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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 | """E05 dense-index backend systems experiment."""
from __future__ import annotations
from dataclasses import asdict
from hashlib import sha256
import json
from pathlib import Path
import statistics
import time
from typing import Any, Sequence
import faiss
import psutil
from .components import Candidate
from .confirmatory_retrieval import extended_metrics, subprocess_git
from .fusion import reciprocal_rank_fusion, unique_files
from .lm_studio_embeddings import LMStudioEmbeddingClient
from .pilot import research_code_revision
from .repository import GitSnapshot, chunk_snapshot
from .retrieval import BM25FuzzyRetriever, DenseRetriever, SQLiteEmbeddingCache
from .specs import (
BackendSpec,
HarnessSpec,
load_backends,
load_embeddings,
load_experiments,
load_harnesses,
load_models,
load_task_split,
load_tasks,
)
from .syntax_index import SyntaxRetriever, parse_snapshot
from .telemetry import EventWriter, RunIdentity, run_directory
from .tokenization import QwenTokenCounter
from .vector_backends import FaissFlatRetriever, FaissHNSWRetriever, SQLiteVecRetriever
class BackendExperimentError(RuntimeError):
"""Raised when E05 cannot execute its frozen backend protocol."""
def percentile(values: Sequence[float], fraction: float) -> float:
ordered = sorted(values)
if not ordered:
raise ValueError("percentile requires observations")
position = (len(ordered) - 1) * fraction
lower = int(position)
upper = min(lower + 1, len(ordered) - 1)
weight = position - lower
return ordered[lower] * (1.0 - weight) + ordered[upper] * weight
def build_backend(
backend: BackendSpec,
dense: DenseRetriever,
index_path: Path,
) -> tuple[Any, int, int]:
process = psutil.Process()
before = process.memory_info().rss
if backend.backend_id == "B001":
instance = FaissFlatRetriever(dense)
faiss.write_index(instance.index, str(index_path))
elif backend.backend_id == "B002":
instance = FaissHNSWRetriever(
dense,
neighbors=int(backend.neighbors or 32),
ef_construction=int(backend.ef_construction or 80),
ef_search=int(backend.ef_search or 64),
)
faiss.write_index(instance.index, str(index_path))
elif backend.backend_id == "B003":
instance = SQLiteVecRetriever(dense, index_path)
else:
raise BackendExperimentError(f"unsupported backend {backend.backend_id}")
after = process.memory_info().rss
return instance, max(after - before, 0), index_path.stat().st_size
def treatment_ranking(
harness: HarnessSpec,
dense_ranking: Sequence[Candidate],
lexical_ranking: Sequence[Candidate],
syntax_ranking: Sequence[Candidate],
limit: int,
) -> tuple[Candidate, ...]:
if harness.harness_id == "H003":
return unique_files(tuple(dense_ranking))[:limit]
if harness.harness_id == "H005":
return reciprocal_rank_fusion([lexical_ranking, dense_ranking], limit)
if harness.harness_id == "H007":
return reciprocal_rank_fusion([lexical_ranking, syntax_ranking, dense_ranking], limit)
raise BackendExperimentError(f"E05 does not implement {harness.harness_id}")
def run_backend_experiment(
root: Path,
repository: Path,
experiment_id: str = "E05",
task_filter: set[str] | None = None,
backend_filter: set[str] | None = None,
harness_filter: set[str] | None = None,
candidate_limit: int = 200,
) -> dict[str, Any]:
revision = research_code_revision(root)
experiments = load_experiments(root)
experiment = experiments.get(experiment_id)
if experiment is None or experiment.mode != "index_backend":
raise BackendExperimentError("runner requires the frozen E05 index_backend experiment")
harness_catalog = load_harnesses(root)
backend_catalog = load_backends(root)
model = load_models(root)[experiment.model_ids[0]]
embedding = load_embeddings(root)[experiment.embedding_id]
task_catalog = load_tasks(root)
split = load_task_split(root / "tasks" / "splits" / f"{experiment.task_split}.txt")
tasks = [task_catalog[item] for item in split if task_filter is None or item in task_filter]
harnesses = [
harness_catalog[item]
for item in experiment.harness_ids
if harness_filter is None or item in harness_filter
]
backends = [
backend_catalog[item]
for item in experiment.backend_ids
if backend_filter is None or item in backend_filter
]
if not tasks or not harnesses or not backends:
raise BackendExperimentError("filters selected no E05 cells")
client = LMStudioEmbeddingClient(embedding, timeout_seconds=120.0)
runtime = client.resolve()
resident = client.loaded_model_keys()
if tuple(resident) != (embedding.model_key,):
raise BackendExperimentError(f"E05 requires exclusive embedding residency; observed {resident}")
tokenizer = QwenTokenCounter()
snapshot = GitSnapshot(repository)
origin = subprocess_git(repository, ["remote", "get-url", "origin"])
rows: list[dict[str, Any]] = []
cache_path = root / "indexes" / "embeddings" / f"{embedding.config_hash}.sqlite3"
with SQLiteEmbeddingCache(cache_path, embedding) as cache:
for task in tasks:
chunks = chunk_snapshot(
snapshot,
task.base_commit,
embedding.chunk_lines,
embedding.chunk_overlap_lines,
embedding.chunk_char_limit,
)
symbols = parse_snapshot(snapshot, task.base_commit)
dense_base, dense_stats = DenseRetriever.build(chunks, embedding, client, cache)
lexical_ranking = BM25FuzzyRetriever(chunks).retrieve(task.statement, candidate_limit)
syntax_ranking = SyntaxRetriever(symbols).retrieve(task.statement, candidate_limit)
index_dir = root / "indexes" / "e05" / task.base_commit
index_dir.mkdir(parents=True, exist_ok=True)
instances: dict[str, tuple[Any, int, int]] = {}
for backend in backends:
suffix = ".sqlite3" if backend.backend_id == "B003" else ".faiss"
instances[backend.backend_id] = build_backend(
backend,
dense_base,
index_dir / f"{backend.backend_id}{suffix}",
)
flat_paths = [
candidate.path
for candidate in unique_files(
tuple(instances["B001"][0].retrieve(task.statement, candidate_limit))
)[:10]
] if "B001" in instances else []
for backend in backends:
instance, ram_delta, disk_bytes = instances[backend.backend_id]
for seed in experiment.seeds:
timings: list[float] = []
dense_ranking: Sequence[Candidate] = ()
for _ in range(backend.query_repetitions):
started = time.perf_counter()
dense_ranking = instance.retrieve(task.statement, candidate_limit)
timings.append((time.perf_counter() - started) * 1000.0)
for harness in harnesses:
treatment_id = f"{harness.harness_id}_{backend.backend_id}"
treatment_hash = sha256(
f"{harness.config_hash}\0{backend.config_hash}".encode("utf-8")
).hexdigest()
identity = RunIdentity(
experiment_id=experiment.experiment_id,
task_id=task.task_id,
harness_id=treatment_id,
harness_hash=treatment_hash,
model_id=model.model_id,
model_key=model.expected_inference_key,
model_config_hash=model.config_hash,
context_budget=experiment.context_budgets[0],
seed=seed,
repetition=0,
repository_sha=task.base_commit,
code_revision=revision,
)
directory = run_directory(root / "results", identity)
if directory.exists():
final_path = directory / "final_metrics.json"
if not final_path.exists():
raise BackendExperimentError(f"incomplete E05 run: {directory}")
final = json.loads(final_path.read_text(encoding="utf-8"))
rows.append({"run_id": identity.run_id, **final})
continue
ranking = treatment_ranking(
harness,
dense_ranking,
lexical_ranking,
syntax_ranking,
candidate_limit,
)
metrics = extended_metrics(
ranking,
task.gold_files,
task.gold_symbols,
symbols,
tokenizer,
experiment.context_budgets[0],
)
backend_top = [candidate.path for candidate in unique_files(tuple(dense_ranking))[:10]]
metrics.update(
{
"experiment_id": experiment.experiment_id,
"task_id": task.task_id,
"harness_id": harness.harness_id,
"backend_id": backend.backend_id,
"seed": seed,
"backend_recall_at_10_vs_flat": (
len(set(backend_top) & set(flat_paths)) / 10.0 if flat_paths else None
),
"index_build_seconds": instance.stats.build_seconds,
"index_ram_bytes_delta": ram_delta,
"index_disk_bytes": disk_bytes,
"query_repetitions": backend.query_repetitions,
"query_mean_ms": statistics.fmean(timings),
"query_p50_ms": percentile(timings, 0.50),
"query_p95_ms": percentile(timings, 0.95),
"dense_cached_chunks": dense_stats.cached_chunks,
"dense_embedded_chunks": dense_stats.embedded_chunks,
}
)
with EventWriter(
root / "results",
identity,
{"harness": asdict(harness), "backend": asdict(backend)},
{
"agent_model_not_loaded": asdict(model),
"embedding_model": asdict(embedding),
"embedding_runtime": runtime,
},
) as writer:
writer.emit("run_started", {"confirmatory": True, "candidate_limit": candidate_limit})
for rank, candidate in enumerate(ranking, 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,
"symbol": candidate.symbol,
"is_gold_file": candidate.path in set(task.gold_files),
},
)
writer.write_artifact(
"final_metrics.json", json.dumps(metrics, indent=2) + "\n"
)
writer.emit("run_finished", {"status": "completed", "metrics": metrics})
rows.append({"run_id": identity.run_id, **metrics})
for instance, _, _ in instances.values():
if isinstance(instance, SQLiteVecRetriever):
instance.close()
summary = {
"schema_version": 1,
"experiment_id": experiment.experiment_id,
"confirmatory": True,
"repository_origin": origin,
"code_revision": revision,
"task_count": len(tasks),
"harness_count": len(harnesses),
"backend_count": len(backends),
"seed_count": len(experiment.seeds),
"run_count": len(rows),
"rows": rows,
}
report = root / "results" / "reports" / f"E05_{revision[:12]}_{int(time.time())}.json"
report.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8")
summary["report_path"] = str(report)
return summary
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