super-rag and rag_blueprint
These are complementary tools: Super-RAG provides the core pipeline infrastructure for retrieval, reranking, and summarization, while RAG Blueprint offers the evaluation and monitoring framework needed to assess and optimize those pipelines in production.
About super-rag
superagent-ai/super-rag
Super performant RAG pipelines for AI apps. Summarization, Retrieve/Rerank and Code Interpreters in one simple API.
This helps AI application developers create powerful question-answering systems by feeding various document types, like PDFs and web pages, into a backend that understands and retrieves relevant information. It takes your raw documents and a user's question, then outputs precise answers, even performing computations if needed. This is for developers building AI-powered chatbots, assistants, or knowledge bases who need robust information retrieval capabilities.
About rag_blueprint
feld-m/rag_blueprint
A modular framework for building and deploying Retrieval-Augmented Generation (RAG) systems with built-in evaluation and monitoring.
This project helps engineering and product teams build robust AI chatbots and question-answering systems that provide accurate information from internal documents. It takes existing knowledge bases like Confluence, Notion, or PDF files, processes them, and delivers an interactive chat interface where users can ask questions and get answers. The ideal user is a developer or technical lead creating a reliable AI knowledge agent for their organization.
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