Qdrant Vector Database Skill

Quick Start Workflow

When working with Qdrant:

1. •

   Check if Qdrant is configured

   • •Look for QDRANT_URL and QDRANT_API_KEY in .env
   • •For local: http://localhost:6333
   • •For cloud: https://xxx.qdrant.io
2. •

   For collection creation

   • •Define vector size (1536 for OpenAI ada-002)
   • •Choose distance metric (Cosine for semantic similarity)
   • •Set up HNSW parameters for performance
3. •

   For content ingestion

   • •Chunk text into 800-character segments with 200-char overlap
   • •Generate embeddings with OpenAI text-embedding-ada-002
   • •Upsert vectors with metadata (chapter, section, file path)
4. •

   For semantic search

   • •Convert user query to embedding
   • •Search with score threshold (>= 0.7 for relevance)
   • •Return top 5 results with metadata

Standard Patterns

Client Setup

import { QdrantClient } from '@qdrant/js-client';

export const qdrant = new QdrantClient({
  url: process.env.QDRANT_URL,
  apiKey: process.env.QDRANT_API_KEY,
});

Collection Configuration

await qdrant.createCollection('textbook_chunks', {
  vectors: {
    size: 1536, // OpenAI ada-002
    distance: 'Cosine',
  },
  hnsw_config: {
    m: 16,
    ef_construct: 100,
  },
});

Best Practices

For Physical AI textbook RAG:

• •Collection name: textbook_chunks
• •Vector size: 1536 (OpenAI ada-002 embeddings)
• •Chunk size: 800 characters with 200 overlap
• •Score threshold: 0.7 minimum for relevance
• •Batch size: 100 vectors per upsert operation
• •Metadata: Always include chapter, section, file path

Knowledge Base

For detailed information, see:

• •Docker Setup → references/docker-setup.md
• •Collection Management → references/collections.md
• •Embedding Generation → references/embeddings.md
• •Search Patterns → references/search-patterns.md
• •Performance Tuning → references/performance.md