CodeFuse-ChatBot: An Intelligent AI Assistant for Software Development
Codefuse-ChatBot is an innovative open-source AI assistant developed by the Ant CodeFuse team. This project is designed to enhance and streamline different stages of the software development lifecycle. By integrating a collaborative scheduling mechanism known as Multi-Agent, alongside a comprehensive library of tools, code, and knowledge as well as a sandbox environment, this platform enables LLM (Large Language Model) to efficiently execute complex tasks in the field of DevOps.
Project Updates
CodeFuse-ChatBot has seen several key updates:
- On January 29, 2024, the configurable multi-agent framework, codefuse-muAgent, was released to the public.
- On December 26, 2023, the platform opened capabilities for incorporating open-source and privatized large models and model interfaces through FastChat.
- As of December 14, 2023, a feature article was published on the Quantum Dimension public account.
- As of December 1, 2023, functionalities for Multi-Agent and code repository retrieval were made available.
- On November 15, 2023, a question-and-answer enhancement mode based on local code repositories was introduced.
- As of September 15, 2023, the sandbox feature for local and isolated environments was launched, employing web crawlers for specified URL knowledge retrieval.
Introduction
The core aim of Codefuse-ChatBot is to construct an AI assistant that augments the entire software development lifecycle through Retrieval Augmented Generation (RAG), Tool Learning, and sandbox environments. The move from traditional operational modes, reliant on dispersed platforms and manual research, to an intelligent and automated DevOps system marks a significant transition.
Key Technologies and Functionalities
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Smart Scheduling Core: A robust and comprehensive scheduling core that supports multi-pattern configurations, simplifying the process significantly.
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Comprehensive Code Analysis: Offers deep analysis at the repository level and detailed code generation at the project file level, enhancing development efficiency.
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Enhanced Document Analysis: Combines document knowledge bases and knowledge graphs for a deeper level of document analysis through retrieval and inference enhancement.
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Custom Knowledge Libraries: Tailored to the DevOps domain, these libraries facilitate easy and convenient one-click construction of specialized knowledge bases.
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Domain Model Compatibility: Ensures compatibility with DevOps-related platforms, promoting technological ecosystem integration for smaller models in the DevOps domain.
The project builds upon open-source LLM and Embedding models, allowing offline private deployment based on open-source models. Furthermore, it supports the use of OpenAI API. Core development teams have focused on extensive R&D in AIOps + NLP, inviting wide contributions to enhance the solution for a more seamless development process.
Demonstrative Video
A series of demonstration videos have been prepared to provide a visual understanding of Codefuse-ChatBot’s capabilities and functionalities. These can assist users in quickly grasping the core features and operational processes of the project.
- Importing Knowledge Base and Q&A
- Local Code Repository Import and Q&A
Technical Roadmap
Key technical modules include:
- Multi-Agent Schedule Core: An easily configurable core for constructing interactive agents.
- Multi Source Web Crawl: Enables the gathering of information via web crawlers across specific URLs.
- Data Processor: Facilitates document loading, data cleansing, and text segmentation across various data sources.
- Text Embedding & Index: Simplifies document retrieval through the upload of user files to enhance document analysis.
- Vector Database & Graph Database: Offers flexible and robust solutions for data management.
- Prompt Control & Management: Accurately defines the contextual environment of an intelligent agent.
- SandBox: Provides a safe environment for code compilation and execution.
- LLM: Supports various open-source models and LLM interfaces.
- API Management: Tools that hasten the integration of open-source components and operational platforms.
Quick Start
Codefuse-ChatBot is versatile and can be deployed on various systems. For Apple Silicon users, specific instructions might be necessary, such as using brew install qpdf.
Begin by setting up the Python environment, installing dependencies, and starting services as specified in the quick start guide. More detailed instructions for Docker installation, private LLM access, and service initiation are provided in the official documentation.
Contribution Guide
Codefuse welcomes suggestions, feedback, criticism, comments, and contributions from everyone. Contributions can range from code implementation and test creation to documentation enhancement. All contributions are valued and will be acknowledged in the contributor's list.
Acknowledgments
This project is heavily based on the extensive work done by langchain-chatchat and codebox-api. Thanks to their open-source contributions, Codefuse stands as a comprehensive AI solution in the DevOps field.
Contact and Support
For further assistance or enthusiast discussions, potential contributors and users can find contact details and support channels on the project's main page.