MCP Server - Vector Search

@miosomos

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

miosomos

关于 MCP Server - Vector Search

MCP Server to improve LLM context through vector search.

#neo4j #mcp #knowledge-graph #llm

MCP Server - Vector Search 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-vector-search-miosomos": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

MCP Server - Vector Search 工具

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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is 🔍 MCP Server - Vector Search?

🔍 MCP Server - Vector Search is a Model Context Protocol server built with FastMCP that combines Neo4j’s graph database with vector search using embeddings. It enables intelligent semantic search across a knowledge graph through natural language queries, converting them into 1536-dimensional vectors via OpenAI (or a sentence-transformers fallback) and returning ranked results from a Neo4j vector index.

How to use 🔍 MCP Server - Vector Search?

Install with uv, clone the repository, set up a .env file with Neo4j credentials and an optional OpenAI API key, create a vector index in Neo4j, then run python main.py. The server exposes a single tool vector_search_neo4j(prompt) that accepts natural language queries and returns semantically similar nodes with similarity scores.

Key features of 🔍 MCP Server - Vector Search

Combines Neo4j graph database with vector search
Uses OpenAI embeddings or sentence-transformers as fallback
Single powerful tool for semantic search
Asynchronous, non-blocking I/O for high throughput
Easy integration with Claude Desktop via MCP
Dependency management with uv (10‑100x faster resolution)

Use cases of 🔍 MCP Server - Vector Search

Semantic document retrieval in knowledge-graph-powered applications
Natural‑language querying of structured and unstructured data stored in Neo4j
Connecting AI assistants (e.g., Claude) to graph knowledge for context‑aware answers
Intelligent search across enterprise knowledge bases that use graph and vector indexes

常见问题

What does the `vector_search_neo4j` tool do?

It converts a natural‑language prompt into a 1536‑dimensional vector (using OpenAI or sentence‑transformers), searches the Neo4j vector index for the most semantically similar nodes, and returns ranked results with similarity scores.

How do I set up the Neo4j vector index?

Run the Cypher command: `CREATE VECTOR INDEX embeddableIndex FOR (n:Document) ON (n.embedding) OPTIONS {indexConfig: {\`vector.dimensions\`: 1536, \`vector.similarity_function\`: 'cosine'}}`. The index requires the APOC plugin.

What happens if I don’t provide an OpenAI API key?

If `OPENAI_API_KEY` is not set, the server falls back to the `all-MiniLM-L6-v2` sentence‑transformers model for generating embeddings.

What are the runtime dependencies?

Python 3.8+, `uv` package manager, Neo4j 5.0+ with the APOC plugin, and the Python packages `fastmcp`, `neo4j`, `openai`, `python-dotenv`, `sentence-transformers`, and `pydantic`.

Can I use this server without Neo4j?

No, a running Neo4j database with a vector index is required for both storing node embeddings and performing similarity searches.

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