openai_batch_api_jsonl_file_upload_and_job_creation.py

1import json
2from openai import OpenAI
3
4client = OpenAI()
5
6# 1. Prepare your batch file (JSONL format)
7# Each line represents a single request in the batch
8tasks = [
9    {
10        "custom_id": "request-1",
11        "method": "POST",
12        "url": "/v1/chat/completions",
13        "body": {
14            "model": "gpt-4o-mini",
15            "messages": [
16                {"role": "system", "content": "You are a helpful assistant."},
17                {"role": "user", "content": "Hello world!"}
18            ],
19            "max_tokens": 1000
20        }
21    },
22    {
23        "custom_id": "request-2",
24        "method": "POST",
25        "url": "/v1/chat/completions",
26        "body": {
27            "model": "gpt-4o-mini",
28            "messages": [
29                {"role": "system", "content": "You are an unhelpful assistant."},
30                {"role": "user", "content": "Goodbye world!"}
31            ],
32            "max_tokens": 1000
33        }
34    }
35]
36
37file_name = "batch_tasks.jsonl"
38with open(file_name, "w") as f:
39    for task in tasks:
40        f.write(json.dumps(task) + "\n")
41
42# 2. Upload the file to OpenAI
43batch_input_file = client.files.create(
44    file=open(file_name, "rb"),
45    purpose="batch"
46)
47
48# 3. Create the batch job
49batch_job = client.batches.create(
50    input_file_id=batch_input_file.id,
51    endpoint="/v1/chat/completions",
52    completion_window="24h",
53    metadata={
54        "description": "nightly eval job"
55    }
56)
57
58print(f"Batch Job Created: {batch_job.id}")
59
60# 4. Check the status of the batch (Optional polling)
61# retrieve_status = client.batches.retrieve(batch_job.id)
62# print(f"Status: {retrieve_status.status}")