ragflow/plugin

Plugins

This directory contains the plugin mechanism for RAGFlow.

RAGFlow will load plugins from embedded_plugins subdirectory recursively.

Supported plugin types

Currently, the only supported plugin type is llm_tools.

• llm_tools: A tool for LLM to call.

How to add a plugin

Add a LLM tool plugin is simple: create a plugin file, put a class inherits the LLMToolPlugin class in it, then implement the get_metadata and the invoke methods.

• get_metadata method: This method returns a LLMToolMetadata object, which contains the description of this tool. The description will be provided to LLM, and the RAGFlow web frontend for displaying.

• invoke method: This method accepts parameters generated by LLM, and return a str containing the tool execution result. All the execution logic of this tool should go into this method.

When you start RAGFlow, you can see your plugin was loaded in the log:

2025-05-15 19:29:08,959 INFO     34670 Recursively importing plugins from path `/some-path/ragflow/plugin/embedded_plugins`
2025-05-15 19:29:08,960 INFO     34670 Loaded llm_tools plugin BadCalculatorPlugin version 1.0.0

Or it may contain some errors for you to fix your plugin.

Demo

We will demonstrate how to add a plugin with a calculator tool which will give wrong answers.

First, create a plugin file bad_calculator.py under the embedded_plugins/llm_tools directory.

Then, we create a BadCalculatorPlugin class, extending the LLMToolPlugin base class:

class BadCalculatorPlugin(LLMToolPlugin):
    _version_ = "1.0.0"

The _version_ field is required, which specifies the version of the plugin.

Our calculator has two numbers a and b as inputs, so we add a invoke method to our BadCalculatorPlugin class:

def invoke(self, a: int, b: int) -> str:
    return str(a + b + 100)

The invoke method will be called by LLM. It can have many parameters, but the return type must be a str.

Finally, we have to add a get_metadata method, to tell LLM how to use our bad_calculator:

@classmethod
def get_metadata(cls) -> LLMToolMetadata:
    return {
        # Name of this tool, providing to LLM
        "name": "bad_calculator",
        # Display name of this tool, providing to RAGFlow frontend
        "displayName": "$t:bad_calculator.name",
        # Description of the usage of this tool, providing to LLM
        "description": "A tool to calculate the sum of two numbers (will give wrong answer)",
        # Description of this tool, providing to RAGFlow frontend
        "displayDescription": "$t:bad_calculator.description",
        # Parameters of this tool
        "parameters": {
            # The first parameter - a
            "a": {
                # Parameter type, options are: number, string, or whatever the LLM can recognise
                "type": "number",
                # Description of this parameter, providing to LLM
                "description": "The first number",
                # Description of this parameter, provding to RAGFlow frontend
                "displayDescription": "$t:bad_calculator.params.a",
                # Whether this parameter is required
                "required": True
            },
            # The second parameter - b
            "b": {
                "type": "number",
                "description": "The second number",
                "displayDescription": "$t:bad_calculator.params.b",
                "required": True
            }
        }

The get_metadata method is a classmethod. It will provide the description of this tool to LLM.

The fields starts with display can use a special notation: $t:xxx, which will use the i18n mechanism in the RAGFlow frontend, getting text from the llmTools category. The frontend will display what you put here if you don't use this notation.

Now our tool is ready. You can select it in the Generate component and try it out.