Implementation:Groq Groq python JSONL Construction
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| Knowledge Sources | |
|---|---|
| Domains | Batch_Processing, Data_Preparation |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
User-defined pattern for constructing JSONL batch input files for the Groq batch processing API.
Description
This is a Pattern Doc — there is no library API for this step. Users construct the JSONL file themselves using Python's built-in json module. Each line must conform to the batch request schema with custom_id, method, url, and body fields.
Usage
Construct the JSONL file before uploading via client.files.create(). Ensure each line is valid JSON and the file is under 100 MB.
Interface Specification
# Required schema for each JSONL line
{
"custom_id": str, # Unique identifier for this request
"method": "POST", # HTTP method (always POST for completions)
"url": "/v1/chat/completions", # API endpoint path
"body": {
"model": str, # Model identifier
"messages": [ # Chat messages array
{"role": str, "content": str},
...
],
# Optional: temperature, max_tokens, etc.
}
}
I/O Contract
Inputs
| Name | Type | Required | Description |
|---|---|---|---|
| data | List[dict] | Yes | List of request specifications to batch |
Outputs
| Name | Type | Description |
|---|---|---|
| (file) | .jsonl file | One JSON object per line, up to 100 MB |
Usage Examples
import json
requests = [
{
"custom_id": "req-1",
"method": "POST",
"url": "/v1/chat/completions",
"body": {
"model": "llama-3.3-70b-versatile",
"messages": [
{"role": "user", "content": "Summarize quantum computing"}
]
}
},
{
"custom_id": "req-2",
"method": "POST",
"url": "/v1/chat/completions",
"body": {
"model": "llama-3.3-70b-versatile",
"messages": [
{"role": "user", "content": "Explain machine learning"}
]
}
},
]
with open("batch_input.jsonl", "w") as f:
for req in requests:
f.write(json.dumps(req) + "\n")
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