Effectively managing large document volumes is essential for modern applications, particularly to maintain fast and reliable querying. With Azure Logic Apps, you can now automate document indexing into Azure Cosmos DB, in addition to the existing capability of indexing in AI Search, offering the flexibility to use either service as a vector store.

In this post, we’ll walk through a scenario where Logic Apps automates the ingestion and indexing of documents, such as PDFs, into Azure Cosmos DB. This approach not only reduces operational overhead but also ensures that your data remains highly accessible and queryable.

Why use Logic Apps for document indexing in Cosmos DB?

Automated Workflows: By automating document indexing, you eliminate manual tasks and ensure that documents are indexed as soon as they are uploaded.
Scalability: As your document volume grows, Azure Cosmos DB’s global distribution ensures your data remains scalable and highly available.
Seamless Integration: Logic App enables you to easily integrate with other Azure services, such as Blob Storage and AI models, enhancing your document indexing with intelligence and automation.

Scenario Overview

In this scenario, we automate the ingestion of document content from Azure Blob Storage, parsing it, and indexing it into Azure Cosmos DB. When a blob (such as a PDF or text document) is uploaded, a Logic App workflow is triggered to process the document and store its data in a Cosmos DB container, making it easily retrievable and queryable. Here is what the workflow will look like: 

Key steps in the workflow:

Blob Upload Detection: The Logic App starts by detecting when a new blob (document) is added or updated in Azure Blob Storage using the event-based trigger.
Read Blob Content: The workflow reads the content of the uploaded blob and prepares it for further processing.
Document Parsing: Logic Apps parses the document, extracting the relevant content, such as text or metadata. This can include PDF extraction or text chunking for larger documents.
Chunk Text: For larger documents, the content is split into manageable chunks to ensure smooth processing and indexing.
Generate Embeddings Using AI: Using Azure AI, the Logic App generates embeddings from the document content. These embeddings allow for enhanced data processing, categorization, and structure mapping within Cosmos DB.
Map to Schema: The extracted data and embeddings are mapped to a predefined schema to ensure consistency in how documents are indexed within Cosmos DB.
Bulk Update in Cosmos DB: Finally, the processed document is stored and indexed in Cosmos DB. The “Create or update many items in bulk” action ensures that multiple items are processed efficiently for fast querying.

Here is a GitHub sample logic app that has the ingestion workflow to index data in Azure Cosmos DB.

Conclusion

By leveraging Azure Logic Apps to automate document indexing into Azure Cosmos DB, you can streamline data workflows, reduce manual intervention, and ensure your data is organized for optimal performance. This powerful integration simplifies the process, making it easier for teams to manage large volumes of documents and scale as needed. 

We are also planning on adding support to allow retrieval of the indexed content soon. Stay tuned for more updates and please let us know your thoughts and feedback.

​Microsoft Tech Community – Latest Blogs –Read More