TranscriptionTools MCP Server

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import { FileHandler } from '../utils/file-handler.js';
import { Logger } from '../utils/logger.js';

/**
 * Interface for the summary_text function parameters
 */
export interface SummaryTextParams {
  input_text: string;
  is_file_path?: boolean;
  constraint_type?: 'time' | 'chars' | 'words' | null;
  constraint_value?: number | null;
}

/**
 * Generate intelligent summaries of processed transcripts using ACE cognitive methodology
 * @param params - Parameters for the summary process
 * @returns Object containing the summary text
 */
export async function summaryText(params: SummaryTextParams): Promise<{ summary: string }> {
  try {
    const { 
      input_text, 
      is_file_path = false, 
      constraint_type = null,
      constraint_value = null
    } = params;
    
    // Resolve content (either direct text or from file)
    const textContent = await FileHandler.resolveTextContent(input_text, is_file_path);
    
    // Create a session ID and logger
    const logger = new Logger();
    const sessionId = logger.getSessionId();
    
    // In a real implementation, this would be a sophisticated NLP pipeline
    // using deep learning models for summarization. Here we'll simulate the process.
    
    // ACE-Driven Processing
    // Step 1: Priming Stage
    const contentLength = textContent.length;
    const wordCount = textContent.split(/\s+/).length;
    
    // Determine target length based on constraint
    let targetLength: number;
    if (constraint_type === 'time') {
      // Base rate: 150 words/minute
      const baseRate = 150;
      targetLength = constraint_value ? constraint_value * baseRate / 60 : wordCount * 0.3;
    } else if (constraint_type === 'chars') {
      targetLength = constraint_value ? constraint_value : contentLength * 0.3;
    } else if (constraint_type === 'words') {
      targetLength = constraint_value ? constraint_value : wordCount * 0.3;
    } else {
      // Default constraint: 30% of original
      targetLength = wordCount * 0.3;
    }
    
    // Simulate domain context identification
    const primingFactors = [
      'Document length analysis',
      'Content type recognition',
      'Terminological evaluation',
      'Priority patterns identified'
    ];
    
    // Step 2: Comprehension Stage
    const paragraphs = textContent.split(/\n\n+/);
    
    // Simulate comprehension metrics
    const comprehensionMetrics = {
      coreThemeCount: Math.min(5, paragraphs.length),
      relationshipNodes: paragraphs.length * 2,
      causalChains: Math.floor(paragraphs.length / 2),
      hierarchyLevels: 3
    };
    
    // Step 3: Context Clarification Stage
    const sentences = textContent.match(/[^.!?]+[.!?]+/g) || [];
    
    // Map importance scores to sentences (simulated)
    const sentenceScores = sentences.map((sentence, index) => {
      // In a real implementation, this would use NLP models to score importance
      // Here we'll use position and length as proxies for importance
      const positionScore = 1 - (index / sentences.length); // Early sentences get higher scores
      const lengthScore = Math.min(1, sentence.length / 100); // Longer sentences (up to a point) get higher scores
      return { sentence, score: (positionScore * 0.7) + (lengthScore * 0.3) };
    });
    
    // Sort by importance score
    sentenceScores.sort((a, b) => b.score - a.score);
    
    // Simulated context maps 
    const contextMaps = {
      semanticUnits: sentences.length,
      densityEvaluation: 'Completed',
      dependencyGraph: 'Generated',
      narrativeThreads: 'Mapped'
    };
    
    // Step 4: Expanding Stage
    // Select top sentences based on constraint
    let selectedSentences: string[] = [];
    let currentLength = 0;
    const targetMetric = constraint_type === 'chars' ? 'chars' : 'words';
    
    // Keep adding sentences until we hit the target length
    for (const item of sentenceScores) {
      const sentenceLength = targetMetric === 'chars' 
        ? item.sentence.length 
        : item.sentence.split(/\s+/).length;
        
      if (currentLength + sentenceLength <= targetLength) {
        selectedSentences.push(item.sentence);
        currentLength += sentenceLength;
      } else if (selectedSentences.length === 0) {
        // Always include at least one sentence
        selectedSentences.push(item.sentence);
        break;
      } else {
        break;
      }
    }
    
    // Step 5: Recursive Stage
    // In a real implementation, this would iteratively refine the summary
    // For simulation, we'll just reorder the sentences to follow the original text flow
    
    // Get original order of selected sentences
    const sentenceIndexes = selectedSentences.map(sentence => {
      return sentences.findIndex(s => s === sentence);
    });
    
    // Sort by original position
    const sortedIndexes = [...sentenceIndexes].sort((a, b) => a - b);
    selectedSentences = sortedIndexes.map(index => {
      return sentences[index];
    });
    
    // Join the selected sentences to form the summary
    let summary = selectedSentences.join(' ').trim();
    
    // Simulate refinement
    const expansionIterations = 3;
    const recursiveOptimizations = 2;
    
    // Log the summary process
    const constraintDetails = {
      type: constraint_type,
      target: constraint_value,
      achieved: currentLength
    };
    
    const processStats = {
      primingFactors,
      comprehensionMetrics,
      contextMaps,
      expansionIterations,
      recursiveOptimizations
    };
    
    await logger.logSummaryProcess(constraintDetails, processStats);
    
    return { summary };
  } catch (error) {
    throw new Error(`Summary process failed: ${error instanceof Error ? error.message : String(error)}`);
  }
}