This is a node.js project leveraging the OpenAI API for vector embedding, seamlessly integrating with MongoDB to store embedded data and facilitating efficient query-based retrieval for enhanced knowledge management

RAG_llama3.3 is an advanced Retrieval-Augmented Generation (RAG) system using Llama 3.3 language model. This project integrates state-of-the-art natural language processing (NLP) techniques to enable accurate and context-aware question answering.

LangChain Chatbot is a conversational AI system designed to assist users with legal queries and provide relevant information. It utilizes various natural language processing techniques, including OpenAI's GPT-3.5 model, Sentence Transformers, and Pinecone indexing, to understand user queries, refine them, and find the most relevant responses.

llama2 flask api for generative AI

Created a custom chatbot that will reply to your question based on the data stored in it's memory.Technology used are prisma ORM, mongoDB, pineconeDB to store vectors, openAI Text-embedding-ada-002-v2 to embed the text. I have followed a youtube tutorial to learn this.

This repository delves into the power of vector embeddings, from text to images, bridging the gap between high-dimensional data and insightful relationships. Learn to preprocess, generate, and store embeddings efficiently, while exploring advanced techniques like GCNs, GATs, and beyond.

OpenAI-chatbot-note-app

This AI tool helps you to chat with your PDFs just by uploading it in the web interface.