You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This demo repository illustrates how to use Python to scrape news articles from Google based on a given keyword. The scraped articles are then processed by Azure OpenAI Service (AOAI)'s GPT-3 model, which generates concise summaries of the main points. The summaries are then formatted and sent via email using MailJet API.
I have improved the demo by using Azure OpenAI’s Embedding model (text-embedding-ada-002), which has a powerful word embedding capability. This model can also vectorize product key phrases and recommend products based on cosine similarity, but with better results. You can find the updated repo here.
This repository contains various examples of how to use LangChain, a way to use natural language to interact with LLM, a large language model from Azure OpenAI Service.
This code repo demonstrates how to use the word embedding model from Azure OpenAI Service to perform a semantic search on a grocery store dataset. This enhanced/completed version used Streamlit to build a web user experience to semantic search and display the most relevant items
In this repository, you will discover how Streamlit, can work seamlessly with Azure OpenAI Service's Embedding and GPT 3.5 models. These tools make it possible to create a user-friendly web application that enables users to ask questions in natural language about a PDF file they have uploaded.
In this repository, you will find an example code for creating an interactive chat experience that allows you to ask questions about your CSV data with chart visualization capabilities.