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Download scripts/check_qdrant.py from tenperformer/Matchlens-API: direct link, hf CLI and curl.
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- Download file 2.25 kB
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https://huggingface.co/spaces/tenperformer/Matchlens-API/resolve/main/scripts/check_qdrant.py
- Command line
-
hf download hf://spaces/tenperformer/Matchlens-API/scripts/check_qdrant.py
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curl -L -o check_qdrant.py https://huggingface.co/spaces/tenperformer/Matchlens-API/resolve/main/scripts/check_qdrant.py
2.25 kB
| import os | |
| import sys | |
| from pathlib import Path | |
| backend_dir = Path(__file__).resolve().parent.parent | |
| sys.path.insert(0, str(backend_dir)) | |
| from dotenv import load_dotenv | |
| load_dotenv(backend_dir / ".env") | |
| from app.config import get_settings | |
| from qdrant_client import QdrantClient | |
| def main(): | |
| settings = get_settings() | |
| url = settings.qdrant_url | |
| key = settings.qdrant_api_key | |
| collection = settings.qdrant_collection | |
| print("=" * 65) | |
| print("MatchLens Qdrant Cloud Inspection Tool") | |
| print("=" * 65) | |
| print(f"Target URL: {url}") | |
| print(f"Collection Name: {collection}") | |
| if not url or not key: | |
| print("ERROR: QDRANT_URL or QDRANT_API_KEY is not set in backend/.env") | |
| return | |
| client = QdrantClient(url=url, api_key=key, timeout=15) | |
| # 1. Cluster collections | |
| colls = client.get_collections() | |
| collection_names = [c.name for c in colls.collections] | |
| print(f"Available Collections in Cluster: {collection_names}") | |
| if collection not in collection_names: | |
| print(f"WARNING: Collection '{collection}' does not exist on cluster!") | |
| return | |
| # 2. Collection statistics | |
| info = client.get_collection(collection) | |
| print("-" * 65) | |
| print(f"Collection Status: {info.status}") | |
| print(f"Total Points: {info.points_count}") | |
| print(f"Vector Dimensions: {info.config.params.vectors.size}") | |
| print(f"Distance Metric: {info.config.params.vectors.distance}") | |
| print("-" * 65) | |
| # 3. Scroll and display all ingested document payloads | |
| points, _ = client.scroll( | |
| collection_name=collection, | |
| limit=20, | |
| with_payload=True, | |
| with_vectors=False, | |
| ) | |
| print(f"Stored Documents & Embeddings ({len(points)} points retrieved):\n") | |
| for idx, pt in enumerate(points, 1): | |
| payload = pt.payload or {} | |
| print(f"[{idx}] Point ID: {pt.id}") | |
| print(f" Title: {payload.get('title')}") | |
| print(f" Source: {payload.get('source_url')}") | |
| content = payload.get('content', '') | |
| print(f" Preview: {content[:130].strip()}...") | |
| print() | |
| print("=" * 65) | |
| print("All points are verified and indexed in Qdrant Cloud.") | |
| if __name__ == "__main__": | |
| main() | |