Chat with multiple web pages

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.

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.

This web application processes text files convert them into embeddings using OpenAIEmbeddings and stores them in Chroma DB. It's built using Python 3.9 and Streamlit and offers a user-friendly interface for text file processing.

This app proves to be a very useful tool for data profiling with an intelligent ai chatbot to explore the data in an interactive way. Users can also download the generated data profile report. It provides interface for data profiling, excel sheet view and pair plots.

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

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.

Question & Answer on PDF as knowledge data using langchain