Update README.md

README.md

@@ -1,262 +1,9 @@
-# DataQualityEnv
 ---
 title: data-quality-env
+sdk: docker
 emoji: 🚀
 colorFrom: blue
 colorTo: green
-sdk: docker
-pinned: false
----
-## Environment description
-DataQualityEnv is an OpenEnv-compliant RL environment where an agent acts as a data quality auditor.  
-For each episode, the environment generates a seeded dirty relational dataset, loads it into in-memory DuckDB, and exposes schema + row count.  
-The agent performs multi-turn SQL `SELECT` investigation and submits a structured JSON audit report for deterministic grading.
-
-## Plain-English summary
-This project trains and evaluates an AI agent that behaves like a data quality analyst.
-
-- The environment creates broken data on purpose.
-- The agent investigates the data with safe SQL queries.
-- The agent writes a final audit report.
-- The grader scores how accurately the report matches the hidden faults.
-
-In short: **inspect the data, reason about the problems, and submit a correct audit report**.
-
-### Motivation (real-world utility)
-Modern analytics pipelines fail silently when null explosions, schema drift, and referential drift go unnoticed.  
-This environment simulates a real data quality analyst workflow: inspect tables, run targeted SQL diagnostics, and submit an actionable incident report.
-
-### Why this is useful
-- It models a real job that people actually do in production.
-- It gives agents a meaningful multi-step reasoning task.
-- It provides deterministic scores, which makes it suitable for RL training and benchmarking.
-- It is safe by design because only non-destructive SQL is allowed.
-
-## How the environment works
-1. Call `reset(task_id, seed)`.
-2. The environment creates a reproducible dirty dataset and loads it into DuckDB.
-3. The agent reads the schema and row count.
-4. The agent uses `step(query)` to inspect the data.
-5. The environment returns query results and partial reward signals.
-6. When the agent is ready, it submits `step(submit_report)`.
-7. The grader compares the report with the hidden truth and returns the final score.
-
-### Score meaning
-- `1.0` = perfect audit report
-- `0.7` = partially correct, some key evidence missing
-- `0.0` = wrong or empty report
-
-## Action space
-- query: `{"action_type": "query", "sql": "SELECT ..."}`
-- submit_report: `{"action_type": "submit_report", "report": AuditReport}`
-
-## Observation space
-`task_description`, `table_name`, `schema`, `row_count`, `step`, `max_steps`, `last_query_result`, `last_action_error`
-
-## Tasks
-| ID | Name | Difficulty | What agent must find |
-|----|------|-----------|---------------------|
-| 1  | Null & duplicate detection | Easy | Null counts per column, duplicate rows |
-| 2  | Schema violation repair | Medium | Type mismatches, range violations |
-| 3  | Silent data drift | Hard | Statistical shift, new categories, referential drift |
-
-## What each task teaches
-- Task 1: basic data profiling and deduplication logic
-- Task 2: schema validation and data cleaning checks
-- Task 3: cross-snapshot drift analysis and anomaly detection
-
-## Reward design
-- Final reward (on `submit_report`) is task score in `[0.0, 1.0]` from deterministic graders.
-- Intermediate query reward gives partial credit for meaningful investigative probes.
-  - Example: detecting null-focused SQL probes, duplicate-analysis queries, cross-snapshot drift probes.
-- Safety penalty: destructive SQL attempts (`DROP`, `TRUNCATE`, etc.) return `-0.2`.
-- Efficiency penalty: repeating the exact same query incurs a small negative penalty.
-
-## Recommended way to run this project
-If you are starting from the `meta` folder, use the helper scripts:
-
-```bash
-./run_env_server.sh
-./run_high_grade_agent.sh
-```
-
-If you want to run the environment directly:
-
-```bash
-cd /Users/hemanthkunta/meta/data-quality-env
-python3 -m uvicorn env.app:app --app-dir /Users/hemanthkunta/meta/data-quality-env --host 0.0.0.0 --port 7860
-```
-
-Then verify it:
-
-```bash
-curl http://localhost:7860/health
-```
-
-## Baseline scores (seed=42, model=meta-llama/Llama-3.1-8B-Instruct)
-Task 1: ~0.82  
-Task 2: ~0.61  
-Task 3: ~0.34
-
-## Setup
-```bash
-docker build -t data-quality-env .
-docker run -p 7860:7860 \
-  -e API_BASE_URL=https://router.huggingface.co/v1 \
-  -e MODEL_NAME=meta-llama/Llama-3.1-8B-Instruct \
-  -e HF_TOKEN=your_token \
-  -e ENV_URL=http://localhost:7860 \
-  data-quality-env
-```
-
-## Local server run
-If you are running from the `meta` folder, start the server with the helper script:
-
-```bash
-./run_env_server.sh
-```
-
-Or directly:
-
-```bash
-cd /Users/hemanthkunta/meta/data-quality-env
-python3 -m uvicorn env.app:app --app-dir /Users/hemanthkunta/meta/data-quality-env --host 0.0.0.0 --port 7860
-```
-
-## Running inference
-```bash
-python inference.py
-```
-
-### Judge compatibility (important)
-- Judges set `API_BASE_URL`, `HF_TOKEN`, and `MODEL_NAME`, then run `python inference.py`.
-- `inference.py` must use those env vars directly and execute real OpenAI-compatible chat calls.
-- If env vars are ignored (or values are hardcoded), the run can produce no LLM output and score `0`.
-- Deterministic fallback is intended only for missing/invalid credentials or explicit override.
-- For Phase 2 behavior, avoid replacing successful LLM execution with heuristic shortcuts.
-
-## Chat-style assistant mode (ChatGPT/Gemini/Claude-like UX)
-You can run a conversational wrapper over the same OpenEnv backend:
-
-```bash
-python chat_agent.py --task-id 1 --seed 42
-```
-
-This adds a natural chat loop while preserving hackathon-required endpoints (`/reset`, `/step`, `/state`) and graders.
-
-## High-grade hybrid tool agent
-For a stronger agentic runner (policy-guided query ordering + OpenAI report polishing):
-
-```bash
-python high_grade_agent.py
-```
-
-Optional:
-- train local RL policy first and reuse it for ordering probes:
-  ```bash
-  python scripts/train_rl_agent.py train --episodes 300 --output outputs/rl_policy.json
-  RL_POLICY_PATH=outputs/rl_policy.json python high_grade_agent.py
-  ```
-
-Advanced mode details:
-- Query planning uses an explicit bank of `100,000` deterministic algorithm configurations.
-- Each candidate algorithm is checked against environment safety/step constraints before selection.
-- Selection balances coverage, statistical signal, novelty, safety risk, and efficiency.
-- SQL planning is augmented with a reusable SQL probe library (`env/sql_brain.py`) and reference guide (`SQL_AGENT_MIND.md`).
-
-Validate the 100k bank:
-```bash
-python scripts/check_100k_algorithms.py
-```
-
-Read the full SQL command/function guide:
-```bash
-cat SQL_AGENT_MIND.md
-```
-
-Run deeper multi-seed scoring (robust test):
-```bash
-python scripts/deep_evaluate_agent.py --seed-start 42 --runs 5
-```
-
-If you are in the `meta` folder:
-```bash
-python3 deep_evaluate_agent.py --seed-start 42 --runs 5
-```
-
-## Advanced shield architecture
-This project now includes all requested advanced components while staying hackathon-compliant:
-
-- **LLM reasoning**: hypothesis hints before planning (`high_grade_agent.py`)
-- **Planner-Executor-Critic loop**: LLM planner proposes extra probes, executor runs SQL tools, critic repairs final report schema
-- **RL fine-tuning**: tabular Q-learning policy training (`scripts/train_rl_agent.py`)
-- **Tool use**: SQL querying + report submission via `/step`
-- **Memory**: persistent successful plans (`env/agent_memory.py`, `outputs/agent_memory.json`)
-- **Knowledge brain**: deterministic evidence-to-report auto-fixer (`env/knowledge_brain.py`)
-- **Self-improvement loop**: iterative train + evaluate (`scripts/self_improve_loop.py`)
-- **Chat-style assistant**: multi-agent conversation wrapper (`chat_agent.py`) with planner/critic behavior
-
-If `API_BASE_URL` / `MODEL_NAME` / `HF_TOKEN` are missing, the advanced agent runs in deterministic fallback mode (no LLM calls) and still functions.
-
-Run full self-improvement cycle:
-```bash
-python scripts/self_improve_loop.py --cycles 3 --episodes-per-cycle 200
-```
-
-Or via make:
-```bash
-make self-improve
-```
-
-## Self-learning RL policy (optional advanced track)
-This repo includes a lightweight tabular Q-learning trainer that learns a query policy from shaped rewards:
-
-```bash
-python scripts/train_rl_agent.py train --episodes 300 --output outputs/rl_policy.json
-python scripts/train_rl_agent.py eval --policy outputs/rl_policy.json --episodes-per-task 5
-```
-
-If you are in the `meta` folder, you can also run the root wrapper:
-
-```bash
-python3 train_rl_agent.py train --episodes 300 --output data-quality-env/outputs/rl_policy.json
-```
-
-Notes:
-- This is a practical local RL loop over a compact action set (SQL probe selection + submit).
-- It is designed for hackathon constraints (2 vCPU / 8GB RAM, <20 minute runtime).
-- Frontier-scale LLM RL (GRPO/PPO over billions of params) is out of scope for the submission runtime budget, but this environment is compatible with external RL trainers.
-
-## Validate before submission
-```bash
-openenv validate
-./validate-submission.sh http://localhost:7860
-python scripts/local_qa.py
-python scripts/check_graders.py
-```
-
-## Troubleshooting
-- If you see `ModuleNotFoundError: No module named 'env'`, you started the server from the wrong directory. Use `./run_env_server.sh`.
-- If you see `address already in use`, the server is already running on port `7860`.
-- If the agent says the server is unreachable, run `curl http://localhost:7860/health` first.
-- If you want LLM-backed behavior, set `API_BASE_URL`, `MODEL_NAME`, and `HF_TOKEN`.
-
-## Hugging Face Spaces deployment (Docker SDK)
-1. Create a public Docker Space.
-2. Add `openenv` tag in Space settings.
-3. Set variables/secrets:
-  - `API_BASE_URL`
-  - `MODEL_NAME`
-  - `HF_TOKEN`
-  - `ENV_URL` is not required for the Space UI path.
-  - Keep these set in the Space even during evaluation so the app remains healthy 24/7.
-  - Judges still inject their own values when they run `inference.py`.
-4. The Space entrypoint is `space_app.py`, which mounts a Gradio UI and calls the environment in-process.
-4. Verify:
-  - `GET /health`
-  - `POST /reset`
-  - run `validate-submission.sh` against the Space URL.
-
 ---
 
 ## Description
@@ -352,4 +99,4 @@ python inference.py
 ## Validation
 ```bash
 ./validate-submission.sh https://your-space.hf.space
-```
+```