DeepRepo FAQs

Question 1

What is DeepRepo and what problem does it solve?

Accepted Answer

DeepRepo is a production-grade Python library designed for Retrieval Augmented Generation (RAG) on local codebases. It enables AI models to understand and interact with your code, solving the challenge of providing up-to-date, context-aware responses from your private code repositories.

Question 2

What makes DeepRepo different from other RAG frameworks?

Accepted Answer

DeepRepo distinguishes itself with a raw Python implementation, avoiding heavy frameworks like LangChain/LlamaIndex or external vector databases. It uses a NumPy-powered vector store, offering a lightweight, efficient, and highly customizable solution for RAG on code.

Question 3

Which AI providers can I use with DeepRepo?

Accepted Answer

DeepRepo offers extensive flexibility by supporting multiple AI providers for both embeddings and LLMs, including Ollama (local), HuggingFace, OpenAI, Anthropic, and Gemini. You can mix and match providers to optimize for cost, performance, or specific model capabilities.

Question 4

Can DeepRepo integrate with existing AI coding assistants?

Accepted Answer

Yes, DeepRepo features an MCP (Model Context Protocol) server, allowing seamless integration with popular AI assistants like Cursor, Claude Desktop, and Antigravity. This enables your AI assistant to leverage DeepRepo's codebase RAG capabilities directly.

Question 5

How can I deploy and integrate DeepRepo into my workflow?

Accepted Answer

DeepRepo provides a RESTful API for easy integration into any application. It's fully Docker containerized for straightforward deployment and supports environment variables for flexible configuration, making it ready for production environments.

DeepRepo

DeepRepo

主要功能

使用案例

主要功能

使用案例