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POST
https://api.keywordsai.co
/
api
/
datasets
/
{dataset_id}
/
logs
{
  "input": {
    "customer_id": "cust_12345",
    "query_type": "billing",
    "message": "I need help with my subscription",
    "priority": "high",
    "user_context": {
      "plan": "premium",
      "account_age_days": 365
    }
  },
  "output": {
    "response": "I'd be happy to help with your subscription. Let me look that up for you.",
    "agent_id": "agent_789",
    "resolution_time_seconds": 120,
    "actions_taken": ["account_lookup", "plan_verification"],
    "satisfaction_score": 4.5,
    "follow_up_required": true
  },
  "metadata": {
    "model": "gpt-4",
    "log_type": "chat",
    "session_id": "sess_abc123",
    "custom_identifier": "support-interaction-v2"
  },
  "metrics": {
    "prompt_tokens": 25,
    "completion_tokens": 18,
    "total_tokens": 43,
    "cost": 0.002,
    "latency": 1.8
  }
}

{
  "message": "Dataset log request completed synchronously (debug mode)!",
  "log_data": {
    "unique_id": "2844b4f401854250aec6ee1ffd8f38be",
    "input": "{\"customer_id\": \"cust_12345\", \"query_type\": \"billing\", \"message\": \"I need help with my subscription\"}",
    "output": {
      "response": "I'd be happy to help with your subscription. Let me look that up for you.",
      "agent_id": "agent_789",
      "resolution_time_seconds": 120,
      "satisfaction_score": 4.5
    },
    "model": "gpt-4",
    "log_type": "chat",
    "prompt_tokens": 25,
    "completion_tokens": 18,
    "cost": 0.002,
    "latency": 1.8,
    "dataset_id": "6828c7e1-51fa-4b05-89be-382567fc2703",
    "status": "success",
    "session_id": "sess_abc123",
    "custom_identifier": "support-interaction-v2"
  }
}
Create an individual dataset log from unified format data. Datasets record end-to-end input/output of workflows, so both input and output can be arbitrary JSON structures.

​
Authentication

  • API key: Authorization: Bearer <API key>

​
Parameters

​
input
any
required
The input data - can be any JSON structure (objects, arrays, strings, etc.)
​
output
any
required
The output data - can be any JSON structure (objects, arrays, strings, etc.)
​
metadata
object
Additional metadata fields (model, log_type, custom_identifier, etc.)
​
metrics
object
Metric fields (prompt_tokens, completion_tokens, cost, latency, etc.)
Note: Fields can be provided either at top-level or within metadata/metrics objects. This endpoint supports any workflow type - customer support, data processing, API transformations, calculations, etc.

​
Request Example

{
  "input": {
    "customer_id": "cust_12345",
    "query_type": "billing",
    "message": "I need help with my subscription",
    "priority": "high",
    "user_context": {
      "plan": "premium",
      "account_age_days": 365
    }
  },
  "output": {
    "response": "I'd be happy to help with your subscription. Let me look that up for you.",
    "agent_id": "agent_789",
    "resolution_time_seconds": 120,
    "actions_taken": ["account_lookup", "plan_verification"],
    "satisfaction_score": 4.5,
    "follow_up_required": true
  },
  "metadata": {
    "model": "gpt-4",
    "log_type": "chat",
    "session_id": "sess_abc123",
    "custom_identifier": "support-interaction-v2"
  },
  "metrics": {
    "prompt_tokens": 25,
    "completion_tokens": 18,
    "total_tokens": 43,
    "cost": 0.002,
    "latency": 1.8
  }
}

​
Examples

import requests

url = "https://api.keywordsai.co/api/datasets/{dataset_id}/logs/"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {
    "input": {
        "customer_id": "cust_12345",
        "query_type": "billing",
        "message": "I need help with my subscription",
        "priority": "high",
        "user_context": {
            "plan": "premium",
            "account_age_days": 365
        }
    },
    "output": {
        "response": "I'd be happy to help with your subscription. Let me look that up for you.",
        "agent_id": "agent_789",
        "resolution_time_seconds": 120,
        "actions_taken": ["account_lookup", "plan_verification"],
        "satisfaction_score": 4.5,
        "follow_up_required": True
    },
    "metadata": {
        "model": "gpt-4",
        "log_type": "chat",
        "session_id": "sess_abc123",
        "custom_identifier": "support-interaction-v2"
    },
    "metrics": {
        "prompt_tokens": 25,
        "completion_tokens": 18,
        "total_tokens": 43,
        "cost": 0.002,
        "latency": 1.8
    }
}

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

​
Response

{
  "message": "Dataset log request completed synchronously (debug mode)!",
  "log_data": {
    "unique_id": "2844b4f401854250aec6ee1ffd8f38be",
    "input": "{\"customer_id\": \"cust_12345\", \"query_type\": \"billing\", \"message\": \"I need help with my subscription\"}",
    "output": {
      "response": "I'd be happy to help with your subscription. Let me look that up for you.",
      "agent_id": "agent_789",
      "resolution_time_seconds": 120,
      "satisfaction_score": 4.5
    },
    "model": "gpt-4",
    "log_type": "chat",
    "prompt_tokens": 25,
    "completion_tokens": 18,
    "cost": 0.002,
    "latency": 1.8,
    "dataset_id": "6828c7e1-51fa-4b05-89be-382567fc2703",
    "status": "success",
    "session_id": "sess_abc123",
    "custom_identifier": "support-interaction-v2"
  }
}

​
Errors

  • 400 Bad Request — Invalid unified format data
  • 401 Unauthorized — Missing/invalid authentication
  • 404 Not Found — Dataset not found or not in your organization