Registry / llm-agents / llama-index-vector-stores-lancedb

llama-index-vector-stores-lancedb

library0.5.0pypypiunverified

LanceDB vector store integration for LlamaIndex. Current version 0.5.0. Provides a LanceDBVectorStore class for storing and querying vector embeddings. Release cadence is irregular; follows LlamaIndex package structure. Supports hybrid search and column filtering in LanceDB.

llm-agents vector-search ai-ml database

pip install llama-index-vector-stores-lancedb

package ↗

INSTALL

IMPORT

SIG · LLAMA-INDEX-VECTOR

llama-index-vector-stores-lancedb

llm-agentspythonv0.5.0

Install

24.1s avg

Import

—

Disk

725MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.5.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 24.1s · import 0.000s · 716MB

725MB installed

● package 725MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

LanceDBVectorStore

✓ from llama_index.vector_stores.lancedb import LanceDBVectorStore

✗ from llama_index.vector_stores.lancedb import LanceDBVectorStore

Minimal working example: connect to LanceDB, create a vector store, build an index, and query.

import lancedb from llama_index.core import SimpleDirectoryReader, StorageContext, VectorStoreIndex from llama_index.vector_stores.lancedb import LanceDBVectorStore # Create LanceDB connection and table uri = "data/sample-lancedb" db = lancedb.connect(uri) table_name = "my_table" table = db.create_table(table_name, data=[{"vector": [0.0]*768, "text": "placeholder"}], mode="overwrite") # Initialize vector store vector_store = LanceDBVectorStore(uri=uri, table_name=table_name) # Create storage context storage_context = StorageContext.from_defaults(vector_store=vector_store) # Load documents and build index documents = SimpleDirectoryReader("data").load_data() index = VectorStoreIndex.from_documents(documents, storage_context=storage_context) # Query query_engine = index.as_query_engine() response = query_engine.query("What is LanceDB?") print(response)

Debug

Known issues

breakingLanceDBVectorStore no longer accepts 'connection' parameter; use 'uri' instead. As of v0.4.0, the constructor requires 'uri' and 'table_name' directly.

fix

Replace `LanceDBVectorStore(connection=db)` with `LanceDBVectorStore(uri=uri, table_name=table_name)`.

affects: >=0.4.0

gotchaThe vector dimension must match the embedding model dimension. LanceDB does not automatically validate dimensions; mismatches cause runtime errors.

fix

Ensure embedding dimension passed to LanceDBVectorStore matches your embedding model's dimension. Default is 1536 for OpenAI but can be set via 'dim' parameter.

affects: all

gotchaTable must exist before using LanceDBVectorStore; the store does not automatically create tables. Use lancedb.connect(uri).create_table(table_name, data) first.

fix

Create the table explicitly using the LanceDB Python API before using the vector store.

affects: all

Upgrade

Version history

0.5.0latest on PyPI · released Mar 12, 2026

Audit

Dependencies

llama-index-corerequiredCore LlamaIndex abstractions (BaseNode, VectorStoreQuery)

lancedbrequiredLanceDB Python SDK

Agent activity

52 hits · last 30 days

node

Amazon

OpenAI (training)

Resources

packagehttps://pypi.org/project/llama-index-vector-stores-lancedb/ ↗