AI/ragflow
1
0
Fork 0
mirror of https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git synced 2025-08-15 00:55:54 +08:00
Code Issues Packages Projects Releases Wiki Activity

Docs: add mcp self-host mode (#7163)

Browse Source 
### What problem does this PR solve?

Add mcp self-host mode documentation, a complement of #7141.

### Type of change

- [x] Documentation Update

---------

Co-authored-by: writinwaters <93570324+writinwaters@users.noreply.github.com>
This commit is contained in:
Yongteng Lei 2025-04-22 10:03:38 +08:00 committed by GitHub
parent 8362ab405c
commit 91c5a5c08f
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 43 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

52
docs/guides/mcp.md → docs/develop/mcp.md
View File

@ -1,6 +1,7 @@
______________________________________________________________________
---
sidebar_position: 7
slug: /MCP server
sidebar_position: 4
slug: /mcp_server
---
# Overview of RAGFlow Model Context Protocol (MCP) Server
@ -9,27 +10,53 @@ The RAGFlow MCP server operates as an independent component that complements the
The MCP server currently offers a specific tool to assist users in searching for relevant information powered by RAGFlow DeepDoc technology:
• **retrieve**: Fetches relevant chunks from specified `dataset_ids` and optional `document_ids` using the RAGFlow retrieve interface, based on a given question. Details of all available datasets, namely, `id` and `description`, are provided within the tool description for each individual dataset.
- **retrieve**: Fetches relevant chunks from specified `dataset_ids` and optional `document_ids` using the RAGFlow retrieve interface, based on a given question. Details of all available datasets, namely, `id` and `description`, are provided within the tool description for each individual dataset.
## Launching the MCP Server
Similar to launching the RAGFlow server, the MCP server can be started either from source code or via Docker.
### Launch Modes
The MCP server supports two launch modes:
1. **Self-Host Mode**:
- In this mode, the MCP server is launched to access a specific tenant's datasets.
- This is the default mode.
- The `--api_key` argument is **required** to authenticate the server with the RAGFlow server.
- Example:
```bash
uv run mcp/server/server.py --host=127.0.0.1 --port=9382 --base_url=http://127.0.0.1:9380 --mode=self-host --api_key=ragflow-xxxxx
```
1. **Host Mode**:
- In this mode, the MCP server allows each user to access their own datasets.
- To ensure secure access, a valid API key must be included in the request headers to identify the user.
- The `--api_key` argument is **not required** during server launch but must be provided in the headers on each client request for user authentication.
- Example:
```bash
uv run mcp/server/server.py --host=127.0.0.1 --port=9382 --base_url=http://127.0.0.1:9380 --mode=host
```
### Launching from Source Code
All you need to do is stand on the right place and strike out command, assuming you are on the project working directory.
```bash
uv run mcp/server/server.py --host=127.0.0.1 --port=9382 --base_url=http://127.0.0.1:9380
uv run mcp/server/server.py --host=127.0.0.1 --port=9382 --base_url=http://127.0.0.1:9380 --api_key=ragflow-xxxxx
```
For testing purposes, there is an [MCP client example](#example_mcp_client) provided, free to take!
#### Required Arguments
• **`host`**: Specifies the server's host address.
• **`port`**: Defines the server's listening port.
• **`base_url`**: The address of the RAGFlow server that is already running and ready to handle tasks.
- **`host`**: Specifies the server's host address.
- **`port`**: Defines the server's listening port.
- **`base_url`**: The address of the RAGFlow server that is already running and ready to handle tasks.
- **`mode`**: Launch mode, only accept `self-host` or `host`.
- **`api_key`**: Required when `mode` is `self-host` to authenticate the MCP server with the RAGFlow server.
Here are three augments required, the first two,`host` and `port`, are self-explained. The`base_url` is the address of the ready-to-serve RAGFlow server to actually perform the task.
@ -54,6 +81,8 @@ services:
- --mcp-port=9382
- --mcp-base-url=http://127.0.0.1:9380
- --mcp-script-path=/ragflow/mcp/server/server.py
- --mcp-mode=self-host # `self-host` or `host`
- --mcp--host-api-key="ragflow-xxxxxxx" # only need to privide when mode is `self-host`
```
Then launch it normally `docker compose -f docker-compose.yml`.
@ -69,6 +98,7 @@ ragflow-server | | |\/| | | | |_) | \___ \| _| | |_) \ \ / /| _| | |_) |
ragflow-server | | | | | |___| __/ ___) | |___| _ < \ V / | |___| _ <
ragflow-server | |_| |_|\____|_| |____/|_____|_| \_\ \_/ |_____|_| \_\
ragflow-server |
ragflow-server | MCP launch mode: self-host
ragflow-server | MCP host: 0.0.0.0
ragflow-server | MCP port: 9382
ragflow-server | MCP base_url: http://127.0.0.1:9380
@ -130,13 +160,17 @@ Typically, there are various ways to utilize an MCP server. You can integrate it
# See the License for the specific language governing permissions and
# limitations under the License.
#
from mcp.client.session import ClientSession
from mcp.client.sse import sse_client
async def main():
try:
async with sse_client("http://localhost:9382/sse", headers={"api_key": "ragflow-IyMGI1ZDhjMTA2ZTExZjBiYTMyMGQ4Zm"}) as streams:
# To access RAGFlow server in `host` mode, you need to attach `api_key` for each request to indicate identification.
# async with sse_client("http://localhost:9382/sse", headers={"api_key": "ragflow-IyMGI1ZDhjMTA2ZTExZjBiYTMyMGQ4Zm"}) as streams:
async with sse_client("http://localhost:9382/sse") as streams:
async with ClientSession(
streams[0],
streams[1],
@ -144,7 +178,7 @@ async def main():
await session.initialize()
tools = await session.list_tools()
print(f"{tools.tools=}")
response = await session.call_tool(name="ragflow_retrival", arguments={"dataset_ids": ["ce3bb17cf27a11efa69751e139332ced"], "document_ids": [], "question": "How to install neovim?"})
response = await session.call_tool(name="ragflow_retrieval", arguments={"dataset_ids": ["ce3bb17cf27a11efa69751e139332ced"], "document_ids": [], "question": "How to install neovim?"})
print(f"Tool response: {response.model_dump()}")
except Exception as e: