使用 LangChain 把 PDF 生成音频播客

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.

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

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

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.

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.

RAG over PDFs. Ask any document a question, get a grounded answer with source passages cited. Live at inferlens.latentaxis.io

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

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.

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

Local PDF Q&A with Ollama

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.

A PDF-based RAG QA system built with FastAPI, SentenceTransformers, and FAISS.

Web app to query PDFs in natural language. Upload a PDF, ask questions, get answers with source pages. RAG (Chroma + BM25) + OpenAI; Vue 3 frontend.

Streamlit PDF question-answering app using LangChain, FAISS retrieval, and Groq chat models

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

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.

Chat with annual reports and financial statements using LangChain, Redis Vector Store, and Streamlit — fully Dockerized RAG pipeline

An AI-powered research assistant that answers academic questions from uploaded PDFs or links (arXiv, PubMed) and returns context-rich answers with citation support using LangChain, LLaMA 3 (Groq), and FAISS.