--- title: project1 app_file: app.py sdk: gradio sdk_version: 4.43.0 --- # [LAMBDA](https://www.polyu.edu.hk/ama/cmfai/lambda.html) - Multi-Agent Data Analysis System ![lambda_mix](https://github.com/user-attachments/assets/db5574aa-9441-4c9d-b44d-3b225d11e0cc) We introduce **LAMBDA**, a novel open-source, code-free multi-agent data analysis system that harnesses the power of large models. LAMBDA is designed to address data analysis challenges in complex data-driven applications through the use of innovatively designed data agents that operate iteratively and generatively using natural language. ## Key Features - **Code-Free Data Analysis**: Perform complex data analysis tasks through human language instruction. - **Multi-Agent System**: Utilizes two key agent roles, the programmer and the inspector, to generate and debug code seamlessly. - **User Interface**: This includes a robust user interface that allows direct user intervention in the operational loop. - **Model Integration**: Flexibly integrates external models and algorithms to cater to customized data analysis needs. - **Automatic Report Generation**: Concentrate on high-value tasks, rather than spending time and resources on report writing and formatting. ## Getting Started ### Installation First, clone the repository. ```bash git clone https://github.com/Stephen-SMJ/LAMBDA.git cd LAMBDA ``` Then, we recommend creating a [Conda](https://docs.conda.io/en/latest/) environment for this project and install the dependencies by following commands: ```bash conda create -n lambda python=3.10 conda activate lambda ``` Next, you should install the Jupyter kernel to create a local Code Interpreter: ```bash ipython kernel install --name lambda --user ``` ### Configuration 1. To use the Large Language Model, you should have an API key from [OpenAI](https://platform.openai.com/docs/guides/authentication) or other companies. Also, you can call your local LLMs once deployed by frameworks such as [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory). 2. **We used Aliyun Cloud Server to store the caches (like showing figures, models and so on). Currently, you should buy a [OSS（Object Storage Service](https://cn.aliyun.com/product/oss?from_alibabacloud=) from Aliyun to use it. But we will release a new version without the cloud server for easier use soon.** 3. Set your API key, models, working path, and OSS-related items in the config.yaml: ```bash #================================================================================================ # Config of the LLMs #================================================================================================ conv_model : "gpt-4o-mini" # the conversation model programmer_model : "gpt-4o-mini" inspector_model : "gpt-4o-mini" api_key : "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" base_url_conv_model : 'https://api.openai.com/v1' base_url_programmer : 'https://api.openai.com/v1' base_url_inspector : 'htts://api.openai.com/v1' max_token_conv_model: 4096 # the max token of the conversation model, this will determine the maximum length of the report. #================================================================================================ # Config of the system #================================================================================================ streaming : True #cache_related oss_endpoint: "" oss_access_key_id: "" oss_access_secret: "" oss_bucket_name: "" expired_time: 36000 # The expired time of the link in cache cache_dir : "" # local cache dir max_attempts : 5 # The max attempts of self-correcting max_exe_time: 18000 # max time for the execution #knowledge integration retrieval : False # whether to start a knowledge retrieval. If you don't create your knowledge base, you should set it to False mode : "full" # the mode of the #knowledge integration ``` Finally, Run the following command to start the demo with GUI: ```bash python app.py ``` ## Demonstration Videos The performance of LAMBDA in solving data science problems is demonstrated in several case studies including: - **[Data Analysis](https://www.polyu.edu.hk/ama/cmfai/files/lambda/lambda.mp4)** - **[Integrating Human Intelligence](https://www.polyu.edu.hk/ama/cmfai/files/lambda/knw.mp4)** - **[Education](https://www.polyu.edu.hk/ama/cmfai/files/lambda/LAMBDA_education.mp4)** ## Planning works - Code refactoring. - Remove the cloud cache module for easier use. - Add a Docker image for easier use. - Documentation writing. - Replace Gradio with other GUI frameworks. ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Acknowledgements Thank the contributors and the communities for their support and feedback. --- > If you find our work useful in your research, consider citing our paper by: ```bash @article{sun2024lambda, title={LAMBDA: A Large Model Based Data Agent}, author={Sun, Maojun and Han, Ruijian and Jiang, Binyan and Qi, Houduo and Sun, Defeng and Yuan, Yancheng and Huang, Jian}, journal={arXiv preprint arXiv:2407.17535}, year={2024} } ``` ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=Stephen-SMJ/LAMBDA&type=Timeline)](https://star-history.com/#Stephen-SMJ/LAMBDA&Timeline)