Metadata-Version: 2.1
Name: lagent
Version: 0.1.2
Summary: A lightweight framework for building LLM-based agents
Home-page: https://github.com/InternLM/lagent
License: Apache 2.0
Description: <div align="center">
          <img src="docs/imgs/lagent_logo.png" width="450"/>
        
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        English | [简体中文](README_zh-CN.md) | [日本語](README_ja_JP.md) | [हिंदी](README_in_HIN.md) | [বাংলা](README_in_beng.md)
        
        </div>
        
        <p align="center">
            👋 join us on <a href="https://twitter.com/intern_lm" target="_blank">Twitter</a>, <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://r.vansin.top/?r=internwx" target="_blank">WeChat</a>
        </p>
        
        ## Introduction
        
        Lagent is a lightweight open-source framework that allows users to efficiently build large language model(LLM)-based agents. It also provides some typical tools to augment LLM. The overview of our framework is shown below:
        
        ![image](https://github.com/InternLM/lagent/assets/24351120/cefc4145-2ad8-4f80-b88b-97c05d1b9d3e)
        
        ### Major Features
        
        **0.1.2** was released in 24/10/2023:
        
        - **Support efficient inference engine.** Lagent now supports efficient inference engine [lmdeploy turbomind](https://github.com/InternLM/lmdeploy/tree/main).
        
        - **Support multiple kinds of agents out of box.** Lagent now supports [ReAct](https://arxiv.org/abs/2210.03629), [AutoGPT](https://github.com/Significant-Gravitas/Auto-GPT) and [ReWOO](https://arxiv.org/abs/2305.18323), which can drive the large language models(LLMs) for multiple trials of reasoning and function calling.
        
        - **Extremely simple and easy to extend.** The framework is quite simple with a clear structure. With only 20 lines of code, you are able to construct your own agent. It also supports three typical tools: Python interpreter, API call, and google search.
        
        - **Support various large language models.** We support different LLMs, including API-based (GPT-3.5/4) and open-source (LLaMA 2, InternLM) models.
        
        ## Getting Started
        
        Please see the [overview](docs/en/get_started/overview.md) for the general introduction of Lagent. Meanwhile, we provide extremely simple code for quick start. You may refer to [examples](examples/) for more details.
        
        ### Installation
        
        Install with pip (Recommended).
        
        ```bash
        pip install lagent
        ```
        
        Optionally, you could also build Lagent from source in case you want to modify the code:
        
        ```bash
        git clone https://github.com/InternLM/lagent.git
        cd lagent
        pip install -e .
        ```
        
        ### Run ReAct Web Demo
        
        ```bash
        # You need to install streamlit first
        # pip install streamlit
        streamlit run examples/react_web_demo.py
        ```
        
        Then you can chat through the UI shown as below
        ![image](https://github.com/InternLM/lagent/assets/24622904/3aebb8b4-07d1-42a2-9da3-46080c556f68)
        
        ### Run a ReWOO agent with GPT-3.5
        
        Below is an example for running ReWOO with GPT-3.5
        
        ```python
        from lagent.agents import ReWOO
        from lagent.actions import ActionExecutor, GoogleSearch, LLMQA
        from lagent.llms import GPTAPI
        
        llm = GPTAPI(model_type='gpt-3.5-turbo', key=['Your OPENAI_API_KEY'])
        search_tool = GoogleSearch(api_key='Your SERPER_API_KEY')
        llmqa_tool = LLMQA(llm)
        
        chatbot = ReWOO(
            llm=llm,
            action_executor=ActionExecutor(
                actions=[search_tool, llmqa_tool]),
        )
        
        response = chatbot.chat('What profession does Nicholas Ray and Elia Kazan have in common')
        print(response.response)
        >>> Film director.
        ```
        
        ### Run a ReAct agent with InternLM
        
        NOTE: If you want to run a HuggingFace model, please run `pip install -e .[all]` first.
        
        ```python
        from lagent.agents import ReAct
        from lagent.actions import ActionExecutor, GoogleSearch, PythonInterpreter
        from lagent.llms import HFTransformer
        
        llm = HFTransformer('internlm/internlm-chat-7b-v1_1')
        search_tool = GoogleSearch(api_key='Your SERPER_API_KEY')
        python_interpreter = PythonInterpreter()
        
        chatbot = ReAct(
            llm=llm,
            action_executor=ActionExecutor(
                actions=[search_tool, python_interpreter]),
        )
        
        response = chatbot.chat('若$z=-1+\sqrt{3}i$,则$\frac{z}{{z\overline{z}-1}}=\left(\ \ \right)$')
        print(response.response)
        >>> $-\\frac{1}{3}+\\frac{{\\sqrt{3}}}{3}i$
        ```
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE).
        
Keywords: artificial general intelligence,agent,agi,llm
Platform: UNKNOWN
Description-Content-Type: text/markdown
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