CodeFuse-ChatBot is an assistant aimed at the whole software development lifecycle — design, coding, testing, deployment, and operations. It combines retrieval-augmented generation, tool learning, and a sandbox so a model can act on DevOps tasks rather than only answer questions about them.

The scheduling core is a multi-agent framework, released separately as the codefuse-muagent package, which is configured rather than coded to build interactive agents. Around it sit a web crawler for named URLs, a data processor for document loading, cleaning, and text splitting, text embedding and indexing, vector and graph databases, prompt management, a sandbox for compiling and running code, and an API layer for wiring in open-source components and operations platforms.

Two retrieval modes are specific to development work: repository-level code analysis, which reads a whole code base and can write and generate code at file level, and document analysis that pairs a document knowledge base with a knowledge graph. A DevOps-specific knowledge base can be built in one step for a given domain.

Models can be open-source and self-hosted for an offline, private deployment, reached through FastChat, or accessed through the OpenAI API. The project builds on Langchain-Chatchat and codebox-api.

Model	Size	GPU memory	Quantisation
chatgpt	–	–	–
codellama-34b-int4	34b	20g	int4

Features

Multi-agent scheduling core: a configurable dispatcher that assembles interactive agents without code changes
Repository-level code analysis: whole-repository understanding plus file-level code writing and generation
Document analysis: a document knowledge base combined with a knowledge graph for retrieval and inference
Domain knowledge bases: one-step construction of a DevOps-specific knowledge base
Sandbox: an isolated local environment for compiling and executing code and actions
Web crawling: retrieval of content from specified URLs to fill the knowledge base
Storage: vector and graph databases behind embedding and indexing
Model access: open-source models served through FastChat for offline deployment, or the OpenAI API

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