Chrome Extension to Summarize or Chat with Web Pages/Local Documents Using locally running LLMs. Keep all of your data and conversations private. 🔐

Completely local RAG. Chat with your PDF documents (with open LLM) and UI to that uses LangChain, Streamlit, Ollama (Llama 3.1), Qdrant and advanced methods like reranking and semantic chunking.

Simple Chat UI as well as chat with documents using LLMs with Ollama (mistral model) locally, LangChaiin and Chainlit

A "chat with your data" example: using a large language models (LLM) to interact with our own (local) data. Everything is local: the embedding model, the LLM, the vector database. This is an example of retrieval-augmented generation (RAG): we find relevant sections from our documents and pass it to the LLM as part of the prompt (see pics).

Chat with Documents from scratch using LLMs and a vector databse

AI-powered Streamlit app for analyzing, summarizing, and chatting with documents (PDF, DOCX, CSV, images, etc.) using LLMs and Together AI.

Vortex is a self-hosted RAG (Retrieval-Augmented Generation) application that lets you chat with your documents using any LLM provider. Upload PDFs, ingest URLs, and get accurate answers grounded in your own knowledge bases — all with a clean, modern interface.

Learn the fundamentals of LLMs & Retrieval-Augmented Generation (RAG) through hands-on notebooks, then build your own AI-powered Q&A chat app using Streamlit. Bring your own documents and get started with our video guide!

A python LLM chat app using Django Async, ReactJS and LLAMA2, that allows you to chat with multiple pdf documents. Components are chosen so everything can be self-hosted.

Chat with documents via a chatbot using LLMs

A minimalist command-line tool for Retrieval-Augmented Generation (RAG) chat with your documents using local LLMs via Ollama.

DocInferX is a fully-local, privacy-focused document intelligence system. It ingests PDFs and images, performs OCR, cleans text, chunks content, embeds it into a vector database, and lets you chat with your documents offline using a lightweight LLM (Phi-2).

CDoc lets you chat with your documents using local LLMs, combining Ollama, ChromaDB, and LangChain for offline, secure, and efficient information extraction. Perfect for researchers, developers, and professionals seeking quick insights from their documents.

A Retrieval-Augmented Generation (RAG) based question-answering proof-of-concept (PoC) that enables users to query documents using natural language. This system leverages local LLMs via Ollama for enhanced privacy and performance and features a chat-based interface. Built entirely with Python, including both backend an frontend.

Chat With Documents is a Streamlit application designed to facilitate interactive, context-aware conversations with large language models (LLMs) by leveraging Retrieval-Augmented Generation (RAG). Users can upload documents or provide URLs, and the app indexes the content using a vector store called Chroma to supply relevant context during chats.

BMCS2123 NATURAL LANGUAGE PROCESSING course assignment, a full-stack web app that enables interactive chat with one or more PDF documents using LLMs.

A private, local RAG (Retrieval-Augmented Generation) system using Flowise, Ollama, and open-source LLMs to chat with your documents securely and offline.

A local, private AI assistant for querying your own documents. Upload PDFs, text files, images, and web links—MindPalace parses, embeds, and stores them in-memory, letting you chat with your data using a fully local LLM. No cloud. No leaks.

Chat-With-PDFs: An end-to-end RAG system using LangChain and LLMs for interacting with PDF content. Upload PDFs, retrieve relevant document chunks, and have contextual, conversation-like interactions. Ideal for research, business, or educational purposes with streamlined retrieval and response.

A Telegram bot built with Python that summarizes PDF files using an LLM model. Users can send PDFs to the bot and receive concise summaries or answers to their queries directly in chat. The project streamlines document analysis and information retrieval via Telegram.

Use locally hosted or private cloud scaled up LLM to securely chat with your local documents

Cassette: A Real-Time Speech-to-Text Tool. Document Notes in Google or Notion and Chat with Your Data using LLM's

In this project, we are creating a question answer chat bot which will take any document, research paper, book or article as input and will answer any question related to that document. For this purpose, we are using langchain as our framework along with different LLMs (Large language models).

This project enables a conversational AI chatbot capable of processing and answering questions from multiple document formats, including CSV, JSON, PDF, and DOCX. It uses LangChain and Hugging Face's pre-trained models to extract information from these documents and provide relevant responses.

Chat with a LLaMa LLM using a body of documents

Chat with pdf document using LLM and RAG.

This is a RAG application to chat with data in your PDF documents implemented using LangChain, OpenAI LLM, Faiss Vector Store and Streamlit for UI

A lightweight AI tool to chat with your PDF study materials. Privacy-focused RAG system using local LLMs for instant document insights.

AI-powered assistant using RAG and Groq LLMs to answer questions from PDFs, DOCX, and websites. Features persistent chat history, fast responses, and a clean Streamlit UI. Built with HuggingFace embeddings for accurate document-grounded answers.

Chat with your documents using local LLMs — Full-stack RAG agent with iOS & Android apps