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

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.

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.

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

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.

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.

Local PDF Q&A with Ollama

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.

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

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.

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

Multimodal PDF Q&A with Late / Early / Hybrid fusion — FastAPI + Streamlit + Gemini, GPU-ready (E5 + CLIP + CrossEncoder).

PDF RAG chatbot — PyPDFLoader + RecursiveCharacterTextSplitter + IBM Slate 30M embeddings + ChromaDB + Mistral Small 3.1 24B via Watsonx + Gradio UI

AI-powered PDF Question Answering system using RAG architecture, Groq LLaMA 3.3 70B and FAISS vector search.

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