AgentDB Vector Search

Semantic vector search for RAG is built by generating document embeddings, indexing them with HNSW, and applying quantization and caching to retrieve relevant context in milliseconds. This approach grounds AI answers in source material by matching meaning rather than exact keywords.

Combining vector similarity matching with metadata filtering creates hybrid search workflows that narrow the vector space before ranking. This yields higher-quality retrieval by ensuring returned passages meet specific structural or categorical criteria alongside semantic relevance.

HNSW indexing and quantization directly enable low-latency document retrieval by structuring embeddings in a navigable graph and reducing memory footprint. Caching further accelerates response times for frequently queried semantic contexts.

Indexing product docs and support articles requires generating embeddings for the text and storing them in a searchable vector database. Once indexed, the system instantly surfaces the most relevant passages to answer user questions based on semantic similarity.

AgentDB vector search operates across local databases and MCP integrations for context-aware retrieval. It applies semantic similarity workflows to diverse data sources, requiring embedding generation and metadata filtering to return relevant documents.

Semantic search returns better results than exact keyword matching by understanding the contextual meaning of queries. It transforms documents into searchable vectors, eliminating brittle text lookups and retrieving passages that share semantic similarity with the user's intent.