🔍 MCP Server - Vector Search

What is MCP Server - Vector Search?

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.

How to use MCP Server - Vector Search?

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.

Key features of MCP Server - Vector Search?

• Seamless integration with Neo4j for graph database capabilities.
• Fast vector search using embeddings for semantic queries.
• Supports natural language processing for intuitive user interaction.

Use cases of MCP Server - Vector Search?

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.

FAQ from MCP Server - Vector Search?

• 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.