| """Vector-only retrieval check. Avoids the reranker and LLM rewrite, which
|
| together with the embedded Qdrant client have been crashing this process
|
| (0xC0000005) on Windows.
|
| """
|
|
|
| from __future__ import annotations
|
|
|
| import sys
|
| from pathlib import Path
|
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
|
|
| from backend.app.agent.embeddings import get_embeddings
|
| from backend.app.agent.resources import get_qdrant
|
| from backend.app.config import get_settings
|
|
|
| QUERIES = [
|
| "What are the main competitive risks Alphabet identifies in AI and cloud?",
|
| "antitrust lawsuits Google Search advertising",
|
| "cybersecurity risk management and incidents",
|
| ]
|
|
|
|
|
| def main() -> None:
|
| settings = get_settings()
|
| embedder = get_embeddings()
|
| client = get_qdrant()
|
| print(f"collection {settings.collection_name}: "
|
| f"{client.get_collection(settings.collection_name).points_count} points\n")
|
|
|
| for query in QUERIES:
|
| print(f"Q: {query}")
|
| hits = client.query_points(
|
| collection_name=settings.collection_name,
|
| query=embedder.embed_query(query),
|
| limit=5,
|
| with_payload=True,
|
| ).points
|
| for rank, hit in enumerate(hits, 1):
|
| payload = hit.payload or {}
|
| section = payload.get("section") or "(none)"
|
| summary = " ".join(str(payload.get("summary") or payload.get("content") or "").split())
|
| print(f" {rank}. {hit.score:.4f} [{section}] {summary[:140]}")
|
| print()
|
|
|
|
|
| if __name__ == "__main__":
|
| main()
|
|
|
