ragflow/api/apps/plugin_app.py

31 lines
1.0 KiB
Python
Raw Normal View History

#
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
Feat: Support tool calling in Generate component (#7572) ### 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: ![image](https://github.com/user-attachments/assets/74a21fdf-9333-4175-991b-43df6524c5dc) And with the `bad_calculator` tool, it results this with the `qwen-max` model: ![image](https://github.com/user-attachments/assets/93aff9c4-8550-414a-90a2-1a15a5249d94) ### 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>
2025-05-16 16:32:19 +08:00
from flask import Response
from flask_login import login_required
from api.utils.api_utils import get_json_result
from plugin import GlobalPluginManager
Feat: Support tool calling in Generate component (#7572) ### 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: ![image](https://github.com/user-attachments/assets/74a21fdf-9333-4175-991b-43df6524c5dc) And with the `bad_calculator` tool, it results this with the `qwen-max` model: ![image](https://github.com/user-attachments/assets/93aff9c4-8550-414a-90a2-1a15a5249d94) ### 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>
2025-05-16 16:32:19 +08:00
@manager.route('/llm_tools', methods=['GET']) # noqa: F821
@login_required
def llm_tools() -> Response:
tools = GlobalPluginManager.get_llm_tools()
tools_metadata = [t.get_metadata() for t in tools]
return get_json_result(data=tools_metadata)