mirror of https://github.com/InternLM/InternLM
74 lines
2.5 KiB
Markdown
74 lines
2.5 KiB
Markdown
# Lagnet
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[English](lagent.md) | 简体中文
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## 简介
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[Lagent](https://github.com/InternLM/lagent) 是一个轻量级、开源的基于大语言模型的智能体（agent）框架，支持用户快速地将一个大语言模型转变为多种类型的智能体，并提供了一些典型工具为大语言模型赋能。它的整个框架图如下:
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本文主要介绍 Lagent 的基本用法。更全面的介绍请参考 Lagent 中提供的 [例子](https://github.com/InternLM/lagent/tree/main/examples)。
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## 安装
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通过 pip 进行安装 (推荐)。
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```bash
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pip install lagent
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```
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同时，如果你想修改这部分的代码，也可以通过以下命令从源码编译 Lagent:
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```bash
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git clone https://github.com/InternLM/lagent.git
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cd lagent
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pip install -e .
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```
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## 运行一个 ReAct 智能体的网页样例
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```bash
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# 需要确保已经安装 streamlit 包
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# pip install streamlit
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streamlit run examples/react_web_demo.py
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```
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然后你就可以在网页端和智能体进行对话了，效果如下图所示
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## 用 InternLM2.5-Chat 构建一个 ReAct 智能体
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\*\*注意：\*\*如果你想要启动一个 HuggingFace 的模型，请先运行 pip install -e .\[all\]。
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```python
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# Import necessary modules and classes from the "lagent" library.
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from lagent.agents import ReAct
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from lagent.actions import ActionExecutor, GoogleSearch, PythonInterpreter
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from lagent.llms import HFTransformer
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# Initialize the HFTransformer-based Language Model (llm) and provide the model name.
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llm = HFTransformer('internlm/internlm2_5-7b-chat')
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# Initialize the Google Search tool and provide your API key.
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search_tool = GoogleSearch(api_key='Your SERPER_API_KEY')
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# Initialize the Python Interpreter tool.
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python_interpreter = PythonInterpreter()
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# Create a chatbot by configuring the ReAct agent.
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chatbot = ReAct(
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llm=llm, # Provide the Language Model instance.
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action_executor=ActionExecutor(
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actions=[search_tool, python_interpreter] # Specify the actions the chatbot can perform.
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),
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)
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# Ask the chatbot a mathematical question in LaTeX format.
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response = chatbot.chat('若$z=-1+\sqrt{3}i$,则$\frac{z}{{z\overline{z}-1}}=\left(\ \ \right)$')
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# Print the chatbot's response.
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print(response.response) # Output the response generated by the chatbot.
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>>> $-\\frac{1}{3}+\\frac{{\\sqrt{3}}}{3}i$
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```
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