Batch API With OpenAI - Theory | Complete OpenAI API GPT Python Tutorial - Part 9

This video is part of a full Udemy course which will be uploaded soon on Udemy. In this video, we dive into the Batch API offered by OpenAI, discussing its theoretical aspects, benefits, limitations, and use cases. Learn how to send a large number of requests at a reduced cost and understand when to opt for batch processing over streaming APIs. In This Video: Overview of OpenAI's Batch API Cost benefits and limitations Rate limits and higher headroom for batch processing Supported input and output types Detailed explanation of a single request format When and why to use Batch API Understanding the response format Timestamps: (00:00) Introduction to Batch API (00:29) Cost benefits and limitations (01:40) Rate limits and higher headroom (02:43) Supported input and output types (03:11) Use cases for Batch API (04:12) Single request format explained (06:16) Privacy concerns and file preparation (07:18) Submitting a batch job (09:12) Fine-tune models and rate limits (10:12) Separate servers for streaming and batch APIs (11:08) Response format and additional details (14:10) Conclusion and upcoming tutorials Other courses: RAG LLMOps in GCP - Deploying a Retrieval Augmented Generation LLM in GCP infrastructure project: https://youtu.be/39PGfKA50As Source code for these tutorials: https://github.com/Sahilvohra58/open_ai_api_tutorials If you found this video helpful, please give it a thumbs up 👍, subscribe to our channel 🔔, and leave a comment below if you have any questions or topics you want us to cover next! Follow Us: YouTube: https://www.youtube.com/@sahilvohra8892 LinkedIn: https://www.linkedin.com/in/sahil-vohra/ GitHub: https://github.com/Sahilvohra58/ Thank you for watching, and happy coding! 🚀