53d37e61dd
Created complete working examples for all 4 vector databases with RAG adaptors: Weaviate Example: - Comprehensive README with hybrid search guide - 3 Python scripts (generate, upload, query) - Sample outputs and query results - Covers hybrid search, filtering, schema design Chroma Example: - Simple, local-first approach - In-memory and persistent storage options - Semantic search and metadata filtering - Comparison with Weaviate FAISS Example: - Facebook AI Similarity Search integration - OpenAI embeddings generation - Index building and persistence - Performance-focused for scale Qdrant Example: - Advanced filtering capabilities - Production-ready features - Complex query patterns - Rust-based performance Each example includes: - Detailed README with setup and troubleshooting - requirements.txt with dependencies - 3 working Python scripts - Sample outputs directory Total files: 20 (4 examples × 5 files each) Documentation: 4 comprehensive READMEs (~800 lines total) Phase 2 of optional enhancements complete. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
83 lines
2.0 KiB
Markdown
83 lines
2.0 KiB
Markdown
# Qdrant Vector Database Example
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Qdrant is a vector similarity search engine with extended filtering support. Built in Rust for maximum performance.
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## Quick Start
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```bash
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# 1. Start Qdrant (Docker)
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docker run -p 6333:6333 qdrant/qdrant:latest
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# 2. Install dependencies
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pip install -r requirements.txt
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# 3. Generate and upload
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python 1_generate_skill.py
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python 2_upload_to_qdrant.py
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# 4. Query
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python 3_query_example.py
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```
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## What Makes Qdrant Special?
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- **Advanced Filtering**: Rich payload queries with AND/OR/NOT
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- **High Performance**: Rust-based, handles billions of vectors
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- **Production Ready**: Clustering, replication, persistence built-in
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- **Flexible Storage**: In-memory or on-disk, cloud or self-hosted
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## Key Features
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### Rich Payload Filtering
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```python
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# Complex filters
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collection.search(
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query_vector=vector,
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query_filter=models.Filter(
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must=[
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models.FieldCondition(
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key="category",
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match=models.MatchValue(value="api")
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)
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],
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should=[
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models.FieldCondition(
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key="type",
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match=models.MatchValue(value="reference")
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)
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]
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),
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limit=5
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)
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```
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### Hybrid Search
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Combine vector similarity with payload filtering:
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- Filter first (fast): Narrow by metadata, then search
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- Search first: Find similar, then filter results
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### Production Features
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- **Snapshots**: Point-in-time backups
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- **Replication**: High availability
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- **Sharding**: Horizontal scaling
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- **Monitoring**: Prometheus metrics
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## Files
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- `1_generate_skill.py` - Package for Qdrant
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- `2_upload_to_qdrant.py` - Upload to Qdrant
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- `3_query_example.py` - Query examples
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## Resources
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- **Qdrant Docs**: https://qdrant.tech/documentation/
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- **API Reference**: https://qdrant.tech/documentation/quick-start/
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- **Cloud**: https://cloud.qdrant.io/
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---
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**Note**: Qdrant excels at production deployments with complex filtering needs. For simpler use cases, try ChromaDB.
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