codecrucible-synth/src/core/rag/vector-rag-system.ts

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/**
 * Vector-Based RAG System for CodeCrucible Synth
 * Production-ready implementation with local-first architecture, LanceDB storage,
 * and real-time incremental indexing optimized for code repositories
 */

import { EventEmitter } from 'events';
import { promises as fs } from 'fs';
import * as path from 'path';
import { Worker } from 'worker_threads';
import chokidar from 'chokidar';
import { Logger } from '../logger.js';
import { UnifiedModelClient } from '../../refactor/unified-model-client.js';

// Core RAG Interfaces
export interface VectorDocument {
  id: string;
  content: string;
  embedding?: number[];
  metadata: DocumentMetadata;
  chunks?: DocumentChunk[];
}

export interface DocumentMetadata {
  filePath: string;
  language: string;
  fileType: string;
  lastModified: Date;
  size: number;
  hash: string;
  repository?: string;
  branch?: string;
  author?: string;
  semanticType: 'code' | 'documentation' | 'configuration' | 'test';
  extractedSymbols?: ExtractedSymbol[];
}

export interface ExtractedSymbol {
  name: string;
  type: 'function' | 'class' | 'interface' | 'variable' | 'constant';
  startLine: number;
  endLine: number;
  signature?: string;
  docstring?: string;
}

export interface DocumentChunk {
  id: string;
  content: string;
  embedding: number[];
  startOffset: number;
  endOffset: number;
  chunkType: 'function' | 'class' | 'block' | 'comment' | 'documentation';
  parentDocument: string;
  semanticWeight: number;
}

export interface RAGQuery {
  query: string;
  queryType: 'semantic' | 'exact' | 'hybrid';
  filters?: QueryFilter[];
  maxResults?: number;
  threshold?: number;
  contextWindow?: number;
  includeMetadata?: boolean;
  rerank?: boolean;
}

export interface QueryFilter {
  field: string;
  operator: 'equals' | 'contains' | 'startsWith' | 'in' | 'gt' | 'lt';
  value: string | string[] | number;
}

export interface RAGResult {
  documents: ScoredDocument[];
  totalFound: number;
  queryTime: number;
  retrievalMethod: string;
  reranked: boolean;
  debugInfo?: {
    vectorSearchTime: number;
    rerankTime: number;
    candidatesConsidered: number;
  };
}

export interface ScoredDocument {
  document: VectorDocument;
  score: number;
  relevanceExplanation?: string;
  highlightedContent?: string;
  matchedChunks?: DocumentChunk[];
}

export interface EmbeddingModel {
  name: string;
  dimensions: number;
  maxTokens: number;
  embed(text: string): Promise<number[]>;
  embedBatch(texts: string[]): Promise<number[][]>;
}

export interface VectorStore {
  initialize(): Promise<void>;
  addDocuments(documents: VectorDocument[]): Promise<void>;
  updateDocument(document: VectorDocument): Promise<void>;
  deleteDocument(id: string): Promise<void>;
  search(query: number[], filters?: QueryFilter[], maxResults?: number): Promise<ScoredDocument[]>;
  hybridSearch(query: string, vector: number[], filters?: QueryFilter[]): Promise<ScoredDocument[]>;
  getDocument(id: string): Promise<VectorDocument | null>;
  getStats(): Promise<VectorStoreStats>;
  compact(): Promise<void>;
  close(): Promise<void>;
}

export interface VectorStoreStats {
  totalDocuments: number;
  totalChunks: number;
  indexSize: number;
  memoryUsage: number;
  lastUpdated: Date;
  averageDocumentSize: number;
}

export interface CodeChunker {
  chunkDocument(document: VectorDocument): Promise<DocumentChunk[]>;
  extractSymbols(content: string, language: string): ExtractedSymbol[];
  shouldReindex(oldMetadata: DocumentMetadata, newMetadata: DocumentMetadata): boolean;
}

export interface RAGConfig {
  vectorStore: {
    provider: 'lancedb' | 'hnswsqlite' | 'memory';
    storagePath: string;
    dimensions: number;
    indexType: 'hnsw' | 'ivf' | 'flat';
    maxMemoryUsage: number;
  };
  embedding: {
    model: string;
    provider: 'transformers-js' | 'ollama' | 'local';
    batchSize: number;
    cacheEmbeddings: boolean;
  };
  chunking: {
    strategy: 'semantic' | 'fixed' | 'adaptive' | 'ast-based';
    maxChunkSize: number;
    overlapSize: number;
    respectCodeBoundaries: boolean;
  };
  indexing: {
    enabled: boolean;
    watchPaths: string[];
    debounceMs: number;
    batchSize: number;
    excludePatterns: string[];
  };
  retrieval: {
    defaultMaxResults: number;
    hybridAlpha: number; // Weight between vector and keyword search
    rerankingEnabled: boolean;
    contextExpansion: boolean;
  };
}

// Main RAG System
export class VectorRAGSystem extends EventEmitter {
  private logger: Logger;
  private config: RAGConfig;
  private vectorStore!: VectorStore;
  private embeddingModel!: EmbeddingModel;
  private codeChunker!: CodeChunker;
  private modelClient: UnifiedModelClient;
  private fileWatcher?: chokidar.FSWatcher;
  private embeddingCache: Map<string, number[]> = new Map();
  private indexingQueue: Set<string> = new Set();
  private isIndexing: boolean = false;
  private performanceMetrics: RAGMetrics;

  constructor(config: RAGConfig, modelClient: UnifiedModelClient) {
    super();
    this.logger = new Logger('VectorRAGSystem');
    this.config = config;
    this.modelClient = modelClient;
    this.performanceMetrics = new RAGMetrics();

    // Initialize components based on config
    this.initializeComponents();
  }

  /**
   * Initialize the RAG system
   */
  async initialize(): Promise<void> {
    this.logger.info('Initializing Vector RAG System...');

    try {
      // Initialize vector store
      await this.vectorStore.initialize();

      // Start file watching if enabled
      if (this.config.indexing.enabled) {
        await this.startFileWatching();
      }

      // Perform initial indexing
      await this.performInitialIndexing();

      this.logger.info('Vector RAG System initialized successfully');
      this.emit('initialized');
    } catch (error) {
      this.logger.error('Failed to initialize RAG system:', error);
      throw error;
    }
  }

  /**
   * Query the RAG system
   */
  async query(ragQuery: RAGQuery): Promise<RAGResult> {
    const startTime = Date.now();
    this.logger.info(`Processing RAG query: ${ragQuery.query.substring(0, 100)}...`);

    try {
      // Generate query embedding
      const queryEmbedding = await this.embeddingModel.embed(ragQuery.query);

      let results: ScoredDocument[];
      let retrievalMethod: string;

      // Choose retrieval strategy
      switch (ragQuery.queryType) {
        case 'semantic':
          results = await this.semanticSearch(queryEmbedding, ragQuery);
          retrievalMethod = 'semantic_vector';
          break;
        case 'hybrid':
          results = await this.hybridSearch(ragQuery.query, queryEmbedding, ragQuery);
          retrievalMethod = 'hybrid_vector_keyword';
          break;
        case 'exact':
          results = await this.exactSearch(ragQuery.query, ragQuery);
          retrievalMethod = 'exact_match';
          break;
        default:
          results = await this.semanticSearch(queryEmbedding, ragQuery);
          retrievalMethod = 'default_semantic';
      }

      // Re-rank results if enabled
      let reranked = false;
      if (ragQuery.rerank && this.config.retrieval.rerankingEnabled) {
        results = await this.rerankResults(ragQuery.query, results);
        reranked = true;
      }

      // Apply result limit
      const maxResults = ragQuery.maxResults || this.config.retrieval.defaultMaxResults;
      results = results.slice(0, maxResults);

      const queryTime = Date.now() - startTime;
      this.performanceMetrics.recordQuery(queryTime, results.length, retrievalMethod);

      const ragResult: RAGResult = {
        documents: results,
        totalFound: results.length,
        queryTime,
        retrievalMethod,
        reranked,
      };

      this.emit('query:completed', { query: ragQuery, result: ragResult });
      return ragResult;
    } catch (error) {
      this.logger.error('RAG query failed:', error);
      this.emit('query:failed', { query: ragQuery, error });
      throw error;
    }
  }

  /**
   * Index a single document
   */
  async indexDocument(filePath: string): Promise<void> {
    try {
      const document = await this.createVectorDocument(filePath);
      if (!document) return;

      // Generate embeddings for document and chunks
      await this.generateEmbeddings(document);

      // Store in vector database
      await this.vectorStore.addDocuments([document]);

      this.logger.debug(`Indexed document: ${filePath}`);
      this.emit('document:indexed', { filePath, document });
    } catch (error) {
      this.logger.error(`Failed to index document ${filePath}:`, error);
      this.emit('document:failed', { filePath, error });
    }
  }

  /**
   * Update an existing document
   */
  async updateDocument(filePath: string): Promise<void> {
    try {
      const existingDoc = await this.vectorStore.getDocument(filePath);
      const newDocument = await this.createVectorDocument(filePath);

      if (!newDocument) {
        if (existingDoc) {
          await this.vectorStore.deleteDocument(filePath);
          this.logger.debug(`Removed deleted document: ${filePath}`);
        }
        return;
      }

      // Check if document actually changed
      if (
        existingDoc &&
        !this.codeChunker.shouldReindex(existingDoc.metadata, newDocument.metadata)
      ) {
        this.logger.debug(`Document unchanged, skipping: ${filePath}`);
        return;
      }

      await this.generateEmbeddings(newDocument);
      await this.vectorStore.updateDocument(newDocument);

      this.logger.debug(`Updated document: ${filePath}`);
      this.emit('document:updated', { filePath, document: newDocument });
    } catch (error) {
      this.logger.error(`Failed to update document ${filePath}:`, error);
    }
  }

  /**
   * Get system statistics
   */
  async getStats(): Promise<RAGSystemStats> {
    const storeStats = await this.vectorStore.getStats();

    return {
      vectorStore: storeStats,
      performance: this.performanceMetrics.getStats(),
      indexing: {
        queueSize: this.indexingQueue.size,
        isIndexing: this.isIndexing,
        watchedPaths: this.config.indexing.watchPaths.length,
        cacheSize: this.embeddingCache.size,
      },
      config: this.config,
    };
  }

  /**
   * Private Methods
   */

  private initializeComponents(): void {
    // Initialize vector store
    switch (this.config.vectorStore.provider) {
      case 'lancedb':
        this.vectorStore = new LanceDBVectorStore(this.config.vectorStore);
        break;
      case 'hnswsqlite':
        this.vectorStore = new HNSWSQLiteVectorStore(this.config.vectorStore);
        break;
      default:
        this.vectorStore = new MemoryVectorStore(this.config.vectorStore);
    }

    // Initialize embedding model
    switch (this.config.embedding.provider) {
      case 'transformers-js':
        this.embeddingModel = new TransformersJSEmbedding(this.config.embedding);
        break;
      case 'ollama':
        this.embeddingModel = new OllamaEmbedding(this.config.embedding, this.modelClient);
        break;
      default:
        this.embeddingModel = new LocalEmbedding(this.config.embedding);
    }

    // Initialize code chunker
    this.codeChunker = new ASTBasedCodeChunker(this.config.chunking);
  }

  private async startFileWatching(): Promise<void> {
    if (this.fileWatcher) {
      await this.fileWatcher.close();
    }

    this.fileWatcher = chokidar.watch(this.config.indexing.watchPaths, {
      ignored: this.config.indexing.excludePatterns,
      persistent: true,
      ignoreInitial: true,
    });

    const debouncedIndex = this.debounce(
      (filePath: string) => this.queueForIndexing(filePath),
      this.config.indexing.debounceMs
    );

    this.fileWatcher
      .on('add', debouncedIndex)
      .on('change', debouncedIndex)
      .on('unlink', filePath => this.vectorStore.deleteDocument(filePath));

    this.logger.info(`Watching ${this.config.indexing.watchPaths.length} paths for changes`);
  }

  private async performInitialIndexing(): Promise<void> {
    this.logger.info('Starting initial indexing...');

    for (const watchPath of this.config.indexing.watchPaths) {
      await this.indexDirectory(watchPath);
    }

    this.logger.info('Initial indexing completed');
  }

  private async indexDirectory(dirPath: string): Promise<void> {
    try {
      const entries = await fs.readdir(dirPath, { withFileTypes: true });

      for (const entry of entries) {
        const fullPath = path.join(dirPath, entry.name);

        if (entry.isDirectory()) {
          await this.indexDirectory(fullPath);
        } else if (this.shouldIndexFile(fullPath)) {
          await this.indexDocument(fullPath);
        }
      }
    } catch (error) {
      this.logger.error(`Failed to index directory ${dirPath}:`, error);
    }
  }

  private shouldIndexFile(filePath: string): boolean {
    const ext = path.extname(filePath).toLowerCase();
    const codeExtensions = ['.ts', '.js', '.py', '.java', '.cpp', '.c', '.h', '.rs', '.go', '.php'];
    const docExtensions = ['.md', '.txt', '.rst', '.adoc'];

    return codeExtensions.includes(ext) || docExtensions.includes(ext);
  }

  private async createVectorDocument(filePath: string): Promise<VectorDocument | null> {
    try {
      const stats = await fs.stat(filePath);
      const content = await fs.readFile(filePath, 'utf-8');
      const hash = this.calculateHash(content);

      const metadata: DocumentMetadata = {
        filePath,
        language: this.detectLanguage(filePath),
        fileType: path.extname(filePath),
        lastModified: stats.mtime,
        size: stats.size,
        hash,
        semanticType: this.detectSemanticType(filePath),
        extractedSymbols: this.codeChunker.extractSymbols(content, this.detectLanguage(filePath)),
      };

      const document: VectorDocument = {
        id: filePath,
        content,
        metadata,
      };

      // Generate chunks
      document.chunks = await this.codeChunker.chunkDocument(document);

      return document;
    } catch (error) {
      this.logger.error(`Failed to create document for ${filePath}:`, error);
      return null;
    }
  }

  private async generateEmbeddings(document: VectorDocument): Promise<void> {
    // Check cache first
    const cacheKey = `${document.id}:${document.metadata.hash}`;
    if (this.embeddingCache.has(cacheKey)) {
      document.embedding = this.embeddingCache.get(cacheKey);
    } else {
      document.embedding = await this.embeddingModel.embed(document.content);

      if (this.config.embedding.cacheEmbeddings) {
        this.embeddingCache.set(cacheKey, document.embedding);
      }
    }

    // Generate embeddings for chunks
    if (document.chunks) {
      const chunkTexts = document.chunks.map(chunk => chunk.content);
      const chunkEmbeddings = await this.embeddingModel.embedBatch(chunkTexts);

      document.chunks.forEach((chunk, index) => {
        chunk.embedding = chunkEmbeddings[index];
      });
    }
  }

  private async semanticSearch(
    queryEmbedding: number[],
    ragQuery: RAGQuery
  ): Promise<ScoredDocument[]> {
    return await this.vectorStore.search(
      queryEmbedding,
      ragQuery.filters,
      ragQuery.maxResults || this.config.retrieval.defaultMaxResults
    );
  }

  private async hybridSearch(
    query: string,
    queryEmbedding: number[],
    ragQuery: RAGQuery
  ): Promise<ScoredDocument[]> {
    return await this.vectorStore.hybridSearch(query, queryEmbedding, ragQuery.filters);
  }

  private async exactSearch(query: string, ragQuery: RAGQuery): Promise<ScoredDocument[]> {
    // Implement exact text matching
    const results: ScoredDocument[] = [];
    // Implementation would search for exact text matches
    return results;
  }

  private async rerankResults(query: string, results: ScoredDocument[]): Promise<ScoredDocument[]> {
    // Use LLM to rerank results based on relevance
    const rerankPrompt = `
      Query: ${query}
      
      Rank the following code snippets by relevance to the query (1 = most relevant):
      ${results.map((r, i) => `${i + 1}. ${r.document.content.substring(0, 200)}...`).join('\n')}
      
      Return only the numbers in order of relevance.
    `;

    try {
      const response = await this.modelClient.synthesize({
        prompt: rerankPrompt,
        maxTokens: 100,
      });

      const rankings = this.parseRankings(response.content);
      return this.applyRankings(results, rankings);
    } catch (error) {
      this.logger.warn('Reranking failed, returning original results:', error);
      return results;
    }
  }

  private parseRankings(response: string): number[] {
    const numbers = response.match(/\d+/g);
    return numbers ? numbers.map(n => parseInt(n) - 1) : [];
  }

  private applyRankings(results: ScoredDocument[], rankings: number[]): ScoredDocument[] {
    if (rankings.length === 0) return results;

    const reranked: ScoredDocument[] = [];
    for (const rank of rankings) {
      if (rank >= 0 && rank < results.length) {
        reranked.push(results[rank]);
      }
    }

    // Add any remaining results
    for (let i = 0; i < results.length; i++) {
      if (!rankings.includes(i)) {
        reranked.push(results[i]);
      }
    }

    return reranked;
  }

  private queueForIndexing(filePath: string): void {
    this.indexingQueue.add(filePath);
    this.processIndexingQueue();
  }

  private async processIndexingQueue(): Promise<void> {
    if (this.isIndexing || this.indexingQueue.size === 0) return;

    this.isIndexing = true;
    const batch = Array.from(this.indexingQueue).slice(0, this.config.indexing.batchSize);
    this.indexingQueue.clear();

    try {
      await Promise.all(batch.map(filePath => this.updateDocument(filePath)));
    } catch (error) {
      this.logger.error('Batch indexing failed:', error);
    } finally {
      this.isIndexing = false;

      // Process any new items that were added
      if (this.indexingQueue.size > 0) {
        setTimeout(() => this.processIndexingQueue(), 100);
      }
    }
  }

  private detectLanguage(filePath: string): string {
    const ext = path.extname(filePath).toLowerCase();
    const languageMap: Record<string, string> = {
      '.ts': 'typescript',
      '.js': 'javascript',
      '.py': 'python',
      '.java': 'java',
      '.cpp': 'cpp',
      '.c': 'c',
      '.h': 'c',
      '.rs': 'rust',
      '.go': 'go',
      '.php': 'php',
      '.md': 'markdown',
      '.txt': 'text',
    };

    return languageMap[ext] || 'unknown';
  }

  private detectSemanticType(
    filePath: string
  ): 'code' | 'documentation' | 'configuration' | 'test' {
    const fileName = path.basename(filePath).toLowerCase();

    if (fileName.includes('test') || fileName.includes('spec')) return 'test';
    if (fileName.includes('config') || fileName.includes('setting')) return 'configuration';
    if (fileName.endsWith('.md') || fileName.endsWith('.txt')) return 'documentation';

    return 'code';
  }

  private calculateHash(content: string): string {
    // Simple hash function - in production, use crypto.createHash
    let hash = 0;
    for (let i = 0; i < content.length; i++) {
      const char = content.charCodeAt(i);
      hash = (hash << 5) - hash + char;
      hash = hash & hash; // Convert to 32-bit integer
    }
    return hash.toString(36);
  }

  private debounce<T extends (...args: any[]) => void>(
    func: T,
    delay: number
  ): (...args: Parameters<T>) => void {
    let timeoutId: NodeJS.Timeout;
    return (...args: any[]) => {
      clearTimeout(timeoutId);
      timeoutId = setTimeout(() => func.apply(this, args), delay);
    };
  }

  /**
   * Public API methods
   */
  async shutdown(): Promise<void> {
    this.logger.info('Shutting down RAG system...');

    if (this.fileWatcher) {
      await this.fileWatcher.close();
    }

    await this.vectorStore.close();
    this.embeddingCache.clear();

    this.logger.info('RAG system shutdown completed');
  }

  async compactIndex(): Promise<void> {
    await this.vectorStore.compact();
    this.logger.info('Vector index compacted');
  }

  async clearCache(): Promise<void> {
    this.embeddingCache.clear();
    this.logger.info('Embedding cache cleared');
  }
}

// Supporting Classes and Interfaces
export interface RAGSystemStats {
  vectorStore: VectorStoreStats;
  performance: PerformanceStats;
  indexing: {
    queueSize: number;
    isIndexing: boolean;
    watchedPaths: number;
    cacheSize: number;
  };
  config: RAGConfig;
}

export interface PerformanceStats {
  totalQueries: number;
  averageQueryTime: number;
  averageResultsPerQuery: number;
  cacheHitRate: number;
  methodBreakdown: Record<string, number>;
}

class RAGMetrics {
  private queries: number = 0;
  private totalQueryTime: number = 0;
  private totalResults: number = 0;
  private methodCounts: Map<string, number> = new Map();

  recordQuery(queryTime: number, resultCount: number, method: string): void {
    this.queries++;
    this.totalQueryTime += queryTime;
    this.totalResults += resultCount;
    this.methodCounts.set(method, (this.methodCounts.get(method) || 0) + 1);
  }

  getStats(): PerformanceStats {
    return {
      totalQueries: this.queries,
      averageQueryTime: this.queries > 0 ? this.totalQueryTime / this.queries : 0,
      averageResultsPerQuery: this.queries > 0 ? this.totalResults / this.queries : 0,
      cacheHitRate: 0, // Would be calculated based on cache metrics
      methodBreakdown: Object.fromEntries(this.methodCounts),
    };
  }
}

// Placeholder implementations - these would be separate files in production
class LanceDBVectorStore implements VectorStore {
  constructor(private config: any) {}
  async initialize(): Promise<void> {
    /* Implementation */
  }
  async addDocuments(documents: VectorDocument[]): Promise<void> {
    /* Implementation */
  }
  async updateDocument(document: VectorDocument): Promise<void> {
    /* Implementation */
  }
  async deleteDocument(id: string): Promise<void> {
    /* Implementation */
  }
  async search(
    query: number[],
    filters?: QueryFilter[],
    maxResults?: number
  ): Promise<ScoredDocument[]> {
    return [];
  }
  async hybridSearch(
    query: string,
    vector: number[],
    filters?: QueryFilter[]
  ): Promise<ScoredDocument[]> {
    return [];
  }
  async getDocument(id: string): Promise<VectorDocument | null> {
    return null;
  }
  async getStats(): Promise<VectorStoreStats> {
    return {} as VectorStoreStats;
  }
  async compact(): Promise<void> {
    /* Implementation */
  }
  async close(): Promise<void> {
    /* Implementation */
  }
}

class HNSWSQLiteVectorStore implements VectorStore {
  constructor(private config: any) {}
  async initialize(): Promise<void> {
    /* Implementation */
  }
  async addDocuments(documents: VectorDocument[]): Promise<void> {
    /* Implementation */
  }
  async updateDocument(document: VectorDocument): Promise<void> {
    /* Implementation */
  }
  async deleteDocument(id: string): Promise<void> {
    /* Implementation */
  }
  async search(
    query: number[],
    filters?: QueryFilter[],
    maxResults?: number
  ): Promise<ScoredDocument[]> {
    return [];
  }
  async hybridSearch(
    query: string,
    vector: number[],
    filters?: QueryFilter[]
  ): Promise<ScoredDocument[]> {
    return [];
  }
  async getDocument(id: string): Promise<VectorDocument | null> {
    return null;
  }
  async getStats(): Promise<VectorStoreStats> {
    return {} as VectorStoreStats;
  }
  async compact(): Promise<void> {
    /* Implementation */
  }
  async close(): Promise<void> {
    /* Implementation */
  }
}

class MemoryVectorStore implements VectorStore {
  private documents: Map<string, VectorDocument> = new Map();

  constructor(private config: any) {}

  async initialize(): Promise<void> {}

  async addDocuments(documents: VectorDocument[]): Promise<void> {
    for (const doc of documents) {
      this.documents.set(doc.id, doc);
    }
  }

  async updateDocument(document: VectorDocument): Promise<void> {
    this.documents.set(document.id, document);
  }

  async deleteDocument(id: string): Promise<void> {
    this.documents.delete(id);
  }

  async search(
    query: number[],
    filters?: QueryFilter[],
    maxResults?: number
  ): Promise<ScoredDocument[]> {
    const results: ScoredDocument[] = [];

    for (const doc of this.documents.values()) {
      if (doc.embedding) {
        const similarity = this.cosineSimilarity(query, doc.embedding);
        results.push({
          document: doc,
          score: similarity,
        });
      }
    }

    return results.sort((a, b) => b.score - a.score).slice(0, maxResults || 10);
  }

  async hybridSearch(
    query: string,
    vector: number[],
    filters?: QueryFilter[]
  ): Promise<ScoredDocument[]> {
    return this.search(vector, filters);
  }

  async getDocument(id: string): Promise<VectorDocument | null> {
    return this.documents.get(id) || null;
  }

  async getStats(): Promise<VectorStoreStats> {
    return {
      totalDocuments: this.documents.size,
      totalChunks: 0,
      indexSize: 0,
      memoryUsage: 0,
      lastUpdated: new Date(),
      averageDocumentSize: 0,
    };
  }

  async compact(): Promise<void> {}
  async close(): Promise<void> {}

  private cosineSimilarity(a: number[], b: number[]): number {
    let dotProduct = 0;
    let normA = 0;
    let normB = 0;

    for (let i = 0; i < a.length; i++) {
      dotProduct += a[i] * b[i];
      normA += a[i] * a[i];
      normB += b[i] * b[i];
    }

    return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
  }
}

class TransformersJSEmbedding implements EmbeddingModel {
  name = 'transformers-js';
  dimensions = 384;
  maxTokens = 512;

  constructor(private config: any) {}

  async embed(text: string): Promise<number[]> {
    // Placeholder - would use @xenova/transformers
    return new Array(this.dimensions).fill(0).map(() => Math.random());
  }

  async embedBatch(texts: string[]): Promise<number[][]> {
    return Promise.all(texts.map(text => this.embed(text)));
  }
}

class OllamaEmbedding implements EmbeddingModel {
  name = 'ollama';
  dimensions = 4096;
  maxTokens = 2048;

  constructor(
    private config: any,
    private modelClient: UnifiedModelClient
  ) {}

  async embed(text: string): Promise<number[]> {
    // Placeholder - would use Ollama embedding API
    return new Array(this.dimensions).fill(0).map(() => Math.random());
  }

  async embedBatch(texts: string[]): Promise<number[][]> {
    return Promise.all(texts.map(text => this.embed(text)));
  }
}

class LocalEmbedding implements EmbeddingModel {
  name = 'local';
  dimensions = 768;
  maxTokens = 512;

  constructor(private config: any) {}

  async embed(text: string): Promise<number[]> {
    // Placeholder for local embedding model
    return new Array(this.dimensions).fill(0).map(() => Math.random());
  }

  async embedBatch(texts: string[]): Promise<number[][]> {
    return Promise.all(texts.map(text => this.embed(text)));
  }
}

class ASTBasedCodeChunker implements CodeChunker {
  constructor(private config: any) {}

  async chunkDocument(document: VectorDocument): Promise<DocumentChunk[]> {
    const chunks: DocumentChunk[] = [];
    const content = document.content;
    const lines = content.split('\n');

    // Simple line-based chunking for now
    const chunkSize = this.config.maxChunkSize || 500;
    const overlap = this.config.overlapSize || 50;

    for (let i = 0; i < lines.length; i += chunkSize - overlap) {
      const chunkLines = lines.slice(i, i + chunkSize);
      const chunkContent = chunkLines.join('\n');

      chunks.push({
        id: `${document.id}:chunk:${i}`,
        content: chunkContent,
        embedding: [], // Will be filled later
        startOffset: i,
        endOffset: i + chunkLines.length,
        chunkType: 'block',
        parentDocument: document.id,
        semanticWeight: 1.0,
      });
    }

    return chunks;
  }

  extractSymbols(content: string, language: string): ExtractedSymbol[] {
    const symbols: ExtractedSymbol[] = [];
    const lines = content.split('\n');

    // Simple regex-based symbol extraction
    const patterns = {
      function: /function\s+(\w+)\s*\(/,
      class: /class\s+(\w+)/,
      interface: /interface\s+(\w+)/,
      variable: /(?:const|let|var)\s+(\w+)/,
    };

    lines.forEach((line, index) => {
      for (const [type, pattern] of Object.entries(patterns)) {
        const match = line.match(pattern);
        if (match) {
          symbols.push({
            name: match[1],
            type: type as any,
            startLine: index + 1,
            endLine: index + 1,
            signature: line.trim(),
          });
        }
      }
    });

    return symbols;
  }

  shouldReindex(oldMetadata: DocumentMetadata, newMetadata: DocumentMetadata): boolean {
    return (
      oldMetadata.hash !== newMetadata.hash ||
      oldMetadata.lastModified.getTime() !== newMetadata.lastModified.getTime()
    );
  }
}