Why is the data source configured for Full Text Search (using a SQL Virtual Machine) not usable for Vector Search?

Question

I have following Question:

• SQL Virtual Machine Compatibility Issue:
• Why is the data source configured for Full Text Search (using a SQL Virtual Machine) not usable for Vector Search?
  • In the "Import and Vectorize" option on the Azure portal, why does using a SQL Virtual Machine configuration result in errors, even though the same configuration works seamlessly for creating indexes and indexers for Full Text Search?
  • Vector Embedding Issue:
    • I updated an existing index to include a vector field and configured a Vector Profile with an associated Vectorizer. However, the embeddings are not being generated for the vector field during indexing.
      • Can you help identify why the vector embeddings are not created, even though the index and vector profile configurations seem to be correct?
• General Inquiry:
• Is there any limitation or specific requirement when using a SQL Virtual Machine as a data source for Vector Search compared to Full Text Search?
  • How can I ensure that embeddings are generated and stored in the vector field "item_desc_vector"during indexing?

Answer 1

@Akhilesh Mishra I have gone ahead and added my responses inline

I have following Question:

• SQL Virtual Machine Compatibility Issue:
• Why is the data source configured for Full Text Search (using a SQL Virtual Machine) not usable for Vector Search?
  • Response In the "Import and Vectorize" option on the Azure portal, why does using a SQL Virtual Machine configuration result in errors, even though the same configuration works seamlessly for creating indexes and indexers for Full Text Search? Using a SQL Virtual Machine (VM) for Full Text Search works seamlessly because it is designed to handle text-based queries and indexing. However, Vector Search involves different requirements and processes. Here are some potential reasons why a SQL VM configured for Full Text Search might not be suitable for Vector Search:
    1. Data Structure Differences: Full Text Search indexes text data, while Vector Search requires numerical vector embeddings. The underlying data structures and storage mechanisms differ significantly.
    2. Resource Requirements: Vector Search often requires more computational resources for generating and querying vector embeddings. SQL VMs might not be optimized for these intensive operations.
    3. Configuration and Compatibility: The "Import and Vectorize" option in Azure might have specific requirements or configurations that are not fully supported by SQL VMs.
  • Vector Embedding Issue:
    • I updated an existing index to include a vector field and configured a Vector Profile with an associated Vectorizer. However, the embeddings are not being generated for the vector field during indexing.
      • Can you help identify why the vector embeddings are not created, even though the index and vector profile configurations seem to be correct?
        • response: If embeddings are not being generated for the vector field during indexing, consider the following:
          1. Vector Profile Configuration: Ensure that the vector profile and associated vectorizer are correctly configured. Double-check the settings and parameters.
          2. Data Source Compatibility: Verify that the data source is compatible with the vectorizer. Some data sources might require additional preprocessing or transformation.
          3. Index Definition: Ensure that the index definition includes the vector field with the correct data type (e.g., Collection(Edm.Single)) and that it is properly mapped.
• General Inquiry:
• Is there any limitation or specific requirement when using a SQL Virtual Machine as a data source for Vector Search compared to Full Text Search?
  • How can I ensure that embeddings are generated and stored in the vector field "item_desc_vector"during indexing?
    • response: When using a SQL Virtual Machine as a data source for Vector Search, there are specific limitations and requirements compared to Full Text Search:
      1. Data Transformation: Vector Search requires transforming text data into numerical vectors, which might not be straightforward with SQL VMs.
      2. Performance Considerations: SQL VMs might not be optimized for the high computational demands of vector operations.
      3. Configuration Requirements: Ensure that the SQL VM and the Azure Search service are configured to support vector operations, including necessary extensions or plugins.
      4. Verify Vectorizer Configuration: Ensure that the vectorizer is correctly configured and associated with the vector profile.
      5. Check Indexing Process: Monitor the indexing process for any errors or warnings related to vector embedding generation.