Glossary
What is a Vector Database?
Enabling semantic search for AI applications.
What is a vector database?
A vector database stores and indexes high-dimensional vectors (embeddings) for fast similarity search. It enables semantic search where queries find conceptually similar content, not just keyword matches.
How It Works
- Embed: Convert text to vectors using embedding models
- Store: Index vectors in the database
- Query: Convert query to vector
- Search: Find nearest neighbors by similarity
Popular Vector Databases
- Pinecone: Managed, serverless
- Weaviate: Open source, hybrid search
- Chroma: Lightweight, developer-friendly
- Qdrant: Open source, high performance
- pgvector: PostgreSQL extension
Use Cases
- RAG retrieval for LLM applications
- Semantic search
- Recommendation systems
- Duplicate detection
Why use for RAG?
Vector databases enable semantic retrieval—finding documents by meaning rather than keywords. This is essential for RAG because user queries rarely match document text exactly. The database finds conceptually relevant content to include in the LLM prompt.
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