Vector Stores
Vector stores save embedding vectors of your ingested document chunks.
Available Vector Stores
Section titled “Available Vector Stores”Available Vector Stores are shown on the sidebar to the left. Additionally the following integrations exist without separate documentation:
- SimpleVectorStore: A simple in-memory vector store with optional persistance to disk.
- AstraDBVectorStore: A cloud-native, scalable Database-as-a-Service built on Apache Cassandra, see datastax.com
- ChromaVectorStore: An open-source vector database, focused on ease of use and performance, see trychroma.com
- MilvusVectorStore: An open-source, high-performance, highly scalable vector database, see milvus.io
- MongoDBAtlasVectorSearch: A cloud-based vector search solution for MongoDB, see mongodb.com
- PGVectorStore: An open-source vector store built on PostgreSQL, see pgvector Github
- PineconeVectorStore: A managed, cloud-native vector database, see pinecone.io
- WeaviateVectorStore: An open-source, ai-native vector database, see weaviate.io
Check the LlamaIndexTS Github for the most up to date overview of integrations.
Note for AI agents: this documentation is built for programmatic access.
- Overview of all docs: https://developers.llamaindex.ai/llms.txt
- Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md
- Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters.
- A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/