Super-Rag provides a high-performance, production-ready REST API for building Retrieval-Augmented Generation (RAG) pipelines. It targets AI application developers seeking a flexible and efficient solution for document summarization, retrieval, reranking, and computational question answering, offering a unified API for complex RAG workflows.

How It Works

Super-Rag employs a modular architecture, allowing customization of document ingestion, chunking, and encoding. It supports various document formats and integrates with multiple vector databases (e.g., Qdrant, Pinecone) and embedding models (e.g., Cohere, HuggingFace, OpenAI). A key feature is its built-in code interpreter powered by E2B.dev, enabling computational Q&A scenarios by executing code within sandboxed environments. Session management via unique IDs facilitates caching for improved performance.

Quick Start & Requirements

• Installation: Clone the repository, set up a virtual environment, install dependencies with poetry install, rename .env.example to .env, and run the server with uvicorn main:app --reload.
• Prerequisites: Python 3.x, Poetry, and potentially an API key for the E2B.dev code interpreter. Configuration of environment variables in .env is required.
• Resources: Local setup involves standard Python dependencies. Cloud API usage is free within limits.
• Links: GitHub Repository

Highlighted Details

• Supports multiple document formats and vector databases.
• Production-ready REST API powered by FastAPI.
• Customizable document splitting and encoding with various providers.
• Built-in code interpreter mode for computational Q&A via E2B.dev.
• Session management for caching.

Maintenance & Community

The project is maintained by superagent-ai. Further community engagement details (Discord, Slack, roadmap) are not explicitly detailed in the README.

Licensing & Compatibility

The README does not specify a license. This requires clarification for commercial use or integration into closed-source projects.

Limitations & Caveats

The license is not specified, which is a significant caveat for adoption. Some planned features like Mistral and Anthropic encoder support, and Chroma vector database integration, are marked as "coming soon."