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### What problem does this PR solve? Hello, our use case requires LLM agent to invoke some tools, so I made a simple implementation here. This PR does two things: 1. A simple plugin mechanism based on `pluginlib`: This mechanism lives in the `plugin` directory. It will only load plugins from `plugin/embedded_plugins` for now. A sample plugin `bad_calculator.py` is placed in `plugin/embedded_plugins/llm_tools`, it accepts two numbers `a` and `b`, then give a wrong result `a + b + 100`. In the future, it can load plugins from external location with little code change. Plugins are divided into different types. The only plugin type supported in this PR is `llm_tools`, which must implement the `LLMToolPlugin` class in the `plugin/llm_tool_plugin.py`. More plugin types can be added in the future. 2. A tool selector in the `Generate` component: Added a tool selector to select one or more tools for LLM:  And with the `bad_calculator` tool, it results this with the `qwen-max` model:  ### Type of change - [ ] Bug Fix (non-breaking change which fixes an issue) - [x] New Feature (non-breaking change which adds functionality) - [ ] Documentation Update - [ ] Refactoring - [ ] Performance Improvement - [ ] Other (please describe): Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
98 lines
3.9 KiB
Markdown
98 lines
3.9 KiB
Markdown
# Plugins
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This directory contains the plugin mechanism for RAGFlow.
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RAGFlow will load plugins from `embedded_plugins` subdirectory recursively.
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## Supported plugin types
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Currently, the only supported plugin type is `llm_tools`.
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- `llm_tools`: A tool for LLM to call.
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## How to add a plugin
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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.
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- `get_metadata` method: This method returns a `LLMToolMetadata` object, which contains the description of this tool.
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The description will be provided to LLM, and the RAGFlow web frontend for displaying.
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- `invoke` method: This method accepts parameters generated by LLM, and return a `str` containing the tool execution result.
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All the execution logic of this tool should go into this method.
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When you start RAGFlow, you can see your plugin was loaded in the log:
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```
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2025-05-15 19:29:08,959 INFO 34670 Recursively importing plugins from path `/some-path/ragflow/plugin/embedded_plugins`
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2025-05-15 19:29:08,960 INFO 34670 Loaded llm_tools plugin BadCalculatorPlugin version 1.0.0
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```
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Or it may contain some errors for you to fix your plugin.
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### Demo
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We will demonstrate how to add a plugin with a calculator tool which will give wrong answers.
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First, create a plugin file `bad_calculator.py` under the `embedded_plugins/llm_tools` directory.
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Then, we create a `BadCalculatorPlugin` class, extending the `LLMToolPlugin` base class:
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```python
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class BadCalculatorPlugin(LLMToolPlugin):
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_version_ = "1.0.0"
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```
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The `_version_` field is required, which specifies the version of the plugin.
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Our calculator has two numbers `a` and `b` as inputs, so we add a `invoke` method to our `BadCalculatorPlugin` class:
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```python
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def invoke(self, a: int, b: int) -> str:
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return str(a + b + 100)
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```
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The `invoke` method will be called by LLM. It can have many parameters, but the return type must be a `str`.
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Finally, we have to add a `get_metadata` method, to tell LLM how to use our `bad_calculator`:
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```python
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@classmethod
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def get_metadata(cls) -> LLMToolMetadata:
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return {
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# Name of this tool, providing to LLM
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"name": "bad_calculator",
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# Display name of this tool, providing to RAGFlow frontend
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"displayName": "$t:bad_calculator.name",
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# Description of the usage of this tool, providing to LLM
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"description": "A tool to calculate the sum of two numbers (will give wrong answer)",
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# Description of this tool, providing to RAGFlow frontend
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"displayDescription": "$t:bad_calculator.description",
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# Parameters of this tool
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"parameters": {
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# The first parameter - a
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"a": {
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# Parameter type, options are: number, string, or whatever the LLM can recognise
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"type": "number",
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# Description of this parameter, providing to LLM
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"description": "The first number",
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# Description of this parameter, provding to RAGFlow frontend
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"displayDescription": "$t:bad_calculator.params.a",
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# Whether this parameter is required
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"required": True
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},
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# The second parameter - b
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"b": {
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"type": "number",
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"description": "The second number",
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"displayDescription": "$t:bad_calculator.params.b",
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"required": True
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}
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}
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```
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The `get_metadata` method is a `classmethod`. It will provide the description of this tool to LLM.
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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.
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Now our tool is ready. You can select it in the `Generate` component and try it out.
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