RAG implemented from scratch without using LangChain and LangGraph - designed specifically for processing and querying PDF documents with advanced support for visual content like tables, charts, and mathematical formulas.

使用 LangChain 把 PDF 生成音频播客

支持多模型对话、PDF知识库问答的AI应用，自带暗黑模式，本地一键部署

Production-ready multilingual RAG system for scientific PDFs. Supports 10+ Indic languages with E5 embeddings, ChromaDB vector store, Gemini 2.5 Flash LLM, and NLLB-200 translation. Ask questions in any language, get accurate answers with citations

RAG-based Multi-PDF Question Answering system with semantic search (FAISS) and LLM inference (Groq & Ollama)

Vision-first document RAG for PDF, DOCX, and image QA/extraction with ColQwen2, Qwen2.5-VL, Qdrant, and FastAPI.

本地化医学文献 RAG 问答系统。覆盖毕业论文文献调研、科研综述撰写、临床指南查阅、药学信息查询等场景 ，同时具备小数据量下的生产级可用性。支持多篇中英文医学论文 PDF（糖尿病、心血管、BMI 等）的智能检索与生成式问答。基于 Faiss 语义向量 + BM25 关键词 + Cross-Encoder 重排序三重检索 pipeline，模块化设计可自由切换嵌入模型、生成模型与重排序模型。全程不依赖任何第三方 API，基于 Ollama 本地部署 bge-m3 嵌入模型与 qwen2:7b 生成模型，确保医学数据隐私安全。实现了多会话隔离、流式 SSE 输出、异步 PDF 上传、前端历史持久化等生产级特性。

AI-powered PDF Q&A chatbot. Upload any document and have a real conversation with it. Built with RAG architecture using LangChain, Groq (Llama 3.3-70B), ChromaDB, and HuggingFace embeddings, completely free to run.

Multi-tool LangGraph agent with RAG over PDFs, live web search, stock prices, and remote MCP integration persistent SQLite memory and Streamlit chat UI powered by Groq. ---↓ project link below :

Sleek Streamlit chat app for Google Gemini (Files API). Dark, gradient UI with model picker, usage dialog, file/image/audio/PDF attach & preview, chat history, image persistence, robust error handling, and token usage tracking. Supports streaming replies and modular backend via google-genai.

Streamlit-based chatbot to interact with PDFs using Retrieval-Augmented Generation (RAG), FAISS, Sentence Transformers, and Mistral LLM

A high-performance Speculative RAG pipeline designed to reduce latency by combining fast draft generation and accurate verification using Groq Llama models, local HuggingFace embeddings, ChromaDB vector search, and end-to-end observability with Langfuse.

Local PDF Q&A with Ollama

Document Q&A system using RAG, FAISS vector search and LLM inference

PDF document QA system using RAG - Google Gemini embeddings, FAISS vector search, and Groq LLaMA 3.3 generation with a Streamlit UI

A local PDF chatbot that uses RAG to answer questions from a single text-based PDF with citations and out-of-scope detection.

RAG-based PDF Q&A system. Upload any PDF, ask questions, get answers grounded in the document. Built with LangChain, FAISS, BGE embeddings, Groq (LLaMA 3.3 70B), and Streamlit.

📄 An interactive AI Research Paper Assistant built with Streamlit and LangChain. Upload research PDFs to ask questions, generate executive summaries, and extract literature reviews powered by Groq (Llama 3.1) and local vector embeddings.

Enables context-aware question answering over PDFs using retrieval-augmented generation with vector embeddings. Built with Next.js App Router and OpenAI models for low-latency document search and response generation.