[Question]: Azure AI search index size is 0 ?

[AmirBraham]

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Question

Hello, I'm following this guide by creating an index. I can run queries using index.as_query_engine(similarity_top_k=3) but when I visit the azure platform , I find that the vector index size is 0 .

I also tried creating the vector store using azure wizard and uploading files directly through azure , but it seems that the index generated fields don't match the description in the LlamaIndex guide ( missing fields such as doc_id and embedding_field_key) Any help would be really appreciated it ! Thanks a lot !

[dosubot[bot]]

Hey @AmirBraham, great to see you diving into new challenges! How's it going?

Based on the information you've provided, it seems like there might be a few things going wrong. Here are some potential issues and solutions:

1. Index Creation and Document Upload Process: If the index size is showing as 0, it could indicate that the documents were not successfully uploaded to the index. This could be due to errors in the document upload process or the index creation process not being completed successfully. Please ensure that you've followed all the steps correctly and that there were no errors during the process.

2. Field Mapping and Index Schema: The doc_id and embedding_field_key fields are crucial for the functioning of the LlamaIndex framework. If these fields are missing, it could be because they were not properly defined in the index schema or were not included in the field mapping configuration. Please ensure that the index schema in Azure AI Search includes these fields and that they are correctly mapped in the code.

3. Document Upload Logic: The document upload logic uploads documents in batches. If there's an issue with how documents are batched or if there's a failure during the upload process (e.g., due to exceeding batch byte size limits), this could result in documents not being added to the index, thus showing an index size of 0. Please check the logs for any errors during the upload process and ensure that documents are correctly formatted and batched according to Azure AI Search requirements.

4. Index Validation: The code includes a validation step to check if the index exists. If there's an issue with the index name or if the index was not created successfully, this validation step could fail, potentially leading to issues with document upload or index usage. Please ensure that the index name is correct and that the index was created successfully.

5. Error Handling and Logging: The provided code includes logging statements that can help diagnose issues during the index creation and document upload process. It's recommended to review the logs for any error messages or warnings that could indicate what went wrong during the process.

Without specific error messages or logs, these are general recommendations based on the provided context. Further investigation into the logs and Azure AI Search configuration would be necessary to pinpoint the exact cause of the issue. If you could provide more specific details about the errors or issues you're encountering, it would be easier to provide a more targeted solution.

For more information, you can refer to the LlamaIndex codebase, specifically the AzureAISearchVectorStore class, which handles the index creation and document upload process for Azure AI Search.

Sources

• llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/llama_index/vector_stores/azureaisearch/base.py
• llama-index-core/llama_index/core/indices/common_tree/base.py

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