rag_blueprint and RAG-Boilerplate
These two tools are competitors, as both provide frameworks and boilerplate for building RAG systems, though B offers a more opinionated and feature-rich boilerplate with specific components like CrewAI orchestration and hybrid search, while A emphasizes modularity and built-in evaluation 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.
About RAG-Boilerplate
mburaksayici/RAG-Boilerplate
RAG boilerplate with semantic/propositional chunking, hybrid search (BM25 + dense), LLM reranking, query enhancement agents, CrewAI orchestration, Qdrant vector search, Redis/Mongo sessioning, Celery ingestion pipeline, Gradio UI, and an evaluation suite (Hit-Rate, MRR, hybrid configs).
This system helps developers build sophisticated AI applications that can 'chat' with custom data. It takes raw text documents, processes them using advanced chunking and search techniques, and allows users to ask questions and receive answers based on the content. The target user is a software developer or AI engineer looking to create conversational AI experiences over domain-specific information.
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