---
id: llama-index-vector-stores-lancedb
version: "0.5.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# llama-index-vector-stores-lancedb — llama-index vector_stores lancedb integration

License: permissive · Maintenance: active · Downloads: 73.5K/mo

## What it is and what it does

This package bridges LlamaIndex and LanceDB, allowing developers to use LanceDB as the vector storage backend for semantic search and retrieval-augmented generation (RAG) applications. LlamaIndex is a framework for building context-augmented LLM applications, and this integration lets you store and query embeddings in LanceDB instead of other vector databases.

The package is a thin adapter layer that implements LlamaIndex's vector store interface for LanceDB. It depends on lancedb for the actual vector database, llama-index-core for the framework abstractions, pylance for type checking support, and tantivy for full-text search capabilities. It's actively maintained and carries no known security vulnerabilities.

Use it for:

- Store embeddings from LlamaIndex document processing pipelines directly into LanceDB for semantic search.
- Build RAG applications that retrieve context from LanceDB before passing queries to an LLM.
- Integrate LanceDB as a drop-in vector store replacement in existing LlamaIndex workflows.
- Combine vector similarity search with full-text search using tantivy's capabilities alongside LanceDB.
- Persist embeddings locally or in a managed LanceDB instance for production retrieval systems.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Integrates LanceDB as a vector store backend for LlamaIndex, enabling semantic search and retrieval-augmented generation workflows with LanceDB's vector database.

Yes, if you are building a LlamaIndex application and prefer LanceDB as your vector store. The package is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive MIT license. Verify that tantivy's dependencies align with your deployment environment before committing to production use.

## Install

pip install llama-index-vector-stores-lancedb
uv add llama-index-vector-stores-lancedb
poetry add llama-index-vector-stores-lancedb

## Installing llama-index-vector-stores-lancedb

Before you install: Low install friction with a pure-Python wheel. Actively maintained as of the latest release. Depends on lancedb, llama-index-core, pylance, and tantivy; no compiled system dependencies noted.

License in practice: MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.

Quickstart:

pip install llama-index-vector-stores-lancedb

from llama_index.vector_stores.lancedb import LanceDBVectorStore

vector_store = LanceDBVectorStore(db_path="./lancedb")

Requires Python 3.10 or later (supports current versions up to <4.0).

Verify before relying:

- Whether tantivy (a Rust-based full-text search library) requires a C/Rust build toolchain on the target system.
- Performance characteristics and scalability limits for typical RAG workloads.
- Whether LanceDB persistence and query capabilities are fully documented for this integration version.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 73.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

lancedb vector store integration, llama-index vector database, semantic search with lancedb, rag vector store backend, lancedb embeddings storage, vector-search, rag, llm-integration

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