Weaviate

Combine keyword and semantic search for more accurate and comprehensive information retrieval.

Understand how vector databases find similar items using high-dimensional embedding comparisons.

A comprehensive guide to RAG - the dominant pattern for building AI applications that answer questions using your own data.

Pre-filter documents by metadata before vector search - tenant isolation, date ranges, document types, and access control.

ColBERT-style multi-vector and token-level retrieval that scores query-document interactions at search time for higher precision.

Compress embedding vectors with product, scalar, and binary quantization to cut memory and speed ANN search with controlled recall tradeoffs.

Discover how AI converts text, images, and data into numerical vectors that capture meaning.

Improve retrieval by rewriting, expanding, or decomposing user queries before search — including query rewriting, multi-query retrieval, query decomposition, and related transformation techniques.

Learn how AI understands the meaning behind queries to find relevant results beyond keyword matching.

Purpose-built databases for storing, indexing, and querying embedding vectors at scale — including managed options like Pinecone (Database + Nexus knowledge engine).

Choosing and evaluating embedding models - OpenAI, Cohere, BGE, E5, and open-source alternatives for production RAG.

Cross-encoder rerankers that re-score retrieved documents for precision - the highest-ROI improvement in most RAG pipelines.

Approximate nearest neighbor indexes - HNSW, IVF, IVF-PQ, DiskANN, ScaNN, and flat - that make vector search fast at scale.

Explore how knowledge graphs enhance RAG pipelines with structured relationships and reasoning.

Production RAG architecture — hybrid retrieval, ACL filters, model routing, caching, guardrails, and observability for multi-tenant enterprise systems.

A complete guide to building retrieval-augmented generation systems - from embeddings to production.