A multi-pdf chatbot based on RAG architecture, allows users to upload multiple pdfs and ask questions from them.

This project is a Streamlit-based web app that lets you upload multiple PDF documents and interact with them in natural language. It uses Google Gemini (via LangChain) for embeddings and question-answering, along with FAISS as the vector store for efficient document retrieval.

An end-to-end RAG chatbot using FastAPI, LangChain, ChromaDB & Streamlit with Multi-LLM support to answer questions from uploaded PDFs.

RAG chatbot deployed on streamlit, its multi-source(PDFs,Text,Url) and use huggingface LLM model and embeding model

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Step-by-Step Guide: Creating a Multi-PDF RAG Chatbot with Langchain and Streamlit

Chat with multiple PDFs

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Chatbot to compare multiple PDF documents with RAG, Chat GPT and LlamaIndex

A multi-pdf chatbot based on RAG architecture, allows users to upload multiple pdfs and ask questions from them.

A basic RAG chatbot using LangChain, ChromaDB & Streamlit with Multi-LLM support to answer questions from uploaded PDFs.

RAG-powered Document Q&A app using Python, Streamlit, LangChain, FAISS, and HuggingFace embeddings. Supports multi-PDF ingestion, vector search, and high-speed Llama-3/Groq & OpenAI inference for accurate, context-aware answers. Modern Generative AI chatbot for knowledge retrieval.

An advanced AI-powered document analysis chatbot enhanced with Agentic RAG and multi-agent collaboration. Supports PDF, DOCX, and TXT files with intelligent retrieval, summarization, and analysis. Built on OpenAI GPT models, CrewAI multi-agent framework, and FAISS vector search, with a clean Streamlit web interface.

A stateful, multi-threaded RAG chatbot built with LangGraph, LangChain, and Streamlit. Supports per-thread PDF ingestion, semantic search using FAISS and OpenAI embeddings, tool-calling LLMs, and persistent chat history via SQLite, with real-time streaming responses.

This RAG Chatbot is a smart, multi-turn conversational agent that allows users to upload any PDF (like a textbook or research article) and chat with it like a human tutor. Whether you want a summary, a factual answer, or insights about the structure or opinion in the text, this bot can handle it all.

A simple RAG based PDF chatbot

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This project combines LLMs, RAG (Retrieval-Augmented Generation), authentication, and a full-stack interface — to create a personalized AI chatbot where each user can upload research papers or URLs and chat with their own data.

An Offline Multi- pdf/docs RAG chatbot

Building a multi pdf chatbot using RAG

A multi-pdf chatbot based on RAG architecture, allows users to upload multiple pdfs and ask questions from them.

Multi-agent Telegram chatbot with RAG retrieval, automated PDF ingestion, and dual-model self-correction for hallucination-free responses.

ChatDocs – Application RAG multi-PDF : Développement d’un chatbot intelligent pour interroger des documents PDF, basé sur LangChain, FAISS et OpenAI/Claude. (Python, Streamlit, LangChain, FAISS, OpenAI API)

📄 A Streamlit-based Multi-Document RAG Chatbot using LangChain, FAISS, HuggingFace embeddings, and Google Gemini. Supports PDF uploads, web fallback, chat export to PDF, and custom UI.

Streamlit-based multi-source chatbot using RAG with PDF, website, and YouTube support. Includes summarization, chat history, and LLM integration via Groq API.

An intelligent multi-PDF chatbot built with Retrieval-Augmented Generation (RAG), using FAISS for semantic search and Mistral via OpenRouter for large language model reasoning.

This project is a conversational RAG (Retrieval-Augmented Generation) chatbot application that enables users to upload PDF documents and engage in multi-turn question-answering sessions.

Retrieval-Augmented Generation (RAG) chatbot for multi-source document Q&A. Upload PDFs/text, get AI answers grounded in your docs. LangChain · FAISS · Streamlit · Hugging Face.