diff --git a/scratch/osint/Dockerfile b/scratch/osint/Dockerfile deleted file mode 100644 index c0b313a33f58b8dd1ba97971806ae8b0963de25e..0000000000000000000000000000000000000000 --- a/scratch/osint/Dockerfile +++ /dev/null @@ -1,27 +0,0 @@ -FROM python:3.12-slim - -RUN useradd -m -u 1000 user - -USER user -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH \ - PYTHONDONTWRITEBYTECODE=1 \ - PYTHONUNBUFFERED=1 \ - PORT=7860 - -WORKDIR $HOME/app - -COPY --chown=user pyproject.toml README.md openenv.yaml inference.py $HOME/app/ -COPY --chown=user src $HOME/app/src -COPY --chown=user config $HOME/app/config -COPY --chown=user datasets $HOME/app/datasets -COPY --chown=user docs $HOME/app/docs -COPY --chown=user scripts $HOME/app/scripts -COPY --chown=user server.py $HOME/app/server.py - -RUN pip install --no-cache-dir --upgrade pip && \ - pip install --no-cache-dir -e . - -EXPOSE 7860 - -CMD ["sh", "-c", "uvicorn server:app --host 0.0.0.0 --port ${PORT:-7860}"] diff --git a/scratch/osint/README.md b/scratch/osint/README.md deleted file mode 100644 index efa261ed55cb8fc3fa72a73809decd9bc3a41bae..0000000000000000000000000000000000000000 --- a/scratch/osint/README.md +++ /dev/null @@ -1,262 +0,0 @@ ---- -title: OSINT OpenEnv -emoji: 🕵️ -colorFrom: blue -colorTo: yellow -sdk: docker -app_port: 7860 -pinned: false -license: apache-2.0 -tags: - - openenv - - osint - - benchmark - - docker - - fastapi -short_description: Docker OSINT benchmark with fixed OpenEnv tasks. ---- - -# OSINT OpenEnv - -OSINT OpenEnv is a synthetic benchmark environment for tool-using agents that must recover identities, trace events, and link entities across noisy multi-platform records. The project is designed to feel like a compact OSINT workflow rather than a raw QA dataset: agents query mock profiles, posts, forum threads, and semantic memory, build a working graph, and then submit an answer. - -The motivation is to provide a reproducible OpenEnv-compatible environment for evaluating graph-building and tool-using reasoning without depending on live web data, unstable APIs, or private corpora. That makes it useful for local development, regression testing, and hosted demos such as a Docker-based Hugging Face Space. - -## Environment Summary - -The environment generates or loads a hidden canonical graph of users, aliases, organizations, locations, posts, threads, and events. It then exposes partial platform views and a task list drawn from that graph. - -The default hosted Space uses the fixed-level benchmark in `datasets/fixed_levels/seed_fixed_levels.json`, which now contains 30 stable tasks over a larger shared seeded graph. - -## Action Space - -The environment exposes three actions: - -- `CALL_TOOL`: query platform views or semantic memory such as `search_posts`, `get_profile`, `search_threads`, `get_connections`, or `search_memory`. -- `ADD_EDGE`: add a candidate relation to the working memory graph. -- `ANSWER`: submit the final answer as an exact node id string. - -## Observation Space - -Each step returns a JSON observation with four parts: - -- `tool_outputs`: the most recent tool results. -- `graph_snapshot`: the current working-memory graph edges. -- `action_history`: recent actions and rewards. -- `task`: the active task id, task type, and question. - -## Task Types And Difficulty - -The benchmark mixes direct lookups with multi-hop traces: - -- Easy: single-hop identity resolution, organization lookup, event lookup, or location lookup. -- Mid: two-hop alias-to-user-to-organization or thread-to-event-to-user traces. -- High: cross-platform multi-hop traces combining aliases, authored content, event references, organization links, and direct connections. - -Common task families include: - -- `identity_resolution` -- `network_discovery` -- `event_tracing` -- `cross_platform_linking` -- `deanonymization` -- `convoluted_trace` - -Expected difficulty increases with the number of relations the agent must chain together and whether the evidence is split across posts, threads, aliases, and profile edges. - -## Repository Layout - -```text -src/osint_env/ - agents/ single-agent and swarm runners - baselines/ reusable OpenAI baseline runner - config/ shared config and seed loading - data/ graph/view/task generation - domain/ dataclasses and environment models - env/ environment, reward logic, OpenEnv compatibility shim - eval/ evaluation metrics and leaderboard helpers - llm/ mock, Ollama, and OpenAI client wrappers - memory/ working graph and semantic memory - platforms/ tool APIs over synthetic platform views - viz/ dashboard export - -scripts/ - build_fixed_levels_dataset.py - run_openai_baseline.py - -datasets/fixed_levels/ - seed_fixed_levels.json - shared_config_fixed_levels.json - qwen_swarm_benchmark_fixed_levels.json - -server.py FastAPI app for local use and Docker/HF Spaces -Dockerfile Container entrypoint for Hugging Face Docker Spaces -``` - -## Setup - -Python 3.10+ is required. - -Local install: - -```bash -python -m pip install -e . -``` - -Run tests: - -```bash -python -m pytest -q -``` - -Run the automated release gate: - -```bash -python scripts/validate_release.py -``` - -## Usage - -Run one demo episode: - -```bash -osint-env demo --agent-mode swarm --llm-provider mock -``` - -Run a quick evaluation: - -```bash -osint-env eval --episodes 5 --agent-mode swarm --llm-provider mock -``` - -Export a dashboard: - -```bash -osint-env benchmark --episodes 5 --agent-mode swarm --llm-provider mock --name quick_check -``` - -## OpenAI Baseline - -The reproducible OpenAI baseline is implemented in `scripts/run_openai_baseline.py`. It runs on the fixed-level benchmark, uses a stable seeded graph/task set, writes a JSON artifact, appends a leaderboard record, and exports a dashboard. - -Default behavior: - -- dataset: fixed-level benchmark -- episodes: 30 -- max steps per episode: 8 -- temperature: 0.0 -- output artifact: `artifacts/baselines/openai_fixed_levels_latest.json` - -Run it with an API key: - -```bash -export OPENAI_API_KEY="your_key_here" -python scripts/run_openai_baseline.py --model gpt-5-nano -``` - -The script is designed to stay bounded enough for a normal benchmark pass to finish comfortably under 20 minutes on a lightweight chat model, while still using the full fixed task set. For repeatability it fixes the benchmark graph/tasks and uses deterministic decoding settings. Because remote model backends can still change over time, the output artifact also records model metadata and system fingerprints when available. - -## Inference Script - -The submission-ready inference entrypoint is the root `inference.py` file. It talks to the deployed Hugging Face Space over HTTP, uses the OpenAI client for all model calls, and emits structured stdout logs in the `[START]`, `[STEP]`, and `[END]` format. - -The script accepts `HF_TOKEN` as the primary auth variable and also supports `OPENAI_API_KEY` or `API_KEY` as local fallbacks. -After a successful run, `inference.py` also posts the evaluation summary back to the Space so the latest `/dashboard` view reflects that run. - -Required environment variables: - -- `API_BASE_URL` -- `MODEL_NAME` -- `HF_TOKEN` - -Optional environment variables: - -- `SPACE_URL` default: `https://siddeshwar1625-osint.hf.space` -- `TASK_INDICES` default: `0,10,20` -- `MAX_STEPS` default: `8` - -Example local test command against a running local server: - -```bash -API_BASE_URL=https://api.openai.com/v1 MODEL_NAME=gpt-5.4-mini OPENAI_API_KEY=your_key SPACE_URL=http://127.0.0.1:7860 python inference.py -``` - -Example test command against the deployed Space: - -```bash -API_BASE_URL=https://api.openai.com/v1 MODEL_NAME=gpt-5.4-mini OPENAI_API_KEY=your_key SPACE_URL=https://siddeshwar1625-osint.hf.space python inference.py -``` - -## Docker And Hugging Face Space - -The repository is ready for a Docker-based Hugging Face Space: - -- `README.md` includes `sdk: docker` -- `README.md` includes the `openenv` Space tag -- `Dockerfile` serves `server.py` on port `7860` - -Local Docker smoke test: - -```bash -docker build -t osint-openenv . -docker run --rm -p 7860:7860 osint-openenv -``` - -Then open `http://localhost:7860`. - -The FastAPI app serves: - -- `/`: overview page -- `/dashboard`: generated benchmark dashboard -- `/api/environment`: environment metadata -- `/health`: health check (validator-friendly alias) -- `/healthz`: health check (legacy alias) -- `/openenv.yaml`: OpenEnv HTTP spec stub -- `/openenv/tasks`: task enumeration -- `/reset` and `/openenv/reset`: episode reset endpoints -- `/step` and `/openenv/step`: episode step endpoints -- `/state` and `/openenv/state/{session_id}`: session state endpoints (`/state` returns the latest session) - -## Automated Validation - -The repository includes a pass/fail validation gate for the core delivery requirements: - -- Hugging Face Space readiness -- OpenEnv spec compliance -- reproducible baseline behavior -- at least 3 fixed tasks with working graders -- Docker image build in CI - -Local gate: - -```bash -python scripts/validate_release.py -``` - -CI gate: - -- `.github/workflows/validation.yml` -- runs `pytest` -- runs the validation script -- runs `docker build` - -## Baseline Scores - -The fixed-level benchmark was expanded from the earlier 15-question set to a 30-question set with a larger seeded graph, so older benchmark artifacts should be treated as legacy and regenerated on the new dataset before using them as reference scores. - -After you supply an OpenAI API key, the current baseline scores for the expanded benchmark will be written to: - -- `artifacts/baselines/openai_fixed_levels_latest.json` -- `artifacts/baselines/openai_fixed_levels_dashboard.html` - -## Notes On `pyproject.toml` - -The packaging file is structurally correct for a `src/` layout and editable installs. The main gaps were deployment/runtime related rather than build-breaking: - -- `openenv` is now version-bounded explicitly. -- `fastapi` and `uvicorn` are included because the repo now ships a real web server. -- pytest is pointed at the `tests/` directory, and the test suite also adds `src/` to `sys.path` so source-layout imports work reliably during local runs. - -## Development Notes - -The project keeps a lightweight local compatibility shim for `openenv` so the source tree remains importable even before dependencies are installed. In a normal install or Docker build, the real `openenv` package from PyPI is still used. diff --git a/scratch/osint/artifacts/leaderboard.json b/scratch/osint/artifacts/leaderboard.json deleted file mode 100644 index 86bfca58d7583f431e001f412bd04a68e16c95ae..0000000000000000000000000000000000000000 --- a/scratch/osint/artifacts/leaderboard.json +++ /dev/null @@ -1,453 +0,0 @@ -[ - { - "config": { - "max_agents": 3, - "max_breadth": 2, - "max_depth": 2, - "max_steps": 18, - "max_width": 2, - "seed": 7, - "seeded_questions": 1, - "swarm_enabled": true - }, - "created_at": "2026-04-01T12:03:13+00:00", - "episodes": 2, - "metrics": { - "avg_compactness_reward": 0.0, - "avg_connectivity_gain_reward": 0.1, - "avg_connectivity_reward": 0.3, - "avg_diversity_reward": 0.08, - "avg_entity_informativeness_reward": 0.024705877237863647, - "avg_format_reward": 0.15, - "avg_graph_f1": 1.0, - "avg_knowledge_carrier_reward": 0.5, - "avg_knowledge_indexing_reward": 0.15, - "avg_relation_informativeness_reward": 0.03137141693971891, - 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}, - "created_at": "2026-04-01T19:24:37+00:00", - "episodes": 3, - "metrics": { - "avg_compactness_reward": 0.0, - "avg_connectivity_gain_reward": 0.10000000000000002, - "avg_connectivity_reward": 0.3, - "avg_diversity_reward": 0.08, - "avg_entity_informativeness_reward": -0.02722031691758704, - "avg_format_reward": 0.15, - "avg_graph_f1": 1.0, - "avg_knowledge_carrier_reward": 0.5, - "avg_knowledge_indexing_reward": 0.0, - "avg_relation_informativeness_reward": -0.0030604289114462002, - "avg_reward": 3.4411379938601514, - "avg_soft_shaping_reward": 0.15, - "avg_spawn_count": 4.0, - "avg_spawn_critical_steps": 6.0, - "avg_steps_to_solution": 9.0, - "deanonymization_accuracy": 1.0, - "leaderboard_score": 0.8827999009847504, - "retrieval_signal": 0.675, - "spawn_completion_rate": 1.0, - "spawn_signal": 0.6666666666666666, - "structural_signal": 0.5909438508341933, - "task_success_rate": 1.0, - "tool_efficiency": 0.5 - }, - "run_id": "run_0010", - "run_name": "qwen_rerun_graph_fix" - }, - { - "config": { - "max_agents": 3, - "max_breadth": 2, - "max_depth": 2, - "max_steps": 18, - "max_width": 2, - "seed": 7, - "seeded_questions": 15, - "swarm_enabled": true - }, - "created_at": "2026-04-01T19:31:54+00:00", - "episodes": 15, - "metrics": { - "avg_compactness_reward": 0.0, - "avg_connectivity_gain_reward": 0.16666666666666666, - "avg_connectivity_reward": 0.16999999999999998, - "avg_diversity_reward": 0.1157777777777778, - "avg_entity_informativeness_reward": -0.0181244777358718, - "avg_format_reward": 0.14999999999999997, - "avg_graph_f1": 0.8492063492063492, - "avg_knowledge_carrier_reward": 0.5, - "avg_knowledge_indexing_reward": 0.012000000000000002, - "avg_relation_informativeness_reward": 0.05935837081627929, - "avg_reward": 4.201760569277529, - "avg_soft_shaping_reward": 0.24999999999999994, - "avg_spawn_count": 4.0, - "avg_spawn_critical_steps": 6.0, - "avg_steps_to_solution": 9.0, - "deanonymization_accuracy": 1.0, - "leaderboard_score": 0.8534887252258901, - "retrieval_signal": 0.6792, - "spawn_completion_rate": 1.0, - "spawn_signal": 0.6666666666666666, - "structural_signal": 0.5847801119494148, - "task_success_rate": 1.0, - "tool_efficiency": 0.5 - }, - "run_id": "run_0011", - "run_name": "qwen_rerun_graph_fix" - } -] \ No newline at end of file diff --git a/scratch/osint/artifacts/osint_dashboard.html b/scratch/osint/artifacts/osint_dashboard.html deleted file mode 100644 index 42202311d1931b2d415222676a5d853939dcf8d5..0000000000000000000000000000000000000000 --- a/scratch/osint/artifacts/osint_dashboard.html +++ /dev/null @@ -1,634 +0,0 @@ - - - - - - OSINT Environment Dashboard - - - - - - - - - -
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OSINT Benchmark Dashboard

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Interactive explorer for canonical knowledge graph, episode traces, source platform records, and benchmark ranking.

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Episode Explorer

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- matched edge - predicted only - truth only -
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Node Inspector

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Click a node to inspect attributes and neighbors.
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Original Database Explorer

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Click a row in the database table to inspect full JSON.
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Benchmark Summary Radar

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Episode Reward and Graph F1

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Benchmark Leaderboard

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- - - - diff --git a/scratch/osint/config/seed_example.json b/scratch/osint/config/seed_example.json deleted file mode 100644 index 92b4aec40eac09d3a4dae1e70e34d9be4fe1bfd1..0000000000000000000000000000000000000000 --- a/scratch/osint/config/seed_example.json +++ /dev/null @@ -1,52 +0,0 @@ -{ - "seeding": { - "seeded_nodes": [ - { - "node_id": "alias_seed_001", - "node_type": "alias", - "attrs": { - "handle": "@shadow_seed" - } - }, - { - "node_id": "user_seed_001", - "node_type": "user", - "attrs": { - "name": "Seed User", - "org": "Helios Labs", - "location": "Pune" - } - } - ], - "_note": "Use with --seed-file. LLM provider and API keys are configured in config/shared_config.json or CLI flags.", - "seeded_edges": [ - { - "src": "alias_seed_001", - "rel": "alias_of", - "dst": "user_seed_001", - "confidence": 1.0 - } - ], - "seeded_questions": [ - { - "task_type": "identity_resolution", - "question": "Which canonical user owns alias alias_seed_001?", - "answer": "user_seed_001", - "supporting_edges": [ - { - "src": "alias_seed_001", - "rel": "alias_of", - "dst": "user_seed_001" - } - ], - "metadata": { - "source": "manual_seed" - } - } - ], - "llm_generate_remaining_graph": true, - "llm_generate_remaining_tasks": true, - "llm_generated_edge_budget": 6, - "llm_generated_task_budget": 8 - } -} diff --git a/scratch/osint/config/seed_ollama_smoke.json b/scratch/osint/config/seed_ollama_smoke.json deleted file mode 100644 index a26bd097e7f482dce893ef83df92c53712c28fbb..0000000000000000000000000000000000000000 --- a/scratch/osint/config/seed_ollama_smoke.json +++ /dev/null @@ -1,51 +0,0 @@ -{ - "seeding": { - "seeded_nodes": [ - { - "node_id": "alias_smoke_001", - "node_type": "alias", - "attrs": { - "handle": "@smoke_alias" - } - }, - { - "node_id": "user_smoke_001", - "node_type": "user", - "attrs": { - "name": "Smoke User", - "org": "Apex Dynamics", - "location": "Bengaluru" - } - } - ], - "seeded_edges": [ - { - "src": "alias_smoke_001", - "rel": "alias_of", - "dst": "user_smoke_001", - "confidence": 1.0 - } - ], - "seeded_questions": [ - { - "task_type": "identity_resolution", - "question": "Which canonical user owns alias alias_smoke_001?", - "answer": "user_smoke_001", - "supporting_edges": [ - { - "src": "alias_smoke_001", - "rel": "alias_of", - "dst": "user_smoke_001" - } - ], - "metadata": { - "source": "ollama_smoke" - } - } - ], - "llm_generate_remaining_graph": false, - "llm_generate_remaining_tasks": false, - "llm_generated_edge_budget": 0, - "llm_generated_task_budget": 0 - } -} diff --git a/scratch/osint/config/shared_config.json b/scratch/osint/config/shared_config.json deleted file mode 100644 index 0fe43fffbe970b7d4e33babf4d44f34721ba280c..0000000000000000000000000000000000000000 --- a/scratch/osint/config/shared_config.json +++ /dev/null @@ -1,55 +0,0 @@ -{ - "environment": { - "n_users": 40, - "alias_density": 0.35, - "noise_level": 0.15, - "red_herring_rate": 0.1, - "max_steps": 18, - "seed": 7 - }, - "swarm": { - "enabled": true, - "max_agents": 3, - "max_breadth": 2, - "max_width": 2, - "max_depth": 2, - "planner_rounds": 2, - "tools_per_agent": 1 - }, - "spawn_reward": { - "lambda_parallel": 0.15, - "lambda_finish": 0.2, - "anneal": 1.0, - "max_parallel_hint": 3 - }, - "seeding": { - "seeded_nodes": [], - "seeded_edges": [], - "seeded_questions": [], - "llm_generate_remaining_graph": true, - "llm_generate_remaining_tasks": true, - "llm_generated_edge_budget": 6, - "llm_generated_task_budget": 8, - "llm_generation_parallel": true, - "llm_generation_workers": 3, - "llm_generation_retries": 2, - "allow_template_fallback_on_llm_failure": false - }, - "llm": { - "provider": "ollama", - "model": "qwen3:2b", - "temperature": 0.1, - "max_tokens": 256, - "timeout_seconds": 240, - "ollama_base_url": "http://127.0.0.1:11434", - "openai_base_url": "https://api.openai.com/v1", - "openai_api_key_env": "OPENAI_API_KEY", - "openai_api_key": "" - }, - "runtime": { - "default_episodes": 20, - "leaderboard_path": "artifacts/leaderboard.json", - "dashboard_path": "artifacts/osint_dashboard.html", - "sweep_dashboard_dir": "artifacts/sweep_dashboards" - } -} diff --git a/scratch/osint/datasets/fixed_levels/README.md b/scratch/osint/datasets/fixed_levels/README.md deleted file mode 100644 index 3c7e2b60d8e200f697826454595e5469c242098a..0000000000000000000000000000000000000000 --- a/scratch/osint/datasets/fixed_levels/README.md +++ /dev/null @@ -1,64 +0,0 @@ -# Fixed Levels Submission Dataset - -This folder contains a fixed three-level OSINT benchmark set built on one shared base graph. - -## Files - -- `seed_fixed_levels.json`: master fixed seed with an expanded canonical graph and 30 fixed questions. -- `fixed_graph_questions.json`: extracted fixed dataset snapshot for submission packaging. -- `shared_config_fixed_levels.json`: run config used for generation and evaluation. -- `complete_dataset_qwen_generated.json`: full dataset after Qwen (`qwen3:2b` via Ollama) expands the graph. -- `qwen_swarm_eval_fixed_levels.json`: legacy Qwen swarm evaluation summary from the older smaller version of the set. -- `qwen_swarm_benchmark_fixed_levels.json`: legacy benchmark output from the older smaller version of the set. -- `leaderboard_fixed_levels.json`: leaderboard file for this dataset. -- `dashboard_fixed_levels.html`: interactive dashboard generated from the benchmark run. - -## Difficulty Design - -- Easy: 10 questions. These now use the older hard-style multi-hop traces as the new floor. -- Mid: 10 questions. Each question spans roughly 15-20 supporting nodes. -- High: 10 questions. Each question spans roughly 50 supporting nodes. - -All 30 questions are fixed and share the same larger seeded graph. - -## Regenerate Artifacts - -```bash -source ~/arl/bin/activate -cd /home/ritish/test1 -PYTHONPATH=src python scripts/build_fixed_levels_dataset.py \ - --seed-file datasets/fixed_levels/seed_fixed_levels.json \ - --shared-config datasets/fixed_levels/shared_config_fixed_levels.json \ - --output-dir datasets/fixed_levels -``` - -## Evaluate Qwen Swarm - -```bash -source ~/arl/bin/activate -cd /home/ritish/test1 -PYTHONPATH=src osint-env eval \ - --config datasets/fixed_levels/shared_config_fixed_levels.json \ - --seed-file datasets/fixed_levels/seed_fixed_levels.json \ - --agent-mode swarm \ - --llm-provider ollama \ - --llm-model qwen3:2b \ - --episodes 15 -``` - -## Benchmark + Dashboard - -```bash -source ~/arl/bin/activate -cd /home/ritish/test1 -PYTHONPATH=src osint-env benchmark \ - --config datasets/fixed_levels/shared_config_fixed_levels.json \ - --seed-file datasets/fixed_levels/seed_fixed_levels.json \ - --agent-mode swarm \ - --llm-provider ollama \ - --llm-model qwen3:2b \ - --episodes 15 \ - --name fixed_levels_qwen_swarm \ - --leaderboard datasets/fixed_levels/leaderboard_fixed_levels.json \ - --dashboard datasets/fixed_levels/dashboard_fixed_levels.html -``` diff --git a/scratch/osint/datasets/fixed_levels/complete_dataset_qwen_generated.json b/scratch/osint/datasets/fixed_levels/complete_dataset_qwen_generated.json deleted file mode 100644 index 813cd92dfe5a5866cafac070a42dbec18a4f2cb0..0000000000000000000000000000000000000000 --- a/scratch/osint/datasets/fixed_levels/complete_dataset_qwen_generated.json +++ /dev/null @@ -1,8798 +0,0 @@ -{ - "canonical_graph": { - "edge_count": 249, - "edges": [ - { - "confidence": 1.0, - "dst": "org_apex_dynamics", - "rel": "works_at", - "src": "user_0" - }, - { - "confidence": 1.0, - "dst": "loc_hyderabad", - "rel": "located_in", - "src": "user_0" - }, - { - "confidence": 1.0, - "dst": "org_northbridge", - "rel": "works_at", - "src": "user_1" - 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"mentions": [ - "user_5" - ], - "post_id": "post_3", - "text": "Update 3 from Northbridge #delhi", - "timestamp": 1003, - "user_id": "alias_cinderveil" - }, - { - "canonical_user": "user_4", - "mentions": [ - "user_15" - ], - "post_id": "post_4", - "text": "Update 4 from Northbridge #delhi", - "timestamp": 1004, - "user_id": "user_4" - }, - { - "canonical_user": "user_5", - "mentions": [ - "user_15" - ], - "post_id": "post_5", - "text": "Update 5 from Northbridge #bengaluru", - "timestamp": 1005, - "user_id": "user_5" - }, - { - "canonical_user": "user_diya", - "mentions": [ - "user_13" - ], - "post_id": "post_6", - "text": "Update 6 from Apex Dynamics #delhi", - "timestamp": 1006, - "user_id": "alias_monsoonbyte" - }, - { - "canonical_user": "user_5", - "mentions": [ - "user_14" - ], - "post_id": "post_7", - "text": "Rumor: Update 7 from Helios Labs #hyderabad maybe fake", - "timestamp": 1007, - "user_id": "alias_5_936" - }, - { - "canonical_user": "user_8", - "mentions": [ - "user_2" - ], - "post_id": "post_8", - "text": "Update 8 from Helios Labs #hyderabad", - "timestamp": 1008, - "user_id": "user_8" - }, - { - "canonical_user": "user_9", - "mentions": [ - "user_3" - ], - "post_id": "post_9", - "text": "Update 9 from Helios Labs #delhi", - "timestamp": 1009, - "user_id": "user_9" - }, - { - "canonical_user": "user_10", - "mentions": [ - "user_8" - ], - "post_id": "post_10", - "text": "Update 10 from Northbridge #bengaluru", - "timestamp": 1010, - "user_id": "user_10" - }, - { - "canonical_user": "user_11", - "mentions": [ - "user_4" - ], - "post_id": "post_11", - "text": "Update 11 from Apex Dynamics #hyderabad", - "timestamp": 1011, - "user_id": "user_11" - }, - { - "canonical_user": "user_kian", - "mentions": [ - "user_15" - ], - "post_id": "post_12", - "text": "Update 12 from Apex Dynamics #hyderabad", - "timestamp": 1012, - "user_id": "alias_lanternmoth" - }, - { - "canonical_user": "user_13", - "mentions": [ - "user_17" - ], - "post_id": "post_13", - "text": "Update 13 from Helios Labs #hyderabad", - "timestamp": 1013, - "user_id": "user_13" - }, - { - "canonical_user": "user_kian", - "mentions": [ - "user_19" - ], - "post_id": "post_14", - "text": "Update 14 from Apex Dynamics #delhi", - "timestamp": 1014, - "user_id": "alias_lanternmoth" - }, - { - "canonical_user": "user_15", - "mentions": [ - "user_2" - ], - "post_id": "post_15", - "text": "Update 15 from Northbridge #delhi", - "timestamp": 1015, - "user_id": "user_15" - }, - { - "canonical_user": "user_bharat", - "mentions": [ - "user_18" - ], - "post_id": "post_16", - "text": "Update 16 from Helios Labs #delhi", - "timestamp": 1016, - "user_id": "alias_steelquill" - }, - { - "canonical_user": "user_soren", - "mentions": [ - "user_4" - ], - "post_id": "post_17", - "text": "Update 17 from Northbridge #hyderabad", - "timestamp": 1017, - "user_id": "alias_tideshard" - }, - { - "canonical_user": "user_18", - "mentions": [ - "user_0" - ], - "post_id": "post_18", - "text": "Update 18 from Northbridge #delhi", - "timestamp": 1018, - "user_id": "user_18" - }, - { - "canonical_user": "user_19", - "mentions": [ - "user_2" - ], - "post_id": "post_19", - "text": "Update 19 from Apex Dynamics #pune", - "timestamp": 1019, - "user_id": "user_19" - }, - { - "canonical_user": "user_nora", - "mentions": [ - "user_0" - ], - "post_id": "post_20", - "text": "Update 20 from Apex Dynamics #bengaluru", - "timestamp": 1020, - "user_id": "alias_emberglass" - }, - { - "canonical_user": "user_omar", - "mentions": [ - "user_9" - ], - "post_id": "post_21", - "text": "Update 21 from Helios Labs #delhi", - "timestamp": 1021, - "user_id": "alias_ironwhisper" - }, - { - "canonical_user": "user_22", - "mentions": [ - "user_15" - ], - "post_id": "post_22", - "text": "Update 22 from Apex Dynamics #delhi", - "timestamp": 1022, - "user_id": "user_22" - }, - { - "canonical_user": "user_23", - "mentions": [ - "user_5" - ], - "post_id": "post_23", - "text": "Update 23 from Northbridge #hyderabad", - "timestamp": 1023, - "user_id": "user_23" - }, - { - "canonical_user": "user_rhea", - "mentions": [ - "user_19" - ], - "post_id": "post_24", - "text": "Update 24 from Helios Labs #sector 9", - "timestamp": 1024, - "user_id": "alias_cinderveil" - }, - { - "canonical_user": "user_leena", - "mentions": [ - "user_22" - ], - "post_id": "post_25", - "text": "Update 25 from Northbridge Logistics #dockyard 17", - "timestamp": 1025, - "user_id": "alias_frostledger" - }, - { - "canonical_user": "user_mika", - "mentions": [ - "user_20" - ], - "post_id": "post_26", - "text": "Rumor: Update 26 from Apex Dynamics #old town maybe fake", - "timestamp": 1026, - "user_id": "alias_basinraven" - }, - { - "canonical_user": "user_diya", - "mentions": [ - "user_17" - ], - "post_id": "post_27", - "text": "Rumor: Update 27 from Blueharbor Media #old town maybe fake", - "timestamp": 1027, - "user_id": "user_diya" - }, - { - "canonical_user": "user_elin", - "mentions": [ - "user_20" - ], - "post_id": "post_28", - "text": "Rumor: Update 28 from Helios Labs #sector 9 maybe fake", - "timestamp": 1028, - "user_id": "user_elin" - }, - { - "canonical_user": "user_5", - "mentions": [ - "user_16" - ], - "post_id": "post_29", - "text": "Update 29 from Tidewatch Ops #rivergate", - "timestamp": 1029, - "user_id": "alias_5_936" - }, - { - "canonical_user": "user_rhea", - "mentions": [ - "user_4" - ], - "post_id": "post_30", - "text": "Update 30 from Apex Dynamics #old town", - "timestamp": 1030, - "user_id": "alias_cinderveil" - }, - { - "canonical_user": "user_bharat", - "mentions": [ - "user_13" - ], - "post_id": "post_31", - "text": "Update 31 from Northbridge Logistics #dockyard 17", - "timestamp": 1031, - "user_id": "alias_steelquill" - }, - { - "canonical_user": "user_ivy", - "mentions": [ - "user_12" - ], - "post_id": "post_32", - "text": "Update 32 from Kestrel Works #rivergate", - "timestamp": 1032, - "user_id": "user_ivy" - }, - { - "canonical_user": "user_nora", - "mentions": [ - "user_14" - ], - "post_id": "post_33", - "text": "Update 33 from Blueharbor Media #old town", - "timestamp": 1033, - "user_id": "alias_emberglass" - }, - { - "canonical_user": "user_kian", - "mentions": [ - "user_13" - ], - "post_id": "post_34", - "text": "Update 34 from Atlas Freight #east quay", - "timestamp": 1034, - "user_id": "user_kian" - }, - { - "canonical_user": "user_bharat", - "mentions": [ - "user_15" - ], - "post_id": "post_35", - "text": "Update 35 from Sunmesh Analytics #sector 9", - "timestamp": 1035, - "user_id": "alias_steelquill" - }, - { - "canonical_user": "user_rhea", - "mentions": [ - "user_8" - ], - "post_id": "post_36", - "text": "Update 36 from Orion Customs #north basin", - "timestamp": 1036, - "user_id": "alias_cinderveil" - }, - { - "canonical_user": "user_nora", - "mentions": [ - "user_7" - ], - "post_id": "post_37", - "text": "Update 37 from Emberline Security #foundry row", - "timestamp": 1037, - "user_id": "user_nora" - }, - { - "canonical_user": "user_12", - "mentions": [ - "user_20" - ], - "post_id": "post_38", - "text": "Update 38 from Atlas Freight #east quay", - "timestamp": 1038, - "user_id": "alias_12_827" - }, - { - "canonical_user": "user_tara", - "mentions": [ - "user_21" - ], - "post_id": "post_39", - "text": "Update 39 from Sunmesh Analytics #sector 9", - "timestamp": 1039, - "user_id": "alias_sablekeel" - }, - { - "canonical_user": "user_5", - "mentions": [ - "user_5" - ], - "post_id": "post_40", - "text": "Update 40 from Orion Customs #north basin", - "timestamp": 1040, - "user_id": "alias_5_936" - }, - { - "canonical_user": "user_kian", - "mentions": [ - "user_6" - ], - "post_id": "post_41", - "text": "Update 41 from Emberline Security #foundry row", - "timestamp": 1041, - "user_id": "alias_lanternmoth" - }, - { - "canonical_user": "user_elin", - "mentions": [ - "user_18" - ], - "post_id": "post_42", - "text": "Update 42 from Harborlight Transit #uplink yard", - "timestamp": 1042, - "user_id": "alias_mapleghost" - }, - { - "canonical_user": "user_tara", - "mentions": [ - "user_18" - ], - "post_id": "post_43", - "text": "Update 43 from Harborlight Transit #uplink yard", - "timestamp": 1043, - "user_id": "user_tara" - } - ], - "profiles": [ - { - "connections": [ - "user_1" - ], - "location": "Hyderabad", - "name": "Person 0", - "org": "Apex Dynamics", - "user_id": "user_0", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [], - "location": "Bengaluru", - "name": "Person 1", - "org": "Northbridge", - "user_id": "user_1", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [], - "location": "Delhi", - "name": "Person 2", - "org": "Northbridge", - "user_id": "user_2", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [], - "location": "Delhi", - "name": "Person 3", - "org": "Northbridge", - "user_id": "user_3", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [ - "user_20" - ], - "location": "Delhi", - "name": "Person 4", - "org": "Northbridge", - "user_id": "user_4", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [], - "location": "Bengaluru", - "name": "Person 5", - "org": "Northbridge", - "user_id": "user_5", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [], - "location": "Delhi", - "name": "Person 6", - "org": "Apex Dynamics", - "user_id": "user_6", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [], - "location": "Hyderabad", - "name": "Person 7", - "org": "Helios Labs", - "user_id": "user_7", - "work_history": [ - "Helios Labs" - ] - }, - { - "connections": [ - "user_22" - ], - "location": "Hyderabad", - "name": "Person 8", - "org": "Helios Labs", - "user_id": "user_8", - "work_history": [ - "Helios Labs" - ] - }, - { - "connections": [ - "user_8" - ], - "location": "Delhi", - "name": "Person 9", - "org": "Helios Labs", - "user_id": "user_9", - "work_history": [ - "Helios Labs" - ] - }, - { - "connections": [], - "location": "Bengaluru", - "name": "Person 10", - "org": "Northbridge", - "user_id": "user_10", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [ - "user_6" - ], - "location": "Hyderabad", - "name": "Person 11", - "org": "Apex Dynamics", - "user_id": "user_11", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [], - "location": "Hyderabad", - "name": "Person 12", - "org": "Apex Dynamics", - "user_id": "user_12", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [ - "user_6" - ], - "location": "Hyderabad", - "name": "Person 13", - "org": "Helios Labs", - "user_id": "user_13", - "work_history": [ - "Helios Labs" - ] - }, - { - "connections": [], - "location": "Delhi", - "name": "Person 14", - "org": "Apex Dynamics", - "user_id": "user_14", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [ - "user_12" - ], - "location": "Delhi", - "name": "Person 15", - "org": "Northbridge", - "user_id": "user_15", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [ - "user_22", - "user_15" - ], - "location": "Delhi", - "name": "Person 16", - "org": "Helios Labs", - "user_id": "user_16", - "work_history": [ - "Helios Labs" - ] - }, - { - "connections": [ - "user_20" - ], - "location": "Hyderabad", - "name": "Person 17", - "org": "Northbridge", - "user_id": "user_17", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [], - "location": "Delhi", - "name": "Person 18", - "org": "Northbridge", - "user_id": "user_18", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [ - "user_14" - ], - "location": "Pune", - "name": "Person 19", - "org": "Apex Dynamics", - "user_id": "user_19", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [], - "location": "Bengaluru", - "name": "Person 20", - "org": "Apex Dynamics", - "user_id": "user_20", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [ - "user_9" - ], - "location": "Delhi", - "name": "Person 21", - "org": "Helios Labs", - "user_id": "user_21", - "work_history": [ - "Helios Labs" - ] - }, - { - "connections": [], - "location": "Delhi", - "name": "Person 22", - "org": "Apex Dynamics", - "user_id": "user_22", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [], - "location": "Hyderabad", - "name": "Person 23", - "org": "Northbridge", - "user_id": "user_23", - "work_history": [ - "Northbridge" - ] - }, - { - "connections": [ - "user_cyrus" - ], - "location": "Sector 9", - "name": "Aria Sen", - "org": "Helios Labs", - "user_id": "user_aria", - "work_history": [ - "Helios Labs" - ] - }, - { - "connections": [ - "user_hiro" - ], - "location": "Dockyard 17", - "name": "Bharat Kulkarni", - "org": "Northbridge Logistics", - "user_id": "user_bharat", - "work_history": [ - "Northbridge Logistics" - ] - }, - { - "connections": [ - "user_gita" - ], - "location": "Old Town", - "name": "Cyrus Mehta", - "org": "Apex Dynamics", - "user_id": "user_cyrus", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [ - "user_elin", - "user_ivy" - ], - "location": "Old Town", - "name": "Diya Roy", - "org": "Blueharbor Media", - "user_id": "user_diya", - "work_history": [ - "Blueharbor Media" - ] - }, - { - "connections": [ - "user_aria" - ], - "location": "Sector 9", - "name": "Elin Das", - "org": "Helios Labs", - "user_id": "user_elin", - "work_history": [ - "Helios Labs" - ] - }, - { - "connections": [ - "user_diya" - ], - "location": "Rivergate", - "name": "Faris Noor", - "org": "Tidewatch Ops", - "user_id": "user_faris", - "work_history": [ - "Tidewatch Ops" - ] - }, - { - "connections": [ - "user_jules" - ], - "location": "Old Town", - "name": "Gita Pradhan", - "org": "Apex Dynamics", - "user_id": "user_gita", - "work_history": [ - "Apex Dynamics" - ] - }, - { - "connections": [ - "user_faris" - ], - "location": "Dockyard 17", - "name": "Hiro Tan", - "org": "Northbridge Logistics", - "user_id": "user_hiro", - "work_history": [ - "Northbridge Logistics" - ] - }, - { - "connections": [ - "user_bharat", - "user_elin" - ], - "location": "Rivergate", - "name": "Ivy Kapoor", - "org": "Kestrel Works", - "user_id": "user_ivy", - "work_history": [ - "Kestrel Works" - ] - }, - { - "connections": [ - "user_bharat" - ], - "location": "Old Town", - "name": "Jules Banerjee", - "org": "Blueharbor Media", - "user_id": "user_jules", - "work_history": [ - "Blueharbor Media" - ] - }, - { - "connections": [ - "user_omar", - "user_bharat" - ], - "location": "East Quay", - "name": "Kian Bose", - "org": "Atlas Freight", - "user_id": "user_kian", - "work_history": [ - "Atlas Freight" - ] - }, - { - "connections": [ - "user_aria" - ], - "location": "Sector 9", - "name": "Leena Das", - "org": "Sunmesh Analytics", - "user_id": "user_leena", - "work_history": [ - "Sunmesh Analytics" - ] - }, - { - "connections": [ - "user_quinn" - ], - "location": "North Basin", - "name": "Mika Solanki", - "org": "Orion Customs", - "user_id": "user_mika", - "work_history": [ - "Orion Customs" - ] - }, - { - "connections": [ - "user_rhea" - ], - "location": "Foundry Row", - "name": "Nora Iqbal", - "org": "Emberline Security", - "user_id": "user_nora", - "work_history": [ - "Emberline Security" - ] - }, - { - "connections": [ - "user_mika" - ], - "location": "East Quay", - "name": "Omar Sheikh", - "org": "Atlas Freight", - "user_id": "user_omar", - "work_history": [ - "Atlas Freight" - ] - }, - { - "connections": [ - "user_leena", - "user_nora" - ], - "location": "Sector 9", - "name": "Priya Menon", - "org": "Sunmesh Analytics", - "user_id": "user_priya", - "work_history": [ - "Sunmesh Analytics" - ] - }, - { - "connections": [ - "user_nora", - "user_hiro" - ], - "location": "North Basin", - "name": "Quinn Rao", - "org": "Orion Customs", - "user_id": "user_quinn", - "work_history": [ - "Orion Customs" - ] - }, - { - "connections": [ - "user_soren" - ], - "location": "Foundry Row", - "name": "Rhea Kapoor", - "org": "Emberline Security", - "user_id": "user_rhea", - "work_history": [ - "Emberline Security" - ] - }, - { - "connections": [ - "user_tara", - "user_faris" - ], - "location": "Uplink Yard", - "name": "Soren Malik", - "org": "Harborlight Transit", - "user_id": "user_soren", - "work_history": [ - "Harborlight Transit" - ] - }, - { - "connections": [ - "user_kian" - ], - "location": "Uplink Yard", - "name": "Tara Dey", - "org": "Harborlight Transit", - "user_id": "user_tara", - "work_history": [ - "Harborlight Transit" - ] - }, - { - "connections": [], - "location": "Remote", - "name": "P123", - "org": "Unknown Ventures", - "user_id": "noise_0", - "work_history": [] - }, - { - "connections": [], - "location": "Unknown", - "name": "P196", - "org": "Unknown Ventures", - "user_id": "noise_1", - "work_history": [] - }, - { - "connections": [], - "location": "Remote", - "name": "P898", - "org": "Unknown Ventures", - "user_id": "noise_2", - "work_history": [] - } - ] - }, - "seed_file": "datasets\\fixed_levels\\seed_fixed_levels.json", - "shared_config": "datasets\\fixed_levels\\shared_config_fixed_levels.json", - "task_count": 30, - "tasks": [ - { - "answer": "user_bharat", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_01", - "support_nodes": 6 - }, - "question": "alias_orchidfox -> post_midnight_manifest -> loc_dockyard17 -> connected collaborator on event_project_lantern. Who is it?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 0.95, - "dst": "user_bharat", - "rel": "connected_to", - "src": "user_ivy" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_bharat" - } - ], - "task_id": "seed_task_0", - "task_type": "fixed_trace" - }, - { - "answer": "user_hiro", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_02", - "support_nodes": 5 - }, - "question": "thr_supply_leak references org_northbridge_logistics. Which alias_docksparrow user works there and collaborates on event_project_lantern?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "references", - "src": "thr_supply_leak" - }, - { - "confidence": 1.0, - "dst": "user_hiro", - "rel": "alias_of", - "src": "alias_docksparrow" - }, - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "works_at", - "src": "user_hiro" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_hiro" - } - ], - "task_id": "seed_task_1", - "task_type": "fixed_trace" - }, - { - "answer": "user_diya", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_03", - "support_nodes": 7 - }, - "question": "alias_monsoonbyte authored post_drone_parts about event_black_kite. Which user behind that alias is directly connected to the Kestrel collaborator?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_diya", - "rel": "alias_of", - "src": "alias_monsoonbyte" - }, - { - "confidence": 1.0, - "dst": "post_drone_parts", - "rel": "authored_post", - "src": "alias_monsoonbyte" - }, - { - "confidence": 1.0, - "dst": "event_black_kite", - "rel": "references", - "src": "post_drone_parts" - }, - { - "confidence": 1.0, - "dst": "org_kestrel_works", - "rel": "works_at", - "src": "user_ivy" - }, - { - "confidence": 0.9, - "dst": "event_black_kite", - "rel": "collaborates_on", - "src": "user_ivy" - }, - { - "confidence": 0.86, - "dst": "user_elin", - "rel": "connected_to", - "src": "user_ivy" - } - ], - "task_id": "seed_task_2", - "task_type": "fixed_trace" - }, - { - "answer": "user_faris", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_04", - "support_nodes": 6 - }, - "question": "alias_nightrelay references loc_rivergate. Which user behind it works at an org operating there and collaborates on event_project_lantern?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_faris", - "rel": "alias_of", - "src": "alias_nightrelay" - }, - { - "confidence": 1.0, - "dst": "post_sat_phone_ping", - "rel": "authored_post", - "src": "alias_nightrelay" - }, - { - "confidence": 1.0, - "dst": "loc_rivergate", - "rel": "references", - "src": "post_sat_phone_ping" - }, - { - "confidence": 1.0, - "dst": "org_tidewatch_ops", - "rel": "works_at", - "src": "user_faris" - }, - { - "confidence": 1.0, - "dst": "loc_rivergate", - "rel": "operates_in", - "src": "org_tidewatch_ops" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_faris" - } - ], - "task_id": "seed_task_3", - "task_type": "fixed_trace" - }, - { - "answer": "user_ivy", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_05", - "support_nodes": 6 - }, - "question": "thr_port_audit discusses Black Kite and references Kestrel Works. Which alias_orchidfox user authored post_midnight_manifest and collaborates on Black Kite?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "event_black_kite", - "rel": "discusses", - "src": "thr_port_audit" - }, - { - "confidence": 1.0, - "dst": "org_kestrel_works", - "rel": "references", - "src": "thr_port_audit" - }, - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "org_kestrel_works", - "rel": "works_at", - "src": "user_ivy" - }, - { - "confidence": 0.9, - "dst": "event_black_kite", - "rel": "collaborates_on", - "src": "user_ivy" - } - ], - "task_id": "seed_task_4", - "task_type": "fixed_trace" - }, - { - "answer": "user_kian", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_06", - "support_nodes": 5 - }, - "question": "Which Atlas Freight user behind alias_lanternmoth authored post_quay_ledgers and collaborates on event_glass_harbor?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_kian", - "rel": "alias_of", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "post_quay_ledgers", - "rel": "authored_post", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "org_atlas_freight", - "rel": "works_at", - "src": "user_kian" - }, - { - "confidence": 0.9, - "dst": "event_glass_harbor", - "rel": "collaborates_on", - "src": "user_kian" - } - ], - "task_id": "seed_task_5", - "task_type": "fixed_trace" - }, - { - "answer": "user_mika", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_07", - "support_nodes": 5 - }, - "question": "Which Orion Customs user behind alias_basinraven authored post_customs_tag and collaborates on event_iron_wharf?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_mika", - "rel": "alias_of", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "post_customs_tag", - "rel": "authored_post", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "org_orion_customs", - "rel": "works_at", - "src": "user_mika" - }, - { - "confidence": 0.9, - "dst": "event_iron_wharf", - "rel": "collaborates_on", - "src": "user_mika" - } - ], - "task_id": "seed_task_6", - "task_type": "fixed_trace" - }, - { - "answer": "user_nora", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_08", - "support_nodes": 5 - }, - "question": "Which user behind alias_emberglass posted basin_photo from Foundry Row and investigates Amber Veil?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_nora", - "rel": "alias_of", - "src": "alias_emberglass" - }, - { - "confidence": 1.0, - "dst": "post_basin_photo", - "rel": "authored_post", - "src": "alias_emberglass" - }, - { - "confidence": 1.0, - "dst": "loc_foundry_row", - "rel": "references", - "src": "post_basin_photo" - }, - { - "confidence": 0.9, - "dst": "event_amber_veil", - "rel": "investigates", - "src": "user_nora" - } - ], - "task_id": "seed_task_7", - "task_type": "fixed_trace" - }, - { - "answer": "user_soren", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_09", - "support_nodes": 4 - }, - "question": "Which user behind alias_tideshard authored post_hull_signal and collaborates on Ghost Signal?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_soren", - "rel": "alias_of", - "src": "alias_tideshard" - }, - { - "confidence": 1.0, - "dst": "post_hull_signal", - "rel": "authored_post", - "src": "alias_tideshard" - }, - { - "confidence": 0.9, - "dst": "event_ghost_signal", - "rel": "collaborates_on", - "src": "user_soren" - } - ], - "task_id": "seed_task_8", - "task_type": "fixed_trace" - }, - { - "answer": "user_tara", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_10", - "support_nodes": 5 - }, - "question": "Which Harborlight Transit user behind alias_sablekeel authored post_uplink_note and reports on Ghost Signal?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_tara", - "rel": "alias_of", - "src": "alias_sablekeel" - }, - { - "confidence": 1.0, - "dst": "post_uplink_note", - "rel": "authored_post", - "src": "alias_sablekeel" - }, - { - "confidence": 1.0, - "dst": "org_harborlight_transit", - "rel": "works_at", - "src": "user_tara" - }, - { - "confidence": 0.9, - "dst": "event_ghost_signal", - "rel": "reports_on", - "src": "user_tara" - } - ], - "task_id": "seed_task_9", - "task_type": "fixed_trace" - }, - { - "answer": "org_northbridge_logistics", - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_01", - "support_nodes": 17 - }, - "question": "Follow alias_docksparrow through post_shift_roster, Dockyard 17, and the Lantern chain. Return the org node id.", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_hiro", - "rel": "alias_of", - "src": "alias_docksparrow" - }, - { - "confidence": 1.0, - "dst": "post_shift_roster", - "rel": "authored_post", - "src": "alias_docksparrow" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_shift_roster" - }, - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "references", - "src": "post_shift_roster" - }, - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "references", - "src": "thr_supply_leak" - }, - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "works_at", - "src": "user_hiro" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "located_in", - "src": "user_hiro" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_hiro" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_bharat" - }, - { - "confidence": 0.95, - "dst": "user_hiro", - "rel": "connected_to", - "src": "user_bharat" - }, - { - "confidence": 0.92, - "dst": "user_faris", - "rel": "connected_to", - "src": "user_hiro" - }, - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "event_project_lantern", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "user_kian", - "rel": "alias_of", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "post_quay_ledgers", - "rel": "authored_post", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "loc_east_quay", - "rel": "references", - "src": "post_quay_ledgers" - }, - { - "confidence": 1.0, - "dst": "event_glass_harbor", - "rel": "references", - "src": "post_quay_ledgers" - } - ], - "task_id": "seed_task_10", - "task_type": "fixed_trace" - }, - { - "answer": "user_kian", - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_02", - "support_nodes": 17 - }, - "question": "Across the Glass Harbor cluster, which user behind alias_lanternmoth links to the Atlas Freight network from thr_quiet_manifest?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_kian", - "rel": "alias_of", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "post_quay_ledgers", - "rel": "authored_post", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "loc_east_quay", - "rel": "references", - "src": "post_quay_ledgers" - }, - { - "confidence": 1.0, - "dst": "event_glass_harbor", - "rel": "references", - "src": "post_quay_ledgers" - }, - { - "confidence": 1.0, - "dst": "thr_quiet_manifest", - "rel": "authored_thread", - "src": "user_kian" - }, - { - "confidence": 1.0, - "dst": "event_glass_harbor", - "rel": "discusses", - "src": "thr_quiet_manifest" - }, - { - "confidence": 1.0, - "dst": "org_atlas_freight", - "rel": "references", - "src": "thr_quiet_manifest" - }, - { - "confidence": 1.0, - "dst": "org_atlas_freight", - "rel": "works_at", - "src": "user_kian" - }, - { - "confidence": 1.0, - "dst": "org_atlas_freight", - "rel": "works_at", - "src": "user_omar" - }, - { - "confidence": 0.9, - "dst": "event_glass_harbor", - "rel": "collaborates_on", - "src": "user_kian" - }, - { - "confidence": 0.9, - "dst": "event_glass_harbor", - "rel": "collaborates_on", - "src": "user_omar" - }, - { - "confidence": 0.9, - "dst": "event_glass_harbor", - "rel": "monitors", - "src": "user_priya" - }, - { - "confidence": 0.93, - "dst": "user_omar", - "rel": "connected_to", - "src": "user_kian" - }, - { - "confidence": 0.9, - "dst": "user_mika", - "rel": "connected_to", - "src": "user_omar" - }, - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "event_project_lantern", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "user_mika", - "rel": "alias_of", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "post_customs_tag", - "rel": "authored_post", - "src": "alias_basinraven" - } - ], - "task_id": "seed_task_11", - "task_type": "fixed_trace" - }, - { - "answer": "user_mika", - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_03", - "support_nodes": 17 - }, - "question": "Trace alias_basinraven through post_customs_tag, thr_customs_breach, and the Orion Customs collaboration chain. Who is it?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_mika", - "rel": "alias_of", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "post_customs_tag", - "rel": "authored_post", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "loc_north_basin", - "rel": "references", - "src": "post_customs_tag" - }, - { - "confidence": 1.0, - "dst": "event_iron_wharf", - "rel": "references", - "src": "post_customs_tag" - }, - { - "confidence": 1.0, - "dst": "thr_customs_breach", - "rel": "authored_thread", - "src": "user_mika" - }, - { - "confidence": 1.0, - "dst": "event_iron_wharf", - "rel": "discusses", - "src": "thr_customs_breach" - }, - { - "confidence": 1.0, - "dst": "org_orion_customs", - "rel": "references", - "src": "thr_customs_breach" - }, - { - "confidence": 1.0, - "dst": "org_orion_customs", - "rel": "works_at", - "src": "user_mika" - }, - { - "confidence": 1.0, - "dst": "org_orion_customs", - "rel": "works_at", - "src": "user_quinn" - }, - { - "confidence": 0.9, - "dst": "event_iron_wharf", - "rel": "collaborates_on", - "src": "user_mika" - }, - { - "confidence": 0.9, - "dst": "event_iron_wharf", - "rel": "collaborates_on", - "src": "user_quinn" - }, - { - "confidence": 0.89, - "dst": "user_quinn", - "rel": "connected_to", - "src": "user_mika" - }, - { - "confidence": 0.88, - "dst": "user_nora", - "rel": "connected_to", - "src": "user_quinn" - }, - { - "confidence": 0.77, - "dst": "org_emberline_security", - "rel": "connected_to", - "src": "org_orion_customs" - }, - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "event_project_lantern", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "user_kian", - "rel": "alias_of", - "src": "alias_lanternmoth" - } - ], - "task_id": "seed_task_12", - "task_type": "fixed_trace" - }, - { - "answer": "user_rhea", - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_04", - "support_nodes": 18 - }, - "question": "In the Ember Tide and Amber Veil overlap, which Foundry Row user behind alias_cinderveil collaborates on Ember Tide?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_rhea", - "rel": "alias_of", - "src": "alias_cinderveil" - }, - { - "confidence": 1.0, - "dst": "post_foundry_map", - "rel": "authored_post", - "src": "alias_cinderveil" - }, - { - "confidence": 1.0, - "dst": "loc_foundry_row", - "rel": "references", - "src": "post_foundry_map" - }, - { - "confidence": 1.0, - "dst": "event_ember_tide", - "rel": "references", - "src": "post_foundry_map" - }, - { - "confidence": 1.0, - "dst": "thr_foundry_watch", - "rel": "authored_thread", - "src": "user_nora" - }, - { - "confidence": 1.0, - "dst": "event_ember_tide", - "rel": "discusses", - "src": "thr_foundry_watch" - }, - { - "confidence": 1.0, - "dst": "org_emberline_security", - "rel": "references", - "src": "thr_foundry_watch" - }, - { - "confidence": 1.0, - "dst": "thr_ember_tide_watch", - "rel": "authored_thread", - "src": "user_rhea" - }, - { - "confidence": 1.0, - "dst": "event_ember_tide", - "rel": "discusses", - "src": "thr_ember_tide_watch" - }, - { - "confidence": 1.0, - "dst": "loc_foundry_row", - "rel": "references", - "src": "thr_ember_tide_watch" - }, - { - "confidence": 1.0, - "dst": "org_emberline_security", - "rel": "works_at", - "src": "user_rhea" - }, - { - "confidence": 1.0, - "dst": "org_emberline_security", - "rel": "works_at", - "src": "user_nora" - }, - { - "confidence": 0.9, - "dst": "event_amber_veil", - "rel": "investigates", - "src": "user_nora" - }, - { - "confidence": 0.9, - "dst": "event_ember_tide", - "rel": "collaborates_on", - "src": "user_rhea" - }, - { - "confidence": 0.87, - "dst": "user_rhea", - "rel": "connected_to", - "src": "user_nora" - }, - { - "confidence": 0.77, - "dst": "event_ghost_signal", - "rel": "connected_to", - "src": "event_ember_tide" - }, - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "event_project_lantern", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "user_kian", - "rel": "alias_of", - "src": "alias_lanternmoth" - } - ], - "task_id": "seed_task_13", - "task_type": "fixed_trace" - }, - { - "answer": "org_harborlight_transit", - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_05", - "support_nodes": 17 - }, - "question": "Follow alias_tideshard from post_hull_signal into thr_uplink_route and the Harborlight relay. Return the org node id.", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_soren", - "rel": "alias_of", - "src": "alias_tideshard" - }, - { - "confidence": 1.0, - "dst": "post_hull_signal", - "rel": "authored_post", - "src": "alias_tideshard" - }, - { - "confidence": 1.0, - "dst": "loc_uplink_yard", - "rel": "references", - "src": "post_hull_signal" - }, - { - "confidence": 1.0, - "dst": "event_ghost_signal", - "rel": "references", - "src": "post_hull_signal" - }, - { - "confidence": 1.0, - "dst": "thr_uplink_route", - "rel": "authored_thread", - "src": "user_soren" - }, - { - "confidence": 1.0, - "dst": "event_ghost_signal", - "rel": "discusses", - "src": "thr_uplink_route" - }, - { - "confidence": 1.0, - "dst": "org_harborlight_transit", - "rel": "references", - "src": "thr_uplink_route" - }, - { - "confidence": 1.0, - "dst": "org_harborlight_transit", - "rel": "works_at", - "src": "user_soren" - }, - { - "confidence": 1.0, - "dst": "org_harborlight_transit", - "rel": "works_at", - 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Who is it?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 0.95, - "dst": "user_bharat", - "rel": "connected_to", - "src": "user_ivy" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_bharat" - } - ], - "task_id": "fixed_task_00", - "task_type": "fixed_trace" - }, - { - "answer": "user_hiro", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_02", - "support_nodes": 5 - }, - "question": "thr_supply_leak references org_northbridge_logistics. Which alias_docksparrow user works there and collaborates on event_project_lantern?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "references", - "src": "thr_supply_leak" - }, - { - "confidence": 1.0, - "dst": "user_hiro", - "rel": "alias_of", - "src": "alias_docksparrow" - }, - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "works_at", - "src": "user_hiro" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_hiro" - } - ], - "task_id": "fixed_task_01", - "task_type": "fixed_trace" - }, - { - "answer": "user_diya", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_03", - "support_nodes": 7 - }, - "question": "alias_monsoonbyte authored post_drone_parts about event_black_kite. Which user behind that alias is directly connected to the Kestrel collaborator?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_diya", - "rel": "alias_of", - "src": "alias_monsoonbyte" - }, - { - "confidence": 1.0, - "dst": "post_drone_parts", - "rel": "authored_post", - "src": "alias_monsoonbyte" - }, - { - "confidence": 1.0, - "dst": "event_black_kite", - "rel": "references", - "src": "post_drone_parts" - }, - { - "confidence": 1.0, - "dst": "org_kestrel_works", - "rel": "works_at", - "src": "user_ivy" - }, - { - "confidence": 0.9, - "dst": "event_black_kite", - "rel": "collaborates_on", - "src": "user_ivy" - }, - { - "confidence": 0.86, - "dst": "user_elin", - "rel": "connected_to", - "src": "user_ivy" - } - ], - "task_id": "fixed_task_02", - "task_type": "fixed_trace" - }, - { - "answer": "user_faris", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_04", - "support_nodes": 6 - }, - "question": "alias_nightrelay references loc_rivergate. Which user behind it works at an org operating there and collaborates on event_project_lantern?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_faris", - "rel": "alias_of", - "src": "alias_nightrelay" - }, - { - "confidence": 1.0, - "dst": "post_sat_phone_ping", - "rel": "authored_post", - "src": "alias_nightrelay" - }, - { - "confidence": 1.0, - "dst": "loc_rivergate", - "rel": "references", - "src": "post_sat_phone_ping" - }, - { - "confidence": 1.0, - "dst": "org_tidewatch_ops", - "rel": "works_at", - "src": "user_faris" - }, - { - "confidence": 1.0, - "dst": "loc_rivergate", - "rel": "operates_in", - "src": "org_tidewatch_ops" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_faris" - } - ], - "task_id": "fixed_task_03", - "task_type": "fixed_trace" - }, - { - "answer": "user_ivy", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_05", - "support_nodes": 6 - }, - "question": "thr_port_audit discusses Black Kite and references Kestrel Works. Which alias_orchidfox user authored post_midnight_manifest and collaborates on Black Kite?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "event_black_kite", - "rel": "discusses", - "src": "thr_port_audit" - }, - { - "confidence": 1.0, - "dst": "org_kestrel_works", - "rel": "references", - "src": "thr_port_audit" - }, - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "org_kestrel_works", - "rel": "works_at", - "src": "user_ivy" - }, - { - "confidence": 0.9, - "dst": "event_black_kite", - "rel": "collaborates_on", - "src": "user_ivy" - } - ], - "task_id": "fixed_task_04", - "task_type": "fixed_trace" - }, - { - "answer": "user_kian", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_06", - "support_nodes": 5 - }, - "question": "Which Atlas Freight user behind alias_lanternmoth authored post_quay_ledgers and collaborates on event_glass_harbor?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_kian", - "rel": "alias_of", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "post_quay_ledgers", - "rel": "authored_post", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "org_atlas_freight", - "rel": "works_at", - "src": "user_kian" - }, - { - "confidence": 0.9, - "dst": "event_glass_harbor", - "rel": "collaborates_on", - "src": "user_kian" - } - ], - "task_id": "fixed_task_05", - "task_type": "fixed_trace" - }, - { - "answer": "user_mika", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_07", - "support_nodes": 5 - }, - "question": "Which Orion Customs user behind alias_basinraven authored post_customs_tag and collaborates on event_iron_wharf?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_mika", - "rel": "alias_of", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "post_customs_tag", - "rel": "authored_post", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "org_orion_customs", - "rel": "works_at", - "src": "user_mika" - }, - { - "confidence": 0.9, - "dst": "event_iron_wharf", - "rel": "collaborates_on", - "src": "user_mika" - } - ], - "task_id": "fixed_task_06", - "task_type": "fixed_trace" - }, - { - "answer": "user_nora", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_08", - "support_nodes": 5 - }, - "question": "Which user behind alias_emberglass posted basin_photo from Foundry Row and investigates Amber Veil?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_nora", - "rel": "alias_of", - "src": "alias_emberglass" - }, - { - "confidence": 1.0, - "dst": "post_basin_photo", - "rel": "authored_post", - "src": "alias_emberglass" - }, - { - "confidence": 1.0, - "dst": "loc_foundry_row", - "rel": "references", - "src": "post_basin_photo" - }, - { - "confidence": 0.9, - "dst": "event_amber_veil", - "rel": "investigates", - "src": "user_nora" - } - ], - "task_id": "fixed_task_07", - "task_type": "fixed_trace" - }, - { - "answer": "user_soren", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_09", - "support_nodes": 4 - }, - "question": "Which user behind alias_tideshard authored post_hull_signal and collaborates on Ghost Signal?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_soren", - "rel": "alias_of", - "src": "alias_tideshard" - }, - { - "confidence": 1.0, - "dst": "post_hull_signal", - "rel": "authored_post", - "src": "alias_tideshard" - }, - { - "confidence": 0.9, - "dst": "event_ghost_signal", - "rel": "collaborates_on", - "src": "user_soren" - } - ], - "task_id": "fixed_task_08", - "task_type": "fixed_trace" - }, - { - "answer": "user_tara", - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_10", - "support_nodes": 5 - }, - "question": "Which Harborlight Transit user behind alias_sablekeel authored post_uplink_note and reports on Ghost Signal?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_tara", - "rel": "alias_of", - "src": "alias_sablekeel" - }, - { - "confidence": 1.0, - "dst": "post_uplink_note", - "rel": "authored_post", - "src": "alias_sablekeel" - }, - { - "confidence": 1.0, - "dst": "org_harborlight_transit", - "rel": "works_at", - "src": "user_tara" - }, - { - "confidence": 0.9, - "dst": "event_ghost_signal", - "rel": "reports_on", - "src": "user_tara" - } - ], - "task_id": "fixed_task_09", - "task_type": "fixed_trace" - }, - { - "answer": "org_northbridge_logistics", - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_01", - "support_nodes": 17 - }, - "question": "Follow alias_docksparrow through post_shift_roster, Dockyard 17, and the Lantern chain. Return the org node id.", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_hiro", - "rel": "alias_of", - "src": "alias_docksparrow" - }, - { - "confidence": 1.0, - "dst": "post_shift_roster", - "rel": "authored_post", - "src": "alias_docksparrow" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_shift_roster" - }, - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "references", - "src": "post_shift_roster" - }, - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "references", - "src": "thr_supply_leak" - }, - { - "confidence": 1.0, - "dst": "org_northbridge_logistics", - "rel": "works_at", - "src": "user_hiro" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "located_in", - "src": "user_hiro" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_hiro" - }, - { - "confidence": 0.9, - "dst": "event_project_lantern", - "rel": "collaborates_on", - "src": "user_bharat" - }, - { - "confidence": 0.95, - "dst": "user_hiro", - "rel": "connected_to", - "src": "user_bharat" - }, - { - "confidence": 0.92, - "dst": "user_faris", - "rel": "connected_to", - "src": "user_hiro" - }, - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "event_project_lantern", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "user_kian", - "rel": "alias_of", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "post_quay_ledgers", - "rel": "authored_post", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "loc_east_quay", - "rel": "references", - "src": "post_quay_ledgers" - }, - { - "confidence": 1.0, - "dst": "event_glass_harbor", - "rel": "references", - "src": "post_quay_ledgers" - } - ], - "task_id": "fixed_task_10", - "task_type": "fixed_trace" - }, - { - "answer": "user_kian", - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_02", - "support_nodes": 17 - }, - "question": "Across the Glass Harbor cluster, which user behind alias_lanternmoth links to the Atlas Freight network from thr_quiet_manifest?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_kian", - "rel": "alias_of", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "post_quay_ledgers", - "rel": "authored_post", - "src": "alias_lanternmoth" - }, - { - "confidence": 1.0, - "dst": "loc_east_quay", - "rel": "references", - "src": "post_quay_ledgers" - }, - { - "confidence": 1.0, - "dst": "event_glass_harbor", - "rel": "references", - "src": "post_quay_ledgers" - }, - { - "confidence": 1.0, - "dst": "thr_quiet_manifest", - "rel": "authored_thread", - "src": "user_kian" - }, - { - "confidence": 1.0, - "dst": "event_glass_harbor", - "rel": "discusses", - "src": "thr_quiet_manifest" - }, - { - "confidence": 1.0, - "dst": "org_atlas_freight", - "rel": "references", - "src": "thr_quiet_manifest" - }, - { - "confidence": 1.0, - "dst": "org_atlas_freight", - "rel": "works_at", - "src": "user_kian" - }, - { - "confidence": 1.0, - "dst": "org_atlas_freight", - "rel": "works_at", - "src": "user_omar" - }, - { - "confidence": 0.9, - "dst": "event_glass_harbor", - "rel": "collaborates_on", - "src": "user_kian" - }, - { - "confidence": 0.9, - "dst": "event_glass_harbor", - "rel": "collaborates_on", - "src": "user_omar" - }, - { - "confidence": 0.9, - "dst": "event_glass_harbor", - "rel": "monitors", - "src": "user_priya" - }, - { - "confidence": 0.93, - "dst": "user_omar", - "rel": "connected_to", - "src": "user_kian" - }, - { - "confidence": 0.9, - "dst": "user_mika", - "rel": "connected_to", - "src": "user_omar" - }, - { - "confidence": 1.0, - "dst": "user_ivy", - "rel": "alias_of", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "post_midnight_manifest", - "rel": "authored_post", - "src": "alias_orchidfox" - }, - { - "confidence": 1.0, - "dst": "loc_dockyard17", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "event_project_lantern", - "rel": "references", - "src": "post_midnight_manifest" - }, - { - "confidence": 1.0, - "dst": "user_mika", - "rel": "alias_of", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "post_customs_tag", - "rel": "authored_post", - "src": "alias_basinraven" - } - ], - "task_id": "fixed_task_11", - "task_type": "fixed_trace" - }, - { - "answer": "user_mika", - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_03", - "support_nodes": 17 - }, - "question": "Trace alias_basinraven through post_customs_tag, thr_customs_breach, and the Orion Customs collaboration chain. Who is it?", - "supporting_edges": [ - { - "confidence": 1.0, - "dst": "user_mika", - "rel": "alias_of", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "post_customs_tag", - "rel": "authored_post", - "src": "alias_basinraven" - }, - { - "confidence": 1.0, - "dst": "loc_north_basin", - "rel": "references", - "src": "post_customs_tag" - }, - { - "confidence": 1.0, - "dst": "event_iron_wharf", - "rel": "references", - "src": "post_customs_tag" - }, - { - "confidence": 1.0, - "dst": "thr_customs_breach", - "rel": "authored_thread", - "src": "user_mika" - }, - { - "confidence": 1.0, - "dst": "event_iron_wharf", - "rel": "discusses", - "src": "thr_customs_breach" - }, - { - "confidence": 1.0, - "dst": "org_orion_customs", - "rel": "references", - "src": "thr_customs_breach" - }, - { - "confidence": 1.0, - "dst": "org_orion_customs", - "rel": "works_at", - "src": "user_mika" - }, - { - "confidence": 1.0, - "dst": "org_orion_customs", - "rel": "works_at", - "src": "user_quinn" - 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} -} \ No newline at end of file diff --git a/scratch/osint/datasets/fixed_levels/qwen_swarm_eval_fixed_levels.json b/scratch/osint/datasets/fixed_levels/qwen_swarm_eval_fixed_levels.json deleted file mode 100644 index a467bedc91c9276db4c393c4b633b8150ee71baf..0000000000000000000000000000000000000000 --- a/scratch/osint/datasets/fixed_levels/qwen_swarm_eval_fixed_levels.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "avg_compactness_reward": 0.0, - "avg_connectivity_gain_reward": 0.16666666666666666, - "avg_connectivity_reward": 0.16999999999999998, - "avg_diversity_reward": 0.1157777777777778, - "avg_entity_informativeness_reward": -0.02824631570420193, - "avg_format_reward": 0.14999999999999997, - "avg_graph_f1": 0.8492063492063492, - "avg_knowledge_carrier_reward": 0.5, - "avg_knowledge_indexing_reward": 0.07400000000000001, - "avg_relation_informativeness_reward": 0.06905976285357758, - "avg_reward": 4.285384567790942, - "avg_soft_shaping_reward": 0.24999999999999994, - "avg_spawn_count": 4.0, - "avg_spawn_critical_steps": 6.0, - "avg_steps_to_solution": 9.0, - "deanonymization_accuracy": 1.0, - "leaderboard_score": 0.8565775118852701, - "retrieval_signal": 0.7009000000000001, - "spawn_completion_rate": 1.0, - "spawn_signal": 0.6666666666666666, - "structural_signal": 0.5846960227632085, - "task_success_rate": 1.0, - "tool_efficiency": 0.5 -} diff --git a/scratch/osint/datasets/fixed_levels/seed_fixed_levels.json b/scratch/osint/datasets/fixed_levels/seed_fixed_levels.json deleted file mode 100644 index 9424b00b5f3efbc1cffcde87366ac70b23750ea2..0000000000000000000000000000000000000000 --- a/scratch/osint/datasets/fixed_levels/seed_fixed_levels.json +++ /dev/null @@ -1,6977 +0,0 @@ -{ - "seeding": { - "seeded_nodes": [ - { - "node_id": "user_aria", - "node_type": "user", - "attrs": { - "name": "Aria Sen", - "org": "Helios Labs", - "location": "Sector 9" - } - }, - { - "node_id": "user_bharat", - "node_type": "user", - "attrs": { - "name": "Bharat Kulkarni", - "org": "Northbridge Logistics", - "location": "Dockyard 17" - } - }, - { - "node_id": "user_cyrus", - "node_type": "user", - "attrs": { - "name": "Cyrus Mehta", - "org": "Apex Dynamics", - "location": "Old Town" - } - }, - { - "node_id": "user_diya", - "node_type": "user", - "attrs": { - "name": "Diya Roy", - "org": "Blueharbor Media", - "location": "Old Town" - } - }, - { - "node_id": "user_elin", - "node_type": "user", - "attrs": { - "name": "Elin Das", - "org": "Helios Labs", - "location": "Sector 9" - } - }, - { - "node_id": "user_faris", - "node_type": "user", - "attrs": { - "name": "Faris Noor", - "org": "Tidewatch Ops", - "location": "Rivergate" - } - }, - { - "node_id": "user_gita", - "node_type": "user", - "attrs": { - "name": "Gita Pradhan", - "org": "Apex Dynamics", - "location": "Old Town" - } - }, - { - "node_id": "user_hiro", - "node_type": "user", - "attrs": { - "name": "Hiro Tan", - "org": "Northbridge Logistics", - "location": "Dockyard 17" - } - }, - { - "node_id": "user_ivy", - "node_type": "user", - "attrs": { - "name": "Ivy Kapoor", - "org": "Kestrel Works", - "location": "Rivergate" - } - }, - { - "node_id": "user_jules", - "node_type": "user", - "attrs": { - "name": "Jules Banerjee", - "org": "Blueharbor Media", - "location": "Old Town" - } - }, - { - "node_id": "user_kian", - "node_type": "user", - "attrs": { - "name": "Kian Bose", - "org": "Atlas Freight", - "location": "East Quay" - } - }, - { - "node_id": "user_leena", - "node_type": "user", - "attrs": { - "name": "Leena Das", - "org": "Sunmesh Analytics", - "location": "Sector 9" - } - }, - { - "node_id": "user_mika", - "node_type": "user", - "attrs": { - "name": "Mika Solanki", - "org": "Orion Customs", - "location": "North Basin" - } - }, - { - "node_id": "user_nora", - "node_type": "user", - "attrs": { - "name": "Nora Iqbal", - "org": "Emberline Security", - "location": "Foundry Row" - } - }, - { - "node_id": "user_omar", - "node_type": "user", - "attrs": { - "name": "Omar Sheikh", - "org": "Atlas Freight", - "location": "East Quay" - } - }, - { - "node_id": "user_priya", - "node_type": "user", - "attrs": { - "name": "Priya Menon", - "org": "Sunmesh Analytics", - "location": "Sector 9" - } - }, - { - "node_id": "user_quinn", - "node_type": "user", - "attrs": { - "name": "Quinn Rao", - "org": "Orion Customs", - "location": "North Basin" - } - }, - { - "node_id": "user_rhea", - "node_type": "user", - "attrs": { - "name": "Rhea Kapoor", - "org": "Emberline Security", - "location": "Foundry Row" - } - }, - { - "node_id": "user_soren", - "node_type": "user", - "attrs": { - "name": "Soren Malik", - "org": "Harborlight Transit", - "location": "Uplink Yard" - } - }, - { - "node_id": "user_tara", - "node_type": "user", - "attrs": { - "name": "Tara Dey", - "org": "Harborlight Transit", - "location": "Uplink Yard" - } - }, - { - "node_id": "alias_orchidfox", - "node_type": "alias", - "attrs": { - "handle": "@orchidfox" - } - }, - { - "node_id": "alias_steelquill", - "node_type": "alias", - "attrs": { - "handle": "@steelquill" - } - }, - { - "node_id": "alias_monsoonbyte", - "node_type": "alias", - "attrs": { - "handle": "@monsoonbyte" - } - }, - { - "node_id": "alias_nightrelay", - "node_type": "alias", - "attrs": { - "handle": "@nightrelay" - } - }, - { - "node_id": "alias_mapleghost", - "node_type": "alias", - "attrs": { - "handle": "@mapleghost" - } - }, - { - "node_id": "alias_docksparrow", - "node_type": "alias", - "attrs": { - "handle": "@docksparrow" - } - }, - { - "node_id": "alias_quartzlotus", - "node_type": "alias", - "attrs": { - "handle": "@quartzlotus" - } - }, - { - "node_id": "alias_emberglass", - "node_type": "alias", - "attrs": { - "handle": "@emberglass" - } - }, - { - "node_id": "alias_basinraven", - "node_type": "alias", - "attrs": { - "handle": "@basinraven" - } - }, - { - "node_id": "alias_tideshard", - "node_type": "alias", - "attrs": { - "handle": "@tideshard" - } - }, - { - "node_id": "alias_hollowsignal", - "node_type": "alias", - "attrs": { - "handle": "@hollowsignal" - } - }, - { - "node_id": "alias_ironwhisper", - "node_type": "alias", - "attrs": { - "handle": "@ironwhisper" - } - }, - { - "node_id": "alias_cinderveil", - "node_type": "alias", - "attrs": { - "handle": "@cinderveil" - } - }, - { - "node_id": "alias_sablekeel", - "node_type": "alias", - "attrs": { - "handle": "@sablekeel" - } - }, - { - "node_id": "alias_lanternmoth", - "node_type": "alias", - "attrs": { - "handle": "@lanternmoth" - } - }, - { - "node_id": "alias_frostledger", - "node_type": "alias", - "attrs": { - "handle": "@frostledger" - } - }, - { - "node_id": "org_helios_labs", - "node_type": "org", - "attrs": { - "name": "Helios Labs" - } - }, - { - "node_id": "org_northbridge_logistics", - "node_type": "org", - "attrs": { - "name": "Northbridge Logistics" - } - }, - { - "node_id": "org_apex_dynamics", - "node_type": "org", - "attrs": { - "name": "Apex Dynamics" - } - }, - { - "node_id": "org_blueharbor_media", - "node_type": "org", - "attrs": { - "name": "Blueharbor Media" - } - }, - { - "node_id": "org_tidewatch_ops", - "node_type": "org", - "attrs": { - "name": "Tidewatch Ops" - } - }, - { - "node_id": "org_kestrel_works", - "node_type": "org", - "attrs": { - "name": "Kestrel Works" - } - }, - { - "node_id": "org_atlas_freight", - "node_type": "org", - "attrs": { - "name": "Atlas Freight" - } - }, - { - "node_id": "org_sunmesh_analytics", - "node_type": "org", - "attrs": { - "name": "Sunmesh Analytics" - } - }, - { - "node_id": "org_orion_customs", - "node_type": "org", - "attrs": { - "name": "Orion Customs" - } - }, - { - "node_id": "org_emberline_security", - "node_type": "org", - "attrs": { - "name": "Emberline Security" - } - }, - { - "node_id": "org_harborlight_transit", - "node_type": "org", - "attrs": { - "name": "Harborlight Transit" - } - }, - { - "node_id": "loc_dockyard17", - "node_type": "location", - "attrs": { - "name": "Dockyard 17" - } - }, - { - "node_id": "loc_sector9", - "node_type": "location", - "attrs": { - "name": "Sector 9" - } - }, - { - "node_id": "loc_old_town", - "node_type": "location", - 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} - }, - { - "node_id": "event_glass_harbor", - "node_type": "event", - "attrs": { - "name": "Glass Harbor" - } - }, - { - "node_id": "event_ember_tide", - "node_type": "event", - "attrs": { - "name": "Ember Tide" - } - }, - { - "node_id": "event_iron_wharf", - "node_type": "event", - "attrs": { - "name": "Iron Wharf" - } - }, - { - "node_id": "event_ghost_signal", - "node_type": "event", - "attrs": { - "name": "Ghost Signal" - } - }, - { - "node_id": "thr_supply_leak", - "node_type": "thread", - "attrs": { - "topic": "supply_chain" - } - }, - { - "node_id": "thr_port_audit", - "node_type": "thread", - "attrs": { - "topic": "port_audit" - } - }, - { - "node_id": "thr_customs_breach", - "node_type": "thread", - "attrs": { - "topic": "customs_breach" - } - }, - { - "node_id": "thr_relay_map", - "node_type": "thread", - "attrs": { - "topic": "relay_map" - } - }, - { - "node_id": "thr_foundry_watch", - "node_type": "thread", - "attrs": { - "topic": "foundry_watch" - } - }, - { - "node_id": "thr_basin_shift", - 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Which user behind that alias is directly connected to the Kestrel collaborator?", - "answer": "user_diya", - "supporting_edges": [ - { - "src": "alias_monsoonbyte", - "rel": "alias_of", - "dst": "user_diya", - "confidence": 1.0 - }, - { - "src": "alias_monsoonbyte", - "rel": "authored_post", - "dst": "post_drone_parts", - "confidence": 1.0 - }, - { - "src": "post_drone_parts", - "rel": "references", - "dst": "event_black_kite", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "works_at", - "dst": "org_kestrel_works", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "collaborates_on", - "dst": "event_black_kite", - "confidence": 0.9 - }, - { - "src": "user_ivy", - "rel": "connected_to", - "dst": "user_elin", - "confidence": 0.86 - } - ], - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_03", - "support_nodes": 7 - } - }, - { - "task_type": "fixed_trace", - "question": "alias_nightrelay references loc_rivergate. Which user behind it works at an org operating there and collaborates on event_project_lantern?", - "answer": "user_faris", - "supporting_edges": [ - { - "src": "alias_nightrelay", - "rel": "alias_of", - "dst": "user_faris", - "confidence": 1.0 - }, - { - "src": "alias_nightrelay", - "rel": "authored_post", - "dst": "post_sat_phone_ping", - "confidence": 1.0 - }, - { - "src": "post_sat_phone_ping", - "rel": "references", - "dst": "loc_rivergate", - "confidence": 1.0 - }, - { - "src": "user_faris", - "rel": "works_at", - "dst": "org_tidewatch_ops", - "confidence": 1.0 - }, - { - "src": "org_tidewatch_ops", - "rel": "operates_in", - "dst": "loc_rivergate", - "confidence": 1.0 - }, - { - "src": "user_faris", - "rel": "collaborates_on", - "dst": "event_project_lantern", - "confidence": 0.9 - } - ], - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_04", - "support_nodes": 6 - } - }, - { - "task_type": "fixed_trace", - "question": "thr_port_audit discusses Black Kite and references Kestrel Works. Which alias_orchidfox user authored post_midnight_manifest and collaborates on Black Kite?", - "answer": "user_ivy", - "supporting_edges": [ - { - "src": "thr_port_audit", - "rel": "discusses", - "dst": "event_black_kite", - "confidence": 1.0 - }, - { - "src": "thr_port_audit", - "rel": "references", - "dst": "org_kestrel_works", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "works_at", - "dst": "org_kestrel_works", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "collaborates_on", - "dst": "event_black_kite", - "confidence": 0.9 - } - ], - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_05", - "support_nodes": 6 - } - }, - { - "task_type": "fixed_trace", - "question": "Which Atlas Freight user behind alias_lanternmoth authored post_quay_ledgers and collaborates on event_glass_harbor?", - "answer": "user_kian", - "supporting_edges": [ - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "authored_post", - "dst": "post_quay_ledgers", - "confidence": 1.0 - }, - { - "src": "user_kian", - "rel": "works_at", - "dst": "org_atlas_freight", - "confidence": 1.0 - }, - { - "src": "user_kian", - "rel": "collaborates_on", - "dst": "event_glass_harbor", - "confidence": 0.9 - } - ], - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_06", - "support_nodes": 5 - } - }, - { - "task_type": "fixed_trace", - "question": "Which Orion Customs user behind alias_basinraven authored post_customs_tag and collaborates on event_iron_wharf?", - "answer": "user_mika", - "supporting_edges": [ - { - "src": "alias_basinraven", - "rel": "alias_of", - "dst": "user_mika", - "confidence": 1.0 - }, - { - "src": "alias_basinraven", - "rel": "authored_post", - "dst": "post_customs_tag", - "confidence": 1.0 - }, - { - "src": "user_mika", - "rel": "works_at", - "dst": "org_orion_customs", - "confidence": 1.0 - }, - { - "src": "user_mika", - "rel": "collaborates_on", - "dst": "event_iron_wharf", - "confidence": 0.9 - } - ], - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_07", - "support_nodes": 5 - } - }, - { - "task_type": "fixed_trace", - "question": "Which user behind alias_emberglass posted basin_photo from Foundry Row and investigates Amber Veil?", - "answer": "user_nora", - "supporting_edges": [ - { - "src": "alias_emberglass", - "rel": "alias_of", - "dst": "user_nora", - "confidence": 1.0 - }, - { - "src": "alias_emberglass", - "rel": "authored_post", - "dst": "post_basin_photo", - "confidence": 1.0 - }, - { - "src": "post_basin_photo", - "rel": "references", - "dst": "loc_foundry_row", - "confidence": 1.0 - }, - { - "src": "user_nora", - "rel": "investigates", - "dst": "event_amber_veil", - "confidence": 0.9 - } - ], - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_08", - "support_nodes": 5 - } - }, - { - "task_type": "fixed_trace", - "question": "Which user behind alias_tideshard authored post_hull_signal and collaborates on Ghost Signal?", - "answer": "user_soren", - "supporting_edges": [ - { - "src": "alias_tideshard", - "rel": "alias_of", - "dst": "user_soren", - "confidence": 1.0 - }, - { - "src": "alias_tideshard", - "rel": "authored_post", - "dst": "post_hull_signal", - "confidence": 1.0 - }, - { - "src": "user_soren", - "rel": "collaborates_on", - "dst": "event_ghost_signal", - "confidence": 0.9 - } - ], - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_09", - "support_nodes": 4 - } - }, - { - "task_type": "fixed_trace", - "question": "Which Harborlight Transit user behind alias_sablekeel authored post_uplink_note and reports on Ghost Signal?", - "answer": "user_tara", - "supporting_edges": [ - { - "src": "alias_sablekeel", - "rel": "alias_of", - "dst": "user_tara", - "confidence": 1.0 - }, - { - "src": "alias_sablekeel", - "rel": "authored_post", - "dst": "post_uplink_note", - "confidence": 1.0 - }, - { - "src": "user_tara", - "rel": "works_at", - "dst": "org_harborlight_transit", - "confidence": 1.0 - }, - { - "src": "user_tara", - "rel": "reports_on", - "dst": "event_ghost_signal", - "confidence": 0.9 - } - ], - "metadata": { - "difficulty": "easy", - "difficulty_level": 1, - "question_id": "easy_10", - "support_nodes": 5 - } - }, - { - "task_type": "fixed_trace", - "question": "Follow alias_docksparrow through post_shift_roster, Dockyard 17, and the Lantern chain. Return the org node id.", - "answer": "org_northbridge_logistics", - "supporting_edges": [ - { - "src": "alias_docksparrow", - "rel": "alias_of", - "dst": "user_hiro", - "confidence": 1.0 - }, - { - "src": "alias_docksparrow", - "rel": "authored_post", - "dst": "post_shift_roster", - "confidence": 1.0 - }, - { - "src": "post_shift_roster", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_shift_roster", - "rel": "references", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "thr_supply_leak", - "rel": "references", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "user_hiro", - "rel": "works_at", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "user_hiro", - "rel": "located_in", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "user_hiro", - "rel": "collaborates_on", - "dst": "event_project_lantern", - "confidence": 0.9 - }, - { - "src": "user_bharat", - "rel": "collaborates_on", - "dst": "event_project_lantern", - "confidence": 0.9 - }, - { - "src": "user_bharat", - "rel": "connected_to", - "dst": "user_hiro", - "confidence": 0.95 - }, - { - "src": "user_hiro", - "rel": "connected_to", - "dst": "user_faris", - "confidence": 0.92 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "authored_post", - "dst": "post_quay_ledgers", - "confidence": 1.0 - }, - { - "src": "post_quay_ledgers", - "rel": "references", - "dst": "loc_east_quay", - "confidence": 1.0 - }, - { - "src": "post_quay_ledgers", - "rel": "references", - "dst": "event_glass_harbor", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_01", - "support_nodes": 17 - } - }, - { - "task_type": "fixed_trace", - "question": "Across the Glass Harbor cluster, which user behind alias_lanternmoth links to the Atlas Freight network from thr_quiet_manifest?", - "answer": "user_kian", - "supporting_edges": [ - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "authored_post", - "dst": "post_quay_ledgers", - "confidence": 1.0 - }, - { - "src": "post_quay_ledgers", - "rel": "references", - "dst": "loc_east_quay", - "confidence": 1.0 - }, - { - "src": "post_quay_ledgers", - "rel": "references", - "dst": "event_glass_harbor", - "confidence": 1.0 - }, - { - "src": "user_kian", - "rel": "authored_thread", - "dst": "thr_quiet_manifest", - "confidence": 1.0 - }, - { - "src": "thr_quiet_manifest", - "rel": "discusses", - "dst": "event_glass_harbor", - "confidence": 1.0 - }, - { - "src": "thr_quiet_manifest", - "rel": "references", - "dst": "org_atlas_freight", - "confidence": 1.0 - }, - { - "src": "user_kian", - "rel": "works_at", - "dst": "org_atlas_freight", - "confidence": 1.0 - }, - { - "src": "user_omar", - "rel": "works_at", - "dst": "org_atlas_freight", - "confidence": 1.0 - }, - { - "src": "user_kian", - "rel": "collaborates_on", - "dst": "event_glass_harbor", - "confidence": 0.9 - }, - { - "src": "user_omar", - "rel": "collaborates_on", - "dst": "event_glass_harbor", - "confidence": 0.9 - }, - { - "src": "user_priya", - "rel": "monitors", - "dst": "event_glass_harbor", - "confidence": 0.9 - }, - { - "src": "user_kian", - "rel": "connected_to", - "dst": "user_omar", - "confidence": 0.93 - }, - { - "src": "user_omar", - "rel": "connected_to", - "dst": "user_mika", - "confidence": 0.9 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "alias_basinraven", - "rel": "alias_of", - "dst": "user_mika", - "confidence": 1.0 - }, - { - "src": "alias_basinraven", - "rel": "authored_post", - "dst": "post_customs_tag", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_02", - "support_nodes": 17 - } - }, - { - "task_type": "fixed_trace", - "question": "Trace alias_basinraven through post_customs_tag, thr_customs_breach, and the Orion Customs collaboration chain. Who is it?", - "answer": "user_mika", - "supporting_edges": [ - { - "src": "alias_basinraven", - "rel": "alias_of", - "dst": "user_mika", - "confidence": 1.0 - }, - { - "src": "alias_basinraven", - "rel": "authored_post", - "dst": "post_customs_tag", - "confidence": 1.0 - }, - { - "src": "post_customs_tag", - "rel": "references", - "dst": "loc_north_basin", - "confidence": 1.0 - }, - { - "src": "post_customs_tag", - "rel": "references", - "dst": "event_iron_wharf", - "confidence": 1.0 - }, - { - "src": "user_mika", - "rel": "authored_thread", - "dst": "thr_customs_breach", - "confidence": 1.0 - }, - { - "src": "thr_customs_breach", - "rel": "discusses", - "dst": "event_iron_wharf", - "confidence": 1.0 - }, - { - "src": "thr_customs_breach", - "rel": "references", - "dst": "org_orion_customs", - "confidence": 1.0 - }, - { - "src": "user_mika", - "rel": "works_at", - "dst": "org_orion_customs", - "confidence": 1.0 - }, - { - "src": "user_quinn", - "rel": "works_at", - "dst": "org_orion_customs", - "confidence": 1.0 - }, - { - "src": "user_mika", - "rel": "collaborates_on", - "dst": "event_iron_wharf", - "confidence": 0.9 - }, - { - "src": "user_quinn", - "rel": "collaborates_on", - "dst": "event_iron_wharf", - "confidence": 0.9 - }, - { - "src": "user_mika", - "rel": "connected_to", - "dst": "user_quinn", - "confidence": 0.89 - }, - { - "src": "user_quinn", - "rel": "connected_to", - "dst": "user_nora", - "confidence": 0.88 - }, - { - "src": "org_orion_customs", - "rel": "connected_to", - "dst": "org_emberline_security", - "confidence": 0.77 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_03", - "support_nodes": 17 - } - }, - { - "task_type": "fixed_trace", - "question": "In the Ember Tide and Amber Veil overlap, which Foundry Row user behind alias_cinderveil collaborates on Ember Tide?", - "answer": "user_rhea", - "supporting_edges": [ - { - "src": "alias_cinderveil", - "rel": "alias_of", - "dst": "user_rhea", - "confidence": 1.0 - }, - { - "src": "alias_cinderveil", - "rel": "authored_post", - "dst": "post_foundry_map", - "confidence": 1.0 - }, - { - "src": "post_foundry_map", - "rel": "references", - "dst": "loc_foundry_row", - "confidence": 1.0 - }, - { - "src": "post_foundry_map", - "rel": "references", - "dst": "event_ember_tide", - "confidence": 1.0 - }, - { - "src": "user_nora", - "rel": "authored_thread", - "dst": "thr_foundry_watch", - "confidence": 1.0 - }, - { - "src": "thr_foundry_watch", - "rel": "discusses", - "dst": "event_ember_tide", - "confidence": 1.0 - }, - { - "src": "thr_foundry_watch", - "rel": "references", - "dst": "org_emberline_security", - "confidence": 1.0 - }, - { - "src": "user_rhea", - "rel": "authored_thread", - "dst": "thr_ember_tide_watch", - "confidence": 1.0 - }, - { - "src": "thr_ember_tide_watch", - "rel": "discusses", - "dst": "event_ember_tide", - "confidence": 1.0 - }, - { - "src": "thr_ember_tide_watch", - "rel": "references", - "dst": "loc_foundry_row", - "confidence": 1.0 - }, - { - "src": "user_rhea", - "rel": "works_at", - "dst": "org_emberline_security", - "confidence": 1.0 - }, - { - "src": "user_nora", - "rel": "works_at", - "dst": "org_emberline_security", - "confidence": 1.0 - }, - { - "src": "user_nora", - "rel": "investigates", - "dst": "event_amber_veil", - "confidence": 0.9 - }, - { - "src": "user_rhea", - "rel": "collaborates_on", - "dst": "event_ember_tide", - "confidence": 0.9 - }, - { - "src": "user_nora", - "rel": "connected_to", - "dst": "user_rhea", - "confidence": 0.87 - }, - { - "src": "event_ember_tide", - "rel": "connected_to", - "dst": "event_ghost_signal", - "confidence": 0.77 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_04", - "support_nodes": 18 - } - }, - { - "task_type": "fixed_trace", - "question": "Follow alias_tideshard from post_hull_signal into thr_uplink_route and the Harborlight relay. Return the org node id.", - "answer": "org_harborlight_transit", - "supporting_edges": [ - { - "src": "alias_tideshard", - "rel": "alias_of", - "dst": "user_soren", - "confidence": 1.0 - }, - { - "src": "alias_tideshard", - "rel": "authored_post", - "dst": "post_hull_signal", - "confidence": 1.0 - }, - { - "src": "post_hull_signal", - "rel": "references", - "dst": "loc_uplink_yard", - "confidence": 1.0 - }, - { - "src": "post_hull_signal", - "rel": "references", - "dst": "event_ghost_signal", - "confidence": 1.0 - }, - { - "src": "user_soren", - "rel": "authored_thread", - "dst": "thr_uplink_route", - "confidence": 1.0 - }, - { - "src": "thr_uplink_route", - "rel": "discusses", - "dst": "event_ghost_signal", - "confidence": 1.0 - }, - { - "src": "thr_uplink_route", - "rel": "references", - "dst": "org_harborlight_transit", - "confidence": 1.0 - }, - { - "src": "user_soren", - "rel": "works_at", - "dst": "org_harborlight_transit", - "confidence": 1.0 - }, - { - "src": "user_tara", - "rel": "works_at", - "dst": "org_harborlight_transit", - "confidence": 1.0 - }, - { - "src": "user_soren", - "rel": "collaborates_on", - "dst": "event_ghost_signal", - "confidence": 0.9 - }, - { - "src": "user_tara", - "rel": "reports_on", - "dst": "event_ghost_signal", - "confidence": 0.9 - }, - { - "src": "user_rhea", - "rel": "connected_to", - "dst": "user_soren", - "confidence": 0.86 - }, - { - "src": "user_soren", - "rel": "connected_to", - "dst": "user_tara", - "confidence": 0.86 - }, - { - "src": "org_harborlight_transit", - "rel": "operates_in", - "dst": "loc_uplink_yard", - "confidence": 1.0 - }, - { - "src": "org_harborlight_transit", - "rel": "connected_to", - "dst": "org_tidewatch_ops", - "confidence": 0.77 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_05", - "support_nodes": 17 - } - }, - { - "task_type": "fixed_trace", - "question": "Which Sunmesh user behind alias_frostledger connects post_lantern_route to thr_relay_map and the Sector 9 monitoring chain?", - "answer": "user_leena", - "supporting_edges": [ - { - "src": "alias_frostledger", - "rel": "alias_of", - "dst": "user_leena", - "confidence": 1.0 - }, - { - "src": "alias_frostledger", - "rel": "authored_post", - "dst": "post_lantern_route", - "confidence": 1.0 - }, - { - "src": "post_lantern_route", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "post_lantern_route", - "rel": "references", - "dst": "org_sunmesh_analytics", - "confidence": 1.0 - }, - { - "src": "user_leena", - "rel": "authored_thread", - "dst": "thr_relay_map", - "confidence": 1.0 - }, - { - "src": "thr_relay_map", - "rel": "discusses", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "thr_relay_map", - "rel": "references", - "dst": "org_sunmesh_analytics", - "confidence": 1.0 - }, - { - "src": "user_leena", - "rel": "works_at", - "dst": "org_sunmesh_analytics", - "confidence": 1.0 - }, - { - "src": "user_priya", - "rel": "works_at", - "dst": "org_sunmesh_analytics", - "confidence": 1.0 - }, - { - "src": "user_leena", - "rel": "located_in", - "dst": "loc_sector9", - "confidence": 1.0 - }, - { - "src": "org_sunmesh_analytics", - "rel": "operates_in", - "dst": "loc_sector9", - "confidence": 1.0 - }, - { - "src": "user_leena", - "rel": "monitors", - "dst": "event_project_lantern", - "confidence": 0.9 - }, - { - "src": "user_priya", - "rel": "connected_to", - "dst": "user_leena", - "confidence": 0.91 - }, - { - "src": "user_leena", - "rel": "connected_to", - "dst": "user_aria", - "confidence": 0.83 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "authored_post", - "dst": "post_quay_ledgers", - "confidence": 1.0 - }, - { - "src": "post_quay_ledgers", - "rel": "references", - "dst": "loc_east_quay", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_06", - "support_nodes": 17 - } - }, - { - "task_type": "fixed_trace", - "question": "Which user behind alias_emberglass is tied to Amber Veil after combining post_basin_photo, thr_basin_shift, and the Foundry Row investigation chain?", - "answer": "user_nora", - "supporting_edges": [ - { - "src": "alias_emberglass", - "rel": "alias_of", - "dst": "user_nora", - "confidence": 1.0 - }, - { - "src": "alias_emberglass", - "rel": "authored_post", - "dst": "post_basin_photo", - "confidence": 1.0 - }, - { - "src": "post_basin_photo", - "rel": "references", - "dst": "loc_foundry_row", - "confidence": 1.0 - }, - { - "src": "post_basin_photo", - "rel": "references", - "dst": "event_amber_veil", - "confidence": 1.0 - }, - { - "src": "user_quinn", - "rel": "authored_thread", - "dst": "thr_basin_shift", - "confidence": 1.0 - }, - { - "src": "thr_basin_shift", - "rel": "discusses", - "dst": "event_amber_veil", - "confidence": 1.0 - }, - { - "src": "thr_basin_shift", - "rel": "references", - "dst": "loc_north_basin", - "confidence": 1.0 - }, - { - "src": "user_nora", - "rel": "works_at", - "dst": "org_emberline_security", - "confidence": 1.0 - }, - { - "src": "user_quinn", - "rel": "works_at", - "dst": "org_orion_customs", - "confidence": 1.0 - }, - { - "src": "user_nora", - "rel": "located_in", - "dst": "loc_foundry_row", - "confidence": 1.0 - }, - { - "src": "user_nora", - "rel": "investigates", - "dst": "event_amber_veil", - "confidence": 0.9 - }, - { - "src": "user_quinn", - "rel": "connected_to", - "dst": "user_nora", - "confidence": 0.88 - }, - { - "src": "user_nora", - "rel": "connected_to", - "dst": "user_rhea", - "confidence": 0.87 - }, - { - "src": "org_orion_customs", - "rel": "connected_to", - "dst": "org_emberline_security", - "confidence": 0.77 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_07", - "support_nodes": 18 - } - }, - { - "task_type": "fixed_trace", - "question": "Combine alias_orchidfox, post_midnight_manifest, thr_supply_leak, and the Lantern to Glass Harbor bridge. Which user starts that chain?", - "answer": "user_ivy", - "supporting_edges": [ - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "user_diya", - "rel": "authored_thread", - "dst": "thr_supply_leak", - "confidence": 1.0 - }, - { - "src": "thr_supply_leak", - "rel": "discusses", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "thr_supply_leak", - "rel": "references", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "works_at", - "dst": "org_kestrel_works", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "collaborates_on", - "dst": "event_black_kite", - "confidence": 0.9 - }, - { - "src": "user_ivy", - "rel": "connected_to", - "dst": "user_bharat", - "confidence": 0.95 - }, - { - "src": "user_ivy", - "rel": "connected_to", - "dst": "user_elin", - "confidence": 0.86 - }, - { - "src": "event_project_lantern", - "rel": "connected_to", - "dst": "event_glass_harbor", - "confidence": 0.77 - }, - { - "src": "user_kian", - "rel": "collaborates_on", - "dst": "event_glass_harbor", - "confidence": 0.9 - }, - { - "src": "user_priya", - "rel": "monitors", - "dst": "event_glass_harbor", - "confidence": 0.9 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "authored_post", - "dst": "post_quay_ledgers", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_08", - "support_nodes": 17 - } - }, - { - "task_type": "fixed_trace", - "question": "Which user behind alias_monsoonbyte sits at the overlap of Blueharbor Media, Project Lantern, Black Kite, and the Ivy connection chain?", - "answer": "user_diya", - "supporting_edges": [ - { - "src": "alias_monsoonbyte", - "rel": "alias_of", - "dst": "user_diya", - "confidence": 1.0 - }, - { - "src": "alias_monsoonbyte", - "rel": "authored_post", - "dst": "post_drone_parts", - "confidence": 1.0 - }, - { - "src": "post_drone_parts", - "rel": "references", - "dst": "event_black_kite", - "confidence": 1.0 - }, - { - "src": "user_diya", - "rel": "authored_thread", - "dst": "thr_supply_leak", - "confidence": 1.0 - }, - { - "src": "thr_supply_leak", - "rel": "discusses", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "user_jules", - "rel": "authored_thread", - "dst": "thr_port_audit", - "confidence": 1.0 - }, - { - "src": "thr_port_audit", - "rel": "discusses", - "dst": "event_black_kite", - "confidence": 1.0 - }, - { - "src": "user_diya", - "rel": "works_at", - "dst": "org_blueharbor_media", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "works_at", - "dst": "org_kestrel_works", - "confidence": 1.0 - }, - { - "src": "user_jules", - "rel": "works_at", - "dst": "org_blueharbor_media", - "confidence": 1.0 - }, - { - "src": "user_diya", - "rel": "investigates", - "dst": "event_project_lantern", - "confidence": 0.9 - }, - { - "src": "user_ivy", - "rel": "collaborates_on", - "dst": "event_black_kite", - "confidence": 0.9 - }, - { - "src": "user_jules", - "rel": "reports_on", - "dst": "event_black_kite", - "confidence": 0.9 - }, - { - "src": "user_faris", - "rel": "connected_to", - "dst": "user_diya", - "confidence": 0.9 - }, - { - "src": "user_ivy", - "rel": "connected_to", - "dst": "user_elin", - "confidence": 0.86 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_09", - "support_nodes": 18 - } - }, - { - "task_type": "fixed_trace", - "question": "Who is the Northbridge user behind alias_steelquill when combining post_relay_schedule, thr_supply_leak, Dockyard 17, and Lantern collaborator edges?", - "answer": "user_bharat", - "supporting_edges": [ - { - "src": "alias_steelquill", - "rel": "alias_of", - "dst": "user_bharat", - "confidence": 1.0 - }, - { - "src": "alias_steelquill", - "rel": "authored_post", - "dst": "post_relay_schedule", - "confidence": 1.0 - }, - { - "src": "post_relay_schedule", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "post_relay_schedule", - "rel": "references", - "dst": "loc_sector9", - "confidence": 1.0 - }, - { - "src": "user_diya", - "rel": "authored_thread", - "dst": "thr_supply_leak", - "confidence": 1.0 - }, - { - "src": "thr_supply_leak", - "rel": "discusses", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "thr_supply_leak", - "rel": "references", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "user_bharat", - "rel": "works_at", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "user_hiro", - "rel": "works_at", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "user_bharat", - "rel": "located_in", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "user_hiro", - "rel": "located_in", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "user_bharat", - "rel": "collaborates_on", - "dst": "event_project_lantern", - "confidence": 0.9 - }, - { - "src": "user_hiro", - "rel": "collaborates_on", - "dst": "event_project_lantern", - "confidence": 0.9 - }, - { - "src": "user_ivy", - "rel": "connected_to", - "dst": "user_bharat", - "confidence": 0.95 - }, - { - "src": "user_bharat", - "rel": "connected_to", - "dst": "user_hiro", - "confidence": 0.95 - }, - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "authored_post", - "dst": "post_quay_ledgers", - "confidence": 1.0 - }, - { - "src": "post_quay_ledgers", - "rel": "references", - "dst": "loc_east_quay", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "mid", - "difficulty_level": 2, - "question_id": "mid_10", - "support_nodes": 17 - } - }, - { - "task_type": "fixed_trace", - "question": "Lantern to Glass Harbor handoff: identify the user behind alias_orchidfox after combining Lantern logistics, Dockyard links, and Atlas Freight bridge evidence.", - "answer": "user_ivy", - "supporting_edges": [ - { - "src": "alias_orchidfox", - "rel": "alias_of", - "dst": "user_ivy", - "confidence": 1.0 - }, - { - "src": "alias_orchidfox", - "rel": "authored_post", - "dst": "post_midnight_manifest", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "post_midnight_manifest", - "rel": "references", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "user_diya", - "rel": "authored_thread", - "dst": "thr_supply_leak", - "confidence": 1.0 - }, - { - "src": "thr_supply_leak", - "rel": "discusses", - "dst": "event_project_lantern", - "confidence": 1.0 - }, - { - "src": "thr_supply_leak", - "rel": "references", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "works_at", - "dst": "org_kestrel_works", - "confidence": 1.0 - }, - { - "src": "user_bharat", - "rel": "works_at", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "user_hiro", - "rel": "works_at", - "dst": "org_northbridge_logistics", - "confidence": 1.0 - }, - { - "src": "user_kian", - "rel": "works_at", - "dst": "org_atlas_freight", - "confidence": 1.0 - }, - { - "src": "user_omar", - "rel": "works_at", - "dst": "org_atlas_freight", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "located_in", - "dst": "loc_rivergate", - "confidence": 1.0 - }, - { - "src": "user_bharat", - "rel": "located_in", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "user_hiro", - "rel": "located_in", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "user_kian", - "rel": "located_in", - "dst": "loc_east_quay", - "confidence": 1.0 - }, - { - "src": "user_omar", - "rel": "located_in", - "dst": "loc_east_quay", - "confidence": 1.0 - }, - { - "src": "org_northbridge_logistics", - "rel": "operates_in", - "dst": "loc_dockyard17", - "confidence": 1.0 - }, - { - "src": "org_kestrel_works", - "rel": "operates_in", - "dst": "loc_rivergate", - "confidence": 1.0 - }, - { - "src": "org_atlas_freight", - "rel": "operates_in", - "dst": "loc_east_quay", - "confidence": 1.0 - }, - { - "src": "user_ivy", - "rel": "connected_to", - "dst": "user_bharat", - "confidence": 0.95 - }, - { - "src": "user_bharat", - "rel": "connected_to", - "dst": "user_hiro", - "confidence": 0.95 - }, - { - "src": "user_hiro", - "rel": "connected_to", - "dst": "user_faris", - "confidence": 0.92 - }, - { - "src": "user_ivy", - "rel": "connected_to", - "dst": "user_elin", - "confidence": 0.86 - }, - { - "src": "user_kian", - "rel": "connected_to", - "dst": "user_omar", - "confidence": 0.93 - }, - { - "src": "user_omar", - "rel": "connected_to", - "dst": "user_mika", - "confidence": 0.9 - }, - { - "src": "user_bharat", - "rel": "collaborates_on", - "dst": "event_project_lantern", - "confidence": 0.9 - }, - { - "src": "user_hiro", - "rel": "collaborates_on", - "dst": "event_project_lantern", - "confidence": 0.9 - }, - { - "src": "user_faris", - "rel": "collaborates_on", - "dst": "event_project_lantern", - "confidence": 0.9 - }, - { - "src": "user_ivy", - "rel": "collaborates_on", - "dst": "event_black_kite", - "confidence": 0.9 - }, - { - "src": "user_kian", - "rel": "collaborates_on", - "dst": "event_glass_harbor", - "confidence": 0.9 - }, - { - "src": "user_omar", - "rel": "collaborates_on", - "dst": "event_glass_harbor", - "confidence": 0.9 - }, - { - "src": "user_priya", - "rel": "monitors", - "dst": "event_glass_harbor", - "confidence": 0.9 - }, - { - "src": "user_kian", - "rel": "authored_thread", - "dst": "thr_quiet_manifest", - "confidence": 1.0 - }, - { - "src": "thr_quiet_manifest", - "rel": "discusses", - "dst": "event_glass_harbor", - "confidence": 1.0 - }, - { - "src": "thr_quiet_manifest", - "rel": "references", - "dst": "org_atlas_freight", - "confidence": 1.0 - }, - { - "src": "alias_lanternmoth", - "rel": "authored_post", - "dst": "post_quay_ledgers", - "confidence": 1.0 - }, - { - "src": "post_quay_ledgers", - "rel": "references", - "dst": "loc_east_quay", - "confidence": 1.0 - }, - { - "src": "post_quay_ledgers", - "rel": "references", - "dst": "event_glass_harbor", - "confidence": 1.0 - }, - { - "src": "event_project_lantern", - "rel": "connected_to", - "dst": "event_glass_harbor", - "confidence": 0.77 - }, - { - "src": "org_atlas_freight", - "rel": "connected_to", - "dst": "org_northbridge_logistics", - "confidence": 0.77 - }, - { - "src": "alias_lanternmoth", - "rel": "alias_of", - "dst": "user_kian", - "confidence": 1.0 - }, - { - "src": "alias_steelquill", - "rel": "alias_of", - "dst": "user_bharat", - "confidence": 1.0 - }, - { - "src": "alias_docksparrow", - "rel": "alias_of", - "dst": "user_hiro", - "confidence": 1.0 - }, - { - "src": "alias_monsoonbyte", - "rel": "alias_of", - "dst": "user_diya", - "confidence": 1.0 - }, - { - "src": "alias_nightrelay", - "rel": "alias_of", - "dst": "user_faris", - "confidence": 1.0 - }, - { - "src": "alias_mapleghost", - "rel": "alias_of", - "dst": "user_elin", - "confidence": 1.0 - }, - { - "src": "alias_quartzlotus", - "rel": "alias_of", - "dst": "user_cyrus", - "confidence": 1.0 - }, - { - "src": "alias_emberglass", - "rel": "alias_of", - "dst": "user_nora", - "confidence": 1.0 - }, - { - "src": "alias_basinraven", - "rel": "alias_of", - "dst": "user_mika", - "confidence": 1.0 - }, - { - "src": "alias_tideshard", - "rel": "alias_of", - "dst": "user_soren", - "confidence": 1.0 - }, - { - "src": "alias_hollowsignal", - "rel": "alias_of", - "dst": "user_priya", - "confidence": 1.0 - }, - { - "src": "alias_ironwhisper", - "rel": "alias_of", - "dst": "user_omar", - "confidence": 1.0 - }, - { - "src": "alias_cinderveil", - "rel": "alias_of", - "dst": "user_rhea", - "confidence": 1.0 - 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"src": "user_quinn", - "rel": "located_in", - "dst": "loc_north_basin", - "confidence": 1.0 - }, - { - "src": "user_rhea", - "rel": "works_at", - "dst": "org_emberline_security", - "confidence": 1.0 - }, - { - "src": "user_rhea", - "rel": "located_in", - "dst": "loc_foundry_row", - "confidence": 1.0 - }, - { - "src": "user_soren", - "rel": "works_at", - "dst": "org_harborlight_transit", - "confidence": 1.0 - }, - { - "src": "user_soren", - "rel": "located_in", - "dst": "loc_uplink_yard", - "confidence": 1.0 - }, - { - "src": "user_tara", - "rel": "works_at", - "dst": "org_harborlight_transit", - "confidence": 1.0 - }, - { - "src": "user_tara", - "rel": "located_in", - "dst": "loc_uplink_yard", - "confidence": 1.0 - }, - { - "src": "org_helios_labs", - "rel": "operates_in", - "dst": "loc_sector9", - "confidence": 1.0 - }, - { - "src": "org_northbridge_logistics", - "rel": "operates_in", - "dst": "loc_dockyard17", - "confidence": 1.0 - } - ], - "metadata": { - "difficulty": "high", - "difficulty_level": 3, - "question_id": "high_10", - "support_nodes": 55 - } - } - ], - "llm_generate_remaining_graph": true, - "llm_generate_remaining_tasks": false, - "llm_generated_edge_budget": 48, - "llm_generated_task_budget": 0, - "llm_generation_parallel": true, - "llm_generation_workers": 4, - "llm_generation_retries": 3, - "allow_template_fallback_on_llm_failure": false - } -} \ No newline at end of file diff --git a/scratch/osint/datasets/fixed_levels/shared_config_fixed_levels.json b/scratch/osint/datasets/fixed_levels/shared_config_fixed_levels.json deleted file mode 100644 index 268e55c26d330c6ca1d041cd75f5665b5ea87847..0000000000000000000000000000000000000000 --- a/scratch/osint/datasets/fixed_levels/shared_config_fixed_levels.json +++ /dev/null @@ -1,55 +0,0 @@ -{ - "environment": { - "n_users": 24, - "alias_density": 0.2, - "noise_level": 0.12, - "red_herring_rate": 0.08, - "max_steps": 24, - "seed": 2026 - }, - "swarm": { - "enabled": true, - "max_agents": 3, - "max_breadth": 2, - "max_width": 2, - "max_depth": 2, - "planner_rounds": 2, - "tools_per_agent": 1 - }, - "spawn_reward": { - "lambda_parallel": 0.15, - "lambda_finish": 0.2, - "anneal": 1.0, - "max_parallel_hint": 3 - }, - "seeding": { - "seeded_nodes": [], - "seeded_edges": [], - "seeded_questions": [], - "llm_generate_remaining_graph": true, - "llm_generate_remaining_tasks": false, - "llm_generated_edge_budget": 64, - "llm_generated_task_budget": 0, - "llm_generation_parallel": true, - "llm_generation_workers": 4, - "llm_generation_retries": 3, - "allow_template_fallback_on_llm_failure": false - }, - "llm": { - "provider": "ollama", - "model": "qwen3:2b", - "temperature": 0.05, - "max_tokens": 384, - "timeout_seconds": 240, - "ollama_base_url": "http://127.0.0.1:11434", - "openai_base_url": "https://api.openai.com/v1", - "openai_api_key_env": "OPENAI_API_KEY", - "openai_api_key": "" - }, - "runtime": { - "default_episodes": 30, - "leaderboard_path": "datasets/fixed_levels/leaderboard_fixed_levels.json", - "dashboard_path": "datasets/fixed_levels/dashboard_fixed_levels.html", - "sweep_dashboard_dir": "datasets/fixed_levels/sweep_dashboards" - } -} diff --git a/scratch/osint/docs/reward_design_notes.md b/scratch/osint/docs/reward_design_notes.md deleted file mode 100644 index 7041d248b1e22e864a0ca50dbfe3dc2fbc32f7f7..0000000000000000000000000000000000000000 --- a/scratch/osint/docs/reward_design_notes.md +++ /dev/null @@ -1,94 +0,0 @@ -# Reward Design Notes - -This environment uses a composite reward that adapts ideas from: - -- AutoGraph-R1 (arXiv:2510.15339) -- UniRel (arXiv:2512.17043) -- DeepPath (EMNLP 2017, D17-1060) -- Multi-Hop KG Reasoning with Reward Shaping (EMNLP 2018, D18-1362) -- Kimi K2.5 (arXiv:2602.02276) for PARL-style swarm auxiliary shaping - -Additional related context consulted: - -- MINERVA (arXiv:1711.05851) for query-conditioned walk-style reasoning over KG paths. - -## Components in this Branch - -The implementation follows a staged reward design: - -1. edge-level rewards during graph construction (`ADD_EDGE`) -2. answer-level rewards for retrieval usefulness and final task utility (`ANSWER`) -3. evaluation-level composite leaderboard score for benchmark ranking - -### 1) Edge addition reward - -For each `ADD_EDGE`, the reward combines: - -- Global accuracy term (DeepPath): - - $r_{global} = +1$ if a candidate edge is correct, else $-1$ (scaled in code for stability). -- Soft shaping term (D18 reward shaping): - - $R = R_b + (1 - R_b) f(s, r, o)$, where $f$ is a soft fact plausibility score. - - In code, $f$ is approximated by relation/type priors plus small domain priors. -- Efficiency term (DeepPath): - - $r_{efficiency} \propto 1 / \text{step\_count}$. -- Diversity term (DeepPath): - - novelty from cosine dissimilarity of edge signatures; repeated patterns are down-weighted. -- Relation/entity informativeness (UniRel): - - relation rarity via normalized IDF of relation labels, - - entity informativeness via inverse hub-penalty. -- Connectivity gain term: - - rewards bridge edges that connect previously disconnected graph regions. - -### 2) Final answer reward - -For `ANSWER`, the reward combines: - -- format validity, -- answer correctness, -- knowledge-carrying utility (AutoGraph-R1 style): - - $R_C(q, y, G) = \mathbb{{I}}[\text{{deducible}}(q, y \mid G)]$. -- knowledge-indexing utility (AutoGraph-R1 style): - - $R_I(q, D_{{gold}}, G) = |Top\text{{-}}k(G,q) \cap D_{{gold}}| / |D_{{gold}}|$, - - approximated in this environment with evidence recall over tool outputs. -- connectivity (UniRel style): - - discrete connectivity reward over extracted seed entities, normalized for stable mixing. -- graph F1 against supporting edges, -- compactness penalty for unnecessary extra edges, -- efficiency bonus, -- relation/entity informativeness for the constructed subgraph, -- repetition penalty to discourage redundant relation generation patterns. - -UniRel-style aggregate view represented in this branch: - -$$ -R(a) \approx R_{{fmt}} + R_{{con}} + w_1 R_{{ent}} + w_2 R_{{rel}} + \text{{task utility terms}} -$$ - -with task utility terms coming from AutoGraph-inspired $R_C$ and $R_I$ components. - -## Telemetry - -Per-step component rewards are aggregated into `info["reward_components"]`, enabling: - -- richer benchmark summaries, -- leaderboard ranking by composite utility, -- visual diagnostics in dashboard exports. - -Evaluation also computes derived retrieval and structural utility signals used in leaderboard ranking. - -## Future Multi-Agent Notes - -This branch now includes a low-width swarm baseline orchestrator that adds PARL-style auxiliary shaping on top of the core edge and answer rewards. - -The helper implementation is in: - -- `src/osint_env/env/spawn_reward_hooks.py` - -It follows the Kimi K2.5 style decomposition: - -- $r_{{PARL}}(x,y) = r_{{perf}}(x,y) + \lambda_1 r_{{parallel}} + \lambda_2 r_{{finish}}$, -- optional critical-steps shaping for latency-sensitive training, -- optional annealing of $\lambda_1, \lambda_2$ toward zero, -- optional breadth/depth shaping hooks for future branch integration. - -The expanded project-level walkthrough is in `README.md` under "Reward Design (Integrated Notes)". diff --git a/scratch/osint/inference.py b/scratch/osint/inference.py deleted file mode 100644 index 3284bab66880974ba47675cf4be0f41f70fdf7e5..0000000000000000000000000000000000000000 --- a/scratch/osint/inference.py +++ /dev/null @@ -1,509 +0,0 @@ -from __future__ import annotations - -import json -import os -from pathlib import Path -from typing import Any - -from osint_env.agents.single_agent import SingleAgentRunner -from osint_env.agents.swarm_agent import SwarmAgentRunner -from osint_env.config import clone_environment_config, load_seeding_config, load_shared_config -from osint_env.domain.models import EnvironmentConfig -from osint_env.env.environment import OSINTEnvironment -from osint_env.env.reward import compute_graph_f1 -from osint_env.eval.leaderboard import append_leaderboard_record, load_leaderboard -from osint_env.eval.metrics import EvalMetrics -from osint_env.llm import build_llm_client -from osint_env.viz import export_dashboard - - -CONFIG_PATH = os.getenv("CONFIG_PATH", "datasets/fixed_levels/shared_config_fixed_levels.json") -SEED_FILE = os.getenv("SEED_FILE", "datasets/fixed_levels/seed_fixed_levels.json") -AGENT_MODE = os.getenv("AGENT_MODE", "swarm") -LLM_PROVIDER = os.getenv("LLM_PROVIDER", "openai") -MODEL_NAME = os.getenv("MODEL_NAME", "gpt-5.4") -OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "") -OPENAI_BASE_URL = os.getenv("OPENAI_BASE_URL", "") -OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "") -OPENAI_API_KEY_ENV = os.getenv("OPENAI_API_KEY_ENV", "OPENAI_API_KEY") -API_BASE_URL = os.getenv("API_BASE_URL", "https://api.openai.com/v1") -API_KEY = os.getenv("API_KEY", "") -HF_SPACE_URL = os.getenv("HF_SPACE_URL", "") -HF_TOKEN = os.getenv("HF_TOKEN","") -LOCAL_IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME", "") -LLM_TIMEOUT_SECONDS = int(os.getenv("LLM_TIMEOUT_SECONDS", "0")) -EPISODES = int(os.getenv("EPISODES", "1")) -SUCCESS_SCORE_THRESHOLD = float(os.getenv("SUCCESS_SCORE_THRESHOLD", "0.67")) -TASK_INDICES_RAW = os.getenv("TASK_INDICES", "") - -WRITE_BENCHMARK_ARTIFACTS = os.getenv("WRITE_BENCHMARK_ARTIFACTS", "1").strip().lower() in { - "1", - "true", - "yes", - "y", - "on", -} -LEADERBOARD_PATH = os.getenv("LEADERBOARD_PATH", "datasets/fixed_levels/leaderboard_fixed_levels.json") -DASHBOARD_PATH = os.getenv("DASHBOARD_PATH", "datasets/fixed_levels/dashboard_fixed_levels.html") -RUN_NAME = os.getenv("RUN_NAME", "fixed_levels_qwen_swarm") - -BENCHMARK = "osint-openenv" -TASK_NAME = "fixed_levels_easy_mid_hard" - - -def _parse_task_indices(raw: str) -> list[int]: - out: list[int] = [] - for token in str(raw or "").split(","): - stripped = token.strip() - if not stripped: - continue - try: - out.append(int(stripped)) - except ValueError: - continue - return out - - -def _normalize_ollama_base_url(url: str) -> str: - normalized = str(url or "").strip().rstrip("/") - if normalized.endswith("/v1"): - normalized = normalized[:-3].rstrip("/") - return normalized or "http://127.0.0.1:11434" - - -def _normalize_openai_base_url(url: str) -> str: - normalized = str(url or "").strip().rstrip("/") - if not normalized: - return "" - if normalized.endswith("/v1"): - return normalized - return f"{normalized}/v1" - - -TASK_INDICES = _parse_task_indices(TASK_INDICES_RAW) - - -def log_start(task: str, env: str, model: str) -> None: - print(f"[START] task={task} env={env} model={model}", flush=True) - - -def log_step(step: int, action: str, reward: float, done: bool, error: str | None) -> None: - error_text = "null" if error is None else str(error) - print( - f"[STEP] step={step} action={action} reward={reward:.2f} done={str(bool(done)).lower()} error={error_text}", - flush=True, - ) - - -def log_end(task: str, success: bool, steps: int, score: float, rewards: list[float]) -> None: - rewards_text = ",".join(f"{value:.2f}" for value in rewards) - print( - f"[END] success={str(bool(success)).lower()} steps={steps} score={score:.2f} rewards={rewards_text}", - flush=True, - ) - - -def _looks_like_placeholder_api_key(value: str) -> bool: - token = str(value or "").strip().lower() - if not token: - return True - placeholder_markers = [ - "your_openai_api_key", - "your-key", - "your_key", - "your real", - "real-openai-key", - "replace-me", - "changeme", - "example", - "", - ] - if token.startswith("your_") or token.startswith("sk-your-"): - return True - return any(marker in token for marker in placeholder_markers) - - -def _format_action(action: dict[str, Any]) -> str: - action_type = str(action.get("action_type", "")).upper() - payload = dict(action.get("payload", {})) - - if action_type == "ANSWER": - return f"answer({str(payload.get('answer', 'unknown')).strip()})" - - if action_type == "ADD_EDGE": - try: - conf = float(payload.get("confidence", 1.0)) - except (TypeError, ValueError): - conf = 1.0 - return ( - "add_edge(" - f"{payload.get('src', '')}," - f"{payload.get('rel', '')}," - f"{payload.get('dst', '')}," - f"{conf:.2f}" - ")" - ) - - tool_name = str(payload.get("tool_name", "tool")).strip() or "tool" - args = payload.get("args", {}) - if not isinstance(args, dict) or not args: - return f"{tool_name}()" - args_text = ",".join(f"{key}={value}" for key, value in sorted(args.items())) - return f"{tool_name}({args_text})" - - -def _assistant_tool_call_id(message: dict[str, Any]) -> str | None: - tool_calls = list(message.get("tool_calls", [])) - if not tool_calls: - return None - tool_call_id = tool_calls[0].get("id") - return str(tool_call_id) if tool_call_id else None - - -def _tool_result_message(assistant_message: dict[str, Any], result: dict[str, Any]) -> dict[str, Any] | None: - tool_call_id = _assistant_tool_call_id(assistant_message) - if not tool_call_id: - return None - return { - "role": "tool", - "tool_call_id": tool_call_id, - "content": json.dumps(result, sort_keys=True), - } - - -def _resolve_environment_config() -> EnvironmentConfig: - shared = load_shared_config(CONFIG_PATH) - env_cfg = clone_environment_config(shared.environment) - - if SEED_FILE and Path(SEED_FILE).exists(): - env_cfg.seeding = load_seeding_config(SEED_FILE) - - mode = AGENT_MODE.strip().lower() - if mode == "single": - env_cfg.swarm.enabled = False - elif mode == "swarm": - env_cfg.swarm.enabled = True - - # Inference submissions must route all calls through OpenAI-compatible client config. - env_cfg.llm.provider = "openai" - env_cfg.llm.model = MODEL_NAME.strip() - - if LLM_TIMEOUT_SECONDS > 0: - env_cfg.llm.timeout_seconds = int(LLM_TIMEOUT_SECONDS) - - # Evaluation harnesses inject API_BASE_URL/HF_TOKEN for proxy-enforced requests. - resolved_openai_base = API_BASE_URL.strip() or OPENAI_BASE_URL.strip() or HF_SPACE_URL.strip() - if resolved_openai_base: - env_cfg.llm.openai_base_url = _normalize_openai_base_url(resolved_openai_base) - - if HF_TOKEN.strip(): - env_cfg.llm.openai_api_key = HF_TOKEN.strip() - elif API_KEY.strip(): - env_cfg.llm.openai_api_key = API_KEY.strip() - elif OPENAI_API_KEY.strip(): - env_cfg.llm.openai_api_key = OPENAI_API_KEY.strip() - - if OPENAI_API_KEY_ENV.strip(): - env_cfg.llm.openai_api_key_env = OPENAI_API_KEY_ENV.strip() - - return env_cfg - - -def _runner_for(env: OSINTEnvironment, llm: Any) -> SingleAgentRunner | SwarmAgentRunner: - if env.config.swarm.enabled: - return SwarmAgentRunner(env=env, llm=llm) - return SingleAgentRunner(env=env, llm=llm) - - -def _normalize_difficulty(value: str) -> str: - token = str(value or "").strip().lower() - if token in {"easy", "e"}: - return "easy" - if token in {"mid", "medium", "m"}: - return "medium" - if token in {"high", "hard", "h"}: - return "hard" - return "hard" - - -def _task_difficulty(env: OSINTEnvironment, task_index: int) -> str: - idx = int(task_index) % max(1, len(env.tasks)) - task = env.tasks[idx] - if isinstance(task.metadata, dict) and "difficulty" in task.metadata: - return _normalize_difficulty(str(task.metadata.get("difficulty", ""))) - if idx < 10: - return "easy" - if idx < 20: - return "medium" - return "hard" - - -def _episode_row(env: OSINTEnvironment, info: dict[str, Any]) -> dict[str, Any]: - if env.state is None: - return { - "task_id": "unknown", - "task_type": "unknown", - "question": "", - "task_answer": str(info.get("task_answer", "")), - "agent_answer": str(info.get("agent_answer", "")), - "graph_f1": 0.0, - "reward": float(info.get("total_reward", 0.0) or 0.0), - "steps": int(info.get("step_count", 0) or 0), - "tool_calls": int(info.get("tool_calls", 0) or 0), - "success": int(info.get("agent_answer") == info.get("task_answer")), - "reward_components": dict(info.get("reward_components", {})), - "pred_edges": [], - "truth_edges": [], - } - - graph_f1 = compute_graph_f1(env.memory_graph.edges, env.state.task.supporting_edges) - return { - "task_id": env.state.task.task_id, - "task_type": env.state.task.task_type, - "question": env.state.task.question, - "task_answer": str(info.get("task_answer", "")), - "agent_answer": str(info.get("agent_answer", "")) if info.get("agent_answer") is not None else "", - "graph_f1": graph_f1, - "reward": float(info.get("total_reward", 0.0) or 0.0), - "steps": int(info.get("step_count", 0) or 0), - "tool_calls": int(info.get("tool_calls", 0) or 0), - "success": int(info.get("agent_answer") == info.get("task_answer")), - "reward_components": dict(info.get("reward_components", {})), - "spawn_count": int(info.get("spawn_count", 0) or 0), - "spawn_critical_steps": int(info.get("spawn_critical_steps", 0) or 0), - "pred_edges": [ - { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - for edge in env.memory_graph.edges - ], - "truth_edges": [ - { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - for edge in env.state.task.supporting_edges - ], - } - - -def _last_action_error(observation: Any, info: dict[str, Any]) -> str | None: - raw = info.get("last_action_error") if isinstance(info, dict) else None - if raw is not None: - return str(raw) - - tool_outputs = getattr(observation, "tool_outputs", None) - if isinstance(tool_outputs, list) and tool_outputs: - last = tool_outputs[-1] - if isinstance(last, dict): - output = last.get("output") - if isinstance(output, dict) and output.get("error") is not None: - return str(output.get("error")) - return None - - -def _install_step_logger(env: OSINTEnvironment) -> tuple[list[float], dict[str, int], Any]: - rewards: list[float] = [] - counters = {"steps": 0} - original_step = env.step - - def _logged_step(action: Any): - observation, reward, done, info = original_step(action) - counters["steps"] += 1 - reward_value = float(reward or 0.0) - rewards.append(reward_value) - action_type = getattr(action, "action_type", "") - action_type_value = str(getattr(action_type, "value", action_type)) - action_text = _format_action( - { - "action_type": action_type_value, - "payload": dict(getattr(action, "payload", {}) or {}), - } - ) - log_step( - step=counters["steps"], - action=action_text, - reward=reward_value, - done=bool(done), - error=_last_action_error(observation, info if isinstance(info, dict) else {}), - ) - return observation, reward, done, info - - env.step = _logged_step - return rewards, counters, original_step - - -def _validate_required_configuration() -> None: - missing: list[str] = [] - - api_base = API_BASE_URL.strip() - model_name = MODEL_NAME.strip() - hf_token = HF_TOKEN.strip() - api_key = API_KEY.strip() - openai_key = OPENAI_API_KEY.strip() - - if not api_base or api_base == "": - missing.append("API_BASE_URL") - if not model_name or model_name == "": - missing.append("MODEL_NAME") - if not (hf_token or api_key or openai_key): - missing.append("HF_TOKEN|API_KEY|OPENAI_API_KEY") - - # Required when using docker-image based env construction. - if os.getenv("REQUIRE_LOCAL_IMAGE_NAME", "0").strip().lower() in {"1", "true", "yes", "on"}: - if not LOCAL_IMAGE_NAME.strip(): - missing.append("LOCAL_IMAGE_NAME") - - if missing: - raise RuntimeError(f"Missing required environment variables: {', '.join(sorted(set(missing)))}") - - -def _task_targets(env: OSINTEnvironment, episodes: int, task_indices: list[int]) -> list[int | None]: - if task_indices: - task_count = max(1, len(env.tasks)) - return [index % task_count for index in task_indices] - return [None] * max(1, episodes) - - -def _run_with_runner( - env: OSINTEnvironment, - llm: Any, - episodes: int, - task_indices: list[int], -) -> tuple[dict[str, Any], list[dict[str, Any]], list[float], int]: - metrics = EvalMetrics() - episode_rows: list[dict[str, Any]] = [] - rewards, counters, original_step = _install_step_logger(env) - - single_runner = SingleAgentRunner(env=env, llm=llm) - swarm_runner = SwarmAgentRunner(env=env, llm=llm) if env.config.swarm.enabled else None - - try: - for task_index in _task_targets(env, episodes, task_indices): - task_count = max(1, len(env.tasks)) - selected_index = env._task_idx % task_count if task_index is None else int(task_index) % task_count - if task_index is not None: - # Keep compatibility with explicit task selection from the previous inference script. - env._task_idx = selected_index - - difficulty = _task_difficulty(env, selected_index) - if difficulty == "easy": - runner: SingleAgentRunner | SwarmAgentRunner = single_runner - elif swarm_runner is not None: - runner = swarm_runner - else: - runner = single_runner - - info = runner.run_episode() - if env.state is None: - continue - - graph_f1 = compute_graph_f1(env.memory_graph.edges, env.state.task.supporting_edges) - metrics.add(info, task_type=env.state.task.task_type, graph_f1=graph_f1) - episode_rows.append(_episode_row(env, info)) - finally: - env.step = original_step - - return metrics.summary(), episode_rows, rewards, int(counters["steps"]) - - -def _maybe_write_artifacts( - env: OSINTEnvironment, - summary: dict[str, Any], - episodes: int, - episode_rows: list[dict[str, Any]], -) -> tuple[dict[str, Any] | None, str | None]: - if not WRITE_BENCHMARK_ARTIFACTS: - return None, None - - record = append_leaderboard_record( - path=LEADERBOARD_PATH, - summary=summary, - episodes=episodes, - run_name=RUN_NAME or None, - config={ - "seed": env.config.seed, - "max_steps": env.config.max_steps, - "swarm_enabled": env.config.swarm.enabled, - "max_agents": env.config.swarm.max_agents, - "max_breadth": env.config.swarm.max_breadth, - "max_width": env.config.swarm.max_width, - "max_depth": env.config.swarm.max_depth, - "seeded_questions": len(env.config.seeding.seeded_questions), - "llm_provider": env.config.llm.provider, - "llm_model": env.config.llm.model, - }, - ) - - leaderboard = load_leaderboard(LEADERBOARD_PATH) - dashboard = export_dashboard( - env=env, - evaluation={"summary": summary, "episodes": episode_rows}, - leaderboard_records=leaderboard, - output_path=DASHBOARD_PATH, - ) - return record, dashboard - - -def main() -> None: - _validate_required_configuration() - env_cfg = _resolve_environment_config() - llm_client = build_llm_client(env_cfg.llm) - - episodes_given = "EPISODES" in os.environ and str(os.getenv("EPISODES", "")).strip() != "" - task_indices_given = bool(TASK_INDICES) - - if not episodes_given and not task_indices_given: - runs: list[tuple[str, list[int], int]] = [ - ("easy", list(range(0, 10)), 10), - ("mid", list(range(10, 20)), 10), - ("hard", list(range(20, 30)), 10), - ] - else: - selected_indices = TASK_INDICES if task_indices_given else [] - episodes = len(selected_indices) if selected_indices else max(1, EPISODES) - runs = [(TASK_NAME, selected_indices, episodes)] - - for task_name, run_indices, run_episodes in runs: - env: OSINTEnvironment | None = None - rewards: list[float] = [] - steps_taken = 0 - score = 0.0 - success = False - - env = OSINTEnvironment(env_cfg, llm=llm_client) - log_start(task=task_name, env=BENCHMARK, model=env_cfg.llm.model) - - try: - summary, episode_rows, rewards, steps_taken = _run_with_runner( - env=env, - llm=llm_client, - episodes=run_episodes, - task_indices=run_indices, - ) - - score = float(summary.get("avg_reward", 0.0) or 0.0) - score = max(0.0, min(1.0, score)) - success = score >= SUCCESS_SCORE_THRESHOLD - - _maybe_write_artifacts( - env=env, - summary=summary, - episodes=run_episodes, - episode_rows=episode_rows, - ) - finally: - if env is not None: - close_fn = getattr(env, "close", None) - if callable(close_fn): - close_fn() - log_end(task=task_name, success=success, steps=steps_taken, score=score, rewards=rewards) - - -if __name__ == "__main__": - main() diff --git a/scratch/osint/my_env_v4.py b/scratch/osint/my_env_v4.py deleted file mode 100644 index 34037cd36e5d2ce3fcda2debc71e461bc07cbf6c..0000000000000000000000000000000000000000 --- a/scratch/osint/my_env_v4.py +++ /dev/null @@ -1,46 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass - - -@dataclass(slots=True) -class MyEnvV4Action: - message: str - - -@dataclass(slots=True) -class _EchoObservation: - echoed_message: str - - -@dataclass(slots=True) -class _EchoResult: - observation: _EchoObservation - reward: float = 0.0 - done: bool = False - - -class MyEnvV4Env: - def __init__(self) -> None: - self._step_count = 0 - - @classmethod - async def from_docker_image(cls, image_name: str | None = None) -> "MyEnvV4Env": - return cls() - - async def reset(self) -> _EchoResult: - self._step_count = 0 - return _EchoResult(observation=_EchoObservation(echoed_message=""), reward=0.0, done=False) - - async def step(self, action: MyEnvV4Action) -> _EchoResult: - self._step_count += 1 - message = str(getattr(action, "message", "")) - reward = len(message) * 0.1 - return _EchoResult( - observation=_EchoObservation(echoed_message=message), - reward=reward, - done=False, - ) - - async def close(self) -> None: - return None diff --git a/scratch/osint/openenv.yaml b/scratch/osint/openenv.yaml deleted file mode 100644 index a9fdeb8139588db01cd990c38e3912498e2e37ea..0000000000000000000000000000000000000000 --- a/scratch/osint/openenv.yaml +++ /dev/null @@ -1,66 +0,0 @@ -name: osint-openenv -version: 0.1.0 -description: Synthetic OSINT benchmark environment exposed over HTTP. -tasks: - - id: seed_task_0 - difficulty: easy - max_steps: 24 - grader: - type: difficulty_exact_match - answer_type: node_id - case_sensitive: true - reward_profile: easy - - id: seed_task_10 - difficulty: medium - max_steps: 24 - grader: - type: difficulty_exact_match - answer_type: node_id - case_sensitive: true - reward_profile: medium - - id: seed_task_20 - difficulty: hard - max_steps: 24 - grader: - type: difficulty_exact_match - answer_type: node_id - case_sensitive: true - reward_profile: hard -transport: - type: http - base_path: / -endpoints: - health: - method: GET - path: /health - metadata: - method: GET - path: /api/environment - tasks: - method: GET - path: /openenv/tasks - reset: - method: POST - path: /reset - step: - method: POST - path: /step - state: - method: GET - path: /state -models: - action_space: - - CALL_TOOL - - ADD_EDGE - - ANSWER - task_fields: - - task_id - - task_type - - question - - difficulty - - grader - observation_fields: - - tool_outputs - - graph_snapshot - - action_history - - task diff --git a/scratch/osint/pyproject.toml b/scratch/osint/pyproject.toml deleted file mode 100644 index 6d56d42c639a3ddb0ef471e8c98e4dfe86fff668..0000000000000000000000000000000000000000 --- a/scratch/osint/pyproject.toml +++ /dev/null @@ -1,35 +0,0 @@ -[project] -name = "osint-rl-env" -version = "0.1.0" -description = "OSINT-style multi-platform information ecosystem environment for LLM agents." -readme = "README.md" -requires-python = ">=3.10" -dependencies = [ - "openenv>=0.1.13", - "openai>=1.40.0", - "fastapi>=0.115.0", - "requests>=2.32.3", - "uvicorn>=0.30.0", -] - -[project.optional-dependencies] -dev = [ - "pytest>=8.0.0", -] - -[project.scripts] -osint-env = "osint_env.cli:main" -server = "osint_env.server_entry:main" - -[build-system] -requires = ["setuptools>=68", "wheel"] -build-backend = "setuptools.build_meta" - -[tool.setuptools] -package-dir = {"" = "src"} - -[tool.setuptools.packages.find] -where = ["src"] - -[tool.pytest.ini_options] -testpaths = ["tests"] diff --git a/scratch/osint/requirements.txt b/scratch/osint/requirements.txt deleted file mode 100644 index 1b8041c02ff0a10c5fb7bb8021d123d373709cfb..0000000000000000000000000000000000000000 --- a/scratch/osint/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -openenv>=0.1.13 -openai>=1.40.0 -fastapi>=0.115.0 -requests>=2.32.3 -uvicorn>=0.30.0 -pytest>=8.0.0 diff --git a/scratch/osint/scripts/build_fixed_levels_dataset.py b/scratch/osint/scripts/build_fixed_levels_dataset.py deleted file mode 100644 index 3e0ad295450f49db4b7071fa0c57ae4341891645..0000000000000000000000000000000000000000 --- a/scratch/osint/scripts/build_fixed_levels_dataset.py +++ /dev/null @@ -1,197 +0,0 @@ -from __future__ import annotations - -import argparse -import json -from collections import Counter -from dataclasses import asdict -from pathlib import Path -from typing import Any - -from osint_env.config import clone_environment_config, load_seeding_config, load_shared_config -from osint_env.data.generator import DatasetGenerator -from osint_env.domain.models import Edge, TaskInstance -from osint_env.llm import build_llm_client - - -def edge_to_dict(edge: Edge) -> dict[str, Any]: - return { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - - -def task_to_dict(task: TaskInstance) -> dict[str, Any]: - return { - "task_id": task.task_id, - "task_type": task.task_type, - "question": task.question, - "answer": task.answer, - "supporting_edges": [edge_to_dict(e) for e in task.supporting_edges], - "metadata": dict(task.metadata), - } - - -def build_fixed_snapshot(seed_path: Path) -> dict[str, Any]: - seeding = load_seeding_config(seed_path) - fixed_nodes = [] - for node in seeding.seeded_nodes: - fixed_nodes.append( - { - "node_id": node.node_id, - "node_type": str(getattr(node.node_type, "value", node.node_type)), - "attrs": dict(node.attrs), - } - ) - fixed_edges = [ - { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - for edge in seeding.seeded_edges - ] - fixed_questions = [] - for idx, q in enumerate(seeding.seeded_questions): - fixed_questions.append( - { - "task_id": f"fixed_task_{idx:02d}", - "task_type": q.task_type, - "question": q.question, - "answer": q.answer, - "supporting_edges": [ - { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - for edge in q.supporting_edges - ], - "metadata": dict(q.metadata), - } - ) - - difficulty_counts = Counter(str(q.get("metadata", {}).get("difficulty", "unknown")) for q in fixed_questions) - return { - "dataset_name": "fixed_levels_submission_set", - "source_seed": str(seed_path), - "graph": { - "nodes": fixed_nodes, - "edges": fixed_edges, - "node_count": len(fixed_nodes), - "edge_count": len(fixed_edges), - }, - "questions": fixed_questions, - "question_count": len(fixed_questions), - "difficulty_counts": dict(difficulty_counts), - } - - -def build_complete_snapshot(shared_config_path: Path, seed_path: Path) -> dict[str, Any]: - shared = load_shared_config(shared_config_path) - env_cfg = clone_environment_config(shared.environment) - env_cfg.seeding = load_seeding_config(seed_path) - - llm_client = build_llm_client(env_cfg.llm) - generator = DatasetGenerator(config=env_cfg, llm=llm_client) - - graph = generator.build_canonical_graph() - views = generator.build_platform_views(graph) - tasks = generator.generate_tasks(graph, views, count=max(15, len(env_cfg.seeding.seeded_questions))) - - difficulty_counts = Counter(str(task.metadata.get("difficulty", "unknown")) for task in tasks) - - return { - "dataset_name": "fixed_levels_submission_set", - "generation_mode": "llm_expanded", - "shared_config": str(shared_config_path), - "seed_file": str(seed_path), - "llm": asdict(env_cfg.llm), - "environment": { - "n_users": env_cfg.n_users, - "alias_density": env_cfg.alias_density, - "noise_level": env_cfg.noise_level, - "red_herring_rate": env_cfg.red_herring_rate, - "seed": env_cfg.seed, - }, - "canonical_graph": { - "node_count": len(graph.nodes), - "edge_count": len(graph.edges), - "nodes": [ - { - "node_id": node.node_id, - "node_type": node.node_type.value, - "attrs": dict(node.attrs), - } - for node in sorted(graph.nodes.values(), key=lambda n: n.node_id) - ], - "edges": [edge_to_dict(edge) for edge in graph.edges], - }, - "platform_views": { - "microblog_posts": views.microblog_posts, - "forum_threads": views.forum_threads, - "profiles": views.profiles, - "counts": { - "microblog_posts": len(views.microblog_posts), - "forum_threads": len(views.forum_threads), - "profiles": len(views.profiles), - }, - }, - "tasks": [task_to_dict(task) for task in tasks], - "task_count": len(tasks), - "difficulty_counts": dict(difficulty_counts), - } - - -def main() -> None: - parser = argparse.ArgumentParser(description="Build fixed difficulty dataset artifacts.") - parser.add_argument( - "--seed-file", - default="datasets/fixed_levels/seed_fixed_levels.json", - help="Path to seeding JSON with fixed graph/questions.", - ) - parser.add_argument( - "--shared-config", - default="datasets/fixed_levels/shared_config_fixed_levels.json", - help="Path to shared config used for LLM-expanded generation.", - ) - parser.add_argument( - "--output-dir", - default="datasets/fixed_levels", - help="Directory where dataset artifacts are written.", - ) - args = parser.parse_args() - - output_dir = Path(args.output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - seed_path = Path(args.seed_file) - shared_path = Path(args.shared_config) - - fixed_snapshot = build_fixed_snapshot(seed_path) - fixed_path = output_dir / "fixed_graph_questions.json" - fixed_path.write_text(json.dumps(fixed_snapshot, indent=2, sort_keys=True), encoding="utf-8") - - complete_snapshot = build_complete_snapshot(shared_path, seed_path) - complete_path = output_dir / "complete_dataset_qwen_generated.json" - complete_path.write_text(json.dumps(complete_snapshot, indent=2, sort_keys=True), encoding="utf-8") - - summary = { - "fixed_dataset": str(fixed_path), - "complete_dataset": str(complete_path), - "fixed_nodes": fixed_snapshot["graph"]["node_count"], - "fixed_edges": fixed_snapshot["graph"]["edge_count"], - "fixed_questions": fixed_snapshot["question_count"], - "complete_nodes": complete_snapshot["canonical_graph"]["node_count"], - "complete_edges": complete_snapshot["canonical_graph"]["edge_count"], - "complete_tasks": complete_snapshot["task_count"], - "difficulty_counts": complete_snapshot["difficulty_counts"], - } - print(json.dumps(summary, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/scratch/osint/scripts/generate_fixed_levels_seed.py b/scratch/osint/scripts/generate_fixed_levels_seed.py deleted file mode 100644 index 65dc10da2dd52d3e6537adaa8c605a163900368a..0000000000000000000000000000000000000000 --- a/scratch/osint/scripts/generate_fixed_levels_seed.py +++ /dev/null @@ -1,109 +0,0 @@ -from collections import Counter, OrderedDict -from pathlib import Path -import json - -U=[('aria','Aria Sen','Helios Labs','Sector 9'),('bharat','Bharat Kulkarni','Northbridge Logistics','Dockyard 17'),('cyrus','Cyrus Mehta','Apex Dynamics','Old Town'),('diya','Diya Roy','Blueharbor Media','Old Town'),('elin','Elin Das','Helios Labs','Sector 9'),('faris','Faris Noor','Tidewatch Ops','Rivergate'),('gita','Gita Pradhan','Apex Dynamics','Old Town'),('hiro','Hiro Tan','Northbridge Logistics','Dockyard 17'),('ivy','Ivy Kapoor','Kestrel Works','Rivergate'),('jules','Jules Banerjee','Blueharbor Media','Old Town'),('kian','Kian Bose','Atlas Freight','East Quay'),('leena','Leena Das','Sunmesh Analytics','Sector 9'),('mika','Mika Solanki','Orion Customs','North Basin'),('nora','Nora Iqbal','Emberline Security','Foundry Row'),('omar','Omar Sheikh','Atlas Freight','East Quay'),('priya','Priya Menon','Sunmesh Analytics','Sector 9'),('quinn','Quinn Rao','Orion Customs','North Basin'),('rhea','Rhea Kapoor','Emberline Security','Foundry Row'),('soren','Soren Malik','Harborlight Transit','Uplink Yard'),('tara','Tara Dey','Harborlight Transit','Uplink Yard')] -A=[('orchidfox','@orchidfox','ivy'),('steelquill','@steelquill','bharat'),('monsoonbyte','@monsoonbyte','diya'),('nightrelay','@nightrelay','faris'),('mapleghost','@mapleghost','elin'),('docksparrow','@docksparrow','hiro'),('quartzlotus','@quartzlotus','cyrus'),('emberglass','@emberglass','nora'),('basinraven','@basinraven','mika'),('tideshard','@tideshard','soren'),('hollowsignal','@hollowsignal','priya'),('ironwhisper','@ironwhisper','omar'),('cinderveil','@cinderveil','rhea'),('sablekeel','@sablekeel','tara'),('lanternmoth','@lanternmoth','kian'),('frostledger','@frostledger','leena')] -L=[('dockyard17','Dockyard 17'),('sector9','Sector 9'),('old_town','Old Town'),('rivergate','Rivergate'),('east_quay','East Quay'),('foundry_row','Foundry Row'),('north_basin','North Basin'),('uplink_yard','Uplink Yard')] -O=[('helios_labs','Helios Labs','sector9'),('northbridge_logistics','Northbridge Logistics','dockyard17'),('apex_dynamics','Apex Dynamics','old_town'),('blueharbor_media','Blueharbor Media','old_town'),('tidewatch_ops','Tidewatch Ops','rivergate'),('kestrel_works','Kestrel Works','rivergate'),('atlas_freight','Atlas Freight','east_quay'),('sunmesh_analytics','Sunmesh Analytics','sector9'),('orion_customs','Orion Customs','north_basin'),('emberline_security','Emberline Security','foundry_row'),('harborlight_transit','Harborlight Transit','uplink_yard')] -E=[('project_lantern','Project Lantern'),('black_kite','Black Kite'),('silent_current','Silent Current'),('amber_veil','Amber Veil'),('glass_harbor','Glass Harbor'),('ember_tide','Ember Tide'),('iron_wharf','Iron Wharf'),('ghost_signal','Ghost Signal')] -T=[('supply_leak','supply_chain'),('port_audit','port_audit'),('customs_breach','customs_breach'),('relay_map','relay_map'),('foundry_watch','foundry_watch'),('basin_shift','basin_shift'),('quiet_manifest','quiet_manifest'),('uplink_route','uplink_route'),('ember_tide_watch','ember_tide'),('ghost_signal_net','ghost_signal')] -P=['shift_roster','midnight_manifest','sat_phone_ping','drone_parts','relay_schedule','quay_ledgers','customs_tag','hull_signal','basin_photo','foundry_map','lantern_route','uplink_note'] - -def uid(x): return f'user_{x}' -def aid(x): return f'alias_{x}' -def oid(x): return f'org_{x}' -def lid(x): return f'loc_{x}' -def eid(x): return f'event_{x}' -def tid(x): return f'thr_{x}' -def pid(x): return f'post_{x}' - -def addn(nodes,nid,nt,attrs): nodes.append({'node_id':nid,'node_type':nt,'attrs':attrs}) - -def build(): - nodes=[]; edges=OrderedDict(); - for s,name,org,loc in U: addn(nodes,uid(s),'user',{'name':name,'org':org,'location':loc}) - for s,handle,user in A: addn(nodes,aid(s),'alias',{'handle':handle}) - for s,name,_ in O: addn(nodes,oid(s),'org',{'name':name}) - for s,name in L: addn(nodes,lid(s),'location',{'name':name}) - for s,name in E: addn(nodes,eid(s),'event',{'name':name}) - for s,topic in T: addn(nodes,tid(s),'thread',{'topic':topic}) - for s in P: addn(nodes,pid(s),'post',{'channel':'microblog'}) - def ae(k,src,rel,dst,c=1.0): edges[k]={'src':src,'rel':rel,'dst':dst,'confidence':c} - for s,_,user in A: ae(f'a_{s}',aid(s),'alias_of',uid(user)) - org_map={name:oid(s) for s,name,_ in O}; loc_map={name:lid(s) for s,name in L} - for s,_,org,loc in U: ae(f'w_{s}',uid(s),'works_at',org_map[org]); ae(f'l_{s}',uid(s),'located_in',loc_map[loc]) - for s,_,loc in O: ae(f'op_{s}',oid(s),'operates_in',lid(loc)) - CP=[('ivy','bharat',.95),('bharat','hiro',.95),('hiro','faris',.92),('faris','diya',.90),('diya','elin',.89),('elin','aria',.87),('aria','cyrus',.84),('cyrus','gita',.83),('gita','jules',.82),('jules','bharat',.81),('diya','ivy',.90),('ivy','elin',.86),('kian','omar',.93),('omar','mika',.90),('mika','quinn',.89),('quinn','nora',.88),('nora','rhea',.87),('rhea','soren',.86),('soren','tara',.86),('tara','kian',.84),('priya','leena',.91),('leena','aria',.83),('priya','nora',.82),('kian','bharat',.80),('soren','faris',.79),('quinn','hiro',.78)] - for i,(a,b,c) in enumerate(CP,1): ae(f'c{i:02d}',uid(a),'connected_to',uid(b),c) - PA={'midnight_manifest':'orchidfox','shift_roster':'docksparrow','sat_phone_ping':'nightrelay','drone_parts':'monsoonbyte','relay_schedule':'steelquill','quay_ledgers':'lanternmoth','customs_tag':'basinraven','hull_signal':'tideshard','basin_photo':'emberglass','foundry_map':'cinderveil','lantern_route':'frostledger','uplink_note':'sablekeel'} - for post,author in PA.items(): ae(f'ap_{post}',aid(author),'authored_post',pid(post)) - PR={'midnight_manifest':['dockyard17','project_lantern'],'shift_roster':['dockyard17','northbridge_logistics'],'sat_phone_ping':['rivergate','project_lantern'],'drone_parts':['black_kite','kestrel_works'],'relay_schedule':['project_lantern','sector9'],'quay_ledgers':['east_quay','glass_harbor'],'customs_tag':['north_basin','iron_wharf'],'hull_signal':['uplink_yard','ghost_signal'],'basin_photo':['foundry_row','amber_veil'],'foundry_map':['foundry_row','ember_tide'],'lantern_route':['project_lantern','sunmesh_analytics'],'uplink_note':['uplink_yard','harborlight_transit']} - for post,refs in PR.items(): - for i,x in enumerate(refs,1): ae(f'r_{post}_{i}',pid(post),'references', lid(x) if x in {y for y,_ in L} else (oid(x) if x in {y for y,_,_ in O} else eid(x))) - TA={'supply_leak':'diya','port_audit':'jules','customs_breach':'mika','relay_map':'leena','foundry_watch':'nora','basin_shift':'quinn','quiet_manifest':'kian','uplink_route':'soren','ember_tide_watch':'rhea','ghost_signal_net':'tara'} - TL={'supply_leak':[('discusses','project_lantern'),('references','northbridge_logistics')],'port_audit':[('discusses','black_kite'),('references','kestrel_works')],'customs_breach':[('discusses','iron_wharf'),('references','orion_customs')],'relay_map':[('discusses','project_lantern'),('references','sunmesh_analytics')],'foundry_watch':[('discusses','ember_tide'),('references','emberline_security')],'basin_shift':[('discusses','amber_veil'),('references','north_basin')],'quiet_manifest':[('discusses','glass_harbor'),('references','atlas_freight')],'uplink_route':[('discusses','ghost_signal'),('references','harborlight_transit')],'ember_tide_watch':[('discusses','ember_tide'),('references','foundry_row')],'ghost_signal_net':[('discusses','ghost_signal'),('references','uplink_yard')]} - for t,u in TA.items(): ae(f'at_{t}',uid(u),'authored_thread',tid(t)) - for t,rels in TL.items(): - for i,(rel,x) in enumerate(rels,1): ae(f'tl_{t}_{i}',tid(t),rel, lid(x) if x in {y for y,_ in L} else (oid(x) if x in {y for y,_,_ in O} else eid(x))) - ER=[('bharat','collaborates_on','project_lantern'),('hiro','collaborates_on','project_lantern'),('faris','collaborates_on','project_lantern'),('diya','investigates','project_lantern'),('leena','monitors','project_lantern'),('ivy','collaborates_on','black_kite'),('cyrus','collaborates_on','black_kite'),('elin','investigates','black_kite'),('jules','reports_on','black_kite'),('kian','collaborates_on','glass_harbor'),('omar','collaborates_on','glass_harbor'),('priya','monitors','glass_harbor'),('mika','collaborates_on','iron_wharf'),('quinn','collaborates_on','iron_wharf'),('nora','investigates','amber_veil'),('rhea','collaborates_on','ember_tide'),('soren','collaborates_on','ghost_signal'),('tara','reports_on','ghost_signal'),('gita','monitors','silent_current'),('jules','reports_on','silent_current')] - for i,(u,rel,e) in enumerate(ER,1): ae(f'er{i:02d}',uid(u),rel,eid(e),.9) - X=[(eid('project_lantern'),'connected_to',eid('glass_harbor')),(eid('black_kite'),'connected_to',eid('amber_veil')),(eid('ember_tide'),'connected_to',eid('ghost_signal')),(oid('atlas_freight'),'connected_to',oid('northbridge_logistics')),(oid('orion_customs'),'connected_to',oid('emberline_security')),(oid('harborlight_transit'),'connected_to',oid('tidewatch_ops'))] - for i,(a,rel,b) in enumerate(X,1): ae(f'x{i:02d}',a,rel,b,.77) - return nodes,edges - -def mk_questions(edges): - def ids(*items): - out=[] - for it in items: - if isinstance(it,list): out.extend(it) - else: out.append(it) - return out - def rng(prefix,a,b): return [f'{prefix}{i:02d}' for i in range(a,b+1)] - def sup(edge_ids): return [edges[e] for e in edge_ids] - def nodes(edge_ids): - s=set() - for e in edge_ids: s|={edges[e]['src'],edges[e]['dst']} - return len(s) - qs=[] - easy=[('easy_01','alias_orchidfox -> post_midnight_manifest -> loc_dockyard17 -> connected collaborator on event_project_lantern. Who is it?','user_bharat',ids('a_orchidfox','ap_midnight_manifest','r_midnight_manifest_1','c01','er01')),('easy_02','thr_supply_leak references org_northbridge_logistics. Which alias_docksparrow user works there and collaborates on event_project_lantern?','user_hiro',ids('tl_supply_leak_2','a_docksparrow','w_hiro','er02')),('easy_03','alias_monsoonbyte authored post_drone_parts about event_black_kite. Which user behind that alias is directly connected to the Kestrel collaborator?','user_diya',ids('a_monsoonbyte','ap_drone_parts','r_drone_parts_1','w_ivy','er06','c12')),('easy_04','alias_nightrelay references loc_rivergate. Which user behind it works at an org operating there and collaborates on event_project_lantern?','user_faris',ids('a_nightrelay','ap_sat_phone_ping','r_sat_phone_ping_1','w_faris','op_tidewatch_ops','er03')),('easy_05','thr_port_audit discusses Black Kite and references Kestrel Works. Which alias_orchidfox user authored post_midnight_manifest and collaborates on Black Kite?','user_ivy',ids('tl_port_audit_1','tl_port_audit_2','a_orchidfox','ap_midnight_manifest','w_ivy','er06')),('easy_06','Which Atlas Freight user behind alias_lanternmoth authored post_quay_ledgers and collaborates on event_glass_harbor?','user_kian',ids('a_lanternmoth','ap_quay_ledgers','w_kian','er10')),('easy_07','Which Orion Customs user behind alias_basinraven authored post_customs_tag and collaborates on event_iron_wharf?','user_mika',ids('a_basinraven','ap_customs_tag','w_mika','er13')),('easy_08','Which user behind alias_emberglass posted basin_photo from Foundry Row and investigates Amber Veil?','user_nora',ids('a_emberglass','ap_basin_photo','r_basin_photo_1','er15')),('easy_09','Which user behind alias_tideshard authored post_hull_signal and collaborates on Ghost Signal?','user_soren',ids('a_tideshard','ap_hull_signal','er17')),('easy_10','Which Harborlight Transit user behind alias_sablekeel authored post_uplink_note and reports on Ghost Signal?','user_tara',ids('a_sablekeel','ap_uplink_note','w_tara','er18'))] - mid=[('mid_01','Follow alias_docksparrow through post_shift_roster, Dockyard 17, and the Lantern chain. Return the org node id.','org_northbridge_logistics',ids('a_docksparrow','ap_shift_roster','r_shift_roster_1','r_shift_roster_2','tl_supply_leak_2','w_hiro','l_hiro','er02','er01','c02','c03')),('mid_02','Across the Glass Harbor cluster, which user behind alias_lanternmoth links to the Atlas Freight network from thr_quiet_manifest?','user_kian',ids('a_lanternmoth','ap_quay_ledgers','r_quay_ledgers_1','r_quay_ledgers_2','at_quiet_manifest','tl_quiet_manifest_1','tl_quiet_manifest_2','w_kian','w_omar','er10','er11','er12','c13','c14')),('mid_03','Trace alias_basinraven through post_customs_tag, thr_customs_breach, and the Orion Customs collaboration chain. Who is it?','user_mika',ids('a_basinraven','ap_customs_tag','r_customs_tag_1','r_customs_tag_2','at_customs_breach','tl_customs_breach_1','tl_customs_breach_2','w_mika','w_quinn','er13','er14','c15','c16','x05')),('mid_04','In the Ember Tide and Amber Veil overlap, which Foundry Row user behind alias_cinderveil collaborates on Ember Tide?','user_rhea',ids('a_cinderveil','ap_foundry_map','r_foundry_map_1','r_foundry_map_2','at_foundry_watch','tl_foundry_watch_1','tl_foundry_watch_2','at_ember_tide_watch','tl_ember_tide_watch_1','tl_ember_tide_watch_2','w_rhea','w_nora','er15','er16','c17','x03')),('mid_05','Follow alias_tideshard from post_hull_signal into thr_uplink_route and the Harborlight relay. Return the org node id.','org_harborlight_transit',ids('a_tideshard','ap_hull_signal','r_hull_signal_1','r_hull_signal_2','at_uplink_route','tl_uplink_route_1','tl_uplink_route_2','w_soren','w_tara','er17','er18','c18','c19','op_harborlight_transit','x06')),('mid_06','Which Sunmesh user behind alias_frostledger connects post_lantern_route to thr_relay_map and the Sector 9 monitoring chain?','user_leena',ids('a_frostledger','ap_lantern_route','r_lantern_route_1','r_lantern_route_2','at_relay_map','tl_relay_map_1','tl_relay_map_2','w_leena','w_priya','l_leena','op_sunmesh_analytics','er05','c21','c22')),('mid_07','Which user behind alias_emberglass is tied to Amber Veil after combining post_basin_photo, thr_basin_shift, and the Foundry Row investigation chain?','user_nora',ids('a_emberglass','ap_basin_photo','r_basin_photo_1','r_basin_photo_2','at_basin_shift','tl_basin_shift_1','tl_basin_shift_2','w_nora','w_quinn','l_nora','er15','c16','c17','x05')),('mid_08','Combine alias_orchidfox, post_midnight_manifest, thr_supply_leak, and the Lantern to Glass Harbor bridge. Which user starts that chain?','user_ivy',ids('a_orchidfox','ap_midnight_manifest','r_midnight_manifest_1','r_midnight_manifest_2','at_supply_leak','tl_supply_leak_1','tl_supply_leak_2','w_ivy','er06','c01','c12','x01','er10','er12')),('mid_09','Which user behind alias_monsoonbyte sits at the overlap of Blueharbor Media, Project Lantern, Black Kite, and the Ivy connection chain?','user_diya',ids('a_monsoonbyte','ap_drone_parts','r_drone_parts_1','at_supply_leak','tl_supply_leak_1','at_port_audit','tl_port_audit_1','w_diya','w_ivy','w_jules','er04','er06','er09','c04','c12')),('mid_10','Who is the Northbridge user behind alias_steelquill when combining post_relay_schedule, thr_supply_leak, Dockyard 17, and Lantern collaborator edges?','user_bharat',ids('a_steelquill','ap_relay_schedule','r_relay_schedule_1','r_relay_schedule_2','at_supply_leak','tl_supply_leak_1','tl_supply_leak_2','w_bharat','w_hiro','l_bharat','l_hiro','er01','er02','c01','c02'))] - big=list(edges.keys())[:58] - hard=[('high_01','Lantern to Glass Harbor handoff: identify the user behind alias_orchidfox after combining Lantern logistics, Dockyard links, and Atlas Freight bridge evidence.','user_ivy',ids('a_orchidfox','ap_midnight_manifest','r_midnight_manifest_1','r_midnight_manifest_2','at_supply_leak','tl_supply_leak_1','tl_supply_leak_2',['w_ivy','w_bharat','w_hiro','w_kian','w_omar'],['l_ivy','l_bharat','l_hiro','l_kian','l_omar'],['op_northbridge_logistics','op_kestrel_works','op_atlas_freight'],rng('c',1,3),['c12','c13','c14'],['er01','er02','er03','er06','er10','er11','er12'],'at_quiet_manifest','tl_quiet_manifest_1','tl_quiet_manifest_2','ap_quay_ledgers','r_quay_ledgers_1','r_quay_ledgers_2','x01','x04','a_lanternmoth','a_steelquill','a_docksparrow')),('high_02','North Basin to Foundry Row escalation: which user behind alias_basinraven anchors the Iron Wharf side before the Emberline handoff?','user_mika',ids('a_basinraven','ap_customs_tag','r_customs_tag_1','r_customs_tag_2','at_customs_breach','tl_customs_breach_1','tl_customs_breach_2','at_basin_shift','tl_basin_shift_1','tl_basin_shift_2','at_foundry_watch','tl_foundry_watch_1','tl_foundry_watch_2',['w_mika','w_quinn','w_nora','w_rhea'],['l_mika','l_quinn','l_nora','l_rhea'],['op_orion_customs','op_emberline_security'],['c15','c16','c17'],['er13','er14','er15','er16'],'ap_basin_photo','r_basin_photo_1','r_basin_photo_2','ap_foundry_map','r_foundry_map_1','r_foundry_map_2','x02','x03','x05','a_emberglass','a_cinderveil','c23','c24')),('high_03','Harborlight ghost-signal relay: identify the user behind alias_tideshard at the Harborlight / Tidewatch junction.','user_soren',ids('a_tideshard','ap_hull_signal','r_hull_signal_1','r_hull_signal_2','a_sablekeel','ap_uplink_note','r_uplink_note_1','r_uplink_note_2','at_uplink_route','tl_uplink_route_1','tl_uplink_route_2','at_ghost_signal_net','tl_ghost_signal_net_1','tl_ghost_signal_net_2',['w_soren','w_tara','w_faris'],['l_soren','l_tara','l_faris'],['op_harborlight_transit','op_tidewatch_ops'],['c18','c19','c20','c25'],['er03','er17','er18'],'ap_sat_phone_ping','r_sat_phone_ping_1','r_sat_phone_ping_2','at_supply_leak','tl_supply_leak_1','er01','er02','x03','x06','a_nightrelay')),('high_04','Blueharbor to Black Kite to Lantern overlap: which user is the Blueharbor origin behind alias_monsoonbyte?','user_diya',ids('a_monsoonbyte','ap_drone_parts','r_drone_parts_1','r_drone_parts_2','at_port_audit','tl_port_audit_1','tl_port_audit_2','at_supply_leak','tl_supply_leak_1','tl_supply_leak_2',['w_diya','w_jules','w_ivy','w_cyrus'],['l_diya','l_jules','l_ivy','l_cyrus'],['op_blueharbor_media','op_kestrel_works','op_apex_dynamics'],['c04','c08','c09','c12'],['er04','er06','er07','er08','er09'],'a_orchidfox','ap_midnight_manifest','r_midnight_manifest_2','x01','x02','at_relay_map','tl_relay_map_1','w_leena','er05')),('high_05','Sector 9 to Dockyard 17 full relay: which user behind alias_steelquill links the Northbridge chain and the Sunmesh monitoring bridge?','user_bharat',ids('a_steelquill','ap_relay_schedule','r_relay_schedule_1','r_relay_schedule_2','a_frostledger','ap_lantern_route','r_lantern_route_1','r_lantern_route_2','at_relay_map','tl_relay_map_1','tl_relay_map_2','at_supply_leak','tl_supply_leak_1','tl_supply_leak_2',['w_bharat','w_hiro','w_leena','w_priya','w_aria'],['l_bharat','l_hiro','l_leena','l_priya','l_aria'],['op_northbridge_logistics','op_sunmesh_analytics','op_helios_labs'],['c01','c02','c05','c06','c07','c21','c22'],['er01','er02','er05'],'x01','x04','a_docksparrow','a_mapleghost','a_hollowsignal')),('high_06','Foundry Row, North Basin, and Uplink Yard spread: identify the user behind alias_emberglass before the Harborlight relay takes over.','user_nora',ids('a_emberglass','ap_basin_photo','r_basin_photo_1','r_basin_photo_2','a_cinderveil','ap_foundry_map','r_foundry_map_1','r_foundry_map_2','a_sablekeel','ap_uplink_note','r_uplink_note_1','r_uplink_note_2','at_foundry_watch','tl_foundry_watch_1','tl_foundry_watch_2','at_ember_tide_watch','tl_ember_tide_watch_1','tl_ember_tide_watch_2','at_uplink_route','tl_uplink_route_1','tl_uplink_route_2',['w_nora','w_rhea','w_soren','w_tara'],['l_nora','l_rhea','l_soren','l_tara'],['op_emberline_security','op_harborlight_transit'],['c17','c18','c19'],['er15','er16','er17','er18'],'x03','x06')),('high_07','Freight and customs bridge: which Atlas Freight user behind alias_lanternmoth connects Glass Harbor with the Northbridge chain?','user_kian',ids('a_lanternmoth','ap_quay_ledgers','r_quay_ledgers_1','r_quay_ledgers_2','at_quiet_manifest','tl_quiet_manifest_1','tl_quiet_manifest_2',['w_kian','w_omar','w_bharat','w_hiro'],['l_kian','l_omar','l_bharat','l_hiro'],['op_atlas_freight','op_northbridge_logistics'],['c13','c14','c24','c02'],['er10','er11','er12','er01','er02'],'ap_shift_roster','r_shift_roster_1','r_shift_roster_2','ap_midnight_manifest','r_midnight_manifest_1','at_supply_leak','tl_supply_leak_2','x04','a_ironwhisper','a_steelquill','a_docksparrow')),('high_08','Black Kite, Amber Veil, and Iron Wharf overlap: which user behind alias_quartzlotus is the Apex-side collaborator?','user_cyrus',ids('a_quartzlotus','w_cyrus','l_cyrus','op_apex_dynamics','er07','at_port_audit','tl_port_audit_1','ap_drone_parts','r_drone_parts_1','er15','at_basin_shift','tl_basin_shift_1','er13','at_customs_breach','tl_customs_breach_1',['w_ivy','w_nora','w_mika','w_quinn'],['l_ivy','l_nora','l_mika','l_quinn'],['op_kestrel_works','op_emberline_security','op_orion_customs'],['c08','c12','c15','c16','c17'],'x02','x05','a_orchidfox','a_basinraven','a_emberglass')),('high_09','Ghost Signal and Ember Tide relay: which user behind alias_sablekeel is the Harborlight reporting endpoint?','user_tara',ids('a_sablekeel','ap_uplink_note','r_uplink_note_1','r_uplink_note_2','a_tideshard','ap_hull_signal','r_hull_signal_1','r_hull_signal_2','at_ghost_signal_net','tl_ghost_signal_net_1','tl_ghost_signal_net_2','at_uplink_route','tl_uplink_route_1','tl_uplink_route_2','at_ember_tide_watch','tl_ember_tide_watch_1','tl_ember_tide_watch_2',['w_tara','w_soren','w_rhea','w_nora'],['l_tara','l_soren','l_rhea','l_nora'],['op_harborlight_transit','op_emberline_security'],['c18','c19','c17'],['er16','er17','er18'],'x03','x06','a_cinderveil','a_emberglass')),('high_10','End-to-end benchmark sweep: across Lantern, Black Kite, Glass Harbor, Iron Wharf, Ember Tide, and Ghost Signal, which user behind alias_hollowsignal anchors the Sunmesh monitoring side?','user_priya',big)] - for diff,level,specs in [('easy',1,easy),('mid',2,mid),('high',3,hard)]: - for qid,q,a,eids in specs: - qs.append({'task_type':'fixed_trace','question':q,'answer':a,'supporting_edges':sup(eids),'metadata':{'difficulty':diff,'difficulty_level':level,'question_id':qid,'support_nodes':nodes(eids)}}) - def edge_key(e): return (e['src'], e['rel'], e['dst']) - mid_pool = sup(ids('a_orchidfox','ap_midnight_manifest','r_midnight_manifest_1','r_midnight_manifest_2','a_lanternmoth','ap_quay_ledgers','r_quay_ledgers_1','r_quay_ledgers_2','a_basinraven','ap_customs_tag','r_customs_tag_1','r_customs_tag_2','a_tideshard','ap_hull_signal','r_hull_signal_1','at_supply_leak','tl_supply_leak_1','at_quiet_manifest','tl_quiet_manifest_1','er01','er02','er06','er10','c01','c02','c13')) - hard_pool = sup(list(edges.keys())[:120]) - for q in qs: - current = {edge_key(e) for e in q['supporting_edges']} - diff = q['metadata']['difficulty'] - if diff == 'mid': - pool = mid_pool - target = 17 - elif diff == 'high': - pool = hard_pool - target = 50 - else: - continue - for e in pool: - if q['metadata']['support_nodes'] >= target: - break - k = edge_key(e) - if k not in current: - q['supporting_edges'].append(dict(e)) - current.add(k) - q['metadata']['support_nodes'] = len({n for edge in q['supporting_edges'] for n in (edge['src'], edge['dst'])}) - return qs - -def main(): - nodes,edges=build(); questions=mk_questions(edges) - payload={'seeding':{'seeded_nodes':nodes,'seeded_edges':list(edges.values()),'seeded_questions':questions,'llm_generate_remaining_graph':True,'llm_generate_remaining_tasks':False,'llm_generated_edge_budget':48,'llm_generated_task_budget':0,'llm_generation_parallel':True,'llm_generation_workers':4,'llm_generation_retries':3,'allow_template_fallback_on_llm_failure':False}} - out=Path('datasets/fixed_levels/seed_fixed_levels.json'); out.write_text(json.dumps(payload,indent=2),encoding='utf-8') - counts=Counter(q['metadata']['difficulty'] for q in questions) - stats={k:sorted(q['metadata']['support_nodes'] for q in questions if q['metadata']['difficulty']==k) for k in ['easy','mid','high']} - print(json.dumps({'nodes':len(nodes),'edges':len(edges),'questions':len(questions),'difficulty_counts':dict(counts),'support_nodes':stats},indent=2)) - -if __name__=='__main__': - main() diff --git a/scratch/osint/scripts/run_openai_baseline.py b/scratch/osint/scripts/run_openai_baseline.py deleted file mode 100644 index 88a6822ba549e0e778dca402ab165ddeedd60618..0000000000000000000000000000000000000000 --- a/scratch/osint/scripts/run_openai_baseline.py +++ /dev/null @@ -1,59 +0,0 @@ -from __future__ import annotations - -import argparse -import json -import os - -from osint_env.baselines import OpenAIBaselineConfig, OpenAIBaselineRunner - - -def build_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser(description="Run the reproducible OpenAI baseline on the fixed-level OSINT benchmark.") - parser.add_argument("--config", default="datasets/fixed_levels/shared_config_fixed_levels.json", help="Shared config JSON.") - parser.add_argument("--seed-file", default="datasets/fixed_levels/seed_fixed_levels.json", help="Fixed seed file JSON.") - parser.add_argument("--output", default="artifacts/baselines/openai_fixed_levels_latest.json", help="Baseline result JSON output path.") - parser.add_argument("--leaderboard", default="artifacts/baselines/openai_fixed_levels_leaderboard.json", help="Leaderboard JSON path.") - parser.add_argument("--dashboard", default="artifacts/baselines/openai_fixed_levels_dashboard.html", help="Dashboard HTML path.") - parser.add_argument("--run-name", default="openai_fixed_levels_baseline", help="Leaderboard run name.") - parser.add_argument("--model", default="gpt-5-nano", help="OpenAI chat model name.") - parser.add_argument("--openai-base-url", default="https://api.openai.com/v1", help="OpenAI-compatible base URL.") - parser.add_argument("--openai-api-key", default="", help="OpenAI API key override.") - parser.add_argument("--openai-api-key-env", default="OPENAI_API_KEY", help="Environment variable name for the API key.") - parser.add_argument("--episodes", type=int, default=30, help="Number of episodes to evaluate.") - parser.add_argument("--max-steps", type=int, default=8, help="Episode step budget to keep runs bounded.") - parser.add_argument("--temperature", type=float, default=0.0, help="Sampling temperature.") - parser.add_argument("--max-tokens", type=int, default=256, help="Maximum completion tokens per step.") - parser.add_argument("--timeout-seconds", type=int, default=60, help="Per-request timeout.") - parser.add_argument("--seed", type=int, default=7, help="Request seed offset used for repeatable runs.") - parser.add_argument("--skip-leaderboard", action="store_true", help="Do not append the run to the leaderboard file.") - return parser - - -def main() -> None: - args = build_parser().parse_args() - api_key = args.openai_api_key or os.getenv(args.openai_api_key_env, "") - config = OpenAIBaselineConfig( - shared_config_path=args.config, - seed_file=args.seed_file, - output_path=args.output, - leaderboard_path=args.leaderboard, - dashboard_path=args.dashboard, - run_name=args.run_name, - model=args.model, - base_url=args.openai_base_url, - api_key=api_key, - api_key_env=args.openai_api_key_env, - temperature=args.temperature, - max_tokens=args.max_tokens, - timeout_seconds=args.timeout_seconds, - episodes=args.episodes, - max_steps=args.max_steps, - seed=args.seed, - append_leaderboard=not args.skip_leaderboard, - ) - result = OpenAIBaselineRunner(config).run() - print(json.dumps({"summary": result["summary"], "output": args.output, "dashboard": args.dashboard}, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/scratch/osint/scripts/validate_release.py b/scratch/osint/scripts/validate_release.py deleted file mode 100644 index 4aa09322ff18b8a6a5fee31aceb14b62626753a9..0000000000000000000000000000000000000000 --- a/scratch/osint/scripts/validate_release.py +++ /dev/null @@ -1,21 +0,0 @@ -from __future__ import annotations - -import json -import sys -from pathlib import Path - -ROOT = Path(__file__).resolve().parents[1] -if str(ROOT) not in sys.path: - sys.path.insert(0, str(ROOT)) - -from osint_env.validation import run_validation_suite - - -def main() -> int: - result = run_validation_suite() - print(json.dumps(result, indent=2, sort_keys=True)) - return 0 if result["passed"] else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/scratch/osint/server.py b/scratch/osint/server.py deleted file mode 100644 index e18181a051826f34f601b6797fa40113d1fa0ae9..0000000000000000000000000000000000000000 --- a/scratch/osint/server.py +++ /dev/null @@ -1,564 +0,0 @@ -from __future__ import annotations - -import json -import os -from collections import Counter -from functools import lru_cache -from pathlib import Path -from threading import Lock -from typing import Any -from uuid import uuid4 - -from fastapi import FastAPI, HTTPException, Request -from fastapi.responses import FileResponse, HTMLResponse, JSONResponse - -from osint_env.api import ( - OpenEnvActionRequest, - OpenEnvInferenceReportRequest, - OpenEnvInferenceReportResponse, - OpenEnvObservationModel, - OpenEnvResetRequest, - OpenEnvResponseEnvelope, - OpenEnvTaskSummary, -) -from osint_env.config import clone_environment_config, load_seeding_config, load_shared_config -from osint_env.domain.models import Action, ActionType -from osint_env.env.environment import OSINTEnvironment -from osint_env.eval.leaderboard import load_leaderboard -from osint_env.eval.runner import run_evaluation -from osint_env.llm import build_llm_client -from osint_env.viz import export_dashboard - - -SPACE_CONFIG_PATH = Path(os.getenv("OSINT_ENV_CONFIG", "datasets/fixed_levels/shared_config_fixed_levels.json")) -SPACE_SEED_PATH = Path(os.getenv("OSINT_ENV_SEED_FILE", "datasets/fixed_levels/seed_fixed_levels.json")) -SPACE_PROVIDER = os.getenv("OSINT_SPACE_LLM_PROVIDER", "mock") -SPACE_MODEL = os.getenv("OSINT_SPACE_LLM_MODEL", "gpt-4o-mini") -SPACE_PORT = int(os.getenv("PORT", "7860")) -SPACE_DASHBOARD = Path("artifacts/space_dashboard.html") -LATEST_BASELINE_OUTPUT = Path("artifacts/baselines/openai_fixed_levels_latest.json") -LATEST_EVALUATION_OUTPUT = Path("artifacts/latest_evaluation.json") -OPENENV_SPEC_PATH = Path("openenv.yaml") - -_SESSION_LOCK = Lock() -_SESSIONS: dict[str, OSINTEnvironment] = {} -_RESET_COUNTER = 0 -_LATEST_SESSION_ID: str | None = None - - -def _load_json(path: Path) -> dict[str, Any] | None: - if not path.exists(): - return None - try: - payload = json.loads(path.read_text(encoding="utf-8")) - except (OSError, json.JSONDecodeError): - return None - return payload if isinstance(payload, dict) else None - - -def _path_mtime(path: Path) -> float: - try: - return path.stat().st_mtime - except OSError: - return 0.0 - - -def _build_environment() -> OSINTEnvironment: - shared = load_shared_config(SPACE_CONFIG_PATH) - env_cfg = clone_environment_config(shared.environment) - if SPACE_SEED_PATH.exists(): - env_cfg.seeding = load_seeding_config(SPACE_SEED_PATH) - env_cfg.llm.provider = SPACE_PROVIDER - env_cfg.llm.model = SPACE_MODEL - try: - llm = build_llm_client(env_cfg.llm) - except Exception: - env_cfg.llm.provider = "mock" - llm = build_llm_client(env_cfg.llm) - return OSINTEnvironment(env_cfg, llm=llm) - - -def _serialize_observation(observation: Any) -> OpenEnvObservationModel: - return OpenEnvObservationModel( - tool_outputs=list(observation.tool_outputs), - graph_snapshot=dict(observation.graph_snapshot), - action_history=list(observation.action_history), - task=dict(observation.task), - ) - - -def _safe_session_info(info: dict[str, Any]) -> dict[str, Any]: - return { - "step_count": int(info.get("step_count", 0)), - "total_reward": float(info.get("total_reward", 0.0)), - "tool_calls": int(info.get("tool_calls", 0)), - "redundant_tool_calls": int(info.get("redundant_tool_calls", 0)), - "task_answer": str(info.get("task_answer", "")), - "agent_answer": "" if info.get("agent_answer") is None else str(info.get("agent_answer", "")), - "graph_f1": float(info.get("graph_f1", 0.0)), - "reward_components": dict(info.get("reward_components", {})), - } - - -def _task_summaries(env: OSINTEnvironment) -> list[OpenEnvTaskSummary]: - return [ - OpenEnvTaskSummary( - task_id=task.task_id, - task_type=task.task_type, - question=task.question, - difficulty=str(task.metadata.get("difficulty", "unknown")), - grader=( - dict(task.metadata.get("grader", {})) - if isinstance(task.metadata.get("grader"), dict) - else { - "type": "exact_match", - "answer_type": "node_id", - "case_sensitive": True, - } - ), - ) - for task in env.tasks - ] - - -def _resolve_task_index(env: OSINTEnvironment, request: OpenEnvResetRequest) -> int: - global _RESET_COUNTER - if request.task_index is not None: - task_index = int(request.task_index) - if task_index < 0 or task_index >= len(env.tasks): - raise HTTPException(status_code=400, detail=f"Invalid task_index {task_index}") - return task_index - if request.task_id: - for idx, task in enumerate(env.tasks): - if task.task_id == request.task_id: - return idx - raise HTTPException(status_code=400, detail=f"Unknown task_id {request.task_id}") - with _SESSION_LOCK: - task_index = _RESET_COUNTER % max(1, len(env.tasks)) - _RESET_COUNTER += 1 - return task_index - - -def _get_session_env(session_id: str) -> OSINTEnvironment: - with _SESSION_LOCK: - env = _SESSIONS.get(session_id) - if env is None: - raise HTTPException(status_code=404, detail=f"Unknown session_id {session_id}") - return env - - -def _store_session(session_id: str, env: OSINTEnvironment) -> None: - global _LATEST_SESSION_ID - with _SESSION_LOCK: - _SESSIONS[session_id] = env - _LATEST_SESSION_ID = session_id - - -def _latest_session_id() -> str: - with _SESSION_LOCK: - if _LATEST_SESSION_ID and _LATEST_SESSION_ID in _SESSIONS: - return _LATEST_SESSION_ID - if _SESSIONS: - return next(reversed(_SESSIONS)) - raise HTTPException(status_code=404, detail="No active session. Call /reset first.") - - -def _resolve_session_id(session_id: str | None) -> str: - token = str(session_id or "").strip() - if token: - return token - return _latest_session_id() - - -def _task_lookup(env: OSINTEnvironment) -> dict[str, Any]: - return {task.task_id: task for task in env.tasks} - - -def _normalize_episode_rows(env: OSINTEnvironment, episodes: list[dict[str, Any]]) -> list[dict[str, Any]]: - tasks_by_id = _task_lookup(env) - normalized: list[dict[str, Any]] = [] - for episode in episodes: - row = dict(episode) - task = tasks_by_id.get(str(row.get("task_id", ""))) - if task is not None: - row.setdefault("task_type", task.task_type) - row.setdefault("question", task.question) - row.setdefault("task_answer", task.answer) - row.setdefault( - "truth_edges", - [ - { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - for edge in task.supporting_edges - ], - ) - row.setdefault("pred_edges", []) - row.setdefault("reward_components", {}) - row.setdefault("graph_f1", 0.0) - row.setdefault("reward", 0.0) - row.setdefault("steps", 0) - row.setdefault("tool_calls", 0) - row.setdefault("success", 0) - normalized.append(row) - return normalized - - -@lru_cache(maxsize=1) -def _base_environment_snapshot() -> dict[str, Any]: - env = _build_environment() - difficulty_counts = Counter(str(task.metadata.get("difficulty", "unknown")) for task in env.tasks) - return { - "task_count": len(env.tasks), - "difficulty_counts": dict(difficulty_counts), - "action_space": ["CALL_TOOL", "ADD_EDGE", "ANSWER"], - "observation_space": { - "tool_outputs": "Last tool results and memory hits.", - "graph_snapshot": "Current working graph edge snapshot.", - "action_history": "Recent action/reward trace.", - "task": "Task id, task type, and question.", - }, - "task_types": sorted({task.task_type for task in env.tasks}), - "config": { - "seed": env.config.seed, - "max_steps": env.config.max_steps, - "swarm_enabled": env.config.swarm.enabled, - "llm_provider": env.config.llm.provider, - "llm_model": env.config.llm.model, - }, - } - - -@lru_cache(maxsize=1) -def _preview_snapshot() -> dict[str, Any]: - env = _build_environment() - evaluation = run_evaluation(env, episodes=3, return_details=True, llm=build_llm_client(env.config.llm)) - dashboard_path = export_dashboard( - env=env, - evaluation=evaluation, - leaderboard_records=[], - output_path=str(SPACE_DASHBOARD), - ) - snapshot = dict(_base_environment_snapshot()) - snapshot["summary"] = evaluation["summary"] - snapshot["dashboard_path"] = dashboard_path - return snapshot - - -def _space_snapshot() -> dict[str, Any]: - snapshot = dict(_base_environment_snapshot()) - - baseline_payload = _load_json(LATEST_BASELINE_OUTPUT) - evaluation_payload = _load_json(LATEST_EVALUATION_OUTPUT) - - candidates: list[tuple[float, str, dict[str, Any]]] = [] - if baseline_payload is not None and isinstance(baseline_payload.get("summary"), dict): - candidates.append((_path_mtime(LATEST_BASELINE_OUTPUT), "baseline_output", baseline_payload)) - if evaluation_payload is not None and isinstance(evaluation_payload.get("summary"), dict): - candidates.append((_path_mtime(LATEST_EVALUATION_OUTPUT), "latest_evaluation", evaluation_payload)) - - if candidates: - _, source, payload = max(candidates, key=lambda item: item[0]) - snapshot["summary"] = dict(payload["summary"]) - snapshot["source"] = source - if source == "baseline_output": - dashboard_path = Path( - str( - ((payload.get("run") or {}).get("dashboard_path")) - or "artifacts/baselines/openai_fixed_levels_dashboard.html" - ) - ) - if dashboard_path.exists(): - snapshot["dashboard_path"] = str(dashboard_path) - return snapshot - - env = _build_environment() - dashboard_path = export_dashboard( - env=env, - evaluation=payload, - leaderboard_records=[], - output_path=str(SPACE_DASHBOARD), - ) - snapshot["dashboard_path"] = dashboard_path - return snapshot - - preview = _preview_snapshot() - preview["source"] = "preview" - return preview - - -app = FastAPI(title="OSINT OpenEnv Space", version="0.1.0") - - -@app.get("/", response_class=HTMLResponse) -def home() -> str: - snapshot = _space_snapshot() - summary = snapshot["summary"] - difficulty_html = "".join( - f"
  • {level}: {count}
    • " - for level, count in sorted(snapshot["difficulty_counts"].items()) - ) - task_type_html = "".join(f"
  • {task_type}
    • " for task_type in snapshot["task_types"]) - return f""" - - - - - OSINT OpenEnv Space - - - -
    -
    -
    -

    OSINT OpenEnv Space

    -

    A containerized OpenEnv-compatible benchmark for synthetic OSINT reasoning over profiles, forum threads, posts, aliases, organizations, locations, and event links.

    -

    The Space boots with the fixed-level benchmark so visitors get a stable environment snapshot instead of a different graph every restart.

    - Open Dashboard - Environment JSON -
    -
    -

    Included Snapshot

    -
    -
    Tasks
    {snapshot["task_count"]}
    -
    Provider
    {snapshot["config"]["llm_provider"]}
    -
    Score
    {summary["leaderboard_score"]:.3f}
    -
    Success
    {summary["task_success_rate"]:.3f}
    -
    -
    -
    - -
    -
    -

    Action Space

    -
      -
    • CALL_TOOL: query platform views or semantic memory.
      • -
    • ADD_EDGE: add a hypothesized relation to the working graph.
      • -
    • ANSWER: submit the final node id answer.
      • -
    -
    -
    -

    Difficulty Mix

    -
      {difficulty_html}
    -
    -
    -

    Task Families

    -
      {task_type_html}
    -
    -
    -
    - -""" - - -@app.get("/healthz") -def healthz() -> JSONResponse: - return JSONResponse({"status": "ok"}) - - -@app.get("/health") -def health() -> JSONResponse: - return healthz() - - -@app.get("/openenv.yaml") -def openenv_spec() -> FileResponse: - return FileResponse(OPENENV_SPEC_PATH, media_type="text/yaml") - - -@app.get("/api/environment") -def environment_metadata() -> JSONResponse: - return JSONResponse(_space_snapshot()) - - -@app.get("/openenv/tasks", response_model=list[OpenEnvTaskSummary]) -def openenv_tasks() -> list[OpenEnvTaskSummary]: - env = _build_environment() - return _task_summaries(env) - - -@app.post("/openenv/reset", response_model=OpenEnvResponseEnvelope) -@app.post("/openenv/reset/", response_model=OpenEnvResponseEnvelope, include_in_schema=False) -@app.post("/reset", response_model=OpenEnvResponseEnvelope, include_in_schema=False) -@app.post("/reset/", response_model=OpenEnvResponseEnvelope, include_in_schema=False) -async def openenv_reset(request: Request) -> OpenEnvResponseEnvelope: - env = _build_environment() - raw_body = await request.body() - if not raw_body.strip(): - payload: dict[str, Any] = {} - else: - try: - parsed_payload = json.loads(raw_body) - except json.JSONDecodeError as exc: - raise HTTPException(status_code=400, detail="Reset body must be valid JSON") from exc - if parsed_payload is None: - payload = {} - elif isinstance(parsed_payload, dict): - payload = parsed_payload - else: - raise HTTPException(status_code=400, detail="Reset body must be a JSON object") - - try: - reset_request = OpenEnvResetRequest.model_validate(payload) - except Exception as exc: - raise HTTPException(status_code=422, detail="Invalid reset request payload") from exc - - env._task_idx = _resolve_task_index(env, reset_request) - observation = env.reset() - session_id = str(uuid4()) - _store_session(session_id, env) - return OpenEnvResponseEnvelope( - session_id=session_id, - observation=_serialize_observation(observation), - reward=0.0, - done=False, - info=_safe_session_info(env._info()), - ) - - -@app.post("/openenv/step", response_model=OpenEnvResponseEnvelope) -@app.post("/openenv/step/", response_model=OpenEnvResponseEnvelope, include_in_schema=False) -@app.post("/step", response_model=OpenEnvResponseEnvelope, include_in_schema=False) -@app.post("/step/", response_model=OpenEnvResponseEnvelope, include_in_schema=False) -def openenv_step(request: OpenEnvActionRequest) -> OpenEnvResponseEnvelope: - session_id = _resolve_session_id(request.session_id) - env = _get_session_env(session_id) - action_type_raw = request.resolved_action_type().strip() - if not action_type_raw: - raise HTTPException(status_code=400, detail="Missing action_type") - try: - action_type = ActionType(action_type_raw) - except ValueError as exc: - raise HTTPException(status_code=400, detail=f"Unsupported action_type {action_type_raw}") from exc - observation, reward, done, info = env.step(Action(action_type=action_type, payload=request.resolved_payload())) - return OpenEnvResponseEnvelope( - session_id=session_id, - observation=_serialize_observation(observation), - reward=float(reward), - done=bool(done), - info=_safe_session_info(info), - ) - - -def _state_response(session_id: str) -> OpenEnvResponseEnvelope: - env = _get_session_env(session_id) - if env.state is None: - raise HTTPException(status_code=400, detail="Session has not been reset yet") - return OpenEnvResponseEnvelope( - session_id=session_id, - observation=_serialize_observation(env._observation()), - reward=0.0, - done=bool(env.state.done), - info=_safe_session_info(env._info()), - ) - - -@app.get("/openenv/state/{session_id}", response_model=OpenEnvResponseEnvelope) -def openenv_state(session_id: str) -> OpenEnvResponseEnvelope: - return _state_response(session_id) - - -@app.get("/openenv/state", response_model=OpenEnvResponseEnvelope, include_in_schema=False) -@app.get("/state", response_model=OpenEnvResponseEnvelope, include_in_schema=False) -@app.get("/state/", response_model=OpenEnvResponseEnvelope, include_in_schema=False) -def openenv_state_latest() -> OpenEnvResponseEnvelope: - return _state_response(_latest_session_id()) - - -@app.post("/openenv/report_inference", response_model=OpenEnvInferenceReportResponse) -def openenv_report_inference(request: OpenEnvInferenceReportRequest) -> OpenEnvInferenceReportResponse: - env = _build_environment() - normalized_episodes = _normalize_episode_rows(env, list(request.episodes)) - payload = { - "run": dict(request.run), - "summary": dict(request.summary), - "episodes": normalized_episodes, - } - LATEST_EVALUATION_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - LATEST_EVALUATION_OUTPUT.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") - dashboard_path = export_dashboard( - env=env, - evaluation=payload, - leaderboard_records=load_leaderboard("artifacts/baselines/openai_fixed_levels_leaderboard.json"), - output_path=str(SPACE_DASHBOARD), - ) - return OpenEnvInferenceReportResponse( - status="ok", - output_path=str(LATEST_EVALUATION_OUTPUT), - dashboard_path=str(dashboard_path), - ) - - -@app.get("/dashboard") -def dashboard() -> FileResponse: - snapshot = _space_snapshot() - return FileResponse(snapshot["dashboard_path"], media_type="text/html") - - -if __name__ == "__main__": - import uvicorn - - uvicorn.run("server:app", host="0.0.0.0", port=SPACE_PORT) diff --git a/scratch/osint/server/app.py b/scratch/osint/server/app.py deleted file mode 100644 index 0fcf128fd22e0af85ebe3f83e325b3f84f06279d..0000000000000000000000000000000000000000 --- a/scratch/osint/server/app.py +++ /dev/null @@ -1,26 +0,0 @@ -from __future__ import annotations - -import importlib.util -import os -from pathlib import Path - -import uvicorn - - -_ROOT_SERVER_PATH = Path(__file__).resolve().parents[1] / "server.py" -_SPEC = importlib.util.spec_from_file_location("osint_root_server", _ROOT_SERVER_PATH) -if _SPEC is None or _SPEC.loader is None: - raise RuntimeError(f"Unable to load server module from {_ROOT_SERVER_PATH}") - -_MODULE = importlib.util.module_from_spec(_SPEC) -_SPEC.loader.exec_module(_MODULE) -app = _MODULE.app - - -def main() -> None: - port = int(os.getenv("PORT", "7860")) - uvicorn.run("server.app:app", host="0.0.0.0", port=port) - - -if __name__ == "__main__": - main() diff --git a/scratch/osint/src/osint_env/__init__.py b/scratch/osint/src/osint_env/__init__.py deleted file mode 100644 index baddf8bf6a3ce397a17fd78ccee11fcb6c8446e8..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""OSINT RL environment package.""" - -from .env.environment import OSINTEnvironment - -__all__ = ["OSINTEnvironment"] diff --git a/scratch/osint/src/osint_env/agents/__init__.py b/scratch/osint/src/osint_env/agents/__init__.py deleted file mode 100644 index 884d50b613e3bf492ff2ea1fbd2aec4656833975..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/agents/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -"""Agent implementations.""" - -from osint_env.agents.single_agent import SingleAgentRunner -from osint_env.agents.swarm_agent import SwarmAgentRunner - -__all__ = ["SingleAgentRunner", "SwarmAgentRunner"] - diff --git a/scratch/osint/src/osint_env/agents/single_agent.py b/scratch/osint/src/osint_env/agents/single_agent.py deleted file mode 100644 index 7e523af80f778911a42486bf380997c982ce10fd..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/agents/single_agent.py +++ /dev/null @@ -1,41 +0,0 @@ -from __future__ import annotations - -from osint_env.domain.models import Action, ActionType -from osint_env.env.environment import OSINTEnvironment -from osint_env.llm.interface import LLMClient, RuleBasedMockLLM - - -class SingleAgentRunner: - def __init__(self, env: OSINTEnvironment, llm: LLMClient | None = None): - self.env = env - self.llm = llm or RuleBasedMockLLM() - - def run_episode(self) -> dict: - obs = self.env.reset() - done = False - info = {} - while not done: - messages = [{"role": "system", "content": f"question: {obs.task['question']}"}] - tools = [] - try: - llm_resp = self.llm.generate(messages, tools) - planned_calls = llm_resp.tool_calls[:2] - except Exception: - planned_calls = [] - - for call in planned_calls: - obs, _, done, info = self.env.step(Action(ActionType.CALL_TOOL, call)) - if done: - break - if done: - break - answer_guess = self._heuristic_answer(obs.task["question"]) - obs, _, done, info = self.env.step(Action(ActionType.ANSWER, {"answer": answer_guess})) - return info - - @staticmethod - def _heuristic_answer(question: str) -> str: - for token in question.replace("?", "").split(): - if token.startswith("alias_") or token.startswith("user_"): - return token - return "unknown" diff --git a/scratch/osint/src/osint_env/agents/swarm_agent.py b/scratch/osint/src/osint_env/agents/swarm_agent.py deleted file mode 100644 index 534d526aea89593f452655f3e95284e1d957d4df..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/agents/swarm_agent.py +++ /dev/null @@ -1,209 +0,0 @@ -from __future__ import annotations - -import re -from typing import Any - -from osint_env.domain.models import Action, ActionType -from osint_env.env.environment import OSINTEnvironment -from osint_env.env.spawn_reward_hooks import critical_steps, parl_style_spawn_reward -from osint_env.llm.interface import LLMClient, RuleBasedMockLLM - - -class SwarmAgentRunner: - """Low-width multi-agent orchestrator over a single environment episode.""" - - def __init__(self, env: OSINTEnvironment, llm: LLMClient | None = None): - self.env = env - self.llm = llm or RuleBasedMockLLM() - - def run_episode(self) -> dict[str, Any]: - obs = self.env.reset() - done = False - info: dict[str, Any] = {} - - swarm_cfg = self.env.config.swarm - spawn_cfg = self.env.config.spawn_reward - - spawn_count = 0 - finished_subtasks = 0 - depth_used = 0 - max_breadth_used = 0 - - stage_main_steps: list[int] = [] - stage_sub_steps: list[list[int]] = [] - - for _ in range(max(1, swarm_cfg.planner_rounds)): - if done: - break - - active_agents = max(1, min(swarm_cfg.max_agents, swarm_cfg.max_breadth, swarm_cfg.max_width)) - max_breadth_used = max(max_breadth_used, active_agents) - depth_used += 1 - spawn_count += active_agents - stage_main_steps.append(1) - - stage_steps: list[int] = [] - for agent_idx in range(active_agents): - if done: - break - - steps_for_agent = 0 - role = self._agent_role(agent_idx) - planned_calls = self._tool_plan( - obs=obs, - agent_idx=agent_idx, - role=role, - limit=swarm_cfg.tools_per_agent, - ) - for call in planned_calls: - obs, _, done, info = self.env.step(Action(ActionType.CALL_TOOL, call)) - steps_for_agent += 1 - if done: - break - - if not done: - edge_payload = self._edge_plan(agent_idx=agent_idx) - if edge_payload is not None: - obs, _, done, info = self.env.step(Action(ActionType.ADD_EDGE, edge_payload)) - steps_for_agent += 1 - - if steps_for_agent > 0: - finished_subtasks += 1 - stage_steps.append(steps_for_agent) - - stage_sub_steps.append(stage_steps) - - if depth_used >= swarm_cfg.max_depth: - break - - if not done: - answer_guess = self._vote_answer() - obs, _, done, info = self.env.step(Action(ActionType.ANSWER, {"answer": answer_guess})) - - crit_steps = critical_steps( - main_steps=stage_main_steps or [1], - parallel_subagent_steps=stage_sub_steps or [[]], - ) - - base_total = float(info.get("total_reward", 0.0)) - shaped_total = parl_style_spawn_reward( - task_outcome_reward=base_total, - spawn_count=spawn_count, - finished_subtasks=finished_subtasks, - critical_steps=max(1, crit_steps), - lambda_parallel=spawn_cfg.lambda_parallel, - lambda_finish=spawn_cfg.lambda_finish, - anneal=spawn_cfg.anneal, - breadth=max_breadth_used, - depth=depth_used, - max_parallel_hint=spawn_cfg.max_parallel_hint, - ) - spawn_aux = shaped_total - base_total - - components = dict(info.get("reward_components", {})) - components["spawn_auxiliary"] = components.get("spawn_auxiliary", 0.0) + float(spawn_aux) - components["spawn_count"] = float(spawn_count) - components["spawn_finished_subtasks"] = float(finished_subtasks) - components["spawn_critical_steps"] = float(crit_steps) - components["spawn_depth"] = float(depth_used) - components["spawn_breadth"] = float(max_breadth_used) - - info["total_reward"] = shaped_total - info["reward_components"] = components - info["spawn_count"] = spawn_count - info["spawn_finished_subtasks"] = finished_subtasks - info["spawn_critical_steps"] = crit_steps - info["spawn_depth"] = depth_used - info["spawn_breadth"] = max_breadth_used - info["swarm_roles"] = [self._agent_role(i) for i in range(max_breadth_used)] - - if self.env.state is not None: - self.env.state.total_reward = shaped_total - self.env.state.reward_components.update(components) - - return info - - @staticmethod - def _agent_role(agent_idx: int) -> str: - roles = ["explorer", "linker", "reasoner"] - return roles[agent_idx % len(roles)] - - def _tool_plan(self, obs: Any, agent_idx: int, role: str, limit: int) -> list[dict[str, Any]]: - messages = [ - { - "role": "system", - "content": ( - f"question: {obs.task['question']}\n" - f"agent_role: {role}_{agent_idx}\n" - "Return concise tool plan." - ), - } - ] - try: - response = self.llm.generate(messages, tools=[]) - except Exception: - response = None - - calls: list[dict[str, Any]] = [] - for call in (response.tool_calls if response is not None else []): - if not isinstance(call, dict): - continue - tool_name = str(call.get("tool_name", "")).strip() - args = call.get("args", {}) - if not tool_name or not isinstance(args, dict): - continue - calls.append({"tool_name": tool_name, "args": args}) - if len(calls) >= max(1, limit): - break - - if calls: - return calls - - question = str(obs.task.get("question", "")).lower() - if role == "explorer": - if "event" in question: - return [{"tool_name": "search_threads", "args": {"topic": "security"}}] - return [{"tool_name": "search_posts", "args": {"query": "Update"}}] - - if role == "linker": - if "alias" in question: - return [{"tool_name": "search_posts", "args": {"query": "alias"}}] - return [{"tool_name": "search_people", "args": {"org": "Apex"}}] - - if role == "reasoner": - return [{"tool_name": "search_memory", "args": {"query": obs.task.get("question", ""), "k": 5}}] - - if "alias" in question: - return [{"tool_name": "search_posts", "args": {"query": "Update"}}] - - user_tokens = re.findall(r"\buser_[a-zA-Z0-9_]+\b", question) - if user_tokens: - return [{"tool_name": "get_profile", "args": {"user_id": user_tokens[0]}}] - - return [{"tool_name": "search_people", "args": {"org": "Apex"}}] - - def _edge_plan(self, agent_idx: int) -> dict[str, Any] | None: - if self.env.state is None or not self.env.state.task.supporting_edges: - return None - edge = self.env.state.task.supporting_edges[agent_idx % len(self.env.state.task.supporting_edges)] - return { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - - def _vote_answer(self) -> str: - if self.env.state is None: - return "unknown" - - truth = {(e.src, e.rel, e.dst) for e in self.env.state.task.supporting_edges} - pred = {(e.src, e.rel, e.dst) for e in self.env.memory_graph.edges} - if truth & pred: - return self.env.state.task.answer - - question = self.env.state.task.question - for token in question.replace("?", "").split(): - if token.startswith("alias_") or token.startswith("user_"): - return token - return "unknown" diff --git a/scratch/osint/src/osint_env/api/__init__.py b/scratch/osint/src/osint_env/api/__init__.py deleted file mode 100644 index ecc3ec5bd47138834c3f4763f951b8cae9a28282..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/api/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -from osint_env.api.models import ( - OpenEnvActionRequest, - OpenEnvInferenceReportRequest, - OpenEnvInferenceReportResponse, - OpenEnvObservationModel, - OpenEnvResetRequest, - OpenEnvResponseEnvelope, - OpenEnvTaskSummary, -) - -__all__ = [ - "OpenEnvActionRequest", - "OpenEnvInferenceReportRequest", - "OpenEnvInferenceReportResponse", - "OpenEnvObservationModel", - "OpenEnvResetRequest", - "OpenEnvResponseEnvelope", - "OpenEnvTaskSummary", -] diff --git a/scratch/osint/src/osint_env/api/models.py b/scratch/osint/src/osint_env/api/models.py deleted file mode 100644 index 67cfae4cd577057ba2ab65953970f391358579c3..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/api/models.py +++ /dev/null @@ -1,73 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from pydantic import BaseModel, Field - - -class OpenEnvTaskSummary(BaseModel): - task_id: str - task_type: str - question: str - difficulty: str = "unknown" - grader: dict[str, Any] = Field(default_factory=dict) - - -class OpenEnvObservationModel(BaseModel): - tool_outputs: list[dict[str, Any]] - graph_snapshot: dict[str, Any] - action_history: list[dict[str, Any]] - task: dict[str, Any] - - -class OpenEnvResetRequest(BaseModel): - task_id: str | None = None - task_index: int | None = None - - -class OpenEnvActionRequest(BaseModel): - session_id: str | None = Field( - default=None, - description="Session identifier. Optional for /step compatibility alias, which uses the latest session.", - ) - action_type: str | None = Field(default=None, description="One of CALL_TOOL, ADD_EDGE, ANSWER.") - payload: dict[str, Any] = Field(default_factory=dict) - action: dict[str, Any] | None = None - - def resolved_action_type(self) -> str: - if self.action_type: - return str(self.action_type) - if isinstance(self.action, dict): - nested = self.action.get("action_type") - if nested: - return str(nested) - return "" - - def resolved_payload(self) -> dict[str, Any]: - if self.payload: - return dict(self.payload) - if isinstance(self.action, dict): - nested = self.action.get("payload") - if isinstance(nested, dict): - return dict(nested) - return {} - - -class OpenEnvResponseEnvelope(BaseModel): - session_id: str - observation: OpenEnvObservationModel - reward: float - done: bool - info: dict[str, Any] - - -class OpenEnvInferenceReportRequest(BaseModel): - run: dict[str, Any] = Field(default_factory=dict) - summary: dict[str, Any] - episodes: list[dict[str, Any]] = Field(default_factory=list) - - -class OpenEnvInferenceReportResponse(BaseModel): - status: str - output_path: str - dashboard_path: str diff --git a/scratch/osint/src/osint_env/baselines/__init__.py b/scratch/osint/src/osint_env/baselines/__init__.py deleted file mode 100644 index 1bba1e79c306b0b02a2760fa37703138be49db22..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/baselines/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from osint_env.baselines.openai_runner import OpenAIBaselineConfig, OpenAIBaselineRunner - -__all__ = ["OpenAIBaselineConfig", "OpenAIBaselineRunner"] - diff --git a/scratch/osint/src/osint_env/baselines/openai_runner.py b/scratch/osint/src/osint_env/baselines/openai_runner.py deleted file mode 100644 index 6fedfc3e830f0b0b542df0fbcf82b46109a9f786..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/baselines/openai_runner.py +++ /dev/null @@ -1,533 +0,0 @@ -from __future__ import annotations - -import json -from dataclasses import asdict, dataclass -from pathlib import Path -from time import perf_counter -from typing import Any - -from osint_env.config import clone_environment_config, load_seeding_config, load_shared_config -from osint_env.domain.models import Action, ActionType, Edge -from osint_env.env.environment import OSINTEnvironment -from osint_env.env.reward import compute_graph_f1 -from osint_env.eval.leaderboard import append_leaderboard_record, load_leaderboard -from osint_env.eval.metrics import EvalMetrics -from osint_env.viz import export_dashboard - - -SYSTEM_PROMPT = """You are an OSINT benchmark agent operating in a synthetic OpenEnv task. - -Available actions are provided as function tools. On every turn, call exactly one tool. - -Rules: -- Solve the question using only tool outputs and the current graph snapshot. -- When you have enough evidence, call submit_answer with the exact node id string. -- Questions may contain exact node ids such as alias_*, user_*, post_*, thr_*, org_*, loc_*, and event_*. -- Prefer direct id lookups when an exact id is present in the question. -- get_post and get_thread retrieve exact seeded records by id. -- Use add_edge only for relationships strongly supported by the evidence you have already collected. -- Prefer concise, high-signal tool queries. -- Never guess free-form prose when a node id answer is required. -""" - - -@dataclass(slots=True) -class OpenAIBaselineConfig: - shared_config_path: str = "datasets/fixed_levels/shared_config_fixed_levels.json" - seed_file: str = "datasets/fixed_levels/seed_fixed_levels.json" - output_path: str = "artifacts/baselines/openai_fixed_levels_latest.json" - leaderboard_path: str = "artifacts/baselines/openai_fixed_levels_leaderboard.json" - dashboard_path: str = "artifacts/baselines/openai_fixed_levels_dashboard.html" - run_name: str = "openai_fixed_levels_baseline" - model: str = "gpt-5-nano" - base_url: str = "https://api.openai.com/v1" - api_key: str = "" - api_key_env: str = "OPENAI_API_KEY" - temperature: float = 0.0 - max_tokens: int = 256 - timeout_seconds: int = 60 - episodes: int = 30 - max_steps: int = 8 - seed: int | None = 7 - append_leaderboard: bool = True - - -def _tool_schema( - name: str, - description: str, - properties: dict[str, Any], - required: list[str], -) -> dict[str, Any]: - return { - "type": "function", - "function": { - "name": name, - "description": description, - "parameters": { - "type": "object", - "properties": properties, - "required": required, - "additionalProperties": False, - }, - }, - } - - -def build_action_tools() -> list[dict[str, Any]]: - return [ - _tool_schema( - "search_posts", - "Search microblog posts by substring over post text, post id, author id, canonical user id, or referenced entity ids/names.", - {"query": {"type": "string", "description": "Substring to search for in post text."}}, - ["query"], - ), - _tool_schema( - "get_post", - "Fetch a specific microblog post by exact post id.", - {"post_id": {"type": "string", "description": "Post node id such as post_midnight_manifest."}}, - ["post_id"], - ), - _tool_schema( - "get_user_posts", - "Fetch posts authored by a user or alias id. Alias ids are resolved to the canonical user and vice versa.", - {"user_id": {"type": "string", "description": "User or alias node id."}}, - ["user_id"], - ), - _tool_schema( - "get_mentions", - "Fetch posts that mention a given canonical user id.", - {"user_id": {"type": "string", "description": "Canonical user node id."}}, - ["user_id"], - ), - _tool_schema( - "search_threads", - "Search forum threads by exact topic name.", - {"topic": {"type": "string", "description": "Thread topic such as security or ai."}}, - ["topic"], - ), - _tool_schema( - "get_thread", - "Fetch a specific forum thread by id.", - {"thread_id": {"type": "string", "description": "Thread node id."}}, - ["thread_id"], - ), - _tool_schema( - "get_user_activity", - "Fetch a user's known forum activity.", - {"user_id": {"type": "string", "description": "Canonical user node id."}}, - ["user_id"], - ), - _tool_schema( - "get_profile", - "Fetch a profile record by canonical user id or alias id.", - {"user_id": {"type": "string", "description": "Canonical user node id or alias id."}}, - ["user_id"], - ), - _tool_schema( - "search_people", - "Search profiles by name, alias id, organization name, or organization id.", - { - "name": {"type": "string", "description": "Optional name substring.", "default": ""}, - "org": {"type": "string", "description": "Optional organization substring.", "default": ""}, - }, - [], - ), - _tool_schema( - "get_connections", - "Fetch explicit profile connections for a user or alias id.", - {"user_id": {"type": "string", "description": "Canonical user node id or alias id."}}, - ["user_id"], - ), - _tool_schema( - "search_memory", - "Search semantic memory over prior observations and tool outputs.", - { - "query": {"type": "string", "description": "Memory retrieval query."}, - "k": {"type": "integer", "description": "Top-k matches.", "default": 5}, - }, - ["query"], - ), - _tool_schema( - "add_edge", - "Add a supported graph edge to the working memory graph.", - { - "src": {"type": "string"}, - "rel": {"type": "string"}, - "dst": {"type": "string"}, - "confidence": {"type": "number", "default": 1.0}, - }, - ["src", "rel", "dst"], - ), - _tool_schema( - "submit_answer", - "Finish the episode by submitting the exact node id answer.", - {"answer": {"type": "string", "description": "Exact node id answer for the task."}}, - ["answer"], - ), - ] - - -def _message_text(message: Any) -> str: - content = getattr(message, "content", "") - if isinstance(content, str): - return content - if isinstance(content, list): - parts: list[str] = [] - for item in content: - if isinstance(item, dict) and item.get("type") == "text": - parts.append(str(item.get("text", ""))) - else: - text = getattr(item, "text", None) - if text: - parts.append(str(text)) - return "\n".join(part for part in parts if part) - return str(content or "") - - -def _safe_info(info: dict[str, Any]) -> dict[str, Any]: - return { - "step_count": int(info.get("step_count", 0)), - "total_reward": float(info.get("total_reward", 0.0)), - "tool_calls": int(info.get("tool_calls", 0)), - "redundant_tool_calls": int(info.get("redundant_tool_calls", 0)), - "reward_components": dict(info.get("reward_components", {})), - } - - -def _observation_payload(env: OSINTEnvironment, observation: Any, step_limit: int) -> dict[str, Any]: - task = dict(observation.task) - return { - "task": { - "task_id": task.get("task_id", ""), - "task_type": task.get("task_type", ""), - "question": task.get("question", ""), - }, - "remaining_steps": max(0, step_limit - int(env.state.step_count if env.state else 0)), - "recent_tool_outputs": list(observation.tool_outputs), - "graph_snapshot": dict(observation.graph_snapshot), - "recent_action_history": list(observation.action_history), - } - - -class OpenAIBaselineRunner: - def __init__(self, config: OpenAIBaselineConfig): - self.config = config - - from openai import OpenAI - - if not config.api_key: - raise ValueError( - "OpenAI baseline requires an API key. " - f"Set {config.api_key_env} or pass --openai-api-key." - ) - - self.client = OpenAI( - api_key=config.api_key, - base_url=config.base_url, - timeout=config.timeout_seconds, - ) - self.tools = build_action_tools() - - @staticmethod - def _is_gpt5_family(model: str) -> bool: - return str(model).strip().lower().startswith("gpt-5") - - @staticmethod - def _supports_reasoning_effort_in_chat_completions(model: str) -> bool: - model_name = str(model).strip().lower() - if model_name.startswith("gpt-5.4-mini"): - return False - return model_name.startswith("gpt-5") - - def _request_kwargs(self, messages: list[dict[str, Any]], episode_index: int) -> dict[str, Any]: - kwargs: dict[str, Any] = { - "model": self.config.model, - "messages": messages, - "tools": self.tools, - "tool_choice": "required", - "parallel_tool_calls": False, - "max_completion_tokens": self.config.max_tokens, - } - if self.config.seed is not None: - kwargs["seed"] = int(self.config.seed) + episode_index - - if self._is_gpt5_family(self.config.model): - # GPT-5 family chat-completions compatibility: - # use max_completion_tokens and avoid temperature for older GPT-5 models. - if self._supports_reasoning_effort_in_chat_completions(self.config.model): - kwargs["reasoning_effort"] = "none" - else: - kwargs["temperature"] = self.config.temperature - - return kwargs - - def _build_environment(self) -> OSINTEnvironment: - shared = load_shared_config(self.config.shared_config_path) - env_cfg = clone_environment_config(shared.environment) - env_cfg.seeding = load_seeding_config(self.config.seed_file) - env_cfg.llm.provider = "mock" - env_cfg.llm.model = self.config.model - env_cfg.llm.temperature = self.config.temperature - env_cfg.llm.max_tokens = self.config.max_tokens - env_cfg.max_steps = min(int(env_cfg.max_steps), int(self.config.max_steps)) - return OSINTEnvironment(env_cfg) - - def _execute_action( - self, - env: OSINTEnvironment, - tool_name: str, - args: dict[str, Any], - ) -> tuple[Any, float, bool, dict[str, Any], dict[str, Any]]: - if tool_name == "submit_answer": - answer = str(args.get("answer", "")).strip() - obs, reward, done, info = env.step(Action(ActionType.ANSWER, {"answer": answer})) - result = {"submitted_answer": answer} - return obs, reward, done, info, result - - if tool_name == "add_edge": - payload = { - "src": str(args.get("src", "")).strip(), - "rel": str(args.get("rel", "")).strip(), - "dst": str(args.get("dst", "")).strip(), - "confidence": float(args.get("confidence", 1.0)), - } - obs, reward, done, info = env.step(Action(ActionType.ADD_EDGE, payload)) - return obs, reward, done, info, payload - - payload = {"tool_name": tool_name, "args": dict(args)} - obs, reward, done, info = env.step(Action(ActionType.CALL_TOOL, payload)) - result = obs.tool_outputs[-1]["output"] if obs.tool_outputs else {} - return obs, reward, done, info, result - - def _episode(self, env: OSINTEnvironment, episode_index: int) -> tuple[dict[str, Any], dict[str, Any]]: - obs = env.reset() - initial_observation = _observation_payload(env, obs, env.config.max_steps) - messages: list[dict[str, Any]] = [ - {"role": "system", "content": SYSTEM_PROMPT}, - { - "role": "user", - "content": json.dumps(initial_observation, indent=2, sort_keys=True), - }, - ] - - turn_trace: list[dict[str, Any]] = [] - raw_fingerprints: list[str] = [] - info: dict[str, Any] = {} - done = False - usage_totals = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} - - while not done and env.state is not None and env.state.step_count < env.config.max_steps: - completion = self.client.chat.completions.create(**self._request_kwargs(messages, episode_index)) - if getattr(completion, "system_fingerprint", None): - raw_fingerprints.append(str(completion.system_fingerprint)) - if getattr(completion, "usage", None) is not None: - usage_totals["prompt_tokens"] += int(getattr(completion.usage, "prompt_tokens", 0) or 0) - usage_totals["completion_tokens"] += int(getattr(completion.usage, "completion_tokens", 0) or 0) - usage_totals["total_tokens"] += int(getattr(completion.usage, "total_tokens", 0) or 0) - - message = completion.choices[0].message - content = _message_text(message) - tool_calls = list(message.tool_calls or []) - if not tool_calls: - fallback_answer = content.strip() or "unknown" - obs, reward, done, info = env.step(Action(ActionType.ANSWER, {"answer": fallback_answer})) - tool_result = { - "submitted_answer": fallback_answer, - "reward": reward, - "done": done, - "observation": _observation_payload(env, obs, env.config.max_steps), - "info": _safe_info(info), - } - messages.append({"role": "assistant", "content": content}) - messages.append({"role": "tool", "tool_call_id": "fallback_submit", "content": json.dumps(tool_result)}) - turn_trace.append( - { - "assistant_content": content, - "tool_name": "submit_answer", - "args": {"answer": fallback_answer}, - "tool_payload": tool_result, - } - ) - break - - tool_call = tool_calls[0] - tool_name = str(tool_call.function.name) - try: - args = json.loads(tool_call.function.arguments or "{}") - except json.JSONDecodeError: - args = {} - if not isinstance(args, dict): - args = {} - - obs, reward, done, info, result = self._execute_action(env, tool_name, args) - tool_payload = { - "tool_name": tool_name, - "args": args, - "result": result, - "reward": reward, - "done": done, - "observation": _observation_payload(env, obs, env.config.max_steps), - "info": _safe_info(info), - } - assistant_message = { - "role": "assistant", - "content": content, - "tool_calls": [ - { - "id": tool_call.id, - "type": "function", - "function": { - "name": tool_name, - "arguments": json.dumps(args, sort_keys=True), - }, - } - ], - } - messages.append(assistant_message) - messages.append({"role": "tool", "tool_call_id": tool_call.id, "content": json.dumps(tool_payload, sort_keys=True)}) - turn_trace.append( - { - "assistant_content": content, - "tool_name": tool_name, - "args": args, - "reward": reward, - "done": done, - "tool_payload": tool_payload, - } - ) - - if not done: - obs, _, done, info = env.step(Action(ActionType.ANSWER, {"answer": "unknown"})) - final_payload = { - "submitted_answer": "unknown", - "reward": 0.0, - "done": done, - "observation": _observation_payload(env, obs, env.config.max_steps), - "info": _safe_info(info), - } - turn_trace.append( - { - "assistant_content": "", - "tool_name": "submit_answer", - "args": {"answer": "unknown"}, - "reward": 0.0, - "done": done, - "tool_payload": final_payload, - } - ) - - info = dict(info) - info["openai_system_fingerprints"] = raw_fingerprints - info["usage"] = usage_totals - return info, {"initial_observation": initial_observation, "turns": turn_trace} - - def run(self) -> dict[str, Any]: - env = self._build_environment() - metrics = EvalMetrics() - episode_rows: list[dict[str, Any]] = [] - - started = perf_counter() - run_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} - for episode_index in range(int(self.config.episodes)): - info, trace = self._episode(env, episode_index) - episode_usage = dict(info.get("usage", {})) - for key in run_usage: - run_usage[key] += int(episode_usage.get(key, 0) or 0) - task_type = env.state.task.task_type if env.state else "unknown" - task_id = env.state.task.task_id if env.state else f"episode_{episode_index}" - truth = env.state.task.supporting_edges if env.state else [] - pred = env.memory_graph.edges if env.state else [] - graph_f1 = compute_graph_f1(pred, truth) - metrics.add(info, task_type=task_type, graph_f1=graph_f1) - episode_rows.append( - { - "task_id": task_id, - "task_type": task_type, - "question": env.state.task.question if env.state else "", - "task_answer": str(info.get("task_answer", "")), - "agent_answer": str(info.get("agent_answer", "")) if info.get("agent_answer") is not None else "", - "graph_f1": graph_f1, - "reward": float(info.get("total_reward", 0.0)), - "steps": int(info.get("step_count", 0)), - "tool_calls": int(info.get("tool_calls", 0)), - "success": int(info.get("agent_answer") == info.get("task_answer")), - "reward_components": dict(info.get("reward_components", {})), - "pred_edges": [ - { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - for edge in pred - ], - "truth_edges": [ - { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - for edge in truth - ], - "trace": trace, - "openai_system_fingerprints": list(info.get("openai_system_fingerprints", [])), - "usage": episode_usage, - } - ) - - summary = metrics.summary() - duration_seconds = perf_counter() - started - if self.config.append_leaderboard: - record = append_leaderboard_record( - path=self.config.leaderboard_path, - summary=summary, - episodes=int(self.config.episodes), - run_name=self.config.run_name, - config={ - "provider": "openai", - "model": self.config.model, - "seed": self.config.seed, - "max_steps": self.config.max_steps, - "shared_config_path": self.config.shared_config_path, - "seed_file": self.config.seed_file, - }, - ) - else: - record = None - dashboard_path = export_dashboard( - env=env, - evaluation={"summary": summary, "episodes": episode_rows}, - leaderboard_records=load_leaderboard(self.config.leaderboard_path), - output_path=self.config.dashboard_path, - ) - - payload: dict[str, Any] = { - "run": { - "name": self.config.run_name, - "model": self.config.model, - "episodes": int(self.config.episodes), - "temperature": float(self.config.temperature), - "max_tokens": int(self.config.max_tokens), - "timeout_seconds": int(self.config.timeout_seconds), - "max_steps": int(self.config.max_steps), - "seed": self.config.seed, - "shared_config_path": self.config.shared_config_path, - "seed_file": self.config.seed_file, - "duration_seconds": duration_seconds, - "dashboard_path": dashboard_path, - }, - "summary": summary, - "usage": run_usage, - "episodes": episode_rows, - } - - output = Path(self.config.output_path) - output.parent.mkdir(parents=True, exist_ok=True) - output.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") - - if record is not None: - payload["record"] = record - output.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") - - return payload diff --git a/scratch/osint/src/osint_env/cli.py b/scratch/osint/src/osint_env/cli.py deleted file mode 100644 index 707f3a492fd2513846fc1c6ee4d491fa4385e7ca..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/cli.py +++ /dev/null @@ -1,339 +0,0 @@ -from __future__ import annotations - -import argparse -import json -from pathlib import Path - -from osint_env.agents.single_agent import SingleAgentRunner -from osint_env.agents.swarm_agent import SwarmAgentRunner -from osint_env.config import clone_environment_config, load_seeding_config, load_shared_config -from osint_env.domain.models import EnvironmentConfig -from osint_env.env.environment import OSINTEnvironment -from osint_env.env.reward import compute_graph_f1 -from osint_env.eval.leaderboard import append_leaderboard_record, load_leaderboard, render_leaderboard_table -from osint_env.eval.runner import run_evaluation -from osint_env.llm import build_llm_client -from osint_env.viz import export_dashboard - - -DEFAULT_EVALUATION_PATH = "artifacts/latest_evaluation.json" - - -def _save_evaluation(path: str, payload: dict) -> None: - out = Path(path) - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") - - -def _load_evaluation(path: str) -> dict | None: - file_path = Path(path) - if not file_path.exists(): - return None - try: - data = json.loads(file_path.read_text(encoding="utf-8")) - except json.JSONDecodeError: - return None - if not isinstance(data, dict): - return None - return data - - -def _add_common_args(parser: argparse.ArgumentParser) -> None: - parser.add_argument("--config", type=str, default="config/shared_config.json") - parser.add_argument("--seed-file", type=str, default="") - parser.add_argument( - "--agent-mode", - type=str, - default="config", - choices=["config", "single", "swarm"], - help="Use shared config mode or override runner mode explicitly.", - ) - parser.add_argument( - "--llm-provider", - type=str, - default="config", - choices=["config", "mock", "ollama", "openai"], - help="Use shared config provider or override explicitly.", - ) - parser.add_argument("--llm-model", type=str, default="", help="Override model name for selected LLM provider.") - parser.add_argument("--llm-timeout-seconds", type=int, default=0, help="Override LLM request timeout in seconds.") - parser.add_argument("--ollama-base-url", type=str, default="", help="Override Ollama base URL.") - parser.add_argument("--openai-base-url", type=str, default="", help="Override OpenAI base URL.") - parser.add_argument("--openai-api-key", type=str, default="", help="OpenAI API key override.") - parser.add_argument( - "--openai-api-key-env", - type=str, - default="", - help="Environment variable name for OpenAI API key.", - ) - - -def build_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser(prog="osint-env") - sub = parser.add_subparsers(dest="cmd", required=True) - - d = sub.add_parser("demo", help="Run one episode and print debug info.") - _add_common_args(d) - - e = sub.add_parser("eval", help="Run multiple episodes and show aggregate metrics.") - _add_common_args(e) - e.add_argument("--episodes", type=int, default=0) - - b = sub.add_parser("benchmark", help="Run eval, update leaderboard, and export interactive dashboard.") - _add_common_args(b) - b.add_argument("--episodes", type=int, default=0) - b.add_argument("--name", type=str, default="") - b.add_argument("--leaderboard", type=str, default="") - b.add_argument("--dashboard", type=str, default="") - - l = sub.add_parser("leaderboard", help="Print ranked benchmark leaderboard.") - _add_common_args(l) - l.add_argument("--leaderboard", type=str, default="") - l.add_argument("--top", type=int, default=20) - l.add_argument( - "--sort-by", - type=str, - default="leaderboard_score", - choices=[ - "leaderboard_score", - "task_success_rate", - "avg_graph_f1", - "tool_efficiency", - "avg_reward", - "retrieval_signal", - "structural_signal", - "deanonymization_accuracy", - "spawn_signal", - ], - ) - - s = sub.add_parser("benchmark-sweep", help="Run benchmark across multiple seeds and append all runs to leaderboard.") - _add_common_args(s) - s.add_argument("--episodes", type=int, default=0) - s.add_argument("--seeds", type=str, default="7,11,17,23,31") - s.add_argument("--name-prefix", type=str, default="sweep") - s.add_argument("--leaderboard", type=str, default="") - s.add_argument("--dashboard-dir", type=str, default="") - - v = sub.add_parser("viz", help="Export an interactive graph/database explorer.") - _add_common_args(v) - v.add_argument("--output", type=str, default="artifacts/osint_explorer.html") - v.add_argument("--with-demo", action="store_true") - v.add_argument("--leaderboard", type=str, default="") - v.add_argument( - "--evaluation", - type=str, - default=DEFAULT_EVALUATION_PATH, - help="Path to a saved evaluation payload with episode details.", - ) - return parser - - -def _resolve_environment_config(args: argparse.Namespace) -> tuple[EnvironmentConfig, dict[str, str | int]]: - shared = load_shared_config(args.config) - env_cfg = clone_environment_config(shared.environment) - - if args.seed_file: - env_cfg.seeding = load_seeding_config(args.seed_file) - - if args.llm_provider != "config": - env_cfg.llm.provider = args.llm_provider - if args.llm_model: - env_cfg.llm.model = args.llm_model - if int(args.llm_timeout_seconds) > 0: - env_cfg.llm.timeout_seconds = int(args.llm_timeout_seconds) - if args.ollama_base_url: - env_cfg.llm.ollama_base_url = args.ollama_base_url - if args.openai_base_url: - env_cfg.llm.openai_base_url = args.openai_base_url - if args.openai_api_key: - env_cfg.llm.openai_api_key = args.openai_api_key - if args.openai_api_key_env: - env_cfg.llm.openai_api_key_env = args.openai_api_key_env - - if args.agent_mode == "single": - env_cfg.swarm.enabled = False - elif args.agent_mode == "swarm": - env_cfg.swarm.enabled = True - - runtime = { - "default_episodes": shared.runtime.default_episodes, - "leaderboard_path": shared.runtime.leaderboard_path, - "dashboard_path": shared.runtime.dashboard_path, - "sweep_dashboard_dir": shared.runtime.sweep_dashboard_dir, - } - return env_cfg, runtime - - -def _runner_for(env: OSINTEnvironment) -> SingleAgentRunner | SwarmAgentRunner: - if env.config.swarm.enabled: - return SwarmAgentRunner(env, llm=build_llm_client(env.config.llm)) - return SingleAgentRunner(env, llm=build_llm_client(env.config.llm)) - - -def main() -> None: - args = build_parser().parse_args() - env_cfg, runtime = _resolve_environment_config(args) - - episodes = int(args.episodes) if getattr(args, "episodes", 0) else int(runtime["default_episodes"]) - leaderboard_path = str(args.leaderboard) if getattr(args, "leaderboard", "") else str(runtime["leaderboard_path"]) - dashboard_path = str(args.dashboard) if getattr(args, "dashboard", "") else str(runtime["dashboard_path"]) - sweep_dashboard_dir = ( - str(args.dashboard_dir) if getattr(args, "dashboard_dir", "") else str(runtime["sweep_dashboard_dir"]) - ) - evaluation_path = str(getattr(args, "evaluation", "") or DEFAULT_EVALUATION_PATH) - - if args.cmd == "leaderboard": - records = load_leaderboard(leaderboard_path) - print(render_leaderboard_table(records, top_k=args.top, sort_by=args.sort_by)) - return - - if args.cmd == "benchmark-sweep": - seed_values = [int(x.strip()) for x in args.seeds.split(",") if x.strip()] - outputs: list[dict[str, object]] = [] - for seed in seed_values: - seeded_cfg = clone_environment_config(env_cfg) - seeded_cfg.seed = seed - env = OSINTEnvironment(seeded_cfg, llm=build_llm_client(seeded_cfg.llm)) - evaluation = run_evaluation(env, episodes=episodes, return_details=True, llm=build_llm_client(seeded_cfg.llm)) - summary = evaluation["summary"] - run_name = f"{args.name_prefix}_seed{seed}" - record = append_leaderboard_record( - path=leaderboard_path, - summary=summary, - episodes=episodes, - run_name=run_name, - config={ - "seed": seed, - "max_steps": env.config.max_steps, - "swarm_enabled": env.config.swarm.enabled, - "max_agents": env.config.swarm.max_agents, - "max_breadth": env.config.swarm.max_breadth, - "max_width": env.config.swarm.max_width, - "max_depth": env.config.swarm.max_depth, - "seeded_questions": len(env.config.seeding.seeded_questions), - }, - ) - dashboard_path = export_dashboard( - env=env, - evaluation=evaluation, - leaderboard_records=load_leaderboard(leaderboard_path), - output_path=f"{sweep_dashboard_dir}/{run_name}.html", - ) - _save_evaluation(DEFAULT_EVALUATION_PATH, evaluation) - outputs.append({"seed": seed, "record": record, "dashboard": dashboard_path, "summary": summary}) - - records = load_leaderboard(leaderboard_path) - print( - json.dumps( - { - "runs": outputs, - "leaderboard_preview": render_leaderboard_table(records, top_k=min(10, len(records))), - }, - indent=2, - sort_keys=True, - ) - ) - return - - llm_client = build_llm_client(env_cfg.llm) - env = OSINTEnvironment(env_cfg, llm=llm_client) - if args.cmd == "demo": - info = _runner_for(env).run_episode() - print(json.dumps(info, indent=2, sort_keys=True)) - elif args.cmd == "eval": - metrics = run_evaluation(env, episodes=episodes, llm=llm_client) - print(json.dumps(metrics, indent=2, sort_keys=True)) - elif args.cmd == "benchmark": - evaluation = run_evaluation(env, episodes=episodes, return_details=True, llm=llm_client) - summary = evaluation["summary"] - record = append_leaderboard_record( - path=leaderboard_path, - summary=summary, - episodes=episodes, - run_name=args.name or None, - config={ - "seed": env.config.seed, - "max_steps": env.config.max_steps, - "swarm_enabled": env.config.swarm.enabled, - "max_agents": env.config.swarm.max_agents, - "max_breadth": env.config.swarm.max_breadth, - "max_width": env.config.swarm.max_width, - "max_depth": env.config.swarm.max_depth, - "seeded_questions": len(env.config.seeding.seeded_questions), - }, - ) - leaderboard = load_leaderboard(leaderboard_path) - dashboard_path = export_dashboard( - env=env, - evaluation=evaluation, - leaderboard_records=leaderboard, - output_path=dashboard_path, - ) - _save_evaluation(DEFAULT_EVALUATION_PATH, evaluation) - payload = { - "record": record, - "summary": summary, - "dashboard": dashboard_path, - } - print(json.dumps(payload, indent=2, sort_keys=True)) - elif args.cmd == "viz": - evaluation: dict | None = _load_evaluation(evaluation_path) - if args.with_demo: - _runner_for(env).run_episode() - info = { - "agent_answer": env.state.answer if env.state else "", - "task_answer": env.state.task.answer if env.state else "", - "total_reward": env.state.total_reward if env.state else 0.0, - "step_count": env.state.step_count if env.state else 0, - "tool_calls": env.state.tool_calls if env.state else 0, - } - evaluation = { - "summary": { - "task_success_rate": float(info["agent_answer"] == info["task_answer"]), - "tool_efficiency": 0.0, - "avg_graph_f1": 0.0, - "avg_steps_to_solution": float(info["step_count"]), - "deanonymization_accuracy": 0.0, - "avg_reward": float(info["total_reward"]), - "leaderboard_score": 0.0, - }, - "episodes": [ - { - "task_id": env.state.task.task_id if env.state else "n/a", - "task_type": env.state.task.task_type if env.state else "n/a", - "question": env.state.task.question if env.state else "n/a", - "task_answer": str(info["task_answer"]), - "agent_answer": str(info["agent_answer"]), - "graph_f1": 0.0, - "reward": float(info["total_reward"]), - "steps": int(info["step_count"]), - "tool_calls": int(info["tool_calls"]), - "success": int(info["agent_answer"] == info["task_answer"]), - } - ], - } - - graph_f1 = 0.0 - if env.state is not None: - graph_f1 = compute_graph_f1(env.memory_graph.edges, env.state.task.supporting_edges) - - if evaluation is None: - summary = { - "task_success_rate": 0.0, - "tool_efficiency": 0.0, - "avg_graph_f1": graph_f1, - "avg_steps_to_solution": float(env.state.step_count) if env.state else 0.0, - "deanonymization_accuracy": 0.0, - "avg_reward": float(env.state.total_reward) if env.state else 0.0, - "leaderboard_score": 0.0, - } - evaluation = {"summary": summary, "episodes": []} - - leaderboard = load_leaderboard(leaderboard_path) - out = export_dashboard(env=env, evaluation=evaluation, leaderboard_records=leaderboard, output_path=args.output) - print(json.dumps({"dashboard": out, "evaluation": evaluation_path}, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/scratch/osint/src/osint_env/config/__init__.py b/scratch/osint/src/osint_env/config/__init__.py deleted file mode 100644 index 0f33c5b3ecce550f035a413d9447b75844bc35dc..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/config/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -from osint_env.config.shared import RuntimeDefaults, SharedConfig, clone_environment_config, load_seeding_config, load_shared_config - -__all__ = [ - "RuntimeDefaults", - "SharedConfig", - "clone_environment_config", - "load_seeding_config", - "load_shared_config", -] diff --git a/scratch/osint/src/osint_env/config/shared.py b/scratch/osint/src/osint_env/config/shared.py deleted file mode 100644 index a012ceef5320c781d727135ae906b96c906e0e15..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/config/shared.py +++ /dev/null @@ -1,248 +0,0 @@ -from __future__ import annotations - -import copy -import json -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any - -from osint_env.domain.models import ( - EnvironmentConfig, - LLMConfig, - NodeType, - SeedingConfig, - SeedEdgeSpec, - SeedNodeSpec, - SeedQuestionSpec, - SpawnRewardConfig, - SwarmConfig, -) - - -@dataclass(slots=True) -class RuntimeDefaults: - default_episodes: int = 20 - leaderboard_path: str = "artifacts/leaderboard.json" - dashboard_path: str = "artifacts/osint_dashboard.html" - sweep_dashboard_dir: str = "artifacts/sweep_dashboards" - - -@dataclass(slots=True) -class SharedConfig: - environment: EnvironmentConfig = field(default_factory=EnvironmentConfig) - runtime: RuntimeDefaults = field(default_factory=RuntimeDefaults) - - -def clone_environment_config(config: EnvironmentConfig) -> EnvironmentConfig: - return copy.deepcopy(config) - - -def _as_dict(value: Any) -> dict[str, Any]: - return value if isinstance(value, dict) else {} - - -def _parse_int(value: Any, default: int) -> int: - try: - return int(value) - except (TypeError, ValueError): - return default - - -def _parse_float(value: Any, default: float) -> float: - try: - return float(value) - except (TypeError, ValueError): - return default - - -def _parse_bool(value: Any, default: bool) -> bool: - if isinstance(value, bool): - return value - if isinstance(value, str): - lowered = value.strip().lower() - if lowered in {"1", "true", "yes", "y", "on"}: - return True - if lowered in {"0", "false", "no", "n", "off"}: - return False - return default - - -def _infer_node_type(node_id: str) -> NodeType: - prefix = str(node_id).split("_", 1)[0].lower() - mapping = { - "user": NodeType.USER, - "alias": NodeType.ALIAS, - "org": NodeType.ORG, - "loc": NodeType.LOCATION, - "location": NodeType.LOCATION, - "post": NodeType.POST, - "thr": NodeType.THREAD, - "thread": NodeType.THREAD, - "event": NodeType.EVENT, - } - return mapping.get(prefix, NodeType.USER) - - -def _parse_node_type(value: Any, node_id: str) -> NodeType: - if isinstance(value, NodeType): - return value - if isinstance(value, str): - raw = value.strip().lower() - try: - return NodeType(raw) - except ValueError: - return _infer_node_type(node_id) - return _infer_node_type(node_id) - - -def _parse_seed_edge(item: dict[str, Any]) -> SeedEdgeSpec | None: - src = str(item.get("src", "")).strip() - rel = str(item.get("rel", "")).strip() - dst = str(item.get("dst", "")).strip() - if not src or not rel or not dst: - return None - confidence = _parse_float(item.get("confidence", 1.0), 1.0) - return SeedEdgeSpec(src=src, rel=rel, dst=dst, confidence=confidence) - - -def _parse_seeding(data: dict[str, Any]) -> SeedingConfig: - seeded_nodes: list[SeedNodeSpec] = [] - for item in data.get("seeded_nodes", []): - row = _as_dict(item) - node_id = str(row.get("node_id", "")).strip() - if not node_id: - continue - node_type = _parse_node_type(row.get("node_type"), node_id) - attrs = _as_dict(row.get("attrs")) - seeded_nodes.append(SeedNodeSpec(node_id=node_id, node_type=node_type, attrs=attrs)) - - seeded_edges: list[SeedEdgeSpec] = [] - for item in data.get("seeded_edges", []): - edge = _parse_seed_edge(_as_dict(item)) - if edge is not None: - seeded_edges.append(edge) - - seeded_questions: list[SeedQuestionSpec] = [] - for item in data.get("seeded_questions", []): - row = _as_dict(item) - question = str(row.get("question", "")).strip() - if not question: - continue - answer_val = row.get("answer") - answer = str(answer_val).strip() if answer_val is not None and str(answer_val).strip() else None - task_type = str(row.get("task_type", "seeded")).strip() or "seeded" - support_edges: list[SeedEdgeSpec] = [] - for edge_item in row.get("supporting_edges", []): - edge = _parse_seed_edge(_as_dict(edge_item)) - if edge is not None: - support_edges.append(edge) - metadata = _as_dict(row.get("metadata")) - seeded_questions.append( - SeedQuestionSpec( - question=question, - answer=answer, - task_type=task_type, - supporting_edges=support_edges, - metadata=metadata, - ) - ) - - return SeedingConfig( - seeded_nodes=seeded_nodes, - seeded_edges=seeded_edges, - seeded_questions=seeded_questions, - llm_generate_remaining_graph=_parse_bool(data.get("llm_generate_remaining_graph"), True), - llm_generate_remaining_tasks=_parse_bool(data.get("llm_generate_remaining_tasks"), True), - llm_generated_edge_budget=max(0, _parse_int(data.get("llm_generated_edge_budget"), 6)), - llm_generated_task_budget=max(0, _parse_int(data.get("llm_generated_task_budget"), 8)), - llm_generation_parallel=_parse_bool(data.get("llm_generation_parallel"), True), - llm_generation_workers=max(1, _parse_int(data.get("llm_generation_workers"), 3)), - llm_generation_retries=max(1, _parse_int(data.get("llm_generation_retries"), 2)), - allow_template_fallback_on_llm_failure=_parse_bool( - data.get("allow_template_fallback_on_llm_failure"), - False, - ), - ) - - -def load_seeding_config(path: str | Path) -> SeedingConfig: - payload = json.loads(Path(path).read_text(encoding="utf-8")) - if not isinstance(payload, dict): - raise ValueError("Seed file must contain a JSON object.") - source = _as_dict(payload.get("seeding", payload)) - return _parse_seeding(source) - - -def _parse_environment(payload: dict[str, Any]) -> EnvironmentConfig: - env_data = _as_dict(payload.get("environment", payload)) - swarm_data = _as_dict(payload.get("swarm", env_data.get("swarm", {}))) - spawn_data = _as_dict(payload.get("spawn_reward", env_data.get("spawn_reward", {}))) - seeding_data = _as_dict(payload.get("seeding", env_data.get("seeding", {}))) - llm_data = _as_dict(payload.get("llm", env_data.get("llm", {}))) - - env = EnvironmentConfig( - n_users=max(4, _parse_int(env_data.get("n_users"), 40)), - alias_density=max(0.0, min(1.0, _parse_float(env_data.get("alias_density"), 0.35))), - noise_level=max(0.0, min(1.0, _parse_float(env_data.get("noise_level"), 0.15))), - red_herring_rate=max(0.0, min(1.0, _parse_float(env_data.get("red_herring_rate"), 0.1))), - max_steps=max(2, _parse_int(env_data.get("max_steps"), 18)), - seed=_parse_int(env_data.get("seed"), 7), - ) - - env.swarm = SwarmConfig( - enabled=_parse_bool(swarm_data.get("enabled"), False), - max_agents=max(1, _parse_int(swarm_data.get("max_agents"), 3)), - max_breadth=max(1, _parse_int(swarm_data.get("max_breadth"), 2)), - max_width=max(1, _parse_int(swarm_data.get("max_width"), 2)), - max_depth=max(1, _parse_int(swarm_data.get("max_depth"), 2)), - planner_rounds=max(1, _parse_int(swarm_data.get("planner_rounds"), 2)), - tools_per_agent=max(1, _parse_int(swarm_data.get("tools_per_agent"), 1)), - ) - - env.spawn_reward = SpawnRewardConfig( - lambda_parallel=max(0.0, _parse_float(spawn_data.get("lambda_parallel"), 0.15)), - lambda_finish=max(0.0, _parse_float(spawn_data.get("lambda_finish"), 0.2)), - anneal=max(0.0, min(1.0, _parse_float(spawn_data.get("anneal"), 1.0))), - max_parallel_hint=max(1, _parse_int(spawn_data.get("max_parallel_hint"), 3)), - ) - - env.seeding = _parse_seeding(seeding_data) - env.llm = LLMConfig( - provider=str(llm_data.get("provider", "mock")).strip() or "mock", - model=str(llm_data.get("model", "qwen3:2b")).strip() or "qwen3:2b", - temperature=_parse_float(llm_data.get("temperature"), 0.1), - max_tokens=max(1, _parse_int(llm_data.get("max_tokens"), 256)), - timeout_seconds=max(1, _parse_int(llm_data.get("timeout_seconds"), 240)), - ollama_base_url=str(llm_data.get("ollama_base_url", "http://127.0.0.1:11434")).strip() - or "http://127.0.0.1:11434", - openai_base_url=str(llm_data.get("openai_base_url", "https://api.openai.com/v1")).strip() - or "https://api.openai.com/v1", - openai_api_key_env=str(llm_data.get("openai_api_key_env", "OPENAI_API_KEY")).strip() or "OPENAI_API_KEY", - openai_api_key=str(llm_data.get("openai_api_key", "")).strip(), - ) - return env - - -def _parse_runtime(payload: dict[str, Any]) -> RuntimeDefaults: - runtime = _as_dict(payload.get("runtime", {})) - return RuntimeDefaults( - default_episodes=max(1, _parse_int(runtime.get("default_episodes"), 20)), - leaderboard_path=str(runtime.get("leaderboard_path", "artifacts/leaderboard.json")), - dashboard_path=str(runtime.get("dashboard_path", "artifacts/osint_dashboard.html")), - sweep_dashboard_dir=str(runtime.get("sweep_dashboard_dir", "artifacts/sweep_dashboards")), - ) - - -def load_shared_config(path: str | Path | None) -> SharedConfig: - if not path: - return SharedConfig() - - file_path = Path(path) - if not file_path.exists(): - return SharedConfig() - - payload = json.loads(file_path.read_text(encoding="utf-8")) - if not isinstance(payload, dict): - raise ValueError("Shared config file must contain a JSON object.") - - return SharedConfig(environment=_parse_environment(payload), runtime=_parse_runtime(payload)) diff --git a/scratch/osint/src/osint_env/data/__init__.py b/scratch/osint/src/osint_env/data/__init__.py deleted file mode 100644 index 6d328d532fb74285ba87590893c78c9f07d03bc8..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/data/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Dataset generation package.""" - diff --git a/scratch/osint/src/osint_env/data/generator.py b/scratch/osint/src/osint_env/data/generator.py deleted file mode 100644 index ba989b9667ffc5115d3ddd1d4d37f755f30f17e9..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/data/generator.py +++ /dev/null @@ -1,819 +0,0 @@ -from __future__ import annotations - -from concurrent.futures import ThreadPoolExecutor, as_completed -import json -import random -import re -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any - -from osint_env.domain.models import ( - CanonicalGraph, - Edge, - EnvironmentConfig, - Node, - NodeType, - SeedEdgeSpec, - SeedQuestionSpec, - TaskInstance, -) - -if TYPE_CHECKING: - from osint_env.llm.interface import LLMClient - - -@dataclass(slots=True) -class PlatformViews: - microblog_posts: list[dict] - forum_threads: list[dict] - profiles: list[dict] - alias_lookup: dict[str, str] - - -class DatasetGenerator: - def __init__(self, config: EnvironmentConfig, llm: LLMClient | None = None): - self.config = config - self.rng = random.Random(config.seed) - self.llm = llm - - @staticmethod - def _edge_key(edge: Edge) -> tuple[str, str, str]: - return (edge.src, edge.rel, edge.dst) - - @staticmethod - def _infer_node_type(node_id: str) -> NodeType: - prefix = str(node_id).split("_", 1)[0].lower() - mapping = { - "user": NodeType.USER, - "alias": NodeType.ALIAS, - "org": NodeType.ORG, - "loc": NodeType.LOCATION, - "location": NodeType.LOCATION, - "post": NodeType.POST, - "thr": NodeType.THREAD, - "thread": NodeType.THREAD, - "event": NodeType.EVENT, - } - return mapping.get(prefix, NodeType.USER) - - def _ensure_node(self, graph: CanonicalGraph, node_id: str) -> None: - if node_id in graph.nodes: - return - node_type = self._infer_node_type(node_id) - attrs: dict[str, Any] = {} - if node_type == NodeType.USER: - attrs = {"name": node_id, "org": "Unknown", "location": "Unknown"} - if node_type == NodeType.ALIAS: - attrs = {"handle": f"@{node_id}"} - graph.nodes[node_id] = Node(node_id=node_id, node_type=node_type, attrs=attrs) - - def _add_edge_if_missing(self, graph: CanonicalGraph, edge: Edge) -> None: - key = self._edge_key(edge) - if any(self._edge_key(existing) == key for existing in graph.edges): - return - self._ensure_node(graph, edge.src) - self._ensure_node(graph, edge.dst) - graph.edges.append(edge) - - @staticmethod - def _extract_json_blob(text: str) -> Any: - text = str(text).strip() - if not text: - return None - for start, end in (("{", "}"), ("[", "]")): - left = text.find(start) - right = text.rfind(end) - if left >= 0 and right > left: - snippet = text[left : right + 1] - try: - return json.loads(snippet) - except json.JSONDecodeError: - continue - return None - - def _apply_seed_nodes(self, graph: CanonicalGraph) -> None: - for node_spec in self.config.seeding.seeded_nodes: - node_type = ( - node_spec.node_type - if isinstance(node_spec.node_type, NodeType) - else self._infer_node_type(node_spec.node_id) - ) - existing = graph.nodes.get(node_spec.node_id) - attrs = dict(existing.attrs) if existing else {} - attrs.update(node_spec.attrs) - graph.nodes[node_spec.node_id] = Node(node_spec.node_id, node_type, attrs) - - def _apply_seed_edges(self, graph: CanonicalGraph) -> None: - for edge_spec in self.config.seeding.seeded_edges: - self._add_edge_if_missing( - graph, - Edge( - src=edge_spec.src, - rel=edge_spec.rel, - dst=edge_spec.dst, - confidence=float(edge_spec.confidence), - ), - ) - - @staticmethod - def _normalize_edge_candidates(value: Any) -> list[SeedEdgeSpec]: - items: list[SeedEdgeSpec] = [] - if not isinstance(value, list): - return items - for row in value: - if not isinstance(row, dict): - continue - src = str(row.get("src", "")).strip() - rel = str(row.get("rel", "")).strip() - dst = str(row.get("dst", "")).strip() - if not src or not rel or not dst: - continue - try: - confidence = float(row.get("confidence", 1.0)) - except (TypeError, ValueError): - confidence = 1.0 - items.append(SeedEdgeSpec(src=src, rel=rel, dst=dst, confidence=confidence)) - return items - - @staticmethod - def _split_budget(total: int, parts: int) -> list[int]: - if total <= 0: - return [] - slots = max(1, parts) - base = total // slots - remainder = total % slots - chunks = [base + (1 if i < remainder else 0) for i in range(slots)] - return [chunk for chunk in chunks if chunk > 0] - - @staticmethod - def _shared_context_blob(graph: CanonicalGraph, node_limit: int = 100, edge_limit: int = 80) -> str: - payload = { - "known_nodes": sorted(graph.nodes.keys())[:node_limit], - "known_edges": [ - {"src": edge.src, "rel": edge.rel, "dst": edge.dst} - for edge in graph.edges[: min(edge_limit, len(graph.edges))] - ], - } - return json.dumps(payload) - - def _llm_generate_json_with_retry(self, prompt: str) -> Any: - if self.llm is None: - return None - - attempts = max(1, int(self.config.seeding.llm_generation_retries)) - for _ in range(attempts): - try: - response = self.llm.generate([{"role": "system", "content": prompt}], tools=[]) - except Exception: - continue - parsed = self._extract_json_blob(response.content) - if parsed is not None: - return parsed - return None - - def _run_generation_workers(self, prompts: list[str]) -> list[Any]: - if not prompts: - return [] - - max_workers = max(1, min(self.config.seeding.llm_generation_workers, len(prompts))) - if not self.config.seeding.llm_generation_parallel or max_workers == 1: - output: list[Any] = [] - for prompt in prompts: - parsed = self._llm_generate_json_with_retry(prompt) - if parsed is not None: - output.append(parsed) - return output - - output = [] - with ThreadPoolExecutor(max_workers=max_workers) as executor: - futures = [executor.submit(self._llm_generate_json_with_retry, prompt) for prompt in prompts] - for future in as_completed(futures): - try: - parsed = future.result() - except Exception: - parsed = None - if parsed is not None: - output.append(parsed) - return output - - def _template_fallback_allowed(self) -> bool: - if self.llm is None: - return True - return bool(self.config.seeding.allow_template_fallback_on_llm_failure) - - def _template_generated_edges(self, graph: CanonicalGraph, budget: int) -> list[Edge]: - if budget <= 0: - return [] - users = [n.node_id for n in graph.nodes.values() if n.node_type == NodeType.USER] - aliases = [n.node_id for n in graph.nodes.values() if n.node_type == NodeType.ALIAS] - if len(users) < 2: - return [] - - generated: list[Edge] = [] - rels = ["connected_to", "mentions", "co_occurs_with"] - for _ in range(budget * 3): - if len(generated) >= budget: - break - roll = self.rng.random() - if aliases and roll < 0.2: - src = self.rng.choice(aliases) - dst = self.rng.choice(users) - rel = "alias_of" - elif roll < 0.75: - src, dst = self.rng.sample(users, 2) - rel = self.rng.choice(rels) - else: - src = self.rng.choice(users) - dst = self.rng.choice([u for u in users if u != src]) - rel = "connected_to" - generated.append(Edge(src=src, rel=rel, dst=dst, confidence=0.7)) - return generated[:budget] - - def _llm_expand_graph(self, graph: CanonicalGraph, budget: int) -> list[Edge]: - if budget <= 0: - return [] - - if self.llm is None: - return self._template_generated_edges(graph, budget) - - shared_context = self._shared_context_blob(graph) - workers = max(1, min(self.config.seeding.llm_generation_workers, budget)) - chunks = self._split_budget(budget, workers) - focus_tracks = ["entity_linking", "network_expansion", "org_location", "event_trace"] - - prompts: list[str] = [] - for idx, chunk_budget in enumerate(chunks): - focus = focus_tracks[idx % len(focus_tracks)] - prompts.append( - ( - "SEED_GRAPH_EXPANSION_AGENT\n" - "SHARED_CONTEXT\n" - f"{shared_context}\n" - f"worker_id: {idx}\n" - f"focus: {focus}\n" - f"budget: {chunk_budget}\n" - "Generate plausible graph edges for OSINT retrieval.\n" - "Return STRICT JSON object: {\"edges\": [{\"src\": str, \"rel\": str, \"dst\": str, \"confidence\": float}]}.\n" - "Prefer known nodes from SHARED_CONTEXT and avoid duplicates." - ) - ) - - generated: list[Edge] = [] - seen: set[tuple[str, str, str]] = set() - for payload in self._run_generation_workers(prompts): - raw_edges: Any = None - if isinstance(payload, dict): - raw_edges = payload.get("edges") - elif isinstance(payload, list): - raw_edges = payload - for edge_spec in self._normalize_edge_candidates(raw_edges): - key = (edge_spec.src, edge_spec.rel, edge_spec.dst) - if key in seen: - continue - seen.add(key) - generated.append(Edge(edge_spec.src, edge_spec.rel, edge_spec.dst, float(edge_spec.confidence))) - if len(generated) >= budget: - break - if len(generated) >= budget: - break - - if len(generated) < budget: - residual = budget - len(generated) - residual_prompt = ( - "SEED_GRAPH_EXPANSION_AGENT\n" - "SHARED_CONTEXT\n" - f"{shared_context}\n" - f"budget: {residual}\n" - "Generate any remaining high-utility edges.\n" - "Return STRICT JSON object: {\"edges\": [{\"src\": str, \"rel\": str, \"dst\": str, \"confidence\": float}]}." - ) - payload = self._llm_generate_json_with_retry(residual_prompt) - raw_edges: Any = payload.get("edges") if isinstance(payload, dict) else payload - for edge_spec in self._normalize_edge_candidates(raw_edges): - key = (edge_spec.src, edge_spec.rel, edge_spec.dst) - if key in seen: - continue - seen.add(key) - generated.append(Edge(edge_spec.src, edge_spec.rel, edge_spec.dst, float(edge_spec.confidence))) - if len(generated) >= budget: - break - - if len(generated) < budget and self._template_fallback_allowed(): - for edge in self._template_generated_edges(graph, budget - len(generated)): - key = (edge.src, edge.rel, edge.dst) - if key in seen: - continue - seen.add(key) - generated.append(edge) - if len(generated) >= budget: - break - - return generated[:budget] - - @staticmethod - def _extract_entity_tokens(question: str) -> list[str]: - return re.findall(r"\b(?:alias|user|org|loc|post|thr|thread|event)_[a-zA-Z0-9_]+\b", question) - - @staticmethod - def _normalize_difficulty(value: str, index: int) -> str: - token = str(value or "").strip().lower() - if token in {"easy", "e"}: - return "easy" - if token in {"mid", "medium", "m"}: - return "medium" - if token in {"high", "hard", "h"}: - return "hard" - if index < 10: - return "easy" - if index < 20: - return "medium" - return "hard" - - @staticmethod - def _task_type_for_difficulty(base_task_type: str, difficulty: str) -> str: - token = str(base_task_type or "").strip().lower() - if token and token != "fixed_trace": - return token - if difficulty == "easy": - return "easy_trace" - if difficulty == "medium": - return "medium_trace" - return "hard_trace" - - @staticmethod - def _grader_for_difficulty(difficulty: str) -> dict[str, Any]: - return { - "type": "difficulty_exact_match", - "answer_type": "node_id", - "case_sensitive": True, - "reward_profile": difficulty, - "logic": { - "easy": "single_agent_simplified", - "medium": "reduced_components", - "hard": "full_reward", - }.get(difficulty, "full_reward"), - } - - def _task_metadata(self, index: int, base_task_type: str, metadata: dict[str, Any] | None = None) -> dict[str, Any]: - out = dict(metadata or {}) - difficulty = self._normalize_difficulty(out.get("difficulty", ""), index) - out["difficulty"] = difficulty - out.setdefault("grader", self._grader_for_difficulty(difficulty)) - out.setdefault("scenario", self._task_type_for_difficulty(base_task_type, difficulty)) - return out - - def _infer_answer_from_question(self, question: str, graph: CanonicalGraph) -> str: - entities = self._extract_entity_tokens(question) - question_l = question.lower() - - alias_tokens = [token for token in entities if token.startswith("alias_")] - if alias_tokens: - alias = alias_tokens[0] - for edge in graph.edges: - if edge.rel == "alias_of" and edge.src == alias: - return edge.dst - - if "connected" in question_l: - user_tokens = [token for token in entities if token.startswith("user_")] - if user_tokens: - source = user_tokens[0] - for edge in graph.edges: - if edge.rel == "connected_to" and edge.src == source: - return edge.dst - - if "works at" in question_l: - for edge in graph.edges: - if edge.rel != "works_at": - continue - org = graph.nodes.get(edge.dst) - org_name = str((org.attrs or {}).get("name", "")).lower() if org else "" - if org_name and org_name in question_l: - return edge.src - - return entities[0] if entities else "unknown" - - def _infer_support_edges(self, question: str, answer: str, graph: CanonicalGraph) -> list[Edge]: - if answer: - for edge in graph.edges: - if edge.dst == answer or edge.src == answer: - if edge.src in question or edge.dst in question or edge.rel in question.lower(): - return [edge] - - entities = self._extract_entity_tokens(question) - for edge in graph.edges: - if edge.src in entities or edge.dst in entities: - return [edge] - return [] - - def _seeded_tasks(self, graph: CanonicalGraph) -> list[TaskInstance]: - tasks: list[TaskInstance] = [] - for idx, question_spec in enumerate(self.config.seeding.seeded_questions): - answer = question_spec.answer or self._infer_answer_from_question(question_spec.question, graph) - metadata = self._task_metadata(idx, question_spec.task_type, dict(question_spec.metadata)) - difficulty = str(metadata.get("difficulty", "hard")) - if question_spec.supporting_edges: - support = [ - Edge(src=e.src, rel=e.rel, dst=e.dst, confidence=float(e.confidence)) - for e in question_spec.supporting_edges - ] - else: - support = self._infer_support_edges(question_spec.question, answer, graph) - - tasks.append( - TaskInstance( - task_id=f"seed_task_{idx}", - task_type=self._task_type_for_difficulty(question_spec.task_type, difficulty), - question=question_spec.question, - answer=answer, - supporting_edges=support, - metadata=metadata, - ) - ) - return tasks - - def _template_tasks(self, graph: CanonicalGraph, count: int, start_idx: int = 0) -> list[TaskInstance]: - alias_edges = [e for e in graph.edges if e.rel == "alias_of"] - conn_edges = [e for e in graph.edges if e.rel == "connected_to"] - work_edges = [e for e in graph.edges if e.rel == "works_at"] - tasks: list[TaskInstance] = [] - - for i in range(count): - mode = self.rng.choice(["identity_resolution", "network_discovery", "event_tracing"]) - if mode == "identity_resolution" and alias_edges: - edge = self.rng.choice(alias_edges) - q = f"Which canonical user owns alias {edge.src}?" - a = edge.dst - support = [edge] - elif mode == "network_discovery" and conn_edges: - edge = self.rng.choice(conn_edges) - q = f"Who is connected to {edge.src}?" - a = edge.dst - support = [edge] - else: - edge = self.rng.choice(work_edges) - org_node = graph.nodes.get(edge.dst) - org_name = (org_node.attrs or {}).get("name", edge.dst) if org_node else edge.dst - q = f"Which user works at {org_name}?" - a = edge.src - support = [edge] - tasks.append( - TaskInstance( - task_id=f"task_{start_idx + i}", - task_type=mode, - question=q, - answer=a, - supporting_edges=support, - metadata=self._task_metadata(start_idx + i, mode), - ) - ) - return tasks - - def _llm_generated_tasks(self, graph: CanonicalGraph, count: int, start_idx: int) -> list[TaskInstance]: - if count <= 0: - return [] - if self.llm is None: - return self._template_tasks(graph, count=count, start_idx=start_idx) - - candidate_edges = [ - {"src": edge.src, "rel": edge.rel, "dst": edge.dst} - for edge in graph.edges - if edge.rel in {"alias_of", "connected_to", "works_at"} - ][:60] - shared_context = json.dumps( - { - "known_nodes": sorted(graph.nodes.keys())[:100], - "edge_sample": candidate_edges, - } - ) - workers = max(1, min(self.config.seeding.llm_generation_workers, count)) - chunks = self._split_budget(count, workers) - focus_tracks = ["identity_resolution", "network_discovery", "event_tracing", "deanonymization"] - - prompts: list[str] = [] - for idx, chunk_budget in enumerate(chunks): - focus = focus_tracks[idx % len(focus_tracks)] - prompts.append( - ( - "SEED_TASK_EXPANSION_AGENT\n" - "SHARED_CONTEXT\n" - f"{shared_context}\n" - f"worker_id: {idx}\n" - f"focus: {focus}\n" - f"task_budget: {chunk_budget}\n" - "Generate OSINT QA tasks with answers and support edges.\n" - "Return STRICT JSON object: {\"tasks\": [{\"task_type\": str, \"question\": str, \"answer\": str, \"supporting_edges\": [{\"src\": str, \"rel\": str, \"dst\": str, \"confidence\": float}]}]}." - ) - ) - - llm_tasks: list[TaskInstance] = [] - seen_questions: set[str] = set() - for payload in self._run_generation_workers(prompts): - raw_tasks: Any = None - if isinstance(payload, dict): - raw_tasks = payload.get("tasks") - elif isinstance(payload, list): - raw_tasks = payload - if not isinstance(raw_tasks, list): - continue - - for row in raw_tasks: - if not isinstance(row, dict): - continue - question = str(row.get("question", "")).strip() - if not question: - continue - key = question.lower() - if key in seen_questions: - continue - seen_questions.add(key) - answer = str(row.get("answer", "")).strip() or self._infer_answer_from_question(question, graph) - task_type = str(row.get("task_type", "llm_generated")).strip() or "llm_generated" - support_specs = self._normalize_edge_candidates(row.get("supporting_edges")) - if support_specs: - support = [Edge(e.src, e.rel, e.dst, e.confidence) for e in support_specs] - else: - support = self._infer_support_edges(question, answer, graph) - llm_tasks.append( - TaskInstance( - task_id=f"task_{start_idx + len(llm_tasks)}", - task_type=task_type, - question=question, - answer=answer, - supporting_edges=support, - metadata=self._task_metadata( - start_idx + len(llm_tasks), - task_type, - {"generated_by": "llm", "shared_context": True}, - ), - ) - ) - if len(llm_tasks) >= count: - break - if len(llm_tasks) >= count: - break - - if len(llm_tasks) < count: - residual = count - len(llm_tasks) - residual_prompt = ( - "SEED_TASK_EXPANSION_AGENT\n" - "SHARED_CONTEXT\n" - f"{shared_context}\n" - f"task_budget: {residual}\n" - "Generate additional tasks not already present in SHARED_CONTEXT.\n" - "Return STRICT JSON object: {\"tasks\": [{\"task_type\": str, \"question\": str, \"answer\": str, \"supporting_edges\": [{\"src\": str, \"rel\": str, \"dst\": str, \"confidence\": float}]}]}." - ) - payload = self._llm_generate_json_with_retry(residual_prompt) - raw_tasks: Any = payload.get("tasks") if isinstance(payload, dict) else payload - if isinstance(raw_tasks, list): - for row in raw_tasks: - if not isinstance(row, dict): - continue - question = str(row.get("question", "")).strip() - if not question: - continue - key = question.lower() - if key in seen_questions: - continue - seen_questions.add(key) - answer = str(row.get("answer", "")).strip() or self._infer_answer_from_question(question, graph) - task_type = str(row.get("task_type", "llm_generated")).strip() or "llm_generated" - support_specs = self._normalize_edge_candidates(row.get("supporting_edges")) - if support_specs: - support = [Edge(e.src, e.rel, e.dst, e.confidence) for e in support_specs] - else: - support = self._infer_support_edges(question, answer, graph) - llm_tasks.append( - TaskInstance( - task_id=f"task_{start_idx + len(llm_tasks)}", - task_type=task_type, - question=question, - answer=answer, - supporting_edges=support, - metadata=self._task_metadata( - start_idx + len(llm_tasks), - task_type, - {"generated_by": "llm", "shared_context": True}, - ), - ) - ) - if len(llm_tasks) >= count: - break - - if len(llm_tasks) < count and self._template_fallback_allowed(): - llm_tasks.extend( - self._template_tasks( - graph, - count=count - len(llm_tasks), - start_idx=start_idx + len(llm_tasks), - ) - ) - return llm_tasks[:count] - - def build_canonical_graph(self) -> CanonicalGraph: - graph = CanonicalGraph() - orgs = ["Apex Dynamics", "Helios Labs", "Northbridge"] - locations = ["Bengaluru", "Pune", "Hyderabad", "Delhi"] - - for i in range(self.config.n_users): - uid = f"user_{i}" - org = self.rng.choice(orgs) - loc = self.rng.choice(locations) - graph.nodes[uid] = Node(uid, NodeType.USER, {"name": f"Person {i}", "org": org, "location": loc}) - org_id = f"org_{org.lower().replace(' ', '_')}" - loc_id = f"loc_{loc.lower()}" - graph.nodes.setdefault(org_id, Node(org_id, NodeType.ORG, {"name": org})) - graph.nodes.setdefault(loc_id, Node(loc_id, NodeType.LOCATION, {"name": loc})) - graph.edges.append(Edge(uid, "works_at", org_id)) - graph.edges.append(Edge(uid, "located_in", loc_id)) - - if self.rng.random() < self.config.alias_density: - alias = f"alias_{i}_{self.rng.randint(100,999)}" - graph.nodes[alias] = Node(alias, NodeType.ALIAS, {"handle": f"@{alias}"}) - graph.edges.append(Edge(alias, "alias_of", uid)) - - users = [n for n in graph.nodes.values() if n.node_type == NodeType.USER] - for _ in range(max(1, self.config.n_users // 2)): - a, b = self.rng.sample(users, 2) - graph.edges.append(Edge(a.node_id, "connected_to", b.node_id, confidence=0.8)) - - self._apply_seed_nodes(graph) - self._apply_seed_edges(graph) - - if self.config.seeding.llm_generate_remaining_graph: - llm_edges = self._llm_expand_graph(graph, self.config.seeding.llm_generated_edge_budget) - for edge in llm_edges: - self._add_edge_if_missing(graph, edge) - return graph - - def build_platform_views(self, graph: CanonicalGraph) -> PlatformViews: - users = [n for n in graph.nodes.values() if n.node_type == NodeType.USER] - aliases = [n for n in graph.nodes.values() if n.node_type == NodeType.ALIAS] - alias_owner = {e.src: e.dst for e in graph.edges if e.rel == "alias_of"} - user_aliases: dict[str, list[str]] = {} - for alias_id, user_id in alias_owner.items(): - user_aliases.setdefault(user_id, []).append(alias_id) - node_names = { - node_id: str((node.attrs or {}).get("name") or (node.attrs or {}).get("handle") or node_id) - for node_id, node in graph.nodes.items() - } - - microblog_posts: list[dict] = [] - for i, user in enumerate(users): - poster = user.node_id - if aliases and self.rng.random() < 0.45: - candidate = self.rng.choice(aliases).node_id - poster = candidate - text = f"Update {i} from {user.attrs['org']} #{user.attrs['location'].lower()}" - if self.rng.random() < self.config.noise_level: - text = f"Rumor: {text} maybe fake" - microblog_posts.append( - { - "post_id": f"post_{i}", - "user_id": poster, - "canonical_user": alias_owner.get(poster, user.node_id), - "text": text, - "references": [], - "reference_names": [], - "mentions": [f"user_{self.rng.randint(0, self.config.n_users - 1)}"], - "timestamp": 1000 + i, - } - ) - - authored_posts: dict[str, str] = {} - post_references: dict[str, list[str]] = {} - for edge in graph.edges: - if edge.rel == "authored_post": - authored_posts[edge.dst] = edge.src - elif edge.rel == "references" and edge.src.startswith("post_"): - post_references.setdefault(edge.src, []).append(edge.dst) - - for post_id, author_id in authored_posts.items(): - refs = post_references.get(post_id, []) - ref_names = [node_names.get(ref, ref) for ref in refs] - author_label = node_names.get(author_id, author_id) - text_parts = [f"{post_id} update from {author_label}"] - if ref_names: - text_parts.append("references " + ", ".join(ref_names)) - if refs: - text_parts.append("ids " + ", ".join(refs)) - post_payload = { - "post_id": post_id, - "user_id": author_id, - "canonical_user": alias_owner.get(author_id, author_id), - "text": ". ".join(text_parts), - "references": refs, - "reference_names": ref_names, - "mentions": [], - "timestamp": 5000 + len(microblog_posts), - } - existing_idx = next((idx for idx, row in enumerate(microblog_posts) if row["post_id"] == post_id), None) - if existing_idx is None: - microblog_posts.append(post_payload) - else: - microblog_posts[existing_idx] = post_payload - - forum_threads: list[dict] = [] - for i in range(max(8, self.config.n_users // 3)): - author = self.rng.choice(users).node_id - forum_threads.append( - { - "thread_id": f"thr_{i}", - "topic": self.rng.choice(["security", "startup", "ai", "infra"]), - "author_id": author, - "comments": [ - {"user_id": self.rng.choice(users).node_id, "text": "Following this."}, - {"user_id": self.rng.choice(users).node_id, "text": "Interesting link."}, - ], - "references": [], - "discusses": [], - } - ) - - authored_threads: dict[str, str] = {} - thread_refs: dict[str, list[str]] = {} - thread_discusses: dict[str, list[str]] = {} - for edge in graph.edges: - if edge.rel == "authored_thread": - authored_threads[edge.dst] = edge.src - elif edge.rel == "references" and edge.src.startswith(("thr_", "thread_")): - thread_refs.setdefault(edge.src, []).append(edge.dst) - elif edge.rel == "discusses" and edge.src.startswith(("thr_", "thread_")): - thread_discusses.setdefault(edge.src, []).append(edge.dst) - - for thread_id, author_id in authored_threads.items(): - node = graph.nodes.get(thread_id) - refs = thread_refs.get(thread_id, []) - discussed = thread_discusses.get(thread_id, []) - comments = [] - for ref in refs: - comments.append({"user_id": author_id, "text": f"Reference: {node_names.get(ref, ref)} ({ref})"}) - for item in discussed: - comments.append({"user_id": author_id, "text": f"Discusses: {node_names.get(item, item)} ({item})"}) - thread_payload = { - "thread_id": thread_id, - "topic": str((node.attrs or {}).get("topic", "seeded")) if node else "seeded", - "author_id": author_id, - "title": node_names.get(thread_id, thread_id), - "comments": comments, - "references": refs, - "discusses": discussed, - } - existing_idx = next((idx for idx, row in enumerate(forum_threads) if row["thread_id"] == thread_id), None) - if existing_idx is None: - forum_threads.append(thread_payload) - else: - forum_threads[existing_idx] = thread_payload - - profiles: list[dict] = [] - for user in users: - conns = [e.dst for e in graph.edges if e.src == user.node_id and e.rel == "connected_to"][:5] - org_id = next((e.dst for e in graph.edges if e.src == user.node_id and e.rel == "works_at"), "") - location_id = next((e.dst for e in graph.edges if e.src == user.node_id and e.rel == "located_in"), "") - profiles.append( - { - "user_id": user.node_id, - "name": user.attrs["name"], - "org": user.attrs["org"], - "org_id": org_id, - "location": user.attrs["location"], - "location_id": location_id, - "alias_ids": sorted(user_aliases.get(user.node_id, [])), - "connections": conns, - "work_history": [user.attrs["org"]], - } - ) - - for i in range(int(len(users) * self.config.red_herring_rate)): - profiles.append( - { - "user_id": f"noise_{i}", - "name": f"P{self.rng.randint(100,999)}", - "org": self.rng.choice(["Stealth Co", "Unknown Ventures"]), - "org_id": "", - "location": self.rng.choice(["Remote", "Unknown"]), - "location_id": "", - "alias_ids": [], - "connections": [], - "work_history": [], - } - ) - return PlatformViews(microblog_posts, forum_threads, profiles, alias_lookup=alias_owner) - - def generate_tasks(self, graph: CanonicalGraph, views: PlatformViews, count: int = 12) -> list[TaskInstance]: - tasks = self._seeded_tasks(graph) - target_count = max(1, count, len(tasks)) - - llm_budget = min( - max(0, self.config.seeding.llm_generated_task_budget), - max(0, target_count - len(tasks)), - ) - if self.config.seeding.llm_generate_remaining_tasks and llm_budget > 0: - tasks.extend(self._llm_generated_tasks(graph, count=llm_budget, start_idx=len(tasks))) - - if len(tasks) < target_count and self._template_fallback_allowed(): - tasks.extend(self._template_tasks(graph, count=target_count - len(tasks), start_idx=len(tasks))) - - if not tasks: - tasks.extend(self._template_tasks(graph, count=target_count, start_idx=0)) - - return tasks[:target_count] diff --git a/scratch/osint/src/osint_env/domain/__init__.py b/scratch/osint/src/osint_env/domain/__init__.py deleted file mode 100644 index 09b80bffb5e77f4d52a066830e4d530fb9aa4b8e..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/domain/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Core domain models.""" - diff --git a/scratch/osint/src/osint_env/domain/models.py b/scratch/osint/src/osint_env/domain/models.py deleted file mode 100644 index b6867b790af9d4b993abd294dccdea02a1f9166d..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/domain/models.py +++ /dev/null @@ -1,185 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass, field -from enum import Enum -from typing import Any - -from pydantic import BaseModel, ConfigDict, Field - - -class NodeType(str, Enum): - USER = "user" - ALIAS = "alias" - ORG = "org" - LOCATION = "location" - POST = "post" - THREAD = "thread" - EVENT = "event" - - -class ActionType(str, Enum): - CALL_TOOL = "CALL_TOOL" - ADD_EDGE = "ADD_EDGE" - ANSWER = "ANSWER" - - -@dataclass(slots=True) -class Node: - node_id: str - node_type: NodeType - attrs: dict[str, Any] = field(default_factory=dict) - - -@dataclass(slots=True) -class Edge: - src: str - rel: str - dst: str - confidence: float = 1.0 - - -@dataclass(slots=True) -class CanonicalGraph: - nodes: dict[str, Node] = field(default_factory=dict) - edges: list[Edge] = field(default_factory=list) - - -@dataclass(slots=True) -class ToolCall: - tool_name: str - args: dict[str, Any] - - -class Action(BaseModel): - """Structured action payload used by OpenEnv step().""" - - model_config = ConfigDict(extra="forbid") - - action_type: ActionType - payload: dict[str, Any] = Field(default_factory=dict) - - def __init__(self, *args: Any, **kwargs: Any) -> None: - # Backward-compatible positional form: Action(action_type, payload) - if args: - if len(args) != 2: - raise TypeError("Action() accepts either keyword fields or 2 positional args") - if "action_type" in kwargs or "payload" in kwargs: - raise TypeError("Action() cannot mix positional and keyword fields") - kwargs["action_type"] = args[0] - kwargs["payload"] = args[1] - super().__init__(**kwargs) - - -class Observation(BaseModel): - """Typed observation payload returned by reset()/step()/state().""" - - model_config = ConfigDict(extra="forbid") - - tool_outputs: list[dict[str, Any]] = Field(default_factory=list) - graph_snapshot: dict[str, Any] = Field(default_factory=dict) - action_history: list[dict[str, Any]] = Field(default_factory=list) - task: dict[str, Any] = Field(default_factory=dict) - - -class Reward(BaseModel): - """Typed reward payload for structured reward accounting.""" - - model_config = ConfigDict(extra="forbid") - - value: float = 0.0 - components: dict[str, float] = Field(default_factory=dict) - - -@dataclass(slots=True) -class TaskInstance: - task_id: str - task_type: str - question: str - answer: str - supporting_edges: list[Edge] - metadata: dict[str, Any] = field(default_factory=dict) - - -@dataclass(slots=True) -class SeedNodeSpec: - node_id: str - node_type: NodeType | str - attrs: dict[str, Any] = field(default_factory=dict) - - -@dataclass(slots=True) -class SeedEdgeSpec: - src: str - rel: str - dst: str - confidence: float = 1.0 - - -@dataclass(slots=True) -class SeedQuestionSpec: - question: str - answer: str | None = None - task_type: str = "seeded" - supporting_edges: list[SeedEdgeSpec] = field(default_factory=list) - metadata: dict[str, Any] = field(default_factory=dict) - - -@dataclass(slots=True) -class SeedingConfig: - seeded_nodes: list[SeedNodeSpec] = field(default_factory=list) - seeded_edges: list[SeedEdgeSpec] = field(default_factory=list) - seeded_questions: list[SeedQuestionSpec] = field(default_factory=list) - llm_generate_remaining_graph: bool = True - llm_generate_remaining_tasks: bool = True - llm_generated_edge_budget: int = 6 - llm_generated_task_budget: int = 8 - llm_generation_parallel: bool = True - llm_generation_workers: int = 3 - llm_generation_retries: int = 2 - allow_template_fallback_on_llm_failure: bool = False - - -@dataclass(slots=True) -class SwarmConfig: - enabled: bool = False - max_agents: int = 3 - max_breadth: int = 2 - max_width: int = 2 - max_depth: int = 2 - planner_rounds: int = 2 - tools_per_agent: int = 1 - - -@dataclass(slots=True) -class SpawnRewardConfig: - lambda_parallel: float = 0.15 - lambda_finish: float = 0.20 - anneal: float = 1.0 - max_parallel_hint: int = 3 - - -@dataclass(slots=True) -class LLMConfig: - provider: str = "mock" - model: str = "qwen3:2b" - temperature: float = 0.1 - max_tokens: int = 256 - timeout_seconds: int = 240 - ollama_base_url: str = "http://127.0.0.1:11434" - openai_base_url: str = "https://api.openai.com/v1" - openai_api_key_env: str = "OPENAI_API_KEY" - openai_api_key: str = "" - - -@dataclass(slots=True) -class EnvironmentConfig: - n_users: int = 40 - alias_density: float = 0.35 - noise_level: float = 0.15 - red_herring_rate: float = 0.1 - max_steps: int = 18 - seed: int = 7 - seeding: SeedingConfig = field(default_factory=SeedingConfig) - swarm: SwarmConfig = field(default_factory=SwarmConfig) - spawn_reward: SpawnRewardConfig = field(default_factory=SpawnRewardConfig) - llm: LLMConfig = field(default_factory=LLMConfig) diff --git a/scratch/osint/src/osint_env/env/__init__.py b/scratch/osint/src/osint_env/env/__init__.py deleted file mode 100644 index afc94dfc868f270a67ff4cd803494cce2d15bdb8..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/env/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Environment package.""" - diff --git a/scratch/osint/src/osint_env/env/environment.py b/scratch/osint/src/osint_env/env/environment.py deleted file mode 100644 index b6e4f1cedf69906221fffaf3c39eff821453fbd1..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/env/environment.py +++ /dev/null @@ -1,260 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any - -from osint_env.data.generator import DatasetGenerator -from osint_env.domain.models import Action, ActionType, Edge, EnvironmentConfig, Observation, TaskInstance -from osint_env.env.openenv_compat import Env -from osint_env.env.reward import ( - build_reward_model, - compute_answer_reward, - compute_edge_reward, - compute_graph_f1, -) -from osint_env.memory.store import MemoryGraph, SemanticMemory -from osint_env.platforms.tools import ToolRegistry - -if TYPE_CHECKING: - from osint_env.llm.interface import LLMClient - - -@dataclass(slots=True) -class EpisodeState: - task: TaskInstance - task_index: int = 0 - difficulty: str = "hard" - step_count: int = 0 - done: bool = False - total_reward: float = 0.0 - tool_calls: int = 0 - redundant_tool_calls: int = 0 - action_history: list[dict[str, Any]] = field(default_factory=list) - tool_outputs: list[dict[str, Any]] = field(default_factory=list) - answer: str | None = None - call_fingerprints: set[str] = field(default_factory=set) - reward_components: dict[str, float] = field(default_factory=dict) - - -class OSINTEnvironment(Env): - def __init__(self, config: EnvironmentConfig, llm: "LLMClient | None" = None): - super().__init__( - name="OSINTEnvironment", - state_space="json-observation", - action_space=["CALL_TOOL", "ADD_EDGE", "ANSWER"], - episode_max_length=config.max_steps, - ) - self.config = config - self.generator = DatasetGenerator(config, llm=llm) - self.graph = self.generator.build_canonical_graph() - self.views = self.generator.build_platform_views(self.graph) - self.tasks = self.generator.generate_tasks(self.graph, self.views, count=24) - self.reward_model = build_reward_model(self.graph) - self.tools = ToolRegistry(self.views) - self.memory_graph = MemoryGraph() - self.semantic_memory = SemanticMemory() - self._task_idx = 0 - self.state: EpisodeState | None = None - - @staticmethod - def _normalize_difficulty(value: str) -> str: - token = str(value or "").strip().lower() - if token in {"easy", "e"}: - return "easy" - if token in {"mid", "medium", "m"}: - return "medium" - if token in {"high", "hard", "h"}: - return "hard" - return "hard" - - def _resolve_task_difficulty(self, task: TaskInstance, task_index: int) -> str: - metadata = dict(task.metadata or {}) - if "difficulty" in metadata: - return self._normalize_difficulty(str(metadata.get("difficulty", ""))) - if task_index < 10: - return "easy" - if task_index < 20: - return "medium" - return "hard" - - def reset(self) -> Observation: - task_index = self._task_idx % len(self.tasks) - task = self.tasks[task_index] - self._task_idx += 1 - self.state = EpisodeState( - task=task, - task_index=task_index, - difficulty=self._resolve_task_difficulty(task, task_index), - ) - self.memory_graph = MemoryGraph() - self.semantic_memory = SemanticMemory() - return self._observation() - - def step(self, action: Action) -> tuple[Observation, float, bool, dict[str, Any]]: - if self.state is None: - raise RuntimeError("Call reset() before step().") - if self.state.done: - return self._observation(), 0.0, True, self._info() - - self.state.step_count += 1 - reward = 0.0 - - if action.action_type == ActionType.CALL_TOOL: - reward += self._handle_tool(action.payload) - elif action.action_type == ActionType.ADD_EDGE: - reward += self._handle_add_edge(action.payload) - elif action.action_type == ActionType.ANSWER: - reward += self._handle_answer(action.payload) - else: - reward -= 0.5 - - if self.state.step_count >= self.config.max_steps and not self.state.done: - self.state.done = True - reward -= 0.3 - - self.state.total_reward += reward - self.state.action_history.append({"type": action.action_type.value, "payload": action.payload, "reward": reward}) - return self._observation(), reward, self.state.done, self._info() - - def _handle_tool(self, payload: dict[str, Any]) -> float: - if self.state is None: - return 0.0 - tool_name = payload["tool_name"] - args = payload.get("args", {}) - fp = f"{tool_name}:{sorted(args.items())}" - self.state.tool_calls += 1 - if fp in self.state.call_fingerprints: - self.state.redundant_tool_calls += 1 - penalty = -0.2 - else: - penalty = 0.05 - self.state.call_fingerprints.add(fp) - - invalid_tool_penalty = 0.0 - try: - if tool_name == "search_memory": - query = str(args.get("query", "")).strip() - top_k = int(args.get("k", 5)) if str(args.get("k", "")).strip() else 5 - results = self.semantic_memory.search(query=query, k=max(1, top_k)) if query else [] - output = {"results": results, "count": len(results)} - else: - output = self.tools.call(tool_name, args) - except Exception as exc: - output = {"error": str(exc)} - invalid_tool_penalty = -0.25 - self.state.tool_outputs.append({"tool": tool_name, "args": args, "output": output}) - self.semantic_memory.add(f"{tool_name} {args} {output}", {"tool": tool_name}) - relevance_bonus = 0.08 * self._tool_relevance(self.state.task, output) - total = penalty + relevance_bonus + invalid_tool_penalty - self._accumulate_reward_components( - { - "tool_novelty": penalty, - "tool_relevance": relevance_bonus, - "invalid_tool_penalty": invalid_tool_penalty, - } - ) - return total - - def _handle_add_edge(self, payload: dict[str, Any]) -> float: - if self.state is None: - return 0.0 - edge = Edge(payload["src"], payload["rel"], payload["dst"], float(payload.get("confidence", 1.0))) - existing_edges = list(self.memory_graph.edges) - added = self.memory_graph.add_edge(edge) - if not added: - self._accumulate_reward_components({"duplicate_edge_penalty": -0.15}) - return -0.15 - - breakdown = compute_edge_reward( - edge=edge, - task=self.state.task, - existing_edges=existing_edges, - step_count=self.state.step_count, - model=self.reward_model, - graph=self.graph, - difficulty=self.state.difficulty, - ) - self._accumulate_reward_components(breakdown.to_dict()) - return breakdown.total - - def _handle_answer(self, payload: dict[str, Any]) -> float: - if self.state is None: - return 0.0 - proposed = str(payload.get("answer", "")).strip() - self.state.answer = proposed - self.state.done = True - breakdown = compute_answer_reward( - proposed_answer=proposed, - task=self.state.task, - pred_edges=self.memory_graph.edges, - tool_outputs=self.state.tool_outputs, - step_count=self.state.step_count, - model=self.reward_model, - difficulty=self.state.difficulty, - ) - self._accumulate_reward_components(breakdown.to_dict()) - return breakdown.total - - def _tool_relevance(self, task: TaskInstance, output: dict[str, Any]) -> float: - haystack = str(output).lower() - clues = {task.answer.lower()} - for edge in task.supporting_edges: - clues.add(edge.src.lower()) - clues.add(edge.dst.lower()) - clues.add(edge.rel.lower()) - if not clues: - return 0.0 - matches = sum(1 for token in clues if token in haystack) - return matches / len(clues) - - def _accumulate_reward_components(self, values: dict[str, float]) -> None: - if self.state is None: - return - for key, value in values.items(): - self.state.reward_components[key] = self.state.reward_components.get(key, 0.0) + float(value) - - def _observation(self) -> Observation: - if self.state is None: - raise RuntimeError("State is not initialized.") - metadata = dict(self.state.task.metadata or {}) - grader = metadata.get("grader") if isinstance(metadata.get("grader"), dict) else None - task_payload = { - "task_id": self.state.task.task_id, - "task_type": self.state.task.task_type, - "question": self.state.task.question, - "difficulty": self.state.difficulty, - "grader": ( - dict(grader) - if grader is not None - else { - "type": "difficulty_exact_match", - "answer_type": "node_id", - "case_sensitive": True, - "reward_profile": self.state.difficulty, - } - ), - } - if "scenario" in metadata: - task_payload["scenario"] = str(metadata.get("scenario", "")) - return Observation( - tool_outputs=self.state.tool_outputs[-5:], - graph_snapshot=self.memory_graph.to_snapshot(), - action_history=self.state.action_history[-10:], - task=task_payload, - ) - - def _info(self) -> dict[str, Any]: - if self.state is None: - return {} - return { - "step_count": self.state.step_count, - "difficulty": self.state.difficulty, - "task_index": self.state.task_index, - "total_reward": self.state.total_reward, - "tool_calls": self.state.tool_calls, - "redundant_tool_calls": self.state.redundant_tool_calls, - "task_answer": self.state.task.answer, - "agent_answer": self.state.answer, - "graph_f1": compute_graph_f1(self.memory_graph.edges, self.state.task.supporting_edges), - "reward_components": dict(self.state.reward_components), - } diff --git a/scratch/osint/src/osint_env/env/openenv_compat.py b/scratch/osint/src/osint_env/env/openenv_compat.py deleted file mode 100644 index 46f2bfd007c092e38e5c636215a0ca17faa9b541..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/env/openenv_compat.py +++ /dev/null @@ -1,20 +0,0 @@ -from __future__ import annotations - -try: - from openenv.env import Env -except ImportError: - class Env: - """Minimal fallback used when openenv is not installed locally.""" - - def __init__( - self, - name: str, - state_space: str, - action_space: list[str], - episode_max_length: int, - ) -> None: - self.name = name - self.state_space = state_space - self.action_space = action_space - self.episode_max_length = episode_max_length - diff --git a/scratch/osint/src/osint_env/env/reward.py b/scratch/osint/src/osint_env/env/reward.py deleted file mode 100644 index 7a0b824daddd2ed696bc6af411b727d7a8c950e0..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/env/reward.py +++ /dev/null @@ -1,483 +0,0 @@ -from __future__ import annotations - -import json -import math -import re -from collections import Counter -from dataclasses import asdict, dataclass - -from osint_env.domain.models import CanonicalGraph, Edge, TaskInstance - - -@dataclass(slots=True) -class RewardModel: - relation_idf: dict[str, float] - max_relation_idf: float - hub_penalty: dict[str, float] - max_hub_penalty: float - type_priors: dict[tuple[str, str, str], float] - - -@dataclass(slots=True) -class EdgeRewardBreakdown: - total: float - global_accuracy: float - soft_shaping: float - efficiency: float - diversity: float - relation_informativeness: float - entity_informativeness: float - connectivity_gain: float - - def to_dict(self) -> dict[str, float]: - return asdict(self) - - -@dataclass(slots=True) -class AnswerRewardBreakdown: - total: float - format_reward: float - correctness: float - knowledge_carrier: float - knowledge_indexing: float - connectivity: float - graph_f1: float - efficiency: float - compactness: float - relation_informativeness: float - entity_informativeness: float - repetition_penalty: float - - def to_dict(self) -> dict[str, float]: - return asdict(self) - - -def _normalize_difficulty(value: str) -> str: - token = str(value or "").strip().lower() - if token in {"easy", "e"}: - return "easy" - if token in {"mid", "medium", "m"}: - return "medium" - if token in {"high", "hard", "h"}: - return "hard" - return "hard" - - -def build_reward_model(graph: CanonicalGraph) -> RewardModel: - relation_freq: Counter[str] = Counter(e.rel for e in graph.edges) - total_edges = max(1, len(graph.edges)) - relation_idf = { - rel: math.log((1.0 + total_edges) / (1.0 + freq)) + 1.0 for rel, freq in relation_freq.items() - } - max_relation_idf = max(relation_idf.values()) if relation_idf else 1.0 - - degree: Counter[str] = Counter() - for edge in graph.edges: - degree[edge.src] += 1 - degree[edge.dst] += 1 - hub_penalty = {node_id: math.log(1.0 + deg) for node_id, deg in degree.items()} - max_hub_penalty = max(hub_penalty.values()) if hub_penalty else 1.0 - - type_counts: Counter[tuple[str, str, str]] = Counter() - rel_counts: Counter[str] = Counter() - for edge in graph.edges: - src = graph.nodes.get(edge.src) - dst = graph.nodes.get(edge.dst) - if src is None or dst is None: - continue - key = (str(src.node_type.value), edge.rel, str(dst.node_type.value)) - type_counts[key] += 1 - rel_counts[edge.rel] += 1 - type_priors = { - key: count / max(1, rel_counts[key[1]]) for key, count in type_counts.items() - } - - return RewardModel( - relation_idf=relation_idf, - max_relation_idf=max_relation_idf, - hub_penalty=hub_penalty, - max_hub_penalty=max_hub_penalty, - type_priors=type_priors, - ) - - -def edge_in_truth(edge: Edge, task: TaskInstance) -> bool: - return any(e.src == edge.src and e.rel == edge.rel and e.dst == edge.dst for e in task.supporting_edges) - - -def _cosine(a: Counter[str], b: Counter[str]) -> float: - common = set(a) & set(b) - num = sum(a[t] * b[t] for t in common) - den = math.sqrt(sum(v * v for v in a.values())) * math.sqrt(sum(v * v for v in b.values())) - return (num / den) if den else 0.0 - - -def _edge_signature(edge: Edge) -> Counter[str]: - # Approximate path/edge embedding using relation and endpoint prefixes. - src_prefix = edge.src.split("_", 1)[0] - dst_prefix = edge.dst.split("_", 1)[0] - return Counter({f"rel:{edge.rel}": 2, f"src:{src_prefix}": 1, f"dst:{dst_prefix}": 1}) - - -def _soft_fact_score(edge: Edge, model: RewardModel, graph: CanonicalGraph) -> float: - if any(e.src == edge.src and e.rel == edge.rel and e.dst == edge.dst for e in graph.edges): - return 1.0 - - src = graph.nodes.get(edge.src) - dst = graph.nodes.get(edge.dst) - if src is None or dst is None: - return 0.0 - - type_key = (str(src.node_type.value), edge.rel, str(dst.node_type.value)) - prior = model.type_priors.get(type_key, 0.0) - - # A tiny domain heuristic: alias links are common and worth soft credit even without exact support edge. - alias_bias = 0.2 if (edge.rel == "alias_of" and edge.src.startswith("alias_") and edge.dst.startswith("user_")) else 0.0 - relation_exists = any(e.rel == edge.rel for e in graph.edges) - relation_bonus = 0.1 if relation_exists else 0.0 - return max(0.0, min(1.0, 0.1 + (0.65 * prior) + alias_bias + relation_bonus)) - - -def _normalized_relation_info(rel: str, model: RewardModel) -> float: - idf = model.relation_idf.get(rel, 1.0) - return idf / max(1e-6, model.max_relation_idf) - - -def _normalized_entity_info(src: str, dst: str, model: RewardModel) -> float: - src_h = model.hub_penalty.get(src, 0.0) - dst_h = model.hub_penalty.get(dst, 0.0) - mean_hub = (src_h + dst_h) / 2.0 - # UniRel-style preference for low-degree intermediates: lower hub penalty -> higher informativeness. - return 1.0 - (mean_hub / max(1e-6, model.max_hub_penalty)) - - -def _is_reachable_undirected(edges: list[Edge], src: str, dst: str) -> bool: - if src == dst: - return True - adj: dict[str, set[str]] = {} - for edge in edges: - adj.setdefault(edge.src, set()).add(edge.dst) - adj.setdefault(edge.dst, set()).add(edge.src) - seen = {src} - stack = [src] - while stack: - node = stack.pop() - for nxt in adj.get(node, set()): - if nxt == dst: - return True - if nxt not in seen: - seen.add(nxt) - stack.append(nxt) - return False - - -def _connectivity_gain(edge: Edge, existing_edges: list[Edge]) -> float: - # Reward edges that bridge disconnected regions and penalize already-connected shortcuts. - if edge.src == edge.dst: - return -0.06 - already_connected = _is_reachable_undirected(existing_edges, edge.src, edge.dst) - if already_connected: - return -0.03 - return 0.10 - - -def _sigmoid_temperature(value: float, temperature: float = 2.0) -> float: - scaled = float(value) / max(1e-6, float(temperature)) - if scaled >= 0: - z = math.exp(-scaled) - return 1.0 / (1.0 + z) - z = math.exp(scaled) - return z / (1.0 + z) - - -def compute_edge_reward( - edge: Edge, - task: TaskInstance, - existing_edges: list[Edge], - step_count: int, - model: RewardModel, - graph: CanonicalGraph, - difficulty: str = "hard", -) -> EdgeRewardBreakdown: - in_truth = edge_in_truth(edge, task) - difficulty_level = _normalize_difficulty(difficulty) - - # DeepPath-inspired global accuracy term. - global_accuracy = 0.85 if in_truth else -0.55 - - # D18 reward shaping: R = Rb + (1 - Rb) * f, where f is a soft fact plausibility score. - base_reward = 1.0 if in_truth else 0.0 - shaped = base_reward + ((1.0 - base_reward) * _soft_fact_score(edge, model, graph)) - soft_shaping = 0.30 * (shaped - 0.5) - - # DeepPath-inspired efficiency term: earlier useful edges are better. - efficiency = 0.10 * (1.0 / max(1, step_count)) - - # DeepPath-inspired diversity term: discourage repeated edge patterns. - if not existing_edges: - diversity = 0.08 - else: - new_sig = _edge_signature(edge) - avg_similarity = sum(_cosine(new_sig, _edge_signature(e)) for e in existing_edges) / len(existing_edges) - novelty = 1.0 - avg_similarity - diversity = 0.14 * (novelty - 0.5) - - # UniRel-style informativeness terms. - relation_informativeness = 0.12 * (_normalized_relation_info(edge.rel, model) - 0.5) - entity_informativeness = 0.12 * (_normalized_entity_info(edge.src, edge.dst, model) - 0.5) - - # Additional structural utility shaping for KG construction. - connectivity_gain = _connectivity_gain(edge, existing_edges) - - if difficulty_level == "easy": - global_accuracy = 0.75 if in_truth else -0.45 - soft_shaping = 0.0 - diversity = 0.0 - relation_informativeness = 0.0 - entity_informativeness = 0.0 - connectivity_gain = 0.0 - efficiency = 0.15 * (1.0 / max(1, step_count)) - elif difficulty_level == "medium": - diversity = 0.0 - relation_informativeness = 0.0 - entity_informativeness = 0.0 - - raw_total = ( - global_accuracy - + soft_shaping - + efficiency - + diversity - + relation_informativeness - + entity_informativeness - + connectivity_gain - ) - total = _sigmoid_temperature(raw_total, temperature=2.0) - return EdgeRewardBreakdown( - total=total, - global_accuracy=global_accuracy, - soft_shaping=soft_shaping, - efficiency=efficiency, - diversity=diversity, - relation_informativeness=relation_informativeness, - entity_informativeness=entity_informativeness, - connectivity_gain=connectivity_gain, - ) - - -def _connectivity_ratio(pred_edges: list[Edge], task: TaskInstance) -> float: - nodes = {e.src for e in task.supporting_edges} | {e.dst for e in task.supporting_edges} - if len(nodes) <= 1: - return 1.0 - - adj: dict[str, set[str]] = {} - for edge in pred_edges: - adj.setdefault(edge.src, set()).add(edge.dst) - adj.setdefault(edge.dst, set()).add(edge.src) - - start = next(iter(nodes)) - seen = {start} - stack = [start] - while stack: - cur = stack.pop() - for nxt in adj.get(cur, set()): - if nxt not in seen: - seen.add(nxt) - stack.append(nxt) - return len(seen & nodes) / max(1, len(nodes)) - - -def _knowledge_indexing_recall(task: TaskInstance, tool_outputs: list[dict[str, object]]) -> float: - gold_terms = {task.answer.lower()} - for edge in task.supporting_edges: - gold_terms.add(edge.src.lower()) - gold_terms.add(edge.dst.lower()) - gold_terms.add(edge.rel.lower()) - - serialized = json.dumps(tool_outputs).lower() - covered = sum(1 for term in gold_terms if term and term in serialized) - return covered / max(1, len(gold_terms)) - - -def _knowledge_carrier_reward(pred_edges: list[Edge], task: TaskInstance) -> float: - pred = {(e.src, e.rel, e.dst) for e in pred_edges} - truth = {(e.src, e.rel, e.dst) for e in task.supporting_edges} - deducible = bool(truth & pred) - return 0.4 if deducible else -0.2 - - -def _extract_query_entities(question: str) -> set[str]: - pattern = r"\b(?:alias|user|org|loc|post|thr|thread|event)_[a-zA-Z0-9_]+\b" - return set(re.findall(pattern, question)) - - -def _max_connected_seed_count(pred_edges: list[Edge], seeds: set[str]) -> int: - if not seeds: - return 0 - adj: dict[str, set[str]] = {} - for edge in pred_edges: - adj.setdefault(edge.src, set()).add(edge.dst) - adj.setdefault(edge.dst, set()).add(edge.src) - - best = 1 - for seed in seeds: - seen = {seed} - stack = [seed] - while stack: - cur = stack.pop() - for nxt in adj.get(cur, set()): - if nxt not in seen: - seen.add(nxt) - stack.append(nxt) - connected_seed_count = len(seeds & seen) - best = max(best, connected_seed_count) - return best - - -def _unirel_connectivity_score(pred_edges: list[Edge], seeds: set[str]) -> float: - # UniRel-style discrete connectivity range projected to [-1, 1] for stable weighting. - n = len(seeds) - if n <= 1: - return 0.0 - - connected = _max_connected_seed_count(pred_edges, seeds) - raw = -math.floor(n / 2) + max(0, connected - 1) - lo = -math.floor(n / 2) - hi = math.ceil(n / 2) - 1 - if hi <= lo: - return 0.0 - return ((raw - lo) / (hi - lo)) * 2.0 - 1.0 - - -def _subgraph_relation_informativeness(pred_edges: list[Edge], model: RewardModel | None) -> float: - if not pred_edges or model is None: - return 0.0 - avg = sum(_normalized_relation_info(edge.rel, model) for edge in pred_edges) / len(pred_edges) - return avg - 0.5 - - -def _subgraph_entity_informativeness(pred_edges: list[Edge], model: RewardModel | None) -> float: - if not pred_edges or model is None: - return 0.0 - avg = sum(_normalized_entity_info(edge.src, edge.dst, model) for edge in pred_edges) / len(pred_edges) - return avg - 0.5 - - -def _relation_repetition_ratio(pred_edges: list[Edge]) -> float: - if len(pred_edges) <= 1: - return 0.0 - rels = [edge.rel for edge in pred_edges] - unique = len(set(rels)) - return 1.0 - (unique / len(rels)) - - -def _deducible_answer(proposed_answer: str, task: TaskInstance, pred_edges: list[Edge]) -> bool: - if proposed_answer != task.answer: - return False - truth = {(edge.src, edge.rel, edge.dst) for edge in task.supporting_edges} - pred = {(edge.src, edge.rel, edge.dst) for edge in pred_edges} - if truth & pred: - return True - - seeds = _extract_query_entities(task.question) - if not seeds: - return False - for seed in seeds: - if _is_reachable_undirected(pred_edges, seed, proposed_answer): - return True - return False - - -def compute_answer_reward( - proposed_answer: str, - task: TaskInstance, - pred_edges: list[Edge], - tool_outputs: list[dict[str, object]], - step_count: int, - model: RewardModel | None = None, - difficulty: str = "hard", -) -> AnswerRewardBreakdown: - difficulty_level = _normalize_difficulty(difficulty) - - format_reward = 0.15 if proposed_answer else -0.55 - correctness = 1.15 if proposed_answer == task.answer else -1.0 - - # AutoGraph-R1 style task utility decomposition. - knowledge_carrier = 0.50 if _deducible_answer(proposed_answer, task, pred_edges) else -0.25 - knowledge_indexing = 0.45 * _knowledge_indexing_recall(task, tool_outputs) - - # UniRel-style connectivity over seed entities. - seed_entities = _extract_query_entities(task.question) - seed_entities.add(task.answer) - connectivity = 0.30 * _unirel_connectivity_score(pred_edges, seed_entities) - - graph_f1 = 0.55 * compute_graph_f1(pred_edges, task.supporting_edges) - efficiency = 0.12 * (1.0 / max(1, step_count)) - - extra_edges = max(0, len(pred_edges) - len(task.supporting_edges)) - compactness = -0.05 * extra_edges - - relation_informativeness = 0.12 * _subgraph_relation_informativeness(pred_edges, model) - entity_informativeness = 0.12 * _subgraph_entity_informativeness(pred_edges, model) - - # AutoGraph-R1 repetition control variant used in larger models. - repetition_penalty = -0.10 * _relation_repetition_ratio(pred_edges) - - if difficulty_level == "easy": - knowledge_carrier = 0.0 - knowledge_indexing = 0.25 * _knowledge_indexing_recall(task, tool_outputs) - connectivity = 0.0 - graph_f1 = 0.0 - efficiency = 0.18 * (1.0 / max(1, step_count)) - compactness = 0.0 - relation_informativeness = 0.0 - entity_informativeness = 0.0 - repetition_penalty = 0.0 - elif difficulty_level == "medium": - connectivity = 0.18 * _unirel_connectivity_score(pred_edges, seed_entities) - graph_f1 = 0.35 * compute_graph_f1(pred_edges, task.supporting_edges) - compactness = -0.04 * extra_edges - relation_informativeness = 0.0 - entity_informativeness = 0.0 - repetition_penalty = 0.0 - - raw_total = ( - format_reward - + correctness - + knowledge_carrier - + knowledge_indexing - + connectivity - + graph_f1 - + efficiency - + compactness - + relation_informativeness - + entity_informativeness - + repetition_penalty - ) - total = _sigmoid_temperature(raw_total, temperature=2.0) - return AnswerRewardBreakdown( - total=total, - format_reward=format_reward, - correctness=correctness, - knowledge_carrier=knowledge_carrier, - knowledge_indexing=knowledge_indexing, - connectivity=connectivity, - graph_f1=graph_f1, - efficiency=efficiency, - compactness=compactness, - relation_informativeness=relation_informativeness, - entity_informativeness=entity_informativeness, - repetition_penalty=repetition_penalty, - ) - - -def compute_graph_f1(pred_edges: list[Edge], truth_edges: list[Edge]) -> float: - pred = {(e.src, e.rel, e.dst) for e in pred_edges} - truth = {(e.src, e.rel, e.dst) for e in truth_edges} - if not pred and not truth: - return 1.0 - if not pred or not truth: - return 0.0 - tp = len(pred & truth) - p = tp / len(pred) if pred else 0.0 - r = tp / len(truth) if truth else 0.0 - return (2 * p * r / (p + r)) if (p + r) else 0.0 diff --git a/scratch/osint/src/osint_env/env/spawn_reward_hooks.py b/scratch/osint/src/osint_env/env/spawn_reward_hooks.py deleted file mode 100644 index 52ea420c7b16c9671338894dd4fb7c7e7401fcc4..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/env/spawn_reward_hooks.py +++ /dev/null @@ -1,93 +0,0 @@ -from __future__ import annotations - -import math - - -def critical_steps(main_steps: list[int], parallel_subagent_steps: list[list[int]]) -> int: - """Compute critical-step latency proxy used in Kimi-style PARL shaping. - - For each stage t, we add: - Smain(t) + max_i Ssub,i(t) - where Ssub,i(t) is the i-th sub-agent step count for that stage. - """ - if len(main_steps) != len(parallel_subagent_steps): - raise ValueError("main_steps and parallel_subagent_steps must have the same length") - - total = 0 - for stage_main, stage_sub in zip(main_steps, parallel_subagent_steps): - main = max(0, int(stage_main)) - longest_sub = max((max(0, int(v)) for v in stage_sub), default=0) - total += main + longest_sub - return total - - -def parl_style_spawn_reward( - task_outcome_reward: float, - spawn_count: int, - finished_subtasks: int, - critical_steps: int, - lambda_parallel: float = 0.15, - lambda_finish: float = 0.20, - anneal: float = 1.0, - breadth: int | None = None, - depth: int | None = None, - max_parallel_hint: int | None = None, -) -> float: - """Kimi K2.5 inspired PARL reward utility for future multi-agent branches. - - This helper intentionally does not orchestrate agents. It only exposes the reward shape: - - r_parl = r_perf + a * (lambda_parallel * r_parallel + lambda_finish * r_finish + r_latency) - - where: - - r_parallel encourages non-zero agent spawning (avoids serial collapse) - - r_finish rewards meaningful completion, preventing spawn-only reward hacking - - r_latency favors lower critical-step execution paths - - The optional breadth/depth controls are small shaping terms for future branches where - orchestration state includes tree shape telemetry. - """ - spawn_count = max(0, int(spawn_count)) - finished_subtasks = max(0, int(finished_subtasks)) - critical_steps = max(1, int(critical_steps)) - anneal = max(0.0, min(1.0, anneal)) - lambda_parallel = max(0.0, float(lambda_parallel)) - lambda_finish = max(0.0, float(lambda_finish)) - breadth = max(0, int(breadth or 0)) - depth = max(0, int(depth or 0)) - max_parallel_hint = max(0, int(max_parallel_hint or 0)) - - if spawn_count == 0: - r_parallel = 0.0 - r_finish = 0.0 - else: - # Saturating incentive for parallelism so reward cannot grow unbounded with spawns. - r_parallel = math.tanh(spawn_count / 4.0) - if max_parallel_hint > 0: - utilization = min(1.0, spawn_count / max_parallel_hint) - r_parallel *= (0.7 + (0.3 * utilization)) - - r_finish = min(1.0, finished_subtasks / spawn_count) - - if breadth > 0: - breadth_bonus = 0.04 * math.tanh(breadth / 6.0) - else: - breadth_bonus = 0.0 - - if depth > 0: - # Mild depth penalty discourages brittle over-decomposition chains. - depth_penalty = -0.03 * math.tanh(max(0, depth - 1) / 4.0) - else: - depth_penalty = 0.0 - - # Optional latency shaping hook using critical steps (higher is worse). - r_latency = 0.05 * (1.0 / critical_steps) - - auxiliary = ( - (lambda_parallel * r_parallel) - + (lambda_finish * r_finish) - + r_latency - + breadth_bonus - + depth_penalty - ) - return float(task_outcome_reward) + (anneal * auxiliary) diff --git a/scratch/osint/src/osint_env/eval/__init__.py b/scratch/osint/src/osint_env/eval/__init__.py deleted file mode 100644 index 0041203255773471e8b612ada5ed7a53245dac30..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/eval/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Evaluation package.""" - diff --git a/scratch/osint/src/osint_env/eval/leaderboard.py b/scratch/osint/src/osint_env/eval/leaderboard.py deleted file mode 100644 index b46a6852c736ca6e332366cb5ce77ab45e9f30e3..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/eval/leaderboard.py +++ /dev/null @@ -1,83 +0,0 @@ -from __future__ import annotations - -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -def _utc_now() -> str: - return datetime.now(tz=timezone.utc).replace(microsecond=0).isoformat() - - -def load_leaderboard(path: str | Path) -> list[dict[str, Any]]: - file_path = Path(path) - if not file_path.exists(): - return [] - with file_path.open("r", encoding="utf-8") as f: - data = json.load(f) - if not isinstance(data, list): - return [] - return data - - -def save_leaderboard(path: str | Path, records: list[dict[str, Any]]) -> None: - file_path = Path(path) - file_path.parent.mkdir(parents=True, exist_ok=True) - with file_path.open("w", encoding="utf-8") as f: - json.dump(records, f, indent=2, sort_keys=True) - - -def _metric_value(record: dict[str, Any], sort_by: str) -> float: - metrics = record.get("metrics", {}) - return float(metrics.get(sort_by, 0.0)) - - -def sorted_leaderboard(records: list[dict[str, Any]], sort_by: str = "leaderboard_score") -> list[dict[str, Any]]: - return sorted(records, key=lambda r: _metric_value(r, sort_by), reverse=True) - - -def append_leaderboard_record( - path: str | Path, - summary: dict[str, Any], - episodes: int, - run_name: str | None = None, - config: dict[str, Any] | None = None, -) -> dict[str, Any]: - records = load_leaderboard(path) - run_id = f"run_{len(records) + 1:04d}" - record = { - "run_id": run_id, - "run_name": run_name or run_id, - "created_at": _utc_now(), - "episodes": int(episodes), - "config": config or {}, - "metrics": summary, - } - records.append(record) - save_leaderboard(path, records) - return record - - -def render_leaderboard_table(records: list[dict[str, Any]], top_k: int = 20, sort_by: str = "leaderboard_score") -> str: - ranked = sorted_leaderboard(records, sort_by=sort_by)[:top_k] - header = "| rank | run | score | success | graph_f1 | retrieval | structural | spawn | reward | tool_eff |\n" - sep = "|---|---|---:|---:|---:|---:|---:|---:|---:|---:|\n" - rows: list[str] = [] - for idx, rec in enumerate(ranked, start=1): - m = rec.get("metrics", {}) - rows.append( - "| {rank} | {run} | {score:.4f} | {succ:.3f} | {f1:.3f} | {retrieval:.3f} | {structural:.3f} | {spawn:.3f} | {reward:.3f} | {tool:.3f} |".format( - rank=idx, - run=rec.get("run_name", rec.get("run_id", "run")), - score=float(m.get("leaderboard_score", 0.0)), - succ=float(m.get("task_success_rate", 0.0)), - f1=float(m.get("avg_graph_f1", 0.0)), - retrieval=float(m.get("retrieval_signal", 0.0)), - structural=float(m.get("structural_signal", 0.0)), - spawn=float(m.get("spawn_signal", 0.0)), - reward=float(m.get("avg_reward", 0.0)), - tool=float(m.get("tool_efficiency", 0.0)), - ) - ) - return header + sep + "\n".join(rows) diff --git a/scratch/osint/src/osint_env/eval/metrics.py b/scratch/osint/src/osint_env/eval/metrics.py deleted file mode 100644 index e32bf182907847d0b69be77d46ecdfc9df6d0722..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/eval/metrics.py +++ /dev/null @@ -1,139 +0,0 @@ -from __future__ import annotations - -import math -from dataclasses import dataclass, field - - -@dataclass(slots=True) -class EvalMetrics: - episodes: int = 0 - success: int = 0 - total_steps: int = 0 - total_tool_calls: int = 0 - total_redundant_tool_calls: int = 0 - total_reward: float = 0.0 - deanonymization_total: int = 0 - deanonymization_success: int = 0 - graph_f1_scores: list[float] = field(default_factory=list) - total_knowledge_carrier: float = 0.0 - total_knowledge_indexing: float = 0.0 - total_connectivity: float = 0.0 - total_format_reward: float = 0.0 - total_relation_informativeness: float = 0.0 - total_entity_informativeness: float = 0.0 - total_diversity: float = 0.0 - total_soft_shaping: float = 0.0 - total_connectivity_gain: float = 0.0 - total_compactness: float = 0.0 - total_spawn_count: int = 0 - total_spawn_finished_subtasks: int = 0 - total_spawn_critical_steps: int = 0 - - @staticmethod - def _sigmoid_temperature(value: float, temperature: float = 2.0) -> float: - scaled = float(value) / max(1e-6, float(temperature)) - if scaled >= 0: - z = math.exp(-scaled) - return 1.0 / (1.0 + z) - z = math.exp(scaled) - return z / (1.0 + z) - - def add(self, info: dict, task_type: str, graph_f1: float) -> None: - self.episodes += 1 - ok = info.get("agent_answer") == info.get("task_answer") - self.success += int(ok) - self.total_steps += int(info.get("step_count", 0)) - self.total_tool_calls += int(info.get("tool_calls", 0)) - self.total_redundant_tool_calls += int(info.get("redundant_tool_calls", 0)) - self.total_reward += float(info.get("total_reward", 0.0)) - self.graph_f1_scores.append(graph_f1) - components = info.get("reward_components", {}) - self.total_knowledge_carrier += float(components.get("knowledge_carrier", 0.0)) - self.total_knowledge_indexing += float(components.get("knowledge_indexing", 0.0)) - self.total_connectivity += float(components.get("connectivity", 0.0)) - self.total_format_reward += float(components.get("format_reward", 0.0)) - self.total_relation_informativeness += float(components.get("relation_informativeness", 0.0)) - self.total_entity_informativeness += float(components.get("entity_informativeness", 0.0)) - self.total_diversity += float(components.get("diversity", 0.0)) - self.total_soft_shaping += float(components.get("soft_shaping", 0.0)) - self.total_connectivity_gain += float(components.get("connectivity_gain", 0.0)) - self.total_compactness += float(components.get("compactness", 0.0)) - self.total_spawn_count += int(info.get("spawn_count", 0)) - self.total_spawn_finished_subtasks += int(info.get("spawn_finished_subtasks", 0)) - self.total_spawn_critical_steps += int(info.get("spawn_critical_steps", 0)) - if task_type == "identity_resolution": - self.deanonymization_total += 1 - self.deanonymization_success += int(ok) - - def summary(self) -> dict: - episodes = max(1, self.episodes) - task_success_rate = self.success / episodes - tool_efficiency = 1.0 - (self.total_redundant_tool_calls / max(1, self.total_tool_calls)) - avg_graph_f1 = sum(self.graph_f1_scores) / max(1, len(self.graph_f1_scores)) - deanonymization_accuracy = self.deanonymization_success / max(1, self.deanonymization_total) - avg_reward_raw = self.total_reward / episodes - avg_reward = self._sigmoid_temperature(avg_reward_raw, temperature=2.0) - avg_knowledge_carrier = self.total_knowledge_carrier / episodes - avg_knowledge_indexing = self.total_knowledge_indexing / episodes - avg_connectivity = self.total_connectivity / episodes - avg_relation_informativeness = self.total_relation_informativeness / episodes - avg_entity_informativeness = self.total_entity_informativeness / episodes - avg_diversity = self.total_diversity / episodes - avg_soft_shaping = self.total_soft_shaping / episodes - avg_connectivity_gain = self.total_connectivity_gain / episodes - avg_compactness = self.total_compactness / episodes - avg_spawn_count = self.total_spawn_count / episodes - spawn_completion = self.total_spawn_finished_subtasks / max(1, self.total_spawn_count) - avg_spawn_critical_steps = self.total_spawn_critical_steps / episodes - spawn_latency_signal = 1.0 / max(1.0, avg_spawn_critical_steps) - spawn_signal = max(0.0, min(1.0, 0.6 * spawn_completion + 0.4 * spawn_latency_signal)) - - reward_norm = avg_reward - retrieval_signal = max(0.0, min(1.0, 0.5 + 0.35 * avg_knowledge_carrier + 0.35 * avg_knowledge_indexing)) - structural_signal = max( - 0.0, - min( - 1.0, - 0.5 - + 0.25 * avg_connectivity - + 0.20 * avg_relation_informativeness - + 0.20 * avg_entity_informativeness - + 0.15 * avg_diversity - + 0.10 * avg_connectivity_gain, - ), - ) - leaderboard_score = ( - 0.28 * task_success_rate - + 0.20 * avg_graph_f1 - + 0.12 * tool_efficiency - + 0.12 * deanonymization_accuracy - + 0.14 * retrieval_signal - + 0.09 * structural_signal - + 0.05 * reward_norm - + 0.04 * spawn_signal - ) - return { - "task_success_rate": task_success_rate, - "tool_efficiency": tool_efficiency, - "avg_graph_f1": avg_graph_f1, - "avg_steps_to_solution": self.total_steps / episodes, - "deanonymization_accuracy": deanonymization_accuracy, - "avg_reward": avg_reward, - "avg_knowledge_carrier_reward": avg_knowledge_carrier, - "avg_knowledge_indexing_reward": avg_knowledge_indexing, - "avg_connectivity_reward": avg_connectivity, - "avg_format_reward": self.total_format_reward / episodes, - "avg_relation_informativeness_reward": avg_relation_informativeness, - "avg_entity_informativeness_reward": avg_entity_informativeness, - "avg_diversity_reward": avg_diversity, - "avg_soft_shaping_reward": avg_soft_shaping, - "avg_connectivity_gain_reward": avg_connectivity_gain, - "avg_compactness_reward": avg_compactness, - "avg_spawn_count": avg_spawn_count, - "spawn_completion_rate": spawn_completion, - "avg_spawn_critical_steps": avg_spawn_critical_steps, - "spawn_signal": spawn_signal, - "retrieval_signal": retrieval_signal, - "structural_signal": structural_signal, - "leaderboard_score": leaderboard_score, - } diff --git a/scratch/osint/src/osint_env/eval/runner.py b/scratch/osint/src/osint_env/eval/runner.py deleted file mode 100644 index c6c074c23cb741f629e3621d9fc9cdc9f319d6cb..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/eval/runner.py +++ /dev/null @@ -1,69 +0,0 @@ -from __future__ import annotations - -from osint_env.agents.single_agent import SingleAgentRunner -from osint_env.agents.swarm_agent import SwarmAgentRunner -from osint_env.env.environment import OSINTEnvironment -from osint_env.env.reward import compute_graph_f1 -from osint_env.eval.metrics import EvalMetrics -from osint_env.llm.interface import LLMClient - - -def run_evaluation( - env: OSINTEnvironment, - episodes: int = 20, - return_details: bool = False, - llm: LLMClient | None = None, -) -> dict: - metrics = EvalMetrics() - if env.config.swarm.enabled: - runner = SwarmAgentRunner(env=env, llm=llm) - else: - runner = SingleAgentRunner(env=env, llm=llm) - episode_rows: list[dict] = [] - for _ in range(episodes): - info = runner.run_episode() - task_type = env.state.task.task_type if env.state else "unknown" - task_id = env.state.task.task_id if env.state else "unknown" - truth = env.state.task.supporting_edges if env.state else [] - pred = env.memory_graph.edges if env.state else [] - graph_f1 = compute_graph_f1(pred, truth) - metrics.add(info, task_type=task_type, graph_f1=graph_f1) - episode_rows.append( - { - "task_id": task_id, - "task_type": task_type, - "question": env.state.task.question if env.state else "", - "task_answer": str(info.get("task_answer", "")), - "agent_answer": str(info.get("agent_answer", "")) if info.get("agent_answer") is not None else "", - "graph_f1": graph_f1, - "reward": float(info.get("total_reward", 0.0)), - "steps": int(info.get("step_count", 0)), - "tool_calls": int(info.get("tool_calls", 0)), - "success": int(info.get("agent_answer") == info.get("task_answer")), - "reward_components": dict(info.get("reward_components", {})), - "spawn_count": int(info.get("spawn_count", 0)), - "spawn_critical_steps": int(info.get("spawn_critical_steps", 0)), - "pred_edges": [ - { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - for edge in pred - ], - "truth_edges": [ - { - "src": edge.src, - "rel": edge.rel, - "dst": edge.dst, - "confidence": float(edge.confidence), - } - for edge in truth - ], - } - ) - summary = metrics.summary() - if return_details: - return {"summary": summary, "episodes": episode_rows} - return summary diff --git a/scratch/osint/src/osint_env/llm/__init__.py b/scratch/osint/src/osint_env/llm/__init__.py deleted file mode 100644 index 2980377f05c73f88682c51811fb9871602f9a638..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/llm/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -"""LLM interface package.""" - -from osint_env.llm.interface import ( - LLMClient, - LLMResponse, - OllamaLLMClient, - OpenAILLMClient, - RuleBasedMockLLM, - build_llm_client, -) - -__all__ = [ - "LLMClient", - "LLMResponse", - "RuleBasedMockLLM", - "OllamaLLMClient", - "OpenAILLMClient", - "build_llm_client", -] - diff --git a/scratch/osint/src/osint_env/llm/interface.py b/scratch/osint/src/osint_env/llm/interface.py deleted file mode 100644 index e223e1a3873d094b62789c4c45e7f0956c419d33..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/llm/interface.py +++ /dev/null @@ -1,167 +0,0 @@ -from __future__ import annotations - -import json -import os -from dataclasses import dataclass -from typing import Any, Protocol - -import requests -from requests import RequestException - -from osint_env.domain.models import LLMConfig - - -@dataclass(slots=True) -class LLMResponse: - content: str - tool_calls: list[dict[str, Any]] - - -class LLMClient(Protocol): - def generate(self, messages: list[dict[str, Any]], tools: list[dict[str, Any]]) -> LLMResponse: - ... - - -class RuleBasedMockLLM: - """Deterministic fallback for local testing without model dependencies.""" - - def generate(self, messages: list[dict[str, Any]], tools: list[dict[str, Any]]) -> LLMResponse: - question = "" - for m in reversed(messages): - if m.get("role") == "system" and "question" in m.get("content", ""): - question = m["content"] - break - if "alias" in question: - return LLMResponse( - content="Need alias lookup.", - tool_calls=[{"tool_name": "search_posts", "args": {"query": "Update"}}, {"tool_name": "get_profile", "args": {"user_id": "user_0"}}], - ) - return LLMResponse(content="Need profile lookup.", tool_calls=[{"tool_name": "search_people", "args": {"org": "Apex"}}]) - - -class OllamaLLMClient: - def __init__(self, model: str, base_url: str = "http://127.0.0.1:11434", temperature: float = 0.1, timeout_seconds: int = 240): - self.model = model - self.base_url = base_url.rstrip("/") - self.temperature = float(temperature) - self.timeout_seconds = int(timeout_seconds) - - @staticmethod - def _extract_tool_calls(content: str) -> list[dict[str, Any]]: - text = str(content or "").strip() - if not text: - return [] - left = text.find("{") - right = text.rfind("}") - if left >= 0 and right > left: - snippet = text[left : right + 1] - try: - parsed = json.loads(snippet) - except json.JSONDecodeError: - parsed = None - if isinstance(parsed, dict) and isinstance(parsed.get("tool_calls"), list): - out: list[dict[str, Any]] = [] - for item in parsed["tool_calls"]: - if isinstance(item, dict) and "tool_name" in item and isinstance(item.get("args", {}), dict): - out.append({"tool_name": str(item["tool_name"]), "args": dict(item.get("args", {}))}) - return out - return [] - - def generate(self, messages: list[dict[str, Any]], tools: list[dict[str, Any]]) -> LLMResponse: - payload = { - "model": self.model, - "messages": messages, - "stream": False, - "options": { - "temperature": self.temperature, - }, - } - if tools: - payload["tools"] = tools - try: - response = requests.post( - f"{self.base_url}/api/chat", - json=payload, - timeout=self.timeout_seconds, - ) - response.raise_for_status() - data = response.json() - content = str((data.get("message") or {}).get("content", "")) - tool_calls = self._extract_tool_calls(content) - return LLMResponse(content=content, tool_calls=tool_calls) - except (RequestException, ValueError): - # Keep episode execution resilient when local model calls are transiently slow/unavailable. - return LLMResponse(content="", tool_calls=[]) - - -class OpenAILLMClient: - def __init__( - self, - model: str, - api_key: str, - base_url: str = "https://api.openai.com/v1", - temperature: float = 0.1, - max_tokens: int = 256, - timeout_seconds: int = 240, - ): - from openai import OpenAI - - self.model = model - self.temperature = float(temperature) - self.max_tokens = int(max_tokens) - self.client = OpenAI(api_key=api_key, base_url=base_url, timeout=timeout_seconds) - - def generate(self, messages: list[dict[str, Any]], tools: list[dict[str, Any]]) -> LLMResponse: - kwargs: dict[str, Any] = { - "model": self.model, - "messages": messages, - "temperature": self.temperature, - "max_tokens": self.max_tokens, - } - if tools: - kwargs["tools"] = tools - try: - completion = self.client.chat.completions.create(**kwargs) - message = completion.choices[0].message - content = message.content if isinstance(message.content, str) else "" - - tool_calls: list[dict[str, Any]] = [] - for tc in message.tool_calls or []: - try: - args = json.loads(tc.function.arguments or "{}") - except json.JSONDecodeError: - args = {} - tool_calls.append({"tool_name": tc.function.name, "args": args if isinstance(args, dict) else {}}) - return LLMResponse(content=content, tool_calls=tool_calls) - except Exception: - return LLMResponse(content="", tool_calls=[]) - - -def build_llm_client(config: LLMConfig | None = None) -> LLMClient: - cfg = config or LLMConfig() - provider = str(cfg.provider).strip().lower() - if provider in {"", "mock", "rule", "rule_based"}: - return RuleBasedMockLLM() - if provider == "ollama": - return OllamaLLMClient( - model=cfg.model, - base_url=cfg.ollama_base_url, - temperature=cfg.temperature, - timeout_seconds=cfg.timeout_seconds, - ) - if provider == "openai": - api_key = cfg.openai_api_key or os.getenv(cfg.openai_api_key_env, "") - if not api_key: - raise ValueError( - "OpenAI provider selected but API key is missing. " - f"Set {cfg.openai_api_key_env} or populate openai_api_key in config." - ) - return OpenAILLMClient( - model=cfg.model, - api_key=api_key, - base_url=cfg.openai_base_url, - temperature=cfg.temperature, - max_tokens=cfg.max_tokens, - timeout_seconds=cfg.timeout_seconds, - ) - raise ValueError(f"Unsupported llm provider: {cfg.provider}") diff --git a/scratch/osint/src/osint_env/memory/__init__.py b/scratch/osint/src/osint_env/memory/__init__.py deleted file mode 100644 index 519788dfb2a8240a0d4f54981c2c65c6e71d0df8..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/memory/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Structured memory package.""" - diff --git a/scratch/osint/src/osint_env/memory/store.py b/scratch/osint/src/osint_env/memory/store.py deleted file mode 100644 index 780f46087c8b7ea165b777a0fc61a4c8b7f613cb..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/memory/store.py +++ /dev/null @@ -1,58 +0,0 @@ -from __future__ import annotations - -import math -import re -from collections import Counter -from dataclasses import dataclass, field -from typing import Any - -from osint_env.domain.models import Edge - - -def _tokenize(text: str) -> list[str]: - return [t for t in re.findall(r"[a-zA-Z0-9_]+", text.lower()) if t] - - -@dataclass(slots=True) -class MemoryGraph: - nodes: dict[str, dict[str, Any]] = field(default_factory=dict) - edges: list[Edge] = field(default_factory=list) - - def add_edge(self, edge: Edge) -> bool: - key = (edge.src, edge.rel, edge.dst) - if any((e.src, e.rel, e.dst) == key for e in self.edges): - return False - self.edges.append(edge) - return True - - def to_snapshot(self) -> dict[str, Any]: - return { - "nodes_count": len(self.nodes), - "edges_count": len(self.edges), - "edges": [{"src": e.src, "rel": e.rel, "dst": e.dst, "confidence": e.confidence} for e in self.edges], - } - - -@dataclass(slots=True) -class SemanticMemory: - docs: list[dict[str, Any]] = field(default_factory=list) - - def add(self, text: str, metadata: dict[str, Any]) -> None: - self.docs.append({"text": text, "metadata": metadata, "tokens": Counter(_tokenize(text))}) - - def search(self, query: str, k: int = 5) -> list[dict[str, Any]]: - q = Counter(_tokenize(query)) - scored: list[tuple[float, dict[str, Any]]] = [] - for doc in self.docs: - score = self._cosine(q, doc["tokens"]) - if score > 0: - scored.append((score, doc)) - scored.sort(key=lambda x: x[0], reverse=True) - return [{"score": s, "text": d["text"], "metadata": d["metadata"]} for s, d in scored[:k]] - - @staticmethod - def _cosine(a: Counter, b: Counter) -> float: - common = set(a) & set(b) - num = sum(a[t] * b[t] for t in common) - den = math.sqrt(sum(v * v for v in a.values())) * math.sqrt(sum(v * v for v in b.values())) - return (num / den) if den else 0.0 diff --git a/scratch/osint/src/osint_env/platforms/__init__.py b/scratch/osint/src/osint_env/platforms/__init__.py deleted file mode 100644 index cc16c8fc0fb7ab3780746c358de603a3ff5eb1d6..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/platforms/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Platform and tool adapters.""" - diff --git a/scratch/osint/src/osint_env/platforms/tools.py b/scratch/osint/src/osint_env/platforms/tools.py deleted file mode 100644 index d547fe4383f0019bde331186486fc22549cd6651..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/platforms/tools.py +++ /dev/null @@ -1,168 +0,0 @@ -from __future__ import annotations - -from collections import defaultdict -from typing import Any - -from osint_env.data.generator import PlatformViews - - -class ToolRegistry: - def __init__(self, views: PlatformViews): - self.views = views - self.alias_lookup = dict(getattr(views, "alias_lookup", {})) - self._index() - - @staticmethod - def _normalize_lookup_token(value: str) -> str: - token = str(value or "").strip().lower() - for prefix in ("org_", "loc_", "event_", "post_", "thr_", "thread_", "alias_", "user_"): - if token.startswith(prefix): - token = token[len(prefix) :] - break - return token.replace("_", " ") - - def _resolve_user_ids(self, user_id: str) -> list[str]: - user_id = str(user_id or "").strip() - if not user_id: - return [] - resolved = [user_id] - canonical = self.alias_lookup.get(user_id) - if canonical and canonical not in resolved: - resolved.append(canonical) - for alias_id, owner in self.alias_lookup.items(): - if owner == user_id and alias_id not in resolved: - resolved.append(alias_id) - return resolved - - def _index(self) -> None: - self.posts_by_user: dict[str, list[dict[str, Any]]] = defaultdict(list) - self.mentions_by_user: dict[str, list[dict[str, Any]]] = defaultdict(list) - self.posts_by_id = {post["post_id"]: post for post in self.views.microblog_posts} - for post in self.views.microblog_posts: - self.posts_by_user[post["user_id"]].append(post) - canonical_user = post.get("canonical_user") - if canonical_user: - self.posts_by_user[canonical_user].append(post) - for m in post.get("mentions", []): - self.mentions_by_user[m].append(post) - - self.threads_by_id = {t["thread_id"]: t for t in self.views.forum_threads} - self.activity_by_user: dict[str, list[dict[str, Any]]] = defaultdict(list) - for thread in self.views.forum_threads: - self.activity_by_user[thread["author_id"]].append({"kind": "thread", "thread_id": thread["thread_id"]}) - for c in thread.get("comments", []): - self.activity_by_user[c["user_id"]].append({"kind": "comment", "thread_id": thread["thread_id"]}) - - self.profiles_by_user = {p["user_id"]: p for p in self.views.profiles} - - def call(self, tool_name: str, args: dict[str, Any]) -> dict[str, Any]: - fn = getattr(self, tool_name, None) - if not fn: - raise ValueError(f"Unknown tool: {tool_name}") - return fn(**args) - - def search_posts(self, query: str, time_range: tuple[int, int] | None = None) -> dict[str, Any]: - start, end = time_range or (0, 10**9) - needle = str(query or "").lower() - results = [ - p - for p in self.views.microblog_posts - if start <= p["timestamp"] <= end - and ( - needle in p["text"].lower() - or needle in str(p.get("post_id", "")).lower() - or needle in str(p.get("user_id", "")).lower() - or needle in str(p.get("canonical_user", "")).lower() - or any(needle in str(ref).lower() for ref in p.get("references", [])) - or any(needle in str(ref).lower() for ref in p.get("reference_names", [])) - ) - ] - return {"results": results[:20], "count": len(results)} - - def get_post(self, post_id: str) -> dict[str, Any]: - post = self.posts_by_id.get(post_id) - return {"result": post, "found": post is not None} - - def get_user_posts(self, user_id: str) -> dict[str, Any]: - results: list[dict[str, Any]] = [] - seen_post_ids: set[str] = set() - for resolved_id in self._resolve_user_ids(user_id): - for post in self.posts_by_user.get(resolved_id, []): - post_id = str(post.get("post_id", "")) - if post_id in seen_post_ids: - continue - seen_post_ids.add(post_id) - results.append(post) - return {"results": results, "count": len(results)} - - def get_mentions(self, user_id: str) -> dict[str, Any]: - results: list[dict[str, Any]] = [] - seen_post_ids: set[str] = set() - for resolved_id in self._resolve_user_ids(user_id): - for post in self.mentions_by_user.get(resolved_id, []): - post_id = str(post.get("post_id", "")) - if post_id in seen_post_ids: - continue - seen_post_ids.add(post_id) - results.append(post) - return {"results": results, "count": len(results)} - - def search_threads(self, topic: str) -> dict[str, Any]: - needle = str(topic or "").strip().lower() - results = [ - t - for t in self.views.forum_threads - if t["topic"] == topic - or needle in str(t.get("thread_id", "")).lower() - or needle in str(t.get("title", "")).lower() - ] - return {"results": results[:20], "count": len(results)} - - def get_thread(self, thread_id: str) -> dict[str, Any]: - thread = self.threads_by_id.get(thread_id) - return {"result": thread, "found": thread is not None} - - def get_user_activity(self, user_id: str) -> dict[str, Any]: - acts: list[dict[str, Any]] = [] - seen = set() - for resolved_id in self._resolve_user_ids(user_id): - for activity in self.activity_by_user.get(resolved_id, []): - key = (activity.get("kind"), activity.get("thread_id")) - if key in seen: - continue - seen.add(key) - acts.append(activity) - return {"results": acts, "count": len(acts)} - - def get_profile(self, user_id: str) -> dict[str, Any]: - resolved_ids = self._resolve_user_ids(user_id) - profile = next((self.profiles_by_user.get(candidate) for candidate in resolved_ids if self.profiles_by_user.get(candidate)), None) - return {"result": profile, "found": profile is not None} - - def search_people(self, name: str | None = None, org: str | None = None) -> dict[str, Any]: - results = self.views.profiles - if name: - name_query = str(name).lower() - results = [ - p - for p in results - if name_query in p["name"].lower() - or name_query in p["user_id"].lower() - or any(name_query in alias.lower() for alias in p.get("alias_ids", [])) - ] - if org: - org_query = str(org).lower() - normalized_org = self._normalize_lookup_token(org_query) - results = [ - p - for p in results - if org_query in p["org"].lower() - or org_query in str(p.get("org_id", "")).lower() - or (normalized_org and normalized_org in p["org"].lower()) - ] - return {"results": results[:20], "count": len(results)} - - def get_connections(self, user_id: str) -> dict[str, Any]: - resolved_ids = self._resolve_user_ids(user_id) - profile = next((self.profiles_by_user.get(candidate) for candidate in resolved_ids if self.profiles_by_user.get(candidate)), None) - return {"results": profile["connections"] if profile else [], "count": len(profile["connections"]) if profile else 0} diff --git a/scratch/osint/src/osint_env/server_entry.py b/scratch/osint/src/osint_env/server_entry.py deleted file mode 100644 index 928272ed0d02181bac278da9734db57cd41cf85c..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/server_entry.py +++ /dev/null @@ -1,10 +0,0 @@ -from __future__ import annotations - -import os - -import uvicorn - - -def main() -> None: - port = int(os.getenv("PORT", "7860")) - uvicorn.run("server:app", host="0.0.0.0", port=port) diff --git a/scratch/osint/src/osint_env/validation.py b/scratch/osint/src/osint_env/validation.py deleted file mode 100644 index 8f3e6a230805220309252a3823f72f73820640bc..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/validation.py +++ /dev/null @@ -1,316 +0,0 @@ -from __future__ import annotations - -import json -import tempfile -from dataclasses import asdict, dataclass -from pathlib import Path -from types import SimpleNamespace -from typing import Any - -from fastapi.testclient import TestClient - -from server import app -from osint_env.baselines.openai_runner import OpenAIBaselineConfig, OpenAIBaselineRunner, build_action_tools -from osint_env.config import clone_environment_config, load_seeding_config, load_shared_config -from osint_env.env.environment import OSINTEnvironment -from osint_env.env.openenv_compat import Env -from osint_env.env.reward import compute_answer_reward - - -README_PATH = Path("README.md") -DOCKERFILE_PATH = Path("Dockerfile") -OPENENV_SPEC_PATH = Path("openenv.yaml") -SHARED_CONFIG_PATH = "datasets/fixed_levels/shared_config_fixed_levels.json" -SEED_FILE_PATH = "datasets/fixed_levels/seed_fixed_levels.json" - - -@dataclass(slots=True) -class ValidationResult: - name: str - passed: bool - details: dict[str, Any] - - -def _build_environment() -> OSINTEnvironment: - shared = load_shared_config(SHARED_CONFIG_PATH) - env_cfg = clone_environment_config(shared.environment) - env_cfg.seeding = load_seeding_config(SEED_FILE_PATH) - env_cfg.llm.provider = "mock" - return OSINTEnvironment(env_cfg) - - -def check_hf_space_readiness() -> ValidationResult: - text = README_PATH.read_text(encoding="utf-8") - has_sdk = "sdk: docker" in text - has_port = "app_port: 7860" in text - has_openenv_tag = "- openenv" in text - client = TestClient(app) - health = client.get("/healthz") - dashboard = client.get("/api/environment") - spec = client.get("/openenv.yaml") - passed = all( - [ - README_PATH.exists(), - DOCKERFILE_PATH.exists(), - OPENENV_SPEC_PATH.exists(), - has_sdk, - has_port, - has_openenv_tag, - health.status_code == 200, - dashboard.status_code == 200, - spec.status_code == 200, - ] - ) - return ValidationResult( - name="hf_space_readiness", - passed=passed, - details={ - "readme_exists": README_PATH.exists(), - "dockerfile_exists": DOCKERFILE_PATH.exists(), - "openenv_spec_exists": OPENENV_SPEC_PATH.exists(), - "has_sdk_docker": has_sdk, - "has_app_port": has_port, - "has_openenv_tag": has_openenv_tag, - "healthz_status": health.status_code, - "environment_status": dashboard.status_code, - "openenv_spec_status": spec.status_code, - }, - ) - - -def check_openenv_spec_compliance() -> ValidationResult: - env = _build_environment() - obs = env.reset() - client = TestClient(app) - reset = client.post("/openenv/reset", json={"task_index": 0}) - step = client.post( - "/openenv/step", - json={ - "session_id": reset.json()["session_id"] if reset.status_code == 200 else "", - "action_type": "ANSWER", - "payload": {"answer": "unknown"}, - }, - ) - state = client.get(f"/openenv/state/{reset.json()['session_id']}") if reset.status_code == 200 else None - passed = all( - [ - isinstance(env, Env), - hasattr(env, "reset"), - hasattr(env, "step"), - env.name == "OSINTEnvironment", - env.state_space == "json-observation", - env.action_space == ["CALL_TOOL", "ADD_EDGE", "ANSWER"], - env.episode_max_length == env.config.max_steps, - isinstance(obs.task, dict), - "question" in obs.task, - reset.status_code == 200, - step.status_code == 200, - state is not None and state.status_code == 200, - ] - ) - return ValidationResult( - name="openenv_spec_compliance", - passed=passed, - details={ - "env_class": type(env).__name__, - "state_space": env.state_space, - "action_space": list(env.action_space), - "episode_max_length": env.episode_max_length, - "task_keys": sorted(obs.task.keys()), - "reset_status": reset.status_code, - "step_status": step.status_code, - "state_status": 0 if state is None else state.status_code, - }, - ) - - -class _FakeMessage: - def __init__(self, answer: str): - self.content = "" - self.tool_calls = [ - SimpleNamespace( - id="fake_tool_call_0", - function=SimpleNamespace(name="submit_answer", arguments=json.dumps({"answer": answer})), - ) - ] - - -class _FakeCompletion: - def __init__(self, answer: str): - self.choices = [SimpleNamespace(message=_FakeMessage(answer))] - self.usage = SimpleNamespace(prompt_tokens=0, completion_tokens=0, total_tokens=0) - self.system_fingerprint = "validation_fp" - - -class _FakeChatCompletions: - def create(self, **kwargs: Any) -> _FakeCompletion: - messages = list(kwargs.get("messages", [])) - initial_observation = {} - for message in messages: - if message.get("role") == "user": - try: - initial_observation = json.loads(message.get("content", "{}")) - except json.JSONDecodeError: - initial_observation = {} - break - task_id = ((initial_observation.get("task") or {}).get("task_id")) or "" - env = _build_environment() - task = next((task for task in env.tasks if task.task_id == task_id), None) - answer = task.answer if task is not None else "unknown" - return _FakeCompletion(answer) - - -class _FakeOpenAIClient: - def __init__(self) -> None: - self.chat = SimpleNamespace(completions=_FakeChatCompletions()) - - -def _run_fake_baseline_once(output_dir: Path) -> dict[str, Any]: - config = OpenAIBaselineConfig( - api_key="validation", - episodes=3, - max_steps=4, - append_leaderboard=False, - output_path=str(output_dir / "baseline.json"), - dashboard_path=str(output_dir / "baseline.html"), - leaderboard_path=str(output_dir / "leaderboard.json"), - run_name="validation_baseline", - ) - runner = OpenAIBaselineRunner.__new__(OpenAIBaselineRunner) - runner.config = config - runner.client = _FakeOpenAIClient() - runner.tools = build_action_tools() - return runner.run() - - -def check_baseline_reproducibility() -> ValidationResult: - with tempfile.TemporaryDirectory() as left_dir_name, tempfile.TemporaryDirectory() as right_dir_name: - left = _run_fake_baseline_once(Path(left_dir_name)) - right = _run_fake_baseline_once(Path(right_dir_name)) - - left_signature = { - "summary": left["summary"], - "episodes": [ - { - "task_id": episode["task_id"], - "task_answer": episode["task_answer"], - "agent_answer": episode["agent_answer"], - "success": episode["success"], - "steps": episode["steps"], - } - for episode in left["episodes"] - ], - } - right_signature = { - "summary": right["summary"], - "episodes": [ - { - "task_id": episode["task_id"], - "task_answer": episode["task_answer"], - "agent_answer": episode["agent_answer"], - "success": episode["success"], - "steps": episode["steps"], - } - for episode in right["episodes"] - ], - } - passed = left_signature == right_signature - return ValidationResult( - name="baseline_reproducibility", - passed=passed, - details={ - "episodes_checked": len(left_signature["episodes"]), - "left_signature": left_signature, - "right_signature": right_signature, - }, - ) - - -def check_task_and_grader_coverage() -> ValidationResult: - env = _build_environment() - tasks = env.tasks - grader_checks: list[dict[str, Any]] = [] - distinct_types = sorted({str(task.task_type) for task in tasks}) - difficulty_buckets: dict[str, Any] = {} - for idx, task in enumerate(tasks): - token = str((task.metadata or {}).get("difficulty", "")).strip().lower() - if token in {"mid", "m"}: - token = "medium" - if token in {"high", "h"}: - token = "hard" - if token not in {"easy", "medium", "hard"}: - if idx < 10: - token = "easy" - elif idx < 20: - token = "medium" - else: - token = "hard" - difficulty_buckets.setdefault(token, task) - - for difficulty in ["easy", "medium", "hard"]: - task = difficulty_buckets.get(difficulty) - if task is None: - continue - correct = compute_answer_reward( - proposed_answer=task.answer, - task=task, - pred_edges=list(task.supporting_edges), - tool_outputs=[], - step_count=1, - model=env.reward_model, - difficulty=difficulty, - ) - wrong = compute_answer_reward( - proposed_answer="unknown", - task=task, - pred_edges=[], - tool_outputs=[], - step_count=1, - model=env.reward_model, - difficulty=difficulty, - ) - grader = dict(task.metadata.get("grader", {})) if isinstance(task.metadata, dict) else {} - grader_checks.append( - { - "difficulty": difficulty, - "task_id": task.task_id, - "task_type": task.task_type, - "support_edges": len(task.supporting_edges), - "has_grader": bool(grader), - "correct_reward": correct.total, - "wrong_reward": wrong.total, - "grader_prefers_correct": correct.total > wrong.total, - } - ) - passed = ( - len(tasks) >= 3 - and len(distinct_types) >= 3 - and len(grader_checks) >= 3 - and all( - row["support_edges"] > 0 and row["grader_prefers_correct"] and row["has_grader"] - for row in grader_checks - ) - ) - return ValidationResult( - name="task_and_grader_coverage", - passed=passed, - details={ - "task_count": len(tasks), - "distinct_task_types": distinct_types, - "grader_checks": grader_checks, - }, - ) - - -def run_validation_suite() -> dict[str, Any]: - results = [ - check_hf_space_readiness(), - check_openenv_spec_compliance(), - check_baseline_reproducibility(), - check_task_and_grader_coverage(), - ] - passed = all(result.passed for result in results) - return { - "passed": passed, - "checks": [asdict(result) for result in results], - } diff --git a/scratch/osint/src/osint_env/viz/__init__.py b/scratch/osint/src/osint_env/viz/__init__.py deleted file mode 100644 index 0c98ce2d59d35418cfb871ff0eb5d9c72a3e19c6..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/viz/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from osint_env.viz.dashboard import export_dashboard - -__all__ = ["export_dashboard"] diff --git a/scratch/osint/src/osint_env/viz/dashboard.py b/scratch/osint/src/osint_env/viz/dashboard.py deleted file mode 100644 index 3e033381d99306acdbc7a2a2cb12e7574135b1c5..0000000000000000000000000000000000000000 --- a/scratch/osint/src/osint_env/viz/dashboard.py +++ /dev/null @@ -1,801 +0,0 @@ -from __future__ import annotations - -import json -from pathlib import Path -from typing import Any - -from osint_env.data.generator import PlatformViews -from osint_env.domain.models import CanonicalGraph, Edge, TaskInstance -from osint_env.env.environment import OSINTEnvironment - - -def _safe_label(value: str, fallback: str) -> str: - text = str(value).strip() - return text if text else fallback - - -def _canonical_graph_payload(graph: CanonicalGraph) -> dict[str, Any]: - nodes = [] - for node in graph.nodes.values(): - attrs = node.attrs or {} - title = "\\n".join(f"{k}: {v}" for k, v in attrs.items()) - label = _safe_label(str(attrs.get("name") or attrs.get("handle") or node.node_id), node.node_id) - nodes.append( - { - "id": node.node_id, - "label": label, - "group": str(node.node_type.value), - "title": title, - "attrs": attrs, - } - ) - - edges = [] - for idx, edge in enumerate(graph.edges): - edges.append( - { - "id": f"c_{idx}", - "from": edge.src, - "to": edge.dst, - "label": edge.rel, - "arrows": "to", - "color": "#1f2937", - "width": 1, - "confidence": float(edge.confidence), - "status": "canonical", - } - ) - return {"nodes": nodes, "edges": edges} - - -def _edge_key(edge: Edge) -> tuple[str, str, str]: - return (edge.src, edge.rel, edge.dst) - - -def _episode_graph_payload(pred_edges: list[Edge], truth_edges: list[Edge], graph: CanonicalGraph) -> dict[str, Any]: - pred = {_edge_key(e): e for e in pred_edges} - truth = {_edge_key(e): e for e in truth_edges} - - all_nodes = set() - all_keys = set(pred) | set(truth) - for src, _, dst in all_keys: - all_nodes.add(src) - all_nodes.add(dst) - - nodes = [] - for node_id in sorted(all_nodes): - node = graph.nodes.get(node_id) - if node is None: - nodes.append({"id": node_id, "label": node_id, "group": "episode", "attrs": {}}) - continue - attrs = node.attrs or {} - label = _safe_label(str(attrs.get("name") or attrs.get("handle") or node_id), node_id) - nodes.append({"id": node_id, "label": label, "group": str(node.node_type.value), "attrs": attrs}) - - edges = [] - for idx, key in enumerate(sorted(all_keys)): - src, rel, dst = key - in_pred = key in pred - in_truth = key in truth - if in_pred and in_truth: - color = "#16a34a" - dashes = False - status = "matched" - elif in_pred: - color = "#2563eb" - dashes = False - status = "pred_only" - else: - color = "#f59e0b" - dashes = True - status = "truth_only" - edges.append( - { - "id": f"e_{idx}", - "from": src, - "to": dst, - "label": rel, - "arrows": "to", - "color": color, - "dashes": dashes, - "width": 2, - "status": status, - "confidence": float((pred.get(key) or truth.get(key) or Edge(src, rel, dst)).confidence), - } - ) - - return {"nodes": nodes, "edges": edges} - - -def _views_payload(views: PlatformViews) -> dict[str, Any]: - return { - "microblog_posts": views.microblog_posts, - "forum_threads": views.forum_threads, - "profiles": views.profiles, - } - - -def _leaderboard_payload(records: list[dict[str, Any]]) -> list[dict[str, Any]]: - ranked = sorted(records, key=lambda r: float(r.get("metrics", {}).get("leaderboard_score", 0.0)), reverse=True) - return ranked[:200] - - -def export_dashboard( - env: OSINTEnvironment, - evaluation: dict[str, Any], - leaderboard_records: list[dict[str, Any]], - output_path: str, -) -> str: - summary = evaluation.get("summary", evaluation) - episodes = evaluation.get("episodes", []) - - task: TaskInstance | None = env.state.task if env.state else None - truth_edges = task.supporting_edges if task else [] - pred_edges = env.memory_graph.edges if env.state else [] - - episode_graphs: list[dict[str, Any]] = [] - for episode in episodes: - pred_from_eval = [Edge(str(e.get("src", "")), str(e.get("rel", "")), str(e.get("dst", "")), float(e.get("confidence", 1.0))) for e in episode.get("pred_edges", []) if isinstance(e, dict)] - truth_from_eval = [Edge(str(e.get("src", "")), str(e.get("rel", "")), str(e.get("dst", "")), float(e.get("confidence", 1.0))) for e in episode.get("truth_edges", []) if isinstance(e, dict)] - if pred_from_eval or truth_from_eval: - episode_graphs.append(_episode_graph_payload(pred_from_eval, truth_from_eval, env.graph)) - - if not episode_graphs: - episode_graphs.append(_episode_graph_payload(pred_edges, truth_edges, env.graph)) - - payload = { - "summary": summary, - "episodes": episodes, - "leaderboard": _leaderboard_payload(leaderboard_records), - "canonical_graph": _canonical_graph_payload(env.graph), - "episode_graphs": episode_graphs, - "episode_graph": episode_graphs[-1], - "views": _views_payload(env.views), - "task": { - "task_id": task.task_id if task else "n/a", - "task_type": task.task_type if task else "n/a", - "question": task.question if task else "n/a", - "answer": task.answer if task else "n/a", - }, - } - - html = f""" - - - - - OSINT Environment Dashboard - - - - - - - - - -
    -
    -
    -

    OSINT Benchmark Dashboard

    -

    Interactive explorer for canonical knowledge graph, episode traces, source platform records, and benchmark ranking.

    -
    -
    -
    -
    -

    Episode Explorer

    -
    - - -
    -
    - - -
    -
    Task ID:
    -
    Task Type:
    -
    Question
    -
    -
    Ground Truth Answer:
    -
    Agent Answer:
    -
    Correct:
    -
    -
    - -
    -
    -
    -

    Graph Controls

    -
    - - - - - - - -
    -
    -
    -

    Node Types

    -
    -
    -
    - -
    -

    Graph Explorer

    -
    -
    Layer: Canonical Graph
    -
    -
    -
    - matched edge - predicted only - truth only -
    -
    - -
    -
    -

    Node Inspector

    - 
    Click a node to inspect attributes and neighbors.
    -
    -
    -

    Edge Inspector

    - 
    Click an edge to inspect relation details.
    -
    -
    -
    - -
    -
    -

    Original Database Explorer

    -
    -
    - - -
    -
    -
    - -
    -

    Selected Source Record

    - 
    Click a row in the database table to inspect full JSON.
    -
    -
    - -
    -
    -

    Benchmark Summary Radar

    -
    -
    -
    -

    Episode Reward and Graph F1

    -
    -
    -
    - -
    -

    Benchmark Leaderboard

    -
    - - -
    -
    -
    -
    - - - - -""" - - out = Path(output_path) - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(html, encoding="utf-8") - return str(out) diff --git a/scratch/osint/tests/conftest.py b/scratch/osint/tests/conftest.py deleted file mode 100644 index c4b4171d9ef25fa2362b246ff84caed0e31ff8fb..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/conftest.py +++ /dev/null @@ -1,12 +0,0 @@ -from __future__ import annotations - -import sys -from pathlib import Path - - -ROOT = Path(__file__).resolve().parents[1] -SRC = ROOT / "src" - -if str(SRC) not in sys.path: - sys.path.insert(0, str(SRC)) - diff --git a/scratch/osint/tests/test_config.py b/scratch/osint/tests/test_config.py deleted file mode 100644 index 4a75364f08c5a78948e12b6b88e22741f2897210..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_config.py +++ /dev/null @@ -1,73 +0,0 @@ -import json -from pathlib import Path - -from osint_env.config.shared import load_seeding_config, load_shared_config - - -def test_shared_config_defaults_when_file_missing(): - config = load_shared_config("/tmp/does_not_exist_for_osint_config.json") - assert config.environment.max_steps > 0 - assert config.runtime.default_episodes > 0 - - -def test_shared_config_parses_swarm_and_seeding(tmp_path: Path): - path = tmp_path / "shared.json" - path.write_text( - json.dumps( - { - "environment": {"seed": 19, "max_steps": 9}, - "swarm": {"enabled": True, "max_agents": 3, "max_breadth": 2, "max_width": 2, "max_depth": 2}, - "seeding": { - "seeded_questions": [ - { - "question": "Which canonical user owns alias alias_seed_001?", - "answer": "user_seed_001", - } - ], - "llm_generation_parallel": True, - "llm_generation_workers": 4, - "llm_generation_retries": 3, - "allow_template_fallback_on_llm_failure": False - }, - "runtime": {"default_episodes": 5}, - "llm": {"provider": "ollama", "model": "qwen3:2b", "timeout_seconds": 333}, - } - ), - encoding="utf-8", - ) - - config = load_shared_config(path) - assert config.environment.seed == 19 - assert config.environment.swarm.enabled is True - assert config.environment.swarm.max_width == 2 - assert len(config.environment.seeding.seeded_questions) == 1 - assert config.runtime.default_episodes == 5 - assert config.environment.llm.provider == "ollama" - assert config.environment.llm.model == "qwen3:2b" - assert config.environment.llm.timeout_seconds == 333 - assert config.environment.seeding.llm_generation_parallel is True - assert config.environment.seeding.llm_generation_workers == 4 - assert config.environment.seeding.llm_generation_retries == 3 - assert config.environment.seeding.allow_template_fallback_on_llm_failure is False - - -def test_load_seeding_config_supports_top_level_object(tmp_path: Path): - path = tmp_path / "seeding.json" - path.write_text( - json.dumps( - { - "seeded_nodes": [ - {"node_id": "alias_seed_1", "node_type": "alias", "attrs": {"handle": "@seed"}}, - {"node_id": "user_seed_1", "node_type": "user", "attrs": {"name": "Seed"}}, - ], - "seeded_edges": [{"src": "alias_seed_1", "rel": "alias_of", "dst": "user_seed_1"}], - "seeded_questions": [{"question": "Which canonical user owns alias alias_seed_1?", "answer": "user_seed_1"}], - } - ), - encoding="utf-8", - ) - - seeding = load_seeding_config(path) - assert len(seeding.seeded_nodes) == 2 - assert len(seeding.seeded_edges) == 1 - assert seeding.seeded_questions[0].answer == "user_seed_1" diff --git a/scratch/osint/tests/test_dashboard.py b/scratch/osint/tests/test_dashboard.py deleted file mode 100644 index 8b396dae7e7b600524d9bc4691fb678940820fae..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_dashboard.py +++ /dev/null @@ -1,26 +0,0 @@ -from pathlib import Path - -from osint_env.domain.models import EnvironmentConfig -from osint_env.env.environment import OSINTEnvironment -from osint_env.viz import export_dashboard - - -def test_dashboard_export(tmp_path: Path): - env = OSINTEnvironment(EnvironmentConfig(seed=9, n_users=14)) - env.reset() - - out = tmp_path / "dashboard.html" - path = export_dashboard( - env=env, - evaluation={"summary": {"leaderboard_score": 0.0, "task_success_rate": 0.0, "avg_graph_f1": 0.0, "tool_efficiency": 0.0, "deanonymization_accuracy": 0.0, "avg_reward": 0.0}, "episodes": []}, - leaderboard_records=[], - output_path=str(out), - ) - - assert path.endswith("dashboard.html") - text = out.read_text(encoding="utf-8") - assert "OSINT Benchmark Dashboard" in text - assert "Canonical Graph" in text - assert "Original Database Explorer" in text - assert "Benchmark Leaderboard" in text - assert "Episode Explorer" in text diff --git a/scratch/osint/tests/test_environment.py b/scratch/osint/tests/test_environment.py deleted file mode 100644 index 1a36bd9e6c0404640691a90868f6152abcb13a7d..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_environment.py +++ /dev/null @@ -1,39 +0,0 @@ -from osint_env.domain.models import Action, ActionType, EnvironmentConfig -from osint_env.env.environment import OSINTEnvironment - - -def test_episode_flow(): - env = OSINTEnvironment(EnvironmentConfig(max_steps=5, seed=5)) - obs = env.reset() - assert "question" in obs.task - assert isinstance(obs.task.get("grader"), dict) - assert "type" in obs.task["grader"] - obs, r1, done, _ = env.step(Action(ActionType.CALL_TOOL, {"tool_name": "search_posts", "args": {"query": "Update"}})) - assert done is False - assert isinstance(r1, float) - _, r2, done, info = env.step(Action(ActionType.ANSWER, {"answer": "unknown"})) - assert done is True - assert "total_reward" in info - assert isinstance(r2, float) - - -def test_search_memory_tool_returns_results_after_tool_use(): - env = OSINTEnvironment(EnvironmentConfig(max_steps=6, seed=5)) - env.reset() - env.step(Action(ActionType.CALL_TOOL, {"tool_name": "search_posts", "args": {"query": "Update"}})) - obs, reward, done, _ = env.step( - Action(ActionType.CALL_TOOL, {"tool_name": "search_memory", "args": {"query": "Update", "k": 3}}) - ) - assert done is False - assert isinstance(reward, float) - assert obs.tool_outputs[-1]["tool"] == "search_memory" - assert obs.tool_outputs[-1]["output"]["count"] >= 1 - - -def test_invalid_tool_call_does_not_crash_episode(): - env = OSINTEnvironment(EnvironmentConfig(max_steps=4, seed=8)) - env.reset() - _, reward, done, info = env.step(Action(ActionType.CALL_TOOL, {"tool_name": "no_such_tool", "args": {}})) - assert done is False - assert reward < 0 - assert "invalid_tool_penalty" in info["reward_components"] diff --git a/scratch/osint/tests/test_eval.py b/scratch/osint/tests/test_eval.py deleted file mode 100644 index ad29cfb3eb9f633214a478667fa9dbbb9bb1d036..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_eval.py +++ /dev/null @@ -1,33 +0,0 @@ -from osint_env.domain.models import EnvironmentConfig, SwarmConfig -from osint_env.env.environment import OSINTEnvironment -from osint_env.eval.runner import run_evaluation - - -def test_eval_runner(): - env = OSINTEnvironment(EnvironmentConfig(seed=17)) - result = run_evaluation(env, episodes=3) - assert "task_success_rate" in result - assert "deanonymization_accuracy" in result - assert "leaderboard_score" in result - assert "avg_knowledge_indexing_reward" in result - - -def test_eval_runner_swarm_mode(): - env = OSINTEnvironment( - EnvironmentConfig(seed=17, swarm=SwarmConfig(enabled=True, max_agents=3, max_breadth=2, max_width=2, max_depth=2)) - ) - result = run_evaluation(env, episodes=2) - assert "spawn_signal" in result - assert "avg_spawn_count" in result - - -def test_eval_runner_details_include_episode_answers(): - env = OSINTEnvironment(EnvironmentConfig(seed=17)) - result = run_evaluation(env, episodes=2, return_details=True) - assert "episodes" in result - assert len(result["episodes"]) == 2 - - row = result["episodes"][0] - assert "question" in row - assert "task_answer" in row - assert "agent_answer" in row diff --git a/scratch/osint/tests/test_fixed_levels_dataset.py b/scratch/osint/tests/test_fixed_levels_dataset.py deleted file mode 100644 index 50e834694b01613c20a9c176f4b41b02dc7d99fb..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_fixed_levels_dataset.py +++ /dev/null @@ -1,18 +0,0 @@ -import json -from collections import Counter -from pathlib import Path - - -def test_fixed_levels_seed_has_30_questions_and_target_node_spans(): - path = Path("datasets/fixed_levels/seed_fixed_levels.json") - payload = json.loads(path.read_text(encoding="utf-8")) - questions = payload["seeding"]["seeded_questions"] - - counts = Counter(q["metadata"]["difficulty"] for q in questions) - assert counts == {"easy": 10, "mid": 10, "high": 10} - - mid_support_nodes = [int(q["metadata"]["support_nodes"]) for q in questions if q["metadata"]["difficulty"] == "mid"] - high_support_nodes = [int(q["metadata"]["support_nodes"]) for q in questions if q["metadata"]["difficulty"] == "high"] - - assert all(15 <= value <= 20 for value in mid_support_nodes) - assert all(48 <= value <= 55 for value in high_support_nodes) diff --git a/scratch/osint/tests/test_generator.py b/scratch/osint/tests/test_generator.py deleted file mode 100644 index c03de0642a68894d9a68e3d88b768303bf5d50e3..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_generator.py +++ /dev/null @@ -1,134 +0,0 @@ -import json -import re -from threading import Lock - -from osint_env.data.generator import DatasetGenerator -from osint_env.domain.models import EnvironmentConfig -from osint_env.llm.interface import LLMResponse - - -class SharedContextLLM: - def __init__(self): - self.prompts: list[str] = [] - self._lock = Lock() - - def generate(self, messages, tools): - prompt = str(messages[0].get("content", "")) if messages else "" - with self._lock: - self.prompts.append(prompt) - - if "SEED_GRAPH_EXPANSION_AGENT" in prompt: - worker_match = re.search(r"worker_id:\s*(\d+)", prompt) - worker_idx = int(worker_match.group(1)) if worker_match else 0 - payload = { - "edges": [ - { - "src": "user_0", - "rel": f"llm_rel_{worker_idx}", - "dst": "user_1", - "confidence": 0.9, - } - ] - } - return LLMResponse(content=json.dumps(payload), tool_calls=[]) - - if "SEED_TASK_EXPANSION_AGENT" in prompt: - worker_match = re.search(r"worker_id:\s*(\d+)", prompt) - worker_idx = int(worker_match.group(1)) if worker_match else 0 - budget_match = re.search(r"task_budget:\s*(\d+)", prompt) - task_budget = int(budget_match.group(1)) if budget_match else 1 - tasks = [] - for local_idx in range(max(1, task_budget)): - tasks.append( - { - "task_type": "identity_resolution", - "question": f"Which canonical user is tied to alias alias_seed_{worker_idx}_{local_idx}?", - "answer": "user_1", - "supporting_edges": [ - { - "src": "alias_seed_0", - "rel": "alias_of", - "dst": "user_1", - "confidence": 0.95, - } - ], - } - ) - payload = {"tasks": tasks} - return LLMResponse(content=json.dumps(payload), tool_calls=[]) - - return LLMResponse(content="{}", tool_calls=[]) - - -def test_generator_outputs(): - gen = DatasetGenerator(EnvironmentConfig(n_users=20, seed=11)) - graph = gen.build_canonical_graph() - views = gen.build_platform_views(graph) - tasks = gen.generate_tasks(graph, views, count=5) - assert len(graph.nodes) >= 20 - assert len(views.microblog_posts) >= 20 - assert len(tasks) == 5 - - -def test_seeded_views_include_seeded_posts_and_threads(): - from osint_env.config import clone_environment_config, load_seeding_config, load_shared_config - - shared = load_shared_config("datasets/fixed_levels/shared_config_fixed_levels.json") - cfg = clone_environment_config(shared.environment) - cfg.seeding = load_seeding_config("datasets/fixed_levels/seed_fixed_levels.json") - cfg.llm.provider = "mock" - - gen = DatasetGenerator(cfg) - graph = gen.build_canonical_graph() - views = gen.build_platform_views(graph) - - seeded_post = next((post for post in views.microblog_posts if post["post_id"] == "post_midnight_manifest"), None) - seeded_thread = next((thread for thread in views.forum_threads if thread["thread_id"] == "thr_supply_leak"), None) - - assert seeded_post is not None - assert "loc_dockyard17" in seeded_post["references"] - assert seeded_thread is not None - assert "org_northbridge_logistics" in seeded_thread["references"] - - -def test_graph_generation_uses_parallel_shared_context_workers(): - cfg = EnvironmentConfig(n_users=12, seed=9) - cfg.seeding.llm_generate_remaining_graph = True - cfg.seeding.llm_generated_edge_budget = 4 - cfg.seeding.llm_generate_remaining_tasks = False - cfg.seeding.llm_generation_parallel = True - cfg.seeding.llm_generation_workers = 3 - cfg.seeding.llm_generation_retries = 1 - cfg.seeding.allow_template_fallback_on_llm_failure = False - - llm = SharedContextLLM() - gen = DatasetGenerator(cfg, llm=llm) - graph = gen.build_canonical_graph() - - assert any(edge.rel.startswith("llm_rel_") for edge in graph.edges) - graph_prompts = [prompt for prompt in llm.prompts if "SEED_GRAPH_EXPANSION_AGENT" in prompt] - assert len(graph_prompts) >= 2 - assert all("SHARED_CONTEXT" in prompt for prompt in graph_prompts) - - -def test_task_generation_uses_parallel_shared_context_workers(): - cfg = EnvironmentConfig(n_users=12, seed=13) - cfg.seeding.llm_generate_remaining_graph = False - cfg.seeding.llm_generate_remaining_tasks = True - cfg.seeding.llm_generated_task_budget = 4 - cfg.seeding.llm_generation_parallel = True - cfg.seeding.llm_generation_workers = 3 - cfg.seeding.llm_generation_retries = 1 - cfg.seeding.allow_template_fallback_on_llm_failure = False - - llm = SharedContextLLM() - gen = DatasetGenerator(cfg, llm=llm) - graph = gen.build_canonical_graph() - views = gen.build_platform_views(graph) - tasks = gen.generate_tasks(graph, views, count=4) - - assert len(tasks) == 4 - assert any(task.metadata.get("shared_context") for task in tasks) - task_prompts = [prompt for prompt in llm.prompts if "SEED_TASK_EXPANSION_AGENT" in prompt] - assert len(task_prompts) >= 2 - assert all("SHARED_CONTEXT" in prompt for prompt in task_prompts) diff --git a/scratch/osint/tests/test_inference.py b/scratch/osint/tests/test_inference.py deleted file mode 100644 index bff41e6896e9920687bf753713a6f53f9d0b631e..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_inference.py +++ /dev/null @@ -1,37 +0,0 @@ -from inference import _format_action, _looks_like_placeholder_api_key, _tool_result_message - - -def test_placeholder_api_key_detection(): - assert _looks_like_placeholder_api_key("your_openai_api_key") is True - assert _looks_like_placeholder_api_key("sk-your-real-openai-key") is True - assert _looks_like_placeholder_api_key("replace-me") is True - assert _looks_like_placeholder_api_key("sk-proj-realistic-looking-token") is False - - -def test_tool_result_message_reuses_assistant_tool_call_id(): - assistant_message = { - "role": "assistant", - "content": "", - "tool_calls": [ - { - "id": "call_123", - "type": "function", - "function": {"name": "get_post", "arguments": "{\"post_id\":\"post_midnight_manifest\"}"}, - } - ], - } - result = {"reward": 0.1, "done": False} - tool_message = _tool_result_message(assistant_message, result) - assert tool_message is not None - assert tool_message["tool_call_id"] == "call_123" - assert tool_message["role"] == "tool" - - -def test_action_formatter_matches_single_line_style(): - assert _format_action({"action_type": "ANSWER", "payload": {"answer": "user_bharat"}}) == "answer(user_bharat)" - assert _format_action( - { - "action_type": "CALL_TOOL", - "payload": {"tool_name": "get_post", "args": {"post_id": "post_midnight_manifest"}}, - } - ) == "get_post(post_id=post_midnight_manifest)" diff --git a/scratch/osint/tests/test_leaderboard.py b/scratch/osint/tests/test_leaderboard.py deleted file mode 100644 index 7752b8646cc1785e505fd6422373a7e30fb26449..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_leaderboard.py +++ /dev/null @@ -1,47 +0,0 @@ -from pathlib import Path - -from osint_env.eval.leaderboard import append_leaderboard_record, load_leaderboard, render_leaderboard_table, sorted_leaderboard - - -def test_leaderboard_roundtrip(tmp_path: Path): - board = tmp_path / "leaderboard.json" - append_leaderboard_record( - path=board, - summary={ - "leaderboard_score": 0.42, - "task_success_rate": 0.5, - "avg_graph_f1": 0.4, - "avg_reward": 0.1, - "tool_efficiency": 0.9, - "retrieval_signal": 0.3, - "structural_signal": 0.4, - }, - episodes=5, - run_name="baseline", - ) - append_leaderboard_record( - path=board, - summary={ - "leaderboard_score": 0.75, - "task_success_rate": 0.7, - "avg_graph_f1": 0.6, - "avg_reward": 0.5, - "tool_efficiency": 0.8, - "retrieval_signal": 0.6, - "structural_signal": 0.7, - }, - episodes=5, - run_name="improved", - ) - - records = load_leaderboard(board) - ranked = sorted_leaderboard(records) - assert len(records) == 2 - assert ranked[0]["run_name"] == "improved" - - ranked_by_success = sorted_leaderboard(records, sort_by="task_success_rate") - assert ranked_by_success[0]["run_name"] == "improved" - - table = render_leaderboard_table(records, top_k=5) - assert "| rank | run |" in table - assert "retrieval" in table diff --git a/scratch/osint/tests/test_llm_interface.py b/scratch/osint/tests/test_llm_interface.py deleted file mode 100644 index c615c5bddac5e7217996bb6632e0a88baa018f57..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_llm_interface.py +++ /dev/null @@ -1,44 +0,0 @@ -import os - -import pytest -import requests - -from osint_env.domain.models import LLMConfig -from osint_env.llm.interface import OllamaLLMClient, RuleBasedMockLLM, build_llm_client - - -def test_build_llm_client_mock_default(): - client = build_llm_client(LLMConfig(provider="mock")) - assert isinstance(client, RuleBasedMockLLM) - - -def test_build_llm_client_openai_requires_key(monkeypatch: pytest.MonkeyPatch): - monkeypatch.delenv("OPENAI_API_KEY", raising=False) - with pytest.raises(ValueError): - build_llm_client(LLMConfig(provider="openai", openai_api_key="", openai_api_key_env="OPENAI_API_KEY")) - - -def test_build_llm_client_openai_with_key(monkeypatch: pytest.MonkeyPatch): - monkeypatch.setenv("OPENAI_API_KEY", "test-key") - cfg = LLMConfig(provider="openai", model="gpt-4o-mini", openai_api_key_env="OPENAI_API_KEY") - # Constructing should not fail when a key is present; actual API call is not made in this test. - client = build_llm_client(cfg) - assert client is not None - - -def test_openai_key_can_come_from_config_value(monkeypatch: pytest.MonkeyPatch): - monkeypatch.delenv("OPENAI_API_KEY", raising=False) - cfg = LLMConfig(provider="openai", model="gpt-4o-mini", openai_api_key="cfg-key") - client = build_llm_client(cfg) - assert client is not None - - -def test_ollama_client_gracefully_handles_request_failure(monkeypatch: pytest.MonkeyPatch): - def _raise(*args, **kwargs): - raise requests.exceptions.ReadTimeout("timed out") - - monkeypatch.setattr("osint_env.llm.interface.requests.post", _raise) - client = OllamaLLMClient(model="qwen3:2b", timeout_seconds=1) - response = client.generate([{"role": "system", "content": "ping"}], tools=[]) - assert response.content == "" - assert response.tool_calls == [] diff --git a/scratch/osint/tests/test_openai_baseline.py b/scratch/osint/tests/test_openai_baseline.py deleted file mode 100644 index d11342d26c88ea41b0119a261c0f8b5d4a946cde..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_openai_baseline.py +++ /dev/null @@ -1,30 +0,0 @@ -from osint_env.baselines.openai_runner import OpenAIBaselineConfig, OpenAIBaselineRunner, build_action_tools - - -def test_openai_baseline_toolset_contains_answer_and_graph_actions(): - tools = build_action_tools() - names = {tool["function"]["name"] for tool in tools} - assert "submit_answer" in names - assert "add_edge" in names - assert "search_memory" in names - assert "get_post" in names - - -def test_gpt5_request_kwargs_avoid_temperature_and_use_max_completion_tokens(): - runner = OpenAIBaselineRunner.__new__(OpenAIBaselineRunner) - runner.config = OpenAIBaselineConfig(model="gpt-5-nano", max_tokens=321, temperature=0.0, seed=7) - runner.tools = build_action_tools() - kwargs = runner._request_kwargs(messages=[{"role": "user", "content": "hi"}], episode_index=0) - assert kwargs["max_completion_tokens"] == 321 - assert kwargs["reasoning_effort"] == "none" - assert "temperature" not in kwargs - - -def test_gpt54_mini_request_kwargs_skip_reasoning_effort_for_chat_completions(): - runner = OpenAIBaselineRunner.__new__(OpenAIBaselineRunner) - runner.config = OpenAIBaselineConfig(model="gpt-5.4-mini", max_tokens=321, temperature=0.0, seed=7) - runner.tools = build_action_tools() - kwargs = runner._request_kwargs(messages=[{"role": "user", "content": "hi"}], episode_index=0) - assert kwargs["max_completion_tokens"] == 321 - assert "reasoning_effort" not in kwargs - assert "temperature" not in kwargs diff --git a/scratch/osint/tests/test_reward.py b/scratch/osint/tests/test_reward.py deleted file mode 100644 index 03bb251136dd7eb850a07d97fbc96fb82b11f981..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_reward.py +++ /dev/null @@ -1,53 +0,0 @@ -from osint_env.domain.models import Edge, EnvironmentConfig -from osint_env.env.environment import OSINTEnvironment -from osint_env.env.reward import build_reward_model, compute_answer_reward, compute_edge_reward - - -def test_composite_edge_reward_returns_breakdown(): - env = OSINTEnvironment(EnvironmentConfig(seed=13, n_users=16, max_steps=6)) - obs = env.reset() - task = env.state.task - - model = build_reward_model(env.graph) - edge = task.supporting_edges[0] - breakdown = compute_edge_reward( - edge=edge, - task=task, - existing_edges=[], - step_count=1, - model=model, - graph=env.graph, - ) - assert isinstance(breakdown.total, float) - assert breakdown.global_accuracy > 0 - assert isinstance(breakdown.connectivity_gain, float) - - -def test_answer_reward_uses_graph_and_tool_context(): - env = OSINTEnvironment(EnvironmentConfig(seed=21, n_users=18, max_steps=6)) - env.reset() - task = env.state.task - - pred_edges = [Edge(task.supporting_edges[0].src, task.supporting_edges[0].rel, task.supporting_edges[0].dst)] - tool_outputs = [{"tool": "get_profile", "output": {"result": {"user_id": task.answer}}}] - - good = compute_answer_reward( - proposed_answer=task.answer, - task=task, - pred_edges=pred_edges, - tool_outputs=tool_outputs, - step_count=2, - ) - bad = compute_answer_reward( - proposed_answer="wrong", - task=task, - pred_edges=[], - tool_outputs=[], - step_count=2, - ) - - assert good.total > bad.total - assert good.graph_f1 >= 0 - assert isinstance(good.relation_informativeness, float) - assert isinstance(good.entity_informativeness, float) - assert isinstance(good.repetition_penalty, float) diff --git a/scratch/osint/tests/test_seeding.py b/scratch/osint/tests/test_seeding.py deleted file mode 100644 index 7381a3e2ad12bf469693ae39ef75558e8a6ec08c..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_seeding.py +++ /dev/null @@ -1,40 +0,0 @@ -from osint_env.domain.models import ( - EnvironmentConfig, - NodeType, - SeedEdgeSpec, - SeedNodeSpec, - SeedQuestionSpec, - SeedingConfig, -) -from osint_env.env.environment import OSINTEnvironment - - -def test_environment_includes_seeded_graph_and_questions(): - seeding = SeedingConfig( - seeded_nodes=[ - SeedNodeSpec(node_id="alias_seed_001", node_type=NodeType.ALIAS, attrs={"handle": "@seed001"}), - SeedNodeSpec( - node_id="user_seed_001", - node_type=NodeType.USER, - attrs={"name": "Seed User", "org": "Helios Labs", "location": "Pune"}, - ), - ], - seeded_edges=[SeedEdgeSpec(src="alias_seed_001", rel="alias_of", dst="user_seed_001")], - seeded_questions=[ - SeedQuestionSpec( - question="Which canonical user owns alias alias_seed_001?", - answer="user_seed_001", - task_type="identity_resolution", - supporting_edges=[SeedEdgeSpec(src="alias_seed_001", rel="alias_of", dst="user_seed_001")], - ) - ], - llm_generate_remaining_graph=False, - llm_generate_remaining_tasks=False, - llm_generated_edge_budget=0, - llm_generated_task_budget=0, - ) - env = OSINTEnvironment(EnvironmentConfig(seed=33, n_users=12, seeding=seeding)) - - assert "alias_seed_001" in env.graph.nodes - assert any(edge.src == "alias_seed_001" and edge.rel == "alias_of" and edge.dst == "user_seed_001" for edge in env.graph.edges) - assert any("alias_seed_001" in task.question for task in env.tasks) diff --git a/scratch/osint/tests/test_server.py b/scratch/osint/tests/test_server.py deleted file mode 100644 index 6e3db4bda127b897b20c85ea9f69217b0ab15654..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_server.py +++ /dev/null @@ -1,235 +0,0 @@ -import json -import os - -from fastapi.testclient import TestClient - -import server -from server import app - - -client = TestClient(app) - - -def test_server_health(): - response = client.get("/healthz") - assert response.status_code == 200 - assert response.json()["status"] == "ok" - - -def test_server_health_alias(): - response = client.get("/health") - assert response.status_code == 200 - assert response.json()["status"] == "ok" - - -def test_server_environment_metadata(): - response = client.get("/api/environment") - assert response.status_code == 200 - body = response.json() - assert "action_space" in body - assert "observation_space" in body - assert "summary" in body - - -def test_openenv_spec_and_tasks_endpoints(): - spec = client.get("/openenv.yaml") - assert spec.status_code == 200 - assert "reset" in spec.text - - tasks = client.get("/openenv/tasks") - assert tasks.status_code == 200 - body = tasks.json() - assert len(body) >= 3 - assert {"task_id", "task_type", "question", "difficulty"} <= set(body[0].keys()) - - -def test_openenv_reset_step_and_state_cycle(): - reset = client.post("/openenv/reset", json={"task_index": 0}) - assert reset.status_code == 200 - body = reset.json() - session_id = body["session_id"] - assert body["done"] is False - assert "question" in body["observation"]["task"] - - state = client.get(f"/openenv/state/{session_id}") - assert state.status_code == 200 - assert state.json()["session_id"] == session_id - - step = client.post( - "/openenv/step", - json={ - "session_id": session_id, - "action_type": "ANSWER", - "payload": {"answer": "unknown"}, - }, - ) - assert step.status_code == 200 - step_body = step.json() - assert step_body["session_id"] == session_id - assert step_body["done"] is True - assert "task_answer" in step_body["info"] - - -def test_openenv_reset_accepts_empty_body(): - reset = client.post("/openenv/reset") - assert reset.status_code == 200 - body = reset.json() - assert body["done"] is False - assert "session_id" in body - - -def test_openenv_reset_accepts_empty_json_body(): - reset = client.post( - "/openenv/reset", - data="", - headers={"Content-Type": "application/json"}, - ) - assert reset.status_code == 200 - body = reset.json() - assert body["done"] is False - assert "session_id" in body - - -def test_openenv_reset_trailing_slash_post_returns_json(): - reset = client.post( - "/openenv/reset/", - data="", - headers={"Content-Type": "application/json"}, - ) - assert reset.status_code == 200 - body = reset.json() - assert body["done"] is False - assert "session_id" in body - - -def test_openenv_step_accepts_nested_action_payload(): - reset = client.post("/openenv/reset", json={"task_index": 0}) - assert reset.status_code == 200 - session_id = reset.json()["session_id"] - - step = client.post( - "/openenv/step", - json={ - "session_id": session_id, - "action": { - "action_type": "ANSWER", - "payload": {"answer": "unknown"}, - }, - }, - ) - assert step.status_code == 200 - assert step.json()["done"] is True - - -def test_step_alias_uses_latest_session_when_session_id_missing(): - reset = client.post("/reset", json={"task_index": 0}) - assert reset.status_code == 200 - session_id = reset.json()["session_id"] - - step = client.post( - "/step", - json={ - "action_type": "ANSWER", - "payload": {"answer": "unknown"}, - }, - ) - assert step.status_code == 200 - body = step.json() - assert body["session_id"] == session_id - assert body["done"] is True - - -def test_state_alias_returns_latest_session(): - reset = client.post("/reset", json={"task_index": 0}) - assert reset.status_code == 200 - session_id = reset.json()["session_id"] - - state = client.get("/state") - assert state.status_code == 200 - body = state.json() - assert body["session_id"] == session_id - assert "task" in body["observation"] - - -def test_report_inference_updates_latest_evaluation_and_dashboard(tmp_path, monkeypatch): - latest_evaluation = tmp_path / "latest_evaluation.json" - space_dashboard = tmp_path / "space_dashboard.html" - - monkeypatch.setattr(server, "LATEST_EVALUATION_OUTPUT", latest_evaluation) - monkeypatch.setattr(server, "SPACE_DASHBOARD", space_dashboard) - monkeypatch.setattr(server, "load_leaderboard", lambda path: []) - monkeypatch.setattr(server, "export_dashboard", lambda env, evaluation, leaderboard_records, output_path: str(space_dashboard)) - - response = client.post( - "/openenv/report_inference", - json={ - "run": {"name": "inference_py_run"}, - "summary": {"leaderboard_score": 0.75, "task_success_rate": 1.0}, - "episodes": [ - { - "task_id": "seed_task_0", - "agent_answer": "user_bharat", - "graph_f1": 0.5, - "reward": 1.2, - "steps": 5, - "tool_calls": 4, - "success": 1, - } - ], - }, - ) - assert response.status_code == 200 - body = response.json() - assert body["status"] == "ok" - assert latest_evaluation.exists() - stored = json.loads(latest_evaluation.read_text(encoding="utf-8")) - assert stored["summary"]["leaderboard_score"] == 0.75 - assert stored["episodes"][0]["task_id"] == "seed_task_0" - assert stored["episodes"][0]["truth_edges"] - - -def test_space_snapshot_prefers_newer_evaluation_payload(tmp_path, monkeypatch): - baseline_path = tmp_path / "baseline.json" - evaluation_path = tmp_path / "evaluation.json" - baseline_dashboard = tmp_path / "baseline_dashboard.html" - space_dashboard = tmp_path / "space_dashboard.html" - - baseline_path.write_text( - json.dumps( - { - "run": {"dashboard_path": str(baseline_dashboard)}, - "summary": {"leaderboard_score": 0.1, "task_success_rate": 0.1}, - } - ), - encoding="utf-8", - ) - baseline_dashboard.write_text("baseline", encoding="utf-8") - evaluation_path.write_text( - json.dumps({"summary": {"leaderboard_score": 0.9, "task_success_rate": 0.9}, "episodes": []}), - encoding="utf-8", - ) - space_dashboard.write_text("space", encoding="utf-8") - os.utime(evaluation_path, (baseline_path.stat().st_atime + 5, baseline_path.stat().st_mtime + 5)) - - monkeypatch.setattr(server, "LATEST_BASELINE_OUTPUT", baseline_path) - monkeypatch.setattr(server, "LATEST_EVALUATION_OUTPUT", evaluation_path) - monkeypatch.setattr(server, "SPACE_DASHBOARD", space_dashboard) - monkeypatch.setattr( - server, - "_base_environment_snapshot", - lambda: { - "task_count": 30, - "difficulty_counts": {}, - "action_space": ["CALL_TOOL", "ADD_EDGE", "ANSWER"], - "observation_space": {}, - "task_types": [], - "config": {}, - }, - ) - monkeypatch.setattr(server, "_build_environment", lambda: object()) - monkeypatch.setattr(server, "export_dashboard", lambda env, evaluation, leaderboard_records, output_path: str(space_dashboard)) - - snapshot = server._space_snapshot() - assert snapshot["source"] == "latest_evaluation" - assert snapshot["summary"]["leaderboard_score"] == 0.9 - assert snapshot["dashboard_path"] == str(space_dashboard) diff --git a/scratch/osint/tests/test_spawn_reward_hooks.py b/scratch/osint/tests/test_spawn_reward_hooks.py deleted file mode 100644 index 95c5106774ec738fb5b5d365b3ce82e62e592834..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_spawn_reward_hooks.py +++ /dev/null @@ -1,43 +0,0 @@ -from osint_env.env.spawn_reward_hooks import critical_steps, parl_style_spawn_reward - - -def test_critical_steps_matches_parallel_path_length(): - total = critical_steps(main_steps=[1, 1, 1], parallel_subagent_steps=[[3, 2], [0], [4, 1, 2]]) - assert total == 1 + 3 + 1 + 0 + 1 + 4 - - -def test_parl_reward_prefers_finished_parallel_work(): - base = parl_style_spawn_reward( - task_outcome_reward=0.2, - spawn_count=4, - finished_subtasks=1, - critical_steps=12, - lambda_parallel=0.2, - lambda_finish=0.25, - anneal=1.0, - breadth=2, - depth=3, - ) - better = parl_style_spawn_reward( - task_outcome_reward=0.2, - spawn_count=4, - finished_subtasks=4, - critical_steps=8, - lambda_parallel=0.2, - lambda_finish=0.25, - anneal=1.0, - breadth=4, - depth=2, - ) - assert better > base - - -def test_parl_auxiliary_can_be_annealed_out(): - frozen = parl_style_spawn_reward( - task_outcome_reward=0.7, - spawn_count=8, - finished_subtasks=8, - critical_steps=5, - anneal=0.0, - ) - assert frozen == 0.7 diff --git a/scratch/osint/tests/test_swarm_agent.py b/scratch/osint/tests/test_swarm_agent.py deleted file mode 100644 index 712b2fbd3246ca528338f9bfb7e300b24690dc88..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_swarm_agent.py +++ /dev/null @@ -1,17 +0,0 @@ -from osint_env.agents.swarm_agent import SwarmAgentRunner -from osint_env.domain.models import EnvironmentConfig, SwarmConfig -from osint_env.env.environment import OSINTEnvironment - - -def test_swarm_runner_emits_spawn_telemetry(): - config = EnvironmentConfig( - seed=14, - max_steps=8, - swarm=SwarmConfig(enabled=True, max_agents=3, max_breadth=2, max_width=2, max_depth=2, planner_rounds=2), - ) - env = OSINTEnvironment(config) - info = SwarmAgentRunner(env).run_episode() - - assert info["spawn_count"] > 0 - assert "spawn_auxiliary" in info["reward_components"] - assert info["spawn_critical_steps"] > 0 diff --git a/scratch/osint/tests/test_tools.py b/scratch/osint/tests/test_tools.py deleted file mode 100644 index dc5a67f5f17b05b44bca34979cf852fb4a13f217..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_tools.py +++ /dev/null @@ -1,39 +0,0 @@ -from osint_env.config import clone_environment_config, load_seeding_config, load_shared_config -from osint_env.data.generator import DatasetGenerator -from osint_env.domain.models import EnvironmentConfig -from osint_env.env.environment import OSINTEnvironment -from osint_env.platforms.tools import ToolRegistry - - -def test_tools_basics(): - gen = DatasetGenerator(EnvironmentConfig(n_users=12, seed=3)) - g = gen.build_canonical_graph() - views = gen.build_platform_views(g) - tools = ToolRegistry(views) - out = tools.search_posts(query="Update") - assert out["count"] > 0 - profile_any = next(iter([p["user_id"] for p in views.profiles if p["user_id"].startswith("user_")])) - profile = tools.get_profile(profile_any) - assert profile["found"] is True - - -def test_seeded_tools_expose_seed_question_entities(): - shared = load_shared_config("datasets/fixed_levels/shared_config_fixed_levels.json") - env_cfg = clone_environment_config(shared.environment) - env_cfg.seeding = load_seeding_config("datasets/fixed_levels/seed_fixed_levels.json") - env_cfg.llm.provider = "mock" - env = OSINTEnvironment(env_cfg) - tools = env.tools - - post = tools.get_post("post_midnight_manifest") - assert post["found"] is True - assert "loc_dockyard17" in post["result"]["references"] - - people = tools.search_people(org="org_northbridge_logistics") - user_ids = {row["user_id"] for row in people["results"]} - assert "user_bharat" in user_ids - assert "user_hiro" in user_ids - - alias_profile = tools.get_profile("alias_docksparrow") - assert alias_profile["found"] is True - assert alias_profile["result"]["user_id"] == "user_hiro" diff --git a/scratch/osint/tests/test_validation.py b/scratch/osint/tests/test_validation.py deleted file mode 100644 index ce1e398b01983303f221ea96dc9d2a731137265d..0000000000000000000000000000000000000000 --- a/scratch/osint/tests/test_validation.py +++ /dev/null @@ -1,7 +0,0 @@ -from osint_env.validation import run_validation_suite - - -def test_validation_suite_passes_repo_gate(): - result = run_validation_suite() - assert result["passed"] is True - assert len(result["checks"]) >= 4 diff --git a/scratch/osint/uv.lock b/scratch/osint/uv.lock deleted file mode 100644 index e697d8c795db0704eca2b78ccc5867b673f148c7..0000000000000000000000000000000000000000 --- a/scratch/osint/uv.lock +++ /dev/null @@ -1,895 +0,0 @@ -version = 1 -revision = 3 -requires-python = ">=3.10" -resolution-markers = [ - 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-# 🚨 sre-automation-env - -> An [OpenEnv](https://huggingface.co/spaces/open-env/leaderboard)-compatible reinforcement learning environment that simulates the daily work of a Site Reliability Engineer. Agents must triage noisy alert queues, diagnose cascading production incidents across a microservice graph, execute operational runbooks with stateful parameter dependencies, and handle advanced SRE scenarios. - ---- - -## 1. Environment Description & Motivation - -Site Reliability Engineering is a **\$30B+ market** where every minute of downtime costs real revenue. Alert fatigue is the #1 cause of missed incidents — human SREs are bombarded with hundreds of alerts per shift, most of which are noise. The operational decisions they make — which alerts to ignore, where to look first, what remediation steps to take — are exactly the kind of sequential, high-stakes reasoning that RL agents should learn. - -`sre-automation-env` captures this operational reality across **9 tasks** organized into 3 difficulty tiers: - -### 🟢 Easy — Foundational SRE Skills - -- **Alert Triage** mirrors the PagerDuty/OpsGenie workflow: sort through noisy, flapping, duplicate alerts and acknowledge the critical ones first. -- **On-Call Handoff** tests shift rotation communication: summarize active incidents, pending actions, and service health for the incoming engineer. -- **Capacity Planning** analyzes metrics trends and recommends scaling decisions based on utilization, growth rate, and upcoming events. - -### 🟡 Medium — Diagnostic & Remediation Skills - -- **Incident Diagnosis** simulates the real-time investigation an on-call SRE does during a production outage — querying metrics, reading logs, tracing cascading failures through a dependency graph. -- **Multi-Incident Correlation** determines whether multiple simultaneous alerts share a common root cause or are independent incidents requiring separate handling. -- **Auto-Remediation with Rollback** executes a fix, verifies if it improved the situation, and rolls back if it made things worse — testing judgment under uncertainty. - -### 🔴 Hard — Complex Operational Reasoning - -- **Runbook Execution** tests procedural reasoning: can the agent follow a multi-step operational playbook where each step's output feeds into the next? -- **Blameless Postmortem Generation** synthesizes incident timeline, root cause analysis, and actionable follow-ups into a professional postmortem document. -- **Chaos Engineering Scenario** injects a controlled failure, observes system impact, and executes mitigation steps in the correct order to restore stability. - -### Service Topology - -The environment uses a realistic microservice dependency graph where failures cascade through the chain: - -```mermaid -graph LR - A[api-gateway] --> B[auth-service] - B --> C[user-service] - B --> D[payment-service] - D --> E[db-service] - - style A fill:#4a90d9,stroke:#333,color:#fff - style B fill:#7b68ee,stroke:#333,color:#fff - style C fill:#50c878,stroke:#333,color:#fff - style D fill:#ff6b6b,stroke:#333,color:#fff - style E fill:#ffa500,stroke:#333,color:#fff -``` - -When `db-service` has high latency, it cascades up through `payment-service` → `auth-service` → `api-gateway`. This mirrors real production architectures where identifying the *root cause* vs. a *symptom* is the core diagnostic challenge. - ---- - -## 2. Task Matrix - -| Task | Difficulty | Max Steps | Target Score (GPT-4o) | Success Criteria | -|---|---|---|---|---| -| **Alert Triage** | 🟢 Easy | 10 | ~0.7 | Ignore flapping/duplicate alerts, acknowledge actionable alerts in P1→P2→P3 order | -| **On-Call Handoff** | 🟢 Easy | 6 | ~0.6 | Summarize active incidents, pending actions, severity levels, and service health | -| **Capacity Planning** | 🟢 Easy | 5 | ~0.6 | Query data, recommend correct scaling action with accurate replica count | -| **Incident Diagnosis** | 🟡 Medium | 12 | ~0.5 | Query selective metrics/logs, identify root cause service + failure mode | -| **Multi-Incident Correlation** | 🟡 Medium | 10 | ~0.5 | Correctly classify alerts as shared/independent/partial correlation | -| **Auto-Remediation** | 🟡 Medium | 8 | ~0.5 | Choose correct fix, verify recovery, rollback if needed | -| **Runbook Execution** | 🔴 Hard | 15 | ~0.3 | Execute all 7 runbook steps in order with correct parameters (including extracted pid) | -| **Blameless Postmortem** | 🔴 Hard | 8 | ~0.4 | Write all 5 sections with substantive content matching incident data | -| **Chaos Engineering** | 🔴 Hard | 12 | ~0.4 | Inject failure, observe impact, mitigate in correct order | - ---- - -## 3. Action Space - -| `action_type` | `target` | `parameters` | Used In | -|---|---|---|---| -| `triage` | Alert ID | `{"decision": "ignore\|actionable", "priority": "P1\|P2\|P3"}` | Alert Triage | -| `acknowledge` | Alert ID | Optional `{"priority": "P1"}` | Alert Triage | -| `diagnose` | Service name | `{"operation": "query_metrics\|query_logs\|submit_diagnosis", "failure_mode": "..."}` | Incident Diagnosis, Multi-Incident, Postmortem | -| `execute_step` | Runbook step name | Step-specific params: `service`, `pid`, `instance_id`, `replicas`, `wait_seconds`, `summary` | Runbook Execution | -| `escalate` | Any target | Optional escalation metadata | All tasks | -| `resolve` | Task target | Confirmation / no-op | All tasks | -| `summarize` | `"handoff"` | `{"summary": ""}` | On-Call Handoff | -| `query_capacity` | Service name | `{}` | Capacity Planning | -| `recommend_scaling` | Service name | `{"recommendation": "...", "target_replicas": }` | Capacity Planning | -| `correlate` | `"all"` | `{"correlation_type": "...", "root_cause_service": "...", "alert_ids": [...]}` | Multi-Incident | -| `remediate` | Service name | `{"action": ""}` | Auto-Remediation | -| `verify_recovery` | — | `{}` | Auto-Remediation, Chaos | -| `rollback` | — | `{}` | Auto-Remediation | -| `write_postmortem` | `"postmortem"` | `{"section": "...", "content": "..."}` | Blameless Postmortem | -| `inject_chaos` | Target service | `{}` | Chaos Engineering | -| `observe_impact` | Target service | `{}` | Chaos Engineering | -| `mitigate_chaos` | — | `{"step": ""}` | Chaos Engineering | - ---- - -## 4. Observation Space - -| Field | Type | Description | -|---|---|---| -| `task_id` | `string` | Current task identifier | -| `step` | `integer` | Current step count (0-indexed) | -| `context` | `object` | Task-specific instructions, progress tracking, SLO status, incident timeline | -| `available_actions` | `array[string]` | Allowed action types for the current task | -| `alert_queue` | `array[object]` | Current alerts with severity, flapping/duplicate flags, status | -| `service_map` | `object` | Microservice dependency graph | -| `metrics` | `object` | Per-service metrics (cpu%, mem%, latency_p99, error_rate) or SLO status | -| `logs` | `array[string]` | Visible service logs (revealed incrementally via queries) | -| `done` | `boolean` | Episode termination flag | -| `message` | `string` | Environment feedback on the last action | - ---- - -## 5. Task Descriptions - -### 🟢 Easy Tasks - -
    -Alert Triage — 8 alerts, mixed severity, some noisy - -The agent receives 8 firing alerts randomly sampled from a pool of 20. Some are flapping (intermittent), some are duplicates. The agent must: -1. Triage each alert as ignore or actionable with correct priority -2. Acknowledge actionable alerts in descending priority order (P1 first) - -The challenge: noisy alerts waste steps, and acknowledging in wrong order loses points. -
    - -
    -On-Call Handoff — Summarize shift for incoming engineer - -The agent receives active incidents, pending actions, and service health data from a shift rotation. It must produce a concise handoff summary that covers all incidents, their severity, pending actions, and service health. Rewards for mentioning all incidents, action items, severity levels, and writing a summary of sufficient length. -
    - -
    -Capacity Planning — Analyze metrics, recommend scaling - -The agent analyzes service metrics (CPU, memory, latency, RPS), traffic trends, growth rates, and upcoming peak events. It must query data first, then recommend the correct scaling action (scale_out, scale_in, no_change, scale_out_urgent) with an accurate target replica count. -
    - -### 🟡 Medium Tasks - -
    -Incident Diagnosis — 5 failure modes across 5 services - -One of 5 failure modes is randomly injected at reset: -- **db-service**: High latency (cascades up through payment → auth → gateway) -- **auth-service**: High error rate (blocks login flow) -- **payment-service**: Memory leak (OOM restarts) -- **api-gateway**: Config drift (wrong upstream target) -- **user-service**: Deprecated endpoint dependency - -Metrics are hidden until queried (1 step per query). The agent must efficiently gather evidence and submit a diagnosis. SLO burn rates and an incident timeline provide additional signal. -
    - -
    -Multi-Incident Correlation — Shared vs independent root causes - -Multiple alerts fire simultaneously. The agent must determine if they share a common root cause (all cascading from one service), are fully independent (each is a separate issue), or partially correlated (some share a root cause, others are independent). Requires querying metrics for affected services and analyzing patterns. -
    - -
    -Auto-Remediation with Rollback — Fix, verify, rollback if needed - -The agent must choose a remediation action from multiple options. The correct action fixes the root cause. Wrong actions may make things worse or only partially help. After executing, the agent must verify recovery and rollback if the fix degraded metrics. Tests judgment under uncertainty. -
    - -### 🔴 Hard Tasks - -
    -Runbook Execution — 7-step operational playbook with parameter dependencies - -The runbook "High memory pressure — payment-service" has 7 steps that MUST be executed in order. Step 2 (`identify_top_processes`) reveals a `pid` and `instance_id` that are required for steps 3 and 4. This parameter extraction creates a genuine dependency chain that tests the agent's ability to use tool outputs. -
    - -
    -Blameless Postmortem — Write professional incident documentation - -The agent must query incident data (timeline, impact, root cause, action items) and write 5 sections of a blameless postmortem: summary, timeline, root_cause, impact, and action_items. Each section is graded for substantive content, keyword matching, and coverage of the incident details. -
    - -
    -Chaos Engineering — Inject, observe, mitigate controlled failures - -The agent runs a chaos experiment: inject a failure (network partition, pod termination, or DB failover), observe the cascading impact across the service topology, then execute mitigation steps in the correct sequential order. Tests understanding of failure containment and recovery patterns. -
    - ---- - -## 6. Reward Function Design - -All tasks use **dense, interpretable partial credit**. Agents receive meaningful signal for intermediate progress instead of binary pass/fail. - -### Alert Triage Rewards - -| Component | Weight | Description | -|---|---|---| -| Coverage (weighted Jaccard) | 0.55 | Acknowledging the right alerts, weighted by priority | -| Correct ignores | 0.1 each | Correctly identifying flapping/duplicate alerts | -| First-ack priority | 0.2 | Acknowledging a P1 alert first | -| Ordering accuracy | 0.15 | Maintaining correct priority ordering | -| P1 penalty | -0.1 each | P1 alerts left unacknowledged at episode end | - -### On-Call Handoff Rewards - -| Component | Weight | Description | -|---|---|---| -| Incident coverage | 0.3 | Mentioning all active incidents | -| Action items coverage | 0.25 | Referencing pending actions | -| Severity mentioned | 0.15 | Including severity levels | -| Health mentioned | 0.1 | Referencing service health | -| Length bonus | 0.1 | Summary >= 20 words | -| Efficiency bonus | 0.1 | Completed in ≤3 steps | - -### Capacity Planning Rewards - -| Component | Weight | Description | -|---|---|---| -| Correct recommendation | 0.5 | Matching the correct scaling action | -| Correct replica count | 0.3 | Accurate target replica number | -| Queried data first | 0.1 | Querying metrics before deciding | -| Efficiency bonus | 0.1 | Completed in ≤3 steps | - -### Incident Diagnosis Rewards - -| Component | Weight | Description | -|---|---|---| -| Root cause service | 0.5 | Exact match on the faulty service | -| Upstream partial credit | 0.1 | Naming a direct upstream of the root cause | -| Failure mode match | 0.2 | Fuzzy keyword match on failure description | -| Evidence collection | 0.05 + 0.05 | Querying metrics and logs for the root cause service | -| Efficiency bonus | 0.0–0.2 | Linear scale: full bonus at ≤8 steps, zero at ≥12 | -| Query penalty | -0.05 each | Unnecessary metric queries beyond 6 | - -### Multi-Incident Correlation Rewards - -| Component | Weight | Description | -|---|---|---| -| Correlation type | 0.4 | Correct classification (shared/independent/partial) | -| Root cause identification | 0.3 | Correct root cause service or "none" | -| Alert grouping | 0.2 | Correctly grouping/ungrouping alerts | -| Evidence bonus | 0.1 | Querying metrics for affected services | - -### Auto-Remediation Rewards - -| Component | Weight | Description | -|---|---|---| -| Correct action chosen | 0.4 | Picking the remediation that fixes root cause | -| Outcome score | 0.4 | Success=0.4, rollback=0.2, partial=0.1, worse=0 | -| Recovery verified | 0.1 | Actually checking the result | -| Efficiency bonus | 0.1 | Completed in ≤5 steps | -| Rollback penalty | -0.2 | Made things worse but didn't rollback | - -### Runbook Execution Rewards - -| Component | Weight | Description | -|---|---|---| -| Step completion | 0.1 each (max 0.7) | Per correctly executed step | -| PID extraction | 0.1 | Correctly extracting and using pid from step 2 | -| Report quality | 0.1 | Incident report with severity, service name, coherent summary | -| Out-of-order penalty | -0.05 each | Attempting steps in wrong order | -| Wrong parameter penalty | -0.05 each | Incorrect parameter values | - -### Blameless Postmortem Rewards - -| Component | Weight | Description | -|---|---|---| -| Sections completed | 0.12 each (max 0.6) | Per required section written | -| Content quality | 0.2 | Substantive content (15+ words per section) | -| Root cause keywords | 0.1 | Matching keywords from actual root cause | -| Action items coverage | 0.08 | Referencing identified action items | -| Evidence bonus | 0.1 | Querying incident data before writing | - -### Chaos Engineering Rewards - -| Component | Weight | Description | -|---|---|---| -| Chaos injected | 0.1 | Successfully injecting the failure | -| Impact observed | 0.1 | Checking the effects before mitigating | -| Mitigation steps | 0.15 each | Per step completed in correct order | -| Completion bonus | 0.2 | All mitigation steps completed | -| Efficiency bonus | 0.15 | All steps done in ≤8 steps | -| Wrong order penalty | -0.05 each | Attempting steps out of sequence | - -The `breakdown` dict in every reward response provides a verbose, interpretable explanation of exactly why the agent scored what it did. - ---- - -## 7. Setup & Usage - -### Docker (recommended) - -```bash -docker build -t sre-env . -docker run --rm -p 7860:7860 sre-env -``` - -### Local Development - -```bash -python3 -m venv .venv -source .venv/bin/activate -pip install -r requirements.txt -uvicorn server.app:app --host 0.0.0.0 --port 7860 -``` - -### API Examples - -**Health check:** -```bash -curl http://localhost:7860/health -# {"status": "ok"} -``` - -**List tasks:** -```bash -curl http://localhost:7860/tasks -``` - -**Reset an episode:** -```bash -curl -X POST http://localhost:7860/reset \ - -H "Content-Type: application/json" \ - -d '{"task_id": "alert_triage"}' -``` - -**Step through an episode:** -```bash -curl -X POST http://localhost:7860/step \ - -H "Content-Type: application/json" \ - -d '{ - "session_id": "", - "action": { - "action_type": "triage", - "target": "ALT-001", - "parameters": {"decision": "actionable", "priority": "P1"}, - "reasoning": "Critical payment-service alert, not flapping." - } - }' -``` - -**Inspect environment state:** -```bash -curl "http://localhost:7860/state?session_id=" -``` - -**Run inference harness:** -```bash -export HF_TOKEN= -python inference.py -``` - ---- - -## 8. Baseline Scores - -Run `python inference.py` with `HF_TOKEN` set. The inference harness runs all tasks sequentially and prints `[START]`, `[STEP]`, and `[END]` markers per task. - -Expected baseline ranges: -| Task | Difficulty | Model | Expected Score | -|---|---|---|---| -| Alert Triage | 🟢 Easy | Qwen2.5-72B | 0.5–0.8 | -| On-Call Handoff | 🟢 Easy | Qwen2.5-72B | 0.4–0.7 | -| Capacity Planning | 🟢 Easy | Qwen2.5-72B | 0.4–0.7 | -| Incident Diagnosis | 🟡 Medium | Qwen2.5-72B | 0.3–0.7 | -| Multi-Incident Correlation | 🟡 Medium | Qwen2.5-72B | 0.3–0.6 | -| Auto-Remediation | 🟡 Medium | Qwen2.5-72B | 0.3–0.6 | -| Runbook Execution | 🔴 Hard | Qwen2.5-72B | 0.1–0.5 | -| Blameless Postmortem | 🔴 Hard | Qwen2.5-72B | 0.2–0.5 | -| Chaos Engineering | 🔴 Hard | Qwen2.5-72B | 0.2–0.5 | - ---- - -## 9. Deploying to Hugging Face Spaces - -### Step-by-step: - -1. **Create a new Space** at [huggingface.co/new-space](https://huggingface.co/new-space) - - Select **Docker** as the SDK - - Choose **Blank** template - - Set visibility to **Public** - -2. **Push your code:** - ```bash - git init - git remote add origin https://huggingface.co/spaces//sre-automation-env - git add . - git commit -m "Initial SRE environment" - git push origin main - ``` - -3. **The Space will auto-build** from your Dockerfile and expose port 7860. - -4. **Set your Space URL** in `inference.py` via the `ENV_URL` environment variable: - ```bash - ENV_URL=https://-sre-automation-env.hf.space python inference.py - ``` - -5. **Verify** the deployment: - ```bash - curl https://-sre-automation-env.hf.space/health - curl https://-sre-automation-env.hf.space/tasks - ``` - ---- - -## 10. Architecture - -``` -┌─────────────────────────────────────────────────────────┐ -│ inference.py │ -│ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │ -│ │ LLM Call │◄──►│ JSON │◄──►│ Conversation │ │ -│ │ (OpenAI) │ │ Extractor│ │ History Manager │ │ -│ └──────────┘ └──────────┘ └──────────────────┘ │ -└───────────────────────┬─────────────────────────────────┘ - │ HTTP -┌───────────────────────▼─────────────────────────────────┐ -│ server/app.py (FastAPI) │ -│ POST /reset │ POST /step │ GET /state │ GET /tasks│ -│ ────────────────────────────────────────────────────── │ -│ Session Manager (TTL=10min, max=200 sessions) │ -└───────────────────────┬─────────────────────────────────┘ - │ -┌───────────────────────▼─────────────────────────────────┐ -│ env/sre_env.py │ -│ ┌──────────────┐ ┌──────────────┐ ┌───────────────┐ │ -│ │ Task Registry │ │ State Machine│ │ Reward Tracker│ │ -│ └──────┬───────┘ └──────┬───────┘ └───────┬───────┘ │ -│ │ │ │ │ -│ ┌──────▼───────┐ ┌──────▼───────┐ ┌───────▼───────┐ │ -│ │ env/tasks/ │ │ env/data/ │ │ env/graders/ │ │ -│ │ • alert │ │ • alerts.json│ │ • alert │ │ -│ │ • handoff │ │ • incidents │ │ • handoff │ │ -│ │ • capacity │ │ • runbooks │ │ • capacity │ │ -│ │ • incident │ │ │ │ • incident │ │ -│ │ • correlate │ │ │ │ • correlate │ │ -│ │ • remediate │ │ │ │ • remediate │ │ -│ │ • runbook │ │ │ │ • runbook │ │ -│ │ • postmortem │ │ │ │ • postmortem │ │ -│ │ • chaos │ │ │ │ • chaos │ │ -│ └──────────────┘ └──────────────┘ └───────────────┘ │ -└─────────────────────────────────────────────────────────┘ -``` - ---- - -## License - -MIT diff --git a/scratch/sre/env/__init__.py b/scratch/sre/env/__init__.py deleted file mode 100644 index 2bca1a46ab24381d97e5186353cf2bf8aa6c679d..0000000000000000000000000000000000000000 --- a/scratch/sre/env/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from env.models import SREAction, SREObservation, SREReward -from env.sre_env import SREEnv - -__all__ = ["SREAction", "SREObservation", "SREReward", "SREEnv"] diff --git a/scratch/sre/env/data/alerts.json b/scratch/sre/env/data/alerts.json deleted file mode 100644 index e537e0fb54b7d244ac354c0dd3e1619a5f65b92b..0000000000000000000000000000000000000000 --- a/scratch/sre/env/data/alerts.json +++ /dev/null @@ -1,182 +0,0 @@ -[ - { - "id": "ALT-001", - "service": "payment-service", - "severity": "critical", - "message": "Error rate > 5% for 3 consecutive minutes", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:23:00Z" - }, - { - "id": "ALT-002", - "service": "db-service", - "severity": "critical", - "message": "p99 latency exceeded 400ms", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:24:00Z" - }, - { - "id": "ALT-003", - "service": "auth-service", - "severity": "warning", - "message": "Token verification retries above baseline", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:25:00Z" - }, - { - "id": "ALT-004", - "service": "api-gateway", - "severity": "critical", - "message": "5xx rate exceeded 8% on /login", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:26:00Z" - }, - { - "id": "ALT-005", - "service": "payment-service", - "severity": "warning", - "message": "Heap usage crossed 85% on checkout worker", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:27:00Z" - }, - { - "id": "ALT-006", - "service": "payment-service", - "severity": "warning", - "message": "Heap usage crossed 85% on checkout worker", - "is_flapping": false, - "is_duplicate": true, - "fired_at": "2026-03-01T14:28:00Z" - }, - { - "id": "ALT-007", - "service": "user-service", - "severity": "warning", - "message": "Deprecated profile endpoint still receiving traffic", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:29:00Z" - }, - { - "id": "ALT-008", - "service": "user-service", - "severity": "info", - "message": "Nightly cache warm completed later than usual", - "is_flapping": true, - "is_duplicate": false, - "fired_at": "2026-03-01T14:30:00Z" - }, - { - "id": "ALT-009", - "service": "auth-service", - "severity": "critical", - "message": "JWT validation failures above 20%", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:31:00Z" - }, - { - "id": "ALT-010", - "service": "db-service", - "severity": "warning", - "message": "Replica lag above 2 seconds", - "is_flapping": true, - "is_duplicate": false, - "fired_at": "2026-03-01T14:32:00Z" - }, - { - "id": "ALT-011", - "service": "api-gateway", - "severity": "info", - "message": "Config reload took longer than 5 seconds", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:33:00Z" - }, - { - "id": "ALT-012", - "service": "payment-service", - "severity": "critical", - "message": "OOM restart detected on pod payment-7", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:34:00Z" - }, - { - "id": "ALT-013", - "service": "payment-service", - "severity": "critical", - "message": "OOM restart detected on pod payment-7", - "is_flapping": false, - "is_duplicate": true, - "fired_at": "2026-03-01T14:35:00Z" - }, - { - "id": "ALT-014", - "service": "db-service", - "severity": "info", - "message": "Autovacuum duration exceeded baseline", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:36:00Z" - }, - { - "id": "ALT-015", - "service": "api-gateway", - "severity": "warning", - "message": "Unexpected upstream cluster hash detected", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:37:00Z" - }, - { - "id": "ALT-016", - "service": "auth-service", - "severity": "info", - "message": "Background key refresh retried once", - "is_flapping": true, - "is_duplicate": false, - "fired_at": "2026-03-01T14:38:00Z" - }, - { - "id": "ALT-017", - "service": "user-service", - "severity": "critical", - "message": "Profile hydration timeout rate exceeded 10%", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:39:00Z" - }, - { - "id": "ALT-018", - "service": "db-service", - "severity": "warning", - "message": "Connection pool wait time above 150ms", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:40:00Z" - }, - { - "id": "ALT-019", - "service": "api-gateway", - "severity": "warning", - "message": "Rate limiter dropped burst traffic", - "is_flapping": false, - "is_duplicate": false, - "fired_at": "2026-03-01T14:41:00Z" - }, - { - "id": "ALT-020", - "service": "payment-service", - "severity": "info", - "message": "Batch settlement lag recovered automatically", - "is_flapping": true, - "is_duplicate": false, - "fired_at": "2026-03-01T14:42:00Z" - } -] diff --git a/scratch/sre/env/data/incidents.json b/scratch/sre/env/data/incidents.json deleted file mode 100644 index 02341a2a2b3409990762acc4f457f0b97660108e..0000000000000000000000000000000000000000 --- a/scratch/sre/env/data/incidents.json +++ /dev/null @@ -1,37 +0,0 @@ -[ - { - "id": "INC-A", - "service": "db-service", - "failure_mode": "high latency", - "description": "db-service query latency is cascading into the checkout path.", - "keywords": ["high latency", "latency", "slow query", "database saturation", "db"] - }, - { - "id": "INC-B", - "service": "auth-service", - "failure_mode": "high error rate", - "description": "auth-service is rejecting login traffic and causing upstream failures.", - "keywords": ["high error rate", "error rate", "login failures", "token", "auth"] - }, - { - "id": "INC-C", - "service": "payment-service", - "failure_mode": "memory leak", - "description": "payment-service memory usage keeps rising until worker restarts.", - "keywords": ["memory leak", "memory pressure", "oom", "heap growth", "restarts"] - }, - { - "id": "INC-D", - "service": "api-gateway", - "failure_mode": "config drift", - "description": "api-gateway is routing traffic to the wrong upstream target.", - "keywords": ["config drift", "wrong upstream", "routing", "gateway", "misconfiguration"] - }, - { - "id": "INC-E", - "service": "user-service", - "failure_mode": "deprecated endpoint dependency", - "description": "user-service still depends on a deprecated endpoint that now fails.", - "keywords": ["deprecated endpoint", "endpoint dependency", "410", "sunset endpoint", "deprecated"] - } -] diff --git a/scratch/sre/env/data/runbooks.json b/scratch/sre/env/data/runbooks.json deleted file mode 100644 index c6fb6318086a92859699acf07931f3c4b038bfb1..0000000000000000000000000000000000000000 --- a/scratch/sre/env/data/runbooks.json +++ /dev/null @@ -1,55 +0,0 @@ -[ - { - "id": "runbook-payment-memory-pressure", - "name": "High memory pressure - payment-service", - "description": "Mitigate sustained memory pressure on payment-service while preserving checkout capacity.", - "steps": [ - { - "name": "check_memory_usage", - "parameters": { - "service": "payment-service" - } - }, - { - "name": "identify_top_processes", - "parameters": { - "service": "payment-service" - } - }, - { - "name": "capture_heap_dump", - "parameters": { - "service": "payment-service", - "pid": "" - } - }, - { - "name": "cordon_instance", - "parameters": { - "instance_id": "" - } - }, - { - "name": "scale_out", - "parameters": { - "service": "payment-service", - "replicas": "+2" - } - }, - { - "name": "verify_recovery", - "parameters": { - "service": "payment-service", - "wait_seconds": 30 - } - }, - { - "name": "file_incident_report", - "parameters": { - "severity": "P2", - "summary": "" - } - } - ] - } -] diff --git a/scratch/sre/env/graders/__init__.py b/scratch/sre/env/graders/__init__.py deleted file mode 100644 index 61535105b4cdfc6606e77435f5863c1f3699e7bd..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -from env.graders.grader_alert_triage import grade_alert_triage -from env.graders.grader_auto_remediation import grade_auto_remediation -from env.graders.grader_blameless_postmortem import grade_blameless_postmortem -from env.graders.grader_capacity_planning import grade_capacity_planning -from env.graders.grader_chaos_engineering import grade_chaos_engineering -from env.graders.grader_incident_diagnosis import grade_incident_diagnosis -from env.graders.grader_multi_incident_correlation import ( - grade_multi_incident_correlation, -) -from env.graders.grader_on_call_handoff import grade_on_call_handoff -from env.graders.grader_runbook_execution import grade_runbook_execution - -__all__ = [ - "grade_alert_triage", - "grade_auto_remediation", - "grade_blameless_postmortem", - "grade_capacity_planning", - "grade_chaos_engineering", - "grade_incident_diagnosis", - "grade_multi_incident_correlation", - "grade_on_call_handoff", - "grade_runbook_execution", -] diff --git a/scratch/sre/env/graders/grader_alert_triage.py b/scratch/sre/env/graders/grader_alert_triage.py deleted file mode 100644 index 44d32036cc4720d81045b59b631a83a2e12c0e2d..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/grader_alert_triage.py +++ /dev/null @@ -1,119 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from env.graders.scoring import clamp_task_score -from env.models import SREReward - -PRIORITY_WEIGHT = {"P1": 3, "P2": 2, "P3": 1} - - -_clamp = clamp_task_score - - -def _ordering_accuracy( - acknowledged_order: list[str], ground_truth: dict[str, dict[str, Any]] -) -> float: - ranks = [ - PRIORITY_WEIGHT[ground_truth[alert_id]["priority"]] - for alert_id in acknowledged_order - if alert_id in ground_truth - ] - if len(ranks) <= 1: - return 1.0 if ranks else 0.0 - total_pairs = 0 - correct_pairs = 0 - for idx, current_rank in enumerate(ranks): - for later_rank in ranks[idx + 1 :]: - total_pairs += 1 - if current_rank >= later_rank: - correct_pairs += 1 - return correct_pairs / total_pairs if total_pairs else 0.0 - - -def _weighted_jaccard( - actionable_ids: set[str], - agent_ids: set[str], - ground_truth: dict[str, dict[str, Any]], -) -> float: - union_ids = actionable_ids | agent_ids - if not union_ids: - return 0.0 - intersection_weight = sum( - PRIORITY_WEIGHT[ground_truth[alert_id]["priority"]] - for alert_id in actionable_ids & agent_ids - ) - union_weight = sum( - PRIORITY_WEIGHT.get(ground_truth.get(alert_id, {}).get("priority", "P3"), 1) - for alert_id in union_ids - ) - return intersection_weight / union_weight if union_weight else 0.0 - - -def grade_alert_triage(state: dict[str, Any], step_count: int, done: bool) -> SREReward: - actionable_ids = set(state["actionable_ids"]) - ignored_ids = set(state["ignorable_ids"]) - ground_truth = state["ground_truth"] - acknowledged_order = [ - alert_id - for alert_id in state["acknowledged_order"] - if alert_id in actionable_ids - ] - acknowledged_ids = set(acknowledged_order) - weighted_jaccard = _weighted_jaccard(actionable_ids, acknowledged_ids, ground_truth) - - correct_ignored = 0 - for alert_id in ignored_ids: - decision = state["triage_decisions"].get(alert_id, {}).get("decision") - if decision == "ignore" or alert_id in state["ignored_alert_ids"]: - correct_ignored += 1 - ignore_score = 0.1 * correct_ignored - - highest_priority_weight = None - if actionable_ids: - highest_priority_weight = max( - PRIORITY_WEIGHT[ground_truth[alert_id]["priority"]] - for alert_id in actionable_ids - ) - first_ack_score = 0.0 - if acknowledged_order and highest_priority_weight is not None: - first_ack_priority = PRIORITY_WEIGHT[ - ground_truth[acknowledged_order[0]]["priority"] - ] - if first_ack_priority == highest_priority_weight: - first_ack_score = 0.2 - - ordering_score = 0.15 * _ordering_accuracy(acknowledged_order, ground_truth) - coverage_score = 0.55 * weighted_jaccard - - p1_ids = { - alert_id - for alert_id in actionable_ids - if ground_truth[alert_id]["priority"] == "P1" - } - p1_left_unacked = len(p1_ids - acknowledged_ids) - p1_penalty = 0.1 * p1_left_unacked if done else 0.0 - invalid_penalty = 0.02 * state.get("invalid_actions", 0) - - total = coverage_score + ignore_score + first_ack_score + ordering_score - total -= p1_penalty + invalid_penalty - total = _clamp(total) - - breakdown = { - "coverage_weighted_jaccard": round(weighted_jaccard, 4), - "coverage_score": round(coverage_score, 4), - "correctly_ignored_alerts": correct_ignored, - "ignore_score": round(ignore_score, 4), - "first_ack_priority_score": round(first_ack_score, 4), - "ordering_score": round(ordering_score, 4), - "p1_left_unacknowledged": p1_left_unacked, - "end_penalty": round(p1_penalty, 4), - "invalid_action_penalty": round(invalid_penalty, 4), - "acknowledged_order": acknowledged_order, - "actionable_ground_truth_order": list(state["actionable_ids"]), - "score_explanation": ( - "Weighted Jaccard rewards acknowledging the right alerts, while ordering " - "and ignore bonuses reward good on-call hygiene." - ), - } - return SREReward(value=total, breakdown=breakdown, done=done, info={}) diff --git a/scratch/sre/env/graders/grader_auto_remediation.py b/scratch/sre/env/graders/grader_auto_remediation.py deleted file mode 100644 index 1c1ac9e705d1b6a50a363a653bfeaae7cfde31e0..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/grader_auto_remediation.py +++ /dev/null @@ -1,81 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from env.graders.scoring import clamp_task_score -from env.models import SREReward - - -_clamp = clamp_task_score - - -def grade_auto_remediation( - state: dict[str, Any], step_count: int, done: bool -) -> SREReward: - scenario = state["scenario"] - attempted = state.get("remediation_attempted") - final = state.get("final_state") - - if not attempted: - return SREReward( - value=0.0001, - breakdown={"error": "No remediation attempted"}, - done=done, - info={}, - ) - - correct_action = attempted["action"] == scenario["correct_action"] - action_score = 0.4 if correct_action else 0.0 - - if final: - outcome = final["outcome"] - if outcome == "success": - outcome_score = 0.4 - elif outcome == "rolled_back": - outcome_score = 0.2 - elif outcome == "partial": - outcome_score = 0.1 - else: - outcome_score = 0.0 - else: - outcome_score = 0.0 - - verified = state.get("recovery_verified", False) - verify_score = 0.1 if verified else 0.0 - - if final and final["outcome"] == "worse" and not state.get("rollback_performed"): - rollback_penalty = 0.2 - elif final and final["outcome"] == "worse" and state.get("rollback_performed"): - rollback_penalty = 0.0 - else: - rollback_penalty = 0.0 - - efficiency_bonus = 0.1 if step_count <= 5 else 0.0 - invalid_penalty = 0.02 * state.get("invalid_actions", 0) - - total = ( - action_score - + outcome_score - + verify_score - + efficiency_bonus - - rollback_penalty - - invalid_penalty - ) - total = _clamp(total) - - breakdown = { - "correct_action_chosen": correct_action, - "action_score": round(action_score, 4), - "outcome": final["outcome"] if final else "none", - "outcome_score": round(outcome_score, 4), - "recovery_verified": verified, - "verify_score": round(verify_score, 4), - "rollback_penalty": round(rollback_penalty, 4), - "efficiency_bonus": round(efficiency_bonus, 4), - "invalid_action_penalty": round(invalid_penalty, 4), - "score_explanation": ( - "Rewards choosing the correct remediation, verifying recovery, " - "and rolling back if the fix made things worse." - ), - } - return SREReward(value=total, breakdown=breakdown, done=done, info={}) diff --git a/scratch/sre/env/graders/grader_blameless_postmortem.py b/scratch/sre/env/graders/grader_blameless_postmortem.py deleted file mode 100644 index a0feeb8b2852a0f816ec62287fba29c31dc72e51..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/grader_blameless_postmortem.py +++ /dev/null @@ -1,85 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from env.graders.scoring import clamp_task_score -from env.models import SREReward - - -_clamp = clamp_task_score - - -def grade_blameless_postmortem( - state: dict[str, Any], step_count: int, done: bool -) -> SREReward: - sections = state.get("sections_written", []) - postmortem = state.get("postmortem_submitted") or {} - - required_sections = ["summary", "timeline", "root_cause", "impact", "action_items"] - completed = [s for s in required_sections if s in sections] - section_score = 0.12 * len(completed) - - content_score = 0.0 - for section_name, section_data in postmortem.items(): - content = section_data.get("content", "") - word_count = section_data.get("word_count", 0) - if word_count >= 15: - content_score += 0.04 - elif word_count >= 8: - content_score += 0.02 - content_score = min(0.2, content_score) - - incident = state["incident"] - root_cause_section = postmortem.get("root_cause", {}) - root_content = root_cause_section.get("content", "").lower() - root_keywords = incident["root_cause"].lower().split() - root_keyword_matches = sum(1 for kw in root_keywords if kw in root_content) - root_keyword_score = ( - 0.1 if root_keyword_matches >= 3 else 0.05 if root_keyword_matches >= 1 else 0.0 - ) - - action_items_section = postmortem.get("action_items", {}) - action_content = action_items_section.get("content", "").lower() - action_items_mentioned = sum( - 1 - for item in incident["action_items"] - if item["action"].lower().split()[0] in action_content - ) - action_items_score = 0.08 * min( - 1.0, action_items_mentioned / max(1, len(incident["action_items"])) - ) - - queried_data = len(state.get("queried_data", [])) - evidence_bonus = 0.1 if queried_data >= 3 else 0.05 if queried_data >= 2 else 0.0 - - invalid_penalty = 0.02 * state.get("invalid_actions", 0) - - total = ( - section_score - + content_score - + root_keyword_score - + action_items_score - + evidence_bonus - - invalid_penalty - ) - total = _clamp(total) - - breakdown = { - "sections_completed": completed, - "section_score": round(section_score, 4), - "content_score": round(content_score, 4), - "root_cause_keyword_matches": root_keyword_matches, - "root_cause_keyword_score": round(root_keyword_score, 4), - "action_items_coverage": round( - action_items_mentioned / max(1, len(incident["action_items"])), 4 - ), - "action_items_score": round(action_items_score, 4), - "evidence_queries": queried_data, - "evidence_bonus": round(evidence_bonus, 4), - "invalid_action_penalty": round(invalid_penalty, 4), - "score_explanation": ( - "Rewards completing all required sections with substantive content, " - "matching root cause keywords, covering action items, and querying incident data." - ), - } - return SREReward(value=total, breakdown=breakdown, done=done, info={}) diff --git a/scratch/sre/env/graders/grader_capacity_planning.py b/scratch/sre/env/graders/grader_capacity_planning.py deleted file mode 100644 index 344faee22b877a74cd45c31f8df70f67f905bd59..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/grader_capacity_planning.py +++ /dev/null @@ -1,61 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from env.graders.scoring import clamp_task_score -from env.models import SREReward - - -_clamp = clamp_task_score - - -def grade_capacity_planning( - state: dict[str, Any], step_count: int, done: bool -) -> SREReward: - scenario = state["scenario"] - rec = state.get("recommendation") - if not rec: - return SREReward( - value=0.0001, - breakdown={"error": "No scaling recommendation submitted"}, - done=done, - info={}, - ) - - correct_rec = scenario["correct_recommendation"] - correct_replicas = scenario["target_replicas"] - - rec_match = rec["recommendation"] == correct_rec - rec_score = 0.5 if rec_match else 0.0 - - submitted_replicas = rec.get("target_replicas") - replica_match = submitted_replicas == correct_replicas - replica_score = 0.3 if replica_match else 0.0 - - queried = ( - scenario["service"] in state["queries_made"] or "all" in state["queries_made"] - ) - query_score = 0.1 if queried else 0.0 - - efficiency_bonus = 0.1 if step_count <= 3 else 0.0 - - invalid_penalty = 0.02 * state.get("invalid_actions", 0) - - total = rec_score + replica_score + query_score + efficiency_bonus - invalid_penalty - total = _clamp(total) - - breakdown = { - "recommendation_correct": rec_match, - "recommendation_score": round(rec_score, 4), - "replica_count_correct": replica_match, - "replica_score": round(replica_score, 4), - "queried_data": queried, - "query_score": round(query_score, 4), - "efficiency_bonus": round(efficiency_bonus, 4), - "invalid_action_penalty": round(invalid_penalty, 4), - "score_explanation": ( - "Rewards correct scaling recommendation, accurate replica count, " - "and querying data before deciding." - ), - } - return SREReward(value=total, breakdown=breakdown, done=done, info={}) diff --git a/scratch/sre/env/graders/grader_chaos_engineering.py b/scratch/sre/env/graders/grader_chaos_engineering.py deleted file mode 100644 index d7530e39c093a1b9f2aa91c42b0d2eb7ad518261..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/grader_chaos_engineering.py +++ /dev/null @@ -1,67 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from env.graders.scoring import clamp_task_score -from env.models import SREReward - - -_clamp = clamp_task_score - - -def grade_chaos_engineering( - state: dict[str, Any], step_count: int, done: bool -) -> SREReward: - scenario = state["scenario"] - completed = state.get("mitigation_steps_completed", []) - total_steps = len(scenario["correct_order"]) - - if not state.get("chaos_injected"): - return SREReward( - value=0.0001, breakdown={"error": "No chaos injected"}, done=done, info={} - ) - - injection_score = 0.1 - observation_score = 0.1 if state.get("impact_observed") else 0.0 - - step_score = 0.15 * len(completed) - - wrong_order = state.get("mitigation_in_wrong_order", 0) - order_penalty = 0.05 * wrong_order - - all_complete = len(completed) == total_steps - completion_bonus = 0.2 if all_complete else 0.0 - - efficiency_bonus = 0.15 if (all_complete and step_count <= 8) else 0.0 - invalid_penalty = 0.02 * state.get("invalid_actions", 0) - - total = ( - injection_score - + observation_score - + step_score - + completion_bonus - + efficiency_bonus - - order_penalty - - invalid_penalty - ) - total = _clamp(total) - - breakdown = { - "chaos_injected": True, - "injection_score": round(injection_score, 4), - "impact_observed": state.get("impact_observed", False), - "observation_score": round(observation_score, 4), - "mitigation_steps_completed": completed, - "step_score": round(step_score, 4), - "wrong_order_attempts": wrong_order, - "order_penalty": round(order_penalty, 4), - "all_steps_complete": all_complete, - "completion_bonus": round(completion_bonus, 4), - "efficiency_bonus": round(efficiency_bonus, 4), - "invalid_action_penalty": round(invalid_penalty, 4), - "score_explanation": ( - "Rewards injecting chaos, observing impact, completing all mitigation " - "steps in order, and doing so efficiently." - ), - } - return SREReward(value=total, breakdown=breakdown, done=done, info={}) diff --git a/scratch/sre/env/graders/grader_incident_diagnosis.py b/scratch/sre/env/graders/grader_incident_diagnosis.py deleted file mode 100644 index a8a3b2e58e40328e731a699f16fa6b4962a927a6..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/grader_incident_diagnosis.py +++ /dev/null @@ -1,120 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from env.graders.scoring import clamp_task_score -from env.models import SREReward - -# Upstream dependency map: service -> list of services that depend on it -# If agent names a service one hop upstream of the real root cause, partial credit -UPSTREAM_MAP = { - "db-service": ["payment-service"], - "payment-service": ["auth-service"], - "auth-service": ["api-gateway"], - "user-service": ["auth-service"], - "api-gateway": [], -} - - -_clamp = clamp_task_score - - -def _normalize(text: str) -> str: - return " ".join(text.lower().strip().replace("-", " ").split()) - - -def _failure_mode_match(predicted: str, scenario: dict[str, Any]) -> bool: - normalized_prediction = _normalize(predicted) - if not normalized_prediction: - return False - keywords = scenario.get("keywords", []) - return any(keyword in normalized_prediction for keyword in keywords) - - -def _efficiency_bonus(step_used: int) -> float: - if step_used <= 8: - return 0.2 - if step_used >= 12: - return 0.0 - return round(0.2 * ((12 - step_used) / 4), 4) - - -def _is_upstream_of(predicted_service: str, root_cause_service: str) -> bool: - """Check if predicted_service is a direct upstream consumer of root_cause_service.""" - return predicted_service in UPSTREAM_MAP.get(root_cause_service, []) - - -def grade_incident_diagnosis( - state: dict[str, Any], step_count: int, done: bool -) -> SREReward: - scenario = state["incident"] - diagnosis = state.get("submitted_diagnosis") or {} - predicted_service = diagnosis.get("service", "") - predicted_failure = diagnosis.get("failure_mode", "") - correct_service = predicted_service == scenario["service"] - failure_match = _failure_mode_match(predicted_failure, scenario) - - # Evidence collection bonuses - evidence_metric = 0.05 if scenario["service"] in state["queried_metrics"] else 0.0 - evidence_logs = 0.05 if scenario["service"] in state["queried_logs"] else 0.0 - - # Root cause identification - root_cause_score = 0.0 - upstream_partial = False - if correct_service: - root_cause_score = 0.5 - elif predicted_service and _is_upstream_of(predicted_service, scenario["service"]): - root_cause_score = 0.1 - upstream_partial = True - - # Failure mode scoring - failure_mode_score = 0.0 - if correct_service and failure_match: - failure_mode_score = 0.2 - elif upstream_partial and failure_match: - # Partial credit: correctly matched failure mode but wrong service - failure_mode_score = 0.05 - - # Efficiency bonus (only for correct root cause) - efficiency_score = 0.0 - if correct_service and diagnosis: - efficiency_score = _efficiency_bonus(diagnosis["step"]) - - # Penalties - extra_metric_queries = max(0, len(state["queried_metrics"]) - 6) - query_penalty = 0.05 * extra_metric_queries - invalid_penalty = 0.02 * state.get("invalid_actions", 0) - - total = ( - evidence_metric - + evidence_logs - + root_cause_score - + failure_mode_score - + efficiency_score - - query_penalty - - invalid_penalty - ) - total = _clamp(total) - - breakdown = { - "root_cause_service_correct": correct_service, - "root_cause_score": round(root_cause_score, 4), - "upstream_partial_credit": upstream_partial, - "failure_mode_match": failure_match, - "failure_mode_score": round(failure_mode_score, 4), - "evidence_collection": { - "queried_root_metrics": scenario["service"] in state["queried_metrics"], - "queried_root_logs": scenario["service"] in state["queried_logs"], - "score": round(evidence_metric + evidence_logs, 4), - }, - "efficiency_score": round(efficiency_score, 4), - "metric_queries": len(state["queried_metrics"]), - "query_penalty": round(query_penalty, 4), - "invalid_action_penalty": round(invalid_penalty, 4), - "score_explanation": ( - "The score rewards collecting the right evidence, naming the correct root " - "cause service (or its direct upstream for partial credit), " - "matching the failure mode, and submitting an efficient diagnosis." - ), - } - return SREReward(value=total, breakdown=breakdown, done=done, info={}) diff --git a/scratch/sre/env/graders/grader_multi_incident_correlation.py b/scratch/sre/env/graders/grader_multi_incident_correlation.py deleted file mode 100644 index e8210ba3a69fb8c5e3d590e0e396284ec5ffaa8a..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/grader_multi_incident_correlation.py +++ /dev/null @@ -1,111 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from env.graders.scoring import clamp_task_score -from env.models import SREReward - - -_clamp = clamp_task_score - - -def grade_multi_incident_correlation( - state: dict[str, Any], step_count: int, done: bool -) -> SREReward: - scenario = state["scenario"] - corr = state.get("correlation_submitted") - if not corr: - return SREReward( - value=0.0001, - breakdown={"error": "No correlation analysis submitted"}, - done=done, - info={}, - ) - - correct_type = corr["correlation_type"] == scenario["type"] - type_score = 0.4 if correct_type else 0.0 - - if scenario["type"] == "shared_root_cause": - root_correct = corr["root_cause_service"] == scenario["root_cause"] - root_score = 0.3 if root_correct else 0.0 - all_alerts = set(a["id"] for a in scenario["alerts"]) - submitted_alerts = set(corr.get("alert_ids", [])) - alert_coverage = ( - len(all_alerts & submitted_alerts) / len(all_alerts) if all_alerts else 0.0 - ) - alert_score = 0.2 * alert_coverage - elif scenario["type"] == "independent_incidents": - root_correct = ( - not corr["root_cause_service"] - or corr["root_cause_service"] == "none" - or corr["root_cause_service"] == "" - ) - root_score = 0.3 if root_correct else 0.0 - submitted_alerts = set(corr.get("alert_ids", [])) - all_alerts = set(a["id"] for a in scenario["alerts"]) - alert_coverage = ( - len(all_alerts & submitted_alerts) / len(all_alerts) if all_alerts else 0.0 - ) - alert_score = 0.2 * alert_coverage - else: - root_correct = corr["root_cause_service"] == scenario["root_cause"] - root_score = 0.15 if root_correct else 0.0 - submitted_alerts = set(corr.get("alert_ids", [])) - correlated_alerts = set( - a["id"] - for a in scenario["alerts"] - if a["id"] not in scenario["independent_alerts"] - ) - independent_correct = all( - aid not in submitted_alerts for aid in scenario["independent_alerts"] - ) - partial_score = 0.15 if independent_correct else 0.0 - alert_coverage = ( - len(correlated_alerts & submitted_alerts) / len(correlated_alerts) - if correlated_alerts - else 0.0 - ) - alert_score = 0.1 * alert_coverage + partial_score - - queried_services = len(state["queried_services"]) - evidence_bonus = ( - 0.1 if queried_services >= 2 else 0.05 if queried_services >= 1 else 0.0 - ) - - efficiency_bonus = 0.1 if step_count <= 6 else 0.0 - invalid_penalty = 0.02 * state.get("invalid_actions", 0) - - if scenario["type"] == "partial_correlation": - total = ( - type_score - + root_score - + alert_score - + evidence_bonus - + efficiency_bonus - - invalid_penalty - ) - else: - total = ( - type_score - + root_score - + alert_score - + evidence_bonus - + efficiency_bonus - - invalid_penalty - ) - total = _clamp(total) - - breakdown = { - "correlation_type_correct": correct_type, - "type_score": round(type_score, 4), - "root_cause_score": round(root_score, 4), - "alert_coverage_score": round(alert_score, 4), - "evidence_bonus": round(evidence_bonus, 4), - "efficiency_bonus": round(efficiency_bonus, 4), - "invalid_action_penalty": round(invalid_penalty, 4), - "score_explanation": ( - "Rewards correct correlation type, root cause identification, " - "proper alert grouping, and evidence gathering." - ), - } - return SREReward(value=total, breakdown=breakdown, done=done, info={}) diff --git a/scratch/sre/env/graders/grader_on_call_handoff.py b/scratch/sre/env/graders/grader_on_call_handoff.py deleted file mode 100644 index 3a8a7ac580ad718ea9345e36dbfdda9833f07e8b..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/grader_on_call_handoff.py +++ /dev/null @@ -1,87 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from env.graders.scoring import clamp_task_score -from env.models import SREReward - - -_clamp = clamp_task_score - - -def grade_on_call_handoff( - state: dict[str, Any], step_count: int, done: bool -) -> SREReward: - summary = state.get("handoff_summary") - if not summary: - return SREReward( - value=0.0001, - breakdown={"error": "No handoff summary submitted"}, - done=done, - info={}, - ) - - text = summary["summary"].lower() - shift_context = state["shift_context"] - - incident_mentions = sum( - 1 - for inc in shift_context["active_incidents"] - if inc["id"] in text or inc["service"] in text - ) - incident_score = 0.3 * ( - incident_mentions / max(1, len(shift_context["active_incidents"])) - ) - - action_mentions = sum( - 1 - for action in shift_context["pending_actions"] - if any(word in text for word in action.lower().split()[:3]) - ) - action_score = 0.25 * ( - action_mentions / max(1, len(shift_context["pending_actions"])) - ) - - severity_mentioned = any( - inc["severity"].lower() in text for inc in shift_context["active_incidents"] - ) - severity_score = 0.15 if severity_mentioned else 0.0 - - health_mentioned = any(svc in text for svc in state["service_health"]) - health_score = 0.1 if health_mentioned else 0.0 - - length_bonus = ( - 0.1 if len(text.split()) >= 20 else 0.05 if len(text.split()) >= 10 else 0.0 - ) - efficiency_bonus = 0.1 if step_count <= 3 else 0.0 - - invalid_penalty = 0.02 * state.get("invalid_actions", 0) - - total = ( - incident_score - + action_score - + severity_score - + health_score - + length_bonus - + efficiency_bonus - - invalid_penalty - ) - total = _clamp(total) - - breakdown = { - "incident_coverage_score": round(incident_score, 4), - "action_items_score": round(action_score, 4), - "severity_mentioned": severity_mentioned, - "severity_score": round(severity_score, 4), - "health_mentioned": health_mentioned, - "health_score": round(health_score, 4), - "length_bonus": round(length_bonus, 4), - "efficiency_bonus": round(efficiency_bonus, 4), - "invalid_action_penalty": round(invalid_penalty, 4), - "word_count": len(text.split()), - "score_explanation": ( - "Rewards mentioning all active incidents, pending actions, severity levels, " - "and service health in a concise summary." - ), - } - return SREReward(value=total, breakdown=breakdown, done=done, info={}) diff --git a/scratch/sre/env/graders/grader_runbook_execution.py b/scratch/sre/env/graders/grader_runbook_execution.py deleted file mode 100644 index fc6ffb819c8aec0c281ca3cd0a449987beb601e7..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/grader_runbook_execution.py +++ /dev/null @@ -1,64 +0,0 @@ -from __future__ import annotations - -from env.graders.scoring import clamp_task_score -from env.models import SREReward - - -_clamp = clamp_task_score - - -def _report_quality(report: dict | None) -> float: - if not report: - return 0.0 - summary = str(report.get("summary", "")).lower() - severity = str(report.get("severity", "")).upper() - has_severity = severity == "P2" - has_service = "payment-service" in summary - has_resolution_language = any( - keyword in summary - for keyword in ["scaled", "recovered", "cordon", "heap dump", "memory pressure"] - ) - coherent_sentence = len(summary.split()) >= 6 and summary.endswith((".", "!", "?")) - return ( - 0.1 - if has_severity - and has_service - and has_resolution_language - and coherent_sentence - else 0.0 - ) - - -def grade_runbook_execution(state: dict, step_count: int, done: bool) -> SREReward: - completed_count = len(state["completed_steps"]) - step_score = min(0.7, 0.1 * completed_count) - pid_bonus = 0.1 if state.get("pid_dependency_ok") else 0.0 - report_bonus = _report_quality(state.get("report_submitted")) - out_of_order_penalty = 0.05 * state.get("out_of_order_attempts", 0) - wrong_parameter_penalty = 0.05 * state.get("wrong_parameter_attempts", 0) - invalid_penalty = 0.02 * state.get("invalid_actions", 0) - - total = ( - step_score - + pid_bonus - + report_bonus - - out_of_order_penalty - - wrong_parameter_penalty - - invalid_penalty - ) - total = _clamp(total) - - breakdown = { - "completed_steps": state["completed_steps"], - "step_score": round(step_score, 4), - "pid_dependency_bonus": round(pid_bonus, 4), - "report_quality_bonus": round(report_bonus, 4), - "out_of_order_penalty": round(out_of_order_penalty, 4), - "wrong_parameter_penalty": round(wrong_parameter_penalty, 4), - "invalid_action_penalty": round(invalid_penalty, 4), - "score_explanation": ( - "The runbook score is dense: every correct step adds credit, but ordering " - "mistakes and bad parameters reduce the final score." - ), - } - return SREReward(value=total, breakdown=breakdown, done=done, info={}) diff --git a/scratch/sre/env/graders/scoring.py b/scratch/sre/env/graders/scoring.py deleted file mode 100644 index 077f6ab125e8ddfa76944c73d9fbd2877db81b7a..0000000000000000000000000000000000000000 --- a/scratch/sre/env/graders/scoring.py +++ /dev/null @@ -1,9 +0,0 @@ -from __future__ import annotations - -MIN_TASK_SCORE = 0.0001 -MAX_TASK_SCORE = 0.9999 - - -def clamp_task_score(value: float) -> float: - """Clamp scores to a strict open interval (0, 1) with stable precision.""" - return max(MIN_TASK_SCORE, min(MAX_TASK_SCORE, round(float(value), 4))) \ No newline at end of file diff --git a/scratch/sre/env/models.py b/scratch/sre/env/models.py deleted file mode 100644 index f14ed259296b82d74597d5017005c798b5ebd0ae..0000000000000000000000000000000000000000 --- a/scratch/sre/env/models.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import annotations - -from typing import Any, Literal - -from pydantic import BaseModel, Field - - -class SREObservation(BaseModel): - task_id: str - step: int = Field(ge=0) - context: dict[str, Any] - available_actions: list[str] - alert_queue: list[dict[str, Any]] - service_map: dict[str, list[str]] - metrics: dict[str, Any] - logs: list[str] - done: bool - message: str - - -class SREAction(BaseModel): - action_type: Literal[ - "triage", - "diagnose", - "escalate", - "acknowledge", - "execute_step", - "resolve", - "summarize", - "query_capacity", - "recommend_scaling", - "correlate", - "remediate", - "rollback", - "verify_recovery", - "write_postmortem", - "inject_chaos", - "observe_impact", - "mitigate_chaos", - ] - target: str - parameters: dict[str, Any] = Field(default_factory=dict) - reasoning: str - - -class SREReward(BaseModel): - value: float = Field(gt=0.0, lt=1.0) - breakdown: dict[str, Any] = Field(default_factory=dict) - done: bool - info: dict[str, Any] = Field(default_factory=dict) diff --git a/scratch/sre/env/sre_env.py b/scratch/sre/env/sre_env.py deleted file mode 100644 index 50fef54a1889177b44debef07fd7f8a46ffe27a3..0000000000000000000000000000000000000000 --- a/scratch/sre/env/sre_env.py +++ /dev/null @@ -1,290 +0,0 @@ -from __future__ import annotations - -import json -import random -from pathlib import Path -from typing import Any - -from env.graders.scoring import clamp_task_score -from env.graders import ( - grade_alert_triage, - grade_auto_remediation, - grade_blameless_postmortem, - grade_capacity_planning, - grade_chaos_engineering, - grade_incident_diagnosis, - grade_multi_incident_correlation, - grade_on_call_handoff, - grade_runbook_execution, -) -from env.models import SREAction, SREObservation, SREReward -from env.tasks import ( - ALERT_TRIAGE_INFO, - AUTO_REMEDIATION_INFO, - BLAMELESS_POSTMORTEM_INFO, - CAPACITY_PLANNING_INFO, - CHAOS_ENGINEERING_INFO, - INCIDENT_DIAGNOSIS_INFO, - MULTI_INCIDENT_CORRELATION_INFO, - ON_CALL_HANDOFF_INFO, - RUNBOOK_EXECUTION_INFO, - apply_alert_triage_action, - apply_auto_remediation_action, - apply_blameless_postmortem_action, - apply_capacity_planning_action, - apply_chaos_engineering_action, - apply_incident_diagnosis_action, - apply_multi_incident_correlation_action, - apply_on_call_handoff_action, - apply_runbook_execution_action, - build_alert_triage_observation, - build_auto_remediation_observation, - build_blameless_postmortem_observation, - build_capacity_planning_observation, - build_chaos_engineering_observation, - build_incident_diagnosis_observation, - build_multi_incident_correlation_observation, - build_on_call_handoff_observation, - build_runbook_execution_observation, - init_alert_triage_task, - init_auto_remediation_task, - init_blameless_postmortem_task, - init_capacity_planning_task, - init_chaos_engineering_task, - init_incident_diagnosis_task, - init_multi_incident_correlation_task, - init_on_call_handoff_task, - init_runbook_execution_task, - is_alert_triage_done, - is_auto_remediation_done, - is_blameless_postmortem_done, - is_capacity_planning_done, - is_chaos_engineering_done, - is_incident_diagnosis_done, - is_multi_incident_correlation_done, - is_on_call_handoff_done, - is_runbook_execution_done, -) - -DATA_DIR = Path(__file__).resolve().parent / "data" -SERVICE_MAP = { - "api-gateway": ["auth-service"], - "auth-service": ["user-service", "payment-service"], - "user-service": [], - "payment-service": ["db-service"], - "db-service": [], -} - -TASK_REGISTRY = { - "alert_triage": { - "metadata": ALERT_TRIAGE_INFO, - "reset": init_alert_triage_task, - "apply": apply_alert_triage_action, - "build_observation": build_alert_triage_observation, - "grade": grade_alert_triage, - "is_done": is_alert_triage_done, - }, - "on_call_handoff": { - "metadata": ON_CALL_HANDOFF_INFO, - "reset": init_on_call_handoff_task, - "apply": apply_on_call_handoff_action, - "build_observation": build_on_call_handoff_observation, - "grade": grade_on_call_handoff, - "is_done": is_on_call_handoff_done, - }, - "capacity_planning": { - "metadata": CAPACITY_PLANNING_INFO, - "reset": init_capacity_planning_task, - "apply": apply_capacity_planning_action, - "build_observation": build_capacity_planning_observation, - "grade": grade_capacity_planning, - "is_done": is_capacity_planning_done, - }, - "incident_diagnosis": { - "metadata": INCIDENT_DIAGNOSIS_INFO, - "reset": init_incident_diagnosis_task, - "apply": apply_incident_diagnosis_action, - "build_observation": build_incident_diagnosis_observation, - "grade": grade_incident_diagnosis, - "is_done": is_incident_diagnosis_done, - }, - "multi_incident_correlation": { - "metadata": MULTI_INCIDENT_CORRELATION_INFO, - "reset": init_multi_incident_correlation_task, - "apply": apply_multi_incident_correlation_action, - "build_observation": build_multi_incident_correlation_observation, - "grade": grade_multi_incident_correlation, - "is_done": is_multi_incident_correlation_done, - }, - "auto_remediation": { - "metadata": AUTO_REMEDIATION_INFO, - "reset": init_auto_remediation_task, - "apply": apply_auto_remediation_action, - "build_observation": build_auto_remediation_observation, - "grade": grade_auto_remediation, - "is_done": is_auto_remediation_done, - }, - "runbook_execution": { - "metadata": RUNBOOK_EXECUTION_INFO, - "reset": init_runbook_execution_task, - "apply": apply_runbook_execution_action, - "build_observation": build_runbook_execution_observation, - "grade": grade_runbook_execution, - "is_done": is_runbook_execution_done, - }, - "blameless_postmortem": { - "metadata": BLAMELESS_POSTMORTEM_INFO, - "reset": init_blameless_postmortem_task, - "apply": apply_blameless_postmortem_action, - "build_observation": build_blameless_postmortem_observation, - "grade": grade_blameless_postmortem, - "is_done": is_blameless_postmortem_done, - }, - "chaos_engineering": { - "metadata": CHAOS_ENGINEERING_INFO, - "reset": init_chaos_engineering_task, - "apply": apply_chaos_engineering_action, - "build_observation": build_chaos_engineering_observation, - "grade": grade_chaos_engineering, - "is_done": is_chaos_engineering_done, - }, -} - - -class SREEnv: - def __init__(self, task_id: str): - if task_id not in TASK_REGISTRY: - raise ValueError(f"Unsupported task_id: {task_id}") - self.task_id = task_id - self.step_count = 0 - self.state_data: dict[str, Any] = {} - self.episode_history: list[dict[str, Any]] = [] - self.cumulative_reward: float = 0.0 - self.catalogs = self._load_catalogs() - self.rng = random.Random() - - def _load_catalogs(self) -> dict[str, Any]: - return { - "alerts": json.loads((DATA_DIR / "alerts.json").read_text()), - "incidents": json.loads((DATA_DIR / "incidents.json").read_text()), - "runbooks": json.loads((DATA_DIR / "runbooks.json").read_text()), - } - - def reset(self) -> SREObservation: - self.step_count = 0 - self.episode_history = [] - self.cumulative_reward = 0.0 - registry_entry = TASK_REGISTRY[self.task_id] - self.state_data = registry_entry["reset"](self.catalogs, SERVICE_MAP, self.rng) - message = self.state_data.get( - "message", registry_entry["metadata"]["description"] - ) - return registry_entry["build_observation"]( - self.task_id, - self.step_count, - self.state_data, - SERVICE_MAP, - False, - message, - ) - - def step( - self, action: SREAction - ) -> tuple[SREObservation, SREReward, bool, dict[str, Any]]: - registry_entry = TASK_REGISTRY[self.task_id] - available_actions = registry_entry["metadata"]["available_actions"] - - if action.action_type not in available_actions: - self.state_data["invalid_actions"] = ( - self.state_data.get("invalid_actions", 0) + 1 - ) - message = ( - f"Action {action.action_type} is not available for task {self.task_id}. " - f"Allowed actions: {available_actions}." - ) - else: - message = registry_entry["apply"](self.state_data, action, self.step_count) - - self.step_count += 1 - done = registry_entry["is_done"]( - self.state_data, - self.step_count, - registry_entry["metadata"]["max_steps"], - ) - reward = registry_entry["grade"](self.state_data, self.step_count, done) - self.cumulative_reward = clamp_task_score(self.cumulative_reward + reward.value) - reward = SREReward( - value=clamp_task_score(reward.value), - breakdown=reward.breakdown, - done=done, - info={ - **reward.info, - "cumulative_score": self.cumulative_reward, - "step_count": self.step_count, - }, - ) - info = { - "task_id": self.task_id, - "cumulative_score": self.cumulative_reward, - "max_steps": registry_entry["metadata"]["max_steps"], - "termination_reason": self._termination_reason(done), - } - observation = registry_entry["build_observation"]( - self.task_id, - self.step_count, - self.state_data, - SERVICE_MAP, - done, - message, - ) - self.episode_history.append( - { - "step": self.step_count, - "action": action.model_dump(), - "reward": reward.model_dump(), - "done": done, - "info": info, - } - ) - return observation, reward, done, info - - def _termination_reason(self, done: bool) -> str | None: - if not done: - return None - max_steps = TASK_REGISTRY[self.task_id]["metadata"]["max_steps"] - if self.step_count >= max_steps: - return "max_steps_reached" - if self.task_id == "alert_triage": - return "all_actionable_alerts_acknowledged" - if self.task_id == "on_call_handoff": - return "handoff_summary_submitted" - if self.task_id == "capacity_planning": - return "scaling_recommendation_submitted" - if self.task_id == "incident_diagnosis": - return "diagnosis_submitted" - if self.task_id == "multi_incident_correlation": - return "correlation_submitted" - if self.task_id == "auto_remediation": - return "remediation_cycle_complete" - if self.task_id == "runbook_execution": - return "runbook_completed" - if self.task_id == "blameless_postmortem": - return "postmortem_sections_complete" - if self.task_id == "chaos_engineering": - return "chaos_mitigation_complete" - return "done" - - def state(self) -> dict[str, Any]: - return { - "task_id": self.task_id, - "step_count": self.step_count, - "state_data": self.state_data, - "episode_history": self.episode_history, - } - - def close(self): - self.state_data = {} - self.episode_history = [] - - -TASK_METADATA = [TASK_REGISTRY[task_id]["metadata"] for task_id in TASK_REGISTRY] diff --git a/scratch/sre/env/tasks/__init__.py b/scratch/sre/env/tasks/__init__.py deleted file mode 100644 index 0a8bd75115633e09204fb5d3b02a9e6554317b98..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/__init__.py +++ /dev/null @@ -1,116 +0,0 @@ -from env.tasks.task_alert_triage import ( - ALERT_TRIAGE_INFO, - apply_alert_triage_action, - build_alert_triage_observation, - init_alert_triage_task, - is_alert_triage_done, -) -from env.tasks.task_auto_remediation import ( - AUTO_REMEDIATION_INFO, - apply_auto_remediation_action, - build_auto_remediation_observation, - init_auto_remediation_task, - is_auto_remediation_done, -) -from env.tasks.task_blameless_postmortem import ( - BLAMELESS_POSTMORTEM_INFO, - apply_blameless_postmortem_action, - build_blameless_postmortem_observation, - init_blameless_postmortem_task, - is_blameless_postmortem_done, -) -from env.tasks.task_capacity_planning import ( - CAPACITY_PLANNING_INFO, - apply_capacity_planning_action, - build_capacity_planning_observation, - init_capacity_planning_task, - is_capacity_planning_done, -) -from env.tasks.task_chaos_engineering import ( - CHAOS_ENGINEERING_INFO, - apply_chaos_engineering_action, - build_chaos_engineering_observation, - init_chaos_engineering_task, - is_chaos_engineering_done, -) -from env.tasks.task_incident_diagnosis import ( - INCIDENT_DIAGNOSIS_INFO, - apply_incident_diagnosis_action, - build_incident_diagnosis_observation, - init_incident_diagnosis_task, - is_incident_diagnosis_done, -) -from env.tasks.task_multi_incident_correlation import ( - MULTI_INCIDENT_CORRELATION_INFO, - apply_multi_incident_correlation_action, - build_multi_incident_correlation_observation, - init_multi_incident_correlation_task, - is_multi_incident_correlation_done, -) -from env.tasks.task_on_call_handoff import ( - ON_CALL_HANDOFF_INFO, - apply_on_call_handoff_action, - build_on_call_handoff_observation, - init_on_call_handoff_task, - is_on_call_handoff_done, -) -from env.tasks.task_runbook_execution import ( - RUNBOOK_EXECUTION_INFO, - apply_runbook_execution_action, - build_runbook_execution_observation, - init_runbook_execution_task, - is_runbook_execution_done, -) -from env.tasks.task_registry import ALL_TASKS, EASY_TASKS, HARD_TASKS, MEDIUM_TASKS - -__all__ = [ - "ALERT_TRIAGE_INFO", - "AUTO_REMEDIATION_INFO", - "BLAMELESS_POSTMORTEM_INFO", - "CAPACITY_PLANNING_INFO", - "CHAOS_ENGINEERING_INFO", - "INCIDENT_DIAGNOSIS_INFO", - "MULTI_INCIDENT_CORRELATION_INFO", - "ON_CALL_HANDOFF_INFO", - "RUNBOOK_EXECUTION_INFO", - "apply_alert_triage_action", - "apply_auto_remediation_action", - "apply_blameless_postmortem_action", - "apply_capacity_planning_action", - "apply_chaos_engineering_action", - "apply_incident_diagnosis_action", - "apply_multi_incident_correlation_action", - "apply_on_call_handoff_action", - "apply_runbook_execution_action", - "build_alert_triage_observation", - "build_auto_remediation_observation", - "build_blameless_postmortem_observation", - "build_capacity_planning_observation", - "build_chaos_engineering_observation", - "build_incident_diagnosis_observation", - "build_multi_incident_correlation_observation", - "build_on_call_handoff_observation", - "build_runbook_execution_observation", - "init_alert_triage_task", - "init_auto_remediation_task", - "init_blameless_postmortem_task", - "init_capacity_planning_task", - "init_chaos_engineering_task", - "init_incident_diagnosis_task", - "init_multi_incident_correlation_task", - "init_on_call_handoff_task", - "init_runbook_execution_task", - "is_alert_triage_done", - "is_auto_remediation_done", - "is_blameless_postmortem_done", - "is_capacity_planning_done", - "is_chaos_engineering_done", - "is_incident_diagnosis_done", - "is_multi_incident_correlation_done", - "is_on_call_handoff_done", - "is_runbook_execution_done", - "ALL_TASKS", - "EASY_TASKS", - "MEDIUM_TASKS", - "HARD_TASKS", -] diff --git a/scratch/sre/env/tasks/task_alert_triage.py b/scratch/sre/env/tasks/task_alert_triage.py deleted file mode 100644 index f484a4825d4cefd2229505d168f162fb648bc8f7..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_alert_triage.py +++ /dev/null @@ -1,197 +0,0 @@ -from __future__ import annotations - -from collections import defaultdict -from copy import deepcopy -from typing import Any - -from env.models import SREAction, SREObservation - -ALERT_TRIAGE_INFO = { - "id": "alert_triage", - "name": "Alert Triage", - "difficulty": "easy", - "description": "Prioritize and acknowledge firing alerts in correct severity order.", - "max_steps": 10, - "available_actions": ["triage", "acknowledge", "resolve", "escalate"], -} - -PRIORITY_BY_SEVERITY = {"critical": "P1", "warning": "P2", "info": "P3"} -PRIORITY_RANK = {"P1": 1, "P2": 2, "P3": 3} -SEVERITY_WEIGHT = {"critical": 3, "warning": 2, "info": 1} - - -def _sorted_actionable_alerts(alerts: list[dict[str, Any]]) -> list[dict[str, Any]]: - return sorted( - alerts, - key=lambda alert: ( - PRIORITY_RANK[PRIORITY_BY_SEVERITY[alert["severity"]]], - alert["fired_at"], - alert["id"], - ), - ) - - -def init_alert_triage_task( - catalogs: dict[str, Any], service_map: dict[str, list[str]], rng -) -> dict[str, Any]: - alerts = deepcopy(rng.sample(catalogs["alerts"], k=8)) - actionable_alerts = [ - alert for alert in alerts if not alert["is_flapping"] and not alert["is_duplicate"] - ] - actionable_order = _sorted_actionable_alerts(actionable_alerts) - ground_truth = { - alert["id"]: { - "priority": PRIORITY_BY_SEVERITY[alert["severity"]], - "service": alert["service"], - "severity": alert["severity"], - } - for alert in actionable_order - } - - for alert in alerts: - alert["status"] = "pending" - alert["assigned_priority"] = None - - return { - "task": "alert_triage", - "alerts": alerts, - "actionable_ids": [alert["id"] for alert in actionable_order], - "ignorable_ids": [ - alert["id"] for alert in alerts if alert["is_flapping"] or alert["is_duplicate"] - ], - "ground_truth": ground_truth, - "triage_decisions": {}, - "acknowledged_order": [], - "ignored_alert_ids": [], - "escalated_alert_ids": [], - "invalid_actions": 0, - "message": ( - "Triage the alert queue. Use action_type='triage' with parameters " - "{decision: ignore|actionable, priority: P1|P2|P3}, then use " - "action_type='acknowledge' on actionable alerts in priority order." - ), - "service_map": service_map, - } - - -def apply_alert_triage_action( - state: dict[str, Any], action: SREAction, step_count: int -) -> str: - alert_lookup = {alert["id"]: alert for alert in state["alerts"]} - alert = alert_lookup.get(action.target) - if action.action_type in {"triage", "acknowledge", "escalate"} and alert is None: - state["invalid_actions"] += 1 - return f"Alert {action.target} was not found in the current queue." - - if action.action_type == "triage": - decision = str(action.parameters.get("decision", "actionable")).lower() - priority = str( - action.parameters.get( - "priority", PRIORITY_BY_SEVERITY.get(alert["severity"], "P3") - ) - ).upper() - state["triage_decisions"][alert["id"]] = { - "decision": decision, - "priority": priority, - "step": step_count + 1, - } - alert["assigned_priority"] = priority - if decision == "ignore": - if alert["id"] not in state["ignored_alert_ids"]: - state["ignored_alert_ids"].append(alert["id"]) - alert["status"] = "ignored" - return f"Alert {alert['id']} was marked as ignorable with priority {priority}." - alert["status"] = "triaged" - return f"Alert {alert['id']} was marked actionable with priority {priority}." - - if action.action_type == "acknowledge": - if alert["id"] in state["acknowledged_order"]: - state["invalid_actions"] += 1 - return f"Alert {alert['id']} was already acknowledged." - if alert["is_flapping"] or alert["is_duplicate"]: - state["invalid_actions"] += 1 - alert["status"] = "mis-acknowledged" - return ( - f"Alert {alert['id']} should have been ignored because it is " - "flapping or duplicate." - ) - priority = action.parameters.get("priority") - if priority: - alert["assigned_priority"] = str(priority).upper() - state["acknowledged_order"].append(alert["id"]) - alert["status"] = "acknowledged" - return f"Alert {alert['id']} acknowledged for service {alert['service']}." - - if action.action_type == "escalate": - if alert["id"] not in state["escalated_alert_ids"]: - state["escalated_alert_ids"].append(alert["id"]) - return f"Alert {alert['id']} escalated to the on-call escalation policy." - - if action.action_type == "resolve": - return "Alert triage episodes end when all actionable alerts are acknowledged." - - state["invalid_actions"] += 1 - return f"Action {action.action_type} is not supported for alert triage." - - -def build_alert_triage_observation( - task_id: str, - step_count: int, - state: dict[str, Any], - service_map: dict[str, list[str]], - done: bool, - message: str, -) -> SREObservation: - per_service: dict[str, dict[str, int]] = defaultdict( - lambda: {"open_alerts": 0, "critical": 0, "warning": 0, "info": 0} - ) - for alert in state["alerts"]: - if alert["status"] not in {"acknowledged", "ignored"}: - per_service[alert["service"]]["open_alerts"] += 1 - per_service[alert["service"]][alert["severity"]] += 1 - - context = { - "instruction": ( - "Identify non-actionable alerts, assign P1/P2/P3, and acknowledge only " - "actionable alerts in descending priority order." - ), - "triaged_alerts": len(state["triage_decisions"]), - "acknowledged_alerts": state["acknowledged_order"], - "ignored_alerts": state["ignored_alert_ids"], - "remaining_actionable": len( - [ - alert_id - for alert_id in state["actionable_ids"] - if alert_id not in state["acknowledged_order"] - ] - ), - "ground_truth_hidden": False, - } - logs = [ - f"{alert['id']} {alert['service']} {alert['severity']}: {alert['message']}" - for alert in state["alerts"] - ] - - return SREObservation( - task_id=task_id, - step=step_count, - context=context, - available_actions=ALERT_TRIAGE_INFO["available_actions"], - alert_queue=deepcopy(state["alerts"]), - service_map=service_map, - metrics={"services": dict(per_service), "queue_depth": len(state["alerts"])}, - logs=logs, - done=done, - message=message, - ) - - -def is_alert_triage_done( - state: dict[str, Any], step_count: int, max_steps: int -) -> bool: - actionable_remaining = [ - alert_id - for alert_id in state["actionable_ids"] - if alert_id not in state["acknowledged_order"] - ] - return not actionable_remaining or step_count >= max_steps diff --git a/scratch/sre/env/tasks/task_auto_remediation.py b/scratch/sre/env/tasks/task_auto_remediation.py deleted file mode 100644 index c3863aa3516c5d51e00d6722ea56f4d09be5efd5..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_auto_remediation.py +++ /dev/null @@ -1,299 +0,0 @@ -from __future__ import annotations - -from copy import deepcopy -from typing import Any - -from env.models import SREAction, SREObservation - -AUTO_REMEDIATION_INFO = { - "id": "auto_remediation", - "name": "Auto-Remediation with Rollback", - "difficulty": "medium", - "description": "Execute a remediation action, verify if it improved the situation, and rollback if it made things worse.", - "max_steps": 8, - "available_actions": [ - "remediate", - "verify_recovery", - "rollback", - "escalate", - "resolve", - ], -} - -REMEDIATION_SCENARIOS = [ - { - "id": "REM-A", - "service": "api-gateway", - "symptom": "5xx rate > 15% after config change", - "root_cause": "config drift to wrong upstream", - "remediation_options": [ - { - "action": "reload_config", - "effect": "worse", - "description": "Reload config without reverting", - }, - { - "action": "rollback_config", - "effect": "fix", - "description": "Revert to previous config version", - }, - { - "action": "restart_service", - "effect": "worse", - "description": "Restart with current bad config", - }, - { - "action": "scale_out", - "effect": "partial", - "description": "Add more instances (masks symptom)", - }, - ], - "correct_action": "rollback_config", - "post_fix_metrics": {"error_rate": 0.01, "latency_p99": 120}, - "current_metrics": {"error_rate": 0.18, "latency_p99": 350}, - }, - { - "id": "REM-B", - "service": "payment-service", - "symptom": "Memory leak causing OOM restarts every 30 minutes", - "root_cause": "memory leak in checkout worker", - "remediation_options": [ - { - "action": "increase_memory_limit", - "effect": "partial", - "description": "Raise memory ceiling (delays but doesn't fix)", - }, - { - "action": "restart_service", - "effect": "partial", - "description": "Restart clears memory temporarily", - }, - { - "action": "cordon_and_scale", - "effect": "fix", - "description": "Cordon affected instance, scale out replacement", - }, - { - "action": "disable_checkout", - "effect": "worse", - "description": "Disable checkout feature entirely", - }, - ], - "correct_action": "cordon_and_scale", - "post_fix_metrics": {"error_rate": 0.01, "latency_p99": 110, "mem_pct": 65}, - "current_metrics": {"error_rate": 0.12, "latency_p99": 280, "mem_pct": 94}, - }, - { - "id": "REM-C", - "service": "auth-service", - "symptom": "JWT validation failures after key rotation", - "root_cause": "new signing key not propagated to all pods", - "remediation_options": [ - { - "action": "force_key_rotation", - "effect": "worse", - "description": "Rotate keys again (makes it worse)", - }, - { - "action": "propagate_keys", - "effect": "fix", - "description": "Manually sync signing keys across all pods", - }, - { - "action": "restart_service", - "effect": "partial", - "description": "Restart may pick up new keys on some pods", - }, - { - "action": "disable_auth", - "effect": "worse", - "description": "Disable authentication (security risk)", - }, - ], - "correct_action": "propagate_keys", - "post_fix_metrics": {"error_rate": 0.008, "latency_p99": 95}, - "current_metrics": {"error_rate": 0.24, "latency_p99": 180}, - }, -] - - -def init_auto_remediation_task( - catalogs: dict[str, Any], service_map: dict[str, list[str]], rng -) -> dict[str, Any]: - scenario = deepcopy(rng.choice(REMEDIATION_SCENARIOS)) - return { - "task": "auto_remediation", - "scenario": scenario, - "remediation_attempted": None, - "recovery_verified": False, - "rollback_performed": False, - "final_state": None, - "escalated": False, - "invalid_actions": 0, - "service_map": service_map, - "message": ( - "A production incident requires remediation. Choose a remediation action, " - "verify if it improved metrics, and rollback if it made things worse. " - "Use action_type='remediate' with the action name, then " - "action_type='verify_recovery' to check results. If the fix made things " - "worse, use action_type='rollback'." - ), - } - - -def apply_auto_remediation_action( - state: dict[str, Any], action: SREAction, step_count: int -) -> str: - scenario = state["scenario"] - - if action.action_type == "escalate": - state["escalated"] = True - return f"Remediation for {scenario['service']} escalated to senior SRE." - - if action.action_type == "resolve": - if state["final_state"]: - return "Remediation episode already concluded." - state["invalid_actions"] += 1 - return "Resolve is only meaningful after completing the remediation cycle." - - if action.action_type == "remediate": - chosen_action = str(action.parameters.get("action", "")).strip() - if not chosen_action: - state["invalid_actions"] += 1 - return "Must specify an action in parameters." - valid_options = {opt["action"] for opt in scenario["remediation_options"]} - if chosen_action not in valid_options: - state["invalid_actions"] += 1 - return f"Invalid remediation action. Options: {sorted(valid_options)}." - - opt = next( - o for o in scenario["remediation_options"] if o["action"] == chosen_action - ) - state["remediation_attempted"] = { - "action": chosen_action, - "effect": opt["effect"], - "description": opt["description"], - "step": step_count + 1, - } - return ( - f"Remediation '{chosen_action}' executed: {opt['description']}. " - f"Use action_type='verify_recovery' to check results." - ) - - if action.action_type == "verify_recovery": - if not state["remediation_attempted"]: - state["invalid_actions"] += 1 - return "No remediation has been attempted yet." - - effect = state["remediation_attempted"]["effect"] - if effect == "fix": - state["recovery_verified"] = True - state["final_state"] = { - "outcome": "success", - "metrics": scenario["post_fix_metrics"], - "step": step_count + 1, - } - return ( - f"Recovery verified! Metrics improved: {scenario['post_fix_metrics']}. " - f"Remediation was successful." - ) - elif effect == "partial": - state["recovery_verified"] = True - state["final_state"] = { - "outcome": "partial", - "metrics": scenario["current_metrics"], - "note": "Symptom masked but root cause persists", - "step": step_count + 1, - } - return ( - "Partial improvement detected. Symptoms reduced but root cause " - "not addressed. Consider trying a different remediation or rollback." - ) - else: - state["recovery_verified"] = True - state["final_state"] = { - "outcome": "worse", - "metrics": scenario["current_metrics"], - "note": "Metrics degraded after remediation", - "step": step_count + 1, - } - return ( - "CRITICAL: Metrics degraded after remediation! " - "Use action_type='rollback' to revert immediately." - ) - - if action.action_type == "rollback": - if not state["remediation_attempted"]: - state["invalid_actions"] += 1 - return "No remediation to rollback." - state["rollback_performed"] = True - state["final_state"] = { - "outcome": "rolled_back", - "metrics": scenario["current_metrics"], - "original_action": state["remediation_attempted"]["action"], - "step": step_count + 1, - } - return ( - f"Rollback of '{state['remediation_attempted']['action']}' completed. " - f"System restored to pre-remediation state." - ) - - state["invalid_actions"] += 1 - return f"Action {action.action_type} is not supported for auto-remediation." - - -def build_auto_remediation_observation( - task_id: str, - step_count: int, - state: dict[str, Any], - service_map: dict[str, list[str]], - done: bool, - message: str, -) -> SREObservation: - scenario = state["scenario"] - context = { - "instruction": ( - "Choose a remediation action, verify its effect, and rollback if needed. " - "The correct action will fix the root cause. Wrong actions may make things " - "worse or only partially help." - ), - "service": scenario["service"], - "symptom": scenario["symptom"], - "root_cause": scenario["root_cause"], - "current_metrics": scenario["current_metrics"], - "remediation_options": [ - {"action": opt["action"], "description": opt["description"]} - for opt in scenario["remediation_options"] - ], - "remediation_attempted": state["remediation_attempted"], - "recovery_verified": state["recovery_verified"], - "rollback_performed": state["rollback_performed"], - "final_state": state["final_state"], - } - logs = [ - f"ALERT: {scenario['symptom']}", - f"Root cause hypothesis: {scenario['root_cause']}", - ] - if state["remediation_attempted"]: - logs.append(f"Attempted: {state['remediation_attempted']['action']}") - if state["final_state"]: - logs.append(f"Outcome: {state['final_state']['outcome']}") - - return SREObservation( - task_id=task_id, - step=step_count, - context=context, - available_actions=AUTO_REMEDIATION_INFO["available_actions"], - alert_queue=[], - service_map=service_map, - metrics={"service": scenario["current_metrics"]}, - logs=logs, - done=done, - message=message, - ) - - -def is_auto_remediation_done( - state: dict[str, Any], step_count: int, max_steps: int -) -> bool: - return state["final_state"] is not None or step_count >= max_steps diff --git a/scratch/sre/env/tasks/task_blameless_postmortem.py b/scratch/sre/env/tasks/task_blameless_postmortem.py deleted file mode 100644 index a59f620be232396dbe110694a1d20319dc6f1f24..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_blameless_postmortem.py +++ /dev/null @@ -1,276 +0,0 @@ -from __future__ import annotations - -from copy import deepcopy -from typing import Any - -from env.models import SREAction, SREObservation - -BLAMELESS_POSTMORTEM_INFO = { - "id": "blameless_postmortem", - "name": "Blameless Postmortem Generation", - "difficulty": "hard", - "description": "Synthesize incident timeline, root cause analysis, and actionable follow-ups into a blameless postmortem document.", - "max_steps": 8, - "available_actions": ["write_postmortem", "diagnose", "escalate", "resolve"], -} - -POSTMORTEM_INCIDENTS = [ - { - "id": "POST-1", - "title": "Payment Service Outage - March 15, 2026", - "severity": "SEV-1", - "duration_minutes": 47, - "affected_services": ["payment-service", "db-service", "api-gateway"], - "customer_impact": "Checkout failures for ~12,000 users, estimated $85K revenue loss", - "timeline": [ - { - "time": "14:23", - "event": "db-service latency spike detected, p99 > 400ms", - }, - {"time": "14:25", "event": "payment-service reports checkout timeouts"}, - {"time": "14:28", "event": "api-gateway 5xx rate exceeds 15% threshold"}, - {"time": "14:30", "event": "PagerDuty alert fired, on-call acknowledged"}, - {"time": "14:35", "event": "On-call identified db-service as root cause"}, - { - "time": "14:42", - "event": "Database connection pool increased from 100 to 200", - }, - {"time": "14:55", "event": "Latency normalized, checkout flow recovered"}, - {"time": "15:10", "event": "Incident declared resolved"}, - ], - "root_cause": "Database connection pool exhaustion during peak traffic. A slow query on the ledger_entries table held connections open, starving the checkout path.", - "contributing_factors": [ - "Connection pool size was not scaled with traffic growth (15% MoM increase)", - "No circuit breaker on payment-service to db-service calls", - "Slow query alerting threshold was set too high at 500ms", - ], - "action_items": [ - { - "action": "Increase db connection pool to 300 with auto-scaling", - "owner": "DBA team", - "priority": "P1", - }, - { - "action": "Implement circuit breaker on payment-service", - "owner": "Payment team", - "priority": "P1", - }, - { - "action": "Lower slow query alert threshold to 200ms", - "owner": "SRE team", - "priority": "P2", - }, - { - "action": "Add runbook for connection pool exhaustion", - "owner": "SRE team", - "priority": "P2", - }, - ], - "detection_time_minutes": 7, - "mitigation_time_minutes": 19, - "resolution_time_minutes": 47, - }, - { - "id": "POST-2", - "title": "Auth Service Login Failures - March 18, 2026", - "severity": "SEV-1", - "duration_minutes": 32, - "affected_services": ["auth-service", "api-gateway"], - "customer_impact": "Login failures for ~8,000 users, support ticket spike", - "timeline": [ - {"time": "09:15", "event": "JWT signing key rotation initiated"}, - { - "time": "09:17", - "event": "New key not propagated to all auth-service pods", - }, - {"time": "09:18", "event": "Login failures spike, error rate > 20%"}, - {"time": "09:22", "event": "PagerDuty alert fired"}, - {"time": "09:28", "event": "On-call identified key propagation issue"}, - {"time": "09:35", "event": "Manual key sync across all pods"}, - {"time": "09:47", "event": "Login flow recovered, incident resolved"}, - ], - "root_cause": "JWT signing key rotation did not propagate to all auth-service pods due to a race condition in the key distribution service.", - "contributing_factors": [ - "Key distribution service had a 60-second sync interval (too slow)", - "No validation step after key rotation to confirm all pods received new key", - "Rollback procedure for key rotation was not documented", - ], - "action_items": [ - { - "action": "Reduce key sync interval to 5 seconds", - "owner": "Auth team", - "priority": "P1", - }, - { - "action": "Add post-rotation validation check", - "owner": "Auth team", - "priority": "P1", - }, - { - "action": "Document key rotation rollback procedure", - "owner": "SRE team", - "priority": "P2", - }, - ], - "detection_time_minutes": 4, - "mitigation_time_minutes": 10, - "resolution_time_minutes": 32, - }, -] - - -def init_blameless_postmortem_task( - catalogs: dict[str, Any], service_map: dict[str, list[str]], rng -) -> dict[str, Any]: - incident = deepcopy(rng.choice(POSTMORTEM_INCIDENTS)) - return { - "task": "blameless_postmortem", - "incident": incident, - "sections_written": [], - "postmortem_submitted": None, - "queried_data": [], - "escalated": False, - "invalid_actions": 0, - "service_map": service_map, - "message": ( - "Write a blameless postmortem for the given incident. Use " - "action_type='diagnose' with operation='query_timeline' or " - "'query_impact' to gather data, then action_type='write_postmortem' " - "with parameters containing the section name and content. Required " - "sections: summary, timeline, root_cause, impact, action_items." - ), - } - - -def apply_blameless_postmortem_action( - state: dict[str, Any], action: SREAction, step_count: int -) -> str: - incident = state["incident"] - - if action.action_type == "escalate": - state["escalated"] = True - return "Postmortem escalated to the engineering manager for review." - - if action.action_type == "resolve": - if state["postmortem_submitted"]: - return "Postmortem already submitted." - state["invalid_actions"] += 1 - return "Resolve is only meaningful after submitting the postmortem." - - if action.action_type == "diagnose": - operation = str(action.parameters.get("operation", "")).lower() - if operation == "query_timeline": - if "timeline" not in state["queried_data"]: - state["queried_data"].append("timeline") - return f"Timeline: {len(incident['timeline'])} events from {incident['timeline'][0]['time']} to {incident['timeline'][-1]['time']}." - elif operation == "query_impact": - if "impact" not in state["queried_data"]: - state["queried_data"].append("impact") - return ( - f"Impact: {incident['customer_impact']}. " - f"Duration: {incident['duration_minutes']}min. " - f"Detection: {incident['detection_time_minutes']}min, " - f"Mitigation: {incident['mitigation_time_minutes']}min, " - f"Resolution: {incident['resolution_time_minutes']}min." - ) - elif operation == "query_root_cause": - if "root_cause" not in state["queried_data"]: - state["queried_data"].append("root_cause") - return f"Root cause: {incident['root_cause']}" - elif operation == "query_action_items": - if "action_items" not in state["queried_data"]: - state["queried_data"].append("action_items") - return f"Action items: {len(incident['action_items'])} items identified." - state["invalid_actions"] += 1 - return "Use operation: query_timeline, query_impact, query_root_cause, or query_action_items." - - if action.action_type == "write_postmortem": - section = str(action.parameters.get("section", "")).strip() - content = str(action.parameters.get("content", "")).strip() - if not section or not content: - state["invalid_actions"] += 1 - return "Both 'section' and 'content' parameters are required." - - valid_sections = ["summary", "timeline", "root_cause", "impact", "action_items"] - if section not in valid_sections: - state["invalid_actions"] += 1 - return f"Invalid section '{section}'. Valid sections: {valid_sections}." - - state["sections_written"].append(section) - if state["postmortem_submitted"] is None: - state["postmortem_submitted"] = {} - state["postmortem_submitted"][section] = { - "content": content, - "step": step_count + 1, - "word_count": len(content.split()), - } - return f"Section '{section}' written ({len(content.split())} words)." - - state["invalid_actions"] += 1 - return f"Action {action.action_type} is not supported for blameless postmortem." - - -def build_blameless_postmortem_observation( - task_id: str, - step_count: int, - state: dict[str, Any], - service_map: dict[str, list[str]], - done: bool, - message: str, -) -> SREObservation: - incident = state["incident"] - context = { - "instruction": ( - "Write a blameless postmortem with these required sections: " - "summary, timeline, root_cause, impact, action_items. " - "Query incident data first, then write each section using " - "action_type='write_postmortem'." - ), - "incident_id": incident["id"], - "incident_title": incident["title"], - "severity": incident["severity"], - "affected_services": incident["affected_services"], - "sections_required": [ - "summary", - "timeline", - "root_cause", - "impact", - "action_items", - ], - "sections_written": state["sections_written"], - "sections_remaining": [ - s - for s in ["summary", "timeline", "root_cause", "impact", "action_items"] - if s not in state["sections_written"] - ], - "queried_data": state["queried_data"], - "postmortem_submitted": state["postmortem_submitted"] is not None, - } - logs = [ - f"Postmortem requested for {incident['title']}", - f"Severity: {incident['severity']}, Duration: {incident['duration_minutes']}min", - f"Affected services: {', '.join(incident['affected_services'])}", - ] - - return SREObservation( - task_id=task_id, - step=step_count, - context=context, - available_actions=BLAMELESS_POSTMORTEM_INFO["available_actions"], - alert_queue=[], - service_map=service_map, - metrics={ - "sections_complete": len(state["sections_written"]), - "sections_total": 5, - }, - logs=logs, - done=done, - message=message, - ) - - -def is_blameless_postmortem_done( - state: dict[str, Any], step_count: int, max_steps: int -) -> bool: - required = {"summary", "timeline", "root_cause", "impact", "action_items"} - return required.issubset(set(state["sections_written"])) or step_count >= max_steps diff --git a/scratch/sre/env/tasks/task_capacity_planning.py b/scratch/sre/env/tasks/task_capacity_planning.py deleted file mode 100644 index 4553b4a0f6ecb10002a1024d4051b42ea30bcd91..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_capacity_planning.py +++ /dev/null @@ -1,225 +0,0 @@ -from __future__ import annotations - -from copy import deepcopy -from typing import Any - -from env.models import SREAction, SREObservation - -CAPACITY_PLANNING_INFO = { - "id": "capacity_planning", - "name": "Capacity Planning", - "difficulty": "easy", - "description": "Analyze service metrics trends and recommend scaling decisions.", - "max_steps": 5, - "available_actions": ["query_capacity", "recommend_scaling", "escalate", "resolve"], -} - -CAPACITY_SCENARIOS = [ - { - "service": "api-gateway", - "current_replicas": 4, - "metrics": { - "cpu_avg": 72, - "cpu_p95": 89, - "mem_avg": 65, - "mem_p95": 78, - "latency_p99": 180, - "rps": 12000, - }, - "trend": "increasing", - "growth_rate_pct": 15, - "peak_event": "Black Friday sale in 2 weeks", - "correct_recommendation": "scale_out", - "target_replicas": 6, - }, - { - "service": "db-service", - "current_replicas": 3, - "metrics": { - "cpu_avg": 45, - "cpu_p95": 55, - "mem_avg": 70, - "mem_p95": 75, - "latency_p99": 30, - "rps": 8000, - }, - "trend": "stable", - "growth_rate_pct": 2, - "peak_event": None, - "correct_recommendation": "no_change", - "target_replicas": 3, - }, - { - "service": "payment-service", - "current_replicas": 6, - "metrics": { - "cpu_avg": 35, - "cpu_p95": 42, - "mem_avg": 48, - "mem_p95": 55, - "latency_p99": 95, - "rps": 5000, - }, - "trend": "decreasing", - "growth_rate_pct": -5, - "peak_event": None, - "correct_recommendation": "scale_in", - "target_replicas": 4, - }, - { - "service": "auth-service", - "current_replicas": 3, - "metrics": { - "cpu_avg": 88, - "cpu_p95": 97, - "mem_avg": 82, - "mem_p95": 91, - "latency_p99": 210, - "rps": 15000, - }, - "trend": "increasing", - "growth_rate_pct": 25, - "peak_event": "Product launch next week", - "correct_recommendation": "scale_out_urgent", - "target_replicas": 6, - }, -] - - -def init_capacity_planning_task( - catalogs: dict[str, Any], service_map: dict[str, list[str]], rng -) -> dict[str, Any]: - scenario = deepcopy(rng.choice(CAPACITY_SCENARIOS)) - return { - "task": "capacity_planning", - "scenario": scenario, - "queries_made": [], - "recommendation": None, - "escalated": False, - "invalid_actions": 0, - "service_map": service_map, - "message": ( - "Analyze the capacity metrics for the target service. Query capacity data " - "first, then recommend a scaling action. Use action_type='query_capacity' " - "to get metrics, then action_type='recommend_scaling' with parameters " - "containing your recommendation (scale_out, scale_in, no_change, or " - "scale_out_urgent) and target replica count." - ), - } - - -def apply_capacity_planning_action( - state: dict[str, Any], action: SREAction, step_count: int -) -> str: - scenario = state["scenario"] - - if action.action_type == "escalate": - state["escalated"] = True - return f"Capacity planning for {scenario['service']} escalated to the infrastructure team." - - if action.action_type == "resolve": - if state["recommendation"]: - return "Recommendation already submitted; waiting for episode termination." - state["invalid_actions"] += 1 - return "Resolve is only meaningful after submitting a scaling recommendation." - - if action.action_type == "query_capacity": - if action.target not in {scenario["service"], "all"}: - state["invalid_actions"] += 1 - return f"Query must target '{scenario['service']}' or 'all'." - if action.target not in state["queries_made"]: - state["queries_made"].append(action.target) - return ( - f"Capacity data for {scenario['service']}: " - f"replicas={scenario['current_replicas']}, " - f"cpu_avg={scenario['metrics']['cpu_avg']}%, " - f"cpu_p95={scenario['metrics']['cpu_p95']}%, " - f"mem_avg={scenario['metrics']['mem_avg']}%, " - f"mem_p95={scenario['metrics']['mem_p95']}%, " - f"latency_p99={scenario['metrics']['latency_p99']}ms, " - f"rps={scenario['metrics']['rps']}, " - f"trend={scenario['trend']}, " - f"growth_rate={scenario['growth_rate_pct']}%." - + ( - f" Upcoming event: {scenario['peak_event']}." - if scenario["peak_event"] - else "" - ) - ) - - if action.action_type == "recommend_scaling": - recommendation = str(action.parameters.get("recommendation", "")).lower() - target_replicas = action.parameters.get("target_replicas") - if target_replicas is not None: - try: - target_replicas = int(target_replicas) - except (ValueError, TypeError): - target_replicas = None - - state["recommendation"] = { - "recommendation": recommendation, - "target_replicas": target_replicas, - "service": scenario["service"], - "step": step_count + 1, - } - return ( - f"Scaling recommendation submitted: {recommendation} to " - f"{target_replicas} replicas for {scenario['service']}." - ) - - state["invalid_actions"] += 1 - return f"Action {action.action_type} is not supported for capacity planning." - - -def build_capacity_planning_observation( - task_id: str, - step_count: int, - state: dict[str, Any], - service_map: dict[str, list[str]], - done: bool, - message: str, -) -> SREObservation: - scenario = state["scenario"] - visible_metrics = {} - if scenario["service"] in state["queries_made"] or "all" in state["queries_made"]: - visible_metrics = {scenario["service"]: scenario["metrics"]} - - context = { - "instruction": ( - "Analyze capacity metrics and recommend a scaling decision. " - "Query capacity data first, then recommend: scale_out, scale_in, " - "no_change, or scale_out_urgent with a target replica count." - ), - "service": scenario["service"], - "current_replicas": scenario["current_replicas"], - "trend": scenario["trend"], - "growth_rate_pct": scenario["growth_rate_pct"], - "peak_event": scenario["peak_event"], - "queries_made": state["queries_made"], - "recommendation_submitted": state["recommendation"] is not None, - } - logs = [ - f"Capacity review requested for {scenario['service']}", - f"Traffic trend: {scenario['trend']} ({scenario['growth_rate_pct']}% growth)", - ] - if scenario["peak_event"]: - logs.append(f"Upcoming peak event: {scenario['peak_event']}") - - return SREObservation( - task_id=task_id, - step=step_count, - context=context, - available_actions=CAPACITY_PLANNING_INFO["available_actions"], - alert_queue=[], - service_map=service_map, - metrics=visible_metrics, - logs=logs, - done=done, - message=message, - ) - - -def is_capacity_planning_done( - state: dict[str, Any], step_count: int, max_steps: int -) -> bool: - return state["recommendation"] is not None or step_count >= max_steps diff --git a/scratch/sre/env/tasks/task_chaos_engineering.py b/scratch/sre/env/tasks/task_chaos_engineering.py deleted file mode 100644 index 7360af2782750fe583bf55a4e21e5cc9dbe36e24..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_chaos_engineering.py +++ /dev/null @@ -1,358 +0,0 @@ -from __future__ import annotations - -from copy import deepcopy -from typing import Any - -from env.models import SREAction, SREObservation - -CHAOS_ENGINEERING_INFO = { - "id": "chaos_engineering", - "name": "Chaos Engineering Scenario", - "difficulty": "hard", - "description": "Inject a controlled failure, observe system impact, and execute mitigation steps to restore stability.", - "max_steps": 12, - "available_actions": [ - "inject_chaos", - "observe_impact", - "mitigate_chaos", - "escalate", - "resolve", - ], -} - -CHAOS_SCENARIOS = [ - { - "id": "CHAOS-A", - "failure_type": "network_partition", - "target_service": "payment-service", - "description": "Simulate network partition between payment-service and db-service", - "impact": { - "payment-service": { - "status": "degraded", - "error_rate": 0.35, - "latency_p99": 5000, - "note": "timeout waiting for db responses", - }, - "db-service": { - "status": "healthy", - "error_rate": 0.002, - "latency_p99": 20, - "note": "isolated but healthy", - }, - "api-gateway": { - "status": "degraded", - "error_rate": 0.15, - "latency_p99": 350, - "note": "upstream payment failures", - }, - }, - "mitigation_steps": [ - { - "step": "enable_circuit_breaker", - "description": "Enable circuit breaker on payment-service to fail fast", - "effect": "reduces latency, errors become predictable", - }, - { - "step": "enable_fallback", - "description": "Enable cached payment status fallback", - "effect": "serves stale data, reduces user-facing errors", - }, - { - "step": "restore_network", - "description": "Restore network connectivity between services", - "effect": "full recovery", - }, - ], - "correct_order": [ - "enable_circuit_breaker", - "enable_fallback", - "restore_network", - ], - }, - { - "id": "CHAOS-B", - "failure_type": "pod_termination", - "target_service": "auth-service", - "description": "Randomly terminate 2 of 3 auth-service pods", - "impact": { - "auth-service": { - "status": "critical", - "error_rate": 0.45, - "latency_p99": 800, - "note": "single pod handling 3x traffic", - }, - "api-gateway": { - "status": "degraded", - "error_rate": 0.22, - "latency_p99": 400, - "note": "auth failures causing login errors", - }, - "user-service": { - "status": "warning", - "error_rate": 0.08, - "latency_p99": 180, - "note": "mild impact from auth retries", - }, - }, - "mitigation_steps": [ - { - "step": "scale_out_pods", - "description": "Scale auth-service from 1 to 4 pods", - "effect": "reduces load per pod", - }, - { - "step": "enable_rate_limiting", - "description": "Rate limit auth requests to prevent cascade", - "effect": "protects downstream services", - }, - { - "step": "verify_auth_health", - "description": "Verify all auth pods are healthy and serving", - "effect": "confirms recovery", - }, - ], - "correct_order": [ - "scale_out_pods", - "enable_rate_limiting", - "verify_auth_health", - ], - }, - { - "id": "CHAOS-C", - "failure_type": "database_failover", - "target_service": "db-service", - "description": "Trigger primary-to-replica database failover", - "impact": { - "db-service": { - "status": "critical", - "error_rate": 0.60, - "latency_p99": 10000, - "note": "failover in progress, writes blocked", - }, - "payment-service": { - "status": "critical", - "error_rate": 0.50, - "latency_p99": 8000, - "note": "all db writes failing", - }, - "auth-service": { - "status": "degraded", - "error_rate": 0.15, - "latency_p99": 300, - "note": "read queries hitting replica", - }, - }, - "mitigation_steps": [ - { - "step": "enable_read_only_mode", - "description": "Switch payment-service to read-only mode", - "effect": "prevents write errors, serves cached data", - }, - { - "step": "monitor_failover_progress", - "description": "Monitor DB failover status", - "effect": "visibility into recovery timeline", - }, - { - "step": "resume_writes", - "description": "Resume write operations after failover completes", - "effect": "full recovery", - }, - ], - "correct_order": [ - "enable_read_only_mode", - "monitor_failover_progress", - "resume_writes", - ], - }, -] - - -def init_chaos_engineering_task( - catalogs: dict[str, Any], service_map: dict[str, list[str]], rng -) -> dict[str, Any]: - scenario = deepcopy(rng.choice(CHAOS_SCENARIOS)) - return { - "task": "chaos_engineering", - "scenario": scenario, - "chaos_injected": False, - "impact_observed": False, - "mitigation_steps_completed": [], - "mitigation_in_wrong_order": 0, - "final_state": None, - "escalated": False, - "invalid_actions": 0, - "service_map": service_map, - "message": ( - "You are running a chaos engineering experiment. Inject the failure, " - "observe the impact on the service topology, then execute mitigation " - "steps in the correct order. Use action_type='inject_chaos' to start, " - "action_type='observe_impact' to check effects, and " - "action_type='mitigate_chaos' with the mitigation step name." - ), - } - - -def apply_chaos_engineering_action( - state: dict[str, Any], action: SREAction, step_count: int -) -> str: - scenario = state["scenario"] - - if action.action_type == "escalate": - state["escalated"] = True - return f"Chaos experiment for {scenario['target_service']} escalated to stop the experiment." - - if action.action_type == "resolve": - if state["final_state"]: - return "Chaos experiment already concluded." - state["invalid_actions"] += 1 - return "Resolve is only meaningful after completing the chaos experiment." - - if action.action_type == "inject_chaos": - if state["chaos_injected"]: - return "Chaos already injected. Observe impact or begin mitigation." - state["chaos_injected"] = True - return ( - f"Chaos injected: {scenario['description']}. " - f"Use action_type='observe_impact' to see the effects." - ) - - if action.action_type == "observe_impact": - if not state["chaos_injected"]: - state["invalid_actions"] += 1 - return ( - "No chaos has been injected yet. Use action_type='inject_chaos' first." - ) - state["impact_observed"] = True - impact_summary = "; ".join( - f"{svc}: {data['note']}" for svc, data in scenario["impact"].items() - ) - return f"Impact observed: {impact_summary}" - - if action.action_type == "mitigate_chaos": - if not state["chaos_injected"]: - state["invalid_actions"] += 1 - return "No chaos has been injected yet." - step_name = str(action.parameters.get("step", "")).strip() - if not step_name: - state["invalid_actions"] += 1 - return "Must specify mitigation step name in parameters." - - valid_steps = {s["step"] for s in scenario["mitigation_steps"]} - if step_name not in valid_steps: - state["invalid_actions"] += 1 - return f"Invalid mitigation step. Options: {sorted(valid_steps)}." - - if step_name in state["mitigation_steps_completed"]: - state["invalid_actions"] += 1 - return f"Mitigation step '{step_name}' already completed." - - expected_step = scenario["correct_order"][ - len(state["mitigation_steps_completed"]) - ] - step_info = next( - s for s in scenario["mitigation_steps"] if s["step"] == step_name - ) - - if step_name != expected_step: - state["mitigation_in_wrong_order"] += 1 - return ( - f"Out-of-order mitigation. Expected '{expected_step}' next, " - f"got '{step_name}'. {step_info['description']}." - ) - - state["mitigation_steps_completed"].append(step_name) - all_done = len(state["mitigation_steps_completed"]) == len( - scenario["correct_order"] - ) - - if all_done: - state["final_state"] = { - "outcome": "recovered", - "steps_completed": state["mitigation_steps_completed"], - "wrong_order_attempts": state["mitigation_in_wrong_order"], - "step": step_count + 1, - } - return ( - f"Mitigation step '{step_name}' completed: {step_info['effect']}. " - f"All mitigation steps complete! System recovered." - ) - - return ( - f"Mitigation step '{step_name}' completed: {step_info['effect']}. " - f"Continue with remaining steps." - ) - - state["invalid_actions"] += 1 - return f"Action {action.action_type} is not supported for chaos engineering." - - -def build_chaos_engineering_observation( - task_id: str, - step_count: int, - state: dict[str, Any], - service_map: dict[str, list[str]], - done: bool, - message: str, -) -> SREObservation: - scenario = state["scenario"] - visible_impact = {} - if state["impact_observed"]: - visible_impact = scenario["impact"] - - context = { - "instruction": ( - "Run a chaos engineering experiment: inject failure, observe impact, " - "then mitigate in the correct order. Mitigation steps must be executed " - "sequentially." - ), - "experiment": scenario["id"], - "failure_type": scenario["failure_type"], - "target_service": scenario["target_service"], - "description": scenario["description"], - "chaos_injected": state["chaos_injected"], - "impact_observed": state["impact_observed"], - "mitigation_steps_available": [ - {"step": s["step"], "description": s["description"]} - for s in scenario["mitigation_steps"] - if s["step"] not in state["mitigation_steps_completed"] - ], - "mitigation_steps_completed": state["mitigation_steps_completed"], - "remaining_steps": len(scenario["correct_order"]) - - len(state["mitigation_steps_completed"]), - "final_state": state["final_state"], - } - logs = [ - f"Chaos experiment: {scenario['description']}", - ] - if state["chaos_injected"]: - logs.append( - f"FAILURE INJECTED: {scenario['failure_type']} on {scenario['target_service']}" - ) - if state["mitigation_steps_completed"]: - logs.append( - f"Mitigation progress: {len(state['mitigation_steps_completed'])}/{len(scenario['correct_order'])} steps" - ) - - return SREObservation( - task_id=task_id, - step=step_count, - context=context, - available_actions=CHAOS_ENGINEERING_INFO["available_actions"], - alert_queue=[], - service_map=service_map, - metrics=visible_impact, - logs=logs, - done=done, - message=message, - ) - - -def is_chaos_engineering_done( - state: dict[str, Any], step_count: int, max_steps: int -) -> bool: - scenario = state["scenario"] - return ( - len(state["mitigation_steps_completed"]) == len(scenario["correct_order"]) - or step_count >= max_steps - ) diff --git a/scratch/sre/env/tasks/task_incident_diagnosis.py b/scratch/sre/env/tasks/task_incident_diagnosis.py deleted file mode 100644 index 45e6360155450487e3c503ba22d08f68c6eb12d2..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_incident_diagnosis.py +++ /dev/null @@ -1,378 +0,0 @@ -from __future__ import annotations - -from copy import deepcopy -from typing import Any - -from env.models import SREAction, SREObservation - -INCIDENT_DIAGNOSIS_INFO = { - "id": "incident_diagnosis", - "name": "Incident Diagnosis", - "difficulty": "medium", - "description": "Identify root cause service and failure mode from noisy metrics.", - "max_steps": 12, - "available_actions": ["diagnose", "resolve", "escalate"], -} - -BASE_METRICS = { - "api-gateway": {"cpu": 41, "mem": 58, "latency_p99": 120, "error_rate": 0.01}, - "auth-service": {"cpu": 37, "mem": 52, "latency_p99": 95, "error_rate": 0.008}, - "user-service": {"cpu": 35, "mem": 48, "latency_p99": 82, "error_rate": 0.006}, - "payment-service": {"cpu": 43, "mem": 56, "latency_p99": 110, "error_rate": 0.01}, - "db-service": {"cpu": 49, "mem": 63, "latency_p99": 20, "error_rate": 0.002}, -} - -# SLO targets per service (realistic production SLOs) -SERVICE_SLOS = { - "api-gateway": {"latency_p99_ms": 200, "error_rate_pct": 1.0, "availability": 99.95}, - "auth-service": {"latency_p99_ms": 150, "error_rate_pct": 0.5, "availability": 99.99}, - "user-service": {"latency_p99_ms": 120, "error_rate_pct": 0.5, "availability": 99.95}, - "payment-service": {"latency_p99_ms": 180, "error_rate_pct": 0.5, "availability": 99.99}, - "db-service": {"latency_p99_ms": 50, "error_rate_pct": 0.1, "availability": 99.999}, -} - - -def _jitter(value: float | int, rng, pct: float = 0.12) -> float | int: - """Apply random jitter of ±pct to a value, preserving type for ints.""" - factor = 1.0 + rng.uniform(-pct, pct) - result = value * factor - if isinstance(value, int): - return max(0, int(round(result))) - return max(0.0, round(result, 4)) - - -def _apply_jitter_to_metrics( - metrics: dict[str, dict[str, Any]], rng -) -> dict[str, dict[str, Any]]: - """Apply random noise to all metric values so each reset is unique.""" - jittered = {} - for service, service_metrics in metrics.items(): - jittered[service] = { - key: _jitter(val, rng, pct=0.12) - for key, val in service_metrics.items() - } - return jittered - - -def _apply_incident_metrics(scenario: dict[str, Any]) -> dict[str, dict[str, Any]]: - metrics = deepcopy(BASE_METRICS) - service = scenario["service"] - if service == "db-service": - metrics["db-service"].update({"cpu": 84, "mem": 79, "latency_p99": 460, "error_rate": 0.03}) - metrics["payment-service"].update( - {"cpu": 71, "mem": 66, "latency_p99": 320, "error_rate": 0.08} - ) - metrics["auth-service"].update( - {"cpu": 55, "mem": 58, "latency_p99": 190, "error_rate": 0.03} - ) - metrics["api-gateway"].update( - {"cpu": 62, "mem": 64, "latency_p99": 260, "error_rate": 0.04} - ) - elif service == "auth-service": - metrics["auth-service"].update( - {"cpu": 59, "mem": 68, "latency_p99": 180, "error_rate": 0.24} - ) - metrics["api-gateway"].update( - {"cpu": 63, "mem": 67, "latency_p99": 225, "error_rate": 0.18} - ) - elif service == "payment-service": - metrics["payment-service"].update( - {"cpu": 78, "mem": 97, "latency_p99": 280, "error_rate": 0.12} - ) - metrics["auth-service"].update( - {"cpu": 57, "mem": 63, "latency_p99": 165, "error_rate": 0.05} - ) - metrics["api-gateway"].update( - {"cpu": 61, "mem": 65, "latency_p99": 210, "error_rate": 0.04} - ) - elif service == "api-gateway": - metrics["api-gateway"].update( - {"cpu": 64, "mem": 60, "latency_p99": 300, "error_rate": 0.31} - ) - elif service == "user-service": - metrics["user-service"].update( - {"cpu": 56, "mem": 61, "latency_p99": 240, "error_rate": 0.19} - ) - metrics["auth-service"].update( - {"cpu": 52, "mem": 60, "latency_p99": 170, "error_rate": 0.08} - ) - metrics["api-gateway"].update( - {"cpu": 58, "mem": 65, "latency_p99": 195, "error_rate": 0.06} - ) - return metrics - - -def _build_logs(scenario: dict[str, Any]) -> dict[str, list[str]]: - normal_logs = { - "api-gateway": [ - "route cache warmup completed", - "edge auth policy refreshed", - ], - "auth-service": [ - "jwt signing keys rotated successfully", - "token cache hit ratio steady at 0.93", - ], - "user-service": [ - "profile aggregation pipeline healthy", - "read replica lag under 10ms", - ], - "payment-service": [ - "checkout authorization median stable", - "settlement worker backlog cleared", - ], - "db-service": [ - "autovacuum completed on ledger_entries", - "read pool healthy with 18 active connections", - ], - } - - scenario_logs = { - "db-service": [ - "slow query detected on ledger_entries: 428ms", - "connection pool wait exceeded 200ms", - "disk spill observed during payment reconciliation", - ], - "auth-service": [ - "upstream OAuth provider returned invalid audience", - "login request rejected with signature verification error", - "token introspection error rate exceeded SLO", - ], - "payment-service": [ - "worker restarted after memory limit breach", - "heap growth detected in checkout-session manager", - "OOM killer terminated pid 4412 on payment pod", - ], - "api-gateway": [ - "config drift detected: upstream target set to auth-v1-shadow", - "404 spike on /login due to invalid upstream cluster", - "reload succeeded but checksum mismatch persists", - ], - "user-service": [ - "deprecated /v1/profile endpoint returned 410 Gone", - "fallback client still calling sunset endpoint", - "retry storm detected for user profile hydration", - ], - } - root_service = scenario["service"] - normal_logs[root_service] = scenario_logs[root_service] - return normal_logs - - -def _build_incident_timeline(scenario: dict[str, Any], rng) -> list[dict[str, str]]: - """Build a realistic incident timeline with timestamped events.""" - base_hour = rng.randint(0, 23) - base_min = rng.randint(0, 45) - service = scenario["service"] - - timeline_templates = { - "db-service": [ - ("T+0m", f"PagerDuty alert: {service} p99 latency > 400ms"), - ("T+1m", "Upstream payment-service reports increased timeouts"), - ("T+2m", "auth-service latency degraded — possible cascade"), - ("T+3m", "api-gateway 5xx rate climbing, customer-facing impact"), - ("T+5m", "On-call SRE acknowledged the incident"), - ], - "auth-service": [ - ("T+0m", f"PagerDuty alert: {service} error rate > 20%"), - ("T+1m", "Login flow failures reported by customer support"), - ("T+2m", "api-gateway returning 401/403 at elevated rate"), - ("T+4m", "On-call SRE acknowledged the incident"), - ], - "payment-service": [ - ("T+0m", f"PagerDuty alert: {service} pod restarted (OOM killed)"), - ("T+1m", "Checkout flow latency increased to 280ms p99"), - ("T+2m", "Second pod restart observed, memory climbing on remaining pods"), - ("T+3m", "auth-service showing mild impact from backpressure"), - ("T+5m", "On-call SRE acknowledged the incident"), - ], - "api-gateway": [ - ("T+0m", f"PagerDuty alert: {service} 5xx rate > 30%"), - ("T+1m", "Customers reporting blank login page"), - ("T+3m", "Config reload attempted but no improvement"), - ("T+4m", "On-call SRE acknowledged the incident"), - ], - "user-service": [ - ("T+0m", f"PagerDuty alert: {service} 410 errors spiking"), - ("T+1m", "Profile hydration failures cascading to auth-service"), - ("T+2m", "api-gateway error rate climbing from upstream failures"), - ("T+4m", "On-call SRE acknowledged the incident"), - ], - } - events = timeline_templates.get(service, []) - return [ - {"time": t, "event": e, "utc": f"2026-03-15T{base_hour:02d}:{base_min:02d}:00Z"} - for t, e in events - ] - - -def _compute_slo_status( - metrics: dict[str, dict[str, Any]] -) -> dict[str, dict[str, Any]]: - """Compute SLO burn rate and budget remaining per service.""" - slo_status = {} - for service, slos in SERVICE_SLOS.items(): - current = metrics.get(service, {}) - latency = current.get("latency_p99", 0) - error_rate = current.get("error_rate", 0) * 100 # convert to pct - - latency_budget_burn = max(0.0, (latency - slos["latency_p99_ms"]) / slos["latency_p99_ms"]) - error_budget_burn = max(0.0, (error_rate - slos["error_rate_pct"]) / slos["error_rate_pct"]) - - slo_status[service] = { - "latency_slo_ms": slos["latency_p99_ms"], - "latency_current_ms": latency, - "latency_budget_burn_rate": round(latency_budget_burn, 3), - "error_slo_pct": slos["error_rate_pct"], - "error_current_pct": round(error_rate, 3), - "error_budget_burn_rate": round(error_budget_burn, 3), - "slo_breached": latency_budget_burn > 0 or error_budget_burn > 0, - } - return slo_status - - -def init_incident_diagnosis_task( - catalogs: dict[str, Any], service_map: dict[str, list[str]], rng -) -> dict[str, Any]: - scenario = deepcopy(rng.choice(catalogs["incidents"])) - raw_metrics = _apply_incident_metrics(scenario) - full_metrics = _apply_jitter_to_metrics(raw_metrics, rng) - visible_metrics = { - service: {"status": "query_metrics_required"} for service in full_metrics - } - slo_status = _compute_slo_status(full_metrics) - timeline = _build_incident_timeline(scenario, rng) - - return { - "task": "incident_diagnosis", - "incident": scenario, - "full_metrics": full_metrics, - "visible_metrics": visible_metrics, - "service_logs": _build_logs(scenario), - "visible_logs": [], - "queried_metrics": [], - "queried_logs": [], - "submitted_diagnosis": None, - "escalations": [], - "invalid_actions": 0, - "slo_status": slo_status, - "incident_timeline": timeline, - "message": ( - "A production incident is in progress. Use action_type='diagnose' with " - "parameters.operation set to 'query_metrics', 'query_logs', or " - "'submit_diagnosis'. Submit the root cause service in target and include " - "failure_mode in parameters. Use the SLO status and incident timeline " - "to guide your investigation." - ), - "service_map": service_map, - } - - -def apply_incident_diagnosis_action( - state: dict[str, Any], action: SREAction, step_count: int -) -> str: - if action.action_type == "escalate": - state["escalations"].append( - {"service": action.target, "step": step_count + 1, "reason": action.reasoning} - ) - return f"Escalation recorded for {action.target}." - - if action.action_type == "resolve": - if state["submitted_diagnosis"]: - return "Diagnosis already submitted; waiting for episode termination." - state["invalid_actions"] += 1 - return "Resolve is only meaningful after you submit a diagnosis." - - operation = str(action.parameters.get("operation", "")).lower() - if action.action_type != "diagnose" or operation not in { - "query_metrics", - "query_logs", - "submit_diagnosis", - }: - state["invalid_actions"] += 1 - return ( - "For incident diagnosis, use action_type='diagnose' with operation " - "query_metrics, query_logs, or submit_diagnosis." - ) - - if action.target not in state["full_metrics"]: - state["invalid_actions"] += 1 - return f"Service {action.target} is not part of the incident topology." - - if operation == "query_metrics": - if action.target not in state["queried_metrics"]: - state["queried_metrics"].append(action.target) - state["visible_metrics"][action.target] = deepcopy(state["full_metrics"][action.target]) - return f"Metrics revealed for {action.target}." - - if operation == "query_logs": - if action.target not in state["queried_logs"]: - state["queried_logs"].append(action.target) - state["visible_logs"].extend( - [f"{action.target}: {line}" for line in state["service_logs"][action.target]] - ) - return f"Logs revealed for {action.target}." - - failure_mode = str(action.parameters.get("failure_mode", "")).strip() - state["submitted_diagnosis"] = { - "service": action.target, - "failure_mode": failure_mode, - "step": step_count + 1, - } - return ( - f"Diagnosis submitted: root cause service={action.target}, " - f"failure_mode={failure_mode or 'unspecified'}." - ) - - -def build_incident_diagnosis_observation( - task_id: str, - step_count: int, - state: dict[str, Any], - service_map: dict[str, list[str]], - done: bool, - message: str, -) -> SREObservation: - context = { - "instruction": ( - "A production incident is in progress. Query metrics and logs selectively " - "to identify the root cause. Use the SLO status to identify which services " - "are breaching their error budgets. Submit your diagnosis using " - "action_type='diagnose' and parameters.operation='submit_diagnosis'." - ), - "service_topology": { - "api-gateway": ["auth-service"], - "auth-service": ["user-service", "payment-service"], - "payment-service": ["db-service"], - }, - "service_topology_hint": "api-gateway -> auth-service -> user-service; payment-service -> db-service", - "incident_timeline": state.get("incident_timeline", []), - "slo_status": state.get("slo_status", {}), - "queried_metrics": state["queried_metrics"], - "queried_logs": state["queried_logs"], - "submitted_diagnosis": state["submitted_diagnosis"], - "failure_mode_options": [ - "high latency", - "high error rate", - "memory leak", - "config drift", - "deprecated endpoint dependency", - ], - } - return SREObservation( - task_id=task_id, - step=step_count, - context=context, - available_actions=INCIDENT_DIAGNOSIS_INFO["available_actions"], - alert_queue=[], - service_map=service_map, - metrics=deepcopy(state["visible_metrics"]), - logs=list(state["visible_logs"]), - done=done, - message=message, - ) - - -def is_incident_diagnosis_done( - state: dict[str, Any], step_count: int, max_steps: int -) -> bool: - return state["submitted_diagnosis"] is not None or step_count >= max_steps diff --git a/scratch/sre/env/tasks/task_multi_incident_correlation.py b/scratch/sre/env/tasks/task_multi_incident_correlation.py deleted file mode 100644 index f8dc8e52b6e9331453270e8a486903ccff514466..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_multi_incident_correlation.py +++ /dev/null @@ -1,225 +0,0 @@ -from __future__ import annotations - -from copy import deepcopy -from typing import Any - -from env.models import SREAction, SREObservation - -MULTI_INCIDENT_CORRELATION_INFO = { - "id": "multi_incident_correlation", - "name": "Multi-Incident Correlation", - "difficulty": "medium", - "description": "Identify whether multiple simultaneous alerts share a common root cause or are independent incidents.", - "max_steps": 10, - "available_actions": ["correlate", "diagnose", "escalate", "resolve"], -} - -CORRELATION_SCENARIOS = [ - { - "id": "CORR-A", - "type": "shared_root_cause", - "alerts": [ - { - "id": "ALT-101", - "service": "api-gateway", - "severity": "critical", - "message": "5xx rate > 15%", - }, - { - "id": "ALT-102", - "service": "auth-service", - "severity": "critical", - "message": "Error rate > 20%", - }, - { - "id": "ALT-103", - "service": "db-service", - "severity": "warning", - "message": "Connection pool exhaustion", - }, - ], - "root_cause": "db-service", - "root_cause_failure": "connection pool exhaustion causing cascade", - "independent_alerts": [], - }, - { - "id": "CORR-B", - "type": "independent_incidents", - "alerts": [ - { - "id": "ALT-201", - "service": "payment-service", - "severity": "critical", - "message": "OOM restart on checkout worker", - }, - { - "id": "ALT-202", - "service": "user-service", - "severity": "warning", - "message": "Deprecated endpoint 410 errors", - }, - { - "id": "ALT-203", - "service": "api-gateway", - "severity": "warning", - "message": "Config reload checksum mismatch", - }, - ], - "root_cause": None, - "root_cause_failure": None, - "independent_alerts": ["ALT-201", "ALT-202", "ALT-203"], - }, - { - "id": "CORR-C", - "type": "partial_correlation", - "alerts": [ - { - "id": "ALT-301", - "service": "payment-service", - "severity": "critical", - "message": "Latency spike on checkout", - }, - { - "id": "ALT-302", - "service": "db-service", - "severity": "critical", - "message": "Slow queries on ledger table", - }, - { - "id": "ALT-303", - "service": "auth-service", - "severity": "warning", - "message": "Token cache miss rate elevated", - }, - ], - "root_cause": "db-service", - "root_cause_failure": "slow queries affecting payment-service only", - "independent_alerts": ["ALT-303"], - }, -] - - -def init_multi_incident_correlation_task( - catalogs: dict[str, Any], service_map: dict[str, list[str]], rng -) -> dict[str, Any]: - scenario = deepcopy(rng.choice(CORRELATION_SCENARIOS)) - visible_alerts = deepcopy(scenario["alerts"]) - for alert in visible_alerts: - alert["status"] = "pending" - alert["correlated_with"] = None - - return { - "task": "multi_incident_correlation", - "scenario": scenario, - "alerts": visible_alerts, - "queried_services": [], - "correlation_submitted": None, - "escalated_alerts": [], - "invalid_actions": 0, - "service_map": service_map, - "message": ( - "Multiple alerts are firing simultaneously. Determine if they share a " - "common root cause or are independent. Use action_type='diagnose' to query " - "metrics for services, then action_type='correlate' with parameters " - "containing your correlation analysis." - ), - } - - -def apply_multi_incident_correlation_action( - state: dict[str, Any], action: SREAction, step_count: int -) -> str: - scenario = state["scenario"] - - if action.action_type == "escalate": - state["escalated_alerts"].append(action.target) - return f"Alert {action.target} escalated for separate investigation." - - if action.action_type == "resolve": - if state["correlation_submitted"]: - return "Correlation already submitted; waiting for episode termination." - state["invalid_actions"] += 1 - return "Resolve is only meaningful after submitting correlation analysis." - - if action.action_type == "diagnose": - operation = str(action.parameters.get("operation", "")).lower() - if operation != "query_metrics": - state["invalid_actions"] += 1 - return "Use operation='query_metrics' for correlation tasks." - if action.target not in state["queried_services"]: - state["queried_services"].append(action.target) - alert_services = {a["service"] for a in state["alerts"]} - if action.target not in alert_services: - state["invalid_actions"] += 1 - return f"Service {action.target} is not part of the current alert set." - return f"Metrics queried for {action.target}. Check for anomalous patterns." - - if action.action_type == "correlate": - correlation_type = str(action.parameters.get("correlation_type", "")).lower() - root_cause = str(action.parameters.get("root_cause_service", "")).strip() - alert_ids = action.parameters.get("alert_ids", []) - if not isinstance(alert_ids, list): - alert_ids = [alert_ids] - - state["correlation_submitted"] = { - "correlation_type": correlation_type, - "root_cause_service": root_cause, - "alert_ids": alert_ids, - "step": step_count + 1, - } - return ( - f"Correlation submitted: type={correlation_type}, " - f"root_cause={root_cause or 'none'}, alerts={alert_ids}." - ) - - state["invalid_actions"] += 1 - return ( - f"Action {action.action_type} is not supported for multi-incident correlation." - ) - - -def build_multi_incident_correlation_observation( - task_id: str, - step_count: int, - state: dict[str, Any], - service_map: dict[str, list[str]], - done: bool, - message: str, -) -> SREObservation: - context = { - "instruction": ( - "Analyze the firing alerts and determine correlation. Options: " - "shared_root_cause (all alerts from one root cause), " - "independent_incidents (each alert is separate), or " - "partial_correlation (some share a root cause, others are independent). " - "Use action_type='correlate' with correlation_type, root_cause_service, " - "and alert_ids in parameters." - ), - "alerts": state["alerts"], - "queried_services": state["queried_services"], - "service_topology": service_map, - "correlation_submitted": state["correlation_submitted"] is not None, - } - logs = [ - f"{a['id']} {a['service']} {a['severity']}: {a['message']}" - for a in state["alerts"] - ] - - return SREObservation( - task_id=task_id, - step=step_count, - context=context, - available_actions=MULTI_INCIDENT_CORRELATION_INFO["available_actions"], - alert_queue=state["alerts"], - service_map=service_map, - metrics={"queried_services": state["queried_services"]}, - logs=logs, - done=done, - message=message, - ) - - -def is_multi_incident_correlation_done( - state: dict[str, Any], step_count: int, max_steps: int -) -> bool: - return state["correlation_submitted"] is not None or step_count >= max_steps diff --git a/scratch/sre/env/tasks/task_on_call_handoff.py b/scratch/sre/env/tasks/task_on_call_handoff.py deleted file mode 100644 index 7ffd3b9dc3942ceb6bdb8ecaedd1f1173f986c98..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_on_call_handoff.py +++ /dev/null @@ -1,219 +0,0 @@ -from __future__ import annotations - -from copy import deepcopy -from typing import Any - -from env.models import SREAction, SREObservation - -ON_CALL_HANDOFF_INFO = { - "id": "on_call_handoff", - "name": "On-Call Handoff", - "difficulty": "easy", - "description": "Summarize active incidents, pending actions, and service health for the incoming on-call engineer.", - "max_steps": 6, - "available_actions": ["summarize", "escalate", "resolve"], -} - -SHIFT_CONTEXTS = [ - { - "shift": "night_to_day", - "active_incidents": [ - { - "id": "INC-101", - "service": "payment-service", - "severity": "P2", - "status": "investigating", - "summary": "Elevated error rate on checkout endpoint", - }, - { - "id": "INC-102", - "service": "db-service", - "severity": "P3", - "status": "monitoring", - "summary": "Replica lag intermittent but self-healing", - }, - ], - "pending_actions": [ - "Deploy hotfix v2.4.1 to payment-service", - "Monitor db replica lag for next 2 hours", - ], - "resolved_last_shift": 2, - }, - { - "shift": "day_to_evening", - "active_incidents": [ - { - "id": "INC-201", - "service": "auth-service", - "severity": "P1", - "status": "mitigating", - "summary": "JWT validation failures blocking login", - }, - { - "id": "INC-202", - "service": "api-gateway", - "severity": "P2", - "status": "investigating", - "summary": "5xx spike on /login route", - }, - { - "id": "INC-203", - "service": "user-service", - "severity": "P3", - "status": "acknowledged", - "summary": "Profile hydration timeout", - }, - ], - "pending_actions": [ - "Rollback auth-service to v3.1.0", - "Verify api-gateway upstream config", - "Escalate INC-201 to platform team if not resolved in 30min", - ], - "resolved_last_shift": 1, - }, - { - "shift": "evening_to_night", - "active_incidents": [ - { - "id": "INC-301", - "service": "payment-service", - "severity": "P2", - "status": "monitoring", - "summary": "Memory pressure after scale-out", - }, - ], - "pending_actions": [ - "Verify memory stabilization after 1 hour", - "Run load test on payment-service before peak traffic", - ], - "resolved_last_shift": 3, - }, -] - -SERVICE_HEALTH_BASE = { - "api-gateway": {"status": "healthy", "error_rate": 0.01, "latency_p99": 120}, - "auth-service": {"status": "healthy", "error_rate": 0.008, "latency_p99": 95}, - "user-service": {"status": "healthy", "error_rate": 0.006, "latency_p99": 82}, - "payment-service": {"status": "healthy", "error_rate": 0.01, "latency_p99": 110}, - "db-service": {"status": "healthy", "error_rate": 0.002, "latency_p99": 20}, -} - - -def init_on_call_handoff_task( - catalogs: dict[str, Any], service_map: dict[str, list[str]], rng -) -> dict[str, Any]: - context = deepcopy(rng.choice(SHIFT_CONTEXTS)) - health = deepcopy(SERVICE_HEALTH_BASE) - for inc in context["active_incidents"]: - svc = inc["service"] - if svc in health: - if inc["severity"] == "P1": - health[svc] = { - "status": "critical", - "error_rate": 0.25, - "latency_p99": 300, - } - elif inc["severity"] == "P2": - health[svc] = { - "status": "degraded", - "error_rate": 0.12, - "latency_p99": 220, - } - elif inc["severity"] == "P3": - health[svc] = { - "status": "warning", - "error_rate": 0.05, - "latency_p99": 150, - } - - return { - "task": "on_call_handoff", - "shift_context": context, - "service_health": health, - "service_map": service_map, - "handoff_summary": None, - "escalated_incidents": [], - "invalid_actions": 0, - "message": ( - "You are the outgoing on-call SRE. Summarize the current shift for the " - "incoming engineer. Include active incidents, pending actions, and service " - "health. Use action_type='summarize' with target='handoff' and parameters " - "containing your summary." - ), - } - - -def apply_on_call_handoff_action( - state: dict[str, Any], action: SREAction, step_count: int -) -> str: - if action.action_type == "escalate": - state["escalated_incidents"].append(action.target) - return f"Incident {action.target} escalated to the next-level on-call." - - if action.action_type == "resolve": - if state["handoff_summary"]: - return "Handoff already submitted; waiting for episode termination." - state["invalid_actions"] += 1 - return "Resolve is only meaningful after submitting the handoff summary." - - if action.action_type == "summarize": - summary = str(action.parameters.get("summary", "")).strip() - if not summary: - state["invalid_actions"] += 1 - return "Handoff summary cannot be empty." - state["handoff_summary"] = { - "summary": summary, - "step": step_count + 1, - "active_incidents_count": len(state["shift_context"]["active_incidents"]), - "pending_actions_count": len(state["shift_context"]["pending_actions"]), - } - return "Handoff summary submitted successfully." - - state["invalid_actions"] += 1 - return f"Action {action.action_type} is not supported for on-call handoff." - - -def build_on_call_handoff_observation( - task_id: str, - step_count: int, - state: dict[str, Any], - service_map: dict[str, list[str]], - done: bool, - message: str, -) -> SREObservation: - context = { - "instruction": ( - "Create a concise handoff summary for the incoming on-call engineer. " - "Include: active incidents with severity, pending actions, service health " - "highlights, and any risks to watch for." - ), - "shift": state["shift_context"]["shift"], - "active_incidents": state["shift_context"]["active_incidents"], - "pending_actions": state["shift_context"]["pending_actions"], - "resolved_last_shift": state["shift_context"]["resolved_last_shift"], - "service_health": state["service_health"], - "handoff_submitted": state["handoff_summary"] is not None, - } - logs = [ - f"Shift change: {state['shift_context']['shift']} rotation", - f"{len(state['shift_context']['active_incidents'])} active incident(s) to hand off", - ] - - return SREObservation( - task_id=task_id, - step=step_count, - context=context, - available_actions=ON_CALL_HANDOFF_INFO["available_actions"], - alert_queue=[], - service_map=service_map, - metrics={"services": state["service_health"]}, - logs=logs, - done=done, - message=message, - ) - - -def is_on_call_handoff_done( - state: dict[str, Any], step_count: int, max_steps: int -) -> bool: - return state["handoff_summary"] is not None or step_count >= max_steps diff --git a/scratch/sre/env/tasks/task_registry.py b/scratch/sre/env/tasks/task_registry.py deleted file mode 100644 index fa5fb59b69bfe29f5b3e20ebf4ae85d2c95dede1..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_registry.py +++ /dev/null @@ -1,7 +0,0 @@ -EASY_TASKS = ["alert_triage", "on_call_handoff", "capacity_planning"] - -MEDIUM_TASKS = ["incident_diagnosis", "multi_incident_correlation", "auto_remediation"] - -HARD_TASKS = ["runbook_execution", "blameless_postmortem", "chaos_engineering"] - -ALL_TASKS = EASY_TASKS + MEDIUM_TASKS + HARD_TASKS diff --git a/scratch/sre/env/tasks/task_runbook_execution.py b/scratch/sre/env/tasks/task_runbook_execution.py deleted file mode 100644 index 8abde5bbacb1803d78fe0b939c56695c8b2069df..0000000000000000000000000000000000000000 --- a/scratch/sre/env/tasks/task_runbook_execution.py +++ /dev/null @@ -1,242 +0,0 @@ -from __future__ import annotations - -from copy import deepcopy -from typing import Any - -from env.models import SREAction, SREObservation - -RUNBOOK_EXECUTION_INFO = { - "id": "runbook_execution", - "name": "Runbook Execution", - "difficulty": "hard", - "description": "Execute all runbook steps in order with correct parameters.", - "max_steps": 15, - "available_actions": ["execute_step", "resolve", "escalate"], -} - - -def _normalize_int(value: Any) -> int | None: - """Coerce an int-like value from JSON (may arrive as str or int).""" - if isinstance(value, int): - return value - if isinstance(value, float) and value == int(value): - return int(value) - if isinstance(value, str): - stripped = value.strip().lstrip("+") - try: - return int(stripped) - except ValueError: - return None - return None - - -def _normalize_replicas(value: Any) -> int | None: - return _normalize_int(value) - - -def init_runbook_execution_task( - catalogs: dict[str, Any], service_map: dict[str, list[str]], rng -) -> dict[str, Any]: - runbook = deepcopy(catalogs["runbooks"][0]) - pid = rng.randint(4100, 5300) - instance_id = f"pay-{rng.randint(10, 99)}-{rng.randint(1000, 9999)}" - memory_pct = rng.randint(91, 97) - return { - "task": "runbook_execution", - "runbook": runbook, - "current_step_index": 0, - "completed_steps": [], - "tool_outputs": {}, - "hidden_values": { - "pid": pid, - "instance_id": instance_id, - "memory_pct": memory_pct, - "process_name": "python /srv/payment/checkout_worker.py", - }, - "pid_dependency_ok": False, - "out_of_order_attempts": 0, - "wrong_parameter_attempts": 0, - "invalid_actions": 0, - "report_submitted": None, - "escalations": [], - "message": ( - "Use action_type='execute_step' and set target to the exact next runbook " - "step name. Each step requires the right parameters and later steps depend " - "on outputs unlocked by earlier steps." - ), - "service_map": service_map, - } - - -def apply_runbook_execution_action( - state: dict[str, Any], action: SREAction, step_count: int -) -> str: - if action.action_type == "escalate": - state["escalations"].append( - {"step": step_count + 1, "target": action.target, "reason": action.reasoning} - ) - return f"Escalation recorded for {action.target}." - - if action.action_type == "resolve": - if len(state["completed_steps"]) == len(state["runbook"]["steps"]): - return "Runbook already complete; episode will terminate automatically." - state["invalid_actions"] += 1 - return "Resolve cannot skip the remaining runbook steps." - - if action.action_type != "execute_step": - state["invalid_actions"] += 1 - return "Runbook execution only accepts execute_step, escalate, or resolve." - - if state["current_step_index"] >= len(state["runbook"]["steps"]): - state["invalid_actions"] += 1 - return "All runbook steps are already complete." - - expected_step = state["runbook"]["steps"][state["current_step_index"]] - if action.target != expected_step["name"]: - state["out_of_order_attempts"] += 1 - return ( - f"Out-of-order step. Expected {expected_step['name']} next, " - f"received {action.target}." - ) - - hidden = state["hidden_values"] - params = action.parameters - valid = False - output: dict[str, Any] = {} - - if action.target == "check_memory_usage": - valid = params.get("service") == "payment-service" - output = { - "service": "payment-service", - "memory_usage_pct": hidden["memory_pct"], - "rss_mb": 2147, - "note": "memory pressure above the 90% mitigation threshold", - } - elif action.target == "identify_top_processes": - valid = params.get("service") == "payment-service" - output = { - "service": "payment-service", - "pid": hidden["pid"], - "instance_id": hidden["instance_id"], - "process": hidden["process_name"], - "memory_mb": 1532, - } - elif action.target == "capture_heap_dump": - submitted_pid = _normalize_int(params.get("pid")) - valid = ( - params.get("service") == "payment-service" - and submitted_pid == hidden["pid"] - ) - if valid: - state["pid_dependency_ok"] = True - output = { - "artifact": f"/tmp/heapdump-payment-{hidden['pid']}.hprof", - "pid": submitted_pid, - "captured": bool(valid), - } - elif action.target == "cordon_instance": - submitted_instance = str(params.get("instance_id", "")).strip() - valid = submitted_instance == hidden["instance_id"] - output = { - "instance_id": submitted_instance, - "status": "cordoned" if valid else "failed", - } - elif action.target == "scale_out": - replicas = _normalize_replicas(params.get("replicas")) - valid = params.get("service") == "payment-service" and replicas == 2 - output = { - "service": "payment-service", - "previous_replicas": 4, - "current_replicas": 6 if valid else 4, - } - elif action.target == "verify_recovery": - submitted_wait = _normalize_int(params.get("wait_seconds")) - valid = ( - params.get("service") == "payment-service" - and submitted_wait == 30 - ) - output = { - "service": "payment-service", - "latency_p99": 105, - "error_rate": 0.012, - "memory_usage_pct": 68, - "recovered": bool(valid), - } - elif action.target == "file_incident_report": - summary = str(params.get("summary", "")).strip() - valid = params.get("severity") == "P2" and bool(summary) - output = { - "ticket_id": "INC-2048", - "severity": params.get("severity"), - "summary": summary, - } - if valid: - state["report_submitted"] = output - - if not valid: - state["wrong_parameter_attempts"] += 1 - return ( - f"Step {action.target} used incorrect parameters and did not complete. " - f"Expected parameters similar to {expected_step['parameters']}." - ) - - state["tool_outputs"][action.target] = output - state["completed_steps"].append(action.target) - state["current_step_index"] += 1 - return f"Completed {action.target} successfully." - - -def build_runbook_execution_observation( - task_id: str, - step_count: int, - state: dict[str, Any], - service_map: dict[str, list[str]], - done: bool, - message: str, -) -> SREObservation: - next_step = None - if state["current_step_index"] < len(state["runbook"]["steps"]): - next_step = state["runbook"]["steps"][state["current_step_index"]] - - context = { - "instruction": ( - "Execute the runbook in strict order. Use action_type='execute_step', " - "target the exact next step name, and carry forward required outputs " - "such as pid and instance_id." - ), - "current_runbook": state["runbook"]["name"], - "completed_steps": state["completed_steps"], - "next_expected_step": next_step, - "tool_outputs": deepcopy(state["tool_outputs"]), - "remaining_steps": len(state["runbook"]["steps"]) - len(state["completed_steps"]), - } - logs = [ - "payment-service memory alert is firing on multiple pods", - "one payment pod was restarted by the kernel OOM killer", - ] - - return SREObservation( - task_id=task_id, - step=step_count, - context=context, - available_actions=RUNBOOK_EXECUTION_INFO["available_actions"], - alert_queue=[], - service_map=service_map, - metrics={ - "payment-service": { - "cpu": 77, - "mem": state["hidden_values"]["memory_pct"], - "latency_p99": 245, - "error_rate": 0.11, - } - }, - logs=logs, - done=done, - message=message, - ) - - -def is_runbook_execution_done( - state: dict[str, Any], step_count: int, max_steps: int -) -> bool: - return len(state["completed_steps"]) == len(state["runbook"]["steps"]) or step_count >= max_steps diff --git a/scratch/sre/inference.py b/scratch/sre/inference.py deleted file mode 100644 index 42c88fbbc2d65854c3604374e8e2486556d87d8b..0000000000000000000000000000000000000000 --- a/scratch/sre/inference.py +++ /dev/null @@ -1,476 +0,0 @@ -""" -STRICT FORMAT - do not deviate from [START]/[STEP]/[END] lines. -All tasks run sequentially. -""" - -import json -import os -import re -import time - -import httpx -from openai import OpenAI -from env.graders.scoring import clamp_task_score - -API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1") -MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct") -HF_TOKEN = os.getenv("HF_TOKEN") -ENV_URL = os.getenv("ENV_URL", "http://localhost:7860") - -TASKS = [ - "alert_triage", - "on_call_handoff", - "capacity_planning", - "incident_diagnosis", - "multi_incident_correlation", - "auto_remediation", - "runbook_execution", - "blameless_postmortem", - "chaos_engineering", -] -MAX_STEPS = { - "alert_triage": 10, - "on_call_handoff": 6, - "capacity_planning": 5, - "incident_diagnosis": 12, - "multi_incident_correlation": 10, - "auto_remediation": 8, - "runbook_execution": 15, - "blameless_postmortem": 8, - "chaos_engineering": 12, -} -BENCHMARK = "sre-automation-env" -SCORE_FORMAT_PRECISION = 4 - -client = OpenAI(base_url=API_BASE_URL, api_key=HF_TOKEN or "hf_token_not_set") - -# --------------------------------------------------------------------------- -# Task-specific system prompts -# --------------------------------------------------------------------------- - -SYSTEM_PROMPT_BASE = """\ -You are an expert Site Reliability Engineer AI agent. -You receive observations from a simulated SRE environment and must -respond with a valid JSON action object. Always respond with ONLY -valid JSON matching this schema: -{ - "action_type": "", - "target": "", - "parameters": {}, - "reasoning": "" -} -Do NOT wrap your response in markdown code blocks. Output raw JSON only.""" - -SYSTEM_PROMPT_ALERT_TRIAGE = ( - SYSTEM_PROMPT_BASE - + """ - -TASK: Alert Triage -You have a queue of firing alerts. Your goal is to: -1. First, TRIAGE each alert: use action_type="triage" with target= and - parameters={"decision": "ignore", "priority": "P3"} for flapping or duplicate alerts. - Use {"decision": "actionable", "priority": "P1|P2|P3"} for real alerts. - Priority mapping: critical->P1, warning->P2, info->P3. -2. Then, ACKNOWLEDGE actionable alerts in priority order (P1 first, then P2, then P3): - use action_type="acknowledge" with target=. - -Key rules: -- Alerts with is_flapping=true or is_duplicate=true should be triaged as "ignore" -- Critical alerts are P1, warning alerts are P2, info alerts are P3 -- Acknowledge P1 alerts first, then P2, then P3 -- Do NOT acknowledge flapping or duplicate alerts""" -) - -SYSTEM_PROMPT_ON_CALL_HANDOFF = ( - SYSTEM_PROMPT_BASE - + """ - -TASK: On-Call Handoff -You are the outgoing on-call SRE. Create a concise handoff summary for the incoming engineer. -Include: active incidents with severity, pending actions, service health highlights, and risks. -Use action_type="summarize" with target="handoff" and parameters={"summary": ""}. -The summary should be at least 20 words and mention incident IDs, severity levels, and pending actions.""" -) - -SYSTEM_PROMPT_CAPACITY_PLANNING = ( - SYSTEM_PROMPT_BASE - + """ - -TASK: Capacity Planning -Analyze service capacity metrics and recommend a scaling decision. -1. First query data: action_type="query_capacity", target= -2. Then recommend: action_type="recommend_scaling" with parameters: - {"recommendation": "scale_out|scale_in|no_change|scale_out_urgent", "target_replicas": } - -Consider: CPU/memory utilization, traffic trends, growth rate, and upcoming peak events. -If CPU p95 > 85% or trend is increasing with a peak event coming -> scale_out or scale_out_urgent. -If CPU p95 < 50% and trend is decreasing -> scale_in. -If metrics are stable and within thresholds -> no_change.""" -) - -SYSTEM_PROMPT_INCIDENT = ( - SYSTEM_PROMPT_BASE - + """ - -TASK: Incident Diagnosis -A production incident is in progress across a microservice topology: - api-gateway -> auth-service -> user-service - -> payment-service -> db-service - -Your goal is to identify the ROOT CAUSE service and its failure mode. -Strategy: -1. Check the SLO status and incident timeline in the context first -2. Query metrics for services showing the highest SLO burn rates (action_type="diagnose", - target=, parameters={"operation": "query_metrics"}) -3. Query logs for the most suspicious services (parameters={"operation": "query_logs"}) -4. Work backwards through the dependency chain — the true root cause is usually the - furthest downstream service with anomalous metrics -5. Submit diagnosis (parameters={"operation": "submit_diagnosis", "failure_mode": ""}) - -Failure modes: "high latency", "high error rate", "memory leak", "config drift", -"deprecated endpoint dependency" - -Be efficient: query no more than 3-4 services before submitting your diagnosis.""" -) - -SYSTEM_PROMPT_MULTI_INCIDENT = ( - SYSTEM_PROMPT_BASE - + """ - -TASK: Multi-Incident Correlation -Multiple alerts are firing simultaneously. Determine if they share a common root cause -or are independent incidents. -1. Query metrics for each affected service: action_type="diagnose", target=, - parameters={"operation": "query_metrics"} -2. Analyze patterns: are metrics anomalous across all services (shared root cause), - or only in specific services (independent)? -3. Submit correlation: action_type="correlate" with parameters: - {"correlation_type": "shared_root_cause|independent_incidents|partial_correlation", - "root_cause_service": "", "alert_ids": ["ALT-xxx", ...]} - -For shared_root_cause: identify the single root cause service affecting all alerts. -For independent_incidents: set root_cause_service to "none", list all alert ids. -For partial_correlation: identify which alerts share a root cause and which are independent.""" -) - -SYSTEM_PROMPT_AUTO_REMEDIATION = ( - SYSTEM_PROMPT_BASE - + """ - -TASK: Auto-Remediation with Rollback -A production incident requires remediation. You must: -1. Choose a remediation action: action_type="remediate" with parameters={"action": ""} - Review the remediation_options in the context and pick the one that addresses the root cause. -2. Verify the result: action_type="verify_recovery" (no parameters needed) -3. If metrics got WORSE, immediately rollback: action_type="rollback" - If metrics improved, you're done. -4. If partial improvement, consider trying a different action. - -The CORRECT action fixes the root cause. Wrong actions may make things worse or only mask symptoms. -Think carefully about which action addresses the stated root cause.""" -) - -SYSTEM_PROMPT_RUNBOOK = ( - SYSTEM_PROMPT_BASE - + """ - -TASK: Runbook Execution -You must execute a runbook for "High memory pressure - payment-service" in STRICT order. -Each step uses action_type="execute_step" with target=. - -Steps IN ORDER: -1. check_memory_usage — params: {"service": "payment-service"} -2. identify_top_processes — params: {"service": "payment-service"} -3. capture_heap_dump — params: {"service": "payment-service", "pid": } -4. cordon_instance — params: {"instance_id": ""} -5. scale_out — params: {"service": "payment-service", "replicas": 2} -6. verify_recovery — params: {"service": "payment-service", "wait_seconds": 30} -7. file_incident_report — params: {"severity": "P2", "summary": ""} - -CRITICAL: After step 2, read the tool_outputs carefully to extract the pid and instance_id. -These MUST be used in steps 3 and 4. The summary in step 7 must mention payment-service -and describe what was done (e.g., memory pressure, scaled out, recovered).""" -) - -SYSTEM_PROMPT_POSTMORTEM = ( - SYSTEM_PROMPT_BASE - + """ - -TASK: Blameless Postmortem Generation -Write a blameless postmortem for a production incident. Required sections: -summary, timeline, root_cause, impact, action_items. - -1. Query incident data first: - - action_type="diagnose", target="incident", parameters={"operation": "query_timeline"} - - action_type="diagnose", target="incident", parameters={"operation": "query_impact"} - - action_type="diagnose", target="incident", parameters={"operation": "query_root_cause"} - - action_type="diagnose", target="incident", parameters={"operation": "query_action_items"} -2. Write each section: action_type="write_postmortem" with parameters: - {"section": "", "content": ""} - -Each section should have substantive content (15+ words). The root_cause section should -include keywords from the actual root cause. The action_items section should reference -the specific action items identified.""" -) - -SYSTEM_PROMPT_CHAOS = ( - SYSTEM_PROMPT_BASE - + """ - -TASK: Chaos Engineering Scenario -Run a chaos engineering experiment: -1. Inject the failure: action_type="inject_chaos", target= -2. Observe the impact: action_type="observe_impact", target= -3. Execute mitigation steps IN ORDER: action_type="mitigate_chaos" with - parameters={"step": ""} - -The mitigation steps must be executed in the correct sequential order. Each step -builds on the previous one. Read the available mitigation_steps in the context -and execute them one by one in order. - -Common patterns: first contain the blast radius (circuit breaker, read-only mode), -then implement workarounds (fallback, rate limiting), then restore full service.""" -) - -TASK_PROMPTS = { - "alert_triage": SYSTEM_PROMPT_ALERT_TRIAGE, - "on_call_handoff": SYSTEM_PROMPT_ON_CALL_HANDOFF, - "capacity_planning": SYSTEM_PROMPT_CAPACITY_PLANNING, - "incident_diagnosis": SYSTEM_PROMPT_INCIDENT, - "multi_incident_correlation": SYSTEM_PROMPT_MULTI_INCIDENT, - "auto_remediation": SYSTEM_PROMPT_AUTO_REMEDIATION, - "runbook_execution": SYSTEM_PROMPT_RUNBOOK, - "blameless_postmortem": SYSTEM_PROMPT_POSTMORTEM, - "chaos_engineering": SYSTEM_PROMPT_CHAOS, -} - -TASK_FALLBACK = { - "alert_triage": lambda obs: { - "action_type": "acknowledge", - "target": (obs.get("alert_queue", [{}])[0] or {}).get("id", "ALT-001"), - "parameters": {}, - "reasoning": "fallback: acknowledging first available alert", - }, - "on_call_handoff": lambda obs: { - "action_type": "summarize", - "target": "handoff", - "parameters": { - "summary": "Active incidents and pending actions need attention." - }, - "reasoning": "fallback: submitting minimal handoff summary", - }, - "capacity_planning": lambda obs: { - "action_type": "recommend_scaling", - "target": obs.get("context", {}).get("service", "api-gateway"), - "parameters": {"recommendation": "no_change", "target_replicas": 3}, - "reasoning": "fallback: recommending no change", - }, - "incident_diagnosis": lambda obs: { - "action_type": "diagnose", - "target": "db-service", - "parameters": {"operation": "query_metrics"}, - "reasoning": "fallback: querying deepest service in dependency chain", - }, - "multi_incident_correlation": lambda obs: { - "action_type": "correlate", - "target": "all", - "parameters": { - "correlation_type": "shared_root_cause", - "root_cause_service": "db-service", - "alert_ids": [], - }, - "reasoning": "fallback: assuming shared root cause", - }, - "auto_remediation": lambda obs: { - "action_type": "remediate", - "target": obs.get("context", {}).get("service", "api-gateway"), - "parameters": {"action": "restart_service"}, - "reasoning": "fallback: attempting service restart", - }, - "runbook_execution": lambda obs: { - "action_type": "execute_step", - "target": (obs.get("context", {}).get("next_expected_step") or {}).get( - "name", "check_memory_usage" - ), - "parameters": {"service": "payment-service"}, - "reasoning": "fallback: attempting next expected runbook step", - }, - "blameless_postmortem": lambda obs: { - "action_type": "write_postmortem", - "target": "postmortem", - "parameters": { - "section": "summary", - "content": "An incident occurred and was resolved.", - }, - "reasoning": "fallback: writing minimal summary section", - }, - "chaos_engineering": lambda obs: { - "action_type": "observe_impact", - "target": obs.get("context", {}).get("target_service", "api-gateway"), - "parameters": {}, - "reasoning": "fallback: observing impact", - }, -} - -# --------------------------------------------------------------------------- -# JSON extraction helpers -# --------------------------------------------------------------------------- - - -def _extract_json(raw: str) -> dict | None: - """Extract JSON from raw LLM output, handling markdown code blocks.""" - text = raw.strip() - - # Try direct parse first - try: - return json.loads(text) - except json.JSONDecodeError: - pass - - # Extract from markdown code blocks: ```json ... ``` or ``` ... ``` - code_block_match = re.search(r"```(?:json)?\s*\n?(.*?)\n?\s*```", text, re.DOTALL) - if code_block_match: - try: - return json.loads(code_block_match.group(1).strip()) - except json.JSONDecodeError: - pass - - # Try to find a JSON object anywhere in the text - brace_match = re.search(r"\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}", text, re.DOTALL) - if brace_match: - try: - return json.loads(brace_match.group(0)) - except json.JSONDecodeError: - pass - - return None - - -# --------------------------------------------------------------------------- -# Episode runner -# --------------------------------------------------------------------------- - -MAX_RETRIES = 2 - - -def _normalize_task_score(value) -> float: - try: - numeric_value = float(value) - except (TypeError, ValueError): - numeric_value = 0.0 - return clamp_task_score(numeric_value) - - -def _format_task_score(value) -> str: - return f"{_normalize_task_score(value):.{SCORE_FORMAT_PRECISION}f}" - - -def run_episode(task_id: str): - resp = httpx.post(f"{ENV_URL}/reset", json={"task_id": task_id}, timeout=30) - resp.raise_for_status() - obs = resp.json() - session_id = obs.get("session_id", task_id) - - print(f"[START] task={task_id} env={BENCHMARK} model={MODEL_NAME}") - - rewards = [] - done = False - step = 0 - score = _normalize_task_score(0.0) - last_error = "null" - system_prompt = TASK_PROMPTS[task_id] - conversation = [{"role": "system", "content": system_prompt}] - - while not done and step < MAX_STEPS[task_id]: - # Build user message from current observation - user_msg = f"Current observation:\n{json.dumps(obs, indent=2)}\n\nWhat action do you take?" - conversation.append({"role": "user", "content": user_msg}) - - # Call LLM with retries - action = None - for attempt in range(MAX_RETRIES + 1): - try: - completion = client.chat.completions.create( - model=MODEL_NAME, - messages=conversation, - max_tokens=400, - temperature=0.1, - ) - raw = completion.choices[0].message.content.strip() - action = _extract_json(raw) - if action: - last_error = "null" - # Store the assistant response for conversation history - conversation.append( - {"role": "assistant", "content": json.dumps(action)} - ) - break - else: - last_error = "json_extraction_failed" - except Exception as e: - last_error = f"llm_error:{type(e).__name__}" - if attempt < MAX_RETRIES: - time.sleep(1) - - if not action: - action = TASK_FALLBACK[task_id](obs) - last_error = last_error or "fallback_used" - conversation.append({"role": "assistant", "content": json.dumps(action)}) - - # Ensure required fields exist - action.setdefault("action_type", "diagnose") - action.setdefault("target", "") - action.setdefault("parameters", {}) - action.setdefault("reasoning", "") - - # Step the environment - try: - step_resp = httpx.post( - f"{ENV_URL}/step", - json={"session_id": session_id, "action": action}, - timeout=30, - ) - step_resp.raise_for_status() - result = step_resp.json() - except Exception as e: - last_error = f"env_error:{type(e).__name__}" - step += 1 - error_reward = _normalize_task_score(0.0) - rewards.append(error_reward) - print( - f"[STEP] step={step} action={action.get('action_type', '?')}({action.get('target', '?')}) " - f"reward={_format_task_score(error_reward)} done=false error={last_error}" - ) - continue - - obs = result["observation"] - reward_val = _normalize_task_score(result["reward"]["value"]) - done = result["done"] - score = _normalize_task_score( - result.get("info", {}).get("cumulative_score", reward_val) - ) - - rewards.append(reward_val) - step += 1 - - print( - f"[STEP] step={step} action={action['action_type']}({action['target']}) " - f"reward={_format_task_score(reward_val)} done={str(done).lower()} error={last_error}" - ) - - # Trim conversation history if too long (keep system + last 6 exchanges) - if len(conversation) > 13: - conversation = [conversation[0]] + conversation[-12:] - - success = score >= 0.5 - rewards_str = ",".join(_format_task_score(reward) for reward in rewards) - print( - f"[END] success={str(success).lower()} steps={step} " - f"score={_format_task_score(score)} rewards={rewards_str}" - ) - return score - - -if __name__ == "__main__": - for task in TASKS: - run_episode(task) diff --git a/scratch/sre/openenv.yaml b/scratch/sre/openenv.yaml deleted file mode 100644 index f2822b2ca7bd16d76177899109ce76985266c295..0000000000000000000000000000000000000000 --- a/scratch/sre/openenv.yaml +++ /dev/null @@ -1,105 +0,0 @@ -name: sre-automation-env -version: "2.0.0" -description: > - An OpenEnv environment simulating Site Reliability Engineering tasks. - Agents must triage alerts, diagnose production incidents, execute - operational runbooks, and handle advanced SRE scenarios across a - simulated microservice topology. -tags: - - openenv - - sre - - operations - - real-world -difficulty_levels: - easy: - - alert_triage - - on_call_handoff - - capacity_planning - medium: - - incident_diagnosis - - multi_incident_correlation - - auto_remediation - hard: - - runbook_execution - - blameless_postmortem - - chaos_engineering -tasks: - # EASY - - id: alert_triage - name: Alert Triage - difficulty: easy - max_steps: 10 - description: "Prioritize and acknowledge firing alerts in correct severity order." - - id: on_call_handoff - name: On-Call Handoff - difficulty: easy - max_steps: 6 - description: "Summarize active incidents, pending actions, and service health for the incoming on-call engineer." - - id: capacity_planning - name: Capacity Planning - difficulty: easy - max_steps: 5 - description: "Analyze service metrics trends and recommend scaling decisions." - # MEDIUM - - id: incident_diagnosis - name: Incident Diagnosis - difficulty: medium - max_steps: 12 - description: "Identify root cause service and failure mode from noisy metrics." - - id: multi_incident_correlation - name: Multi-Incident Correlation - difficulty: medium - max_steps: 10 - description: "Identify whether multiple simultaneous alerts share a common root cause or are independent incidents." - - id: auto_remediation - name: Auto-Remediation with Rollback - difficulty: medium - max_steps: 8 - description: "Execute a remediation action, verify if it improved the situation, and rollback if it made things worse." - # HARD - - id: runbook_execution - name: Runbook Execution - difficulty: hard - max_steps: 15 - description: "Execute all runbook steps in order with correct parameters." - - id: blameless_postmortem - name: Blameless Postmortem Generation - difficulty: hard - max_steps: 8 - description: "Synthesize incident timeline, root cause analysis, and actionable follow-ups into a blameless postmortem document." - - id: chaos_engineering - name: Chaos Engineering Scenario - difficulty: hard - max_steps: 12 - description: "Inject a controlled failure, observe system impact, and execute mitigation steps to restore stability." -observation_space: - type: object - fields: - - name: task_id - type: string - - name: step - type: integer - - name: metrics - type: object - - name: alert_queue - type: array - - name: logs - type: array - - name: available_actions - type: array -action_space: - type: object - fields: - - name: action_type - type: string - enum: [triage, diagnose, escalate, acknowledge, execute_step, resolve, summarize, query_capacity, recommend_scaling, correlate, remediate, rollback, verify_recovery, write_postmortem, inject_chaos, observe_impact, mitigate_chaos] - - name: target - type: string - - name: parameters - type: object - - name: reasoning - type: string -endpoints: - reset: POST /reset - step: POST /step - state: GET /state diff --git a/scratch/sre/pyproject.toml b/scratch/sre/pyproject.toml deleted file mode 100644 index e9c8f5027f2297026763c7619068b679bfbef514..0000000000000000000000000000000000000000 --- a/scratch/sre/pyproject.toml +++ /dev/null @@ -1,32 +0,0 @@ -[build-system] -requires = ["setuptools>=68", "wheel"] -build-backend = "setuptools.build_meta" - -[project] -name = "sre-automation-env" -version = "1.0.0" -description = "OpenEnv-compatible SRE automation environment" -readme = "README.md" -requires-python = ">=3.10" -authors = [ - { name = "Rutul Patel" } -] -dependencies = [ - "fastapi>=0.110.0", - "uvicorn>=0.29.0", - "pydantic>=2.6.0", - "openai>=1.25.0", - "python-dotenv>=1.0.0", - "httpx>=0.27.0", - "openenv-core>=0.2.0" -] - -[project.scripts] -server = "server.app:main" - -[tool.setuptools] -include-package-data = true - -[tool.setuptools.packages.find] -where = ["."] -include = ["env*", "server*"] diff --git a/scratch/sre/requirements.txt b/scratch/sre/requirements.txt deleted file mode 100644 index 72278b5f4a1f4ee81234b0224789e8ead8c3b88b..0000000000000000000000000000000000000000 --- a/scratch/sre/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -fastapi>=0.110.0 -uvicorn>=0.29.0 -pydantic>=2.6.0 -openai>=1.25.0 -python-dotenv>=1.0.0 -httpx>=0.27.0 diff --git a/scratch/sre/server/__init__.py b/scratch/sre/server/__init__.py deleted file mode 100644 index 44f48b69da469ffb07b6aa9168ce1318ecfcc6df..0000000000000000000000000000000000000000 --- a/scratch/sre/server/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from server.app import app - -__all__ = ["app"] diff --git a/scratch/sre/server/app.py b/scratch/sre/server/app.py deleted file mode 100644 index e5bbf0ca27b553474d8ece8a6f866bd750488046..0000000000000000000000000000000000000000 --- a/scratch/sre/server/app.py +++ /dev/null @@ -1,160 +0,0 @@ -from __future__ import annotations - -import time -from collections import OrderedDict -from typing import Literal -from uuid import uuid4 - -from fastapi import FastAPI, HTTPException, Query -from fastapi.middleware.cors import CORSMiddleware -from pydantic import BaseModel - -from env.models import SREAction -from env.sre_env import SREEnv, TASK_METADATA - -app = FastAPI(title="sre-automation-env", version="1.0.0") -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - -MAX_SESSIONS = 200 -SESSION_TTL_SECONDS = 600 # 10 minutes - - -class _SessionEntry: - __slots__ = ("env", "created_at", "last_active") - - def __init__(self, env: SREEnv): - self.env = env - self.created_at = time.time() - self.last_active = self.created_at - - def touch(self) -> None: - self.last_active = time.time() - - @property - def expired(self) -> bool: - return (time.time() - self.last_active) > SESSION_TTL_SECONDS - - -SESSIONS: OrderedDict[str, _SessionEntry] = OrderedDict() - - -def _evict_stale_sessions() -> None: - """Remove expired sessions and enforce max session count.""" - now = time.time() - expired = [ - sid - for sid, entry in SESSIONS.items() - if (now - entry.last_active) > SESSION_TTL_SECONDS - ] - for sid in expired: - entry = SESSIONS.pop(sid, None) - if entry: - entry.env.close() - - while len(SESSIONS) > MAX_SESSIONS: - sid, entry = SESSIONS.popitem(last=False) - entry.env.close() - - -def _get_session(session_id: str) -> _SessionEntry: - entry = SESSIONS.get(session_id) - if entry is None: - raise HTTPException(status_code=404, detail="Unknown session_id") - if entry.expired: - SESSIONS.pop(session_id, None) - entry.env.close() - raise HTTPException(status_code=410, detail="Session expired") - entry.touch() - return entry - - -class ResetRequest(BaseModel): - task_id: Literal[ - "alert_triage", - "incident_diagnosis", - "runbook_execution", - "on_call_handoff", - "capacity_planning", - "multi_incident_correlation", - "auto_remediation", - "blameless_postmortem", - "chaos_engineering", - ] = "alert_triage" - - -class StepRequest(BaseModel): - session_id: str = "" - action: SREAction | None = None - - -@app.post("/reset") -def reset_environment(request: ResetRequest | None = None) -> dict: - _evict_stale_sessions() - session_id = str(uuid4()) - task_id = request.task_id if request else "alert_triage" - env = SREEnv(task_id=task_id) - observation = env.reset() - SESSIONS[session_id] = _SessionEntry(env) - return {"session_id": session_id, **observation.model_dump()} - - -@app.post("/step") -def step_environment(request: StepRequest | None = None) -> dict: - if not request or not request.session_id: - raise HTTPException( - status_code=400, detail="Missing session_id in request body" - ) - - entry = _get_session(request.session_id) - action = request.action - if not action: - # Fallback empty action if tester sends malformed action - action = SREAction( - action_type="diagnose", - target="api-gateway", - parameters={}, - reasoning="fallback action due to missing action body", - ) - - observation, reward, done, info = entry.env.step(action) - if done: - # Clean up completed sessions after a small delay - SESSIONS.pop(request.session_id, None) - return { - "observation": observation.model_dump(), - "reward": reward.model_dump(), - "done": done, - "info": info, - } - - -@app.get("/state") -def get_state(session_id: str = Query(...)) -> dict: - entry = _get_session(session_id) - return entry.env.state() - - -@app.get("/tasks") -def list_tasks() -> list[dict]: - return TASK_METADATA - - -@app.get("/health") -def healthcheck() -> dict[str, str]: - return {"status": "ok"} - - -def main() -> None: - import uvicorn - - uvicorn.run("server.app:app", host="0.0.0.0", port=7860) - - -if __name__ == "__main__": - main() diff --git a/scratch/sre/sre_automation_env.egg-info/PKG-INFO b/scratch/sre/sre_automation_env.egg-info/PKG-INFO deleted file mode 100644 index 412134932d69a597cda673841a36bcd99b7d83f7..0000000000000000000000000000000000000000 --- a/scratch/sre/sre_automation_env.egg-info/PKG-INFO +++ /dev/null @@ -1,322 +0,0 @@ -Metadata-Version: 2.4 -Name: sre-automation-env -Version: 1.0.0 -Summary: OpenEnv-compatible SRE automation environment -Author: Rutul Patel -Requires-Python: >=3.10 -Description-Content-Type: text/markdown -Requires-Dist: fastapi>=0.110.0 -Requires-Dist: uvicorn>=0.29.0 -Requires-Dist: pydantic>=2.6.0 -Requires-Dist: openai>=1.25.0 -Requires-Dist: python-dotenv>=1.0.0 -Requires-Dist: httpx>=0.27.0 -Requires-Dist: openenv-core>=0.2.0 - ---- -title: Sre Automation Env -emoji: 😻 -colorFrom: green -colorTo: pink -sdk: docker -pinned: false ---- - -# 🚨 sre-automation-env - -> An [OpenEnv](https://huggingface.co/spaces/open-env/leaderboard)-compatible reinforcement learning environment that simulates the daily work of a Site Reliability Engineer. Agents must triage noisy alert queues, diagnose cascading production incidents across a microservice graph, and execute operational runbooks with stateful parameter dependencies. - ---- - -## 1. Environment Description & Motivation - -Site Reliability Engineering is a **\$30B+ market** where every minute of downtime costs real revenue. Alert fatigue is the #1 cause of missed incidents — human SREs are bombarded with hundreds of alerts per shift, most of which are noise. The operational decisions they make — which alerts to ignore, where to look first, what remediation steps to take — are exactly the kind of sequential, high-stakes reasoning that RL agents should learn. - -`sre-automation-env` captures this operational reality: - -- **Alert Triage** mirrors the PagerDuty/OpsGenie workflow: sort through noisy, flapping, duplicate alerts and acknowledge the critical ones first. -- **Incident Diagnosis** simulates the real-time investigation an on-call SRE does during a production outage — querying metrics, reading logs, tracing cascading failures through a dependency graph. -- **Runbook Execution** tests procedural reasoning: can the agent follow a multi-step operational playbook where each step's output feeds into the next? - -### Service Topology - -The environment uses a realistic microservice dependency graph where failures cascade through the chain: - -```mermaid -graph LR - A[api-gateway] --> B[auth-service] - B --> C[user-service] - B --> D[payment-service] - D --> E[db-service] - - style A fill:#4a90d9,stroke:#333,color:#fff - style B fill:#7b68ee,stroke:#333,color:#fff - style C fill:#50c878,stroke:#333,color:#fff - style D fill:#ff6b6b,stroke:#333,color:#fff - style E fill:#ffa500,stroke:#333,color:#fff -``` - -When `db-service` has high latency, it cascades up through `payment-service` → `auth-service` → `api-gateway`. This mirrors real production architectures where identifying the *root cause* vs. a *symptom* is the core diagnostic challenge. - ---- - -## 2. Action Space - -| `action_type` | `target` | `parameters` | Used In | -|---|---|---|---| -| `triage` | Alert ID | `{"decision": "ignore\|actionable", "priority": "P1\|P2\|P3"}` | Alert Triage | -| `acknowledge` | Alert ID | Optional `{"priority": "P1"}` | Alert Triage | -| `diagnose` | Service name | `{"operation": "query_metrics\|query_logs\|submit_diagnosis", "failure_mode": "..."}` | Incident Diagnosis | -| `execute_step` | Runbook step name | Step-specific params: `service`, `pid`, `instance_id`, `replicas`, `wait_seconds`, `summary` | Runbook Execution | -| `escalate` | Any target | Optional escalation metadata | All tasks | -| `resolve` | Task target | Confirmation / no-op | All tasks | - ---- - -## 3. Observation Space - -| Field | Type | Description | -|---|---|---| -| `task_id` | `string` | Current task identifier | -| `step` | `integer` | Current step count (0-indexed) | -| `context` | `object` | Task-specific instructions, progress tracking, SLO status, incident timeline | -| `available_actions` | `array[string]` | Allowed action types for the current task | -| `alert_queue` | `array[object]` | Current alerts with severity, flapping/duplicate flags, status | -| `service_map` | `object` | Microservice dependency graph | -| `metrics` | `object` | Per-service metrics (cpu%, mem%, latency_p99, error_rate) or SLO status | -| `logs` | `array[string]` | Visible service logs (revealed incrementally via queries) | -| `done` | `boolean` | Episode termination flag | -| `message` | `string` | Environment feedback on the last action | - ---- - -## 4. Task Descriptions - -| Task | Difficulty | Max Steps | Target Score (GPT-4o) | Success Criteria | -|---|---|---|---|---| -| **Alert Triage** | 🟢 Easy | 10 | ~0.7 | Ignore flapping/duplicate alerts, acknowledge actionable alerts in P1→P2→P3 order | -| **Incident Diagnosis** | 🟡 Medium | 12 | ~0.5 | Query selective metrics/logs, identify root cause service + failure mode | -| **Runbook Execution** | 🔴 Hard | 15 | ~0.3 | Execute all 7 runbook steps in order with correct parameters (including extracted pid) | - -### Task Details - -
    -Alert Triage — 8 alerts, mixed severity, some noisy - -The agent receives 8 firing alerts randomly sampled from a pool of 20. Some are flapping (intermittent), some are duplicates. The agent must: -1. Triage each alert as ignore or actionable with correct priority -2. Acknowledge actionable alerts in descending priority order (P1 first) - -The challenge: noisy alerts waste steps, and acknowledging in wrong order loses points. -
    - -
    -Incident Diagnosis — 5 failure modes across 5 services - -One of 5 failure modes is randomly injected at reset: -- **db-service**: High latency (cascades up through payment → auth → gateway) -- **auth-service**: High error rate (blocks login flow) -- **payment-service**: Memory leak (OOM restarts) -- **api-gateway**: Config drift (wrong upstream target) -- **user-service**: Deprecated endpoint dependency - -Metrics are hidden until queried (1 step per query). The agent must efficiently gather evidence and submit a diagnosis. SLO burn rates and an incident timeline provide additional signal. -
    - -
    -Runbook Execution — 7-step operational playbook with parameter dependencies - -The runbook "High memory pressure — payment-service" has 7 steps that MUST be executed in order. Step 2 (`identify_top_processes`) reveals a `pid` and `instance_id` that are required for steps 3 and 4. This parameter extraction creates a genuine dependency chain that tests the agent's ability to use tool outputs. -
    - ---- - -## 5. Reward Function Design - -All three tasks use **dense, interpretable partial credit**. Agents receive meaningful signal for intermediate progress instead of binary pass/fail. - -### Alert Triage Rewards - -| Component | Weight | Description | -|---|---|---| -| Coverage (weighted Jaccard) | 0.55 | Acknowledging the right alerts, weighted by priority | -| Correct ignores | 0.1 each | Correctly identifying flapping/duplicate alerts | -| First-ack priority | 0.2 | Acknowledging a P1 alert first | -| Ordering accuracy | 0.15 | Maintaining correct priority ordering | -| P1 penalty | -0.1 each | P1 alerts left unacknowledged at episode end | - -### Incident Diagnosis Rewards - -| Component | Weight | Description | -|---|---|---| -| Root cause service | 0.5 | Exact match on the faulty service | -| Upstream partial credit | 0.1 | Naming a direct upstream of the root cause | -| Failure mode match | 0.2 | Fuzzy keyword match on failure description | -| Evidence collection | 0.05 + 0.05 | Querying metrics and logs for the root cause service | -| Efficiency bonus | 0.0–0.2 | Linear scale: full bonus at ≤8 steps, zero at ≥12 | -| Query penalty | -0.05 each | Unnecessary metric queries beyond 6 | - -### Runbook Execution Rewards - -| Component | Weight | Description | -|---|---|---| -| Step completion | 0.1 each (max 0.7) | Per correctly executed step | -| PID extraction | 0.1 | Correctly extracting and using pid from step 2 | -| Report quality | 0.1 | Incident report with severity, service name, coherent summary | -| Out-of-order penalty | -0.05 each | Attempting steps in wrong order | -| Wrong parameter penalty | -0.05 each | Incorrect parameter values | - -The `breakdown` dict in every reward response provides a verbose, interpretable explanation of exactly why the agent scored what it did. - ---- - -## 6. Setup & Usage - -### Docker (recommended) - -```bash -docker build -t sre-env . -docker run --rm -p 7860:7860 sre-env -``` - -### Local Development - -```bash -python3 -m venv .venv -source .venv/bin/activate -pip install -r requirements.txt -uvicorn server.app:app --host 0.0.0.0 --port 7860 -``` - -### API Examples - -**Health check:** -```bash -curl http://localhost:7860/health -# {"status": "ok"} -``` - -**List tasks:** -```bash -curl http://localhost:7860/tasks -``` - -**Reset an episode:** -```bash -curl -X POST http://localhost:7860/reset \ - -H "Content-Type: application/json" \ - -d '{"task_id": "alert_triage"}' -``` - -**Step through an episode:** -```bash -curl -X POST http://localhost:7860/step \ - -H "Content-Type: application/json" \ - -d '{ - "session_id": "", - "action": { - "action_type": "triage", - "target": "ALT-001", - "parameters": {"decision": "actionable", "priority": "P1"}, - "reasoning": "Critical payment-service alert, not flapping." - } - }' -``` - -**Inspect environment state:** -```bash -curl "http://localhost:7860/state?session_id=" -``` - -**Run inference harness:** -```bash -export HF_TOKEN= -python inference.py -``` - ---- - -## 7. Baseline Scores - -Run `python inference.py` with `HF_TOKEN` set. The inference harness runs all three tasks sequentially and prints `[START]`, `[STEP]`, and `[END]` markers per task. - -Expected baseline ranges: -| Task | Model | Expected Score | -|---|---|---| -| Alert Triage | Qwen2.5-72B | 0.5–0.8 | -| Incident Diagnosis | Qwen2.5-72B | 0.3–0.7 | -| Runbook Execution | Qwen2.5-72B | 0.1–0.5 | - ---- - -## 8. Deploying to Hugging Face Spaces - -### Step-by-step: - -1. **Create a new Space** at [huggingface.co/new-space](https://huggingface.co/new-space) - - Select **Docker** as the SDK - - Choose **Blank** template - - Set visibility to **Public** - -2. **Push your code:** - ```bash - git init - git remote add origin https://huggingface.co/spaces//sre-automation-env - git add . - git commit -m "Initial SRE environment" - git push origin main - ``` - -3. **The Space will auto-build** from your Dockerfile and expose port 7860. - -4. **Set your Space URL** in `inference.py` via the `ENV_URL` environment variable: - ```bash - ENV_URL=https://-sre-automation-env.hf.space python inference.py - ``` - -5. **Verify** the deployment: - ```bash - curl https://-sre-automation-env.hf.space/health - curl https://-sre-automation-env.hf.space/tasks - ``` - ---- - -## 9. Architecture - -``` -┌─────────────────────────────────────────────────────────┐ -│ inference.py │ -│ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │ -│ │ LLM Call │◄──►│ JSON │◄──►│ Conversation │ │ -│ │ (OpenAI) │ │ Extractor│ │ History Manager │ │ -│ └──────────┘ └──────────┘ └──────────────────┘ │ -└───────────────────────┬─────────────────────────────────┘ - │ HTTP -┌───────────────────────▼─────────────────────────────────┐ -│ server/app.py (FastAPI) │ -│ POST /reset │ POST /step │ GET /state │ GET /tasks│ -│ ────────────────────────────────────────────────────── │ -│ Session Manager (TTL=10min, max=200 sessions) │ -└───────────────────────┬─────────────────────────────────┘ - │ -┌───────────────────────▼─────────────────────────────────┐ -│ env/sre_env.py │ -│ ┌──────────────┐ ┌──────────────┐ ┌───────────────┐ │ -│ │ Task Registry │ │ State Machine│ │ Reward Tracker│ │ -│ └──────┬───────┘ └──────┬───────┘ └───────┬───────┘ │ -│ │ │ │ │ -│ ┌──────▼───────┐ ┌──────▼───────┐ ┌───────▼───────┐ │ -│ │ env/tasks/ │ │ env/data/ │ │ env/graders/ │ │ -│ │ • alert │ │ • alerts.json│ │ • alert │ │ -│ │ • incident │ │ • incidents │ │ • incident │ │ -│ │ • runbook │ │ • runbooks │ │ • runbook │ │ -│ └──────────────┘ └──────────────┘ └───────────────┘ │ -└─────────────────────────────────────────────────────────┘ -``` - ---- - -## License - -MIT diff --git a/scratch/sre/sre_automation_env.egg-info/SOURCES.txt b/scratch/sre/sre_automation_env.egg-info/SOURCES.txt deleted file mode 100644 index f64a337a165a7f6c23c1dffc191e15a161052c12..0000000000000000000000000000000000000000 --- a/scratch/sre/sre_automation_env.egg-info/SOURCES.txt +++ /dev/null @@ -1,21 +0,0 @@ -README.md -pyproject.toml -env/__init__.py -env/models.py -env/sre_env.py -env/graders/__init__.py -env/graders/grader_alert_triage.py -env/graders/grader_incident_diagnosis.py -env/graders/grader_runbook_execution.py -env/tasks/__init__.py -env/tasks/task_alert_triage.py -env/tasks/task_incident_diagnosis.py -env/tasks/task_runbook_execution.py -server/__init__.py -server/app.py -sre_automation_env.egg-info/PKG-INFO -sre_automation_env.egg-info/SOURCES.txt -sre_automation_env.egg-info/dependency_links.txt -sre_automation_env.egg-info/entry_points.txt -sre_automation_env.egg-info/requires.txt -sre_automation_env.egg-info/top_level.txt \ No newline at end of file diff --git a/scratch/sre/sre_automation_env.egg-info/dependency_links.txt b/scratch/sre/sre_automation_env.egg-info/dependency_links.txt deleted file mode 100644 index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000 --- a/scratch/sre/sre_automation_env.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/scratch/sre/sre_automation_env.egg-info/entry_points.txt b/scratch/sre/sre_automation_env.egg-info/entry_points.txt deleted file mode 100644 index 0847d637cdd6379cd6383fb5d669c11b5489bdeb..0000000000000000000000000000000000000000 --- a/scratch/sre/sre_automation_env.egg-info/entry_points.txt +++ /dev/null @@ -1,2 +0,0 @@ -[console_scripts] -server = server.app:main diff --git a/scratch/sre/sre_automation_env.egg-info/requires.txt b/scratch/sre/sre_automation_env.egg-info/requires.txt deleted file mode 100644 index 14b942e4edcfb5e3fd8edd254a0f2fe704791f55..0000000000000000000000000000000000000000 --- a/scratch/sre/sre_automation_env.egg-info/requires.txt +++ /dev/null @@ -1,7 +0,0 @@ -fastapi>=0.110.0 -uvicorn>=0.29.0 -pydantic>=2.6.0 -openai>=1.25.0 -python-dotenv>=1.0.0 -httpx>=0.27.0 -openenv-core>=0.2.0 diff --git a/scratch/sre/sre_automation_env.egg-info/top_level.txt b/scratch/sre/sre_automation_env.egg-info/top_level.txt deleted file mode 100644 index 5ba10e09b5f6e1844ca3eac220c4a1fb4e44ba5a..0000000000000000000000000000000000000000 --- a/scratch/sre/sre_automation_env.egg-info/top_level.txt +++ /dev/null @@ -1,2 +0,0 @@ -env -server diff --git a/scratch/sre/tests/test_score_constraints.py b/scratch/sre/tests/test_score_constraints.py deleted file mode 100644 index 8dd5cd0facba4b7fa6d264f1fa7240b693d8e9de..0000000000000000000000000000000000000000 --- a/scratch/sre/tests/test_score_constraints.py +++ /dev/null @@ -1,104 +0,0 @@ -from __future__ import annotations - -import io -import unittest -from contextlib import redirect_stdout -from types import SimpleNamespace -from unittest.mock import patch - -import inference -from env.models import SREAction -from env.sre_env import SREEnv - - -class _FakeResponse: - def __init__(self, payload): - self._payload = payload - - def raise_for_status(self) -> None: - return None - - def json(self): - return self._payload - - -class ScoreConstraintTests(unittest.TestCase): - def test_format_task_score_keeps_boundaries_open(self): - self.assertEqual(inference._format_task_score(0.0), "0.0001") - self.assertEqual(inference._format_task_score(1.0), "0.9999") - self.assertEqual(inference._format_task_score(None), "0.0001") - - def test_run_episode_emits_open_interval_score_even_if_upstream_rounds_to_edge(self): - completion = SimpleNamespace( - choices=[ - SimpleNamespace( - message=SimpleNamespace( - content='{"action_type":"resolve","target":"noop","parameters":{},"reasoning":"finish"}' - ) - ) - ] - ) - - reset_payload = { - "session_id": "session-1", - "task_id": "alert_triage", - "step": 0, - "context": {}, - "available_actions": ["resolve"], - "alert_queue": [], - "service_map": {}, - "metrics": {}, - "logs": [], - "done": False, - "message": "ready", - } - - step_payload = { - "observation": {**reset_payload, "step": 1, "done": True}, - "reward": {"value": 1.0, "breakdown": {}, "done": True, "info": {}}, - "done": True, - "info": {"cumulative_score": 1.0}, - } - - with patch("inference.httpx.post") as mock_post, patch.object( - inference.client.chat.completions, "create", return_value=completion - ): - mock_post.side_effect = [ - _FakeResponse(reset_payload), - _FakeResponse(step_payload), - ] - stdout = io.StringIO() - with redirect_stdout(stdout): - score = inference.run_episode("alert_triage") - - self.assertEqual(score, 0.9999) - self.assertIn("score=0.9999", stdout.getvalue()) - self.assertNotIn("score=1.0000", stdout.getvalue()) - - def test_env_rewards_and_cumulative_scores_stay_inside_open_interval(self): - for task_id in inference.TASKS: - with self.subTest(task_id=task_id): - env = SREEnv(task_id) - observation = env.reset() - done = False - steps_taken = 0 - - while not done and steps_taken < inference.MAX_STEPS[task_id]: - action_payload = inference.TASK_FALLBACK[task_id]( - observation.model_dump() - ) - action = SREAction(**action_payload) - observation, reward, done, info = env.step(action) - - self.assertGreater(reward.value, 0.0) - self.assertLess(reward.value, 1.0) - self.assertGreater(info["cumulative_score"], 0.0) - self.assertLess(info["cumulative_score"], 1.0) - - steps_taken += 1 - - self.assertGreater(steps_taken, 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/scratch/sre/uv.lock b/scratch/sre/uv.lock deleted file mode 100644 index 6676839ab90a90366a0b49900c9a9cac676b4e5c..0000000000000000000000000000000000000000 --- a/scratch/sre/uv.lock +++ /dev/null @@ -1,2794 +0,0 @@ -version = 1 -requires-python = ">=3.10" -resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'win32'", - 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