Fifty thousand support tickets get classified overnight at half the price of doing it synchronously, and you never write a rate-limit backoff. Batch requests bill at 0.5x the sync rate.

One JSONL file in, one JSONL file out, joined back on your own custom_id.

What you need

An FC API key with scope api:write
pip install openai
A corpus of inputs (we use a list of ticket strings below)

Full code

python
# classify_tickets.py
import io, json, os, time
from openai import OpenAI

client = OpenAI(api_key=os.environ["FC_API_KEY"], base_url="https://api.fightclub.pro/v1")

# 1. Prepare the JSONL batch input.
tickets = [
    {"id": "t_001", "body": "My login is broken, please help"},
    {"id": "t_002", "body": "Can I get a refund for last month?"},
    {"id": "t_003", "body": "Your pricing page has a typo on it"},
    # ... imagine 50,000 rows here
]

SYSTEM = (
    "Classify the ticket into one of: account, billing, bug, feature_request, other. "
    "Respond with JUST the label."
)

buf = io.BytesIO()
for t in tickets:
    row = {
        "custom_id": t["id"],
        "method": "POST",
        "url": "/v1/chat/completions",
        "body": {
            "model": "fc:openai/gpt-4o-mini",
            "messages": [
                {"role": "system", "content": SYSTEM},
                {"role": "user", "content": t["body"]},
            ],
            "max_tokens": 8,
        },
    }
    buf.write((json.dumps(row) + "\n").encode())
buf.seek(0)

# 2. Upload the JSONL file.
uploaded = client.files.create(file=("tickets.jsonl", buf), purpose="batch")
print(f"file: {uploaded.id} ({uploaded.bytes} bytes)")

# 3. Create the batch.
batch = client.batches.create(
    input_file_id=uploaded.id,
    endpoint="/v1/chat/completions",
    completion_window="24h",
    metadata={"source": "ticket_backlog_apr21"},
)
print(f"batch: {batch.id} status={batch.status}")

# 4. Poll until terminal.
while batch.status in ("validating", "in_progress", "finalizing"):
    time.sleep(60)
    batch = client.batches.retrieve(batch.id)
    print(f"  {batch.status}  {batch.request_counts.completed}/{batch.request_counts.total}")

if batch.status != "completed":
    raise SystemExit(f"batch ended: {batch.status} (error file: {batch.error_file_id})")

# 5. Download the output JSONL.
out_content = client.files.content(batch.output_file_id).read().decode()
classifications = {}
for line in out_content.splitlines():
    row = json.loads(line)
    cid = row["custom_id"]
    label = row["response"]["body"]["choices"][0]["message"]["content"].strip()
    classifications[cid] = label

print(f"classified {len(classifications)} tickets")
# merge back into your ticket DB here...

Walkthrough

The custom_id field on each input row is how you correlate output rows back to your records. Ringside does not reorder, but some rows may fail (rate limits, upstream errors); those appear in batch.error_file_id with the same custom_id. Always join on custom_id, never on position.

Polling every 60 seconds is fine, batches are rate-limit-free and you're not billed for polls. For jobs that might run overnight, prefer subscribing to the batch.completed webhook instead of polling. The event payload includes output_file_id and request_counts.

Batch children are billed at 0.5x the sync markup. A sync chat request costing $0.02 costs $0.01 in a batch. It shows up as api_usage_events.batch_markup_multiplier = 0.5 and rolls into /v1/margin at the discounted rate.

24-hour completion_window is the only value supported in v2.1, tunable windows are a v2.2 item.

Run it

bash
export FC_API_KEY=ko_0d7f2a91c4e35b86af10d2c7e94b6f3a5d81c02e7b4936af18d5c60e2a7f9b34
python classify_tickets.py

You'll see status transitions validating → in_progress → finalizing → completed over ~10 minutes for 3 rows, longer for 50k.

Overnight batch classifications

What you need

Full code

Walkthrough

Run it

What's next