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- OpenClaw
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- Extract archive
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- OpenClaw
- Primary doc
- SKILL.md
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Doubleword API
Create, submit, monitor, and retrieve asynchronous batch AI inference jobs via the Doubleword API using JSONL files for large or cost-sensitive workloads.
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Create, submit, monitor, and retrieve asynchronous batch AI inference jobs via the Doubleword API using JSONL files for large or cost-sensitive workloads.
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- Tencent SkillHub
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- SKILL.md, scripts/create_batch_file.py, references/api_reference.md
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- Tencent SkillHub
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- Version
- 1.0.0
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Documentation
Doubleword Batch Inference
Process multiple AI inference requests asynchronously using the Doubleword batch API.
When to Use Batches
Batches are ideal for: Multiple independent requests that can run simultaneously Workloads that don't require immediate responses Large volumes that would exceed rate limits if sent individually Cost-sensitive workloads (24h window offers better pricing)
Quick Start
Basic workflow for any batch job: Create JSONL file with requests (one JSON object per line) Upload file to get file ID Create batch using file ID Poll status until complete Download results from output_file_id
Step 1: Create Batch Request File
Create a .jsonl file where each line contains a single request: {"custom_id": "req-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "anthropic/claude-3-5-sonnet", "messages": [{"role": "user", "content": "What is 2+2?"}]}} {"custom_id": "req-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "anthropic/claude-3-5-sonnet", "messages": [{"role": "user", "content": "What is the capital of France?"}]}} Required fields per line: custom_id: Unique identifier (max 64 chars) - use descriptive IDs like "user-123-question-5" for easier result mapping method: Always "POST" url: Always "/v1/chat/completions" body: Standard API request with model and messages Optional body parameters: temperature: 0-2 (default: 1.0) max_tokens: Maximum response tokens top_p: Nucleus sampling parameter stop: Stop sequences File limits: Max size: 200MB Format: JSONL only (JSON Lines - newline-delimited JSON) Split large batches into multiple files if needed Helper script: Use scripts/create_batch_file.py to generate JSONL files programmatically: python scripts/create_batch_file.py output.jsonl Modify the script's requests list to generate your specific batch requests.
Step 2: Upload File
Upload the JSONL file: curl https://api.doubleword.ai/v1/files \ -H "Authorization: Bearer $DOUBLEWORD_API_KEY" \ -F purpose="batch" \ -F file="@batch_requests.jsonl" Response contains id field - save this file ID for next step.
Step 3: Create Batch
Create the batch job using the file ID: curl https://api.doubleword.ai/v1/batches \ -H "Authorization: Bearer $DOUBLEWORD_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "input_file_id": "file-abc123", "endpoint": "/v1/chat/completions", "completion_window": "24h" }' Parameters: input_file_id: File ID from upload step endpoint: Always "/v1/chat/completions" completion_window: Choose "24h" (better pricing) or "1h" (50% premium, faster results) Response contains batch id - save this for status polling.
Step 4: Poll Status
Check batch progress: curl https://api.doubleword.ai/v1/batches/batch-xyz789 \ -H "Authorization: Bearer $DOUBLEWORD_API_KEY" Status progression: validating - Checking input file format in_progress - Processing requests completed - All requests finished Other statuses: failed - Batch failed (check error_file_id) expired - Batch timed out cancelling/cancelled - Batch cancelled Response includes: output_file_id - Download results here error_file_id - Failed requests (if any) request_counts - Total/completed/failed counts Polling frequency: Check every 30-60 seconds during processing. Early access: Results available via output_file_id before batch fully completes - check X-Incomplete header.
Step 5: Download Results
Download completed results: curl https://api.doubleword.ai/v1/files/file-output123/content \ -H "Authorization: Bearer $DOUBLEWORD_API_KEY" \ > results.jsonl Response headers: X-Incomplete: true - Batch still processing, more results coming X-Last-Line: 45 - Resume point for partial downloads Output format (each line): { "id": "batch-req-abc", "custom_id": "request-1", "response": { "status_code": 200, "body": { "id": "chatcmpl-xyz", "choices": [{ "message": { "role": "assistant", "content": "The answer is 4." } }] } } } Download errors (if any): curl https://api.doubleword.ai/v1/files/file-error123/content \ -H "Authorization: Bearer $DOUBLEWORD_API_KEY" \ > errors.jsonl Error format (each line): { "id": "batch-req-def", "custom_id": "request-2", "error": { "code": "invalid_request", "message": "Missing required parameter" } }
List All Batches
curl https://api.doubleword.ai/v1/batches?limit=10 \ -H "Authorization: Bearer $DOUBLEWORD_API_KEY"
Cancel Batch
curl https://api.doubleword.ai/v1/batches/batch-xyz789/cancel \ -X POST \ -H "Authorization: Bearer $DOUBLEWORD_API_KEY" Notes: Unprocessed requests are cancelled Already-processed results remain downloadable Cannot cancel completed batches
Processing Results
Parse JSONL output line-by-line: import json with open('results.jsonl') as f: for line in f: result = json.loads(line) custom_id = result['custom_id'] content = result['response']['body']['choices'][0]['message']['content'] print(f"{custom_id}: {content}")
Handling Partial Results
Check for incomplete batches and resume: import requests response = requests.get( 'https://api.doubleword.ai/v1/files/file-output123/content', headers={'Authorization': f'Bearer {api_key}'} ) if response.headers.get('X-Incomplete') == 'true': last_line = int(response.headers.get('X-Last-Line', 0)) print(f"Batch incomplete. Processed {last_line} requests so far.") # Continue polling and download again later
Retry Failed Requests
Extract failed requests from error file and resubmit: import json failed_ids = [] with open('errors.jsonl') as f: for line in f: error = json.loads(line) failed_ids.append(error['custom_id']) print(f"Failed requests: {failed_ids}") # Create new batch with only failed requests
Best Practices
Descriptive custom_ids: Include context in IDs for easier result mapping Good: "user-123-question-5" Bad: "1", "req1" Validate JSONL locally: Ensure each line is valid JSON before upload Split large files: Keep under 200MB limit Choose appropriate window: Use 24h for cost savings, 1h only when time-sensitive Handle errors gracefully: Always check error_file_id and retry failed requests Monitor request_counts: Track progress via completed/total ratio Save file IDs: Store batch_id, input_file_id, output_file_id for later retrieval
Reference Documentation
For complete API details including authentication, rate limits, and advanced parameters, see: API Reference: references/api_reference.md - Full endpoint documentation and schemas
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
2 Docs1 Scripts
- SKILL.md
- references/api_reference.md
- scripts/create_batch_file.py