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Mcp Memory Bank

@bsmi021

关于 Mcp Memory Bank

A powerful, production-ready context management system for Large Language Models (LLMs). Built with ChromaDB and modern embedding technologies, it provides persistent, project-specific memory capabilities that enhance your AI's understanding and response quality.

基本信息

分类

记忆与知识

许可证

MIT

运行时

node

传输方式

stdio

发布者

bsmi021

配置

暂无标准配置

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代码仓库

工具

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概览

What is Mcp Memory Bank?

How to use Mcp Memory Bank?

Deploy using Docker Compose. Run docker-compose up --build -d to build and start the application and its required ChromaDB vector database.

Key features of Mcp Memory Bank

  • Docker-ready deployment with Docker Compose
  • Uses ChromaDB as vector database
  • Configurable embedding model via environment variable
  • Persistent data volumes for ChromaDB
  • HTTP API on port 3000
  • Non-root user inside container

Use cases of Mcp Memory Bank

FAQ from Mcp Memory Bank

What environment variables are required?

Default environment variables include CHROMADB_URL, TRANSPORT, HTTP_PORT, MCP_MEMBANK_EMBEDDING_MODEL, NODE_ENV, and NODE_OPTIONS. They can be overridden in your environment or in docker-compose.yml.

What is the default embedding model?

The default embedding model is Xenova/all-MiniLM-L6-v2, configurable via MCP_MEMBANK_EMBEDDING_MODEL.

Is ChromaDB data persisted?

Yes, ChromaDB data is persisted in the named Docker volume chromadb-data.

What ports does the application use?

Port 3000 for the main application HTTP API, and port 8000 for ChromaDB.

Does the application run as root?

No, the application runs inside the container as a non-root user.

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