| |
| """ |
| verify_eqc_qa.py — sanity-check the eqc_qa Qdrant collection. |
| |
| - point count == embedded chunk count |
| - BM25 (sparse-only) probes [always works, no API] |
| - hybrid dense+BM25 RRF probes [needs one query embedding per probe; degrades |
| to BM25-only if the Gemini quota 429s] |
| |
| Probes: SST consistency, satellite soil moisture completeness, multi-origin atlas. |
| Prints top hits with report_id + dataset_id. |
| """ |
| import json |
| import sys |
| import threading |
| from pathlib import Path |
|
|
| sys.path.insert(0, "/Users/dmpantiu/copernicus_mcp/marine_rag") |
| import net_ipv4 |
|
|
| from qdrant_client import QdrantClient, models |
| from fastembed import SparseTextEmbedding |
|
|
| ROOT = Path(__file__).resolve().parent |
| COLLECTION = "eqc_qa" |
| DENSE_DIM = 768 |
| LOCAL_DB = ROOT / "qdrant_db" |
| INPUT = ROOT / "chunks_embedded.jsonl" |
|
|
| _bm25 = None |
| _lock = threading.Lock() |
|
|
|
|
| def log(*a): |
| print(*a, file=sys.stderr, flush=True) |
|
|
|
|
| def resolve_key() -> str: |
| import os |
| for var in ("GOOGLE_API_KEY", "GEMINI_API_KEY"): |
| if os.environ.get(var): |
| return os.environ[var] |
| for env in (Path("/Users/dmpantiu/copernicus_mcp/.env"),): |
| if env.exists(): |
| for line in env.read_text().splitlines(): |
| line = line.strip() |
| if "api_key" in line.lower() and "=" in line and not line.startswith("#"): |
| return line.split("=", 1)[1].strip().strip('"').strip("'") |
| raise SystemExit("no key") |
|
|
|
|
| def embed_query(q: str): |
| from google import genai |
| from google.genai import types |
| import numpy as np |
| client = genai.Client(api_key=resolve_key()) |
| r = client.models.embed_content( |
| model="gemini-embedding-2-preview", contents=q, |
| config=types.EmbedContentConfig(task_type="RETRIEVAL_QUERY", output_dimensionality=DENSE_DIM)) |
| v = np.array(list(r.embeddings[0].values), dtype=np.float32) |
| n = np.linalg.norm(v) |
| return (v / n).tolist() if n > 0 else v.tolist() |
|
|
|
|
| def sparse_query(q: str): |
| global _bm25 |
| with _lock: |
| if _bm25 is None: |
| _bm25 = SparseTextEmbedding(model_name="Qdrant/bm25") |
| sp = list(_bm25.query_embed(q))[0] |
| return models.SparseVector(indices=sp.indices.tolist(), values=sp.values.tolist()) |
|
|
|
|
| def search(client, query, top_k=5): |
| sparse = sparse_query(query) |
| dense = None |
| try: |
| dense = embed_query(query) |
| except Exception as e: |
| log(f" [dense unavailable: {str(e)[:70]}] BM25-only") |
| if dense is not None: |
| res = client.query_points( |
| collection_name=COLLECTION, |
| prefetch=[ |
| models.Prefetch(query=dense, using="dense", limit=50), |
| models.Prefetch(query=sparse, using="sparse", limit=50), |
| ], |
| query=models.FusionQuery(fusion=models.Fusion.RRF), |
| limit=top_k, with_payload=True, |
| ) |
| mode = "hybrid dense+BM25 RRF" |
| else: |
| res = client.query_points(collection_name=COLLECTION, query=sparse, |
| using="sparse", limit=top_k, with_payload=True) |
| mode = "BM25-only" |
| return res.points, mode |
|
|
|
|
| def main(): |
| client = QdrantClient(path=str(LOCAL_DB)) |
| n_pts = client.get_collection(COLLECTION).points_count |
| n_emb = sum(1 for _ in open(INPUT)) if INPUT.exists() else 0 |
| print(f"points={n_pts} embedded_chunks={n_emb} match={'OK' if n_pts == n_emb else 'MISMATCH'}") |
|
|
| probes = [ |
| "sea surface temperature consistency assessment", |
| "completeness of satellite soil moisture", |
| "multi-origin atlas quality", |
| ] |
| for q in probes: |
| pts, mode = search(client, q, top_k=5) |
| print(f"\n=== '{q}' [{mode}] ===") |
| for i, p in enumerate(pts, 1): |
| pl = p.payload |
| print(f" #{i} score={p.score:.4f} report={pl['report_id']}") |
| print(f" dataset={pl['dataset_id']} aspect={pl['aspect']} " |
| f"conf={pl.get('match_confidence','')} sec='{pl.get('section','')[:50]}'") |
| print(f" {pl.get('text_raw','')[:150].strip()}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
