RAG Conversational Agent

What it does

A powerful Retrieval-Augmented Generation (RAG) system for analyzing PDF documents with optional AI-powered conversational responses. Easy to Use - CLI, Python API, and Web Demo available

RAG Conversational Agent - PDF query tool using retrieval augmented generation - rushi-12320/RAG-Conversational-Agent

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  RAGstack2023 · github.com · ▲111

  Deploy a private ChatGPT alternative hosted within your VPC

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  Demystifying Advanced RAG Pipelines2023 · github.com · ▲131

  I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…

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  Dive deep into AI Retrieval Augmented Generation (RAG)

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  PDF RAG2025 · github.com · ▲8

  RAG pipeline with PDF OCR, vector search and chat UI

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  PageIndex – Vectorless RAG2025 · github.com · ▲192

  Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans approach reading: navigating through sections and context rather than matching embeddings. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit reasoning…

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  Turn any website into a knowledge base for LLMs2024 · embedding.io · ▲305

  I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...

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