PipesHub is a fully extensible and explainable workplace AI platform for enterprise search and workflow automation

RAG-GPT, leveraging LLM and RAG technology, learns from user-customized knowledge bases to provide contextually relevant answers for a wide range of queries, ensuring rapid and accurate information retrieval.

An agentic AI application that allows you to chat with your papers and gather also information from papers on ArXiv and on PubMed

Python, LlamaIndex, LangChain, Docker Compose: 8 PG Graph, 3 RDF Graph, 10 Vector, OpenSearch, Elasticsearch, Alfresco DBs. 13 data sources (9 auto-sync), KG auto-building, Ontologies, LLMs, Docling or LlamaParse doc processing, GraphRAG, RAG only, Hybrid Search, AI Chat. TypeScript React, Vue, Angular frontends, FastAPI REST backend, MCP Server🌟!

Building and deploying a document Q&A application on the Hugging Face cloud using Docker.

Python SDK for OCR and document parsing in the cloud with LlamaParse

Turn PDF into Notes in seconds📝

RAG system for intelligent question answering over PDF documents

A chatbot for asking about internal information

Chainlit app for RAG chat with documents Parsing PDF documents using LlamaParse, Qdrant, and the Groq model

📄 Simplify data extraction from PDFs and documents using AI APIs for efficient parsing, structuring, and saving to JSON, CSV, or databases.

A RAG app with streamlit as UI app, flask as backend api. Bot trả lời về document, về data cụ thể. Bot trả lời về document của công ty, trả lời về tờ hướng dẫn sử dụng hay gì đó. Data: bot có khả năng query để lấy dữ liệu (dạng dữ liệu có cấu trúc) như csv

Simplify context preparation for large language models. This Streamlit application uses llama-parse and files-to-prompt to combine PDF and text files into a single, Claude XML-formatted context file for LLMs.

Convert PDFs, PowerPoints, Word docs, and other documents to WCAG 2.1 AA compliant, screen-reader-compatible HTML

Build a full-stack AI chat app using FastAPI, React, PostgreSQL, and OpenAI for scalable, maintainable, and intelligent web interaction.

SmartRAG-Assistant/GenAI-Assistant leverages advanced LLM models and Nvidia APIs for efficient query handling and document summarization. It integrates LlamaParse for structured data extraction, HuggingFace embeddings for vectorization, and PineconeDB for efficient retrieval, ensuring precise answers to user queries.

Llama Parse PDF: Extract tables from PDF documents and convert them into Excel format, simplifying the process of managing transaction data.

extract and analyze content from various file formats including PDFs, text files, and images.

Q&A Chatbot capable of answering questions related to the financial reports of large public companies. This chatbot can be directly hosted in Heroku platform. The RAG (Retrieval-Augmented Generation) approach is used.

compare pdfs