In past code repo, "Semantic Search by Azure OpenAI Embedding model (text-embedding-ada-002)", it demonstrates how to use the word embedding model from Azure OpenAI Service to perform a semantic search on a grocery store dataset. The dataset contains 50 items with their names only. The word embedding model (text-embedding-ada-002) converts the items and search terms into high-dimensional vectors and computes their cosine similarity.
This enhanced/completed version used Streamlit to build a web user experience to semantic search and display the most relevant items.
To run this Streamlit web app
streamlit run app.py
Enjoy!