import { PineconeClient } from '@pinecone-database/pinecone';
import { ClaudeCode } from '@anthropic-ai/claude-code-sdk';
 
const pinecone = new PineconeClient();
await pinecone.init({
  apiKey: process.env.PINECONE_API_KEY,
  environment: 'us-east-1'
});
 
const index = pinecone.Index('code-search');

import weaviate from 'weaviate-ts-client';
 
const client = weaviate.client({
  scheme: 'http',
  host: 'localhost:8080',
});
 
// Create schema for code embeddings
const codeSchema = {
  class: 'CodeSnippet',
  vectorizer: 'text2vec-openai',
  properties: [
    { name: 'content', dataType: ['text'] },
    { name: 'filepath', dataType: ['string'] },
    { name: 'language', dataType: ['string'] },
    { name: 'ast', dataType: ['text'] }
  ]
};

import { CodeEmbedder } from './embeddings';
import { parse } from '@babel/parser';
 
class CodeIndexer {
  constructor(
    private claude: ClaudeCode,
    private vectorDB: VectorDatabase
  ) {}
 
  async indexCode(filePath: string, content: string) {
    // Parse AST for structural understanding
    const ast = parse(content, {
      sourceType: 'module',
      plugins: ['typescript', 'jsx']
    });
 
    // Generate semantic embedding
    const embedding = await this.generateEmbedding(content, ast);
    
    // Store in vector database
    await this.vectorDB.upsert({
      id: filePath,
      values: embedding,
      metadata: {
        filepath: filePath,
        language: this.detectLanguage(filePath),
        lastModified: Date.now(),
        astFeatures: this.extractASTFeatures(ast)
      }
    });
  }
 
  private async generateEmbedding(code: string, ast: any) {
    // Combine code content with AST features
    const enhancedContext = `
      Code: ${code}
      Structure: ${this.summarizeAST(ast)}
      Purpose: ${await this.claude.analyze(code)}
    `;
 
    return await this.embedder.embed(enhancedContext);
  }
}

class SemanticCodeSearch {
  async search(query: string, options: SearchOptions = {}) {
    // Enhance query with context
    const enhancedQuery = await this.enhanceQuery(query);
    
    // Generate query embedding
    const queryEmbedding = await this.embedder.embed(enhancedQuery);
    
    // Search vector database
    const results = await this.vectorDB.query({
      vector: queryEmbedding,
      topK: options.limit || 10,
      includeMetadata: true,
      filter: this.buildFilter(options)
    });
 
    // Re-rank with Claude for better relevance
    return await this.rerank(query, results);
  }
 
  private async enhanceQuery(query: string) {
    const response = await this.claude.complete({
      prompt: `Enhance this code search query with related concepts:
        Query: ${query}
        Include: synonyms, related patterns, common implementations`,
      max_tokens: 100
    });
 
    return `${query} ${response}`;
  }
 
  private async rerank(query: string, results: SearchResult[]) {
    const reranked = await this.claude.complete({
      prompt: `Rerank these code search results for query "${query}":
        ${results.map((r, i) => `${i}: ${r.snippet}`).join('\n')}
        
        Order by relevance, considering:
        - Exact behavior match
        - Code quality
        - Modern patterns`,
      max_tokens: 50
    });
 
    return this.parseRanking(reranked, results);
  }
}

// Combine code, comments, and documentation
class MultiModalIndexer {
  async index(file: CodeFile) {
    const embeddings = await Promise.all([
      this.embedCode(file.content),
      this.embedComments(file.comments),
      this.embedDocs(file.documentation)
    ]);
 
    // Weighted combination
    const combined = this.combineEmbeddings(embeddings, {
      code: 0.5,
      comments: 0.3,
      docs: 0.2
    });
 
    await this.vectorDB.upsert({
      id: file.path,
      values: combined,
      metadata: file.metadata
    });
  }
}

// Enhance embeddings with structural information
class ASTEnhancer {
  enhanceWithAST(code: string, language: Language) {
    const ast = this.parseAST(code, language);
    
    const features = {
      complexity: this.calculateComplexity(ast),
      patterns: this.detectPatterns(ast),
      dependencies: this.extractDependencies(ast),
      symbols: this.extractSymbols(ast)
    };
 
    return this.combineWithEmbedding(code, features);
  }
 
  private detectPatterns(ast: AST) {
    return {
      hasAsync: this.hasAsyncPatterns(ast),
      usesGenerics: this.hasGenerics(ast),
      designPattern: this.identifyDesignPattern(ast)
    };
  }
}

// Efficiently update embeddings on code changes
class IncrementalIndexer {
  async updateIndex(changes: FileChange[]) {
    const batches = this.batchChanges(changes);
    
    for (const batch of batches) {
      const updates = await Promise.all(
        batch.map(async (change) => {
          if (change.type === 'delete') {
            return { delete: change.path };
          }
          
          const embedding = await this.generateEmbedding(change);
          return {
            upsert: {
              id: change.path,
              values: embedding,
              metadata: this.extractMetadata(change)
            }
          };
        })
      );
 
      await this.vectorDB.batch(updates);
    }
  }
}

class ClaudeCodeIntegration {
  async analyzeAndIndex(filePath: string) {
    const content = await fs.readFile(filePath, 'utf-8');
    
    // Use Claude to understand code purpose
    const analysis = await this.claude.analyze({
      code: content,
      instructions: `Analyze this code and provide:
        1. Main purpose and functionality
        2. Key algorithms or patterns used
        3. Dependencies and interactions
        4. Potential search terms`
    });
 
    // Generate rich embedding
    const embedding = await this.embedder.embed({
      code: content,
      analysis: analysis,
      metadata: await this.extractMetadata(filePath)
    });
 
    await this.vectorDB.index({
      path: filePath,
      embedding: embedding,
      searchableText: analysis.searchTerms
    });
  }
}

// Use Claude to improve search queries
async function enhancedSearch(query: string) {
  // Get Claude's interpretation
  const interpretation = await claude.complete({
    prompt: `Interpret this code search query:
      "${query}"
      
      Provide:
      1. What the user is likely looking for
      2. Related concepts and synonyms
      3. Common implementation patterns`,
    max_tokens: 200
  });
 
  // Search with enhanced understanding
  const results = await vectorSearch.search({
    original: query,
    enhanced: interpretation,
    weights: {
      exact: 0.3,
      semantic: 0.5,
      pattern: 0.2
    }
  });
 
  return results;
}

class EmbeddingCache {
  private cache = new LRUCache<string, Float32Array>({
    max: 10000,
    ttl: 1000 * 60 * 60 * 24 // 24 hours
  });
 
  async getEmbedding(content: string) {
    const hash = this.hash(content);
    
    if (this.cache.has(hash)) {
      return this.cache.get(hash);
    }
 
    const embedding = await this.generate(content);
    this.cache.set(hash, embedding);
    
    return embedding;
  }
}

// Process files in batches for efficiency
async function batchIndex(files: string[]) {
  const BATCH_SIZE = 100;
  
  for (let i = 0; i < files.length; i += BATCH_SIZE) {
    const batch = files.slice(i, i + BATCH_SIZE);
    
    const embeddings = await Promise.all(
      batch.map(file => generateEmbedding(file))
    );
 
    await vectorDB.batchUpsert(
      batch.map((file, idx) => ({
        id: file,
        values: embeddings[idx]
      }))
    );
  }
}

// Natural language queries that work with semantic search
const exampleQueries = [
  "authentication middleware that validates JWT tokens",
  "React hooks for managing form state",
  "functions that implement retry logic with exponential backoff",
  "database connection pooling implementations",
  "code similar to Redux but using Context API"
];
 
// Query with filters
const results = await search.query({
  text: "error handling with retry logic",
  filters: {
    language: "typescript",
    minQuality: 0.8,
    hasTests: true,
    modifiedAfter: "2024-01-01"
  }
});