Create Evaluators

curl --request GET \
     --url https://my.orq.ai/v2/evaluators \
     --header 'accept: application/json' \
     --header 'authorization: Bearer ORQ_API_KEY'

import { Orq } from "@orq-ai/node";

const orq = new Orq({ apiKey: process.env["ORQ_API_KEY"] ?? "" });

const result = await orq.evals.all({});
console.log(result);

from orq_ai_sdk import Orq
import os

with Orq(api_key=os.getenv("ORQ_API_KEY", "")) as orq:
    res = orq.evals.all(limit=10)
    print(res)

curl --request POST \
  --url 'https://my.orq.ai/v3/evaluators/<evaluator_id>/invoke' \
  --header "Authorization: Bearer $ORQ_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "context": {
      "input": {
        "user_query": "What is the capital of France?",
        "expected_output": "Paris"
      },
      "output": {
        "response": "The capital of France is Paris."
      }
    }
  }'

import { Orq } from "@orq-ai/node";

const orq = new Orq({ apiKey: process.env["ORQ_API_KEY"] ?? "" });

const evaluation = await orq.evals.invoke({
    id: "01JN5J8W4J5JP8ZSD0TADK11GJ",
    requestBody: {
        context: {
            input: {
                user_query: "What is the capital of France?",
                expected_output: "Paris"
            },
            output: {
                response: "The capital of France is Paris."
            }
        }
    }
});
console.log(evaluation);

from orq_ai_sdk import Orq
import os

orq = Orq(api_key=os.getenv("ORQ_API_KEY", ""))

evaluation = orq.evals.invoke(
    id="01JN5J8W4J5JP8ZSD0TADK11GJ",
    context={
        "input": {
            "user_query": "What is the capital of France?",
            "expected_output": "Paris",
        },
        "output": {"response": "The capital of France is Paris."},
    },
)
print(evaluation)

npm install @orq-ai/evaluatorq

pip install evaluatorq

import { evaluatorq, job } from "@orq-ai/evaluatorq";

const textAnalyzer = job("text-analyzer", async (data) => {
    const text = data.inputs.text;
    return {
        length: text.length,
        wordCount: text.split(" ").length,
        uppercase: text.toUpperCase(),
    };
});

await evaluatorq("text-analysis", {
    data: [
        { inputs: { text: "Hello world" } },
        { inputs: { text: "Testing evaluation" } },
    ],
    jobs: [textAnalyzer],
    evaluators: [
        {
            name: "length-check",
            scorer: async ({ output }) => {
                const passesCheck = output.length > 10;
                return {
                    value: passesCheck ? 1 : 0,
                    explanation: passesCheck
                        ? "Output length is sufficient"
                        : `Output too short (${output.length} chars, need >10)`,
                };
            },
        },
    ],
});

import asyncio
from evaluatorq import evaluatorq, job, DataPoint, EvaluationResult

@job("text-analyzer")
async def text_analyzer(data: DataPoint, row: int):
    text = data.inputs["text"]
    return {
        "length": len(text),
        "word_count": len(text.split()),
        "uppercase": text.upper(),
    }

async def length_check_scorer(params):
    output = params["output"]
    passes_check = output["length"] > 10
    return EvaluationResult(
        value=1 if passes_check else 0,
        explanation=(
            "Output length is sufficient"
            if passes_check
            else f"Output too short ({output['length']} chars, need >10)"
        )
    )

async def main():
    await evaluatorq(
        "text-analysis",
        data=[
            DataPoint(inputs={"text": "Hello world"}),
            DataPoint(inputs={"text": "Testing evaluation"}),
        ],
        jobs=[text_analyzer],
        evaluators=[{"name": "length-check", "scorer": length_check_scorer}],
    )

if __name__ == "__main__":
    asyncio.run(main())