Used to download and install the uv package installer, which is recommended for dependency management.
Enables cloning repositories from GitHub to access code for the RAGFlow MCP server.
Provides the runtime environment for the RAGFlow MCP server, requiring Python 3.12 specifically.
Supports configuration management through pyproject.toml for dependency resolution and installation.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@RAGFlow MCP search for recent AI research papers about RAG systems"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Simple RAGFlow MCP. Only useful until the RAGFlow team releases the official MCP server
We provide two installation methods. Method 2 (using uv) is recommended for faster installation and better dependency management.
Create a new conda environment:
conda create -n ragflow_mcp python=3.12
conda activate ragflow_mcpClone the repository:
git clone https://github.com/oraichain/ragflow-mcp.git
cd ragflow-mcpInstall dependencies:
pip install -r requirements.txtInstall uv (A fast Python package installer and resolver):
curl -LsSf https://astral.sh/uv/install.sh | shClone the repository:
git clone https://github.com/oraichain/ragflow-mcp.git
cd ragflow-mcpCreate a new virtual environment and activate it:
uv venv --python 3.12
source .venv/bin/activate # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activateInstall dependencies:
uv pip install -r pyproject.tomlStart the MCP server
Start the inspector using the following command:
# you can choose a different server
SERVER_PORT=9000 npx @modelcontextprotocol/inspectorThis server cannot be deployed
MCP server for AI dialogue using various LLM models via AceDataCloud
Remote MCP server for supportsheep: run AI interviews and manage support content for your blog.
