import { TOOL_SELECTION_CLASSIFICATION_EVALUATOR_CONFIG } from "../__generated__/default_templates";
import { CreateClassificationEvaluatorArgs } from "../types/evals";
import { ClassificationEvaluator } from "./ClassificationEvaluator";
import { createClassificationEvaluator } from "./createClassificationEvaluator";
export interface ToolSelectionEvaluatorArgs<
RecordType extends Record<string, unknown> = ToolSelectionEvaluationRecord,
> extends Omit<
CreateClassificationEvaluatorArgs<RecordType>,
"promptTemplate" | "choices" | "optimizationDirection" | "name"
> {
optimizationDirection?: CreateClassificationEvaluatorArgs<RecordType>["optimizationDirection"];
name?: CreateClassificationEvaluatorArgs<RecordType>["name"];
choices?: CreateClassificationEvaluatorArgs<RecordType>["choices"];
promptTemplate?: CreateClassificationEvaluatorArgs<RecordType>["promptTemplate"];
}
/**
* A record to be evaluated by the tool selection evaluator.
*/
export type ToolSelectionEvaluationRecord = {
/**
* The input query or conversation context.
*/
input: string;
/**
* The available tools that the LLM could use.
*/
availableTools: string;
/**
* The tool or tools selected by the LLM.
*/
toolSelection: string;
};
/**
* Creates a tool selection evaluator function.
*
* This function returns an evaluator that determines whether the correct tool
* was selected for a given context. Unlike the tool invocation evaluator which
* checks if the tool was called correctly with proper arguments, this evaluator
* focuses on whether the right tool was chosen in the first place.
*
* The evaluator checks for:
* - Whether the LLM chose the best available tool for the user query
* - Whether the tool name exists in the available tools list
* - Whether the correct number of tools were selected for the task
* - Whether the tool selection is safe and appropriate
*
* @param args - The arguments for creating the tool selection evaluator.
* @param args.model - The model to use for classification.
* @param args.choices - The possible classification choices (defaults to correct/incorrect).
* @param args.promptTemplate - The prompt template to use (defaults to TOOL_SELECTION_TEMPLATE).
* @param args.telemetry - The telemetry to use for the evaluator.
*
* @returns An evaluator function that takes a {@link ToolSelectionEvaluationRecord} and returns
* a classification result indicating whether the tool selection is correct or incorrect.
*
* @example
* ```ts
* const evaluator = createToolSelectionEvaluator({ model: openai("gpt-4o-mini") });
*
* const result = await evaluator.evaluate({
* input: "User: What is the weather in San Francisco?",
* availableTools: `WeatherTool: Get the current weather for a location.
* NewsTool: Stay connected to global events with our up-to-date news around the world.
* MusicTool: Create playlists, search for music, and check the latest music trends.`,
* toolSelection: "WeatherTool"
* });
* console.log(result.label); // "correct" or "incorrect"
* ```
*/
export function createToolSelectionEvaluator<
RecordType extends Record<string, unknown> = ToolSelectionEvaluationRecord,
>(
args: ToolSelectionEvaluatorArgs<RecordType>
): ClassificationEvaluator<RecordType> {
const {
choices = TOOL_SELECTION_CLASSIFICATION_EVALUATOR_CONFIG.choices,
promptTemplate = TOOL_SELECTION_CLASSIFICATION_EVALUATOR_CONFIG.template,
optimizationDirection = TOOL_SELECTION_CLASSIFICATION_EVALUATOR_CONFIG.optimizationDirection,
name = TOOL_SELECTION_CLASSIFICATION_EVALUATOR_CONFIG.name,
...rest
} = args;
return createClassificationEvaluator<RecordType>({
...rest,
promptTemplate,
choices,
optimizationDirection,
name,
});
}
