Vector Databases¶
A comprehensive guide to vector databases and similarity search in AI applications.
Introduction¶
Vector databases are specialized database systems designed to store and efficiently query high-dimensional vectors, which are essential for modern AI applications.
Key Concepts¶
Vector Embeddings¶
- Understanding vector representations
- Embedding models and techniques
- Dimensionality considerations
- Quality and consistency
Similarity Search¶
- Distance metrics
- Approximate Nearest Neighbors (ANN)
- Trade-offs in accuracy vs. speed
- Filtering and hybrid search
Indexing Methods¶
- HNSW
- IVF
- Product Quantization
- Tree-based methods
Popular Solutions¶
Open Source¶
- Milvus
- Weaviate
- Qdrant
- FAISS
Cloud Services¶
- Pinecone
- Weaviate Cloud
- Azure Vector Search
- AWS OpenSearch
Best Practices¶
Performance Optimization¶
- Index tuning
- Batch processing
- Caching strategies
- Hardware considerations
Scalability¶
- Sharding strategies
- Replication
- Load balancing
- Monitoring and metrics
Integration Patterns¶
- Bulk operations
- Real-time updates
- Error handling
- Backup and recovery