LanceDB

Overview

LanceDB is an open-source, developer-friendly vector database designed to store, manage, and search vector embeddings alongside their metadata. It's built on the efficient, column-oriented Lance data format, which is optimized for large-scale data processing, including multimodal AI data.

The database provides high-performance vector similarity search using state-of-the-art indexing algorithms like DiskANN and HNSW. It also supports efficient filtering and querying of metadata using SQL, allowing complex search criteria. LanceDB integrates seamlessly with popular machine learning libraries and frameworks like PyTorch, TensorFlow, LangChain, and LlamaIndex.

Its unique value proposition lies in its serverless and embedded nature, allowing it to run locally as a library or scale effortlessly on cloud object storage like S3, GCS, and Azure Blob Storage. This flexibility makes it suitable for a wide range of AI applications, from local development and prototyping to production-scale inference and data pipelines.

Key Features

• Built on the Lance data format for efficient storage and I/O
• High-performance vector similarity search (DiskANN, HNSW)
• Metadata filtering with SQL queries
• Serverless and embedded deployment options
• Scales on cloud object storage (AWS S3, GCS, Azure Blob)
• Seamless integration with ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Integrations with LangChain and LlamaIndex
• Supports various data types (vectors, text, images, audio, video)
• Python, JavaScript, and Rust SDKs

Supported Platforms

• Web Browser (for documentation/dashboard)
• Python (Library)
• JavaScript/Node.js (Library)
• Rust (Library)
• Cloud Storage (AWS S3, GCS, Azure Blob)

Integrations

• LangChain
• LlamaIndex
• PyTorch
• TensorFlow
• Hugging Face
• scikit-learn
• DuckDB