@plust/datasleuth/dist/steps/analyze.d.ts

Build LLM-powered research pipelines and output structured data.

import { createStep } from '../utils/steps.js';
import { z } from 'zod';
import { LanguageModel } from 'ai';
/**
 * Schema for analysis results
 *
 * Defines the structure for analysis output, including insights, confidence score,
 * supporting evidence, and recommendations.
 *
 * @private
 */
declare const analysisResultSchema: z.ZodObject<{
    focus: z.ZodString;
    insights: z.ZodArray<z.ZodString, "many">;
    confidence: z.ZodNumber;
    supportingEvidence: z.ZodOptional<z.ZodArray<z.ZodString, "many">>;
    limitations: z.ZodOptional<z.ZodArray<z.ZodString, "many">>;
    recommendations: z.ZodOptional<z.ZodArray<z.ZodString, "many">>;
}, "strip", z.ZodTypeAny, {
    confidence: number;
    focus: string;
    insights: string[];
    supportingEvidence?: string[] | undefined;
    limitations?: string[] | undefined;
    recommendations?: string[] | undefined;
}, {
    confidence: number;
    focus: string;
    insights: string[];
    supportingEvidence?: string[] | undefined;
    limitations?: string[] | undefined;
    recommendations?: string[] | undefined;
}>;
export type AnalysisResult = z.infer<typeof analysisResultSchema>;
/**
 * Options for the analysis step
 */
export interface AnalyzeOptions {
    /** Focus area for analysis (e.g., 'market-trends', 'technical-details') */
    focus: string;
    /** Model to use for analysis (from the AI SDK) */
    llm?: LanguageModel;
    /** Temperature for the LLM (0.0 to 1.0) */
    temperature?: number;
    /** Depth of analysis ('basic', 'detailed', 'comprehensive') */
    depth?: 'basic' | 'detailed' | 'comprehensive';
    /** Whether to include supporting evidence in the analysis */
    includeEvidence?: boolean;
    /** Whether to include recommendations in the analysis */
    includeRecommendations?: boolean;
    /** Whether to add the analysis to the final results */
    includeInResults?: boolean;
    /** Custom prompt for analysis */
    customPrompt?: string;
    /** Whether to proceed if no content is available */
    allowEmptyContent?: boolean;
    /** Maximum content size for analysis (in characters) */
    maxContentSize?: number;
    /** Retry configuration for LLM calls */
    retry?: {
        /** Maximum number of retries */
        maxRetries?: number;
        /** Base delay between retries in ms */
        baseDelay?: number;
    };
}
/**
 * Creates an analysis step for the research pipeline
 *
 * This function creates a step that analyzes research data using AI to extract insights,
 * identify patterns, and provide recommendations based on the specified focus area.
 * The step will process available data from previous steps, such as extracted content
 * and fact-checked statements.
 *
 * @param options - Configuration options for the analysis step
 * @param options.focus - The focus area for analysis (e.g., 'market-trends', 'technical-details')
 * @param options.llm - Language model to use (falls back to state.defaultLLM if not provided)
 * @param options.temperature - Temperature setting for the LLM (default: 0.3)
 * @param options.depth - Depth of analysis: 'basic', 'detailed', or 'comprehensive' (default: 'detailed')
 * @param options.includeEvidence - Whether to include supporting evidence (default: true)
 * @param options.includeRecommendations - Whether to include recommendations (default: true)
 * @param options.includeInResults - Whether to include analysis in final results (default: true)
 * @param options.allowEmptyContent - Whether to proceed if no content is available (default: false)
 * @param options.maxContentSize - Maximum content size in characters (default: 10000)
 *
 * @returns A configured analysis step for the research pipeline
 *
 * @example
 * ```typescript
 * import { research, analyze } from '@plust/datasleuth';
 * import { openai } from '@ai-sdk/openai';
 *
 * const results = await research({
 *   query: "Impact of AI on healthcare",
 *   steps: [
 *     // Other steps...
 *     analyze({
 *       focus: 'ethical-considerations',
 *       llm: openai('gpt-4o'),
 *       depth: 'comprehensive',
 *       includeRecommendations: true
 *     })
 *   ],
 *   outputSchema: outputSchema
 * });
 * ```
 */
export declare function analyze(options: AnalyzeOptions): ReturnType<typeof createStep>;
export {};