🔍 MCP Server - Vector Search

Created By
miosomosa year ago
MCP Server to improve LLM context through vector search.
Overview

MCP Server - Vector Search is a high-performance server designed to enhance the context of large language models (LLMs) through advanced vector search capabilities, utilizing Neo4j's graph database.

To use the MCP Server, set up your environment with Python 3.8+, install the necessary dependencies, configure your Neo4j database, and launch the server. You can then perform vector searches using natural language queries.

  • Seamless integration with Neo4j for graph database capabilities.
  • Fast vector search using embeddings for semantic queries.
  • Supports natural language processing for intuitive user interaction.
  1. Enhancing search capabilities in knowledge graphs.
  2. Enabling intelligent document retrieval based on semantic similarity.
  3. Supporting AI applications that require contextual understanding of data.
  • What are the prerequisites for using MCP Server?

You need Python 3.8+, Neo4j Database (v5.0+), and an OpenAI API key.

  • Is there a specific Neo4j configuration required?

Yes, you need to set up a vector index for 1536-dimensional OpenAI embeddings and ensure the APOC plugin is installed.

  • Can I use this server for any type of data?

The server is optimized for use with knowledge graphs and data that can be represented in vector form.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
miosomos
Star
0
Language
Python
License
-

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