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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 | """Blinded, single-call LLM localization over a frozen retrieval ranking."""
from __future__ import annotations
from dataclasses import asdict
from hashlib import sha256
import json
from pathlib import Path
import time
from typing import Any, Sequence
from .lm_studio import LMStudioClient, LMStudioError
from .pilot import PilotError, research_code_revision, retrieval_metrics
from .repository import GitSnapshot
from .specs import load_experiments, load_harnesses, load_models, load_tasks
from .telemetry import EventWriter, RunIdentity
SYSTEM_PROMPT = """You are performing blinded bug localization in a large Go repository.
Use only the issue and candidate snippets supplied by the harness. Select the files that
would most likely need source-code changes. Do not propose a patch. Return one JSON object
with exactly these keys: {"files":["path/to/file.go"],"reasoning":"brief rationale"}.
The files array must contain 1-10 distinct paths copied exactly from the candidates. Keep
the rationale below 200 words and do not wrap the JSON in Markdown."""
class LocalizationError(RuntimeError):
"""Raised when a blinded localization run is invalid or cannot be completed."""
def load_ranking(path: Path, limit: int = 10) -> tuple[list[dict[str, Any]], str]:
raw = path.read_bytes()
try:
value = json.loads(raw)
except json.JSONDecodeError as exc:
raise LocalizationError(f"Invalid ranking JSON: {path}") from exc
if not isinstance(value, list) or not value:
raise LocalizationError("Ranking must be a non-empty JSON array")
candidates: list[dict[str, Any]] = []
seen: set[str] = set()
for record in value:
if not isinstance(record, dict):
raise LocalizationError("Every ranking entry must be an object")
try:
candidate = {
"rank": int(record["rank"]),
"path": str(record["path"]),
"line_start": int(record["line_start"]),
"line_end": int(record["line_end"]),
"score": float(record["score"]),
"source": str(record["source"]),
}
except (KeyError, TypeError, ValueError) as exc:
raise LocalizationError(f"Malformed ranking entry: {record!r}") from exc
path_value = candidate["path"]
if Path(path_value).is_absolute() or ".." in Path(path_value).parts:
raise LocalizationError(f"Unsafe candidate path: {path_value}")
if path_value not in seen:
candidates.append(candidate)
seen.add(path_value)
if len(candidates) >= limit:
break
if not candidates:
raise LocalizationError("Ranking has no usable candidate files")
return candidates, sha256(raw).hexdigest()
def build_prompt(
statement: str,
snapshot: GitSnapshot,
commit: str,
candidates: Sequence[dict[str, Any]],
) -> str:
blocks = [f"ISSUE:\n{statement}\n\nCANDIDATE SNIPPETS:"]
for candidate in candidates:
source = snapshot.read_file(commit, candidate["path"])
lines = source.text.splitlines()
start = max(candidate["line_start"], 1)
end = min(candidate["line_end"], len(lines))
numbered = "\n".join(
f"{line_number:>6}: {lines[line_number - 1]}"
for line_number in range(start, end + 1)
)
blocks.append(
f"\n--- Candidate {candidate['rank']}: {candidate['path']} "
f"(lines {start}-{end}) ---\n{numbered}"
)
return "\n".join(blocks)
def parse_selection(response: dict[str, Any], allowed_paths: set[str]) -> dict[str, Any]:
try:
message = response["choices"][0]["message"]
content = message["content"]
except (KeyError, IndexError, TypeError) as exc:
raise LocalizationError("Chat completion has no assistant content") from exc
if not isinstance(content, str):
raise LocalizationError("Assistant content is not text")
stripped = content.strip()
if stripped.startswith("```"):
stripped = stripped.removeprefix("```json").removeprefix("```")
stripped = stripped.removesuffix("```").strip()
try:
value = json.loads(stripped)
except json.JSONDecodeError:
start, end = stripped.find("{"), stripped.rfind("}")
if start < 0 or end <= start:
raise LocalizationError(f"Assistant did not return JSON: {content!r}")
try:
value = json.loads(stripped[start : end + 1])
except json.JSONDecodeError as exc:
raise LocalizationError(f"Assistant returned invalid JSON: {content!r}") from exc
if not isinstance(value, dict) or set(value) != {"files", "reasoning"}:
raise LocalizationError("Assistant JSON must contain exactly files and reasoning")
files = value["files"]
if (
not isinstance(files, list)
or not 1 <= len(files) <= 10
or not all(isinstance(item, str) for item in files)
or len(files) != len(set(files))
):
raise LocalizationError("Assistant files must be 1-10 distinct path strings")
unknown = set(files) - allowed_paths
if unknown:
raise LocalizationError(f"Assistant selected paths outside the candidates: {sorted(unknown)}")
if not isinstance(value["reasoning"], str):
raise LocalizationError("Assistant reasoning must be text")
return {"files": files, "reasoning": value["reasoning"]}
def _exclusive_agent_residency(discovery: Any, expected_key: str) -> tuple[str, ...]:
loaded = tuple(
str(record.get("key"))
for record in discovery.native_models
if record.get("loaded_instances")
)
if loaded != (expected_key,):
raise LocalizationError(
"LLM localization requires exclusive agent-model residency; "
f"expected {(expected_key,)}, observed {loaded}"
)
return loaded
def run_localization(
root: Path,
repository: Path,
ranking_path: Path,
task_id: str,
harness_id: str,
experiment_id: str = "E06",
candidate_limit: int = 10,
timeout_seconds: float = 900.0,
) -> dict[str, Any]:
revision = research_code_revision(root)
experiments = load_experiments(root)
harnesses = load_harnesses(root)
models = load_models(root)
tasks = load_tasks(root)
try:
experiment = experiments[experiment_id]
harness = harnesses[harness_id]
model = models[experiment.model_ids[0]]
task = tasks[task_id]
except KeyError as exc:
raise LocalizationError(f"Unknown experiment, harness, model, or task: {exc}") from exc
if harness_id not in experiment.harness_ids:
raise LocalizationError(f"{harness_id} is not assigned to {experiment_id}")
candidates, ranking_hash = load_ranking(ranking_path, candidate_limit)
snapshot = GitSnapshot(repository)
snapshot.verify_commit(task.base_commit)
prompt = build_prompt(task.statement, snapshot, task.base_commit, candidates)
prompt_hash = sha256(prompt.encode("utf-8")).hexdigest()
client = LMStudioClient(model, timeout_seconds=timeout_seconds)
discovery, resolved = client.resolve()
loaded_models = _exclusive_agent_residency(discovery, resolved.inference_key)
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.model_id,
model_key=resolved.inference_key,
model_config_hash=model.config_hash,
context_budget=experiment.context_budgets[0],
seed=model.seed,
repetition=1,
repository_sha=task.base_commit,
code_revision=revision,
)
resolved_model = resolved.to_dict()
resolved_model.update(
{
"exclusive_loaded_models": loaded_models,
"ranking_sha256": ranking_hash,
"prompt_sha256": prompt_hash,
"candidate_limit": len(candidates),
}
)
with EventWriter(
root / "results",
identity,
asdict(harness),
resolved_model,
) as writer:
writer.emit(
"run_started",
{
"development_only": True,
"blinded_prompt": True,
"ranking_path": str(ranking_path.resolve()),
"prompt_chars": len(prompt),
"candidate_count": len(candidates),
},
)
started = time.monotonic()
try:
response = client.chat_completions(
resolved.inference_key,
[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": prompt},
],
max_tokens=model.max_tokens,
)
elapsed = time.monotonic() - started
writer.emit(
"model_call",
{
"elapsed_seconds": elapsed,
"usage": response.get("usage", {}),
"finish_reason": response.get("choices", [{}])[0].get("finish_reason"),
},
)
selection = parse_selection(response, {item["path"] for item in candidates})
metrics = retrieval_metrics(selection["files"], task.gold_files)
final = {
"run_id": identity.run_id,
"experiment_id": experiment.experiment_id,
"task_id": task.task_id,
"harness_id": harness.harness_id,
"selected_files": selection["files"],
"reasoning": selection["reasoning"],
"metrics": metrics,
"usage": response.get("usage", {}),
"elapsed_seconds": elapsed,
"prompt_chars": len(prompt),
"prompt_sha256": prompt_hash,
"ranking_sha256": ranking_hash,
}
writer.write_artifact("model_response.json", json.dumps(response, indent=2) + "\n")
writer.write_artifact("selection.json", json.dumps(selection, indent=2) + "\n")
writer.write_artifact("final_metrics.json", json.dumps(final, indent=2) + "\n")
writer.emit("run_finished", final)
return {**final, "run_directory": str(writer.directory)}
except (LMStudioError, LocalizationError) as exc:
writer.emit("run_finished", {"status": "failed", "error": str(exc)})
raise
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