249 lines
10 KiB
Python
249 lines
10 KiB
Python
"""Evaluate local FTS retrieval with temporary Arabic documents and API cleanup."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import argparse
|
|
import atexit
|
|
import json
|
|
import shutil
|
|
import sys
|
|
import tempfile
|
|
from datetime import datetime, timezone
|
|
from pathlib import Path
|
|
from urllib.error import HTTPError
|
|
from urllib.request import Request, urlopen
|
|
|
|
|
|
ROOT = Path(__file__).resolve().parents[1]
|
|
DATASET = ROOT / "evals" / "knowledge_retrieval.jsonl"
|
|
|
|
|
|
def post_json(
|
|
url: str,
|
|
payload: dict,
|
|
timeout: float = 15.0,
|
|
*,
|
|
token: str | None = None,
|
|
) -> dict:
|
|
body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
|
|
headers = {"Content-Type": "application/json"}
|
|
if token:
|
|
headers["Authorization"] = f"Bearer {token}"
|
|
request = Request(url, data=body, headers=headers, method="POST")
|
|
try:
|
|
with urlopen(request, timeout=timeout) as response:
|
|
return json.loads(response.read().decode("utf-8"))
|
|
except HTTPError as error:
|
|
detail = error.read().decode("utf-8", errors="replace")
|
|
raise RuntimeError(f"HTTP {error.code} from {url}: {detail[:1000]}") from error
|
|
|
|
|
|
def delete_json(
|
|
url: str,
|
|
payload: dict,
|
|
timeout: float = 15.0,
|
|
*,
|
|
token: str | None = None,
|
|
) -> dict:
|
|
body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
|
|
headers = {"Content-Type": "application/json"}
|
|
if token:
|
|
headers["Authorization"] = f"Bearer {token}"
|
|
request = Request(
|
|
url,
|
|
data=body,
|
|
headers=headers,
|
|
method="DELETE",
|
|
)
|
|
try:
|
|
with urlopen(request, timeout=timeout) as response:
|
|
return json.loads(response.read().decode("utf-8"))
|
|
except HTTPError as error:
|
|
detail = error.read().decode("utf-8", errors="replace")
|
|
raise RuntimeError(f"HTTP {error.code} from {url}: {detail[:1000]}") from error
|
|
|
|
|
|
def main() -> int:
|
|
parser = argparse.ArgumentParser(description=__doc__)
|
|
parser.add_argument("--base-url", default="http://127.0.0.1:8000")
|
|
parser.add_argument("--dataset", type=Path, default=DATASET)
|
|
parser.add_argument("--output", type=Path, default=None)
|
|
parser.add_argument("--request-timeout", type=float, default=180.0)
|
|
parser.add_argument("--case", action="append", dest="case_ids")
|
|
parser.add_argument(
|
|
"--agent-answers",
|
|
action="store_true",
|
|
help="Also ask the local agent to answer each selected case from indexed knowledge.",
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
cases = [
|
|
json.loads(line)
|
|
for line in args.dataset.read_text(encoding="utf-8").splitlines()
|
|
if line.strip()
|
|
]
|
|
if args.case_ids:
|
|
cases = [case for case in cases if case["id"] in args.case_ids]
|
|
missing = set(args.case_ids) - {case["id"] for case in cases}
|
|
if missing:
|
|
parser.error(f"Unknown case ID(s): {', '.join(sorted(missing))}")
|
|
if not cases or len({case["id"] for case in cases}) != len(cases):
|
|
raise ValueError("Retrieval dataset must be non-empty with unique IDs.")
|
|
|
|
results: list[dict] = []
|
|
index_summary: dict[str, object] = {}
|
|
files = sorted({case["document"] for case in cases})
|
|
index_url = args.base_url.rstrip("/") + "/v1/agent/knowledge/index"
|
|
search_url = args.base_url.rstrip("/") + "/v1/agent/knowledge/search"
|
|
base_url = args.base_url.rstrip("/")
|
|
session = post_json(base_url + "/v1/auth/local-session", {})
|
|
token = session["access_token"]
|
|
|
|
def logout() -> None:
|
|
try:
|
|
post_json(base_url + "/v1/auth/logout", {}, token=token)
|
|
except Exception:
|
|
pass
|
|
|
|
atexit.register(logout)
|
|
with tempfile.TemporaryDirectory(prefix="sovereignai-rag-eval-") as temporary:
|
|
workspace_path = Path(temporary)
|
|
for case in cases:
|
|
destination = workspace_path / case["document"]
|
|
destination.parent.mkdir(parents=True, exist_ok=True)
|
|
source_path = case.get("source_path")
|
|
if source_path:
|
|
source = (ROOT / source_path).resolve(strict=True)
|
|
if not source.is_relative_to(ROOT) or not source.is_file():
|
|
raise ValueError(f"Evaluation source must be a file inside the project: {source_path}")
|
|
shutil.copyfile(source, destination)
|
|
elif not destination.exists():
|
|
destination.write_text(case["text"], encoding="utf-8")
|
|
index_payload = {"workspace_path": str(workspace_path), "files": files}
|
|
index_attempted = False
|
|
try:
|
|
index_attempted = True
|
|
index_result = post_json(
|
|
index_url, index_payload, timeout=args.request_timeout, token=token
|
|
)
|
|
if len(index_result.get("indexed", [])) != len(files):
|
|
raise RuntimeError("The API did not confirm every fixture document.")
|
|
indexed_documents = index_result["indexed"]
|
|
index_summary = {
|
|
"semantic_indexed_documents": sum(
|
|
bool(item.get("semantic_indexed")) for item in indexed_documents
|
|
),
|
|
"documents": len(indexed_documents),
|
|
"embedding_models": sorted(
|
|
{
|
|
item["embedding_model"]
|
|
for item in indexed_documents
|
|
if item.get("embedding_model")
|
|
}
|
|
),
|
|
}
|
|
for case in cases:
|
|
response = post_json(
|
|
search_url,
|
|
{"workspace_path": str(workspace_path), "task": case["query"]},
|
|
timeout=args.request_timeout,
|
|
token=token,
|
|
)
|
|
retrieved = response.get("results", [])
|
|
ranked_paths = list(dict.fromkeys(item["path"] for item in retrieved))
|
|
rank = ranked_paths.index(case["document"]) + 1 if case["document"] in ranked_paths else None
|
|
evidence_found = any(
|
|
case["expected_evidence"].casefold() in item.get("text", "").casefold()
|
|
for item in retrieved
|
|
if item.get("path") == case["document"]
|
|
)
|
|
result = {
|
|
"id": case["id"],
|
|
"query": case["query"],
|
|
"expected_document": case["document"],
|
|
"expected_evidence": case["expected_evidence"],
|
|
"rank": rank,
|
|
"top_paths": ranked_paths,
|
|
"expected_document_chunks": [
|
|
item.get("chunk")
|
|
for item in retrieved
|
|
if item.get("path") == case["document"]
|
|
],
|
|
"evidence_found": evidence_found,
|
|
"search_mode": response.get("search_mode", "unknown"),
|
|
}
|
|
if args.agent_answers:
|
|
agent_response = post_json(
|
|
base_url + "/v1/agent/run",
|
|
{
|
|
"workspace_path": str(workspace_path),
|
|
"task": (
|
|
"Search the indexed knowledge base and answer the user's question "
|
|
"using only retrieved evidence. State when the evidence is insufficient. "
|
|
f"Question: {case['query']}"
|
|
),
|
|
},
|
|
timeout=args.request_timeout,
|
|
token=token,
|
|
)
|
|
result["agent_answer"] = agent_response.get(
|
|
"result", agent_response.get("answer", "")
|
|
)
|
|
result["agent_tool"] = agent_response.get("tool")
|
|
result["agent_files"] = agent_response.get("files", [])
|
|
result["agent_steps"] = agent_response.get("steps", [])
|
|
result["human_rating"] = None
|
|
results.append(result)
|
|
finally:
|
|
if index_attempted:
|
|
original_error = sys.exc_info()[0] is not None
|
|
try:
|
|
deletion = delete_json(
|
|
index_url,
|
|
index_payload,
|
|
timeout=args.request_timeout,
|
|
token=token,
|
|
)
|
|
if len(deletion.get("deleted", [])) != len(files):
|
|
raise RuntimeError(
|
|
"The API did not confirm cleanup for every fixture document."
|
|
)
|
|
except Exception as cleanup_error:
|
|
if original_error:
|
|
print(f"Cleanup also failed: {cleanup_error}", file=sys.stderr)
|
|
else:
|
|
raise
|
|
|
|
logout()
|
|
|
|
total = len(results)
|
|
metrics = {
|
|
"cases": total,
|
|
"hit_at_1": sum(item["rank"] == 1 for item in results) / total,
|
|
"hit_at_3": sum(item["rank"] is not None and item["rank"] <= 3 for item in results) / total,
|
|
"mean_reciprocal_rank": sum(1 / item["rank"] if item["rank"] else 0 for item in results) / total,
|
|
"evidence_rate": sum(item["evidence_found"] for item in results) / total,
|
|
}
|
|
timestamp = datetime.now(timezone.utc)
|
|
output = args.output or ROOT / "evals" / "results" / f"retrieval_{timestamp.strftime('%Y-%m-%d_%H%M%S')}.json"
|
|
report = {
|
|
"created_at_utc": timestamp.isoformat(),
|
|
"base_url": args.base_url,
|
|
"dataset": str(args.dataset.relative_to(ROOT) if args.dataset.is_relative_to(ROOT) else args.dataset),
|
|
"indexing": index_summary,
|
|
"metrics": metrics,
|
|
"results": results,
|
|
"note": (
|
|
"Small deterministic fixture set. Retrieval metrics cover source ranking and evidence presence, "
|
|
"not general RAG quality. Optional agent answers are retained for human review and are not auto-scored."
|
|
),
|
|
}
|
|
output.parent.mkdir(parents=True, exist_ok=True)
|
|
output.write_text(json.dumps(report, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
|
|
print(json.dumps({"output": str(output), "metrics": metrics}, ensure_ascii=False, indent=2))
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
raise SystemExit(main())
|