/**
 * Reasoning-Enhanced RAG System
 *
 * This module enhances the existing RAG system by adding reasoning capabilities
 * using Xenova transformers during the document retrieval process.
 * It can perform summarization, question-answering, and analysis on retrieved chunks.
 */

import type { Document } from '@langchain/core/documents';
import { queryVectorStore } from './simple-pdf-indexer.js';
import log from '../../logger.js';

/**
 * Reasoning task types supported by the system
 */
export type ReasoningTask =
  'summarization' | 'question-answering' | 'analysis' | 'classification';

/**
 * Enhanced RAG response with reasoning
 */
export interface EnhancedRAGResponse {
  query: string;
  retrievedChunks: Document[];
  reasoningResults: ReasoningResult[];
  summary: string;
  answer: string;
  sources: string[];
}

/**
 * Configuration for reasoning models
 */
interface ReasoningConfig {
  task: ReasoningTask;
  modelName: string;
  maxLength?: number;
  minLength?: number;
}

/**
 * Result from reasoning process
 */
interface ReasoningResult {
  originalChunk: string;
  reasoningOutput: string;
  confidence?: number;
  metadata: Record<string, any>;
}

/**
 * Reasoning-enhanced RAG processor
 */
export class ReasoningRAG {
  private transformers: any = null;
  private models: Map<string, any> = new Map();
  private isInitialized: boolean = false;

  constructor() {
    this.initializeTransformers();
  }

  /**
   * Initialize the transformers library dynamically
   */
  private async initializeTransformers(): Promise<void> {
    if (this.transformers) {
      return;
    }

    try {
      // Use eval to avoid CommonJS/ESM conflict during compilation
      const importTransformers = new Function(
        'return import("@xenova/transformers")'
      );
      this.transformers = await importTransformers();
      this.isInitialized = true;
      log.info('Xenova transformers initialized for reasoning');
    } catch (error) {
      log.error('Error importing @xenova/transformers:', error);
      throw new Error(
        `Failed to import @xenova/transformers: ${error instanceof Error ? error.message : String(error)}`
      );
    }
  }

  /**
   * Get or create a model pipeline for a specific task
   */
  private async getModel(config: ReasoningConfig): Promise<any> {
    const modelKey = `${config.task}-${config.modelName}`;

    if (this.models.has(modelKey)) {
      return this.models.get(modelKey);
    }

    await this.initializeTransformers();

    log.info(`Loading model for ${config.task}: ${config.modelName}`);

    try {
      const model = await this.transformers.pipeline(
        config.task,
        config.modelName
      );
      this.models.set(modelKey, model);
      log.info(`Successfully loaded model: ${config.modelName}`);
      return model;
    } catch (error) {
      log.error(`Error loading model ${config.modelName}:`, error);
      throw new Error(
        `Failed to load model: ${error instanceof Error ? error.message : String(error)}`
      );
    }
  }

  /**
   * Perform reasoning on a text chunk
   */
  private async performReasoning(
    text: string,
    config: ReasoningConfig,
    query?: string
  ): Promise<ReasoningResult> {
    const model = await this.getModel(config);

    try {
      let reasoningOutput: string;
      let confidence: number | undefined;

      switch (config.task) {
        case 'summarization':
          const summaryResult = await model(text, {
            max_length: config.maxLength || 150,
            min_length: config.minLength || 30,
            do_sample: false,
          });
          reasoningOutput = summaryResult[0].summary_text;
          confidence = summaryResult[0].score;
          break;

        case 'question-answering':
          if (!query) {
            throw new Error('Query is required for question-answering task');
          }
          const qaResult = await model({
            question: query,
            context: text,
          });
          reasoningOutput = qaResult.answer;
          confidence = qaResult.score;
          break;

        case 'analysis':
          // For analysis, we can use a text generation model
          const analysisResult = await model(
            `Analyze the following text and provide key insights:\n\n${text}\n\nAnalysis:`,
            {
              max_length: config.maxLength || 200,
              temperature: 0.7,
              do_sample: true,
            }
          );
          reasoningOutput =
            analysisResult[0].generated_text.split('Analysis:')[1]?.trim() ||
            'No analysis generated';
          break;

        case 'classification':
          // For classification, we can use a classification model
          const classResult = await model(text);
          reasoningOutput = `Classification: ${classResult[0].label} (confidence: ${classResult[0].score.toFixed(3)})`;
          confidence = classResult[0].score;
          break;

        default:
          throw new Error(`Unsupported reasoning task: ${config.task}`);
      }

      return {
        originalChunk: text,
        reasoningOutput,
        confidence,
        metadata: {
          task: config.task,
          model: config.modelName,
          timestamp: new Date().toISOString(),
        },
      };
    } catch (error) {
      log.error(`Error performing reasoning with ${config.task}:`, error);
      return {
        originalChunk: text,
        reasoningOutput: `Error during reasoning: ${error instanceof Error ? error.message : String(error)}`,
        metadata: {
          task: config.task,
          model: config.modelName,
          error: true,
          timestamp: new Date().toISOString(),
        },
      };
    }
  }

  /**
   * Process multiple chunks with reasoning in parallel
   */
  private async processChunksWithReasoning(
    chunks: Document[],
    configs: ReasoningConfig[],
    query?: string
  ): Promise<ReasoningResult[]> {
    const results: ReasoningResult[] = [];

    // Process chunks in batches to avoid overwhelming the system
    const batchSize = 5;
    for (let i = 0; i < chunks.length; i += batchSize) {
      const batch = chunks.slice(i, i + batchSize);

      const batchPromises = batch.flatMap((chunk) =>
        configs.map((config) =>
          this.performReasoning(chunk.pageContent, config, query)
        )
      );

      const batchResults = await Promise.all(batchPromises);
      results.push(...batchResults);

      // Log progress for large batches
      if (chunks.length > batchSize) {
        log.info(
          `Processed reasoning for ${Math.min(i + batchSize, chunks.length)}/${chunks.length} chunks`
        );
      }
    }

    return results;
  }

  /**
   * Generate a comprehensive summary from reasoning results
   */
  private async generateComprehensiveSummary(
    reasoningResults: ReasoningResult[],
    query: string
  ): Promise<string> {
    // Extract all reasoning outputs
    const summaries = reasoningResults
      .filter((result) => result.metadata.task === 'summarization')
      .map((result) => result.reasoningOutput);

    const analyses = reasoningResults
      .filter((result) => result.metadata.task === 'analysis')
      .map((result) => result.reasoningOutput);

    const qaResults = reasoningResults
      .filter((result) => result.metadata.task === 'question-answering')
      .map((result) => result.reasoningOutput);

    // Combine all insights
    let comprehensiveSummary = `## Query: ${query}\n\n`;

    if (summaries.length > 0) {
      comprehensiveSummary += `### Key Summaries:\n${summaries.map((s, i) => `${i + 1}. ${s}`).join('\n')}\n\n`;
    }

    if (analyses.length > 0) {
      comprehensiveSummary += `### Analysis Insights:\n${analyses.map((a, i) => `${i + 1}. ${a}`).join('\n')}\n\n`;
    }

    if (qaResults.length > 0) {
      comprehensiveSummary += `### Direct Answers:\n${qaResults.map((qa, i) => `${i + 1}. ${qa}`).join('\n')}\n\n`;
    }

    return comprehensiveSummary;
  }

  /**
   * Enhanced RAG query with reasoning capabilities
   */
  async queryWithReasoning(
    query: string,
    options: {
      topK?: number;
      reasoningTasks?: ReasoningTask[];
      customConfigs?: ReasoningConfig[];
    } = {}
  ): Promise<EnhancedRAGResponse> {
    const {
      topK = 50,
      reasoningTasks = ['summarization', 'question-answering'],
      customConfigs,
    } = options;

    try {
      log.info(`Starting reasoning-enhanced RAG query: "${query}"`);

      // Step 1: Retrieve relevant chunks using existing RAG
      log.info(`Retrieving top ${topK} relevant chunks...`);
      const retrievedChunks = await queryVectorStore(query, topK);

      if (!retrievedChunks || retrievedChunks.length === 0) {
        return {
          query,
          retrievedChunks: [],
          reasoningResults: [],
          summary: 'No relevant information found in the documentation.',
          answer: 'No relevant information found to answer your query.',
          sources: [],
        };
      }

      log.info(`Retrieved ${retrievedChunks.length} chunks for reasoning`);

      // Step 2: Configure reasoning models
      const configs: ReasoningConfig[] = customConfigs || [
        // Summarization using T5
        {
          task: 'summarization',
          modelName: 'Xenova/t5-small',
          maxLength: 150,
          minLength: 30,
        },
        // Question answering using DistilBERT
        {
          task: 'question-answering',
          modelName: 'Xenova/distilbert-base-cased-distilled-squad',
        },
      ];

      // Filter configs based on requested tasks
      const filteredConfigs = configs.filter((config) =>
        reasoningTasks.includes(config.task)
      );

      // Step 3: Perform reasoning on retrieved chunks
      log.info(
        `Performing reasoning with ${filteredConfigs.length} different models...`
      );
      const reasoningResults = await this.processChunksWithReasoning(
        retrievedChunks,
        filteredConfigs,
        query
      );

      // Step 4: Generate comprehensive summary
      log.info('Generating comprehensive summary...');
      const summary = await this.generateComprehensiveSummary(
        reasoningResults,
        query
      );

      // Step 5: Extract best answer from reasoning results
      const qaResults = reasoningResults.filter(
        (result) =>
          result.metadata.task === 'question-answering' &&
          !result.metadata.error
      );

      const bestAnswer =
        qaResults.length > 0
          ? qaResults.sort(
              (a, b) => (b.confidence || 0) - (a.confidence || 0)
            )[0].reasoningOutput
          : summary;

      // Step 6: Extract sources
      const sources = retrievedChunks
        .map(
          (doc: any) =>
            doc.metadata?.relativePath ||
            doc.metadata?.filename ||
            doc.metadata?.source
        )
        .filter(
          (source: any, index: number, arr: any[]) =>
            source && arr.indexOf(source) === index
        );

      log.info(
        `Reasoning-enhanced RAG completed. Generated ${reasoningResults.length} reasoning results from ${sources.length} sources`
      );

      return {
        query,
        retrievedChunks,
        reasoningResults,
        summary,
        answer: bestAnswer,
        sources,
      };
    } catch (error) {
      log.error('Error in reasoning-enhanced RAG:', error);
      throw new Error(
        `Reasoning-enhanced RAG failed: ${error instanceof Error ? error.message : String(error)}`
      );
    }
  }

  /**
   * Get available reasoning models and their capabilities
   */
  getAvailableModels(): Record<ReasoningTask, string[]> {
    return {
      summarization: [
        'Xenova/t5-small',
        'Xenova/t5-base',
        'Xenova/bart-large-cnn',
      ],
      'question-answering': [
        'Xenova/distilbert-base-cased-distilled-squad',
        'Xenova/roberta-base-squad2',
      ],
      analysis: ['Xenova/gpt2', 'Xenova/distilgpt2'],
      classification: [
        'Xenova/distilbert-base-uncased-finetuned-sst-2-english',
        'Xenova/bert-base-uncased',
      ],
    };
  }

  /**
   * Clean up resources
   */
  async cleanup(): Promise<void> {
    this.models.clear();
    log.info('Reasoning RAG resources cleaned up');
  }
}

// Export a singleton instance
export const reasoningRAG = new ReasoningRAG();