Process multiple prompts in parallel with Azure OpenAI Batch API

Process Multiple Prompts in Parallel with Azure OpenAI Batch API Who is this for? This workflow is designed for developers and data scientists who want to efficiently send multiple prompts to the Azure OpenAI Batch API and retrieve responses in a single batch process. It is particularly useful for applications that require processing large volumes of text data, such as chatbots, content generation, or data analysis. What problem is this workflow solving? Sending multiple prompts to the Azure OpenAI API can be time-consuming and inefficient if done sequentially. This workflow automates the process of batching requests, allowing users to submit multiple prompts at once and retrieve the results in a streamlined manner. This not only saves time but also optimizes resource usage. What this workflow does This workflow: 1. Accepts an array of requests, each containing a prompt and associated parameters. 2. Converts the requests into a JSONL format suitable for batch processing. 3. Uploads the batch file to the Azure OpenAI API. 4. Creates a batch job to process the prompts. 5. Polls for the job status and retrieves the output once processing is complete. 6. Parses the output and returns the results. Key Features of Azure OpenAI Batch API The Azure OpenAI Batch API is designed to handle large-scale and high-volume processing tasks efficiently. Key features include: - Asynchronous Processing: Handle groups of requests with separate quotas, targeting a 24-hour turnaround at 50% less cost than global standards. - Batch Requests: Send a large number of requests in a single file, avoiding disruption to online workloads. Key Use Cases - Large-Scale Data Processing: Quickly analyze extensive datasets in parallel. - Content Generation: Create large volumes of text, such as product descriptions or articles. - Document Review and Summarization: Automate the review and summarization of lengthy documents. - Customer Support Automation: Handle numerous queries simultaneously for faster responses. - Data Extraction and Analysis: Extract and analyze information from vast amounts of unstructured data. - Natural Language Processing (NLP) Tasks: Perform tasks like sentiment analysis or translation on large datasets. - Marketing and Personalization: Generate personalized content and recommendations at scale. Setup 1. Azure OpenAI Credentials: Ensure you have your Azure OpenAI API credentials set up in n8n. 2. Configure the Workflow: - Set the azopenaiendpoint in the "Setup defaults" node to your Azure OpenAI endpoint. - Adjust the api-version in the "Set desired 'api-version'" node if necessary. 3. Run the Workflow: Trigger the workflow using the "Run example" node to see it in action. How to customize this workflow to your needs - Modify Prompts: Change the prompts in the "One query example" node to suit your application. - Adjust Parameters: Update the parameters in the requests to customize the behavior of the OpenAI model. - Add More Requests: You can add more requests in the input array to process additional prompts. Example Input Example Output Additional Notes - Job Management: You can cancel a job at any time, and any remaining work will be canceled while already completed work is returned. You will be charged for any completed work. - Data Residency: Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location. - Exponential Backoff: If your batch jobs are large and hitting the enqueued token limit, certain regions support queuing multiple batch jobs with exponential backoff. This template provides a comprehensive solution for efficiently processing multiple prompts using the Azure OpenAI Batch API, making it a valuable tool for developers and data scientists alike.