qdrant-vector-search

Malicious tool definition detected

Tool: SKILL.md [1/2] Description: --- name: qdrant-vector-search description: High-performance vector similarity search engine for RAG and semantic search.

Tool: SKILL.md [2/2] Description: quantized in RAM ) ) ) # Search with rescoring results = client.search( collection_name="quantized", query_vector=query, search_params={"quantization": {"rescore": True}}, # Rescore top results limit=10 ) ``` ## Payload indexing ```python from qdrant_client.models import PayloadSchemaType # Create payload index for faster filtering client.create_payload_index( collection_name="documents", field_name="category", field_schema=PayloadSchemaType.KEYWORD ) client.cre

Malicious tool definition detected

Tool: references/advanced-usage.md [1/2] Description: # Qdrant Advanced Usage Guide ## Distributed Deployment ### Cluster Setup Qdrant uses Raft consensus for distributed coordination.

Tool: references/advanced-usage.md [2/2]

Malicious tool definition detected

Tool: references/troubleshooting.md [1/2] Description: # Qdrant Troubleshooting Guide ## Installation Issues ### Docker Issues **Error**: `Cannot connect to Docker daemon` **Fix**: ```bash # Start Docker daemon sudo systemctl start docker # Or use Docker Desktop on Mac/Windows open -a Docker ``` **Error**: `Port 6333 already in use` **Fix**: ```bash # Find process using port lsof -i :6333 # Kill process or use different port docker run -p 6334:6333 qdrant/qdrant ``` ### Python Client Issues **Er

Tool: references/troubleshooting.md [2/2] Description: critical writes client.upsert( collection_name="documents", points=points, ordering=WriteOrdering.STRONG ) ``` ## Performance Tuning ### Benchmark Configuration ```python import time import numpy as np def benchmark_search(client, collection, n_queries=100, dimension=384): # Generate random queries queries = [np.random.rand(dimension).tolist() for _ in range(n_queries)] # Warmup for q in queries[:10]: client.search(collection_name=collection