Exploring the new OpenAI Batch API in web and in Python code

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AI Summary

## Summary: OpenAI's New Batch API  
  
- **Purpose**:   
  - Run multiple requests simultaneously.  
  - Ideal for evaluations, classifying datasets, and getting large-scale embeddings.  
  
- **Features**:  
  - Up to 50,000 requests.  
  - 50% lower cost than regular calls.  
  - Higher rate limits.  
  - 24-hour turnaround time (often faster).  
  
- **Use Cases**:  
  - Suitable for jobs that don't need immediate responses.  
  
- **Rate Limits Example**:  
  - GPT-4 Turbo: 1.8 million tokens, 10,000 requests/minute.  
  - Batch API: 300 million tokens/day.  
  
- **How to Use**:  
  - Prepare a JSONL file with custom IDs.  
  - Select the endpoint (e.g., GPT-3.5 Turbo).  
  - Set parameters like max tokens.  
  - Upload the file to the OpenAI platform.  
  - Choose an endpoint (completions or embeddings).  
  - Create the batch and wait for processing.  
  
- **Python Integration**:  
  - Define `OpenAIBatchProcessor` class.  
  - Use `process_batch` method with input file path, endpoint, and completion window.  
  - Monitor batch status and retrieve results upon completion.  
  
- **Additional Information**:  
  - Patron benefits include access to code files, courses, and one-on-one connections.  
  - A preview of coding the batch processor in Python is provided.  
  - Full video and code available on Patreon.