{"repo":"mburaksayici/RAG-Boilerplate","free":true,"listed":false,"github":"https://github.com/mburaksayici/RAG-Boilerplate","clone":"git clone https://github.com/mburaksayici/RAG-Boilerplate.git","description":"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).","language":"Python","stars":76,"topics":["ai-agents","crewai","hybrid-search","llm","propositional-models","qdrant","rag","rag-evaluation","rag-pipeline","reranking"],"license":null,"category":"ai-agents","readme_excerpt":"RAG Boilerplate Note : Anyone can pick from TO-DO, create issue, and PR. AI-assisted PRs are perfectly welcome! 🎥 Watch the demo on Vimeo A RAG system that is : - Chunking with propositional model + late chunking, and simple recursive overlap retrieval - Using Qdrant as a Vector DB, utilising its hybrid search (BM25 + Dense Search) - Reranks via LLMs - Using query enhancement agent via LLMs - Using crewAI for conversation+retrieval agent - Allows you to chat with data - Dividing conversations into sessions, saving/caching in Redis, cold stage to MongoDB - Having simple GUI on Gradio - Creates evaluation data specialised in hit rate, that allows to compare different retrieval combinations (with/without reranker/query enhancer) Read my blog on RAG Systems. Read README SYSTEM DESIGN.md for system design overviews. Read src/chunking/README.md to see chunking pipeline on action. Table of Contents - RAG Boilerplate - Table of Contents - How to run - Container Ports \\& Services - Python Project Template - Python/Package Manager - Database Choice - Data Parsing \\& Ingestion System - 📊 Progress Tracking Metrics - 📋 Legacy Comparison - Vector DB - Embedding - Vector DB — Vector Index Decision - Zilliz / Milvus Strategy - Heuristic to Use Index - Assumptions for Estimation - Tech Stack Alternatives - Qdrant + Redis + MongoDB - Qdrant (or other) + Elasticsearch + MongoDB - Elasticsearch + MongoDB - PostgreSQL + pgvector (+ optional Redis) - Chunking Strategy - 1. Proposition Model - 2","default_branch":null,"files":null,"tree":[],"storefront":"/r/mburaksayici","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/mburaksayici/RAG-Boilerplate/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}