Best Vector Databases for LlamaIndex
LlamaIndex (formerly GPT Index) specializes in connecting LLMs with external data sources, making vector database selection crucial for building performant RAG applications. LlamaIndex's VectorStoreIndex abstracts the complexity of embedding, indexing, and retrieval across multiple providers. The databases below integrate natively with LlamaIndex, supporting features like metadata filtering, hybrid search, and streaming ingestion for production-grade AI applications.
12 databases compatible with LlamaIndex
Why use LlamaIndex with a vector database?
LlamaIndex (formerly GPT Index) specializes in connecting LLMs with external data sources, making vector database selection crucial for building performant RAG applications. LlamaIndex's VectorStoreIndex abstracts the complexity of embedding, indexing, and retrieval across multiple providers. The databases below integrate natively with LlamaIndex, supporting features like metadata filtering, hybrid search, and streaming ingestion for production-grade AI applications.
How to get started with LlamaIndex
- 1Install LlamaIndex and a vector store package: pip install llama-index llama-index-vector-stores-qdrant
- 2Set up your embedding model and LLM (OpenAI, local, or other provider)
- 3Create a VectorStoreIndex with your chosen database backend
- 4Build a query engine and start asking questions over your indexed data
FAQ — LlamaIndex & Vector Databases
Which vector database is best for LlamaIndex RAG?
Qdrant and Pinecone are top choices for production LlamaIndex RAG. ChromaDB is ideal for local development, and Weaviate works well when you need built-in vectorization.
How does LlamaIndex connect to vector databases?
LlamaIndex uses VectorStoreIndex with pluggable backends. Each database has a dedicated integration package (e.g., llama-index-vector-stores-pinecone) that handles connection, indexing, and querying.
Can LlamaIndex use multiple vector stores simultaneously?
Yes. LlamaIndex supports composable indices, so you can route queries to different vector stores based on the data type or use case, combining results from multiple sources.