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d61821a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 | """Executable static-retrieval pilot with immutable per-run artifacts."""
from __future__ import annotations
from dataclasses import asdict
import json
import math
from pathlib import Path
import resource
import subprocess
import time
from typing import Any, Sequence
from .lm_studio_embeddings import LMStudioEmbeddingClient
from .repository import GitSnapshot, SourceChunk, chunk_snapshot
from .retrieval import (
BM25FuzzyRetriever,
DenseRetriever,
ExactRetriever,
SQLiteEmbeddingCache,
unique_file_ranking,
)
from .specs import (
ExperimentSpec,
HarnessSpec,
TaskSpec,
load_embeddings,
load_experiments,
load_harnesses,
load_models,
load_task_split,
load_tasks,
)
from .telemetry import EventWriter, RunIdentity
class PilotError(RuntimeError):
"""Raised when the development pilot cannot produce an auditable run."""
def _git_text(repository: Path, arguments: list[str]) -> str:
result = subprocess.run(
["git", *arguments],
cwd=repository,
check=False,
capture_output=True,
text=True,
timeout=30,
)
if result.returncode != 0:
raise PilotError(result.stderr.strip() or f"git {' '.join(arguments)} failed")
return result.stdout.strip()
def research_code_revision(root: Path) -> str:
revision = _git_text(root, ["rev-parse", "HEAD"])
if _git_text(root, ["status", "--porcelain"]):
raise PilotError("Research worktree is dirty; commit the exact implementation before a run")
return revision
def retrieval_metrics(ranked_paths: Sequence[str], gold_files: Sequence[str]) -> dict[str, Any]:
gold = set(gold_files)
if not gold:
raise PilotError("retrieval task has no gold files")
metrics: dict[str, Any] = {}
for cutoff in (1, 5, 10):
retrieved = set(ranked_paths[:cutoff])
metrics[f"file_recall_at_{cutoff}"] = len(gold & retrieved) / len(gold)
first_gold_rank = next(
(index for index, path in enumerate(ranked_paths, start=1) if path in gold),
None,
)
metrics["first_gold_rank"] = first_gold_rank
metrics["mrr"] = 0.0 if first_gold_rank is None else 1.0 / first_gold_rank
dcg = sum(
1.0 / math.log2(rank + 1)
for rank, path in enumerate(ranked_paths[:10], start=1)
if path in gold
)
ideal_hits = min(len(gold), 10)
ideal_dcg = sum(1.0 / math.log2(rank + 1) for rank in range(1, ideal_hits + 1))
metrics["ndcg_at_10"] = dcg / ideal_dcg
metrics["all_gold_in_top_10"] = gold.issubset(set(ranked_paths[:10]))
return metrics
def _memory_sample() -> dict[str, Any]:
usage = resource.getrusage(resource.RUSAGE_SELF)
return {"process_max_rss_platform_units": usage.ru_maxrss}
def _select_retriever(
harness: HarnessSpec,
chunks: Sequence[SourceChunk],
embedding_spec: Any,
embedding_client: LMStudioEmbeddingClient,
cache: SQLiteEmbeddingCache,
) -> tuple[Any, dict[str, Any]]:
started = time.monotonic()
if harness.harness_id == "H000":
return ExactRetriever(chunks), {
"index_build_seconds": time.monotonic() - started,
"index_kind": "literal_term_scan",
}
if harness.harness_id == "H001":
retriever = BM25FuzzyRetriever(chunks)
return retriever, {
"index_build_seconds": time.monotonic() - started,
"index_kind": "in_memory_bm25_fuzzy",
}
if harness.harness_id == "H003":
retriever, stats = DenseRetriever.build(
chunks,
embedding_spec,
embedding_client,
cache,
)
return retriever, {"index_kind": "python_flat_cosine", **asdict(stats)}
raise PilotError(f"E00 runner does not implement {harness.harness_id}")
def run_static_retrieval_pilot(
root: Path,
repository: Path,
experiment_id: str = "E00",
task_filter: set[str] | None = None,
harness_filter: set[str] | None = None,
candidate_limit: int = 200,
) -> dict[str, Any]:
code_revision = research_code_revision(root)
experiments = load_experiments(root)
harnesses = load_harnesses(root)
models = load_models(root)
embeddings = load_embeddings(root)
tasks = load_tasks(root)
try:
experiment: ExperimentSpec = experiments[experiment_id]
except KeyError as exc:
raise PilotError(f"Unknown experiment {experiment_id}") from exc
if experiment.mode != "static_retrieval":
raise PilotError("pilot runner only supports static_retrieval experiments")
split = load_task_split(root / "tasks" / "splits" / f"{experiment.task_split}.txt")
selected_tasks = [tasks[item] for item in split if task_filter is None or item in task_filter]
selected_harnesses = [
harnesses[item]
for item in experiment.harness_ids
if harness_filter is None or item in harness_filter
]
if not selected_tasks or not selected_harnesses:
raise PilotError("task or harness filters selected no pilot cells")
if any(task.validation_status not in {"retrieval_ready", "end_to_end_ready"} for task in selected_tasks):
raise PilotError("pilot split contains a task that is not retrieval-ready")
model_spec = models[experiment.model_ids[0]]
embedding_spec = embeddings[experiment.embedding_id]
embedding_client = LMStudioEmbeddingClient(embedding_spec, timeout_seconds=60.0)
embedding_runtime = embedding_client.resolve()
resident_models = embedding_client.loaded_model_keys()
if set(resident_models) != {embedding_spec.model_key}:
raise PilotError(
"Memory-safe E00 requires only the embedding model to be resident; "
f"observed {resident_models}"
)
snapshot = GitSnapshot(repository)
origin = _git_text(repository, ["remote", "get-url", "origin"])
expected_origins = {task.repository_url for task in selected_tasks}
if expected_origins != {origin}:
raise PilotError(f"Repository remote mismatch: expected {expected_origins}, observed {origin!r}")
cache_path = root / "indexes" / "embeddings" / f"{embedding_spec.config_hash}.sqlite3"
summary_rows: list[dict[str, Any]] = []
chunk_cache: dict[str, tuple[SourceChunk, ...]] = {}
with SQLiteEmbeddingCache(cache_path, embedding_spec) as embedding_cache:
for task in selected_tasks:
snapshot.verify_commit(task.base_commit)
snapshot.verify_commit(task.gold_commit)
if task.base_commit not in chunk_cache:
chunk_cache[task.base_commit] = chunk_snapshot(
snapshot,
task.base_commit,
embedding_spec.chunk_lines,
embedding_spec.chunk_overlap_lines,
embedding_spec.chunk_char_limit,
)
chunks = chunk_cache[task.base_commit]
snapshot_paths = {chunk.path for chunk in chunks}
missing_gold = set(task.gold_files) - snapshot_paths
if missing_gold:
raise PilotError(f"{task.task_id} gold files absent at base commit: {sorted(missing_gold)}")
for harness in selected_harnesses:
identity = RunIdentity(
experiment_id=experiment.experiment_id,
task_id=task.task_id,
harness_id=harness.harness_id,
harness_hash=harness.config_hash,
model_id=model_spec.model_id,
model_key=model_spec.expected_inference_key,
model_config_hash=model_spec.config_hash,
context_budget=experiment.context_budgets[0],
seed=experiment.seeds[0],
repetition=0,
repository_sha=task.base_commit,
code_revision=code_revision,
)
with EventWriter(
root / "results",
identity,
asdict(harness),
{
"agent_model": asdict(model_spec),
"embedding_model": asdict(embedding_spec),
"embedding_runtime": embedding_runtime,
},
) as writer:
writer.emit(
"run_started",
{
"task_config_hash": task.config_hash,
"task_validation_status": task.validation_status,
"repository_origin": origin,
"base_commit": task.base_commit,
"gold_commit": task.gold_commit,
"source_file_count": len(snapshot_paths),
"source_chunk_count": len(chunks),
"candidate_limit": candidate_limit,
},
)
writer.emit("resource_sample", _memory_sample())
retriever, index_stats = _select_retriever(
harness,
chunks,
embedding_spec,
embedding_client,
embedding_cache,
)
query_started = time.monotonic()
candidates = tuple(retriever.retrieve(task.statement, candidate_limit))
query_seconds = time.monotonic() - query_started
files = unique_file_ranking(candidates)
ranked_paths = [candidate.path for candidate in files]
metrics = retrieval_metrics(ranked_paths, task.gold_files)
metrics.update(
{
"candidate_count": len(candidates),
"unique_file_count": len(files),
"query_seconds": query_seconds,
**index_stats,
}
)
for rank, candidate in enumerate(candidates, start=1):
writer.emit(
"retrieval_candidate",
{
"rank": rank,
"path": candidate.path,
"line_start": candidate.line_start,
"line_end": candidate.line_end,
"source": candidate.source,
"score": candidate.score,
"chunk_id": (candidate.metadata or {}).get("chunk_id"),
"is_gold_file": candidate.path in set(task.gold_files),
},
)
ranking_payload = [
{
"rank": rank,
"path": candidate.path,
"line_start": candidate.line_start,
"line_end": candidate.line_end,
"score": candidate.score,
"source": candidate.source,
"is_gold_file": candidate.path in set(task.gold_files),
}
for rank, candidate in enumerate(files, start=1)
]
writer.write_artifact("ranking.json", json.dumps(ranking_payload, indent=2) + "\n")
writer.write_artifact("final_metrics.json", json.dumps(metrics, indent=2) + "\n")
writer.emit("resource_sample", _memory_sample())
writer.emit("run_finished", {"status": "completed", "metrics": metrics})
summary_rows.append(
{
"run_id": identity.run_id,
"task_id": task.task_id,
"harness_id": harness.harness_id,
**metrics,
}
)
summary = {
"schema_version": 1,
"experiment_id": experiment.experiment_id,
"development_only": True,
"repository": str(repository.resolve()),
"repository_origin": origin,
"code_revision": code_revision,
"embedding_config_hash": embedding_spec.config_hash,
"resident_models": resident_models,
"task_count": len(selected_tasks),
"harness_count": len(selected_harnesses),
"run_count": len(summary_rows),
"runs": summary_rows,
}
report_path = root / "results" / "reports" / f"{experiment.experiment_id}_{code_revision[:12]}.json"
report_path.parent.mkdir(parents=True, exist_ok=True)
with report_path.open("x", encoding="utf-8") as handle:
json.dump(summary, handle, indent=2, sort_keys=True)
handle.write("\n")
return summary
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