openai-whisper-talk is a sample voice conversation application powered by OpenAI technologies such as Whisper, Completions, Embeddings, and the latest Text-to-Speech. The application is built using Nuxt, a Javascript framework based on Vue.js.

AI Github assistant for your repo. Your proactive GitHub bot that auto-detects duplicates using OpenAI embeddings and Supabase magic!

Harness the power of Retrieval-Augmented Generation with the Personal AI Assistant, an innovative tool designed to extract and synthesize information from web and PDF sources efficiently. This cutting-edge solution transforms complex data into concise, actionable insights, making it indispensable for researchers and professionals alike.

Automate your customer support using Retrieval Augmented Generation (RAG): OpenAI + Pinecone

Node Proxima converts your repo into embeddings using OpenAI.

Make multiple Collections on Qdrant with Langchain & Openai

Forecasting Private Capital Market using published research and patents. Project developed at Michigan State University under the guidance of Dr. Mohammed Ghassemi for JP Morgan Chase.

Personal Project | A personal and private recommendation engine for the internet

Learning NextJs 14 with app router. Messing with proper server/client component architecture, Next API routes, Next streaming, Next cache beta, retrieval augmented generation with OpenAI embeddings API, PineconeDB to store context/query embeddings, and chatbot with OpenAI prompt.

Nodejs a REST API is designed to provide users with an interactive chat interface where they can ask questions and receive responses generated by an AI model. The application utilizes OpenAI embeddings and Langchain to process the user's input and generate relevant responses based on the context of the conversation.

A RAG chatbot which enables user to chat with their pdf documents

Chat with any website using Python and Langchain

A lightweight, unobtrusive R companion for some common NLP & web scraping tasks.

LangChain is a customer support chatbot that uses OpenAI's GPT-3.5-turbo model to answer questions based on a set of documents.