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page_type languages name products description urlFragment
sample
python
Integrated vectorization (Python)
azure
azure-cognitive-search
azure-openai
Using azure-search-documents and the Azure SDK for Python, apply data chunking and vectorization in an indexer pipeline.
integrated-vectors-python

Integrated vectorization using Python (Azure AI Search)

The Python notebook creates vectorized data on Azure AI Search and runs a series of queries.

The code reads the data/text-sample.json file, which contains the input strings for which embeddings are generated. Output is a combination of human-readable text and embeddings that are pushed into a search index.

Prerequisites

  • An Azure subscription, with access to Azure OpenAI.

  • Azure AI Search, any version, but make sure search service capacity is sufficient for the workload. We recommend Basic or higher for this demo.

  • Azure Storage, with a blob container contaning documents to load, chunk, and vectorize. We recommend the PDFs in the data/documents folder.

  • A deployment of the text-embedding-ada-002 embedding model in your Azure OpenAI service. We recommend Azure OpenAI REST API version 2023-05-15. As a naming convention, we name deployments after the model name: "text-embedding-ada-002".

  • Python (these instructions were tested with version 3.11.x)

We used Visual Studio Code with the Python extension and Jupyter extension to test this sample.

Run the code

  1. Use the code/.env-sample as a template for a new .env file located in the subfolder containing the notebook. Review the variables to make sure you have values for Azure AI Search and Azure OpenAI.

  2. Open demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb file in Visual Studio Code.

  3. Optionally, create a virtual environment so that you can control which package versions are used. Use Ctrl+shift+P to open a command palette. Search for Python: Create environment. Select Venv to create an environment within the current workspace.

  4. Execute the cells one by one, or select Run or Shift+Enter.

Troubleshoot errors

If you get error 429 from Azure OpenAI, it means the resource is over capacity:

  • Check the Activity Log of the Azure OpenAI service to see what else might be running.

  • Check the Tokens Per Minute (TPM) on the deployed model. On a system that isn't running other jobs, a TPM of 33K or higher should be sufficient to generate vectors for the sample data. You can try a model with more capacity if 429 errors persist.

  • Review these articles for information on rate limits: Understanding rate limits and A Guide to Azure OpenAI Service's Rate Limits and Monitoring.