pdf-rag

Enterprise-ready Spring AI platform for RAG, tool calling, async ingestion, JWT/RBAC security, and observability.

Pdfelement Pro Edge

PDF-Assistant-RAG is a complete, production-ready AI document assistant that lets users upload complex PDFs, financial reports, legal contracts, and research papers — then chat with an AI that provides accurate, cited answers powered by a multi-stage Retrieval-Augmented Generation pipeline.

✌️ A dynamic Retrieval-Augmented Generation (RAG) system with support for PDF indexing, website crawling, and semantic Q&A powered by OpenAI, Qdrant, and Streamlit.

A PDF Question-Answering App built with RAG (Retrieval-Augmented Generation), allowing users to upload PDFs and ask context-based questions. Powered by Streamlit, LangChain, Ollama, and Chroma for efficient and accurate answers.

PageIndex-inspired agentic RAG app for vectorless document QA, FastAPI, multi-document retrieval, context compaction, and self-hosted AI workspaces.

An insurance PDF RAG system leveraging MongoDB Atlas Vector Search capabilities

A microservices-based RAG platform that supports multi-format document parsing, semantic search, and conversational AI, powered by Google AI and Qdrant.

📄 Transform your PDF documents into actionable insights with this RAG-based Question-Answering App for efficient and accurate responses.

LLM-based application leveraging LangChain for Retrieval-Augmented Generation (RAG) on imported PDF documents. Enables users to interactively query and converse with PDF content using vector-based retrieval.

A full-stack AI-powered application that lets users upload and chat with their PDF documents. It combines seamless PDF processing, intelligent responses, and a minimalistic design to deliver a smooth and intuitive user experience.

A system for ingesting, chunking, and querying PDFs using Retrieval-Augmented Generation (RAG) techniques. It integrates FastAPI, Inngest, Google's Gemini API, and Qdrant for AI-powered document search and question answering.

Rumi GPT is a chatbot built using LLM that provides Rumi quotes to help motivate and inspire you 😇

A RAG pipeline for PDF question answering using LangChain, ChromaDB and Groq LLM (From basics to WEB-UI)

Artifact-aware PDF RAG system with Qwen embeddings, vector search, reranking, grounded answers, and citations.

Local-first PDF RAG engine built with FastAPI and Qdrant. Retrieves and reranks source-attributed context, then delegates LLM inference to Inference Lab (M0).

Cognivia AI is a powerful AI-powered PDF search and question-answering system built with LangChain, Pinecone Vector Store, OpenAI, and Supabase. Upload PDFs, ask questions, and get intelligent answers with persistent conversation memory.

Backend service for Retrieval-Augmented Generation (RAG) using AWS Bedrock, Superduper, and MongoDB Atlas Vector Search.

AI-powered platform for research paper discovery, semantic search, PDF-RAG, cited Q&A, summaries, and exports.

Production-grade multimodal AI lab: 12 notebooks building Visual RAG (ColPali), voice agents (Whisper, code-switching ZH-EN), and video search (frames + audio + RRF). Includes sanity tests + per-phase eval harness.