Local RAG app chat, search and analyse PDF files

Privacy-First Retrieval-Augmented Generation (RAG) app for macOS. Search, index, and chat with your local documents directly on your Mac. No cloud, no tracking — everything runs fully offline for maximum privacy.

Building a PDF Chat App with RAG in .NET Using Local Ollama

Offline Streamlit RAG app that turns any PDF into an interactive study tool: summary, questions, chat & quiz. Fully private & local using Ollama. Tech: Streamlit • LangChain • Ollama (qwen2.5:3b) • FAISS • HuggingFace Embeddings • PyPDF Perfect for students & AI engineers exploring local RAG pipelines.

A fully local, privacy-friendly RAG-powered chat app using Reflex, LangChain, Huggingface, FAISS, and Ollama.

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

A user-friendly web app that lets you chat with your favorite LLMs, powered by a local RAG system and enhanced with MCP client functionality.

Local RAG Chat Bot Web App

Building a Local RAG Chat App with Reflex, LangChain, Huggingface, and Ollama https://www.apideck.com/blog/building-a-local-rag-chat-app-with-reflex-langchain-huggingface-and-ollama

Local LLM chat app with RAG support for Android and iOS. Privacy-first, offline-capable app powered by local open source Large Language Models (LLMs)

This project is based on google gemini pro llm model. Having many apps like chat app, copy of chatgpt, music expert sys, healthcare system, chat with local data(RAG)

Local PDF chat app using RAG: Upload documents, ask questions, get AI-powered answers. Privacy-focused with Ollama, LangChain, FAISS embeddings.

A local RAG (Retrieval-Augmented Generation) app built with LangChain, Chroma, and Gradio — lets you chat with your uploaded documents using OpenAI or Ollama models.

Intelligent RAG app for semantic Q&A with PDFs. Built with LangChain, Streamlit, ChromaDB. Supports local (Ollama) & cloud (OpenAI) LLMs. Chat with your documents to extract insights and information efficiently.

RAG Simplified uses Python, Ollama, and Llama3 to build a chat app for your documents. Features advanced ML techniques for natural Q&A, document parsing, semantic search, local model hosting, and customizable conversation—an ideal showcase of modern AI and ML skills.

A Retrieval-Augmented Generation (RAG) app for chatting with content from uploaded PDFs. Built using Streamlit (frontend), FAISS (vector store), Langchain (conversation chains), and local models for word embeddings. Hugging Face API powers the LLM, supporting natural language queries to retrieve relevant PDF information.

An end‑to‑end, Gradio-based Retrieval-Augmented Generation (RAG) app for exploring and querying your own developer documentation. You can ingest local files and web pages, embed them into a Pinecone vector index using OpenAI embeddings, and chat with an OpenAI model grounded on your data.

A simple but powerful Streamlit app that lets you: - 🔎 Search arXiv for research papers by topic. - 📄 Upload your own PDF/TXT research papers. - 💬 Ask questions about your documents using retrieval-augmented generation (RAG) powered by a fully local LLM (DeepSeek-Coder-1.3B-Instruct, fallback to TinyLlama-1.1B-Chat).

App Chat local RAG com Deepseek

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A local ai chat RAG app. + cloud demo

Local RAG App to chat with your PDF database

A local RAG app to chat with PDF documents

Local llm chat rag app with mongo for vector store

Local RAG app — chat with your documents using Ollama + ChromaDB + FastAPI

This QA-RAG app allows you to chat with your own PDF files. It is a local RAG APP.

Local AI-powered PDF chat app using RAG with Streamlit, LangChain, FAISS, and Ollama

A local Retrieval-Augmented Generation (RAG) app that lets you chat with your own documents.