Evaluations API

Name: Brokle
Author: Brokle

The Evaluations API provides endpoints for submitting quality scores and feedback, and querying evaluation results.

Endpoints Overview

Method	Endpoint	Description	Auth
POST	`/v1/evaluations`	Submit evaluation scores	API Key
GET	`/api/v1/evaluations`	List evaluations	JWT
GET	`/api/v1/evaluations/{evaluationId}`	Get evaluation details	JWT
POST	`/api/v1/evaluations/batch`	Batch create evaluations	JWT
DELETE	`/api/v1/evaluations/{evaluationId}`	Delete evaluation	JWT

Submit Evaluation

POST /v1/evaluations

Submit evaluation scores for traces or spans.

Authentication

Header: X-API-Key: bk_your_api_key
Required Scope: evaluations:write

Request Body

{
  "traceId": "0af7651916cd43dd8448eb211c80319c",
  "spanId": "b7ad6b7169203331",
  "evaluations": [
    {
      "name": "relevance",
      "score": 0.95,
      "type": "numeric",
      "comment": "Response directly addresses the question"
    },
    {
      "name": "hallucination",
      "score": 0.0,
      "type": "boolean",
      "comment": "No factual errors detected"
    },
    {
      "name": "quality",
      "value": "excellent",
      "type": "categorical",
      "options": ["poor", "fair", "good", "excellent"]
    }
  ],
  "evaluator": {
    "type": "llm",
    "model": "gpt-4",
    "version": "1.0"
  },
  "metadata": {
    "dataset": "test_set_v1",
    "runId": "run_abc123"
  }
}

Evaluation Types

Type	Score Range	Description
`numeric`	0.0 - 1.0	Continuous score
`boolean`	0 or 1	Binary pass/fail
`categorical`	string	One of predefined options
`integer`	1-5, 1-10, etc.	Discrete rating scale

Response

{
  "data": {
    "id": "eval_abc123",
    "traceId": "0af7651916cd43dd8448eb211c80319c",
    "spanId": "b7ad6b7169203331",
    "evaluations": [
      {
        "name": "relevance",
        "score": 0.95,
        "type": "numeric"
      }
    ],
    "createdAt": "2024-01-15T10:31:00Z"
  }
}

Examples

curl -X POST https://api.brokle.com/v1/evaluations \
  -H "Content-Type: application/json" \
  -H "X-API-Key: bk_your_api_key" \
  -d '{
    "traceId": "0af7651916cd43dd8448eb211c80319c",
    "evaluations": [
      {
        "name": "relevance",
        "score": 0.95,
        "type": "numeric"
      },
      {
        "name": "hallucination",
        "score": 0.0,
        "type": "boolean"
      }
    ]
  }'

import requests

response = requests.post(
    "https://api.brokle.com/v1/evaluations",
    headers={
        "Content-Type": "application/json",
        "X-API-Key": "bk_your_api_key"
    },
    json={
        "traceId": "0af7651916cd43dd8448eb211c80319c",
        "evaluations": [
            {
                "name": "relevance",
                "score": 0.95,
                "type": "numeric",
                "comment": "Highly relevant response"
            },
            {
                "name": "factual_accuracy",
                "score": 1.0,
                "type": "boolean"
            }
        ],
        "evaluator": {
            "type": "llm",
            "model": "gpt-4"
        }
    }
)

print(response.json())

const response = await fetch('https://api.brokle.com/v1/evaluations', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'X-API-Key': 'bk_your_api_key'
  },
  body: JSON.stringify({
    traceId: '0af7651916cd43dd8448eb211c80319c',
    evaluations: [
      {
        name: 'relevance',
        score: 0.95,
        type: 'numeric'
      }
    ],
    evaluator: {
      type: 'llm',
      model: 'gpt-4'
    }
  })
});

const data = await response.json();
console.log(data);

List Evaluations

GET /api/v1/evaluations

Retrieve evaluations with filtering and pagination.

Authentication

Header: Authorization: Bearer {jwt_token}

Query Parameters

Parameter	Type	Required	Description
`projectId`	string	Yes	Filter by project
`traceId`	string	No	Filter by trace
`spanId`	string	No	Filter by span
`name`	string	No	Filter by evaluation name
`evaluatorType`	string	No	`llm`, `human`, `automated`
`minScore`	float	No	Minimum score filter
`maxScore`	float	No	Maximum score filter
`startTime`	ISO 8601	No	Start of time range
`endTime`	ISO 8601	No	End of time range
`page`	integer	No	Page number
`pageSize`	integer	No	Items per page

Response

{
  "data": [
    {
      "id": "eval_abc123",
      "traceId": "0af7651916cd43dd8448eb211c80319c",
      "spanId": "b7ad6b7169203331",
      "name": "relevance",
      "score": 0.95,
      "type": "numeric",
      "comment": "Highly relevant response",
      "evaluator": {
        "type": "llm",
        "model": "gpt-4"
      },
      "createdAt": "2024-01-15T10:31:00Z"
    }
  ],
  "pagination": {
    "page": 1,
    "pageSize": 20,
    "totalItems": 150,
    "totalPages": 8
  }
}

Get Evaluation

GET /api/v1/evaluations/{evaluationId}

Retrieve details of a specific evaluation.

Response

{
  "data": {
    "id": "eval_abc123",
    "traceId": "0af7651916cd43dd8448eb211c80319c",
    "spanId": "b7ad6b7169203331",
    "name": "relevance",
    "score": 0.95,
    "type": "numeric",
    "comment": "Highly relevant response addressing the user's question directly",
    "evaluator": {
      "type": "llm",
      "model": "gpt-4",
      "version": "1.0",
      "prompt": "Evaluate the relevance of the response..."
    },
    "metadata": {
      "dataset": "test_set_v1",
      "runId": "run_abc123"
    },
    "trace": {
      "id": "trace_xyz",
      "name": "chat_completion",
      "input": {
        "messages": [{"role": "user", "content": "..."}]
      },
      "output": {
        "content": "..."
      }
    },
    "createdAt": "2024-01-15T10:31:00Z",
    "updatedAt": "2024-01-15T10:31:00Z"
  }
}

Batch Create Evaluations

POST /api/v1/evaluations/batch

Create multiple evaluations in a single request.

Request Body

{
  "evaluations": [
    {
      "traceId": "trace_1",
      "name": "relevance",
      "score": 0.9,
      "type": "numeric"
    },
    {
      "traceId": "trace_2",
      "name": "relevance",
      "score": 0.85,
      "type": "numeric"
    },
    {
      "traceId": "trace_3",
      "name": "relevance",
      "score": 0.95,
      "type": "numeric"
    }
  ],
  "evaluator": {
    "type": "llm",
    "model": "gpt-4"
  }
}

Response

{
  "data": {
    "created": 3,
    "failed": 0,
    "evaluations": [
      {"id": "eval_1", "traceId": "trace_1", "score": 0.9},
      {"id": "eval_2", "traceId": "trace_2", "score": 0.85},
      {"id": "eval_3", "traceId": "trace_3", "score": 0.95}
    ]
  }
}

Evaluation Aggregations

Aggregate Scores

GET /api/v1/evaluations/aggregate

Aggregate evaluation scores for analytics.

Query Parameters

Parameter	Type	Description
`projectId`	string	Project ID (required)
`name`	string	Evaluation name (required)
`groupBy`	string	`model`, `day`, `week`, `evaluator`
`startTime`	ISO 8601	Start of time range
`endTime`	ISO 8601	End of time range

Response

{
  "data": {
    "name": "relevance",
    "overall": {
      "mean": 0.87,
      "median": 0.90,
      "stdDev": 0.12,
      "min": 0.45,
      "max": 1.0,
      "count": 1500,
      "p50": 0.90,
      "p90": 0.98,
      "p99": 1.0
    },
    "byGroup": [
      {
        "group": "gpt-4",
        "mean": 0.92,
        "count": 500
      },
      {
        "group": "gpt-3.5-turbo",
        "mean": 0.82,
        "count": 1000
      }
    ],
    "trend": [
      {
        "date": "2024-01-15",
        "mean": 0.85,
        "count": 150
      },
      {
        "date": "2024-01-16",
        "mean": 0.88,
        "count": 175
      }
    ]
  }
}

Human Feedback

Submit human feedback through the dashboard API:

POST /api/v1/feedback

Request Body

{
  "traceId": "0af7651916cd43dd8448eb211c80319c",
  "type": "thumbs",
  "value": "up",
  "comment": "Great response!",
  "userId": "user_123",
  "metadata": {
    "source": "chatbot",
    "sessionId": "session_456"
  }
}

Feedback Types

Type	Values	Description
`thumbs`	`up`, `down`	Simple thumbs up/down
`rating`	1-5	Star rating
`categorical`	custom	Predefined categories
`text`	string	Free-form feedback

Response

{
  "data": {
    "id": "feedback_abc123",
    "traceId": "0af7651916cd43dd8448eb211c80319c",
    "type": "thumbs",
    "value": "up",
    "createdAt": "2024-01-15T10:32:00Z"
  }
}

Built-in Evaluators

Brokle provides built-in evaluators accessible via API:

POST /api/v1/evaluators/run

Request Body

{
  "evaluator": "relevance",
  "traceIds": ["trace_1", "trace_2", "trace_3"],
  "options": {
    "model": "gpt-4",
    "threshold": 0.8
  }
}

Available Evaluators

Evaluator	Description	Output
`relevance`	Response relevance to query	0.0 - 1.0
`hallucination`	Factual accuracy check	boolean
`toxicity`	Harmful content detection	0.0 - 1.0
`coherence`	Response coherence	0.0 - 1.0
`completeness`	Answer completeness	0.0 - 1.0
`groundedness`	Grounded in context	0.0 - 1.0

Response

{
  "data": {
    "runId": "run_abc123",
    "evaluator": "relevance",
    "results": [
      {
        "traceId": "trace_1",
        "score": 0.95,
        "reasoning": "Response directly addresses..."
      },
      {
        "traceId": "trace_2",
        "score": 0.72,
        "reasoning": "Partially relevant but..."
      }
    ],
    "summary": {
      "mean": 0.84,
      "passRate": 0.67
    }
  }
}

Custom Evaluators

POST /api/v1/evaluators/custom

Request Body

{
  "name": "custom_relevance",
  "type": "llm",
  "prompt": "Evaluate the relevance of the assistant's response to the user's question.\n\nQuestion: {{input}}\nResponse: {{output}}\n\nScore from 0 to 1:",
  "model": "gpt-4",
  "outputParser": {
    "type": "numeric",
    "min": 0,
    "max": 1
  },
  "metadata": {
    "version": "1.0",
    "author": "team@company.com"
  }
}

Custom evaluators are project-scoped and can be shared across your organization.

Evaluation Datasets

Create datasets for systematic evaluation:

POST /api/v1/datasets

Request Body

{
  "name": "qa_test_set",
  "description": "Question-answering test dataset",
  "items": [
    {
      "input": {"question": "What is AI?"},
      "expectedOutput": {"answer": "AI is..."},
      "metadata": {"difficulty": "easy"}
    }
  ]
}

Run Evaluation on Dataset

POST /api/v1/datasets/{datasetId}/evaluate

{
  "evaluators": ["relevance", "hallucination"],
  "model": "gpt-4",
  "options": {
    "temperature": 0.7
  }
}

Webhooks

Configure webhooks for evaluation events:

{
  "url": "https://your-server.com/webhook",
  "events": ["evaluation.created", "evaluation.threshold_breached"],
  "filters": {
    "evaluationNames": ["relevance", "hallucination"],
    "thresholds": {
      "relevance": {"min": 0.8},
      "hallucination": {"max": 0.1}
    }
  }
}

Best Practices

Consistent Naming

Use consistent evaluation names across your project:

# Good: Consistent naming convention
evaluations = [
    {"name": "qa_relevance", "score": 0.9},
    {"name": "qa_accuracy", "score": 0.95},
    {"name": "qa_completeness", "score": 0.85}
]

# Bad: Inconsistent naming
evaluations = [
    {"name": "relevance", "score": 0.9},
    {"name": "Accuracy Score", "score": 0.95},
    {"name": "complete", "score": 0.85}
]

Include Context

Always include helpful context:

{
    "name": "relevance",
    "score": 0.72,
    "comment": "Response addresses the question but includes unnecessary information",
    "evaluator": {
        "type": "llm",
        "model": "gpt-4",
        "version": "1.0"
    },
    "metadata": {
        "dataset": "production_sample",
        "evaluationRun": "weekly_check_2024_01_15"
    }
}

Evaluations API

The Evaluations API provides endpoints for submitting quality scores and feedback, and querying evaluation results.

Endpoints Overview

Method	Endpoint	Description	Auth
POST	`/v1/evaluations`	Submit evaluation scores	API Key
GET	`/api/v1/evaluations`	List evaluations	JWT
GET	`/api/v1/evaluations/{evaluationId}`	Get evaluation details	JWT
POST	`/api/v1/evaluations/batch`	Batch create evaluations	JWT
DELETE	`/api/v1/evaluations/{evaluationId}`	Delete evaluation	JWT

Submit Evaluation

POST /v1/evaluations

Submit evaluation scores for traces or spans.

Authentication

Header: X-API-Key: bk_your_api_key
Required Scope: evaluations:write

Request Body

{
  "traceId": "0af7651916cd43dd8448eb211c80319c",
  "spanId": "b7ad6b7169203331",
  "evaluations": [
    {
      "name": "relevance",
      "score": 0.95,
      "type": "numeric",
      "comment": "Response directly addresses the question"
    },
    {
      "name": "hallucination",
      "score": 0.0,
      "type": "boolean",
      "comment": "No factual errors detected"
    },
    {
      "name": "quality",
      "value": "excellent",
      "type": "categorical",
      "options": ["poor", "fair", "good", "excellent"]
    }
  ],
  "evaluator": {
    "type": "llm",
    "model": "gpt-4",
    "version": "1.0"
  },
  "metadata": {
    "dataset": "test_set_v1",
    "runId": "run_abc123"
  }
}

Evaluation Types

Type	Score Range	Description
`numeric`	0.0 - 1.0	Continuous score
`boolean`	0 or 1	Binary pass/fail
`categorical`	string	One of predefined options
`integer`	1-5, 1-10, etc.	Discrete rating scale

Response

{
  "data": {
    "id": "eval_abc123",
    "traceId": "0af7651916cd43dd8448eb211c80319c",
    "spanId": "b7ad6b7169203331",
    "evaluations": [
      {
        "name": "relevance",
        "score": 0.95,
        "type": "numeric"
      }
    ],
    "createdAt": "2024-01-15T10:31:00Z"
  }
}

Examples

curl -X POST https://api.brokle.com/v1/evaluations \
  -H "Content-Type: application/json" \
  -H "X-API-Key: bk_your_api_key" \
  -d '{
    "traceId": "0af7651916cd43dd8448eb211c80319c",
    "evaluations": [
      {
        "name": "relevance",
        "score": 0.95,
        "type": "numeric"
      },
      {
        "name": "hallucination",
        "score": 0.0,
        "type": "boolean"
      }
    ]
  }'

import requests

response = requests.post(
    "https://api.brokle.com/v1/evaluations",
    headers={
        "Content-Type": "application/json",
        "X-API-Key": "bk_your_api_key"
    },
    json={
        "traceId": "0af7651916cd43dd8448eb211c80319c",
        "evaluations": [
            {
                "name": "relevance",
                "score": 0.95,
                "type": "numeric",
                "comment": "Highly relevant response"
            },
            {
                "name": "factual_accuracy",
                "score": 1.0,
                "type": "boolean"
            }
        ],
        "evaluator": {
            "type": "llm",
            "model": "gpt-4"
        }
    }
)

print(response.json())

const response = await fetch('https://api.brokle.com/v1/evaluations', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'X-API-Key': 'bk_your_api_key'
  },
  body: JSON.stringify({
    traceId: '0af7651916cd43dd8448eb211c80319c',
    evaluations: [
      {
        name: 'relevance',
        score: 0.95,
        type: 'numeric'
      }
    ],
    evaluator: {
      type: 'llm',
      model: 'gpt-4'
    }
  })
});

const data = await response.json();
console.log(data);

List Evaluations

GET /api/v1/evaluations

Retrieve evaluations with filtering and pagination.

Authentication

Header: Authorization: Bearer {jwt_token}

Query Parameters

Parameter	Type	Required	Description
`projectId`	string	Yes	Filter by project
`traceId`	string	No	Filter by trace
`spanId`	string	No	Filter by span
`name`	string	No	Filter by evaluation name
`evaluatorType`	string	No	`llm`, `human`, `automated`
`minScore`	float	No	Minimum score filter
`maxScore`	float	No	Maximum score filter
`startTime`	ISO 8601	No	Start of time range
`endTime`	ISO 8601	No	End of time range
`page`	integer	No	Page number
`pageSize`	integer	No	Items per page

Response

{
  "data": [
    {
      "id": "eval_abc123",
      "traceId": "0af7651916cd43dd8448eb211c80319c",
      "spanId": "b7ad6b7169203331",
      "name": "relevance",
      "score": 0.95,
      "type": "numeric",
      "comment": "Highly relevant response",
      "evaluator": {
        "type": "llm",
        "model": "gpt-4"
      },
      "createdAt": "2024-01-15T10:31:00Z"
    }
  ],
  "pagination": {
    "page": 1,
    "pageSize": 20,
    "totalItems": 150,
    "totalPages": 8
  }
}

Get Evaluation

GET /api/v1/evaluations/{evaluationId}

Retrieve details of a specific evaluation.

Response

{
  "data": {
    "id": "eval_abc123",
    "traceId": "0af7651916cd43dd8448eb211c80319c",
    "spanId": "b7ad6b7169203331",
    "name": "relevance",
    "score": 0.95,
    "type": "numeric",
    "comment": "Highly relevant response addressing the user's question directly",
    "evaluator": {
      "type": "llm",
      "model": "gpt-4",
      "version": "1.0",
      "prompt": "Evaluate the relevance of the response..."
    },
    "metadata": {
      "dataset": "test_set_v1",
      "runId": "run_abc123"
    },
    "trace": {
      "id": "trace_xyz",
      "name": "chat_completion",
      "input": {
        "messages": [{"role": "user", "content": "..."}]
      },
      "output": {
        "content": "..."
      }
    },
    "createdAt": "2024-01-15T10:31:00Z",
    "updatedAt": "2024-01-15T10:31:00Z"
  }
}

Batch Create Evaluations

POST /api/v1/evaluations/batch

Create multiple evaluations in a single request.

Request Body

{
  "evaluations": [
    {
      "traceId": "trace_1",
      "name": "relevance",
      "score": 0.9,
      "type": "numeric"
    },
    {
      "traceId": "trace_2",
      "name": "relevance",
      "score": 0.85,
      "type": "numeric"
    },
    {
      "traceId": "trace_3",
      "name": "relevance",
      "score": 0.95,
      "type": "numeric"
    }
  ],
  "evaluator": {
    "type": "llm",
    "model": "gpt-4"
  }
}

Response

{
  "data": {
    "created": 3,
    "failed": 0,
    "evaluations": [
      {"id": "eval_1", "traceId": "trace_1", "score": 0.9},
      {"id": "eval_2", "traceId": "trace_2", "score": 0.85},
      {"id": "eval_3", "traceId": "trace_3", "score": 0.95}
    ]
  }
}

Evaluation Aggregations

Aggregate Scores

GET /api/v1/evaluations/aggregate

Aggregate evaluation scores for analytics.

Query Parameters

Parameter	Type	Description
`projectId`	string	Project ID (required)
`name`	string	Evaluation name (required)
`groupBy`	string	`model`, `day`, `week`, `evaluator`
`startTime`	ISO 8601	Start of time range
`endTime`	ISO 8601	End of time range

Response

{
  "data": {
    "name": "relevance",
    "overall": {
      "mean": 0.87,
      "median": 0.90,
      "stdDev": 0.12,
      "min": 0.45,
      "max": 1.0,
      "count": 1500,
      "p50": 0.90,
      "p90": 0.98,
      "p99": 1.0
    },
    "byGroup": [
      {
        "group": "gpt-4",
        "mean": 0.92,
        "count": 500
      },
      {
        "group": "gpt-3.5-turbo",
        "mean": 0.82,
        "count": 1000
      }
    ],
    "trend": [
      {
        "date": "2024-01-15",
        "mean": 0.85,
        "count": 150
      },
      {
        "date": "2024-01-16",
        "mean": 0.88,
        "count": 175
      }
    ]
  }
}

Human Feedback

Submit human feedback through the dashboard API:

POST /api/v1/feedback

Request Body

{
  "traceId": "0af7651916cd43dd8448eb211c80319c",
  "type": "thumbs",
  "value": "up",
  "comment": "Great response!",
  "userId": "user_123",
  "metadata": {
    "source": "chatbot",
    "sessionId": "session_456"
  }
}

Feedback Types

Type	Values	Description
`thumbs`	`up`, `down`	Simple thumbs up/down
`rating`	1-5	Star rating
`categorical`	custom	Predefined categories
`text`	string	Free-form feedback

Response

{
  "data": {
    "id": "feedback_abc123",
    "traceId": "0af7651916cd43dd8448eb211c80319c",
    "type": "thumbs",
    "value": "up",
    "createdAt": "2024-01-15T10:32:00Z"
  }
}

Built-in Evaluators

Brokle provides built-in evaluators accessible via API:

POST /api/v1/evaluators/run

Request Body

{
  "evaluator": "relevance",
  "traceIds": ["trace_1", "trace_2", "trace_3"],
  "options": {
    "model": "gpt-4",
    "threshold": 0.8
  }
}

Available Evaluators

Evaluator	Description	Output
`relevance`	Response relevance to query	0.0 - 1.0
`hallucination`	Factual accuracy check	boolean
`toxicity`	Harmful content detection	0.0 - 1.0
`coherence`	Response coherence	0.0 - 1.0
`completeness`	Answer completeness	0.0 - 1.0
`groundedness`	Grounded in context	0.0 - 1.0

Response

{
  "data": {
    "runId": "run_abc123",
    "evaluator": "relevance",
    "results": [
      {
        "traceId": "trace_1",
        "score": 0.95,
        "reasoning": "Response directly addresses..."
      },
      {
        "traceId": "trace_2",
        "score": 0.72,
        "reasoning": "Partially relevant but..."
      }
    ],
    "summary": {
      "mean": 0.84,
      "passRate": 0.67
    }
  }
}

Custom Evaluators

POST /api/v1/evaluators/custom

Request Body

{
  "name": "custom_relevance",
  "type": "llm",
  "prompt": "Evaluate the relevance of the assistant's response to the user's question.\n\nQuestion: {{input}}\nResponse: {{output}}\n\nScore from 0 to 1:",
  "model": "gpt-4",
  "outputParser": {
    "type": "numeric",
    "min": 0,
    "max": 1
  },
  "metadata": {
    "version": "1.0",
    "author": "team@company.com"
  }
}

Custom evaluators are project-scoped and can be shared across your organization.

Evaluation Datasets

Create datasets for systematic evaluation:

POST /api/v1/datasets

Request Body

{
  "name": "qa_test_set",
  "description": "Question-answering test dataset",
  "items": [
    {
      "input": {"question": "What is AI?"},
      "expectedOutput": {"answer": "AI is..."},
      "metadata": {"difficulty": "easy"}
    }
  ]
}

Run Evaluation on Dataset

POST /api/v1/datasets/{datasetId}/evaluate

{
  "evaluators": ["relevance", "hallucination"],
  "model": "gpt-4",
  "options": {
    "temperature": 0.7
  }
}

Webhooks

Configure webhooks for evaluation events:

{
  "url": "https://your-server.com/webhook",
  "events": ["evaluation.created", "evaluation.threshold_breached"],
  "filters": {
    "evaluationNames": ["relevance", "hallucination"],
    "thresholds": {
      "relevance": {"min": 0.8},
      "hallucination": {"max": 0.1}
    }
  }
}

Best Practices

Consistent Naming

Use consistent evaluation names across your project:

# Good: Consistent naming convention
evaluations = [
    {"name": "qa_relevance", "score": 0.9},
    {"name": "qa_accuracy", "score": 0.95},
    {"name": "qa_completeness", "score": 0.85}
]

# Bad: Inconsistent naming
evaluations = [
    {"name": "relevance", "score": 0.9},
    {"name": "Accuracy Score", "score": 0.95},
    {"name": "complete", "score": 0.85}
]

Include Context

Always include helpful context:

{
    "name": "relevance",
    "score": 0.72,
    "comment": "Response addresses the question but includes unnecessary information",
    "evaluator": {
        "type": "llm",
        "model": "gpt-4",
        "version": "1.0"
    },
    "metadata": {
        "dataset": "production_sample",
        "evaluationRun": "weekly_check_2024_01_15"
    }
}

Evaluations API

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Evaluations API

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