AgentDB Vector Search

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Wire it into RAG systems, semantic search engines, and knowledge bases.

This skill builds retrieval on AgentDB. It stores embeddings for documents, runs similarity search with metadata-aware filtering, returns context-relevant chunks for grounding LLM answers, and structures the retrieval layer for RAG applications, semantic search engines, and intelligent knowledge bases.

When to use

Use when adding semantic vector search or document retrieval to a RAG system, search engine, or knowledge base with AgentDB.

Examples

Semantic document search

Retrieve by meaning, not keywords

Set up AgentDB vector search over our support articles and return the top passages for a natural-language query

Ground a RAG answer

Context-aware retrieval

Use AgentDB to fetch the most relevant chunks and pass them as context to the LLM for a grounded answer