Talk_with_PDF is a powerful, AI-driven solution designed to automate the extraction of information and generation of answers based on PDF documents. By integrating OpenAI's advanced language models and embeddings, this system provides accurate and contextually relevant responses, making it an invaluable tool for education, business, and research.

Q & A with multiple pdf App is a Python application that allows you to ask questions about the PDFs you upload using natural language model to generate accurate answers to your queries.

Chat with multiple web pages

It’s a very useful app to perform EDA in an interactive way. The LLM based chatbot suggests which Machine Learning Algorithm is appropriate for the provided dataset. Users can also download the generated data profile report. Offers interface for data profiling, excel view and pair plots.

Web application that processes text files by converting their contents into embeddings using a Retrieval-Augmented Generation (RAG) approach.

Chatbot Memo - Backend is a chatbot server which uses langchain framework

Experimenting with retrieval augmented generation (RAG) with the LangChain framework to identify tablets/capsules by their text description

This Python Flask application is designed to process and rank resumes based on job descriptions. It uses Azure's Document Analysis Client for document processing, and a MongoDB database for storing job descriptions and resumes. The application also generates embeddings for the processed documents using AzureOpenAI.

Question & Answer on PDF as knowledge data using langchain

Explore "AI Meets Classroom: Optimizing Transformer-Based Language Models for Education"—a proof-of-concept project using RAG models, emphasizing FATE principles. Discover robust evaluation methods and help advance the future of learning!