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PATCH
https://api.keywordsai.co
/
api
/
request-logs
/
{unique_id}
import requests

unique_id = "log_abc123..."  # Replace with actual log ID
url = f"https://api.keywordsai.co/api/request-logs/{unique_id}/"

headers = {
    "Authorization": f"Bearer {YOUR_KEYWORDS_AI_API_KEY}",
    "Content-Type": "application/json"
}

data = {
    "metadata": {
        "user_feedback": "helpful",
        "quality_rating": 5
    },
    "custom_identifier": "ticket_12345"
}

response = requests.patch(url, headers=headers, json=data)
print(response.json())

{
  "id": "log_abc123...",
  "unique_id": "log_abc123...",
  "organization_id": "org_xyz789",
  "environment": "prod",
  "timestamp": "2025-12-26T10:30:00Z",
  "start_time": "2025-12-26T10:29:58Z",
  "log_type": "chat",
  "model": "gpt-4o-mini",
  "cost": 0.000705,
  "latency": 1.234,
  "status": "success",
  "custom_identifier": "ticket_12345",
  "metadata": {
    "user_feedback": "helpful",
    "quality_rating": 5,
    "previous_metadata_key": "previous_value"
  },
  "input": "[{\"role\":\"user\",\"content\":\"Hello\"}]",
  "output": "{\"role\":\"assistant\",\"content\":\"Hi there!\"}",
  // ... rest of log fields
}
The Update Log endpoint allows you to modify specific fields of a single log. This is useful for adding annotations, updating metadata, or correcting information.
For updating multiple logs at once, use the Batch Update endpoint.

​
Updatable fields

The following fields can be updated:
  • metadata - Custom metadata properties
  • custom_identifier - Custom identifier
  • blurred - Privacy flag to blur log content

​
Core log data is immutable

Fields like input, output, log_type, model, usage, cost, latency, and other telemetry fields cannot be modified to maintain data integrity.

​
Path parameters

​
unique_id
string
required
The unique ID of the log to update. You can get this from the List Logs endpoint.

​
Body parameters

​
metadata
object
Update the log’s metadata with custom properties. This can be used for tagging, categorization, or adding context.
{
  "metadata": {
    "user_feedback": "helpful",
    "quality_rating": 5,
    "reviewed": true,
    "reviewer": "[email protected]"
  }
}
​
custom_identifier
string
Update the log’s custom identifier. Useful for linking logs to external systems.
{
  "custom_identifier": "ticket_12345_resolved"
}
​
blurred
boolean
Set whether the log content should be blurred for privacy reasons.
{
  "blurred": true
}

​
Response

Returns the complete updated log object (same structure as the Get Log endpoint). 
import requests

unique_id = "log_abc123..."  # Replace with actual log ID
url = f"https://api.keywordsai.co/api/request-logs/{unique_id}/"

headers = {
    "Authorization": f"Bearer {YOUR_KEYWORDS_AI_API_KEY}",
    "Content-Type": "application/json"
}

data = {
    "metadata": {
        "user_feedback": "helpful",
        "quality_rating": 5
    },
    "custom_identifier": "ticket_12345"
}

response = requests.patch(url, headers=headers, json=data)
print(response.json())

{
  "id": "log_abc123...",
  "unique_id": "log_abc123...",
  "organization_id": "org_xyz789",
  "environment": "prod",
  "timestamp": "2025-12-26T10:30:00Z",
  "start_time": "2025-12-26T10:29:58Z",
  "log_type": "chat",
  "model": "gpt-4o-mini",
  "cost": 0.000705,
  "latency": 1.234,
  "status": "success",
  "custom_identifier": "ticket_12345",
  "metadata": {
    "user_feedback": "helpful",
    "quality_rating": 5,
    "previous_metadata_key": "previous_value"
  },
  "input": "[{\"role\":\"user\",\"content\":\"Hello\"}]",
  "output": "{\"role\":\"assistant\",\"content\":\"Hi there!\"}",
  // ... rest of log fields
}

​
Use Cases

# Add thumbs up/down feedback to a log
import requests

def add_feedback(log_id, is_positive, api_key):
    url = f"https://api.keywordsai.co/api/request-logs/{log_id}/"
    
    response = requests.patch(
        url,
        headers={"Authorization": f"Bearer {api_key}"},
        json={
            "metadata": {
                "user_feedback": "positive" if is_positive else "negative",
                "feedback_timestamp": datetime.now().isoformat()
            }
        }
    )
    return response.json()

# Usage
add_feedback("log_abc123", True, YOUR_API_KEY)

# Flag logs that need human review
import requests

def mark_for_review(log_id, reason, priority, api_key):
    url = f"https://api.keywordsai.co/api/request-logs/{log_id}/"
    
    response = requests.patch(
        url,
        headers={"Authorization": f"Bearer {api_key}"},
        json={
            "metadata": {
                "needs_review": True,
                "review_reason": reason,
                "review_priority": priority,
                "flagged_at": datetime.now().isoformat()
            }
        }
    )
    return response.json()

# Usage
mark_for_review(
    "log_abc123",
    "Potential PII in response",
    "high",
    YOUR_API_KEY
)

# Blur logs containing sensitive information
import requests

def blur_log(log_id, reason, api_key):
    url = f"https://api.keywordsai.co/api/request-logs/{log_id}/"
    
    response = requests.patch(
        url,
        headers={"Authorization": f"Bearer {api_key}"},
        json={
            "blurred": True,
            "metadata": {
                "blur_reason": reason,
                "blurred_at": datetime.now().isoformat()
            }
        }
    )
    return response.json()

# Usage
blur_log("log_abc123", "Contains PII", YOUR_API_KEY)

# Tag logs with A/B test or experiment information
import requests

def tag_experiment(log_id, experiment_name, variant, api_key):
    url = f"https://api.keywordsai.co/api/request-logs/{log_id}/"
    
    response = requests.patch(
        url,
        headers={"Authorization": f"Bearer {api_key}"},
        json={
            "metadata": {
                "experiment": experiment_name,
                "variant": variant,
                "experiment_tagged_at": datetime.now().isoformat()
            }
        }
    )
    return response.json()

# Usage
tag_experiment(
    "log_abc123",
    "prompt_optimization_v2",
    "variant_b",
    YOUR_API_KEY
)

​
Metadata Best Practices

​
Organizing metadata

  1. Use consistent keys: Establish naming conventions across your organization
  2. Add timestamps: Include _at or _timestamp fields for tracking when metadata was added
  3. Namespace related fields: Use prefixes like review_, feedback_, experiment_ to group related fields
  4. Keep it queryable: Use simple data types (strings, numbers, booleans) for fields you’ll filter on
  5. Document your schema: Maintain documentation of your metadata structure

​
Error Responses

{
  "detail": "Log not found"
}

{
  "error": "Cannot update immutable field",
  "detail": "The field 'cost' cannot be modified"
}