L

Lancedb MCP Server

A model context protocol server based on LanceDB, providing functions such as vector storage, similarity search, and metadata management
2.5 points
26

What is the LanceDB MCP Server?

The LanceDB MCP Server is a server specifically designed for vector data. It helps you store, manage, and quickly search high-dimensional vector data. These vectors usually represent the mathematical representation of text, images, or other content, enabling the computer to understand the similarity between contents.

How to use the LanceDB MCP Server?

You can create tables, add vector data, and perform similarity searches through simple API calls. The server can be easily integrated into existing applications, especially suitable for scenarios that require content retrieval functions.

Use cases

Suitable for scenarios that require efficient vector similarity calculation, such as recommendation systems, semantic search, image retrieval, and anomaly detection.

Main features

Vector storageEfficiently store high-dimensional vector data, supporting custom dimensions
Similarity searchQuickly find the vectors most similar to the query vector
Metadata managementStore associated text metadata for each vector
Table managementCreate and manage multiple vector tables, each table can be configured with different dimensions

Advantages and limitations

Advantages
Efficient vector storage and retrieval performance
Simple REST API interface, easy to integrate
Support for associated storage of vectors and metadata
Configurable vector dimensions to adapt to different models
Limitations
The vector dimension needs to be known in advance
Large-scale data may require optimized storage configuration
Currently mainly supports text metadata

How to use

Install the server
Clone the repository and install dependencies
Configure Claude Desktop
Add the server to the claude_desktop_config.json configuration file
Create a vector table
Create a new vector table through the API
Add vector data
Add vectors and associated metadata to the table
Perform a similarity search
Search for the vectors most similar to the query vector

Usage examples

Document retrieval systemBuild a semantic-based document retrieval system where users can use natural language queries to find relevant documents
Product recommendation engineRecommend similar products based on the user's historical behavior vectors
Image search applicationFind similar images through image feature vectors

Frequently Asked Questions

What does vector dimension mean?
How to choose the appropriate vector dimension?
How much vector data can be stored?
What is the search speed like?
What types of metadata are supported?

Related resources

LanceDB official documentation
Official documentation for the LanceDB database
Introduction to vector similarity search
Introduction to the concept and application of vector similarity search
GitHub repository
Project source code
API reference
Complete API interface documentation
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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