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Introduction

orq.ai exposes API to manipulate Evaluators. These APIs are used to manage Evaluators programmatically. In this page we’ll see the common use cases for creating, and fetching Evaluators through the API.

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Prerequisite

To get started, an API key is needed to use within SDKs or HTTP API.
To get an API key ready, see Authentication.

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SDKs

Node.js

Python

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Creating an Evaluator

To create an Evaluator we’ll use the Create an Evaluator API call. We then need to decide what type of Evaluator we’ll create:

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HTTP Evaluator

Here is a valid payload to create an HTTP evaluator: 
{
  "type": "http_eval",
  "method": "POST",
  "headers": {
    "header-key": "header-value"
  },
  "payload": {
    "body-key": "body-value"
  },
  "url": "https://myevaluatorendpoint.com/api",
  "path": "Default/Evaluators",
  "key": "MyEvaluator"
}
To learn more about building HTTP Evaluators, see Creating an HTTP Evaluator.

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JSON Evaluator

Here is a valid payload to create a JSON evaluator:
Make sure to correctly escape the JSON Schema payload. 
{
  "guardrail_config": {
    "enabled": true,
    "type": "boolean",
    "value": true
  },
  "type": "json_schema",
  "schema": "{   \"$schema\": \"http://json-schema.org/draft-07/schema#\",   \"$id\": \"https://example.com/person.schema.json\",   \"title\": \"Person\",   \"description\": \"A person object\",   \"type\": \"object\",   \"properties\": {     \"firstName\": {       \"type\": \"string\",       \"description\": \"The person's first name\"     },     \"lastName\": {       \"type\": \"string\",       \"description\": \"The person's last name\"     },     \"age\": {       \"type\": \"integer\",       \"minimum\": 0,       \"maximum\": 150,       \"description\": \"Age in years\"     },     \"email\": {       \"type\": \"string\",       \"format\": \"email\",       \"description\": \"Email address\"     },     \"address\": {       \"type\": \"object\",       \"properties\": {         \"street\": {           \"type\": \"string\"         },         \"city\": {           \"type\": \"string\"         },         \"zipCode\": {           \"type\": \"string\",           \"pattern\": \"^[0-9]{5}(-[0-9]{4})?$\"         }       },       \"required\": [\"street\", \"city\", \"zipCode\"]     },     \"hobbies\": {       \"type\": \"array\",       \"items\": {         \"type\": \"string\"       },       \"uniqueItems\": true     }   },   \"required\": [\"firstName\", \"lastName\", \"email\"],   \"additionalProperties\": false }"
}
To learn more about building JSON Evaluators, see Creating a JSON Evaluator.

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LLM Evaluator

Here is a valid payload to create an LLM evaluator: 
{
  "type": "llm_eval",
  "prompt": "Give a number response from 0 to 1, 0 for innapropriate, 10 for perfectly appropriate {{log.output}}",
  "path": "Default/evaluators",
  "model": "openai/gpt-4o",
  "key": "myKey"
}
To learn more about building LLM Evaluators, see Creating an LLM Evaluator.

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Python Evaluator

Here’s a valid Python Evaluator:
Use \n to indicate newlines in code. 
{
  "type": "python_eval",
  "path": "Default/Evaluators",
  "key": "MyEvaluator",
  "code": "def evaluate(log):\n  output_size = len(log[\"output\"])\n  reference_size = len(log[\"reference\"])\n  return abs(output_size - reference_size)\n"
}
To learn more about building Python Evaluators, see Creating a Python Evaluator.

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Guardrail Configuration

For each Evaluator payload you can also define a guardrail payload looking as follows and add it into the creation payload 
{
  "guardrail_config": {
    "enabled": true,
    "type": "number", // can be also boolean
    "value": 5, // value needs to match type
    "operator": "lte" // defines operator to compare value with
  }
}

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Calling the API

Here’s an example end-to-end API call and response: 
curl --request POST \
     --url https://api.orq.ai/v2/evaluators \
     --header 'accept: application/json' \
     --header 'authorization: Bearer ORQ_API_KEY' \
     --header 'content-type: application/json' \
     --data '
{
  "guardrail_config": {
    "enabled": true,
    "type": "number",
    "value": 5,
    "operator": "lte"
  },
  "type": "python_eval",
  "path": "Default/Evaluators",
  "key": "MyEvaluator",
  "code": "def evaluate(log):\n  output_size = len(log[\"output\"])\n  reference_size = len(log[\"reference\"])\n  return abs(output_size - reference_size)\n"
}
'
The expected response is the following: 
{
  "_id":"EVALUATOR_ID",
  "key":"MyEvaluator",
  "description":"",
  "created":"2025-06-26T11:37:02.132Z",
  "updated":"2025-06-26T11:37:02.132Z",
  "type":"python_eval",
  "code":"def evaluate(log):\n  output_size = len(log[\"output\"])\n  reference_size = len(log[\"reference\"])\n  return abs(output_size - reference_size)\n"
}

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Listing Evaluators

To list evaluators we’re using the Listing Evaluators API. We’re making the following call: 
curl --request GET \
     --url https://api.orq.ai/v2/evaluators \
     --header 'accept: application/json' \
     --header 'authorization: Bearer ORQ_API_KEY'
The resulting payload is the following: 
{
  "object": "list",
  "data": [
    {
      "_id": "EVALUATOR_ID",
      "key": "BERT Score",
      "description": "Computes the similarity of two sentences as a sum of cosine similarities between their tokens embeddings",
      "created": "2024-12-16T12:36:40.359Z",
      "updated": "2024-12-16T12:36:40.359Z",
      "guardrail_config": {
        "enabled": false,
        "type": "number",
        "value": 0.3,
        "operator": "gt"
      },
      "type": "function_eval",
      "function_params": {
        "type": "bert_score"
      }
    },
    ...
}
Creating an Evaluator Evaluator Library