Building a Specialized LLM for PCB and Electronic Component Design A Comprehensive Guide

// Example: Web scraping component datasheets
import fs from 'fs';
import fetch from 'node-fetch';
import { JSDOM } from 'jsdom';
 
async function scrapeDatasheets(manufacturerUrl, outputDir) {
  console.log(`Scraping datasheets from ${manufacturerUrl}`);
  
  // Create output directory if it doesn't exist
  if (!fs.existsSync(outputDir)) {
    fs.mkdirSync(outputDir, { recursive: true });
  }
  
  try {
    // Fetch the manufacturer's datasheet page
    const response = await fetch(manufacturerUrl);
    const html = await response.text();
    const dom = new JSDOM(html);
    const document = dom.window.document;
    
    // Find all datasheet links (this selector would need to be adjusted for each site)
    const datasheetLinks = Array.from(document.querySelectorAll('a[href$=".pdf"]'));
    
    console.log(`Found ${datasheetLinks.length} potential datasheets`);
    
    // Download each datasheet
    for (let i = 0; i < datasheetLinks.length; i++) {
      const link = datasheetLinks[i];
      const href = link.href;
      const filename = href.split('/').pop();
      
      console.log(`Downloading ${filename} (${i+1}/${datasheetLinks.length})`);
      
      // Download the PDF
      const pdfResponse = await fetch(href);
      const pdfBuffer = await pdfResponse.buffer();
      
      // Save to file
      fs.writeFileSync(`${outputDir}/${filename}`, pdfBuffer);
      
      // Respect rate limits
      await new Promise(resolve => setTimeout(resolve, 1000));
    }
    
    console.log('Datasheet collection complete');
  } catch (error) {
    console.error('Error scraping datasheets:', error);
  }
}
 
// Usage
scrapeDatasheets('https://example-manufacturer.com/datasheets', './data/datasheets');

// Example: Extracting text from PDF datasheets
import fs from 'fs';
import path from 'path';
import { PDFDocument } from 'pdf-lib';
import pdf from 'pdf-parse';
 
async function extractTextFromPDFs(inputDir, outputDir) {
  console.log(`Processing PDFs from ${inputDir}`);
  
  if (!fs.existsSync(outputDir)) {
    fs.mkdirSync(outputDir, { recursive: true });
  }
  
  const files = fs.readdirSync(inputDir).filter(file => file.endsWith('.pdf'));
  
  for (const file of files) {
    const inputPath = path.join(inputDir, file);
    const outputPath = path.join(outputDir, file.replace('.pdf', '.txt'));
    
    console.log(`Processing ${file}`);
    
    try {
      // Read the PDF file
      const dataBuffer = fs.readFileSync(inputPath);
      
      // Extract text
      const data = await pdf(dataBuffer);
      const text = data.text;
      
      // Save extracted text
      fs.writeFileSync(outputPath, text);
      
      console.log(`Extracted ${text.length} characters from ${file}`);
    } catch (error) {
      console.error(`Error processing ${file}:`, error);
    }
  }
  
  console.log('Text extraction complete');
}
 
// Usage
extractTextFromPDFs('./data/datasheets', './data/extracted_text');

// Example: Cleaning and normalizing extracted text
import fs from 'fs';
import path from 'path';
 
function cleanAndNormalizeText(inputDir, outputDir) {
  console.log(`Cleaning and normalizing text from ${inputDir}`);
  
  if (!fs.existsSync(outputDir)) {
    fs.mkdirSync(outputDir, { recursive: true });
  }
  
  const files = fs.readdirSync(inputDir).filter(file => file.endsWith('.txt'));
  
  for (const file of files) {
    const inputPath = path.join(inputDir, file);
    const outputPath = path.join(outputDir, file);
    
    console.log(`Processing ${file}`);
    
    try {
      // Read the text file
      let text = fs.readFileSync(inputPath, 'utf8');
      
      // Basic cleaning operations
      text = text
        // Remove excessive whitespace
        .replace(/\s+/g, ' ')
        // Normalize component values (e.g., "4.7 kΩ", "4.7k", "4k7" to "4.7 kOhm")
        .replace(/(\d+(?:\.\d+)?)\s*[kK](?:Ω|Ohm)?/g, '$1 kOhm')
        .replace(/(\d+(?:\.\d+)?)\s*[mM](?:Ω|Ohm)?/g, '$1 mOhm')
        // Normalize capacitor values
        .replace(/(\d+(?:\.\d+)?)\s*[uµ]F/g, '$1 uF')
        .replace(/(\d+(?:\.\d+)?)\s*[nN]F/g, '$1 nF')
        .replace(/(\d+(?:\.\d+)?)\s*[pP]F/g, '$1 pF')
        // Normalize voltage ratings
        .replace(/(\d+(?:\.\d+)?)\s*[vV]\b/g, '$1 V')
        // Fix common OCR errors
        .replace(/0hm/g, 'Ohm')
        .replace(/l\b/g, '1') // Replace lowercase l with 1 when it's alone
        .trim();
      
      // Save cleaned text
      fs.writeFileSync(outputPath, text);
      
      console.log(`Cleaned and normalized ${file}`);
    } catch (error) {
      console.error(`Error processing ${file}:`, error);
    }
  }
  
  console.log('Text cleaning and normalization complete');
}
 
// Usage
cleanAndNormalizeText('./data/extracted_text', './data/cleaned_text');

// Example: Extracting component parameters from text
import fs from 'fs';
import path from 'path';
 
function extractComponentParameters(inputDir, outputDir) {
  console.log(`Extracting component parameters from ${inputDir}`);
  
  if (!fs.existsSync(outputDir)) {
    fs.mkdirSync(outputDir, { recursive: true });
  }
  
  const files = fs.readdirSync(inputDir).filter(file => file.endsWith('.txt'));
  
  for (const file of files) {
    const inputPath = path.join(inputDir, file);
    const outputPath = path.join(outputDir, file.replace('.txt', '.json'));
    
    console.log(`Processing ${file}`);
    
    try {
      // Read the cleaned text file
      const text = fs.readFileSync(inputPath, 'utf8');
      
      // Extract component parameters using regex patterns
      const parameters = {
        partNumber: extractPartNumber(text),
        description: extractDescription(text),
        specifications: {
          voltage: extractVoltageRatings(text),
          current: extractCurrentRatings(text),
          resistance: extractResistanceValues(text),
          capacitance: extractCapacitanceValues(text),
          temperature: extractTemperatureRanges(text),
          dimensions: extractDimensions(text)
        },
        features: extractFeatures(text),
        applications: extractApplications(text),
        pinout: extractPinout(text)
      };
      
      // Save structured data
      fs.writeFileSync(outputPath, JSON.stringify(parameters, null, 2));
      
      console.log(`Extracted parameters from ${file}`);
    } catch (error) {
      console.error(`Error processing ${file}:`, error);
    }
  }
  
  console.log('Parameter extraction complete');
}
 
// Helper functions for parameter extraction
function extractPartNumber(text) {
  // Example regex for part numbers (would need customization)
  const match = text.match(/Part\s+Number:?\s*([A-Z0-9\-]+)/i);
  return match ? match[1] : null;
}
 
function extractVoltageRatings(text) {
  const ratings = [];
  const regex = /(\d+(?:\.\d+)?)\s*V(?:olt)?s?\b/gi;
  let match;
  
  while ((match = regex.exec(text)) !== null) {
    ratings.push(parseFloat(match[1]));
  }
  
  return ratings;
}
 
// Additional extraction functions would be implemented similarly
 
// Usage
extractComponentParameters('./data/cleaned_text', './data/structured_data');

// Example: Generating synthetic PCB design questions and answers
import fs from 'fs';
import path from 'path';
 
function generateSyntheticQAPairs(componentData, outputPath, count = 1000) {
  console.log(`Generating ${count} synthetic Q&A pairs`);
  
  const questionTemplates = [
    "What is the maximum operating temperature of {component}?",
    "Can I use {component} in a {application} circuit?",
    "What's the pinout for {component}?",
    "How should I decouple {component} on my PCB?",
    "What's the recommended footprint for {component}?",
    "Is {component} suitable for {environment} environments?",
    "What's the power dissipation of {component}?",
    "Can {component} be used with {voltage}V supply?",
    "What's the typical current consumption of {component}?",
    "How do I calculate the value of {parameter} for {component}?"
  ];
  
  const environmentTypes = [
    "automotive", "industrial", "medical", "consumer", "aerospace", 
    "high-temperature", "low-temperature", "high-humidity", "marine"
  ];
  
  const applicationTypes = [
    "power supply", "amplifier", "filter", "oscillator", "sensor interface",
    "motor control", "battery charging", "signal conditioning", "communication"
  ];
  
  const qaPairs = [];
  
  for (let i = 0; i < count; i++) {
    // Randomly select a component
    const component = componentData[Math.floor(Math.random() * componentData.length)];
    
    // Randomly select a question template
    const template = questionTemplates[Math.floor(Math.random() * questionTemplates.length)];
    
    // Fill in the template
    let question = template
      .replace('{component}', component.partNumber)
      .replace('{application}', applicationTypes[Math.floor(Math.random() * applicationTypes.length)])
      .replace('{environment}', environmentTypes[Math.floor(Math.random() * environmentTypes.length)])
      .replace('{voltage}', [3.3, 5, 12, 24, 48][Math.floor(Math.random() * 5)])
      .replace('{parameter}', ['resistance', 'capacitance', 'inductance'][Math.floor(Math.random() * 3)]);
    
    // Generate an answer based on the question and component data
    const answer = generateAnswer(question, component);
    
    qaPairs.push({ question, answer });
  }
  
  // Save the Q&A pairs
  fs.writeFileSync(outputPath, JSON.stringify(qaPairs, null, 2));
  
  console.log(`Generated ${qaPairs.length} Q&A pairs`);
}
 
function generateAnswer(question, component) {
  // This would be a complex function that generates appropriate answers
  // based on the question type and component data
  // Simplified example:
  if (question.includes('maximum operating temperature')) {
    return `The maximum operating temperature of ${component.partNumber} is ${component.specifications.temperature.max}°C.`;
  }
  
  if (question.includes('pinout')) {
    return `The pinout for ${component.partNumber} is as follows: ${JSON.stringify(component.pinout)}`;
  }
  
  // More answer generation logic would be implemented here
  
  return `Information about ${component.partNumber}: ${component.description}`;
}
 
// Load component data
const componentData = JSON.parse(fs.readFileSync('./data/structured_data/components.json', 'utf8'));
 
// Generate synthetic Q&A pairs
generateSyntheticQAPairs(componentData, './data/training/synthetic_qa.json');

// Example: Transforming component specifications into different formats
import fs from 'fs';
import path from 'path';
 
function transformComponentData(inputDir, outputDir) {
  console.log(`Transforming component data from ${inputDir}`);
  
  if (!fs.existsSync(outputDir)) {
    fs.mkdirSync(outputDir, { recursive: true });
  }
  
  const files = fs.readdirSync(inputDir).filter(file => file.endsWith('.json'));
  
  for (const file of files) {
    const inputPath = path.join(inputDir, file);
    
    console.log(`Processing ${file}`);
    
    try {
      // Read the component data
      const component = JSON.parse(fs.readFileSync(inputPath, 'utf8'));
      
      // Transform to different formats
      
      // 1. Generate SPICE model description
      const spiceModel = generateSpiceModel(component);
      fs.writeFileSync(
        path.join(outputDir, `${component.partNumber}_spice.txt`),
        spiceModel
      );
      
      // 2. Generate KiCad symbol description
      const kicadSymbol = generateKiCadSymbol(component);
      fs.writeFileSync(
        path.join(outputDir, `${component.partNumber}_kicad_sym.txt`),
        kicadSymbol
      );
      
      // 3. Generate PCB footprint description
      const footprint = generateFootprint(component);
      fs.writeFileSync(
        path.join(outputDir, `${component.partNumber}_footprint.txt`),
        footprint
      );
      
      // 4. Generate design considerations
      const designConsiderations = generateDesignConsiderations(component);
      fs.writeFileSync(
        path.join(outputDir, `${component.partNumber}_design_notes.txt`),
        designConsiderations
      );
      
      console.log(`Generated transformations for ${component.partNumber}`);
    } catch (error) {
      console.error(`Error processing ${file}:`, error);
    }
  }
  
  console.log('Data transformation complete');
}
 
function generateSpiceModel(component) {
  // This would generate a SPICE model description based on component parameters
  // Simplified example:
  if (component.type === 'resistor') {
    return `.MODEL ${component.partNumber} RES (R=${component.specifications.resistance} TC1=${component.specifications.tempco || 0})`;
  }
  
  if (component.type === 'capacitor') {
    return `.MODEL ${component.partNumber} CAP (C=${component.specifications.capacitance} V=${component.specifications.voltage.max})`;
  }
  
  // More model generation logic would be implemented here
  
  return `* SPICE model for ${component.partNumber}\n* ${component.description}`;
}
 
// Additional transformation functions would be implemented similarly
 
// Usage
transformComponentData('./data/structured_data', './data/transformed_data');

// Example: Creating a training dataset in JSONL format
import fs from 'fs';
import path from 'path';
 
function createTrainingDataset(inputDirs, outputPath) {
  console.log('Creating training dataset');
  
  const trainingExamples = [];
  
  // Process each input directory
  for (const inputDir of inputDirs) {
    console.log(`Processing ${inputDir}`);
    
    const files = fs.readdirSync(inputDir);
    
    for (const file of files) {
      const filePath = path.join(inputDir, file);
      
      try {
        // Different processing based on file type
        if (file.endsWith('.json')) {
          // Process structured data
          const data = JSON.parse(fs.readFileSync(filePath, 'utf8'));
          
          if (Array.isArray(data)) {
            // If it's an array (like Q&A pairs), add each item
            trainingExamples.push(...data.map(formatTrainingExample));
          } else {
            // If it's a single object, format it
            trainingExamples.push(formatTrainingExample(data));
          }
        } else if (file.endsWith('.txt')) {
          // Process text data
          const text = fs.readFileSync(filePath, 'utf8');
          
          // Create examples from text (e.g., chunk into sections)
          const examples = chunkTextIntoExamples(text, file);
          trainingExamples.push(...examples);
        }
      } catch (error) {
        console.error(`Error processing ${file}:`, error);
      }
    }
  }
  
  // Shuffle the examples
  shuffleArray(trainingExamples);
  
  // Write to JSONL file
  const jsonlData = trainingExamples.map(example => JSON.stringify(example)).join('\n');
  fs.writeFileSync(outputPath, jsonlData);
  
  console.log(`Created training dataset with ${trainingExamples.length} examples`);
}
 
function formatTrainingExample(data) {
  // Format the data as a training example
  // This would depend on the model architecture and training approach
  
  // For instruction tuning format:
  if (data.question && data.answer) {
    return {
      messages: [
        { role: "user", content: data.question },
        { role: "assistant", content: data.answer }
      ]
    };
  }
  
  // For component data:
  if (data.partNumber) {
    // Create a description task
    return {
      messages: [
        { role: "user", content: `Describe the ${data.partNumber} component and its key specifications.` },
        { role: "assistant", content: generateComponentDescription(data) }
      ]
    };
  }
  
  // Default format
  return {
    messages: [
      { role: "user", content: "Tell me about electronic components." },
      { role: "assistant", content: data.text || JSON.stringify(data) }
    ]
  };
}
 
function chunkTextIntoExamples(text, filename) {
  // Split text into chunks and create examples
  // Simplified implementation:
  const chunks = [];
  const paragraphs = text.split('\n\n');
  
  for (let i = 0; i < paragraphs.length; i += 3) {
    const chunk = paragraphs.slice(i, i + 3).join('\n\n');
    if (chunk.trim().length > 100) { // Only use substantial chunks
      chunks.push({
        messages: [
          { role: "user", content: `Explain this technical information: "${chunk.substring(0, 50)}..."` },
          { role: "assistant", content: chunk }
        ]
      });
    }
  }
  
  return chunks;
}
 
function shuffleArray(array) {
  for (let i = array.length - 1; i > 0; i--) {
    const j = Math.floor(Math.random() * (i + 1));
    [array[i], array[j]] = [array[j], array[i]];
  }
}
 
// Usage
createTrainingDataset(
  ['./data/structured_data', './data/transformed_data', './data/training'],
  './data/final/training_data.jsonl'
);

// Example: Creating validation datasets
import fs from 'fs';
import path from 'path';
 
function createValidationDatasets(trainingDataPath, outputDir, validationRatio = 0.1) {
  console.log('Creating validation datasets');
  
  if (!fs.existsSync(outputDir)) {
    fs.mkdirSync(outputDir, { recursive: true });
  }
  
  // Read the training data
  const trainingData = fs.readFileSync(trainingDataPath, 'utf8')
    .split('\n')
    .filter(line => line.trim())
    .map(line => JSON.parse(line));
  
  console.log(`Read ${trainingData.length} training examples`);
  
  // Shuffle the data
  shuffleArray(trainingData);
  
  // Split into training and validation
  const validationCount = Math.floor(trainingData.length * validationRatio);
  const validationData = trainingData.slice(0, validationCount);
  const newTrainingData = trainingData.slice(validationCount);
  
  console.log(`Split into ${newTrainingData.length} training and ${validationData.length} validation examples`);
  
  // Create specialized validation sets
  
  // 1. Component specification validation
  const componentSpecs = validationData.filter(example => 
    example.messages[0].content.includes('specifications') || 
    example.messages[0].content.includes('datasheet')
  );
  
  // 2. PCB design validation
  const pcbDesign = validationData.filter(example => 
    example.messages[0].content.includes('PCB') || 
    example.messages[0].content.includes('layout') ||
    example.messages[0].content.includes('routing')
  );
  
  // 3. Circuit analysis validation
  const circuitAnalysis = validationData.filter(example => 
    example.messages[0].content.includes('circuit') || 
    example.messages[0].content.includes('schematic') ||
    example.messages[0].content.includes('analyze')
  );
  
  // Write the datasets
  writeJsonlFile(path.join(outputDir, 'training.jsonl'), newTrainingData);
  writeJsonlFile(path.join(outputDir, 'validation.jsonl'), validationData);
  writeJsonlFile(path.join(outputDir, 'validation_component_specs.jsonl'), componentSpecs);
  writeJsonlFile(path.join(outputDir, 'validation_pcb_design.jsonl'), pcbDesign);
  writeJsonlFile(path.join(outputDir, 'validation_circuit_analysis.jsonl'), circuitAnalysis);
  
  console.log('Validation datasets created');
}
 
function writeJsonlFile(filePath, data) {
  const jsonlData = data.map(example => JSON.stringify(example)).join('\n');
  fs.writeFileSync(filePath, jsonlData);
  console.log(`Wrote ${data.length} examples to ${filePath}`);
}
 
function shuffleArray(array) {
  for (let i = array.length - 1; i > 0; i--) {
    const j = Math.floor(Math.random() * (i + 1));
    [array[i], array[j]] = [array[j], array[i]];
  }
}
 
// Usage
createValidationDatasets(
  './data/final/training_data.jsonl',
  './data/final/splits'
);

// Example: Configuration for full fine-tuning with Hugging Face Transformers
import { writeFileSync } from 'fs';
 
function createFullFinetuningConfig(modelName, trainingDataPath, outputDir, epochs = 3) {
  const config = {
    model_name_or_path: modelName,
    do_train: true,
    do_eval: true,
    train_file: trainingDataPath,
    validation_file: './data/final/splits/validation.jsonl',
    output_dir: outputDir,
    overwrite_output_dir: true,
    num_train_epochs: epochs,
    per_device_train_batch_size: 4,
    per_device_eval_batch_size: 4,
    gradient_accumulation_steps: 8,
    evaluation_strategy: "steps",
    eval_steps: 500,
    save_steps: 1000,
    save_total_limit: 3,
    learning_rate: 2e-5,
    weight_decay: 0.01,
    warmup_steps: 500,
    logging_dir: `${outputDir}/logs`,
    logging_steps: 100,
    fp16: true,
    push_to_hub: false
  };
  
  writeFileSync('./config/full_finetuning.json', JSON.stringify(config, null, 2));
  console.log('Created full fine-tuning configuration');
}

import fs from 'fs';
import path from 'path';
import { pipeline } from '@huggingface/transformers';
import { PDFLoader } from 'langchain/document_loaders/fs/pdf';
 
// Example of a data processing pipeline for technical documents
async function processElectronicsDocuments(dataDir) {
  console.log("Processing electronics documents...");
  
  // Create document processors for different file types
  const processors = {
    pdf: async (filePath) => {
      const loader = new PDFLoader(filePath);
      return await loader.load();
    },
    txt: async (filePath) => {
      const text = fs.readFileSync(filePath, 'utf8');
      return [{ pageContent: text, metadata: { source: filePath } }];
    },
    // Add processors for other file types (Gerber, Eagle, KiCad, etc.)
  };
 
  const documents = [];
  const files = fs.readdirSync(dataDir);
  
  for (const file of files) {
    const filePath = path.join(dataDir, file);
    const extension = path.extname(file).substring(1).toLowerCase();
    
    if (processors[extension]) {
      try {
        const docs = await processors[extension](filePath);
        documents.push(...docs);
        console.log(`Processed ${file}: ${docs.length} documents extracted`);
      } catch (error) {
        console.error(`Error processing ${file}:`, error);
      }
    }
  }
  
  return documents;
}

async function cleanElectronicsData(documents) {
  console.log("Cleaning electronics data...");
  
  const cleanedDocuments = documents.map(doc => {
    let text = doc.pageContent;
    
    // Normalize component designators
    text = text.replace(/([CR][0-9]+)/g, match => match.toUpperCase());
    
    // Standardize units
    text = text.replace(/(\d+)(\s*)(mΩ|mohm|milliohm)/gi, '$1 mΩ');
    text = text.replace(/(\d+)(\s*)(pF|picofarad)/gi, '$1 pF');
    
    // Handle special characters in datasheets
    text = text.replace(/±/g, '+/-');
    
    // Remove excessive whitespace
    text = text.replace(/\s+/g, ' ').trim();
    
    return { ...doc, pageContent: text };
  });
  
  return cleanedDocuments;
}

function augmentElectronicsData(documents) {
  console.log("Augmenting electronics data...");
  
  const augmentedDocuments = [];
  
  for (const doc of documents) {
    // Add original document
    augmentedDocuments.push(doc);
    
    // Create variations with different component values
    if (doc.pageContent.includes('resistor') || doc.pageContent.includes('capacitor')) {
      const valueVariations = createComponentValueVariations(doc.pageContent);
      for (const variation of valueVariations) {
        augmentedDocuments.push({
          ...doc,
          pageContent: variation,
          metadata: { ...doc.metadata, augmented: true }
        });
      }
    }
    
    // Create variations with different temperature ranges
    if (doc.pageContent.includes('temperature') || doc.pageContent.includes('thermal')) {
      const tempVariations = createTemperatureVariations(doc.pageContent);
      for (const variation of tempVariations) {
        augmentedDocuments.push({
          ...doc,
          pageContent: variation,
          metadata: { ...doc.metadata, augmented: true }
        });
      }
    }
  }
  
  return augmentedDocuments;
}
 
function createComponentValueVariations(text) {
  // Implementation details for creating variations of component values
  // This would substitute different resistor/capacitor values
  return [text.replace(/(\d+)(k|M)?(Ω|ohm)/gi, '4.7kΩ'), 
          text.replace(/(\d+)(k|M)?(Ω|ohm)/gi, '10kΩ')];
}

async function annotateElectronicsData(documents) {
  console.log("Annotating electronics data...");
  
  // Example annotation schema for PCB design data
  const annotationSchema = {
    entities: [
      'COMPONENT_NAME', 'COMPONENT_VALUE', 'PIN_NUMBER', 
      'SIGNAL_NAME', 'FOOTPRINT', 'PACKAGE_TYPE'
    ],
    relations: [
      'CONNECTS_TO', 'PART_OF', 'ALTERNATIVE_FOR'
    ],
    attributes: [
      'VOLTAGE_RATING', 'CURRENT_RATING', 'TOLERANCE',
      'TEMPERATURE_RANGE', 'MANUFACTURER'
    ]
  };
  
  // This would typically involve human annotators or semi-automated tools
  // Here's a simplified example of what the annotation process might produce
  const annotatedDocuments = documents.map(doc => {
    // Use regex patterns to identify and tag entities
    let annotatedText = doc.pageContent;
    
    // Tag component names and values
    annotatedText = annotatedText.replace(
      /([CR][0-9]+)\s*[=:]\s*([0-9.]+)\s*(p|n|u|m|k|M)?(F|Ω|ohm)/g,
      '<COMPONENT_NAME>$1</COMPONENT_NAME> = <COMPONENT_VALUE>$2$3$4</COMPONENT_VALUE>'
    );
    
    // Tag pin numbers
    annotatedText = annotatedText.replace(
      /pin\s+([0-9]+)/gi,
      'pin <PIN_NUMBER>$1</PIN_NUMBER>'
    );
    
    return {
      ...doc,
      pageContent: annotatedText,
      annotations: {
        schema: annotationSchema,
        // Additional annotation metadata would go here
      }
    };
  });
  
  return annotatedDocuments;
}

import { pipeline } from '@huggingface/transformers';
 
async function createPCBSpecializedArchitecture() {
  console.log("Creating specialized architecture for PCB design...");
  
  // This is conceptual code - actual implementation would be in Python with PyTorch/TensorFlow
  
  // 1. Start with a base transformer architecture
  const baseModel = await pipeline('text-generation', 'mistralai/Mistral-7B-v0.1');
  
  // 2. Add domain-specific embeddings
  // - Component embeddings (resistors, capacitors, ICs)
  // - Footprint embeddings
  // - Manufacturing constraint embeddings
  
  // 3. Add specialized attention mechanisms
  // - Cross-attention for schematic-to-layout relationships
  // - Graph attention for netlist processing
  
  // 4. Add retrieval-augmented generation capabilities
  // - Component datasheet retrieval
  // - Design rule lookup
  
  // 5. Add multimodal processing for schematics and layouts
  // - Vision encoder for PCB layouts
  // - Diagram understanding module
  
  console.log("Architecture modifications conceptualized");
  return "PCB-specialized architecture";
}

async function customizePCBTokenizer(baseTokenizer, vocabularyPath) {
  console.log("Customizing tokenizer for PCB domain...");
  
  // Load domain-specific vocabulary
  const domainVocabulary = JSON.parse(fs.readFileSync(vocabularyPath, 'utf8'));
  
  // Example of electronics-specific tokens to add
  const specialTokens = [
    // Component designators
    'R1', 'R2', 'C1', 'C2', 'L1', 'Q1', 'U1',
    
    // Common part numbers
    'LM358', 'NE555', 'ATmega328', 'ESP32',
    
    // PCB terminology
    'VIA', 'TRACE', 'PAD', 'SILKSCREEN',
    
    // Units and measurements
    'mΩ', 'kΩ', 'MHz', 'pF', 'nF', 'μF',
    
    // Manufacturing terms
    'HASL', 'ENIG', 'GERBER', 'DRILL', 'SOLDERMASK'
  ];
  
  // Add special tokens to base tokenizer
  // In practice, this would involve retraining the tokenizer with the new vocabulary
  
  console.log(`Added ${specialTokens.length} domain-specific tokens to tokenizer`);
  return "Customized PCB tokenizer";
}

// Conceptual code for distributed training setup
// Actual implementation would be in Python with frameworks like PyTorch Lightning
 
function configurePCBModelTraining(modelConfig) {
  console.log("Configuring distributed training for PCB model...");
  
  const trainingConfig = {
    // Model configuration
    model: {
      type: modelConfig.architecture,
      size: modelConfig.parameters,
      precision: 'bfloat16', // Mixed precision for efficiency
    },
    
    // Distributed training settings
    distributed: {
      strategy: 'deepspeed_stage_3', // Zero-3 optimization
      worldSize: 8, // Number of GPUs
      gradientAccumulation: 8,
      shardedDataParallel: true,
    },
    
    // Optimization settings
    optimization: {
      optimizer: 'AdamW',
      learningRate: 1e-5,
      weightDecay: 0.01,
      scheduler: 'cosine_with_warmup',
      warmupSteps: 500,
    },
    
    // Training loop configuration
    training: {
      batchSize: 32,
      maxSteps: 100000,
      evalFrequency: 1000,
      checkpointFrequency: 5000,
      mixedPrecision: true,
      gradientClipping: 1.0,
    },
    
    // Memory optimization
    memoryOptimization: {
      activationCheckpointing: true,
      cpuOffloading: true,
      attentionSoftmaxInFp32: true,
    }
  };
  
  console.log("Training configuration prepared");
  return trainingConfig;
}

function setupTrainingMonitoring() {
  console.log("Setting up training monitoring...");
  
  const monitoringConfig = {
    // Metrics to track
    metrics: [
      // Training metrics
      { name: 'loss', frequency: 'step' },
      { name: 'learning_rate', frequency: 'step' },
      { name: 'gpu_memory', frequency: 'step' },
      { name: 'tokens_per_second', frequency: 'step' },
      
      // Validation metrics
      { name: 'val_loss', frequency: 'epoch' },
      { name: 'perplexity', frequency: 'epoch' },
      
      // Domain-specific metrics
      { name: 'component_accuracy', frequency: 'epoch' },
      { name: 'schematic_consistency', frequency: 'epoch' },
      { name: 'design_rule_adherence', frequency: 'epoch' },
    ],
    
    // Visualization tools
    visualization: {
      tensorboard: true,
      wandb: {
        project: 'pcb-llm',
        entity: 'electronics-ai-team',
      },
    },
    
    // Alerting
    alerting: {
      discordWebhook: 'https://discord.com/api/webhooks/...',
      thresholds: {
        loss_spike: 2.0,
        gpu_memory_critical: 0.95,
      },
    },
    
    // Artifact logging
    artifacts: {
      saveCheckpoints: true,
      saveEvalSamples: true,
      saveConfusionMatrix: true,
    }
  };
  
  console.log("Monitoring configuration prepared");
  return monitoringConfig;
}

async function prePCBModelTraining(baseModel, domainCorpus) {
  console.log("Preparing PCB model pre-training...");
  
  // Pre-training configuration
  const preTrainingConfig = {
    // Training objective
    objective: 'causal_language_modeling',
    
    // Curriculum learning stages
    curriculum: [
      {
        name: 'general_electronics',
        dataSubset: 'electronics_textbooks',
        epochs: 2,
        learningRate: 5e-5,
      },
      {
        name: 'component_datasheets',
        dataSubset: 'component_datasheets',
        epochs: 3,
        learningRate: 3e-5,
      },
      {
        name: 'pcb_design_documents',
        dataSubset: 'pcb_design_guides',
        epochs: 5,
        learningRate: 1e-5,
      },
    ],
    
    // Domain-adaptive pre-training settings
    domainAdaptation: {
      vocabularyExpansion: true,
      continueLmHeadTraining: true,
      freezeLayers: ['embeddings', 'first_4_layers'],
    }
  };
  
  console.log("Pre-training configuration prepared");
  return preTrainingConfig;
}

async function supervisedFineTuning(preTrainedModel, instructionDataset) {
  console.log("Preparing supervised fine-tuning for PCB model...");
  
  // Example instruction tuning dataset structure
  const sftExamples = [
    {
      instruction: "Recommend a suitable decoupling capacitor for a 3.3V microcontroller power supply.",
      response: "For a 3.3V microcontroller power supply, I recommend using a 0.1μF ceramic capacitor with X7R dielectric and 0805 or 0603 package size. Place it as close as possible to the power pins of the microcontroller. For additional filtering, consider adding a 10μF tantalum or ceramic capacitor. The 0.1μF capacitor handles high-frequency noise while the 10μF capacitor provides bulk charge storage for transient current demands."
    },
    {
      instruction: "Check if this trace width is sufficient for a 2A current on a 1oz copper PCB.",
      response: "For a 2A current on a 1oz (35μm) copper PCB, the minimum trace width depends on the acceptable temperature rise and whether the trace is internal or external. Using the IPC-2152 standard:\n\nFor an external trace with a 10°C temperature rise: approximately 40 mils (1mm) width is required.\nFor an external trace with a 20°C temperature rise: approximately 25 mils (0.635mm) width is required.\n\nIf your trace is narrower than these values, consider widening it or using thicker copper (2oz) to prevent excessive heating and potential failure."
    },
    // Many more examples would be included
  ];
  
  // SFT configuration
  const sftConfig = {
    // Training approach
    method: 'instruction_tuning',
    
    // Dataset configuration
    dataset: {
      format: 'instruction_response_pairs',
      size: sftExamples.length,
      validationSplit: 0.1,
    },
    
    // Training parameters
    training: {
      epochs: 3,
      batchSize: 4,
      learningRate: 2e-5,
      weightDecay: 0.01,
      maxLength: 2048,
    },
    
    // LoRA configuration for parameter-efficient fine-tuning
    lora: {
      enabled: true,
      r: 16,
      alpha: 32,
      dropout: 0.05,
      targetModules: ['q_proj', 'v_proj', 'k_proj', 'o_proj'],
    }
  };
  
  console.log("Supervised fine-tuning configuration prepared");
  return sftConfig;
}

async function configurePCBModelRLHF(sftModel) {
  console.log("Configuring RLHF for PCB model...");
  
  // RLHF configuration
  const rlhfConfig = {
    // Reward model training
    rewardModel: {
      architecture: 'same_as_policy',
      trainingData: 'ranked_responses',
      pairwiseComparisons: 10000,
      evaluators: ['electronics_engineers', 'pcb_designers'],
    },
    
    // PPO configuration
    ppo: {
      epochs: 4,
      miniBatchSize: 4,
      kl_coef: 0.1,
      gamma: 1.0,
      lam: 0.95,
      clipRange: 0.2,
      valueClipRange: 0.2,
      vfCoef: 0.1,
    },
    
    // Domain-specific reward components
    domainRewards: [
      {
        name: 'technical_accuracy',
        weight: 0.4,
        description: 'Correctness of component values, pinouts, and specifications',
      },
      {
        name: 'design_rule_compliance',
        weight: 0.3,
        description: 'Adherence to PCB design rules and manufacturing constraints',
      },
      {
        name: 'clarity_and_helpfulness',
        weight: 0.2,
        description: 'Clear explanations and actionable recommendations',
      },
      {
        name: 'safety_considerations',
        weight: 0.1,
        description: 'Attention to electrical safety and reliability concerns',
      },
    ],
    
    // Example prompts for RLHF
    examplePrompts: [
      "Design a power supply circuit for an Arduino project",
      "Troubleshoot why my I2C communication is failing",
      "Calculate the appropriate trace width for a 5A power line",
      "Recommend component values for a low-pass filter at 1kHz",
    ]
  };
  
  console.log("RLHF configuration prepared");
  return rlhfConfig;
}

import { VectorDBQAChain } from "langchain/chains";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { Chroma } from "langchain/vectorstores/chroma";
 
async function setupPCBKnowledgeRetrieval(documents) {
  console.log("Setting up retrieval system for PCB knowledge...");
  
  // Create embeddings for documents
  const embeddings = new OpenAIEmbeddings();
  
  // Create vector store from documents
  const vectorStore = await Chroma.fromDocuments(
    documents,
    embeddings,
    { collectionName: "pcb_knowledge_base" }
  );
  
  // Configure retrieval system
  const retrievalConfig = {
    // Vector database configuration
    vectorDb: {
      type: 'chroma',
      dimensions: 1536,
      metric: 'cosine',
      collections: [
        {
          name: 'component_datasheets',
          documentCount: documents.filter(d => d.metadata.type === 'datasheet').length,
        },
        {
          name: 'design_guidelines',
          documentCount: documents.filter(d => d.metadata.type === 'guideline').length,
        },
        {
          name: 'reference_designs',
          documentCount: documents.filter(d => d.metadata.type === 'reference').length,
        },
      ],
    },
    
    // Retrieval strategies
    retrieval: {
      defaultStrategy: 'hybrid',
      topK: 5,
      reranking: true,
      contextWindow: 4096,
      strategies: {
        bm25: { weight: 0.3 },
        semantic: { weight: 0.7 },
      },
    },
    
    // Domain-specific retrieval enhancements
    domainEnhancements: {
      componentLookup: {
        enabled: true,
        exactMatchBoost: 2.0,
      },
      schematicDiagramRetrieval: {
        enabled: true,
        modalityFusion: 'early',
      },
      manufacturingConstraintsLookup: {
        enabled: true,
        contextualFiltering: true,
      },
    }
  };
  
  console.log("Retrieval system configuration prepared");
  return { vectorStore, retrievalConfig };
}

async function evaluatePCBModelAccuracy(model, testDataset) {
  console.log("Evaluating PCB model technical accuracy...");
  
  // Define evaluation metrics
  const evaluationMetrics = {
    // Component knowledge metrics
    componentKnowledge: [
      { name: 'component_identification', weight: 0.2 },
      { name: 'parameter_accuracy', weight: 0.3 },
      { name: 'pinout_correctness', weight: 0.2 },
      { name: 'package_knowledge', weight: 0.1 },
      { name: 'alternative_suggestions', weight: 0.2 },
    ],
    
    // Circuit analysis metrics
    circuitAnalysis: [
      { name: 'circuit_function_understanding', weight: 0.3 },
      { name: 'signal_path_tracing', weight: 0.2 },
      { name: 'failure_mode_analysis', weight: 0.3 },
      { name: 'performance_prediction', weight: 0.2 },
    ],
    
    // PCB design metrics
    pcbDesign: [
      { name: 'design_rule_compliance', weight: 0.3 },
      { name: 'layout_optimization', weight: 0.2 },
      { name: 'thermal_considerations', weight: 0.2 },
      { name: 'manufacturing_feasibility', weight: 0.3 },
    ],
    
    // Example test cases
    testCases: [
      {
        query: "What's the appropriate trace width for a 3.3V signal with 20mA current?",
        expectedAnswer: {
          key_points: [
            "For a 20mA signal, minimum trace width is not determined by current capacity",
            "Standard practice is 6-10 mil (0.15-0.25mm) for signal traces",
            "Impedance control may be more important than current capacity",
            "For high-speed signals, transmission line effects should be considered"
          ]
        }
      },
      // Many more test cases would be included
    ]
  };
  
  console.log("Evaluation metrics defined");
  return evaluationMetrics;
}

async function evaluateDesignRuleChecking(model) {
  console.log("Evaluating design rule checking capabilities...");
  
  // Test cases for design rule checking
  const designRuleTests = [
    {
      description: "Minimum trace spacing violation",
      scenario: "Two 5V traces are spaced 4 mils apart on an inner layer.",
      expectedIssues: [
        "Trace spacing (4 mils) is below minimum requirement (typically 5-6 mils for inner layers)",
        "Risk of short circuit during manufacturing",
        "Recommendation to increase spacing to at least 6 mils"
      ]
    },
    {
      description: "Thermal relief missing",
      scenario: "A large ground plane connects directly to a through-hole component pad without thermal relief.",
      expectedIssues: [
        "Missing thermal relief on pad connection to ground plane",
        "Will cause soldering difficulties due to heat sinking",
        "Recommendation to add thermal relief spokes to the connection"
      ]
    },
    // Many more test cases would be included
  ];
  
  // Evaluation procedure
  const evaluationProcedure = {
    method: "expert_review",
    metrics: {
      issue_identification: { weight: 0.4 },
      explanation_quality: { weight: 0.3 },
      recommendation_quality: { weight: 0.3 }
    },
    scoring: {
      0: "Completely missed the issue",
      1: "Partially identified the issue but with significant errors",
      2: "Correctly identified the issue but with incomplete explanation",
      3: "Correctly identified and explained the issue with appropriate recommendations"
    }
  };
  
  console.log("Design rule checking evaluation prepared");
  return { designRuleTests, evaluationProcedure };
}

async function evaluateComponentSelection(model) {
  console.log("Evaluating component selection capabilities...");
  
  // Test cases for component selection
  const componentSelectionTests = [
    {
      description: "Decoupling capacitor selection",
      scenario: "Select appropriate decoupling capacitors for a 1.8V digital IC with 100MHz clock.",
      expectedResponse: {
        components: [
          { type: "Ceramic capacitor", value: "0.1μF", package: "0603/0402", dielectric: "X7R/X5R" },
          { type: "Ceramic capacitor", value: "1-10μF", package: "0805/1206", dielectric: "X5R/X7R" }
        ],
        placement: "As close as possible to power pins",
        considerations: [
          "Self-resonant frequency above clock frequency",
          "Low ESR for effective high-frequency filtering",
          "Temperature stability across operating range"
        ]
      }
    },
    {
      description: "Power regulator selection",
      scenario: "Select a voltage regulator to convert 12V to 3.3V at 500mA with minimal heat dissipation.",
      expectedResponse: {
        recommendation: "Switching regulator (buck converter)",
        justification: [
          "Linear regulator would dissipate (12-3.3)*0.5 = 4.35W of heat",
          "Switching regulator offers higher efficiency (>85%)",
          "Lower heat dissipation reduces thermal management requirements"
        ],
        examples: [
          { part: "LM2596", type: "Buck converter", features: "Simple, widely available" },
          { part: "MP2307", type: "Buck converter", features: "Small footprint" },
          { part: "TPS54331", type: "Buck converter", features: "High efficiency" }
        ]
      }
    },
    // Many more test cases would be included
  ];
  
  console.log("Component selection evaluation prepared");
  return componentSelectionTests;
}

async function optimizePCBModel(trainedModel, targetPlatform) {
  console.log("Optimizing PCB model for deployment...");
  
  // Optimization techniques
  const optimizationTechniques = {
    // Quantization options
    quantization: {
      method: 'post_training_quantization',
      precision: 'int8',
      calibrationDataset: 'pcb_design_queries',
      perChannelQuantization: true,
    },
    
    // Pruning options
    pruning: {
      method: 'magnitude_pruning',
      sparsity: 0.3,
      schedule: 'cubic',
      layerDistribution: 'uniform',
    },
    
    // Knowledge distillation
    distillation: {
      teacherModel: 'full_pcb_model_70b',
      studentModel: 'pcb_model_7b',
      temperature: 2.0,
      alphaKD: 0.5,
      distillationDataset: 'pcb_design_conversations',
    },
    
    // Model merging
    modelMerging: {
      baseModel: 'pcb_specialized_base',
      expertModels: [
        { name: 'component_expert', weight: 0.3 },
        { name: 'layout_expert', weight: 0.4 },
        { name: 'manufacturing_expert', weight: 0.3 },
      ],
      mergingMethod: 'slerp',
    },
    
    // Optimization for specific hardware
    hardwareOptimization: {
      target: targetPlatform,
      operators: 'fused',
      memoryLayout: 'optimized',
      cacheUtilization: 'maximized',
      tensorCores: targetPlatform === 'nvidia' ? true : false,
    }
  };
  
  console.log(`Model optimized for ${targetPlatform}`);
  return optimizationTechniques;
}

function configurePCBModelServing() {
  console.log("Configuring serving infrastructure for PCB model...");
  
  // Serving configuration
  const servingConfig = {
    // Deployment options
    deployment: {
      containerization: 'docker',
      orchestration: 'kubernetes',
      scaling: 'horizontal',
      regions: ['us-east', 'eu-west', 'ap-southeast'],
    },
    
    // Inference optimization
    inference: {
      batchSize: 1, // For real-time responses
      maxTokens: 2048,
      topK: 40,
      topP: 0.9,
      temperature: 0.7,
      repetitionPenalty: 1.1,
      caching: {
        enabled: true,
        ttl: 3600, // 1 hour
        strategy: 'lru',
      },
    },
    
    // Hardware configuration
    hardware: {
      gpu: 'nvidia-a10g',
      cpuRatio: 4, // 4 vCPUs per GPU
      memory: '24Gi',
      storage: {
        type: 'ssd',
        size: '100Gi',
      },
    },
    
    // Monitoring and observability
    monitoring: {
      metrics: [
        'latency_p50', 'latency_p95', 'latency_p99',
        'throughput', 'gpu_utilization', 'memory_usage',
        'error_rate', 'token_generation_speed',
      ],
      logging: {
        level: 'info',
        format: 'json',
        retention: '7d',
      },
      alerting: {
        latencyThreshold: 2000, // ms
        errorRateThreshold: 0.01, // 1%
        uptimeTarget: 0.995, // 99.5%
      },
    },
    
    // API configuration
    api: {
      authentication: 'oauth2',
      rateLimit: {
        tokensPerMinute: 10000,
        burstLimit: 1000,
      },
      endpoints: [
        { path: '/generate', method: 'POST', description: 'Generate PCB design recommendations' },
        { path: '/analyze', method: 'POST', description: 'Analyze existing PCB design' },
        { path: '/components', method: 'POST', description: 'Recommend components' },
      ],
    }
  };
  
  console.log("Serving configuration prepared");
  return servingConfig;
}