Documents Chatting with Gemini AI API, MiniLM Embedding model, Chroma Vector DB, RAG and Gradio UI

A Langchain app that allows you to chat with multiple PDFs

Ask‑Docs is an app that lets you upload documents (PDF, DOC/DOCX, TXT), extracts their text in the browser, and asks questions powered by OpenAI GPT‑5. Answers are grounded to the uploaded document content and include clear fallback behavior when the answer isn’t in the file.

Open-source AI knowledge base & custom chatbot builder — production-ready Next.js SaaS with RAG, document upload, URL scraping, Q&A training, citations, and embeddable chatbot widgets. Stripe, credits, NextAuth, Prisma.

DocChat RAG is a lightweight AI-powered document assistant built with Python and vanilla JavaScript. It supports PDF/TXT uploads, semantic search, and context-aware question answering using RAG architecture, SQLite vector storage, and Groq/OpenAI APIs without heavy frameworks or external databases.

Production-ready Generative AI RAG system that enables intelligent document querying using LangChain, Gemini LLM, FAISS vector search, and HuggingFace embeddings with source-aware responses.

🚀 Revolutionize your data interaction with a cutting-edge chatbot built on Retrieval-Augmented Generation (RAG) and OpenAI’s GPT-4. Upload documents, create custom knowledge bases, and get precise, contextual answers. Ideal for research, business operations, customer support, and more!

SparkDocs is an AI-powered document Q&A system that allows users to upload PDF, PPTX, or DOCX files, ask questions, and receive comprehensive answers based on the document's content.

DocuChat is a document chat application that allows you to have conversations with your documents, powered by a serverless vector database for scalable, efficient retrieval. Upload your files and ask questions in natural language to get answers based on their content.

chatPdf is an AI-powered document Q&A system that allows users to upload PDF, PPTX, or DOCX files, ask questions, and receive comprehensive answers based on the document's content.

Chat with your PDF, Word, and PowerPoint documents using Retrieval-Augmented Generation (RAG) with LangChain, Ollama (Mistral), and Streamlit

Built an AI-based multi-document chatbot using Retrieval-Augmented Generation (RAG) that enables conversational querying of PDFs with semantic search.

A Document ChatBot based on Conversational RAG(Retrieval-augmented generation) that retrieves and summarizes information from uploaded documents

AI-powered document question answering system using Retrieval-Augmented Generation (RAG). Upload PDFs, retrieve relevant content using embeddings + FAISS, and generate contextual answers with Gemini LLM using Streamlit.

RAG chatbot for answering questions about PDF reports using FAISS and Sentence Transformers

Fully local Retrieval-Augmented Generation (RAG) chatbot powered by FAISS vector search and Ollama LLMs. Supports PDF, TXT, and Markdown ingestion, fast similarity search, model switching, and intelligent document-aware Q&A. Everything runs locally with zero external API calls, wrapped in a clean Streamlit interface.

Use RAG with Langchain to chat with your data and display the retrieved source(s)

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

Self-hosted RAG workspace for document intelligence. Upload PDFs and DOCX files, ask questions, and get grounded answers with FastAPI, ChromaDB, and Groq.

An AI-based document chatbot that answers questions by first searching uploaded PDFs and files, then responding with accurate, context-aware results. Built in Python using Hugging Face Transformers and modern NLP techniques.