{"id":19513882,"url":"https://github.com/easonlai/product_semantic_search_streamlit","last_synced_at":"2025-07-06T01:06:34.019Z","repository":{"id":177345048,"uuid":"660257957","full_name":"easonlai/product_semantic_search_streamlit","owner":"easonlai","description":"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. 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The dataset contains 50 items with their names only. The [word embedding model (text-embedding-ada-002)](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/concepts/models#embeddings-models-1) converts the items and search terms into high-dimensional vectors and computes their cosine similarity.\n\nThis enhanced/completed version used [Streamlit](https://streamlit.io/) to build a web user experience to semantic search and display the most relevant items.\n\n![alt text](https://github.com/easonlai/product_semantic_search_streamlit/blob/main/git-images/git-image-1.png)\n\nTo run this Streamlit web app\n```\nstreamlit run app.py\n```\n\nEnjoy!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feasonlai%2Fproduct_semantic_search_streamlit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feasonlai%2Fproduct_semantic_search_streamlit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feasonlai%2Fproduct_semantic_search_streamlit/lists"}