Vector databases have become essential for modern AI applications, particularly in Retrieval Augmented Generation (RAG) systems. This technique focuses on optimizing vector database usage for maximum performance, accuracy, and efficiency.
Vector databases excel in semantic search optimization within RAG pipelines, enabling efficient document retrieval and high-performance similarity search. They support both real-time vector search in AI applications and hybrid approaches combining vector-based similarity with traditional keyword-based methods.
Indexing:
Optimize Chunking
Smart chunking implementations focus on semantic coherence using recursive chunking with overlapping windows. Dynamic chunk sizing adapts to different content types, while metadata-rich chunking enables enhanced filtering capabilities. Prioritize semantic chunking with appropriate overlapping strategies.
Pipeline
Implement regular reindexing and data validation procedures
Embedding
Careful consideration of embedding models
Meta Data
Index Metadata, they enhance filtering capabilities
Retrieval
Use pre-filtering to reduce search spaces. See RAG Architecture Pattern for more details on different retrieval strategies.