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a1f06a4fdc
### 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>
52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
from typing import Any, TypedDict
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import pluginlib
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from .common import PLUGIN_TYPE_LLM_TOOLS
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class LLMToolParameter(TypedDict):
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type: str
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description: str
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displayDescription: str
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required: bool
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class LLMToolMetadata(TypedDict):
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name: str
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displayName: str
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description: str
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displayDescription: str
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parameters: dict[str, LLMToolParameter]
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@pluginlib.Parent(PLUGIN_TYPE_LLM_TOOLS)
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class LLMToolPlugin:
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@classmethod
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@pluginlib.abstractmethod
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def get_metadata(cls) -> LLMToolMetadata:
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pass
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def invoke(self, **kwargs) -> str:
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raise NotImplementedError
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def llm_tool_metadata_to_openai_tool(llm_tool_metadata: LLMToolMetadata) -> dict[str, Any]:
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return {
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"type": "function",
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"function": {
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"name": llm_tool_metadata["name"],
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"description": llm_tool_metadata["description"],
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"parameters": {
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"type": "object",
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"properties": {
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k: {
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"type": p["type"],
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"description": p["description"]
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}
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for k, p in llm_tool_metadata["parameters"].items()
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},
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"required": [k for k, p in llm_tool_metadata["parameters"].items() if p["required"]]
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}
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}
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}
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