Index documents in Azure CosmosDB with Logic Apps

Effectively managing large documents is essential to maintaining modern applications, especially fast and reliable queries. With Azure Logic Apps, you can now automate document indexing with Azure Cosmos DB in addition to existing capabilities. Indexing in AI SearchThis gives you the flexibility to use either service as your vector store.

In this post, we’ll explore 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 keeps the data highly accessible and queryable.

Why does Cosmos DB use Logic Apps for document indexing?

• Automated Workflow: Automating document indexing eliminates manual work and ensures documents are indexed as soon as they are uploaded.
• scalability: As your document volume grows, your data remains scalable and available through Azure Cosmos DB’s global distribution.
• Seamless integration: Logic App allows you to easily integrate with other Azure services, such as Blob storage and AI models, to enhance document indexing with intelligence and automation.

Scenario Overview

This scenario automates the collection, parsing, and indexing of document content in Azure Blob storage. 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 that data in a Cosmos DB container for easy searching and querying. The workflow is as follows:

Key steps in the workflow:

1. Blob upload detection: Logic apps start by using event-based triggers to detect when new blobs (documents) are added or updated in Azure Blob storage.
2. Read blob content: The workflow reads the content of the uploaded blob and prepares it for further processing.
3. Document parsing: Logic Apps parse documents to extract relevant content, including text and metadata. This may include extracting PDFs or chunking text from large documents.
4. chunk text: For large documents, split content into manageable chunks for smooth processing and indexing.
5. Generate Embeddings Using AI: The logic app uses Azure AI to generate embeddings from document content. These inclusions enable improved data processing, classification, and structure mapping within Cosmos DB.
6. Mapping to Schema: Extracted data and embeddings are mapped to a predefined schema, ensuring consistency in how documents are indexed within Cosmos DB.
7. Bulk updates in Cosmos DB: The final processed document is stored and indexed in Cosmos DB. “Create or update many items in bulk” operations ensure that multiple items are processed efficiently for fast queries.

Here GitHub sample logic app I have an ingestion workflow that indexes data in Azure Cosmos DB.

conclusion

by utilizing Azure Logic App To automate document indexing Azure Cosmos DBhelps you streamline your data workflow, reduce manual intervention, and organize your data for optimal performance. This powerful integration simplifies processes, making it easier for teams to manage large volumes of documents and scale as needed.

We also plan to add support for searching indexed content soon. Stay tuned for more updates and let us know your thoughts and feedback.