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This document introduces how to retry review tasks via REST API. When a review task execution fails or needs to be reviewed again, you can retry the entire review task or retry a specific rule in the review task.
Review tasks support two retry methods:
  1. Retry entire review task: Re-execute all rules in the entire review task
  2. Retry a specific rule: Re-execute only a specific rule in the review task

​
Retry Entire Review Task

Re-execute the entire review task, which will re-review all rules in the task: 
curl -X POST \
  -H "Content-Type: application/json" \
  -H "x-ti-app-id: <your-app-id>" \
  -H "x-ti-secret-code: <your-secret-code>" \
  -d '{
    "workspace_id": "<your-workspace-id>",
    "task_id": "31415926"
  }' \
  "https://docflow.textin.com/api/app-api/sip/platform/v2/review/task/retry"
Request Parameters:
  • workspace_id (required): Workspace ID
  • task_id (required): Review task ID
Response Example: 
{
  "code": 200,
  "msg": "success"
}

​
Retry a Specific Rule in Review Task

Re-execute only a specific rule in the review task, suitable for cases where only a specific rule needs to be reviewed again: 
curl -X POST \
  -H "Content-Type: application/json" \
  -H "x-ti-app-id: <your-app-id>" \
  -H "x-ti-secret-code: <your-secret-code>" \
  -d '{
    "workspace_id": "<your-workspace-id>",
    "task_id": "31415926",
    "rule_id": "31415926"
  }' \
  "https://docflow.textin.com/api/app-api/sip/platform/v2/review/task/rule/retry"
Request Parameters:
  • workspace_id (required): Workspace ID
  • task_id (required): Review task ID
  • rule_id (required): Review rule ID
Response Example: 
{
  "code": 200,
  "msg": "success"
}

​
Parameter Description

​
Review Task ID (task_id)

The review task ID is the identifier of a created review task. You can obtain it through:

​
Review Rule ID (rule_id)

The review rule ID is the identifier of a specific rule in the review task. You can obtain it through:
  • Querying task results via the Get Review Result API, from the groups[].review_tasks[].rule_id field
  • Obtaining rule IDs from the rule repository via the Rule Management API
Example: Get Rule ID from Review Result
Python
import requests

ti_app_id = "<your-app-id>"
ti_secret_code = "<your-secret-code>"
workspace_id = "<your-workspace-id>"
task_id = "31415926"

host = "https://docflow.textin.com"
url = "/api/app-api/sip/platform/v2/review/task/result"

# Get review result
payload = {
    "workspace_id": workspace_id,
    "task_id": task_id
}

resp = requests.post(
    url=f"{host}{url}",
    json=payload,
    headers={
        "x-ti-app-id": ti_app_id,
        "x-ti-secret-code": ti_secret_code,
    },
    timeout=60,
)

result = resp.json()
if result.get("code") == 200:
    review_result = result.get("result", {})
    groups = review_result.get("groups", [])
    
    # Iterate through all rules to get rule IDs
    rule_ids = []
    for group in groups:
        review_tasks = group.get("review_tasks", [])
        for review_task in review_tasks:
            rule_id = review_task.get("rule_id")
            rule_name = review_task.get("rule_name")
            if rule_id:
                rule_ids.append({
                    "rule_id": rule_id,
                    "rule_name": rule_name
                })
    
    print(f"Found {len(rule_ids)} rules:")
    for rule in rule_ids:
        print(f"  Rule ID: {rule['rule_id']}, Rule Name: {rule['rule_name']}")

​
Use Cases

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Retry Entire Review Task

Suitable for the following scenarios:
  1. Review task execution failure: When the entire review task execution fails, you can retry the entire task
  2. After rule repository update: When rules in the rule repository are updated, you can retry the entire review task to apply the new rules
  3. After extraction result update: When the associated extraction task results are updated, you can retry the review task to re-review based on the new extraction results

​
Retry a Specific Rule

Suitable for the following scenarios:
  1. Single rule execution failure: When a specific rule execution fails, you can retry only that rule without affecting other rules
  2. After rule configuration adjustment: When a specific rule’s configuration is adjusted, you can retry only that rule
  3. Improve efficiency: When only a specific rule needs to be reviewed again, retrying a single rule is more efficient than retrying the entire task

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Notes

  1. Asynchronous execution: Retry operations are executed asynchronously. After retrying, you need to query the review status via the Get Review Result API
  2. Task status: Ensure the review task exists and can be retried. Deleted tasks cannot be retried
  3. Rule status: When retrying a rule, ensure the rule ID is correct and belongs to the specified review task
  4. Retry frequency: It is recommended to control retry frequency to avoid excessive system load from frequent retries
  5. Result overwrite: Retrying will regenerate review results, and the original review results will be overwritten