🔍 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, leveraging 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?
- Fast semantic search across knowledge graphs using vector embeddings.
- Integration with Neo4j for efficient data retrieval.
- Supports natural language queries converted into vector representations.
Use cases of MCP Server - Vector Search?
- Enhancing AI applications with contextually relevant information retrieval.
- Enabling intelligent search functionalities in knowledge management systems.
- Supporting research and data analysis through advanced querying capabilities.
FAQ from MCP Server - Vector Search?
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What are the prerequisites for using MCP Server?
You need Python 3.8+, Neo4j Database (v5.0+), and an OpenAI API key.
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Is there a specific setup required for Neo4j?
Yes, you need to install the APOC plugin and create a vector index for embeddings.
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Can I use this server for any type of data?
The server is optimized for semantic searches in knowledge graphs, particularly with data structured for vector embeddings.