@arizeai/phoenix-evals/dist/esm/utils/bindEvaluator.js

A library for running evaluations for AI use cases

/**
 * Binds an evaluator to a specific data structure using input mapping.
 *
 * This function creates a new evaluator instance that automatically transforms
 * your data structure to match what the evaluator expects. This is particularly
 * useful when your data schema doesn't match the evaluator's expected input format.
 *
 * @param evaluator - The evaluator to bind (e.g., a hallucination evaluator)
 * @param context - The binding context containing the input mapping configuration
 * @returns A new evaluator instance with the input mapping applied
 *
 * @example
 * **Basic usage with simple field mapping:**
 * ```typescript
 * import { bindEvaluator, createHallucinationEvaluator } from "@arizeai/phoenix-evals";
 * import { openai } from "@ai-sdk/openai";
 *
 * type MyData = {
 *   question: string;
 *   context: string;
 *   answer: string;
 * };
 *
 * const evaluator = bindEvaluator<MyData>(
 *   createHallucinationEvaluator({ model: openai("gpt-4") }),
 *   {
 *     inputMapping: {
 *       input: "question",      // Evaluator expects "input", map from "question"
 *       reference: "context",   // Evaluator expects "reference", map from "context"
 *       output: "answer",        // Evaluator expects "output", map from "answer"
 *     },
 *   }
 * );
 *
 * // Now you can evaluate with your data structure
 * const result = await evaluator.evaluate({
 *   question: "What is AI?",
 *   context: "AI is artificial intelligence...",
 *   answer: "AI stands for artificial intelligence",
 * });
 * ```
 *
 * @example
 * **Using nested property access:**
 * ```typescript
 * type ApiResponse = {
 *   request: {
 *     body: {
 *       query: string;
 *       context: string;
 *     };
 *   };
 *   response: {
 *     data: {
 *       text: string;
 *     };
 *   };
 * };
 *
 * const evaluator = bindEvaluator<ApiResponse>(
 *   createHallucinationEvaluator({ model: openai("gpt-4") }),
 *   {
 *     inputMapping: {
 *       input: "request.body.query",
 *       reference: "request.body.context",
 *       output: "response.data.text",
 *     },
 *   }
 * );
 * ```
 *
 * @example
 * **Using function-based mapping for data transformation:**
 * ```typescript
 * type RawData = {
 *   question: string;
 *   contexts: string[];  // Array of context strings
 *   answer: string;
 * };
 *
 * const evaluator = bindEvaluator<RawData>(
 *   createHallucinationEvaluator({ model: openai("gpt-4") }),
 *   {
 *     inputMapping: {
 *       input: "question",
 *       // Transform array to single string
 *       reference: (data) => data.contexts.join("\n\n"),
 *       output: "answer",
 *     },
 *   }
 * );
 * ```
 *
 * @example
 * **Using JSONPath for complex queries:**
 * ```typescript
 * type ComplexData = {
 *   conversation: {
 *     messages: Array<{ role: string; content: string }>;
 *   };
 *   metadata: {
 *     sources: string[];
 *   };
 * };
 *
 * const evaluator = bindEvaluator<ComplexData>(
 *   createHallucinationEvaluator({ model: openai("gpt-4") }),
 *   {
 *     inputMapping: {
 *       // Extract last user message
 *       input: "$.conversation.messages[?(@.role=='user')].content[-1]",
 *       // Extract all sources
 *       reference: "$.metadata.sources[*]",
 *       // Extract last assistant message
 *       output: "$.conversation.messages[?(@.role=='assistant')].content[-1]",
 *     },
 *   }
 * );
 * ```
 *
 * @example
 * **Binding multiple evaluators with different mappings:**
 * ```typescript
 * type EvaluationData = {
 *   userQuery: string;
 *   systemContext: string;
 *   modelOutput: string;
 *   expectedOutput?: string;
 * };
 *
 * // Hallucination evaluator
 * const hallucinationEvaluator = bindEvaluator<EvaluationData>(
 *   createHallucinationEvaluator({ model: openai("gpt-4") }),
 *   {
 *     inputMapping: {
 *       input: "userQuery",
 *       reference: "systemContext",
 *       output: "modelOutput",
 *     },
 *   }
 * );
 *
 * // Document relevancy evaluator (if it exists)
 * const relevancyEvaluator = bindEvaluator<EvaluationData>(
 *   createDocumentRelevanceEvaluator({ model: openai("gpt-4") }),
 *   {
 *     inputMapping: {
 *       query: "userQuery",
 *       document: "systemContext",
 *       output: "modelOutput",
 *     },
 *   }
 * );
 * ```
 */
export function bindEvaluator(evaluator, context) {
    let boundEvaluator = evaluator;
    if (context.inputMapping) {
        boundEvaluator = boundEvaluator.bindInputMapping(context.inputMapping);
    }
    return boundEvaluator;
}
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