@genkit-ai/checks/lib/evaluation.mjs

Google Checks AI Safety plugins for classifying the safety of text against Checks AI safety policies.

import { z } from "genkit";
import { runInNewSpan } from "genkit/tracing";
import {
  isConfig
} from "./metrics";
function checksEvaluators(ai, auth, metrics, projectId) {
  const policy_configs = metrics.map(
    (metric) => {
      const metricType = isConfig(metric) ? metric.type : metric;
      const threshold = isConfig(metric) ? metric.threshold : void 0;
      return {
        type: metricType,
        threshold
      };
    }
  );
  return createPolicyEvaluator(projectId, auth, ai, policy_configs);
}
const ResponseSchema = z.object({
  policyResults: z.array(
    z.object({
      policyType: z.string(),
      score: z.number(),
      violationResult: z.string()
    })
  )
});
function createPolicyEvaluator(projectId, auth, ai, policy_config) {
  return ai.defineEvaluator(
    {
      name: "checks/guardrails",
      displayName: "checks/guardrails",
      definition: `Evaluates input text against the Checks ${policy_config.map((policy) => policy.type)} policies.`
    },
    async (datapoint) => {
      const partialRequest = {
        input: {
          text_input: {
            content: datapoint.output
          }
        },
        policies: policy_config.map((config) => {
          return {
            policy_type: config.type,
            threshold: config.threshold
          };
        })
      };
      const response = await checksEvalInstance(
        ai,
        projectId,
        auth,
        partialRequest,
        ResponseSchema
      );
      const evaluationResults = response.policyResults.map((result) => {
        return {
          id: result.policyType,
          score: result.score,
          details: {
            reasoning: `Status ${result.violationResult}`
          }
        };
      });
      return {
        evaluation: evaluationResults,
        testCaseId: datapoint.testCaseId
      };
    }
  );
}
async function checksEvalInstance(ai, projectId, auth, partialRequest, responseSchema) {
  return await runInNewSpan(
    ai,
    {
      metadata: {
        name: "EvaluationService#evaluateInstances"
      }
    },
    async (metadata, _otSpan) => {
      const request = {
        ...partialRequest
      };
      metadata.input = request;
      const client = await auth.getClient();
      const url = "https://checks.googleapis.com/v1alpha/aisafety:classifyContent";
      const response = await client.request({
        url,
        method: "POST",
        body: JSON.stringify(request),
        headers: {
          "x-goog-user-project": projectId,
          "Content-Type": "application/json"
        }
      });
      metadata.output = response.data;
      try {
        return responseSchema.parse(response.data);
      } catch (e) {
        throw new Error(`Error parsing ${url} API response: ${e}`);
      }
    }
  );
}
export {
  checksEvaluators
};
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