🧠 纯原生 Python 实现的 RAG 框架 | FAISS + BM25 混合检索 | 支持 Ollama / SiliconFlow | 适合新手入门学习

RAG for Local LLM, chat with PDF/doc/txt files, ChatPDF. 纯原生实现RAG功能，基于本地LLM、embedding模型、reranker模型实现，支持GraphRAG，无须安装任何第三方agent库。

A full-stack demo showcasing a local RAG (Retrieval Augmented Generation) pipeline to chat with your PDFs.

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.

Local RAG app chat, search and analyse PDF files

Simple demo for chatting with a PDF - and optionally point the RAG implementation to a local LLM

Local RAG + offline LLM demo with Streamlit chat UI, FAISS vector DB, and LoRA tuning/merge utilities — fully powered by Ollama models running locally. Tech Stack: Python 3.11+ · Streamlit · FAISS · Ollama (local LLM + embeddings) · nomic-embed-text · qwen2.5-instruct · PDF ingestion + chunking · LoRA fine-tuning helpers

Offgrid PDF is a fully local, privacy-focused PDF Chat Application that uses local LLMs powered by RAG for interactive document analysis.

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

Geo 3D Chatbot is an AI assistant for the Contractor Transparency Hub, connecting clients and contractors in construction/3D modeling. It features RAG for local knowledge, web search for real-time info, file analysis (PDFs, DOCX, images), concise/detailed modes, and clear chat option. Built with Streamlit and Gemini AI for seamless project queries.

Flask-based NYU course advisor using local Qwen + RAG over syllabus PDFs, with memory-aware chat and automatic weekly schedule generation/conflict checking.

A simple local RAG pipeline designed to extract questions from PDF’s, store them in a vector database, and provides functionality to search and chat, with the stored data. The script comes with a easy-to-use CLI interface developed using python.

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Implementation of RAG with local FAISS database. Ingests a pdf document and can chat with RAG.

A Local RAG Chatbot powered by DeepSeek V3 & RapidOCR. Chat with scanned PDFs, images, and documents privately. Built with Streamlit & LangChain.

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

"A high-speed RAG application using LangChain 0.3, Groq (Llama 3.3), and local Hugging Face embeddings to chat with PDF documents.

Notebook-style RAG chat for your documents. Upload PDFs/DOCX/MD/TXT, rebuild an index with live progress, and chat with citations using Groq or local Ollama—switchable at runtime.

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 lightweight local web chatbot for chatting with PDF files (focused on scientific papers), to get context-powered answers. Uses RAG (Retrieval-Augmented Generation) to combine vector search and generate LLM-based answers that cite sources.

This is Local RAG Chat to Pdf File using SLM Model: Phi 3

A fully local, chat-based Research Assistant using Retrieval Augmented Generation (RAG) for PDF documents

A free, offline alternative to NotebookLM. Chat with your PDFs using a local LLM via Ollama + RAG

A RAG (Retrieval-Augmented Generation) application that allows you to chat with your PDF documents using a local LLM (Ollama) and Vector Database (Qdrant).

A high-performance, fully free RAG system built with FastAPI and Groq. Chat with any PDF in seconds using Llama 3 and local HuggingFace embeddings.

A lightweight RAG system that lets you chat with PDF, TXT, and CSV documents using local LLMs via Ollama, semantic search with FAISS, and a Streamlit interface.

PatentHub is a self-hosted platform for analyzing patents and scientific documents. It combines global search with a local RAG system to index and chat with PDFs, generate AI-powered technical summaries, and manage documents with OCR and local storage.

Offline Multimodal RAG An offline Retrieval-Augmented Generation system using Ollama, ChromaDB & Streamlit. Upload PDFs, DOCX, or images, index content, and get grounded answers via a local LLM in a chat-style interface.

Privacy-first Local RAG Server: Chat with PDF & DOCX using GGUF models via llama.cpp and Qdrant. A lightweight, standalone FastAPI server with a clean HTML UI. High-performance, fully offline document intelligence. No Ollama, no cloud, no API keys.

OllamaRAG Nexus is a professional-grade suite that leverages local Ollama models and LangChain to provide secure, high-speed document intelligence. Featuring a dynamic RAG pipeline, it allows users to upload PDF/TXT files into a persistent ChromaDB vector store for instant, context-aware chatting. Experience premium, private AI locally.