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GitHub - SciPhi-AI/R2R: A framework for rapid development and deployment of production-ready RAG systems

Feb 26, 2024 - github.com
R2R is a semi-opinionated RAG (Retrieval-Augmented Generation) framework developed by SciPhi-AI. It is designed to bridge the gap between experimental RAG models and robust, production-ready systems. The framework simplifies the complexity of deploying, adapting, and maintaining RAG pipelines in production environments. It prioritizes simplicity and practicality, aiming to set a new industry standard for ease of use and effectiveness.

The framework revolves around three core abstractions: the Ingestion Pipeline, the Embedding Pipeline, and the RAG Pipeline. The Ingestion Pipeline prepares embeddable 'Documents' from various data formats. The Embedding Pipeline manages the transformation of text into stored vector embeddings. The RAG Pipeline works similarly to the embedding pipeline but incorporates an LLM provider to produce text completions. Each pipeline incorporates a logging database for operation tracking and observability. The project also includes several basic examples demonstrating application deployment and interaction.

Key takeaways:

  • R2R is a semi-opinionated RAG framework designed to bridge the gap between experimental RAG models and robust, production-ready systems.
  • The framework offers a straightforward path to deploy, adapt, and maintain RAG pipelines in production, prioritizing simplicity and practicality.
  • R2R provides key features such as rapid deployment, flexible standardization, easy modification, versioning, extensibility, OSS driven, and deployment support.
  • The framework revolves around three core abstractions: the Ingestion Pipeline, the Embedding Pipeline, and the RAG Pipeline, each incorporating a logging database for operation tracking and observability.
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