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Aleph-10: Vector Memory MCP Server

@bjkemp

代码仓库

关于 Aleph-10: Vector Memory MCP Server

Vector Memory MCP Server - An MCP server with vector-based memory storage capabilities

#mathgpt#math-solver#math-assistant

基本信息

分类

记忆与知识

运行时

node

传输方式

stdio

发布者

bjkemp

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "aleph-10": {
      "command": "node",
      "args": [
        "build/index.js"
      ]
    }
  }
}

工具

未检测到工具

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 Aleph-10: Vector Memory MCP Server?

Aleph-10: Vector Memory MCP Server is a Model Context Protocol server that combines weather data retrieval from the National Weather Service API with vector‑based semantic memory storage. It is intended for developers who need an MCP‑compatible server for both weather information and persistent, searchable memory via embeddings.

How to use Aleph-10: Vector Memory MCP Server?

Install Node.js 18+, pnpm, clone the repository, run pnpm install, configure environment variables in a .env file, then pnpm build and start with node build/index.js. The server exposes tools such as get‑alerts, get‑forecast, memory‑store, memory‑retrieve, memory‑update, memory‑delete, and memory‑stats.

Key features of Aleph-10: Vector Memory MCP Server

  • Retrieves weather alerts and forecasts via the National Weather Service API
  • Stores and retrieves information using semantic vector search
  • Supports both Google Gemini (cloud) and Ollama (local) embedding providers
  • Allows metadata to be attached and filtered on memory entries

Use cases of Aleph-10: Vector Memory MCP Server

  • Fetching live weather alerts for any US state during an MCP session
  • Obtaining weather forecasts for given latitude/longitude coordinates
  • Storing conversational context as vector embeddings for later retrieval
  • Searching for semantically similar text entries across stored memories
  • Updating or deleting individual memory entries by ID

FAQ from Aleph-10: Vector Memory MCP Server

What are the prerequisites to run the server?

Node.js 18.x or higher and the pnpm package manager are required.

How do I configure the embedding provider?

Set the environment variable EMBEDDING_PROVIDER to gemini or ollama. If using Gemini, provide a GEMINI_API_KEY. For Ollama, set OLLAMA_BASE_URL (default: http://localhost:11434).

Where is the vector database stored?

The vector database is stored at the path specified by VECTOR_DB_PATH, which defaults to ./data/vector_db.

What weather data source is used?

Weather data is sourced from the National Weather Service API.

Can I run embeddings locally without an internet connection?

Yes, by setting EMBEDDING_PROVIDER to ollama and running a local Ollama instance.

评论

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