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

This repository contains implementations of Retrieval-Augmented Generation (RAG) in Jupyter notebooks. It includes examples of building chatbots with and without history, processing PDFs with RAG, and using DeepSeek models for local RAG and financial document analysis.

A local LLM chatbot with RAG for PDF input files

(RAG) Developing a chatbot capable of utilizing PDF files as a knowledge base to answer questions. Additionally, we employed the vision capability of GPT-4V to identify and describe graphs from each page. Describing the charts and their illustrated trends improves the chatbot's accuracy in answering and providing additional context.

A simple local Retrieval-Augmented Generation (RAG) chatbot that can answer to questions by acquiring information from personal PDF documents.

An AI-powered chatbot that leverages RAG (Retrieval-Augmented Generation) to answer your questions based on the content of uploaded PDFs

JARVIS Chatbot: a local simple RAG assistant with PDF files

RAG-ChatBot using Google's Gemini-Pro model, Langchain, ChromaDB and Streamlit. The ChatBot can answer questions asked in natural language about PDF documents that the user uploads

A chatbot powered by CrateDB using RAG techniques and data from PDF files.

Retrieval-Augmented Generation on PDF for Free, Integrated with Memory to recall previous interactions, it operates as a sophisticated lang-chain application.

A powerful chatbot assistant leveraging Retrieval-Augmented Generation (RAG) to answer questions from multiple PDF documents. RAGbot generates accurate responses and provides source references, making it an ideal assistant for working with domain-specific PDFs.

🐶 PetCare AI Chatbot, powered by Langchain, Ollama, and Flask, utilizes RAG for context-aware answers from PDF documents.

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.

A domain-adaptable AI chatbot powered by RAG, FAISS, and LangChain to answer questions from your custom PDFs using HuggingFace LLMs.

No description available

(PDF) Information and Inference, Retrieval-Augmented Generation [ RAG ]

Simple RAG chatbot built with LangChain and Ollama that chats with your own PDFs. Blog post: https://medium.com/@tschechd/retrieval-augmented-generation-rag-in-practice-implementing-a-chatbot-with-langchain-and-ollama-79d6d19642f7

Python package that constructs a structured markdown knowledge base from external sources such as PDFs, websites, and GitHub repos with LLM summarization. Ideal for RAG, search-friendly LLM contexts (/llms.txt), and chatbots.

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

No description available

User-friendly chatbot that summarizes and interacts with content fromuser-uploaded PDFs using RAG with Ensemble Retriever

RAG‑PDF‑Analyzer is a Streamlit chatbot that lets users upload PDFs and query them with natural language. It uses PyPDF2 for text extraction, HuggingFace embeddings with FAISS for semantic search, and Mistral LLM via LangGraph to deliver context‑aware answers from documents.

AI chatbot with multiple trending LLMs and a RAG option to chat with your PDF documents

Medical Chatbot using Retrieval-Augmented Generation (RAG) to answer medical queries. PDFs are converted into embeddings and stored in Pinecone. LangChain retrieves context for LLM responses. Built with Flask and deployable on AWS using Docker and GitHub Actions for scalable access.

Index a pdf library for light-rag using gpt-4o-mini + chatbot

An AI-powered chatbot that answers student questions using university PDFs with the help of Google's Gemini API and RAG (Retrieval-Augmented Generation) architecture.

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

No description available

This repository develops a RAG-powered chatbot that answers questions based on a specific PDF document. Key objectives include: Chatbot Development: Chatbot using Retrieval-Augmented Generation (RAG) Dataset Creation, Performance Evaluation

RAG Gemini AI Chatbot. Users can upload PDF, Image to be used as context. Chat history is also available.