Step I : Create Embeddings and Metadata

1A. Create Embeddings with the notebook azure_ai_vector_search\notebooks\00.create_embeddings.ipynb
1B. Create Azure OPEN AI Embeddings with the notebook azure_ai_vector_search\notebooks\00.create_embeddings_azure.ipynb

The embeddings are created in azure_ai_vector_search\output
The embeddings are docvectors.json and docvectors_azure.json

Step II : Create Index and Insert Documents[ Embeddings and Metadata ]

2. Create the index with the notebook azure_ai_vector_search\notebooks\01.azure_ai_vector_search_index_creation.ipynb

Step III : Search

3. Vector Search , Hybrid Search , Exhaustive KNN exact nearest neighbor search, Semantic Hybrid Search using the notebook azure_ai_vector_search\notebooks\02.azure_ai_vector_search.ipynb

Step IV : Python files for the application

4. azure_ai_vector_search/backend/azure_ai_vector_search.py is the backend code for the vector search. The code in the notebook is put in a python file for easy deployment.

5. azure_ai_vector_search\backend\biz_azure_ai_search.py is the wrapper for the backend code present in azure_ai_vector_search/backend/azure_ai_vector_search.py. This file is used to call the backend code from the frontend.

6. azure_ai_vector_search\backend\config.py has the configurations code.