By [Your Name], Senior Journalist and Editor
Microsoft has recently released a public preview of built-in document parsing and chunking operations for Logic Apps Standard, aiming to simplify the RAG (Retrieval-Augmented Generation) ingestion process for generative AI applications. This move further strengthens the company’s AI capabilities within its low-code offerings.
According to Microsoft,these out-of-the-box operations allow developers to easily ingest documents or files, including structured and unstructured data, into AI Search without the need for writing or managing any code. The newly added Data Operations, Document Parsing and Text Chunking, transform content from formats like PDF, CSV, and Excel into tokenized strings, subsequently splitting them into manageable chunks based on token count. This feature ensures compatibility with Azure AI Search and Azure OpenAI, which require tokenized input andhave token limitations.
Divya Swarnkar, a Program Manager at Microsoft, explains, These operations are built upon the Apache Tika toolkit and parser libraries, enabling developers to parse thousands of file types in multiple languages, including PDF, DOCX, PPT, HTML, and more. You can seamlessly read and parsedocuments from virtually any source without custom logic or configuration!
Wessel Beulink, a Cloud Architect at Rubicon, highlights the potential of these new operations in a blog post: Azure Logic Apps’ document parsing and chunking capabilities unlock numerous automation possibilities. From legal workflows to customer support, these features empower businesses to leverageAI for more innovative document handling.
By leveraging low-code RAG ingestion, organizations can streamline AI model integration, achieving smoother data ingestion, enhanced searchability, and more effective knowledge management. Beulink outlines various use cases, including integrating parsing functionality into AI workflows for simplified document processing, enabling AI chatbots to access and retrieverelevant information for customer support, and breaking down data into manageable segments for improved knowledge management and searchability.
Furthermore, Logic Apps provides ready-made templates for RAG ingestion, allowing developers to connect to familiar data sources like SharePoint, Azure File, SFTP, and Azure Blob Storage, saving them valuable time and effort.
These new operations represent a significant step towards making generative AI more accessible and efficient, particularly for developers working with document-heavy applications. With the ability to seamlessly integrate document parsing and chunking into their workflows, developers can focus on building innovative AI solutions that leverage the power of RAG.
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