PDFs you can talk to.
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Updated
Feb 17, 2026 - TypeScript
PDFs you can talk to.
Private, self-hosted document chat for attorneys: parse legal PDFs and query them with local open-source LLMs (Ollama) + verifiable page citations. One-click desktop app at docuchat.app.
Self-hosted PDF Q&A API with streaming answers, citations, Weaviate/pgvector, and Azure OpenAI, OpenAI-compatible, Vertex AI, or local embeddings.
Chat with your PDF documents.
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.
Advanced local-first RAG system powered by Ollama and LangGraph. Optimized for high-performance sLLM orchestration featuring adaptive intent routing, semantic chunking, intelligent hybrid search (FAISS + BM25), and real-time thought streaming. Includes integrated PDF analysis and secure vector caching.
Local-first AI assistant for macOS — chat with your PDFs, spreadsheets, CSVs and code using a local LLM via Ollama. Model-generated Python runs in a Seatbelt sandbox with no network. No cloud, no telemetry, no API keys.
Local cognitive search on a pdf file.
Chatting with PDF documents using large language models (GPT)
LocalDoc RAG: browser-only local document RAG for PDF/TXT/DOCX/CSV chat with Ollama, Qdrant, and plain JavaScript.
Chat with your documents — privately, offline, on your own machine. Local-first RAG over PDFs/DOCX/images with GPU-accelerated streaming, optional voice mode, multi-conversation history, and citation-anchored sources. Bilingual (中/EN). FastAPI + React + llama.cpp.
A chatbot assistant app that allows you to talk to a pdf using gemini api
RAG-powered PDF Q&A engine — upload any document, ask questions, get answers with page-level citations using FAISS + Gemini
Doctype.io: A production-ready RAG engine that turns static PDFs into intelligent conversations. Built with FastAPI, Redis, LangChain, and Google Gemini.
A NotebookLM-inspired agent that runs locally
AI-powered web app for chatting with PDF documents through semantic search (RAG), built with Next.js, LangChain, OpenAI Embeddings, and Astra DB.
Streamlit RAG app for uploading PDFs, asking document questions, and viewing source-backed answers with Mistral and FAISS.
Chat with your documents in real-time. A high-performance RAG engine built with FastAPI, PostgreSQL (pgvector), and OpenAI.
AI-powered research assistant built with FastAPI, React, RAG, and LLMs for intelligent document retrieval and context-aware conversations.
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