Retry Review Task

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

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.ai/api/app-api/sip/platform/v2/review/task/retry"

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.ai"
url = "/api/app-api/sip/platform/v2/review/task/retry"

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:
    print("Review task retry successful")
else:
    print(f"Retry failed: {result.get('msg')}")

• workspace_id (required): Workspace ID
• task_id (required): Review task ID

{
  "code": 200,
  "msg": "success"
}

​

Retry a Specific Rule in Review 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",
    "rule_id": "31415926"
  }' \
  "https://docflow.textin.ai/api/app-api/sip/platform/v2/review/task/rule/retry"

import requests

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

host = "https://docflow.textin.ai"
url = "/api/app-api/sip/platform/v2/review/task/rule/retry"

payload = {
    "workspace_id": workspace_id,
    "task_id": task_id,
    "rule_id": rule_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:
    print("Rule retry successful")
else:
    print(f"Retry failed: {result.get('msg')}")

• workspace_id (required): Workspace ID
• task_id (required): Review task ID
• rule_id (required): Review rule ID

{
  "code": 200,
  "msg": "success"
}

​

Parameter Description

​

Review Task ID (task_id)

• Returned when creating a task via the Create Review Task API
• Obtained when querying tasks via the Get Review Result API

​

Review Rule ID (rule_id)

• 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

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.ai"
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

​

Retry Entire Review Task

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

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

​

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

​

Related Pages

• Create Review Task - Learn how to create review tasks
• Get Review Result - Learn how to get review results
• Rule Management - Learn how to manage review rule repositories