Core Storage Components
Foundation components for vector storage management:Vector Database Backends
Choose from multiple production-ready vector database backends:Redis Vector Database
High-performance Redis-based vector storage
MongoDB Vector Database
MongoDB vector search capabilities
Qdrant Vector Database
Specialized Qdrant vector database integration
In Memory Vector Database
Fast in-memory storage for development and testing
Key Features
Storage components provide:- Multiple Backends: Support for various vector database technologies
- Performance Optimization: Choose the right backend for your performance needs
- Scalability: From in-memory testing to distributed production storage
- Compatibility: Unified interface across all storage backends
- Production Ready: Battle-tested integrations with popular vector databases
Each vector database backend is optimized for different use cases. Redis offers high performance, MongoDB provides rich querying, Qdrant specializes in vector search, and in-memory storage enables rapid development.
Backend Selection Guide
Choose the right vector database for your needs:- Redis: Best for high-performance scenarios with frequent updates
- MongoDB: Ideal when you need rich metadata querying alongside vector search
- Qdrant: Optimized specifically for vector similarity search operations
- In-Memory: Perfect for development, testing, and small datasets
- TopK: Specialized for scenarios requiring only top-K nearest neighbor results
Start with in-memory storage during development for fast iteration, then choose a production backend based on your specific performance and scalability requirements.
Storage Architecture
Storage components handle:- Vector Indexing: Efficient organization of high-dimensional vectors
- Similarity Search: Fast nearest neighbor and similarity queries
- Metadata Management: Storage and retrieval of associated metadata
- Performance Optimization: Index tuning and query optimization