Thought Remover Component

Required Parameters

• llmResponse: Raw LLM response text

Output Format

{
  "cleaned_message": string,
  "metadata": {
    "original_length": number,
    "cleaned_length": number,
    "removal_count": {
      "thought_patterns": number,
      "reasoning_blocks": number,
      "metadata_sections": number
    },
    "processing_time": number
  },
  "removed_content": {
    "thoughts": string[],
    "reasoning": string[],
    "metadata": string[]
  },
  "statistics": {
    "reduction_percentage": number,
    "pattern_matches": number,
    "content_preserved": number
  }
}

Features

• Pattern-based removal
• Content preservation
• Metadata extraction
• Performance metrics
• Format validation
• Error handling
• Content statistics
• Removal tracking

Example Usage

const thoughtRemover = new ThoughtRemover();

// Example with thought process
const result1 = await thoughtRemover.clean({
  llmResponse: `
    Let me think about this...
    First, I'll analyze the requirements.
    1. User needs a summary
    2. Content should be concise
    3. Format must be clear
    
    Based on this analysis, here's the summary:
    The project deadline is next week and requires
    three main deliverables.
  `
});

// Example with metadata
const result2 = await thoughtRemover.clean({
  llmResponse: `
    [PROCESSING]
    Analyzing input...
    [CONFIDENCE: 0.95]
    
    The weather forecast shows sunny conditions
    with a high of 75°F tomorrow.
    
    [SOURCES: weather-api, local-data]
  `
});

// Example with reasoning steps
const result3 = await thoughtRemover.clean({
  llmResponse: `
    Step 1: Examine the code
    Found potential memory leak
    
    Step 2: Analyze impact
    Critical for long-running processes
    
    Step 3: Propose solution
    Implement proper cleanup in destructor
    
    Recommendation: Add destructor method to
    handle resource cleanup properly.
  `
});