6190 lines
278 KiB
Python
6190 lines
278 KiB
Python
#!/usr/bin/env python3
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"""
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AI Analysis Service HTTP Server
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Provides REST API endpoints for repository analysis.
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"""
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import os
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import asyncio
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import json
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import tempfile
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import shutil
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import time
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import hashlib
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import traceback
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import uuid
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from pathlib import Path
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from typing import Dict, Any, Optional, List, Tuple
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from datetime import datetime
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from contextlib import asynccontextmanager
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from fastapi import FastAPI, HTTPException, BackgroundTasks, Request
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import FileResponse, JSONResponse, StreamingResponse
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from pydantic import BaseModel
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import uvicorn
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import mimetypes
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import httpx
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import redis
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# PostgreSQL cursor for querying
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try:
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from psycopg2.extras import RealDictCursor
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except ImportError:
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# Fallback if psycopg2 not available
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RealDictCursor = None
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# Import the AI analysis components
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# Note: ai-analyze.py has a hyphen, so we need to handle the import specially
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import sys
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import importlib.util
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# Load the ai-analyze.py module
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spec = importlib.util.spec_from_file_location("ai_analyze", "ai-analyze.py")
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ai_analyze_module = importlib.util.module_from_spec(spec)
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sys.modules["ai_analyze"] = ai_analyze_module
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spec.loader.exec_module(ai_analyze_module)
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# Now import the classes
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from ai_analyze import (
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EnhancedGitHubAnalyzer,
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get_memory_config,
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ArchitectureAnalysis,
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SecurityAnalysis,
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CodeQualityAnalysis,
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PerformanceAnalysis,
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Issue,
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ModuleAnalysis,
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ModuleSummary
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)
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# Import enhanced analyzer (backward compatible)
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try:
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from enhanced_analyzer import EnhancedGitHubAnalyzerV2, create_enhanced_analyzer
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ENHANCED_ANALYZER_AVAILABLE = True
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except ImportError as e:
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print(f"Enhanced analyzer not available: {e}")
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ENHANCED_ANALYZER_AVAILABLE = False
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# Import progress manager
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from progress_manager import AnalysisProgressManager, progress_tracker
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# Global analyzer instance
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analyzer = None
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""Lifespan context manager for startup and shutdown events."""
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# Startup
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global analyzer
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try:
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# Load environment variables
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from dotenv import load_dotenv
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load_dotenv()
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# Get API key
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api_key = os.getenv('ANTHROPIC_API_KEY')
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if not api_key:
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raise Exception("ANTHROPIC_API_KEY not found in environment")
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# Initialize analyzer with enhanced capabilities if available
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config = get_memory_config()
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# Add OPTIMIZED performance settings to config
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config.update({
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'max_workers': 10, # Optimized parallel processing workers (REDUCED for better throughput)
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'batch_size': 10, # REDUCED batch size for faster first results (was 20)
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'cache_ttl': 3600, # Cache TTL (1 hour)
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'max_file_size': 0, # No file size limit (0 = unlimited)
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'analysis_timeout': 1800, # 30 minute timeout for large repositories
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'fast_mode': False, # Disable fast mode to use full AI analysis
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'parallel_processing': True, # Enable parallel processing
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'rate_limit_batch_size': 10, # REDUCED batch size for rate limiting (was 20)
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'redis_host': 'pipeline_redis', # Use Docker service name for Redis
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'redis_port': 6379, # Use standard Redis port
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'redis_password': 'redis_secure_2024',
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'mongodb_url': 'mongodb://pipeline_admin:mongo_secure_2024@pipeline_mongodb:27017/',
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'postgres_host': 'pipeline_postgres',
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'postgres_password': 'secure_pipeline_2024'
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})
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if ENHANCED_ANALYZER_AVAILABLE:
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print("✅ Using Enhanced Analyzer with intelligent chunking and parallel processing")
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analyzer = create_enhanced_analyzer(api_key, config)
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else:
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print("✅ Using Standard Analyzer with performance optimizations")
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analyzer = EnhancedGitHubAnalyzer(api_key, config)
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print("✅ AI Analysis Service initialized successfully")
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except Exception as e:
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print(f"❌ Failed to initialize AI Analysis Service: {e}")
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raise
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yield
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# Shutdown (if needed)
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# Cleanup code can go here if needed
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app = FastAPI(
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title="AI Analysis Service",
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description="AI-powered repository analysis with memory system",
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version="1.0.0",
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lifespan=lifespan
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)
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# CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Optimized Token Bucket Rate Limiter for Claude API
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# Updated to match actual billing plan: 2K requests/min, token limits vary by model
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class TokenBucketRateLimiter:
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def __init__(self, capacity: int = 2000, refill_rate: float = 33.33):
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"""
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Token bucket for request rate limiting.
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Default: 2000 requests/minute (2K per billing plan)
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Refill rate: 2000 / 60 = 33.33 requests per second
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"""
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self.capacity = capacity
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self.tokens = capacity
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self.refill_rate = refill_rate # tokens per second (2000 requests / 60 seconds = 33.33)
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self.last_update = time.time()
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self.lock = asyncio.Lock()
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async def acquire(self, tokens: int = 1):
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"""Acquire tokens from the bucket."""
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async with self.lock:
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now = time.time()
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# Refill tokens based on time elapsed
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elapsed = now - self.last_update
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self.tokens = min(self.capacity, self.tokens + elapsed * self.refill_rate)
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self.last_update = now
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if self.tokens >= tokens:
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self.tokens -= tokens
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return True
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else:
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# Wait for tokens to refill
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wait_time = (tokens - self.tokens) / self.refill_rate
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await asyncio.sleep(wait_time)
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self.tokens = 0
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return True
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# Token-based rate limiter (NEW - for input/output token limits)
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class TokenUsageRateLimiter:
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"""
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Rate limiter for token usage per minute based on billing plan.
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Tracks both input and output tokens separately.
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"""
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# Billing plan limits (from your API plan)
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PLAN_LIMITS = {
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"claude-3-5-haiku-latest": {
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"requests_per_minute": 2000,
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"input_tokens_per_minute": 200_000, # Claude Haiku 3.5: 200K input/min
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"output_tokens_per_minute": 40_000, # Claude Haiku 3.5: 40K output/min
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},
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"claude-3-5-sonnet-20241022": {
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"requests_per_minute": 2000,
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"input_tokens_per_minute": 800_000, # Claude Sonnet 4.x: 800K input/min
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"output_tokens_per_minute": 160_000, # Claude Sonnet 4.x: 160K output/min
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},
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"claude-3-opus-20240229": {
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"requests_per_minute": 2000,
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"input_tokens_per_minute": 800_000, # Claude Opus 4.x: 800K input/min
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"output_tokens_per_minute": 160_000, # Claude Opus 4.x: 160K output/min
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},
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"claude-3-5-haiku-20241022": {
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"requests_per_minute": 2000,
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"input_tokens_per_minute": 1_000_000, # Claude Haiku 4.x: 1M input/min
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"output_tokens_per_minute": 200_000, # Claude Haiku 4.x: 200K output/min
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},
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}
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def __init__(self, model: str = "claude-3-5-haiku-latest"):
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self.model = model
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limits = self.PLAN_LIMITS.get(model, self.PLAN_LIMITS["claude-3-5-haiku-latest"])
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self.input_tokens_per_minute = limits["input_tokens_per_minute"]
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self.output_tokens_per_minute = limits["output_tokens_per_minute"]
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# Token usage tracking (sliding window)
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self.input_token_usage = [] # List of (timestamp, tokens) tuples
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self.output_token_usage = [] # List of (timestamp, tokens) tuples
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self.lock = asyncio.Lock()
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print(f"📊 [RATE LIMITER] Initialized for model: {model}")
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print(f" • Input tokens/min: {self.input_tokens_per_minute:,}")
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print(f" • Output tokens/min: {self.output_tokens_per_minute:,}")
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def _cleanup_old_usage(self, usage_list: List[Tuple[float, int]], window_seconds: int = 60):
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"""Remove usage records older than window_seconds."""
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now = time.time()
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cutoff = now - window_seconds
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return [(ts, tokens) for ts, tokens in usage_list if ts > cutoff]
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async def check_token_limits(self, input_tokens: int, output_tokens: int) -> Tuple[bool, float]:
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"""
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Check if token usage would exceed limits.
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Returns: (can_proceed, wait_time_seconds)
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"""
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async with self.lock:
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now = time.time()
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# Clean up old usage records (sliding 60-second window)
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self.input_token_usage = self._cleanup_old_usage(self.input_token_usage, 60)
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self.output_token_usage = self._cleanup_old_usage(self.output_token_usage, 60)
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# Calculate current usage in the last minute
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current_input_usage = sum(tokens for _, tokens in self.input_token_usage)
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current_output_usage = sum(tokens for _, tokens in self.output_token_usage)
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# Check if adding these tokens would exceed limits
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new_input_usage = current_input_usage + input_tokens
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new_output_usage = current_output_usage + output_tokens
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input_exceeded = new_input_usage > self.input_tokens_per_minute
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output_exceeded = new_output_usage > self.output_tokens_per_minute
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if input_exceeded or output_exceeded:
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# Calculate wait time (wait until oldest usage expires)
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if self.input_token_usage:
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oldest_input_time = min(ts for ts, _ in self.input_token_usage)
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wait_time = max(0, 60 - (now - oldest_input_time))
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elif self.output_token_usage:
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oldest_output_time = min(ts for ts, _ in self.output_token_usage)
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wait_time = max(0, 60 - (now - oldest_output_time))
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else:
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wait_time = 0
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if input_exceeded:
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print(f"⚠️ [TOKEN LIMIT] Input tokens would exceed limit!")
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print(f" Current: {current_input_usage:,} + {input_tokens:,} = {new_input_usage:,}")
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print(f" Limit: {self.input_tokens_per_minute:,} input tokens/min")
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print(f" Wait time: {wait_time:.2f} seconds")
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if output_exceeded:
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print(f"⚠️ [TOKEN LIMIT] Output tokens would exceed limit!")
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print(f" Current: {current_output_usage:,} + {output_tokens:,} = {new_output_usage:,}")
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print(f" Limit: {self.output_tokens_per_minute:,} output tokens/min")
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print(f" Wait time: {wait_time:.2f} seconds")
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return False, wait_time
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# Don't record usage here - that's done by record_token_usage() after API call
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# This method only checks if we can proceed
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# Log usage if approaching limits (80% threshold)
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input_usage_pct = (current_input_usage / self.input_tokens_per_minute) * 100
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output_usage_pct = (current_output_usage / self.output_tokens_per_minute) * 100
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if input_usage_pct > 80 or output_usage_pct > 80:
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print(f"⚠️ [TOKEN USAGE] Approaching limits:")
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print(f" Input: {current_input_usage:,}/{self.input_tokens_per_minute:,} ({input_usage_pct:.1f}%)")
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print(f" Output: {current_output_usage:,}/{self.output_tokens_per_minute:,} ({output_usage_pct:.1f}%)")
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return True, 0.0
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async def record_token_usage(self, input_tokens: int, output_tokens: int):
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"""
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Record token usage without checking limits (for actual usage after API call).
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This is used to update the limiter with actual usage after an API call completes.
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"""
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async with self.lock:
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now = time.time()
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# Record usage
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self.input_token_usage.append((now, input_tokens))
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self.output_token_usage.append((now, output_tokens))
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def get_current_usage(self) -> Dict[str, Any]:
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"""Get current token usage statistics."""
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async def _get_usage():
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async with self.lock:
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now = time.time()
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self.input_token_usage = self._cleanup_old_usage(self.input_token_usage, 60)
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self.output_token_usage = self._cleanup_old_usage(self.output_token_usage, 60)
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current_input = sum(tokens for _, tokens in self.input_token_usage)
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current_output = sum(tokens for _, tokens in self.output_token_usage)
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||
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return {
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"model": self.model,
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"input_tokens_used": current_input,
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"input_tokens_limit": self.input_tokens_per_minute,
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"input_tokens_remaining": max(0, self.input_tokens_per_minute - current_input),
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"input_usage_percent": (current_input / self.input_tokens_per_minute) * 100,
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"output_tokens_used": current_output,
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"output_tokens_limit": self.output_tokens_per_minute,
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"output_tokens_remaining": max(0, self.output_tokens_per_minute - current_output),
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"output_usage_percent": (current_output / self.output_tokens_per_minute) * 100,
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}
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# This is a sync method, but we need async - return a coroutine
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import asyncio
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try:
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loop = asyncio.get_event_loop()
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if loop.is_running():
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# If loop is running, we can't use run_until_complete
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||
# Return a dict that will be updated
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||
return {
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"model": self.model,
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"input_tokens_limit": self.input_tokens_per_minute,
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"output_tokens_limit": self.output_tokens_per_minute,
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"note": "Usage stats require async context"
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}
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||
else:
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return loop.run_until_complete(_get_usage())
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except:
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return {
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"model": self.model,
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"input_tokens_limit": self.input_tokens_per_minute,
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"output_tokens_limit": self.output_tokens_per_minute,
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||
}
|
||
|
||
# Batch Rate Limiter for parallel processing
|
||
# Updated to match billing plan: 2K requests/minute
|
||
class BatchRateLimiter:
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||
def __init__(self, batch_size: int = 10, requests_per_minute: int = 2000):
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"""
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Batch rate limiter for parallel chunk processing.
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||
Default: 2000 requests/minute (2K per billing plan)
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"""
|
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self.batch_size = batch_size
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self.requests_per_minute = requests_per_minute
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# Fixed calculation: batches_per_minute = requests_per_minute / files_per_batch
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# For smart batching: 5 files per batch, so batches_per_minute = requests_per_minute / 5
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# batch_interval = 60 / batches_per_minute
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self.files_per_batch = 5 # Smart batching uses 5 files per batch
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self.batches_per_minute = requests_per_minute / self.files_per_batch
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self.batch_interval = 60 / self.batches_per_minute # Time between batches
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||
self.last_batch_time = 0
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||
self.lock = asyncio.Lock()
|
||
|
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async def wait_for_batch(self):
|
||
"""Wait for the next batch slot (only if needed)."""
|
||
async with self.lock:
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||
now = time.time()
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time_since_last = now - self.last_batch_time
|
||
|
||
# Only wait if we're sending batches too fast
|
||
if time_since_last < self.batch_interval:
|
||
wait_time = self.batch_interval - time_since_last
|
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if wait_time > 0.01: # Only wait if more than 10ms
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await asyncio.sleep(wait_time)
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||
|
||
self.last_batch_time = time.time()
|
||
|
||
# Legacy rate limiter for backward compatibility
|
||
# Updated to match billing plan: 2K requests/minute
|
||
class ClaudeRateLimiter:
|
||
def __init__(self, requests_per_minute: int = 2000):
|
||
"""
|
||
Rate limiter for Claude API requests.
|
||
Default: 2000 requests/minute (2K per billing plan)
|
||
"""
|
||
self.token_bucket = TokenBucketRateLimiter(requests_per_minute, requests_per_minute / 60)
|
||
|
||
async def wait_if_needed(self):
|
||
"""Wait if rate limit would be exceeded."""
|
||
await self.token_bucket.acquire(1)
|
||
|
||
# Git Integration Service Client
|
||
class GitIntegrationClient:
|
||
def __init__(self):
|
||
self.base_url = os.getenv('GIT_INTEGRATION_SERVICE_URL', 'http://git-integration:8012')
|
||
self.timeout = 30.0
|
||
|
||
async def get_repository_info(self, repository_id: str, user_id: str) -> Dict[str, Any]:
|
||
"""Get repository information from git-integration service."""
|
||
try:
|
||
print(f"🔍 [DEBUG] Getting repository info for ID: {repository_id}, User: {user_id}")
|
||
print(f"🔍 [DEBUG] Git integration URL: {self.base_url}")
|
||
|
||
async with httpx.AsyncClient(timeout=self.timeout) as client:
|
||
# Get repository info from the diffs endpoint
|
||
url = f"{self.base_url}/api/diffs/repositories"
|
||
headers = {'x-user-id': user_id}
|
||
|
||
print(f"🔍 [DEBUG] Making request to: {url}")
|
||
print(f"🔍 [DEBUG] Headers: {headers}")
|
||
|
||
response = await client.get(url, headers=headers)
|
||
|
||
print(f"🔍 [DEBUG] Response status: {response.status_code}")
|
||
print(f"🔍 [DEBUG] Response headers: {dict(response.headers)}")
|
||
|
||
if response.status_code == 200:
|
||
data = response.json()
|
||
print(f"🔍 [DEBUG] Response data: {data}")
|
||
|
||
if data.get('success') and 'data' in data:
|
||
repositories = data['data'].get('repositories', [])
|
||
print(f"🔍 [DEBUG] Found {len(repositories)} repositories")
|
||
|
||
for repo in repositories:
|
||
print(f"🔍 [DEBUG] Checking repo: {repo.get('id')} vs {repository_id}")
|
||
if repo.get('id') == repository_id:
|
||
result = {
|
||
'id': repo.get('id'),
|
||
'name': repo.get('repository_name'),
|
||
'owner': repo.get('owner_name'),
|
||
'provider': repo.get('provider_name', 'github'),
|
||
'local_path': f"/tmp/attached-repos/{repo.get('owner_name')}__{repo.get('repository_name')}__main",
|
||
'repository_url': f"https://github.com/{repo.get('owner_name')}/{repo.get('repository_name')}"
|
||
}
|
||
print(f"🔍 [DEBUG] Found repository: {result}")
|
||
return result
|
||
|
||
print(f"❌ [DEBUG] Repository {repository_id} not found in {len(repositories)} repositories")
|
||
raise Exception(f"Repository {repository_id} not found")
|
||
else:
|
||
print(f"❌ [DEBUG] Invalid response format: {data}")
|
||
raise Exception(f"Invalid response format: {data}")
|
||
else:
|
||
print(f"❌ [DEBUG] HTTP error: {response.status_code} - {response.text}")
|
||
raise Exception(f"Failed to get repository info: {response.text}")
|
||
|
||
except Exception as e:
|
||
print(f"❌ [DEBUG] Exception in get_repository_info: {e}")
|
||
raise Exception(f"Git-integration service communication failed: {e}")
|
||
|
||
# Analysis Cache
|
||
class AnalysisCache:
|
||
def __init__(self):
|
||
try:
|
||
self.redis = redis.Redis(
|
||
host=os.getenv('REDIS_HOST', 'redis'),
|
||
port=int(os.getenv('REDIS_PORT', 6379)),
|
||
password=os.getenv('REDIS_PASSWORD', ''),
|
||
decode_responses=True
|
||
)
|
||
self.cache_ttl = 86400 # 24 hours
|
||
except Exception as e:
|
||
print(f"Warning: Redis connection failed: {e}")
|
||
self.redis = None
|
||
|
||
async def get_cached_analysis(self, file_hash: str) -> Optional[Dict[str, Any]]:
|
||
"""Get cached analysis result."""
|
||
if not self.redis:
|
||
return None
|
||
|
||
try:
|
||
cache_key = f"analysis:{file_hash}"
|
||
cached_data = self.redis.get(cache_key)
|
||
return json.loads(cached_data) if cached_data else None
|
||
except Exception:
|
||
return None
|
||
|
||
async def cache_analysis(self, file_hash: str, result: Dict[str, Any]):
|
||
"""Cache analysis result."""
|
||
if not self.redis:
|
||
return
|
||
|
||
try:
|
||
cache_key = f"analysis:{file_hash}"
|
||
self.redis.setex(cache_key, self.cache_ttl, json.dumps(result))
|
||
except Exception as e:
|
||
print(f"Warning: Failed to cache analysis: {e}")
|
||
|
||
# Optimized Content Optimizer
|
||
class ContentOptimizer:
|
||
@staticmethod
|
||
def optimize_content_for_claude(content: str, max_tokens: int = 4000) -> str:
|
||
"""Optimize file content for Claude API limits with intelligent truncation."""
|
||
if content is None:
|
||
return ""
|
||
|
||
# Rough token estimation (4 chars per token)
|
||
if len(content) <= max_tokens * 4:
|
||
return content
|
||
|
||
lines = content.split('\n')
|
||
important_lines = []
|
||
|
||
# Keep important lines (imports, functions, classes, comments)
|
||
for line in lines:
|
||
stripped = line.strip()
|
||
if (stripped.startswith(('import ', 'from ', 'def ', 'class ', 'export ', 'const ', 'let ', 'var ')) or
|
||
stripped.startswith(('function ', 'interface ', 'type ', 'enum ')) or
|
||
stripped.startswith(('//', '#', '/*', '*', '<!--')) or # Comments
|
||
stripped.startswith(('@', 'use ', 'require(', 'include')) or # Annotations and includes
|
||
'async ' in stripped or 'await ' in stripped or # Async patterns
|
||
'try:' in stripped or 'except ' in stripped or 'finally:' in stripped or # Error handling
|
||
'if __name__' in stripped or 'main(' in stripped): # Entry points
|
||
important_lines.append(line)
|
||
|
||
# If we have too many important lines, prioritize by length and content
|
||
if len(important_lines) > 100:
|
||
# Sort by importance (shorter lines first, then by content type)
|
||
def line_priority(line):
|
||
stripped = line.strip()
|
||
if stripped.startswith(('import ', 'from ')):
|
||
return 1 # Highest priority
|
||
elif stripped.startswith(('def ', 'class ', 'function ', 'interface ')):
|
||
return 2
|
||
elif stripped.startswith(('//', '#', '/*')):
|
||
return 3
|
||
else:
|
||
return 4
|
||
|
||
important_lines.sort(key=lambda x: (line_priority(x), len(x)))
|
||
important_lines = important_lines[:100]
|
||
|
||
optimized_content = '\n'.join(important_lines)
|
||
|
||
# Add summary information
|
||
optimized_content += f"\n\n... [Content optimized for analysis - {len(content)} chars total, {len(lines)} lines]"
|
||
|
||
return optimized_content
|
||
|
||
# Sanitizers to ensure JSON-serializable, primitive types
|
||
def sanitize_analysis_result(analysis):
|
||
"""Ensure analysis object only contains JSON-serializable types."""
|
||
try:
|
||
print(f"🔍 Sanitizing analysis object...")
|
||
|
||
# Sanitize repo_path
|
||
try:
|
||
if hasattr(analysis, 'repo_path'):
|
||
analysis.repo_path = str(analysis.repo_path) if analysis.repo_path else ""
|
||
except Exception as e:
|
||
print(f"⚠️ Error sanitizing repo_path: {e}")
|
||
analysis.repo_path = ""
|
||
|
||
# Sanitize file_analyses list
|
||
try:
|
||
if hasattr(analysis, 'file_analyses') and analysis.file_analyses:
|
||
print(f"🔍 Sanitizing {len(analysis.file_analyses)} file analyses...")
|
||
for idx, fa in enumerate(analysis.file_analyses):
|
||
try:
|
||
# Path to string
|
||
if hasattr(fa, 'path'):
|
||
fa.path = str(fa.path)
|
||
|
||
# issues_found to list of strings
|
||
if hasattr(fa, 'issues_found'):
|
||
issues = fa.issues_found
|
||
if isinstance(issues, str):
|
||
fa.issues_found = [issues]
|
||
elif isinstance(issues, (list, tuple)):
|
||
fa.issues_found = [str(x) for x in issues]
|
||
else:
|
||
fa.issues_found = []
|
||
else:
|
||
fa.issues_found = []
|
||
|
||
# recommendations to list of strings
|
||
if hasattr(fa, 'recommendations'):
|
||
recs = fa.recommendations
|
||
if isinstance(recs, str):
|
||
fa.recommendations = [recs]
|
||
elif isinstance(recs, (list, tuple)):
|
||
fa.recommendations = [str(x) for x in recs]
|
||
else:
|
||
fa.recommendations = []
|
||
else:
|
||
fa.recommendations = []
|
||
|
||
except Exception as fa_err:
|
||
print(f"⚠️ Error sanitizing file[{idx}]: {fa_err}")
|
||
# Ensure fields exist even if there's an error
|
||
if not hasattr(fa, 'path'):
|
||
fa.path = ""
|
||
if not hasattr(fa, 'issues_found'):
|
||
fa.issues_found = []
|
||
if not hasattr(fa, 'recommendations'):
|
||
fa.recommendations = []
|
||
except Exception as files_err:
|
||
print(f"⚠️ Error iterating file_analyses: {files_err}")
|
||
|
||
print(f"✅ Analysis object sanitized successfully")
|
||
return analysis
|
||
except Exception as e:
|
||
print(f"❌ Critical sanitization error: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return analysis
|
||
|
||
# Global instances
|
||
# UPDATED: Claude API billing plan - 2K requests/minute for all models
|
||
# Token limits vary by model (see TokenUsageRateLimiter.PLAN_LIMITS)
|
||
requests_per_minute = int(os.getenv('CLAUDE_REQUESTS_PER_MINUTE', '2000')) # 2K per billing plan
|
||
rate_limiter = ClaudeRateLimiter(requests_per_minute=requests_per_minute)
|
||
batch_rate_limiter = BatchRateLimiter(batch_size=10, requests_per_minute=requests_per_minute) # 2K requests/minute per billing plan
|
||
|
||
# Token-based rate limiter (initialized with default model, updated per request)
|
||
default_model = os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest")
|
||
token_usage_limiter = TokenUsageRateLimiter(model=default_model)
|
||
|
||
git_client = GitIntegrationClient()
|
||
analysis_cache = AnalysisCache()
|
||
content_optimizer = ContentOptimizer()
|
||
|
||
# ============================================================================
|
||
# TOKEN USAGE & COST TRACKING (NEW)
|
||
# ============================================================================
|
||
|
||
# Global token usage tracker per analysis run
|
||
_token_usage_tracker = {
|
||
'current_run_id': None,
|
||
'total_input_tokens': 0,
|
||
'total_output_tokens': 0,
|
||
'total_requests': 0,
|
||
'total_estimated_cost_usd': 0.0,
|
||
'file_bundles_sent': [],
|
||
'api_errors': []
|
||
}
|
||
|
||
def estimate_tokens_from_text(text: str) -> int:
|
||
"""Estimate token count from text (rough approximation: ~4 chars per token)."""
|
||
if not text:
|
||
return 0
|
||
# Claude uses ~4 characters per token on average
|
||
return len(text) // 4
|
||
|
||
def calculate_estimated_cost(input_tokens: int, output_tokens: int, model: str = "claude-3-5-haiku-latest") -> float:
|
||
"""
|
||
Calculate estimated cost in USD based on Claude API pricing.
|
||
Pricing (as of 2024):
|
||
- Claude 3.5 Haiku: $0.25 per 1M input tokens, $1.25 per 1M output tokens
|
||
- Claude 3.5 Sonnet: $3.00 per 1M input tokens, $15.00 per 1M output tokens
|
||
"""
|
||
pricing = {
|
||
"claude-3-5-haiku-latest": {"input": 0.25 / 1_000_000, "output": 1.25 / 1_000_000},
|
||
"claude-3-5-sonnet-20241022": {"input": 3.00 / 1_000_000, "output": 15.00 / 1_000_000},
|
||
"claude-3-opus-20240229": {"input": 15.00 / 1_000_000, "output": 75.00 / 1_000_000},
|
||
}
|
||
|
||
model_pricing = pricing.get(model, pricing["claude-3-5-haiku-latest"])
|
||
input_cost = input_tokens * model_pricing["input"]
|
||
output_cost = output_tokens * model_pricing["output"]
|
||
return input_cost + output_cost
|
||
|
||
def log_token_usage(
|
||
run_id: str,
|
||
request_type: str,
|
||
input_tokens: int = None,
|
||
output_tokens: int = None,
|
||
prompt_text: str = None,
|
||
response_obj = None,
|
||
file_bundle_size: int = 0,
|
||
model: str = "claude-3-5-haiku-latest",
|
||
error: Exception = None
|
||
):
|
||
"""
|
||
Log token usage and cost for each API call.
|
||
|
||
Args:
|
||
run_id: Analysis run ID
|
||
request_type: Type of request (e.g., "chunk_analysis", "synthesis", "report")
|
||
input_tokens: Actual input tokens (if available from response)
|
||
output_tokens: Actual output tokens (if available from response)
|
||
prompt_text: Prompt text (to estimate tokens if not provided)
|
||
response_obj: Claude API response object (contains usage info)
|
||
file_bundle_size: Number of files in this bundle
|
||
model: Model used
|
||
error: Exception if API call failed
|
||
"""
|
||
global _token_usage_tracker
|
||
|
||
# Initialize tracker for new run
|
||
if _token_usage_tracker['current_run_id'] != run_id:
|
||
_token_usage_tracker = {
|
||
'current_run_id': run_id,
|
||
'total_input_tokens': 0,
|
||
'total_output_tokens': 0,
|
||
'total_requests': 0,
|
||
'total_estimated_cost_usd': 0.0,
|
||
'file_bundles_sent': [],
|
||
'api_errors': []
|
||
}
|
||
|
||
# Get actual token usage from response if available
|
||
if response_obj and hasattr(response_obj, 'usage'):
|
||
actual_input = response_obj.usage.input_tokens if hasattr(response_obj.usage, 'input_tokens') else input_tokens
|
||
actual_output = response_obj.usage.output_tokens if hasattr(response_obj.usage, 'output_tokens') else output_tokens
|
||
else:
|
||
# Estimate from prompt if not available
|
||
if prompt_text and input_tokens is None:
|
||
actual_input = estimate_tokens_from_text(prompt_text)
|
||
else:
|
||
actual_input = input_tokens or 0
|
||
|
||
actual_output = output_tokens or 0
|
||
|
||
# Calculate cost
|
||
estimated_cost = calculate_estimated_cost(actual_input, actual_output, model)
|
||
|
||
# Update tracker
|
||
_token_usage_tracker['total_input_tokens'] += actual_input
|
||
_token_usage_tracker['total_output_tokens'] += actual_output
|
||
_token_usage_tracker['total_requests'] += 1
|
||
_token_usage_tracker['total_estimated_cost_usd'] += estimated_cost
|
||
|
||
if file_bundle_size > 0:
|
||
_token_usage_tracker['file_bundles_sent'].append({
|
||
'type': request_type,
|
||
'files': file_bundle_size,
|
||
'input_tokens': actual_input,
|
||
'output_tokens': actual_output,
|
||
'cost_usd': estimated_cost
|
||
})
|
||
|
||
if error:
|
||
error_msg = str(error)
|
||
is_balance_error = "credit balance" in error_msg.lower() or "too low" in error_msg.lower()
|
||
_token_usage_tracker['api_errors'].append({
|
||
'type': request_type,
|
||
'error': error_msg,
|
||
'is_balance_exhausted': is_balance_error,
|
||
'timestamp': datetime.now().isoformat()
|
||
})
|
||
|
||
# Log detailed information
|
||
print(f"\n{'='*80}")
|
||
print(f"💰 [TOKEN USAGE] Request: {request_type}")
|
||
print(f"{'='*80}")
|
||
print(f" 📊 Bundle Size: {file_bundle_size} files")
|
||
print(f" 📥 Input Tokens: {actual_input:,} tokens")
|
||
print(f" 📤 Output Tokens: {actual_output:,} tokens")
|
||
print(f" 💵 Estimated Cost: ${estimated_cost:.6f} USD")
|
||
print(f" 🤖 Model: {model}")
|
||
|
||
if error:
|
||
print(f" ❌ Error: {error_msg}")
|
||
if is_balance_error:
|
||
print(f" ⚠️ BALANCE EXHAUSTED - This is a billing issue, not code issue!")
|
||
print(f" 💡 Solution: Add credits to Anthropic account at https://console.anthropic.com/")
|
||
else:
|
||
print(f" ⚠️ API Error - Check API key and account status")
|
||
|
||
# Get token limit information
|
||
global token_usage_limiter
|
||
try:
|
||
token_limits = token_usage_limiter.get_current_usage()
|
||
input_limit = token_limits.get('input_tokens_limit', 0)
|
||
output_limit = token_limits.get('output_tokens_limit', 0)
|
||
input_used = token_limits.get('input_tokens_used', 0)
|
||
output_used = token_limits.get('output_tokens_used', 0)
|
||
input_pct = token_limits.get('input_usage_percent', 0)
|
||
output_pct = token_limits.get('output_usage_percent', 0)
|
||
except:
|
||
input_limit = output_limit = input_used = output_used = input_pct = output_pct = 0
|
||
|
||
print(f"\n 📈 CUMULATIVE USAGE (Run: {run_id[:30]}...):")
|
||
print(f" • Total Requests: {_token_usage_tracker['total_requests']}")
|
||
print(f" • Total Input Tokens: {_token_usage_tracker['total_input_tokens']:,}")
|
||
print(f" • Total Output Tokens: {_token_usage_tracker['total_output_tokens']:,}")
|
||
print(f" • Total Estimated Cost: ${_token_usage_tracker['total_estimated_cost_usd']:.4f} USD")
|
||
print(f" • Total Bundles Sent: {len(_token_usage_tracker['file_bundles_sent'])}")
|
||
|
||
if input_limit > 0:
|
||
print(f"\n 📊 TOKEN LIMITS (Current Minute - {model}):")
|
||
print(f" • Input: {input_used:,}/{input_limit:,} ({input_pct:.1f}%)")
|
||
print(f" • Output: {output_used:,}/{output_limit:,} ({output_pct:.1f}%)")
|
||
if input_pct > 80 or output_pct > 80:
|
||
print(f" ⚠️ WARNING: Approaching token limits!")
|
||
|
||
if _token_usage_tracker['api_errors']:
|
||
balance_errors = sum(1 for e in _token_usage_tracker['api_errors'] if e.get('is_balance_exhausted'))
|
||
print(f"\n ⚠️ API Errors: {len(_token_usage_tracker['api_errors'])} ({balance_errors} balance-related)")
|
||
if balance_errors > 0:
|
||
print(f" ❌ Balance exhausted - Add credits at https://console.anthropic.com/")
|
||
|
||
print(f"{'='*80}\n")
|
||
|
||
def get_token_usage_summary(run_id: str) -> Dict:
|
||
"""Get summary of token usage for a run."""
|
||
global _token_usage_tracker
|
||
if _token_usage_tracker['current_run_id'] != run_id:
|
||
return {}
|
||
|
||
return {
|
||
'run_id': run_id,
|
||
'total_requests': _token_usage_tracker['total_requests'],
|
||
'total_input_tokens': _token_usage_tracker['total_input_tokens'],
|
||
'total_output_tokens': _token_usage_tracker['total_output_tokens'],
|
||
'total_tokens': _token_usage_tracker['total_input_tokens'] + _token_usage_tracker['total_output_tokens'],
|
||
'total_estimated_cost_usd': _token_usage_tracker['total_estimated_cost_usd'],
|
||
'file_bundles': _token_usage_tracker['file_bundles_sent'],
|
||
'api_errors': _token_usage_tracker['api_errors'],
|
||
'balance_exhausted': any(e.get('is_balance_exhausted') for e in _token_usage_tracker['api_errors'])
|
||
}
|
||
|
||
# ============================================================================
|
||
# CODE EVIDENCE EXTRACTION FUNCTIONS (NEW)
|
||
# ============================================================================
|
||
|
||
def extract_code_evidence_from_files(file_analyses):
|
||
"""
|
||
Extract specific code evidence with line numbers and snippets for report proof.
|
||
Returns list of evidence items with actual code examples.
|
||
NOTE: Content field may be empty after storage, so evidence is generated from issues/recommendations.
|
||
"""
|
||
evidence_items = []
|
||
|
||
try:
|
||
for fa in file_analyses:
|
||
if not fa:
|
||
continue
|
||
|
||
# Content might be empty if loaded from database, that's OK
|
||
file_path = str(fa.path)
|
||
language = fa.language or 'text'
|
||
lines = []
|
||
|
||
# Only process content if available
|
||
if hasattr(fa, 'content') and fa.content:
|
||
lines = fa.content.split('\n')
|
||
|
||
# Extract evidence from issues with line numbers
|
||
if hasattr(fa, 'issues_found') and fa.issues_found:
|
||
for issue in fa.issues_found[:5]: # Top 5 issues per file
|
||
try:
|
||
issue_text = str(issue) if not isinstance(issue, dict) else issue.get('title', str(issue))
|
||
|
||
# Find relevant code snippet for this issue
|
||
evidence_snippet = None
|
||
if lines: # Only if we have content
|
||
evidence_snippet = find_code_snippet_for_issue(lines, issue_text, language)
|
||
|
||
if evidence_snippet:
|
||
evidence_items.append({
|
||
'file': file_path,
|
||
'issue': issue_text,
|
||
'line_number': evidence_snippet['line_number'],
|
||
'code_snippet': evidence_snippet['code'],
|
||
'language': language,
|
||
'recommendation': evidence_snippet['recommendation'],
|
||
'severity': 'HIGH' if any(keyword in issue_text.lower()
|
||
for keyword in ['security', 'vulnerability', 'sql injection', 'xss', 'csrf']) else 'MEDIUM'
|
||
})
|
||
else:
|
||
# Generate generic evidence when content unavailable
|
||
evidence_items.append({
|
||
'file': file_path,
|
||
'issue': issue_text,
|
||
'line_number': None,
|
||
'code_snippet': f"Issue found in {file_path}",
|
||
'language': language,
|
||
'recommendation': f"Review and fix: {issue_text}",
|
||
'severity': 'HIGH' if any(keyword in issue_text.lower()
|
||
for keyword in ['security', 'vulnerability', 'sql injection', 'xss', 'csrf']) else 'MEDIUM'
|
||
})
|
||
except Exception as e:
|
||
print(f"Warning: Could not extract evidence for issue: {e}")
|
||
continue
|
||
|
||
# Extract evidence from recommendations
|
||
if hasattr(fa, 'recommendations') and fa.recommendations:
|
||
for rec in fa.recommendations[:3]: # Top 3 recommendations per file
|
||
try:
|
||
rec_text = str(rec) if not isinstance(rec, dict) else rec.get('text', str(rec))
|
||
|
||
# Find code snippet that needs this recommendation
|
||
rec_snippet = None
|
||
if lines: # Only if we have content
|
||
rec_snippet = find_code_snippet_for_recommendation(lines, rec_text, language)
|
||
|
||
if rec_snippet:
|
||
evidence_items.append({
|
||
'file': file_path,
|
||
'issue': f"Improvement needed: {rec_text}",
|
||
'line_number': rec_snippet['line_number'],
|
||
'code_snippet': rec_snippet['code'],
|
||
'language': language,
|
||
'recommendation': rec_snippet['recommendation'],
|
||
'severity': 'MEDIUM'
|
||
})
|
||
else:
|
||
# Generate generic evidence when content unavailable
|
||
evidence_items.append({
|
||
'file': file_path,
|
||
'issue': f"Improvement needed: {rec_text}",
|
||
'line_number': None,
|
||
'code_snippet': f"Recommendation applies to {file_path}",
|
||
'language': language,
|
||
'recommendation': f"Implement: {rec_text}",
|
||
'severity': 'MEDIUM'
|
||
})
|
||
except Exception as e:
|
||
print(f"Warning: Could not extract recommendation evidence: {e}")
|
||
continue
|
||
|
||
# Sort by severity (HIGH first, then MEDIUM)
|
||
evidence_items.sort(key=lambda x: (x['severity'] != 'HIGH', x['file']))
|
||
|
||
return evidence_items[:15] # Return top 15 most critical evidence items
|
||
|
||
except Exception as e:
|
||
print(f"Error extracting code evidence: {e}")
|
||
return []
|
||
|
||
def find_code_snippet_for_issue(lines, issue_text, language):
|
||
"""Find relevant code snippet that demonstrates the issue."""
|
||
try:
|
||
# Keywords that help identify problematic code
|
||
issue_keywords = {
|
||
'password': ['password', 'pwd', 'auth', 'login'],
|
||
'sql': ['query', 'select', 'insert', 'update', 'delete', 'sql'],
|
||
'security': ['token', 'key', 'secret', 'auth', 'session'],
|
||
'validation': ['input', 'req.body', 'params', 'validate'],
|
||
'error': ['try', 'catch', 'error', 'throw', 'exception'],
|
||
'performance': ['loop', 'for', 'while', 'n+1', 'query'],
|
||
'memory': ['memory', 'leak', 'connection', 'close']
|
||
}
|
||
|
||
# Find lines that match the issue context
|
||
issue_lower = issue_text.lower()
|
||
|
||
for category, keywords in issue_keywords.items():
|
||
if any(keyword in issue_lower for keyword in keywords):
|
||
# Look for code lines containing these keywords
|
||
for i, line in enumerate(lines):
|
||
line_lower = line.strip().lower()
|
||
if any(keyword in line_lower for keyword in keywords) and len(line.strip()) > 10:
|
||
# Get 3 lines of context (previous, current, next)
|
||
start_line = max(0, i-1)
|
||
end_line = min(len(lines), i+2)
|
||
context_lines = lines[start_line:end_line]
|
||
|
||
return {
|
||
'line_number': i + 1,
|
||
'code': '\n'.join(context_lines),
|
||
'recommendation': generate_recommendation_for_issue(issue_text, line.strip())
|
||
}
|
||
|
||
# Fallback: return first significant code line
|
||
for i, line in enumerate(lines[:50]): # Check first 50 lines
|
||
if line.strip() and not line.strip().startswith('//') and not line.strip().startswith('#') and len(line.strip()) > 15:
|
||
return {
|
||
'line_number': i + 1,
|
||
'code': line.strip(),
|
||
'recommendation': f"Review this code section for: {issue_text}"
|
||
}
|
||
|
||
return None
|
||
except:
|
||
return None
|
||
|
||
def find_code_snippet_for_recommendation(lines, rec_text, language):
|
||
"""Find code snippet that needs the recommendation."""
|
||
try:
|
||
# Look for code patterns that match the recommendation
|
||
rec_lower = rec_text.lower()
|
||
|
||
# Common recommendation patterns
|
||
if 'error handling' in rec_lower or 'try-catch' in rec_lower:
|
||
# Find functions without try-catch
|
||
for i, line in enumerate(lines):
|
||
if any(keyword in line.lower() for keyword in ['function', 'def ', 'async ', 'await']):
|
||
# Check next 10 lines for try-catch
|
||
has_error_handling = any('try' in lines[j].lower() or 'catch' in lines[j].lower()
|
||
for j in range(i, min(len(lines), i+10)))
|
||
if not has_error_handling:
|
||
return {
|
||
'line_number': i + 1,
|
||
'code': line.strip(),
|
||
'recommendation': 'Add try-catch error handling to this function'
|
||
}
|
||
|
||
elif 'validation' in rec_lower:
|
||
# Find input handling without validation
|
||
for i, line in enumerate(lines):
|
||
if any(keyword in line.lower() for keyword in ['req.body', 'input', 'params']):
|
||
return {
|
||
'line_number': i + 1,
|
||
'code': line.strip(),
|
||
'recommendation': 'Add input validation before processing'
|
||
}
|
||
|
||
# Generic fallback
|
||
for i, line in enumerate(lines[:20]):
|
||
if line.strip() and len(line.strip()) > 10:
|
||
return {
|
||
'line_number': i + 1,
|
||
'code': line.strip(),
|
||
'recommendation': rec_text
|
||
}
|
||
|
||
return None
|
||
except:
|
||
return None
|
||
|
||
def generate_recommendation_for_issue(issue_text, code_line):
|
||
"""Generate specific recommendation based on issue and code."""
|
||
issue_lower = issue_text.lower()
|
||
code_lower = code_line.lower()
|
||
|
||
if 'password' in issue_lower and 'hash' not in code_lower:
|
||
return "Hash passwords using bcrypt before storing"
|
||
elif 'sql' in issue_lower and 'prepared' not in code_lower:
|
||
return "Use prepared statements to prevent SQL injection"
|
||
elif 'token' in issue_lower and 'expire' not in code_lower:
|
||
return "Add expiration time to JWT tokens"
|
||
elif 'validation' in issue_lower:
|
||
return "Add input validation and sanitization"
|
||
elif 'error' in issue_lower:
|
||
return "Add proper error handling with try-catch blocks"
|
||
else:
|
||
return f"Fix: {issue_text}"
|
||
|
||
class AnalysisRequest(BaseModel):
|
||
repo_path: str
|
||
output_format: str = "pdf" # pdf, json
|
||
max_files: int = 50
|
||
|
||
class RepositoryAnalysisRequest(BaseModel):
|
||
repository_id: str
|
||
user_id: str
|
||
output_format: str = "pdf" # pdf, json
|
||
max_files: int = 0 # 0 = unlimited files
|
||
analysis_type: str = "full" # fast, basic, full
|
||
|
||
class AnalysisResponse(BaseModel):
|
||
success: bool
|
||
message: str
|
||
analysis_id: Optional[str] = None
|
||
report_path: Optional[str] = None
|
||
stats: Optional[Dict[str, Any]] = None
|
||
|
||
@app.get("/health")
|
||
async def health_check():
|
||
"""Health check endpoint."""
|
||
return {
|
||
"status": "healthy",
|
||
"service": "ai-analysis-service",
|
||
"timestamp": datetime.now().isoformat(),
|
||
"version": "1.0.0"
|
||
}
|
||
|
||
@app.get("/progress/{analysis_id}")
|
||
async def stream_progress(analysis_id: str, request: Request):
|
||
"""
|
||
Server-Sent Events endpoint for real-time progress updates
|
||
|
||
Usage:
|
||
const eventSource = new EventSource('/api/ai-analysis/progress/analysis_id');
|
||
eventSource.onmessage = (event) => {
|
||
const data = JSON.parse(event.data);
|
||
console.log(data);
|
||
};
|
||
"""
|
||
async def event_generator():
|
||
# Get or create progress manager
|
||
manager = progress_tracker.get_manager(analysis_id)
|
||
|
||
if not manager:
|
||
# Send error and close
|
||
yield f"data: {json.dumps({'error': 'Analysis not found'})}\n\n"
|
||
return
|
||
|
||
# Subscribe to updates
|
||
queue = manager.subscribe()
|
||
|
||
try:
|
||
# Send historical events first
|
||
history = await manager.get_progress_history()
|
||
for event in history:
|
||
if await request.is_disconnected():
|
||
break
|
||
yield f"data: {json.dumps(event)}\n\n"
|
||
|
||
# Stream new events
|
||
while True:
|
||
if await request.is_disconnected():
|
||
break
|
||
|
||
try:
|
||
# Wait for next event with timeout
|
||
event = await asyncio.wait_for(queue.get(), timeout=30.0)
|
||
yield f"data: {json.dumps(event)}\n\n"
|
||
|
||
# If analysis completed, close stream
|
||
if event.get('event') in ['analysis_completed', 'analysis_error']:
|
||
break
|
||
|
||
except asyncio.TimeoutError:
|
||
# Send keepalive ping
|
||
yield f": keepalive\n\n"
|
||
continue
|
||
|
||
finally:
|
||
manager.unsubscribe(queue)
|
||
|
||
return StreamingResponse(
|
||
event_generator(),
|
||
media_type="text/event-stream",
|
||
headers={
|
||
"Cache-Control": "no-cache",
|
||
"Connection": "keep-alive",
|
||
"X-Accel-Buffering": "no" # Disable nginx buffering
|
||
}
|
||
)
|
||
|
||
@app.post("/analyze")
|
||
async def analyze_repository(request: AnalysisRequest, background_tasks: BackgroundTasks):
|
||
"""Analyze a repository using direct file path."""
|
||
try:
|
||
if not analyzer:
|
||
raise HTTPException(status_code=500, detail="Analyzer not initialized")
|
||
|
||
# Generate unique analysis ID
|
||
analysis_id = f"analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||
|
||
# Create temporary directory for this analysis
|
||
temp_dir = tempfile.mkdtemp(prefix=f"ai_analysis_{analysis_id}_")
|
||
|
||
try:
|
||
# Run analysis
|
||
analysis = await analyzer.analyze_repository_with_memory(
|
||
request.repo_path
|
||
)
|
||
# Ensure fields are JSON-safe and types are normalized
|
||
analysis = sanitize_analysis_result(analysis)
|
||
|
||
# DEBUG: Log field types
|
||
print(f"DEBUG: repo_path type: {type(analysis.repo_path)}")
|
||
if analysis.file_analyses:
|
||
for i, fa in enumerate(analysis.file_analyses[:3]): # Check first 3
|
||
print(f"DEBUG FA[{i}]: path type={type(fa.path)}, issues_found type={type(fa.issues_found)}, recommendations type={type(fa.recommendations)}")
|
||
if fa.issues_found:
|
||
print(f" issues_found[0] type: {type(fa.issues_found[0])}")
|
||
if fa.recommendations:
|
||
print(f" recommendations[0] type: {type(fa.recommendations[0])}")
|
||
|
||
# Generate report
|
||
if request.output_format == "pdf":
|
||
report_path = f"reports/{analysis_id}_analysis.pdf"
|
||
try:
|
||
analyzer.create_pdf_report(analysis, report_path)
|
||
except Exception as pdf_err:
|
||
print(f"❌ PDF generation failed: {pdf_err}")
|
||
raise HTTPException(status_code=500, detail=f"PDF generation failed: {pdf_err}")
|
||
|
||
# Calculate stats - ensure all fields are properly typed
|
||
stats = {
|
||
"total_files": analysis.total_files,
|
||
"total_lines": analysis.total_lines,
|
||
"languages": analysis.languages,
|
||
"code_quality_score": analysis.code_quality_score,
|
||
"high_quality_files": len([fa for fa in analysis.file_analyses if fa.severity_score >= 8]),
|
||
"medium_quality_files": len([fa for fa in analysis.file_analyses if 5 <= fa.severity_score < 8]),
|
||
"low_quality_files": len([fa for fa in analysis.file_analyses if fa.severity_score < 5]),
|
||
"total_issues": sum(len(fa.issues_found) if isinstance(fa.issues_found, (list, tuple)) else 0 for fa in analysis.file_analyses)
|
||
}
|
||
|
||
# Pre-sanitize all file analyses before stats calculation
|
||
if hasattr(analysis, 'file_analyses'):
|
||
for fa in analysis.file_analyses:
|
||
# Force issues_found to be a list
|
||
if not isinstance(fa.issues_found, list):
|
||
if isinstance(fa.issues_found, tuple):
|
||
fa.issues_found = list(fa.issues_found)
|
||
else:
|
||
fa.issues_found = []
|
||
# Force recommendations to be a list
|
||
if not isinstance(fa.recommendations, list):
|
||
if isinstance(fa.recommendations, tuple):
|
||
fa.recommendations = list(fa.recommendations)
|
||
else:
|
||
fa.recommendations = []
|
||
|
||
# Now calculate stats safely
|
||
stats = {
|
||
"total_files": analysis.total_files,
|
||
"total_lines": analysis.total_lines,
|
||
"languages": analysis.languages,
|
||
"code_quality_score": analysis.code_quality_score,
|
||
"high_quality_files": len([fa for fa in analysis.file_analyses if fa.severity_score >= 8]),
|
||
"medium_quality_files": len([fa for fa in analysis.file_analyses if 5 <= fa.severity_score < 8]),
|
||
"low_quality_files": len([fa for fa in analysis.file_analyses if fa.severity_score < 5]),
|
||
"total_issues": sum(len(fa.issues_found) for fa in analysis.file_analyses)
|
||
}
|
||
|
||
# Use dictionary instead of Pydantic model to avoid serialization issues
|
||
return {
|
||
"success": True,
|
||
"message": "Analysis completed successfully",
|
||
"analysis_id": analysis_id,
|
||
"report_path": report_path,
|
||
"stats": stats
|
||
}
|
||
|
||
finally:
|
||
# Cleanup temporary directory
|
||
if os.path.exists(temp_dir):
|
||
shutil.rmtree(temp_dir)
|
||
|
||
except Exception as e:
|
||
return AnalysisResponse(
|
||
success=False,
|
||
message=f"Analysis failed: {str(e)}",
|
||
analysis_id=None,
|
||
report_path=None,
|
||
stats=None
|
||
)
|
||
|
||
@app.post("/analyze-repository")
|
||
async def analyze_repository_by_id(request: RepositoryAnalysisRequest, background_tasks: BackgroundTasks):
|
||
"""Analyze a repository by ID using git-integration service."""
|
||
global os, shutil, tempfile, json
|
||
# Ensure we're using the module-level imports, not shadowed local variables
|
||
try:
|
||
print(f"🔍 [DEBUG] Analysis request received: {request}")
|
||
if not analyzer:
|
||
raise HTTPException(status_code=500, detail="Analyzer not initialized")
|
||
|
||
# Generate unique analysis ID
|
||
analysis_id = f"repo_analysis_{request.repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||
|
||
# Create and initialize progress manager
|
||
progress_mgr = progress_tracker.create_manager(analysis_id)
|
||
await progress_mgr.connect_redis()
|
||
|
||
# Emit analysis started event
|
||
await progress_mgr.emit_event("analysis_started", {
|
||
"message": "Analysis started",
|
||
"repository_id": request.repository_id,
|
||
"analysis_type": request.analysis_type,
|
||
"timestamp": datetime.now().isoformat()
|
||
})
|
||
|
||
# Get repository information from git-integration service with timeout
|
||
try:
|
||
import asyncio
|
||
repo_info = await asyncio.wait_for(
|
||
git_client.get_repository_info(request.repository_id, request.user_id),
|
||
timeout=10.0 # 10 second timeout for repository info
|
||
)
|
||
local_path = repo_info.get('local_path') # Keep for compatibility but don't check file system
|
||
|
||
# Note: We no longer check local_path existence since we use API approach
|
||
except asyncio.TimeoutError:
|
||
await progress_mgr.emit_event("analysis_error", {
|
||
"message": "Repository info request timed out",
|
||
"error": "Timeout getting repository information"
|
||
})
|
||
raise HTTPException(
|
||
status_code=504,
|
||
detail="Repository info request timed out"
|
||
)
|
||
except Exception as e:
|
||
await progress_mgr.emit_event("analysis_error", {
|
||
"message": f"Failed to get repository info: {str(e)}",
|
||
"error": str(e)
|
||
})
|
||
raise HTTPException(
|
||
status_code=500,
|
||
detail=f"Failed to get repository info: {str(e)}"
|
||
)
|
||
|
||
# Create temporary directory for this analysis
|
||
temp_dir = tempfile.mkdtemp(prefix=f"ai_analysis_{analysis_id}_")
|
||
|
||
# Handle max_files: 0 or None means unlimited
|
||
max_files = getattr(request, 'max_files', None)
|
||
if max_files == 0:
|
||
max_files = None # 0 means unlimited
|
||
|
||
print(f"🔍 [DEBUG] Processing with max_files={max_files} (None/unlimited if not set)")
|
||
|
||
# Start analysis in background and return immediately
|
||
background_tasks.add_task(
|
||
run_analysis_background,
|
||
analysis_id,
|
||
local_path,
|
||
request.repository_id,
|
||
request.user_id,
|
||
max_files,
|
||
progress_mgr,
|
||
temp_dir
|
||
)
|
||
|
||
# Return immediately with analysis ID for progress tracking
|
||
return AnalysisResponse(
|
||
success=True,
|
||
message="Analysis started successfully",
|
||
analysis_id=analysis_id,
|
||
report_path=None, # Will be available when analysis completes
|
||
stats=None # Will be available when analysis completes
|
||
)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
import traceback
|
||
traceback.print_exc()
|
||
print(f"❌ Repository analysis failed: {str(e)}")
|
||
return {
|
||
"success": False,
|
||
"message": f"Repository analysis failed: {str(e)}",
|
||
"analysis_id": None,
|
||
"report_path": None,
|
||
"stats": None
|
||
}
|
||
|
||
async def analyze_repository_fast(local_path: str, repository_id: str, user_id: str, max_files: int = 50):
|
||
"""Fast analysis with timeout and limited files for quick results."""
|
||
try:
|
||
print(f"🚀 Starting FAST analysis for repository {repository_id}")
|
||
|
||
# Set a timeout for fast analysis
|
||
import asyncio
|
||
timeout_seconds = 60 # 1 minute timeout for fast analysis
|
||
|
||
async def run_analysis():
|
||
# Get repository files from API (limited to max_files)
|
||
files_data = await get_repository_files_from_api(repository_id, user_id, max_files)
|
||
|
||
if not files_data:
|
||
raise Exception("No files found in repository")
|
||
|
||
print(f"📁 Found {len(files_data)} files for fast analysis")
|
||
|
||
# Create a simple analysis without AI processing
|
||
from ai_analyze import FileAnalysis, RepositoryAnalysis
|
||
|
||
file_analyses = []
|
||
total_lines = 0
|
||
languages = set()
|
||
|
||
for file_path, content in files_data[:max_files]: # Limit to max_files
|
||
# files_data is a list of tuples (file_path, content)
|
||
|
||
# Basic analysis without AI
|
||
lines = len(content.splitlines()) if content else 0
|
||
total_lines += lines
|
||
|
||
# Enhanced language detection
|
||
language = "Unknown"
|
||
if '.' in file_path:
|
||
ext = '.' + file_path.split('.')[-1].lower()
|
||
language_map = {
|
||
'.py': 'Python', '.js': 'JavaScript', '.ts': 'TypeScript', '.tsx': 'TypeScript',
|
||
'.jsx': 'JavaScript', '.java': 'Java', '.cpp': 'C++', '.c': 'C', '.cs': 'C#',
|
||
'.go': 'Go', '.rs': 'Rust', '.php': 'PHP', '.rb': 'Ruby', '.swift': 'Swift',
|
||
'.kt': 'Kotlin', '.html': 'HTML', '.htm': 'HTML', '.css': 'CSS', '.scss': 'SCSS',
|
||
'.sass': 'SASS', '.sql': 'SQL', '.json': 'JSON', '.yaml': 'YAML', '.yml': 'YAML',
|
||
'.md': 'Markdown', '.txt': 'Text', '.xml': 'XML', '.sh': 'Shell', '.bash': 'Shell',
|
||
'.zsh': 'Shell', '.fish': 'Shell', '.dockerfile': 'Docker', '.dockerignore': 'Docker',
|
||
'.gitignore': 'Git', '.gitattributes': 'Git', '.env': 'Environment', '.ini': 'Config',
|
||
'.cfg': 'Config', '.conf': 'Config', '.toml': 'TOML', '.lock': 'Lock File',
|
||
'.log': 'Log', '.tmp': 'Temporary', '.temp': 'Temporary'
|
||
}
|
||
language = language_map.get(ext, 'Unknown')
|
||
else:
|
||
# Try to detect from filename
|
||
filename = file_path.lower()
|
||
if 'dockerfile' in filename:
|
||
language = 'Docker'
|
||
elif 'makefile' in filename:
|
||
language = 'Makefile'
|
||
elif 'readme' in filename:
|
||
language = 'Markdown'
|
||
elif 'license' in filename:
|
||
language = 'Text'
|
||
elif 'changelog' in filename:
|
||
language = 'Text'
|
||
|
||
languages.add(language)
|
||
|
||
# Perform smart fast analysis
|
||
issues_found = []
|
||
recommendations = []
|
||
complexity_score = 5.0
|
||
severity_score = 7.0
|
||
|
||
# Basic code quality analysis
|
||
if lines > 500:
|
||
issues_found.append("Large file - consider breaking into smaller modules")
|
||
recommendations.append("Split into smaller, focused files")
|
||
complexity_score += 2
|
||
severity_score -= 1
|
||
|
||
if lines < 10:
|
||
issues_found.append("Very small file - might be incomplete")
|
||
recommendations.append("Review if this file is necessary")
|
||
severity_score -= 0.5
|
||
|
||
# Language-specific analysis
|
||
if language == "Python":
|
||
if "import" not in content and "def" not in content and "class" not in content:
|
||
issues_found.append("Python file without imports, functions, or classes")
|
||
recommendations.append("Add proper Python structure")
|
||
severity_score -= 1
|
||
|
||
if "print(" in content and "def " not in content:
|
||
issues_found.append("Contains print statements - consider logging")
|
||
recommendations.append("Use proper logging instead of print statements")
|
||
complexity_score += 1
|
||
|
||
elif language == "JavaScript":
|
||
if "console.log" in content and "function" not in content:
|
||
issues_found.append("Contains console.log statements")
|
||
recommendations.append("Use proper logging or remove debug statements")
|
||
complexity_score += 1
|
||
|
||
elif language == "Markdown":
|
||
if lines < 5:
|
||
issues_found.append("Very short documentation")
|
||
recommendations.append("Add more detailed documentation")
|
||
severity_score += 1
|
||
|
||
# Calculate final scores
|
||
complexity_score = max(1.0, min(10.0, complexity_score))
|
||
severity_score = max(1.0, min(10.0, severity_score))
|
||
|
||
# Generate detailed analysis
|
||
detailed_analysis = f"Fast analysis of {file_path}: {lines} lines, {language} code. "
|
||
if issues_found:
|
||
detailed_analysis += f"Issues found: {len(issues_found)}. "
|
||
else:
|
||
detailed_analysis += "No major issues detected. "
|
||
detailed_analysis += f"Complexity: {complexity_score:.1f}/10, Quality: {severity_score:.1f}/10"
|
||
|
||
# Create smart file analysis
|
||
file_analysis = FileAnalysis(
|
||
path=str(file_path),
|
||
language=language,
|
||
lines_of_code=lines,
|
||
complexity_score=complexity_score,
|
||
issues_found=issues_found if issues_found else ["No issues detected in fast analysis"],
|
||
recommendations=recommendations if recommendations else ["File appears well-structured"],
|
||
detailed_analysis=detailed_analysis,
|
||
severity_score=severity_score,
|
||
content=content # Store file content for code examples
|
||
)
|
||
file_analyses.append(file_analysis)
|
||
|
||
# Create language count dictionary
|
||
language_counts = {}
|
||
for file_analysis in file_analyses:
|
||
lang = file_analysis.language
|
||
language_counts[lang] = language_counts.get(lang, 0) + 1
|
||
|
||
# Create repository analysis
|
||
analysis = RepositoryAnalysis(
|
||
repo_path=local_path,
|
||
total_files=len(file_analyses),
|
||
total_lines=total_lines,
|
||
languages=language_counts,
|
||
code_quality_score=7.5, # Default good score
|
||
architecture_assessment="Fast analysis - architecture details require full analysis",
|
||
security_assessment="Fast analysis - security details require full analysis",
|
||
executive_summary=f"Fast analysis completed for {len(file_analyses)} files. Total lines: {total_lines}. Languages: {', '.join(language_counts.keys())}",
|
||
file_analyses=file_analyses
|
||
)
|
||
|
||
return analysis
|
||
|
||
# Run with timeout
|
||
analysis = await asyncio.wait_for(run_analysis(), timeout=timeout_seconds)
|
||
print(f"✅ Fast analysis completed in under {timeout_seconds} seconds")
|
||
return analysis
|
||
|
||
except asyncio.TimeoutError:
|
||
print(f"⏰ Fast analysis timed out after {timeout_seconds} seconds")
|
||
raise Exception(f"Fast analysis timed out after {timeout_seconds} seconds")
|
||
except Exception as e:
|
||
print(f"❌ Fast analysis failed: {e}")
|
||
raise e
|
||
|
||
async def get_repository_files_from_api(repository_id: str, user_id: str, max_files: Optional[int] = None):
|
||
"""Get repository files from Git Integration Service API."""
|
||
try:
|
||
print(f"🔍 [DEBUG] Getting repository files for {repository_id} with user {user_id}")
|
||
|
||
# Get all files by scanning all directories recursively
|
||
async with httpx.AsyncClient(timeout=30.0) as client:
|
||
# First, get all directories from the repository
|
||
print(f"🔍 [DEBUG] Getting all directories for repository")
|
||
|
||
# Get all directories from database
|
||
directories_query = f"""
|
||
SELECT DISTINCT rd.relative_path
|
||
FROM repository_directories rd
|
||
WHERE rd.repository_id = '{repository_id}'
|
||
ORDER BY rd.relative_path
|
||
"""
|
||
|
||
# We need to get all directories and then scan each one
|
||
# Let's use a different approach - get all files directly from the database
|
||
all_files_query = f"""
|
||
SELECT
|
||
file->>'relative_path' as relative_path,
|
||
file->>'filename' as filename
|
||
FROM repository_files rf,
|
||
jsonb_array_elements(rf.files) as file
|
||
WHERE rf.repository_id = '{repository_id}'
|
||
ORDER BY file->>'relative_path'
|
||
"""
|
||
|
||
# Get all directories by making multiple structure requests
|
||
all_directories = set()
|
||
all_directories.add('') # Add root directory
|
||
|
||
# First, get root structure
|
||
structure_response = await client.get(
|
||
f"{git_client.base_url}/api/github/repository/{repository_id}/structure",
|
||
headers={'x-user-id': user_id}
|
||
)
|
||
|
||
if structure_response.status_code != 200:
|
||
raise Exception(f"Failed to get repository structure: {structure_response.text}")
|
||
|
||
structure_data = structure_response.json()
|
||
if not structure_data.get('success'):
|
||
raise Exception(f"Git Integration Service error: {structure_data.get('message', 'Unknown error')}")
|
||
|
||
# Get all directories from root structure
|
||
structure_items = structure_data.get('data', {}).get('structure', [])
|
||
directories_to_scan = []
|
||
|
||
for item in structure_items:
|
||
if isinstance(item, dict) and item.get('type') == 'directory':
|
||
dir_path = item.get('path', '')
|
||
if dir_path:
|
||
all_directories.add(dir_path)
|
||
directories_to_scan.append(dir_path)
|
||
print(f"🔍 [DEBUG] Found directory: {dir_path}")
|
||
|
||
# Now scan each directory to find subdirectories
|
||
for directory in directories_to_scan:
|
||
try:
|
||
print(f"🔍 [DEBUG] Getting structure for directory: '{directory}'")
|
||
dir_structure_response = await client.get(
|
||
f"{git_client.base_url}/api/github/repository/{repository_id}/structure",
|
||
params={'path': directory},
|
||
headers={'x-user-id': user_id}
|
||
)
|
||
|
||
if dir_structure_response.status_code == 200:
|
||
dir_structure_data = dir_structure_response.json()
|
||
if dir_structure_data.get('success'):
|
||
dir_items = dir_structure_data.get('data', {}).get('structure', [])
|
||
for item in dir_items:
|
||
if isinstance(item, dict) and item.get('type') == 'directory':
|
||
subdir_path = item.get('path', '')
|
||
if subdir_path and subdir_path not in all_directories:
|
||
all_directories.add(subdir_path)
|
||
directories_to_scan.append(subdir_path)
|
||
print(f"🔍 [DEBUG] Found subdirectory: {subdir_path}")
|
||
else:
|
||
print(f"⚠️ [DEBUG] Failed to get structure for directory '{directory}': {dir_structure_data.get('message')}")
|
||
else:
|
||
print(f"⚠️ [DEBUG] Failed to get structure for directory '{directory}': HTTP {dir_structure_response.status_code}")
|
||
except Exception as e:
|
||
print(f"⚠️ [DEBUG] Error getting structure for directory '{directory}': {e}")
|
||
|
||
print(f"🔍 [DEBUG] Found {len(all_directories)} total directories to scan")
|
||
|
||
# Scan each directory for files
|
||
files_to_analyze = []
|
||
for directory in all_directories:
|
||
try:
|
||
print(f"🔍 [DEBUG] Scanning directory: '{directory}'")
|
||
files_response = await client.get(
|
||
f"{git_client.base_url}/api/github/repository/{repository_id}/files",
|
||
params={'directory_path': directory} if directory else {},
|
||
headers={'x-user-id': user_id}
|
||
)
|
||
|
||
if files_response.status_code == 200:
|
||
files_data = files_response.json()
|
||
if files_data.get('success'):
|
||
dir_files = files_data.get('data', {}).get('files', [])
|
||
for file_info in dir_files:
|
||
file_path = file_info.get('relative_path', '')
|
||
if file_path:
|
||
files_to_analyze.append((file_path, None))
|
||
print(f"🔍 [DEBUG] Found file in '{directory}': {file_path}")
|
||
else:
|
||
print(f"⚠️ [DEBUG] Failed to get files from directory '{directory}': {files_data.get('message')}")
|
||
else:
|
||
print(f"⚠️ [DEBUG] Failed to get files from directory '{directory}': HTTP {files_response.status_code}")
|
||
except Exception as e:
|
||
print(f"⚠️ [DEBUG] Error scanning directory '{directory}': {e}")
|
||
|
||
print(f"🔍 [DEBUG] Found {len(files_to_analyze)} total files after scanning all directories")
|
||
|
||
print(f"🔍 [DEBUG] Found {len(files_to_analyze)} files to analyze")
|
||
|
||
# Limit files if needed (0 means unlimited)
|
||
if max_files and max_files > 0 and len(files_to_analyze) > max_files:
|
||
files_to_analyze = files_to_analyze[:max_files]
|
||
print(f"🔍 [DEBUG] Limited to {max_files} files")
|
||
else:
|
||
print(f"🔍 [DEBUG] Processing all {len(files_to_analyze)} files (no limit)")
|
||
|
||
# Fetch file content for each file
|
||
files_with_content = []
|
||
failed_files = []
|
||
skipped_files = []
|
||
|
||
for i, (file_path, _) in enumerate(files_to_analyze):
|
||
try:
|
||
print(f"🔍 [DEBUG] Fetching content for file {i+1}/{len(files_to_analyze)}: {file_path}")
|
||
|
||
# Get file content from Git Integration Service
|
||
content_response = await client.get(
|
||
f"{git_client.base_url}/api/github/repository/{repository_id}/file-content",
|
||
params={'file_path': file_path},
|
||
headers={'x-user-id': user_id}
|
||
)
|
||
|
||
if content_response.status_code == 200:
|
||
content_data = content_response.json()
|
||
if content_data.get('success'):
|
||
# Content is nested in data.content
|
||
content = content_data.get('data', {}).get('content', '')
|
||
# Handle None, empty string, and non-empty content
|
||
if content is None:
|
||
# Content is None, skip this file
|
||
skipped_files.append(file_path)
|
||
print(f"⚠️ Skipped file with None content: {file_path}")
|
||
elif isinstance(content, str):
|
||
# Content is a string, check if it's empty or has content
|
||
if len(content.strip()) >= 0: # Include empty files too
|
||
files_with_content.append((file_path, content))
|
||
print(f"🔍 [DEBUG] Successfully got content for {file_path} ({len(content)} chars)")
|
||
else:
|
||
skipped_files.append(file_path)
|
||
print(f"⚠️ Skipped empty file: {file_path}")
|
||
else:
|
||
# Content is not None and not a string (e.g., binary data)
|
||
# Skip binary files or non-string content
|
||
skipped_files.append(file_path)
|
||
print(f"⚠️ Skipped non-text file: {file_path} (type: {type(content).__name__})")
|
||
else:
|
||
failed_files.append((file_path, content_data.get('message', 'Unknown error')))
|
||
print(f"⚠️ Warning: Failed to get content for {file_path}: {content_data.get('message')}")
|
||
else:
|
||
failed_files.append((file_path, f"HTTP {content_response.status_code}"))
|
||
print(f"⚠️ Warning: Failed to get content for {file_path}: HTTP {content_response.status_code}")
|
||
|
||
except Exception as e:
|
||
failed_files.append((file_path, str(e)))
|
||
print(f"⚠️ Warning: Error getting content for {file_path}: {e}")
|
||
continue
|
||
|
||
print(f"🔍 [DEBUG] Successfully fetched {len(files_with_content)} files")
|
||
if failed_files:
|
||
print(f"⚠️ [DEBUG] Failed to fetch {len(failed_files)} files: {failed_files[:10]}...") # Show first 10 failures
|
||
if skipped_files:
|
||
print(f"⚠️ [DEBUG] Skipped {len(skipped_files)} empty files")
|
||
|
||
print(f"🔍 [DEBUG] Returning {len(files_with_content)} files with content")
|
||
return files_with_content
|
||
|
||
except Exception as e:
|
||
print(f"Error getting repository files from API: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return []
|
||
|
||
async def run_analysis_background(analysis_id: str, local_path: str, repository_id: str, user_id: str, max_files: Optional[int], progress_mgr: AnalysisProgressManager, temp_dir: str):
|
||
"""Run analysis in background and emit progress events."""
|
||
try:
|
||
print(f"🚀 [BACKGROUND] Starting analysis {analysis_id}")
|
||
|
||
# Check if fast mode is enabled
|
||
if False: # Disable fast mode for now, always use full analysis
|
||
# Run fast analysis with timeout
|
||
analysis = await analyze_repository_fast(
|
||
local_path,
|
||
repository_id,
|
||
user_id,
|
||
max_files
|
||
)
|
||
else:
|
||
# Run full analysis with PARALLEL processing and optimized rate limiting
|
||
analysis = await analyze_repository_with_optimizations_parallel(
|
||
local_path,
|
||
repository_id,
|
||
user_id,
|
||
max_files,
|
||
progress_mgr # Pass progress manager
|
||
)
|
||
|
||
# Normalize types before serialization/PDF
|
||
analysis = sanitize_analysis_result(analysis)
|
||
|
||
# Generate report
|
||
try:
|
||
# Emit report generation started event
|
||
await progress_mgr.emit_event("report_generation_started", {
|
||
"message": "Generating comprehensive PDF report",
|
||
"percent": 85
|
||
})
|
||
|
||
# Generate report using new multi-level method
|
||
report_path = f"reports/{analysis_id}_analysis.pdf"
|
||
try:
|
||
# Get run_id (use analysis_id as run_id)
|
||
run_id = analysis_id
|
||
|
||
# Get session_id from analyzer
|
||
session_id = getattr(analyzer, 'session_id', None)
|
||
if not session_id:
|
||
session_id = str(uuid.uuid4())
|
||
|
||
# Retrieve comprehensive context for detailed report
|
||
print(f"📊 [REPORT] Retrieving comprehensive context for report generation...")
|
||
comprehensive_context = await retrieve_comprehensive_report_context(
|
||
run_id=run_id,
|
||
repository_id=repository_id,
|
||
session_id=session_id
|
||
)
|
||
|
||
# Fallback: If no modules found, create context from RepositoryAnalysis
|
||
if comprehensive_context.get('total_modules', 0) == 0:
|
||
print(f"⚠️ [REPORT] No modules found in memory, creating context from RepositoryAnalysis...")
|
||
# Use analysis_state from comprehensive_context if available, otherwise empty
|
||
fallback_analysis_state = comprehensive_context.get('analysis_state', {})
|
||
fallback_file_analyses = analysis.file_analyses if hasattr(analysis, 'file_analyses') else []
|
||
|
||
# IMPORTANT: Convert FileAnalysis objects to dicts WITHOUT content for storage optimization
|
||
# FileAnalysis objects still have content in memory (needed for analysis), but we exclude it when storing
|
||
fallback_file_analyses_dict = []
|
||
for fa in fallback_file_analyses:
|
||
fa_dict = {
|
||
'file_path': str(fa.path),
|
||
'language': fa.language,
|
||
'lines_of_code': fa.lines_of_code,
|
||
'complexity_score': fa.complexity_score,
|
||
'severity_score': fa.severity_score,
|
||
'issues_found': fa.issues_found if isinstance(fa.issues_found, (list, tuple)) else [],
|
||
'recommendations': fa.recommendations if isinstance(fa.recommendations, (list, tuple)) else [],
|
||
'detailed_analysis': fa.detailed_analysis,
|
||
# NOTE: 'content' field explicitly NOT included to save storage space
|
||
}
|
||
# Explicitly ensure content is NOT in the dict
|
||
if 'content' in fa_dict:
|
||
del fa_dict['content']
|
||
fallback_file_analyses_dict.append(fa_dict)
|
||
|
||
comprehensive_context.update({
|
||
'module_analyses': [], # Empty, will use file_analyses fallback
|
||
'total_modules': 0,
|
||
'synthesis_analysis': comprehensive_context.get('synthesis_analysis', {}),
|
||
'analysis_state': fallback_analysis_state,
|
||
'findings_by_module': comprehensive_context.get('findings_by_module', {}),
|
||
'metrics_by_module': comprehensive_context.get('metrics_by_module', {}),
|
||
'total_findings': sum(len(fa.issues_found) if isinstance(fa.issues_found, (list, tuple)) else 0 for fa in fallback_file_analyses),
|
||
'file_analyses': fallback_file_analyses_dict, # Use dicts without content
|
||
'repository_analysis': analysis # Pass full analysis for fallback (but file_analyses in it still have content - that's OK for PDF generation)
|
||
})
|
||
|
||
# Generate multi-level PDF report
|
||
await analyzer.create_multi_level_pdf_report(
|
||
comprehensive_context=comprehensive_context,
|
||
output_path=report_path,
|
||
repository_id=repository_id,
|
||
run_id=run_id,
|
||
progress_mgr=progress_mgr
|
||
)
|
||
except Exception as pdf_err:
|
||
print(f"❌ PDF generation failed: {pdf_err}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
# Emit error event so frontend can show a message and stop
|
||
await progress_mgr.emit_event("analysis_error", {
|
||
"message": "PDF generation failed",
|
||
"error": str(pdf_err)
|
||
})
|
||
raise
|
||
except Exception as report_err:
|
||
print(f"ERROR during report generation: {report_err}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
raise
|
||
|
||
# Calculate stats with proper error handling
|
||
try:
|
||
total_files = len(analysis.file_analyses) if analysis.file_analyses else 0
|
||
total_lines = sum(fa.lines_of_code for fa in analysis.file_analyses) if analysis.file_analyses else 0
|
||
total_issues = sum(len(fa.issues_found) if isinstance(fa.issues_found, (list, tuple)) else 0 for fa in analysis.file_analyses) if analysis.file_analyses else 0
|
||
|
||
stats = {
|
||
"repository_id": repository_id,
|
||
"total_files": total_files,
|
||
"total_lines": total_lines,
|
||
"languages": analysis.languages if hasattr(analysis, 'languages') and analysis.languages else {},
|
||
"code_quality_score": analysis.code_quality_score if hasattr(analysis, 'code_quality_score') else 0.0,
|
||
"high_quality_files": len([fa for fa in analysis.file_analyses if hasattr(fa, 'severity_score') and fa.severity_score >= 8]) if analysis.file_analyses else 0,
|
||
"medium_quality_files": len([fa for fa in analysis.file_analyses if hasattr(fa, 'severity_score') and 5 <= fa.severity_score < 8]) if analysis.file_analyses else 0,
|
||
"low_quality_files": len([fa for fa in analysis.file_analyses if hasattr(fa, 'severity_score') and fa.severity_score < 5]) if analysis.file_analyses else 0,
|
||
"total_issues": total_issues
|
||
}
|
||
|
||
print(f"📊 Analysis stats calculated: {stats}")
|
||
except Exception as stats_err:
|
||
print(f"❌ Error calculating stats: {stats_err}")
|
||
stats = {
|
||
"repository_id": repository_id,
|
||
"total_files": 0,
|
||
"total_lines": 0,
|
||
"languages": {},
|
||
"code_quality_score": 0.0,
|
||
"high_quality_files": 0,
|
||
"medium_quality_files": 0,
|
||
"low_quality_files": 0,
|
||
"total_issues": 0
|
||
}
|
||
|
||
# Emit analysis completed event with stats
|
||
# Log final token usage summary
|
||
run_id = getattr(analyzer, 'run_id', f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
|
||
token_summary = get_token_usage_summary(run_id)
|
||
if token_summary:
|
||
print(f"\n{'='*80}")
|
||
print(f"💰 [FINAL TOKEN USAGE SUMMARY]")
|
||
print(f"{'='*80}")
|
||
print(f" Run ID: {run_id}")
|
||
print(f" Total API Requests: {token_summary['total_requests']}")
|
||
print(f" Total Input Tokens: {token_summary['total_input_tokens']:,}")
|
||
print(f" Total Output Tokens: {token_summary['total_output_tokens']:,}")
|
||
print(f" Total Tokens: {token_summary['total_tokens']:,}")
|
||
print(f" Total Estimated Cost: ${token_summary['total_estimated_cost_usd']:.4f} USD")
|
||
print(f" File Bundles Sent: {len(token_summary['file_bundles'])}")
|
||
|
||
if token_summary['file_bundles']:
|
||
print(f"\n 📦 Bundle Breakdown:")
|
||
for bundle in token_summary['file_bundles'][:10]: # Top 10 bundles
|
||
print(f" • {bundle['type']}: {bundle['files']} files, "
|
||
f"{bundle['input_tokens']:,} input + {bundle['output_tokens']:,} output tokens, "
|
||
f"${bundle['cost_usd']:.6f} USD")
|
||
|
||
if token_summary['api_errors']:
|
||
balance_errors = [e for e in token_summary['api_errors'] if e.get('is_balance_exhausted')]
|
||
print(f"\n ⚠️ API Errors: {len(token_summary['api_errors'])}")
|
||
if balance_errors:
|
||
print(f" ❌ BALANCE EXHAUSTED: {len(balance_errors)} errors due to low balance")
|
||
print(f" 💡 This indicates your Anthropic account balance is too low")
|
||
print(f" 💡 Solution: Add credits at https://console.anthropic.com/")
|
||
else:
|
||
print(f" ⚠️ Other API errors (not balance-related)")
|
||
|
||
# Get token limit information
|
||
global token_usage_limiter
|
||
try:
|
||
token_limits = token_usage_limiter.get_current_usage()
|
||
input_limit = token_limits.get('input_tokens_limit', 0)
|
||
output_limit = token_limits.get('output_tokens_limit', 0)
|
||
input_used = token_limits.get('input_tokens_used', 0)
|
||
output_used = token_limits.get('output_tokens_used', 0)
|
||
input_pct = token_limits.get('input_usage_percent', 0)
|
||
output_pct = token_limits.get('output_usage_percent', 0)
|
||
|
||
print(f"\n 📊 TOKEN LIMITS (Current Minute - {token_limits.get('model', 'unknown')}):")
|
||
print(f" • Input: {input_used:,}/{input_limit:,} ({input_pct:.1f}%)")
|
||
print(f" • Output: {output_used:,}/{output_limit:,} ({output_pct:.1f}%)")
|
||
|
||
if input_pct > 90 or output_pct > 90:
|
||
print(f" ⚠️ WARNING: Token limits were nearly exceeded!")
|
||
print(f" 💡 Large file bundles may have caused high token usage")
|
||
except:
|
||
pass
|
||
|
||
if token_summary.get('balance_exhausted'):
|
||
print(f"\n 🚨 CRITICAL: Balance exhausted during analysis!")
|
||
print(f" This is why you're seeing 'credit balance too low' errors")
|
||
print(f" Large file bundles ({sum(b['files'] for b in token_summary['file_bundles'])} total files) consumed tokens")
|
||
print(f" Estimated cost: ${token_summary['total_estimated_cost_usd']:.4f} USD")
|
||
print(f" 💡 Solution: Add credits at https://console.anthropic.com/")
|
||
elif token_summary['total_estimated_cost_usd'] > 0.1:
|
||
print(f"\n ⚠️ HIGH COST WARNING:")
|
||
print(f" Estimated cost: ${token_summary['total_estimated_cost_usd']:.4f} USD")
|
||
print(f" Large file bundles ({sum(b['files'] for b in token_summary['file_bundles'])} total files)")
|
||
print(f" 💡 Consider reducing bundle sizes or using smaller models")
|
||
|
||
print(f"{'='*80}\n")
|
||
|
||
await progress_mgr.emit_event("analysis_completed", {
|
||
"message": "Analysis completed successfully",
|
||
"analysis_id": analysis_id,
|
||
"report_path": report_path,
|
||
"percent": 100,
|
||
"stats": stats
|
||
})
|
||
|
||
# Cleanup and disconnect with delay to allow frontend to receive final events
|
||
await asyncio.sleep(2) # Reduced from 10s to 2s - frontend receives events immediately via SSE
|
||
|
||
# Cleanup
|
||
await progress_mgr.disconnect_redis()
|
||
if os.path.exists(temp_dir):
|
||
shutil.rmtree(temp_dir)
|
||
|
||
print(f"✅ [BACKGROUND] Analysis {analysis_id} completed successfully")
|
||
|
||
except Exception as e:
|
||
print(f"❌ [BACKGROUND] Analysis {analysis_id} failed: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
# Emit error event
|
||
await progress_mgr.emit_event("analysis_error", {
|
||
"message": f"Analysis failed: {str(e)}",
|
||
"error": str(e)
|
||
})
|
||
|
||
# Cleanup
|
||
await progress_mgr.disconnect_redis()
|
||
if os.path.exists(temp_dir):
|
||
shutil.rmtree(temp_dir)
|
||
|
||
# ============================================================================
|
||
# INTELLIGENT CHUNKING FUNCTIONS - SEMANTIC MODULE-BASED GROUPING
|
||
# ============================================================================
|
||
|
||
def categorize_by_module(files: List[Tuple[str, str]]) -> Dict[str, List[Tuple[str, str]]]:
|
||
"""Categorize files by module/feature based on directory structure and naming patterns."""
|
||
categorized = {}
|
||
|
||
for file_path, content in files:
|
||
if content is None:
|
||
continue
|
||
|
||
# Extract module name from path
|
||
path_parts = file_path.replace('\\', '/').split('/')
|
||
|
||
# Detect module/feature from directory structure
|
||
module_name = None
|
||
|
||
# Common patterns for module detection
|
||
module_patterns = [
|
||
'auth', 'authentication', 'login', 'user',
|
||
'product', 'products', 'item', 'items',
|
||
'order', 'orders', 'cart', 'checkout',
|
||
'payment', 'payments', 'billing', 'invoice',
|
||
'admin', 'dashboard', 'management',
|
||
'api', 'controller', 'service', 'repository',
|
||
'model', 'entity', 'schema', 'dto',
|
||
'config', 'configuration', 'settings',
|
||
'util', 'utils', 'helper', 'helpers',
|
||
'middleware', 'guard', 'interceptor',
|
||
'test', 'tests', 'spec', 'specs'
|
||
]
|
||
|
||
# Check path for module indicators
|
||
for part in path_parts:
|
||
part_lower = part.lower()
|
||
for pattern in module_patterns:
|
||
if pattern in part_lower:
|
||
# Extract module name (e.g., 'auth' from 'auth.controller.js')
|
||
if pattern in ['auth', 'authentication', 'login', 'user']:
|
||
module_name = 'authentication'
|
||
elif pattern in ['product', 'products', 'item', 'items']:
|
||
module_name = 'products'
|
||
elif pattern in ['order', 'orders', 'cart', 'checkout']:
|
||
module_name = 'orders'
|
||
elif pattern in ['payment', 'payments', 'billing', 'invoice']:
|
||
module_name = 'payments'
|
||
elif pattern in ['admin', 'dashboard', 'management']:
|
||
module_name = 'admin'
|
||
elif pattern in ['config', 'configuration', 'settings']:
|
||
module_name = 'configuration'
|
||
elif pattern in ['util', 'utils', 'helper', 'helpers']:
|
||
module_name = 'utilities'
|
||
elif pattern in ['test', 'tests', 'spec', 'specs']:
|
||
module_name = 'tests'
|
||
elif pattern in ['api', 'controller']:
|
||
# Try to extract module from controller name
|
||
if len(path_parts) > 1:
|
||
parent_dir = path_parts[-2].lower()
|
||
for p in module_patterns:
|
||
if p in parent_dir:
|
||
if p in ['auth', 'authentication']:
|
||
module_name = 'authentication'
|
||
elif p in ['product', 'products']:
|
||
module_name = 'products'
|
||
else:
|
||
module_name = parent_dir
|
||
break
|
||
if not module_name:
|
||
module_name = 'api'
|
||
break
|
||
if module_name:
|
||
break
|
||
if module_name:
|
||
break
|
||
|
||
# If no module detected, use directory name or filename prefix
|
||
if not module_name:
|
||
if len(path_parts) > 1:
|
||
# Use parent directory as module
|
||
parent = path_parts[-2].lower()
|
||
if parent not in ['src', 'lib', 'app', 'components', 'services']:
|
||
module_name = parent
|
||
else:
|
||
# Check filename for module prefix (e.g., auth.controller.js)
|
||
filename = path_parts[-1].lower()
|
||
for pattern in module_patterns:
|
||
if filename.startswith(pattern):
|
||
if pattern in ['auth', 'authentication']:
|
||
module_name = 'authentication'
|
||
elif pattern in ['product', 'products']:
|
||
module_name = 'products'
|
||
else:
|
||
module_name = pattern
|
||
break
|
||
if not module_name:
|
||
module_name = 'misc'
|
||
else:
|
||
module_name = 'misc'
|
||
|
||
# Add to categorized dictionary
|
||
if module_name not in categorized:
|
||
categorized[module_name] = []
|
||
categorized[module_name].append((file_path, content))
|
||
|
||
return categorized
|
||
|
||
def get_overview_files(files: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
||
"""Identify project overview files (README, package.json, entry points, etc.)."""
|
||
overview_files = []
|
||
overview_patterns = [
|
||
'readme', 'readme.md', 'readme.txt',
|
||
'package.json', 'package-lock.json', 'yarn.lock',
|
||
'requirements.txt', 'pom.xml', 'build.gradle',
|
||
'cargo.toml', 'go.mod', 'composer.json',
|
||
'dockerfile', 'docker-compose.yml',
|
||
'index.js', 'index.ts', 'main.py', 'main.js', 'main.ts',
|
||
'app.py', 'app.js', 'app.ts', 'server.py', 'server.js',
|
||
'index.html', 'index.php', 'main.go', 'main.rs',
|
||
'.gitignore', '.env.example', 'config.json',
|
||
'tsconfig.json', 'webpack.config.js', 'vite.config.js'
|
||
]
|
||
|
||
for file_path, content in files:
|
||
if content is None:
|
||
continue
|
||
|
||
filename = file_path.replace('\\', '/').split('/')[-1].lower()
|
||
|
||
# Check if file matches overview patterns
|
||
is_overview = False
|
||
for pattern in overview_patterns:
|
||
if filename == pattern or filename.endswith(pattern):
|
||
is_overview = True
|
||
break
|
||
|
||
# Also check root-level files
|
||
if not is_overview:
|
||
path_parts = file_path.replace('\\', '/').split('/')
|
||
if len(path_parts) <= 2 and filename in ['index.js', 'index.ts', 'main.py', 'app.py']:
|
||
is_overview = True
|
||
|
||
if is_overview:
|
||
overview_files.append((file_path, content))
|
||
|
||
return overview_files
|
||
|
||
def estimate_tokens(files: List[Tuple[str, str]]) -> int:
|
||
"""Estimate token count for files (~4 characters per token)."""
|
||
total_chars = 0
|
||
for file_path, content in files:
|
||
if content:
|
||
total_chars += len(content)
|
||
# Rough estimate: 4 characters per token
|
||
return total_chars // 4
|
||
|
||
def split_by_token_limit(module_files: List[Tuple[str, str]], max_tokens: int = 15000) -> List[List[Tuple[str, str]]]:
|
||
"""Split large module into sub-chunks while preserving related files together."""
|
||
sub_chunks = []
|
||
current_chunk = []
|
||
current_tokens = 0
|
||
|
||
for file_path, content in module_files:
|
||
if content is None:
|
||
continue
|
||
|
||
file_tokens = len(content) // 4
|
||
|
||
if current_tokens + file_tokens > max_tokens and current_chunk:
|
||
# Save current chunk and start new one
|
||
sub_chunks.append(current_chunk)
|
||
current_chunk = [(file_path, content)]
|
||
current_tokens = file_tokens
|
||
else:
|
||
current_chunk.append((file_path, content))
|
||
current_tokens += file_tokens
|
||
|
||
if current_chunk:
|
||
sub_chunks.append(current_chunk)
|
||
|
||
return sub_chunks if sub_chunks else [module_files]
|
||
|
||
def find_dependencies(chunk_files: List[Tuple[str, str]], dependency_graph: Optional[Dict] = None) -> List[str]:
|
||
"""Find chunk dependencies by analyzing imports/requires."""
|
||
dependencies = []
|
||
|
||
# Extract imports from files
|
||
imports = set()
|
||
for file_path, content in chunk_files:
|
||
if not content:
|
||
continue
|
||
|
||
# Common import patterns
|
||
import_patterns = [
|
||
r'import\s+.*?\s+from\s+[\'"]([^\'"]+)[\'"]', # ES6 imports
|
||
r'require\([\'"]([^\'"]+)[\'"]', # CommonJS requires
|
||
r'import\s+[\'"]([^\'"]+)[\'"]', # Dynamic imports
|
||
r'from\s+[\'"]([^\'"]+)[\'"]', # Python imports
|
||
]
|
||
|
||
import re
|
||
for pattern in import_patterns:
|
||
matches = re.findall(pattern, content)
|
||
imports.update(matches)
|
||
|
||
# Map imports to chunk IDs (simplified - would need actual dependency graph)
|
||
# For now, return empty list - can be enhanced with actual dependency tracking
|
||
return dependencies
|
||
|
||
def create_intelligent_chunks(files: List[Tuple[str, str]], dependency_graph: Optional[Dict] = None) -> List[Dict]:
|
||
"""
|
||
Group files by module/feature for semantic analysis.
|
||
Returns chunks with metadata for intelligent processing.
|
||
"""
|
||
chunks = []
|
||
|
||
# Step 1: Categorize files by module
|
||
categorized_files = categorize_by_module(files)
|
||
|
||
# Step 2: Create overview chunk (always first)
|
||
overview_files = get_overview_files(files)
|
||
if overview_files:
|
||
chunks.append({
|
||
'id': 'chunk_001',
|
||
'name': 'project_overview',
|
||
'priority': 1,
|
||
'files': overview_files,
|
||
'context_dependencies': [],
|
||
'chunk_type': 'overview'
|
||
})
|
||
# Remove overview files from categorized to avoid duplication
|
||
for module_name, module_files in categorized_files.items():
|
||
categorized_files[module_name] = [
|
||
(fp, cnt) for fp, cnt in module_files
|
||
if (fp, cnt) not in overview_files
|
||
]
|
||
|
||
# Step 3: Create module chunks
|
||
chunk_counter = len(chunks) + 1
|
||
for module_name, module_files in categorized_files.items():
|
||
if not module_files:
|
||
continue
|
||
|
||
# Check token limit (increased for better context and fewer chunks)
|
||
# With 2000 req/min API limit, we can handle larger chunks
|
||
# Increased from 15000 to 25000 tokens for better module-level context
|
||
module_tokens = estimate_tokens(module_files)
|
||
MAX_TOKENS_PER_CHUNK = 25000 # Increased for more files per chunk
|
||
if module_tokens > MAX_TOKENS_PER_CHUNK:
|
||
# Split large modules
|
||
sub_chunks = split_by_token_limit(module_files, MAX_TOKENS_PER_CHUNK)
|
||
for i, sub_chunk in enumerate(sub_chunks):
|
||
chunks.append({
|
||
'id': f'chunk_{chunk_counter:03d}',
|
||
'name': f'{module_name}_part{i+1}' if len(sub_chunks) > 1 else module_name,
|
||
'priority': 2,
|
||
'files': sub_chunk,
|
||
'context_dependencies': find_dependencies(sub_chunk, dependency_graph),
|
||
'chunk_type': 'module'
|
||
})
|
||
chunk_counter += 1
|
||
else:
|
||
chunks.append({
|
||
'id': f'chunk_{chunk_counter:03d}',
|
||
'name': module_name,
|
||
'priority': 2,
|
||
'files': module_files,
|
||
'context_dependencies': find_dependencies(module_files, dependency_graph),
|
||
'chunk_type': 'module'
|
||
})
|
||
chunk_counter += 1
|
||
|
||
return chunks
|
||
|
||
# Backward compatibility: Keep old function name as alias
|
||
def group_files_by_similarity(files: List[Tuple[str, str]]) -> List[List[Tuple[str, str]]]:
|
||
"""Legacy function: Convert intelligent chunks to simple batch format for backward compatibility."""
|
||
chunks = create_intelligent_chunks(files)
|
||
# Convert chunks back to simple batch format
|
||
batches = []
|
||
for chunk in chunks:
|
||
batches.append(chunk['files'])
|
||
return batches
|
||
|
||
def get_language_from_path(file_path: str) -> str:
|
||
"""Extract programming language from file path."""
|
||
extension = Path(file_path).suffix.lower()
|
||
|
||
language_map = {
|
||
'.py': 'python',
|
||
'.js': 'javascript',
|
||
'.ts': 'typescript',
|
||
'.tsx': 'typescript',
|
||
'.jsx': 'javascript',
|
||
'.java': 'java',
|
||
'.kt': 'kotlin',
|
||
'.swift': 'swift',
|
||
'.go': 'go',
|
||
'.rs': 'rust',
|
||
'.cpp': 'cpp',
|
||
'.c': 'c',
|
||
'.cs': 'csharp',
|
||
'.php': 'php',
|
||
'.rb': 'ruby',
|
||
'.scala': 'scala',
|
||
'.html': 'html',
|
||
'.css': 'css',
|
||
'.scss': 'scss',
|
||
'.sass': 'sass',
|
||
'.json': 'json',
|
||
'.xml': 'xml',
|
||
'.yaml': 'yaml',
|
||
'.yml': 'yaml',
|
||
'.md': 'markdown',
|
||
'.txt': 'text',
|
||
'.sql': 'sql',
|
||
'.sh': 'bash',
|
||
'.dockerfile': 'dockerfile',
|
||
'.gradle': 'gradle',
|
||
'.properties': 'properties'
|
||
}
|
||
|
||
return language_map.get(extension, 'unknown')
|
||
|
||
# ============================================================================
|
||
# PROGRESSIVE CONTEXT HELPER FUNCTIONS
|
||
# ============================================================================
|
||
|
||
def build_context_from_state(analysis_state: Optional[Dict], current_chunk: Dict) -> str:
|
||
"""
|
||
Build context section from analysis_state for progressive learning.
|
||
Returns formatted context string to include in prompt.
|
||
"""
|
||
if not analysis_state:
|
||
return ""
|
||
|
||
context_parts = []
|
||
current_chunk_name = current_chunk.get('name', 'unknown')
|
||
|
||
# 1. Project Overview (if exists and not current chunk)
|
||
project_overview = analysis_state.get('project_overview', '')
|
||
if project_overview and current_chunk_name != 'project_overview':
|
||
context_parts.append("## CONTEXT FROM PREVIOUS ANALYSIS")
|
||
context_parts.append("")
|
||
context_parts.append("### PROJECT OVERVIEW")
|
||
overview_snippet = project_overview[:500] + ("..." if len(project_overview) > 500 else "")
|
||
context_parts.append(overview_snippet)
|
||
context_parts.append("")
|
||
|
||
# 2. Previously Analyzed Modules
|
||
module_summaries = analysis_state.get('module_summaries', {})
|
||
if module_summaries:
|
||
context_parts.append("### PREVIOUSLY ANALYZED MODULES")
|
||
for module_name, summary in module_summaries.items():
|
||
if module_name != current_chunk_name:
|
||
summary_snippet = summary[:200] + ('...' if len(summary) > 200 else '')
|
||
context_parts.append(f"- **{module_name}**: {summary_snippet}")
|
||
context_parts.append("")
|
||
|
||
# 3. Architecture Patterns Found
|
||
architecture_patterns = analysis_state.get('architecture_patterns', [])
|
||
if architecture_patterns:
|
||
context_parts.append("### ARCHITECTURE PATTERNS IDENTIFIED SO FAR")
|
||
context_parts.append(", ".join(architecture_patterns))
|
||
context_parts.append("")
|
||
|
||
# 4. Critical Issues Found
|
||
critical_issues = analysis_state.get('critical_issues', [])
|
||
if critical_issues:
|
||
context_parts.append("### CRITICAL ISSUES IDENTIFIED SO FAR")
|
||
for issue in critical_issues[:5]: # Top 5 issues
|
||
module = issue.get('module', 'unknown')
|
||
issue_text = issue.get('issue', '')
|
||
context_parts.append(f"- **{module}**: {issue_text}")
|
||
context_parts.append("")
|
||
|
||
# 5. Tech Stack Discovered
|
||
tech_stack = analysis_state.get('tech_stack', {})
|
||
if tech_stack:
|
||
context_parts.append("### TECH STACK DISCOVERED")
|
||
tech_stack_str = ", ".join([f"{k}: {v}" for k, v in tech_stack.items()])
|
||
context_parts.append(tech_stack_str)
|
||
context_parts.append("")
|
||
|
||
# 6. Dependency Context
|
||
current_dependencies = current_chunk.get('context_dependencies', [])
|
||
dependency_context = analysis_state.get('dependency_context', {})
|
||
if current_dependencies and dependency_context:
|
||
context_parts.append("### DEPENDENCY CONTEXT")
|
||
for dep_chunk_id in current_dependencies:
|
||
if dep_chunk_id in dependency_context:
|
||
dep_summary = dependency_context[dep_chunk_id]
|
||
dep_summary_snippet = dep_summary[:200] + ('...' if len(dep_summary) > 200 else '')
|
||
context_parts.append(f"- **{dep_chunk_id}**: {dep_summary_snippet}")
|
||
context_parts.append("")
|
||
|
||
if context_parts:
|
||
return "\n".join(context_parts)
|
||
return ""
|
||
|
||
def update_state_with_findings(analysis_state: Dict, chunk: Dict, chunk_analysis: Dict, file_analyses: List) -> Dict:
|
||
"""
|
||
Update analysis_state with findings from current chunk analysis.
|
||
Returns updated analysis_state.
|
||
"""
|
||
chunk_name = chunk.get('name', 'unknown')
|
||
chunk_id = chunk.get('id', 'unknown')
|
||
|
||
# Initialize state if needed
|
||
if 'modules_analyzed' not in analysis_state:
|
||
analysis_state['modules_analyzed'] = []
|
||
if 'module_summaries' not in analysis_state:
|
||
analysis_state['module_summaries'] = {}
|
||
if 'architecture_patterns' not in analysis_state:
|
||
analysis_state['architecture_patterns'] = []
|
||
if 'critical_issues' not in analysis_state:
|
||
analysis_state['critical_issues'] = []
|
||
if 'dependency_context' not in analysis_state:
|
||
analysis_state['dependency_context'] = {}
|
||
|
||
# Add to modules_analyzed
|
||
if chunk_name not in analysis_state['modules_analyzed']:
|
||
analysis_state['modules_analyzed'].append(chunk_name)
|
||
|
||
# Store module summary
|
||
module_overview = chunk_analysis.get('module_overview', '')
|
||
if module_overview:
|
||
analysis_state['module_summaries'][chunk_name] = module_overview
|
||
|
||
# Extract architecture patterns from module_architecture
|
||
module_architecture = chunk_analysis.get('module_architecture', '')
|
||
if module_architecture:
|
||
# Common pattern keywords
|
||
pattern_keywords = ['MVC', 'MVVM', 'MVP', 'Service Layer', 'Repository Pattern',
|
||
'Factory Pattern', 'Singleton', 'Observer', 'Strategy',
|
||
'REST API', 'GraphQL', 'Microservices', 'Monolith',
|
||
'Layered Architecture', 'Clean Architecture', 'Hexagonal']
|
||
|
||
for pattern in pattern_keywords:
|
||
if pattern.lower() in module_architecture.lower():
|
||
if pattern not in analysis_state['architecture_patterns']:
|
||
analysis_state['architecture_patterns'].append(pattern)
|
||
|
||
# Extract critical issues (high severity issues from files)
|
||
for fa in file_analyses:
|
||
if hasattr(fa, 'severity_score') and fa.severity_score is not None:
|
||
# Low quality files (< 5) are critical
|
||
if fa.severity_score < 5:
|
||
issues = fa.issues_found if hasattr(fa, 'issues_found') and fa.issues_found else []
|
||
if issues:
|
||
for issue in issues[:2]: # Top 2 issues per file
|
||
analysis_state['critical_issues'].append({
|
||
'module': chunk_name,
|
||
'file': fa.path if hasattr(fa, 'path') else 'unknown',
|
||
'issue': issue if isinstance(issue, str) else str(issue)
|
||
})
|
||
|
||
# Store dependency context
|
||
if chunk_id and module_overview:
|
||
analysis_state['dependency_context'][chunk_id] = module_overview
|
||
|
||
# If overview chunk, extract tech stack
|
||
if chunk_name == 'project_overview' and module_overview:
|
||
# Try to extract tech stack from overview
|
||
tech_stack = {}
|
||
tech_keywords = {
|
||
'frontend': ['react', 'vue', 'angular', 'svelte', 'next.js', 'nuxt'],
|
||
'backend': ['node.js', 'express', 'django', 'flask', 'spring', 'nest.js', 'fastapi'],
|
||
'database': ['postgresql', 'mysql', 'mongodb', 'redis', 'sqlite'],
|
||
'language': ['javascript', 'typescript', 'python', 'java', 'go', 'rust']
|
||
}
|
||
|
||
overview_lower = module_overview.lower()
|
||
for category, keywords in tech_keywords.items():
|
||
for keyword in keywords:
|
||
if keyword in overview_lower:
|
||
tech_stack[category] = keyword.title()
|
||
break
|
||
|
||
if tech_stack:
|
||
analysis_state['tech_stack'] = tech_stack
|
||
analysis_state['project_overview'] = module_overview
|
||
|
||
return analysis_state
|
||
|
||
def build_intelligent_chunk_prompt(chunk: Dict, analysis_state: Optional[Dict] = None) -> str:
|
||
"""
|
||
Build comprehensive prompt for analyzing a semantically grouped chunk.
|
||
Generates detailed module-level analysis with context awareness.
|
||
Now includes progressive context from previous chunks.
|
||
"""
|
||
chunk_name = chunk.get('name', 'unknown')
|
||
chunk_type = chunk.get('chunk_type', 'module')
|
||
files_batch = chunk.get('files', [])
|
||
dependencies = chunk.get('context_dependencies', [])
|
||
|
||
# Optimize content for each file
|
||
optimized_files = []
|
||
for file_path, content in files_batch:
|
||
if content is None:
|
||
continue
|
||
|
||
# Truncate very large files but preserve more context for intelligent analysis
|
||
if len(content) > 3000:
|
||
optimized_content = content[:3000] + "\n... [truncated for analysis efficiency]"
|
||
else:
|
||
optimized_content = content
|
||
|
||
optimized_files.append((file_path, optimized_content))
|
||
|
||
# Build context from previous analyses (progressive learning)
|
||
context_section = build_context_from_state(analysis_state, chunk)
|
||
|
||
# Build comprehensive prompt with module context
|
||
prompt_parts = [
|
||
f"# COMPREHENSIVE ANALYSIS: {chunk_name.upper()}",
|
||
f"Chunk Type: {chunk_type}",
|
||
"",
|
||
"You are a senior software architect with 30+ years of experience. Analyze this module/chunk comprehensively.",
|
||
""
|
||
]
|
||
|
||
# Add context section if available
|
||
if context_section:
|
||
prompt_parts.append(context_section)
|
||
prompt_parts.append("")
|
||
prompt_parts.append("---")
|
||
prompt_parts.append("")
|
||
|
||
prompt_parts.extend([
|
||
"## ANALYSIS REQUIREMENTS:",
|
||
"",
|
||
"1. **FILE-LEVEL ANALYSIS** (for each file):",
|
||
" - Quality Score (1-10): Overall code quality",
|
||
" - Main Issues (top 3-5): Specific problems found",
|
||
" - Recommendations (top 3-5): Actionable improvements",
|
||
" - Complexity Score (1-10): Code complexity assessment",
|
||
" - Detailed Analysis: Comprehensive explanation",
|
||
"",
|
||
"2. **MODULE-LEVEL ANALYSIS** (for the entire chunk):",
|
||
" - Module Flow: How files work together",
|
||
" - Architecture Patterns: Design patterns used",
|
||
" - Security Assessment: Security concerns",
|
||
" - Overall Module Quality: Aggregate assessment",
|
||
" - Module-Level Recommendations: Improvements for the module",
|
||
"",
|
||
"3. **CONTEXT-AWARE INSIGHTS**:",
|
||
" - Relationships between files in this chunk",
|
||
" - Dependencies and interactions",
|
||
" - Potential refactoring opportunities",
|
||
" - How this module fits with previously analyzed modules (if context provided)",
|
||
" - Identify patterns that match or differ from existing architecture patterns",
|
||
" - Note any issues that relate to problems found in other modules",
|
||
"",
|
||
"## FILES IN THIS CHUNK:",
|
||
""
|
||
])
|
||
|
||
for i, (file_path, content) in enumerate(optimized_files, 1):
|
||
prompt_parts.extend([
|
||
f"### FILE {i}: {file_path}",
|
||
"```",
|
||
content,
|
||
"```",
|
||
""
|
||
])
|
||
|
||
# List all file paths explicitly
|
||
file_paths_list = [fp for fp, _ in optimized_files]
|
||
prompt_parts.extend([
|
||
"## RESPONSE FORMAT:",
|
||
"",
|
||
"⚠️ CRITICAL: You MUST analyze ALL files listed above. Do NOT skip any files.",
|
||
f"Files to analyze ({len(optimized_files)} total):",
|
||
])
|
||
for i, file_path in enumerate(file_paths_list, 1):
|
||
prompt_parts.append(f" {i}. {file_path}")
|
||
|
||
prompt_parts.extend([
|
||
"",
|
||
"⚠️ CRITICAL: Your response MUST be ONLY valid JSON. Do NOT include any explanatory text before or after the JSON.",
|
||
"Start your response directly with '{' and end with '}'. No markdown code blocks, no explanations.",
|
||
"",
|
||
"Provide a comprehensive analysis in JSON format with the following structure:",
|
||
"",
|
||
"{",
|
||
' "module_overview": "Brief description of this module/chunk and its purpose",',
|
||
' "module_quality_score": 8.5,',
|
||
' "module_architecture": "Description of architecture patterns used",',
|
||
' "module_security_assessment": "Security concerns and recommendations",',
|
||
' "module_recommendations": ["rec1", "rec2", "rec3"],',
|
||
' "files": [',
|
||
' {',
|
||
' "file_path": "path/to/file",',
|
||
' "quality_score": 8.5,',
|
||
' "main_issues": ["issue1", "issue2", "issue3"],',
|
||
' "recommendations": ["rec1", "rec2", "rec3"],',
|
||
' "complexity_score": 7.0,',
|
||
' "detailed_analysis": "Comprehensive analysis of this file..."',
|
||
' }',
|
||
' // ... MUST include analysis for ALL files listed above',
|
||
' ]',
|
||
"}",
|
||
"",
|
||
f"⚠️ REQUIREMENT: The 'files' array MUST contain exactly {len(optimized_files)} entries, one for each file listed above.",
|
||
"⚠️ FORMAT REQUIREMENT: Return ONLY the JSON object, no additional text, no markdown, no explanations.",
|
||
"Focus on providing detailed, actionable insights that help understand the complete module context."
|
||
])
|
||
|
||
return "\n".join(prompt_parts)
|
||
|
||
def build_smart_batch_prompt(files_batch: List[Tuple[str, str]]) -> str:
|
||
"""Legacy function: Build prompt for simple batch (backward compatibility)."""
|
||
optimized_files = []
|
||
for file_path, content in files_batch:
|
||
if content is None:
|
||
continue
|
||
if len(content) > 1500:
|
||
optimized_content = content[:1500] + "\n... [truncated for efficiency]"
|
||
else:
|
||
optimized_content = content
|
||
optimized_files.append((file_path, optimized_content))
|
||
|
||
prompt_parts = [
|
||
"Analyze these files for code quality, issues, and recommendations.",
|
||
"Return a JSON response with analysis for each file.",
|
||
"For each file, provide: quality_score (1-10), main_issues (top 3), recommendations (top 3), complexity_score (1-10).",
|
||
"",
|
||
"FILES TO ANALYZE:",
|
||
""
|
||
]
|
||
|
||
for i, (file_path, content) in enumerate(optimized_files, 1):
|
||
prompt_parts.extend([
|
||
f"=== FILE {i}: {file_path} ===",
|
||
content,
|
||
""
|
||
])
|
||
|
||
prompt_parts.extend([
|
||
"RESPONSE FORMAT (JSON):",
|
||
"{",
|
||
' "files": [',
|
||
' {',
|
||
' "file_path": "path/to/file",',
|
||
' "quality_score": 8.5,',
|
||
' "main_issues": ["issue1", "issue2", "issue3"],',
|
||
' "recommendations": ["rec1", "rec2", "rec3"],',
|
||
' "complexity_score": 7.0',
|
||
' }',
|
||
' ]',
|
||
"}"
|
||
])
|
||
|
||
return "\n".join(prompt_parts)
|
||
|
||
def parse_intelligent_chunk_response(response_text: str, chunk: Dict) -> Tuple[List, Dict]:
|
||
"""
|
||
Parse comprehensive chunk-level response from Claude API.
|
||
Returns (file_analyses, chunk_analysis) tuple.
|
||
"""
|
||
from ai_analyze import FileAnalysis
|
||
import json
|
||
import re
|
||
|
||
file_analyses = []
|
||
chunk_analysis = {}
|
||
|
||
# Function to extract JSON from markdown code blocks
|
||
def extract_json_from_text(text: str) -> Optional[str]:
|
||
"""Extract JSON from text, handling markdown code blocks."""
|
||
text = text.strip()
|
||
json_block_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', text, re.DOTALL)
|
||
if json_block_match:
|
||
return json_block_match.group(1)
|
||
|
||
brace_count = 0
|
||
start_idx = -1
|
||
for i, char in enumerate(text):
|
||
if char == '{':
|
||
if brace_count == 0:
|
||
start_idx = i
|
||
brace_count += 1
|
||
elif char == '}':
|
||
brace_count -= 1
|
||
if brace_count == 0 and start_idx >= 0:
|
||
return text[start_idx:i+1]
|
||
return None
|
||
|
||
def clean_json_string(json_str: str) -> str:
|
||
"""Clean JSON string by escaping control characters."""
|
||
# Replace raw newlines, tabs with escaped versions (but preserve in strings)
|
||
# This is a simple approach: replace unescaped control chars
|
||
import re
|
||
|
||
# First, handle common issues: unescaped newlines in string values
|
||
# Pattern: look for ": "...\n..." and replace \n with \\n
|
||
# This is tricky, so let's use a different approach:
|
||
# Just try to fix common control char issues
|
||
|
||
# Replace actual newlines with escaped newlines OUTSIDE of quoted strings
|
||
# Simpler fix: just replace problematic control chars globally
|
||
cleaned = json_str
|
||
|
||
# Handle unescaped newlines in JSON values (common issue)
|
||
# Replace literal newlines with \n
|
||
lines = []
|
||
in_string = False
|
||
escape_next = False
|
||
|
||
for char in cleaned:
|
||
if escape_next:
|
||
lines.append(char)
|
||
escape_next = False
|
||
elif char == '\\':
|
||
lines.append(char)
|
||
escape_next = True
|
||
elif char == '"':
|
||
lines.append(char)
|
||
in_string = not in_string
|
||
elif char in '\n\r\t' and in_string:
|
||
# Inside a string - escape it
|
||
if char == '\n':
|
||
lines.append('\\n')
|
||
elif char == '\r':
|
||
lines.append('\\r')
|
||
elif char == '\t':
|
||
lines.append('\\t')
|
||
else:
|
||
lines.append(char)
|
||
|
||
return ''.join(lines)
|
||
|
||
def try_parse_json(text: str) -> Optional[dict]:
|
||
"""Try to parse JSON from text, with multiple attempts."""
|
||
try:
|
||
result = json.loads(text)
|
||
print(f" ✅ [PARSE-JSON] Parsed full response as JSON")
|
||
return result
|
||
except json.JSONDecodeError as e:
|
||
# This is expected when Claude includes explanatory text - not an error
|
||
# Only log at debug level since we have a fallback
|
||
pass
|
||
|
||
# Extract JSON block from text (handles cases where Claude adds explanatory text)
|
||
extracted = extract_json_from_text(text)
|
||
if extracted:
|
||
print(f" ℹ️ [PARSE-JSON] Extracted JSON block from response (length: {len(extracted)} chars)")
|
||
try:
|
||
result = json.loads(extracted)
|
||
print(f" ✅ [PARSE-JSON] Parsed extracted JSON successfully")
|
||
return result
|
||
except json.JSONDecodeError as e:
|
||
print(f" ⚠️ [PARSE-JSON] Extracted JSON parse failed (trying cleanup): {str(e)[:80]}")
|
||
# Try cleaning control characters
|
||
try:
|
||
cleaned = clean_json_string(extracted)
|
||
result = json.loads(cleaned)
|
||
print(f" ✅ [PARSE-JSON] Parsed cleaned JSON successfully!")
|
||
return result
|
||
except json.JSONDecodeError as e2:
|
||
print(f" ❌ [PARSE-JSON] Cleaned JSON also failed: {str(e2)[:80]}")
|
||
else:
|
||
print(f" ❌ [PARSE-JSON] Could not extract JSON block from response")
|
||
return None
|
||
|
||
# Parse response
|
||
print(f" 📝 [PARSE] Response length: {len(response_text)} chars")
|
||
print(f" 📝 [PARSE] First 100 chars: {response_text[:100]}")
|
||
|
||
response_data = try_parse_json(response_text)
|
||
print(f" 📝 [PARSE] JSON parse result: {response_data is not None}")
|
||
|
||
if response_data:
|
||
# Extract module-level analysis
|
||
chunk_analysis = {
|
||
'module_overview': response_data.get('module_overview', ''),
|
||
'module_quality_score': response_data.get('module_quality_score', 5.0),
|
||
'module_architecture': response_data.get('module_architecture', ''),
|
||
'module_security_assessment': response_data.get('module_security_assessment', ''),
|
||
'module_recommendations': response_data.get('module_recommendations', [])
|
||
}
|
||
|
||
# Extract file-level analyses
|
||
files_batch = chunk.get('files', [])
|
||
analyzed_file_paths = set() # Track which files have been analyzed
|
||
|
||
if "files" in response_data:
|
||
print(f"✅ [PARSE] Successfully parsed chunk response with {len(response_data['files'])} files")
|
||
|
||
for file_data in response_data["files"]:
|
||
file_path = file_data.get("file_path", "")
|
||
quality_score = file_data.get("quality_score", 5.0)
|
||
main_issues = file_data.get("main_issues", [])
|
||
recommendations = file_data.get("recommendations", [])
|
||
complexity_score = file_data.get("complexity_score", 5.0)
|
||
detailed_analysis = file_data.get("detailed_analysis", "")
|
||
|
||
# Find matching file in batch
|
||
matching_file = None
|
||
for batch_file_path, batch_content in files_batch:
|
||
if batch_file_path == file_path or batch_file_path.endswith(file_path) or file_path in batch_file_path:
|
||
matching_file = (batch_file_path, batch_content)
|
||
analyzed_file_paths.add(batch_file_path)
|
||
break
|
||
|
||
if matching_file:
|
||
file_path, content = matching_file
|
||
analysis = FileAnalysis(
|
||
path=file_path,
|
||
language=get_language_from_path(file_path),
|
||
lines_of_code=len(content.splitlines()) if content else 0,
|
||
complexity_score=complexity_score,
|
||
issues_found=main_issues if isinstance(main_issues, list) else [],
|
||
recommendations=recommendations if isinstance(recommendations, list) else [],
|
||
detailed_analysis=detailed_analysis or f"Chunk analysis: {quality_score}/10 quality score",
|
||
severity_score=float(quality_score) if isinstance(quality_score, (int, float)) else 5.0,
|
||
content=content
|
||
)
|
||
file_analyses.append(analysis)
|
||
|
||
# IMPORTANT: Ensure ALL files in chunk are analyzed (fallback for missing files)
|
||
for file_path, content in files_batch:
|
||
if file_path not in analyzed_file_paths:
|
||
print(f"⚠️ [PARSE] File {file_path} not in Claude response, creating fallback analysis")
|
||
analysis = FileAnalysis(
|
||
path=file_path,
|
||
language=get_language_from_path(file_path),
|
||
lines_of_code=len(content.splitlines()) if content else 0,
|
||
complexity_score=5.0,
|
||
issues_found=["Analysis completed via intelligent chunking (fallback)"],
|
||
recommendations=["Review code quality"],
|
||
detailed_analysis=f"Intelligent chunk analysis completed for {file_path}",
|
||
severity_score=5.0,
|
||
content=content
|
||
)
|
||
file_analyses.append(analysis)
|
||
|
||
# Fallback: create basic analyses if parsing completely failed
|
||
if not file_analyses:
|
||
print(f"⚠️ [PARSE] Failed to parse chunk response, using complete fallback for all files")
|
||
for file_path, content in chunk.get('files', []):
|
||
analysis = FileAnalysis(
|
||
path=file_path,
|
||
language=get_language_from_path(file_path),
|
||
lines_of_code=len(content.splitlines()) if content else 0,
|
||
complexity_score=5.0,
|
||
issues_found=["Analysis completed via intelligent chunking"],
|
||
recommendations=["Review code quality"],
|
||
detailed_analysis="Intelligent chunk analysis completed",
|
||
severity_score=5.0,
|
||
content=content
|
||
)
|
||
file_analyses.append(analysis)
|
||
|
||
chunk_analysis = {
|
||
'module_overview': f"Analysis of {chunk.get('name', 'unknown')} module",
|
||
'module_quality_score': 5.0,
|
||
'module_architecture': 'Analysis in progress',
|
||
'module_security_assessment': 'Security assessment pending',
|
||
'module_recommendations': ['Review module structure']
|
||
}
|
||
|
||
return file_analyses, chunk_analysis
|
||
|
||
def parse_smart_batch_response(response_text: str, files_batch: List[Tuple[str, str]]) -> List:
|
||
"""Legacy function: Parse simple batch response (backward compatibility)."""
|
||
from ai_analyze import FileAnalysis
|
||
import json
|
||
import re
|
||
|
||
results = []
|
||
|
||
def extract_json_from_text(text: str) -> Optional[str]:
|
||
text = text.strip()
|
||
json_block_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', text, re.DOTALL)
|
||
if json_block_match:
|
||
return json_block_match.group(1)
|
||
|
||
brace_count = 0
|
||
start_idx = -1
|
||
for i, char in enumerate(text):
|
||
if char == '{':
|
||
if brace_count == 0:
|
||
start_idx = i
|
||
brace_count += 1
|
||
elif char == '}':
|
||
brace_count -= 1
|
||
if brace_count == 0 and start_idx >= 0:
|
||
return text[start_idx:i+1]
|
||
return None
|
||
|
||
def try_parse_json(text: str) -> Optional[dict]:
|
||
try:
|
||
return json.loads(text)
|
||
except json.JSONDecodeError:
|
||
pass
|
||
|
||
extracted = extract_json_from_text(text)
|
||
if extracted:
|
||
try:
|
||
return json.loads(extracted)
|
||
except json.JSONDecodeError:
|
||
pass
|
||
return None
|
||
|
||
response_data = try_parse_json(response_text)
|
||
|
||
if response_data and "files" in response_data:
|
||
print(f"✅ [PARSE] Successfully parsed JSON response with {len(response_data['files'])} files")
|
||
|
||
for file_data in response_data["files"]:
|
||
file_path = file_data.get("file_path", "")
|
||
quality_score = file_data.get("quality_score", 5.0)
|
||
main_issues = file_data.get("main_issues", [])
|
||
recommendations = file_data.get("recommendations", [])
|
||
complexity_score = file_data.get("complexity_score", 5.0)
|
||
|
||
matching_file = None
|
||
for batch_file_path, batch_content in files_batch:
|
||
if batch_file_path == file_path or batch_file_path.endswith(file_path):
|
||
matching_file = (batch_file_path, batch_content)
|
||
break
|
||
|
||
if matching_file:
|
||
file_path, content = matching_file
|
||
analysis = FileAnalysis(
|
||
path=file_path,
|
||
language=get_language_from_path(file_path),
|
||
lines_of_code=len(content.splitlines()) if content else 0,
|
||
complexity_score=complexity_score,
|
||
issues_found=main_issues if isinstance(main_issues, list) else [],
|
||
recommendations=recommendations if isinstance(recommendations, list) else [],
|
||
detailed_analysis=f"Smart batch analysis: {quality_score}/10 quality score",
|
||
severity_score=float(quality_score) if isinstance(quality_score, (int, float)) else 5.0,
|
||
content=content
|
||
)
|
||
results.append(analysis)
|
||
|
||
if not results:
|
||
print(f"⚠️ [PARSE] Failed to parse JSON response, using fallback parsing")
|
||
for file_path, content in files_batch:
|
||
analysis = FileAnalysis(
|
||
path=file_path,
|
||
language=get_language_from_path(file_path),
|
||
lines_of_code=len(content.splitlines()) if content else 0,
|
||
complexity_score=5.0,
|
||
issues_found=["Analysis completed via smart batching"],
|
||
recommendations=["Review code quality"],
|
||
detailed_analysis="Smart batch analysis completed",
|
||
severity_score=5.0,
|
||
content=content
|
||
)
|
||
results.append(analysis)
|
||
|
||
return results
|
||
|
||
async def analyze_intelligent_chunk(chunk: Dict, repository_id: str, progress_mgr: Optional[AnalysisProgressManager] = None, analysis_state: Optional[Dict] = None) -> Tuple[List, Dict, Dict]:
|
||
"""
|
||
Process an intelligent chunk (module-level) with comprehensive analysis.
|
||
Now includes progressive context from previous chunks for better pattern detection.
|
||
Returns (file_analyses, chunk_analysis, updated_analysis_state) tuple.
|
||
"""
|
||
chunk_name = chunk.get('name', 'unknown')
|
||
chunk_id = chunk.get('id', 'unknown')
|
||
chunk_type = chunk.get('chunk_type', 'module')
|
||
files_batch = chunk.get('files', [])
|
||
|
||
# Initialize analysis_state if not provided
|
||
if analysis_state is None:
|
||
analysis_state = {}
|
||
|
||
print(f"🧠 [INTELLIGENT CHUNK] Processing chunk: {chunk_name} ({chunk_id}) - {len(files_batch)} files")
|
||
|
||
# Show context being used
|
||
if analysis_state.get('modules_analyzed'):
|
||
print(f" 📚 Using context from {len(analysis_state.get('modules_analyzed', []))} previously analyzed modules")
|
||
|
||
try:
|
||
# Emit chunk started event
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("intelligent_chunk_started", {
|
||
"message": f"Processing chunk: {chunk_name}",
|
||
"chunk_id": chunk_id,
|
||
"chunk_name": chunk_name,
|
||
"chunk_type": chunk_type,
|
||
"files": [f[0] for f in files_batch],
|
||
"files_count": len(files_batch)
|
||
})
|
||
|
||
# Build comprehensive prompt for chunk with progressive context
|
||
chunk_prompt = build_intelligent_chunk_prompt(chunk, analysis_state)
|
||
|
||
# Rate limiting for the chunk
|
||
await rate_limiter.wait_if_needed()
|
||
|
||
# Make API call using Claude
|
||
try:
|
||
if analyzer and hasattr(analyzer, 'client'):
|
||
# Get number of files for token tracking
|
||
num_files = len(chunk.get('files', []))
|
||
|
||
# Use Claude API directly for comprehensive analysis (synchronous client wrapped in executor)
|
||
def call_claude():
|
||
# Calculate appropriate max_tokens based on number of files (num_files from outer scope)
|
||
# Base tokens: 6000, add 1200 per file for comprehensive analysis
|
||
# CRITICAL: Respect Claude model token limits
|
||
model_name = os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest").lower()
|
||
if "haiku" in model_name:
|
||
model_max = 8000 # Haiku: max 8192, use safe 8000
|
||
elif "sonnet" in model_name:
|
||
model_max = 4000 # Sonnet: max 4096, use safe 4000
|
||
elif "opus" in model_name:
|
||
model_max = 4000 # Opus: max 4096, use safe 4000
|
||
else:
|
||
model_max = 8000 # Default safe value
|
||
|
||
# Calculate tokens: base 6000 + 1200 per file, but cap by model limit
|
||
max_tokens = min(model_max, 6000 + (num_files * 1200))
|
||
# Further reduce if getting too close to model max
|
||
if max_tokens > model_max - 500:
|
||
max_tokens = model_max - 500
|
||
return analyzer.client.messages.create(
|
||
model=os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest"),
|
||
max_tokens=max_tokens,
|
||
temperature=0.3,
|
||
messages=[{"role": "user", "content": chunk_prompt}]
|
||
)
|
||
|
||
# Check token limits before making API call
|
||
model = os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest")
|
||
estimated_input_tokens = estimate_tokens_from_text(chunk_prompt)
|
||
estimated_output_tokens = 2000 # Conservative estimate
|
||
|
||
# Update token limiter for this model if needed (use global)
|
||
global token_usage_limiter
|
||
if token_usage_limiter.model != model:
|
||
token_usage_limiter = TokenUsageRateLimiter(model=model)
|
||
|
||
# Check if we can proceed with this request
|
||
can_proceed, wait_time = await token_usage_limiter.check_token_limits(
|
||
estimated_input_tokens, estimated_output_tokens
|
||
)
|
||
|
||
if not can_proceed:
|
||
print(f"⏳ [TOKEN LIMIT] Waiting {wait_time:.2f} seconds to avoid exceeding token limits...")
|
||
await asyncio.sleep(wait_time)
|
||
|
||
# Run synchronous call in executor to avoid blocking
|
||
loop = asyncio.get_event_loop()
|
||
try:
|
||
message = await loop.run_in_executor(None, call_claude)
|
||
response_text = message.content[0].text if message.content else ""
|
||
|
||
# Record actual token usage in limiter (after API call completes)
|
||
if message and hasattr(message, 'usage'):
|
||
actual_input = message.usage.input_tokens if hasattr(message.usage, 'input_tokens') else estimated_input_tokens
|
||
actual_output = message.usage.output_tokens if hasattr(message.usage, 'output_tokens') else estimated_output_tokens
|
||
# Update limiter with actual usage (replace estimate with actual)
|
||
await token_usage_limiter.record_token_usage(actual_input, actual_output)
|
||
|
||
# Log token usage and cost
|
||
run_id = getattr(analyzer, 'run_id', f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
|
||
log_token_usage(
|
||
run_id=run_id,
|
||
request_type=f"chunk_analysis_{chunk_name}",
|
||
prompt_text=chunk_prompt,
|
||
response_obj=message,
|
||
file_bundle_size=num_files,
|
||
model=model
|
||
)
|
||
except Exception as api_error:
|
||
# Log error with token tracking
|
||
run_id = getattr(analyzer, 'run_id', f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
|
||
log_token_usage(
|
||
run_id=run_id,
|
||
request_type=f"chunk_analysis_{chunk_name}",
|
||
prompt_text=chunk_prompt,
|
||
file_bundle_size=num_files,
|
||
model=os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest"),
|
||
error=api_error
|
||
)
|
||
raise
|
||
else:
|
||
# Fallback to individual analysis
|
||
print("⚠️ Analyzer not available, falling back to individual analysis")
|
||
file_analyses = []
|
||
chunk_analysis = {
|
||
'module_overview': f"Analysis of {chunk_name} module",
|
||
'module_quality_score': 5.0,
|
||
'module_architecture': 'Analysis in progress',
|
||
'module_security_assessment': 'Security assessment pending',
|
||
'module_recommendations': ['Review module structure']
|
||
}
|
||
for file_path, content in files_batch:
|
||
result = await analyze_single_file_parallel(file_path, content, repository_id, progress_mgr=progress_mgr)
|
||
file_analyses.append(result)
|
||
# Update state even with fallback
|
||
updated_state = update_state_with_findings(analysis_state.copy(), chunk, chunk_analysis, file_analyses)
|
||
return file_analyses, chunk_analysis, updated_state
|
||
except Exception as api_error:
|
||
print(f"⚠️ API call failed: {api_error}, falling back to individual analysis")
|
||
file_analyses = []
|
||
chunk_analysis = {
|
||
'module_overview': f"Analysis of {chunk_name} module",
|
||
'module_quality_score': 5.0,
|
||
'module_architecture': 'Analysis in progress',
|
||
'module_security_assessment': 'Security assessment pending',
|
||
'module_recommendations': ['Review module structure']
|
||
}
|
||
for file_path, content in files_batch:
|
||
result = await analyze_single_file_parallel(file_path, content, repository_id, progress_mgr=progress_mgr)
|
||
file_analyses.append(result)
|
||
# Update state even with fallback
|
||
updated_state = update_state_with_findings(analysis_state.copy(), chunk, chunk_analysis, file_analyses)
|
||
return file_analyses, chunk_analysis, updated_state
|
||
|
||
# Parse comprehensive chunk response
|
||
file_analyses, chunk_analysis = parse_intelligent_chunk_response(response_text, chunk)
|
||
|
||
# Update analysis_state with findings from this chunk
|
||
updated_state = update_state_with_findings(analysis_state.copy(), chunk, chunk_analysis, file_analyses)
|
||
|
||
# Emit individual file completions
|
||
for i, result in enumerate(file_analyses):
|
||
if progress_mgr:
|
||
file_path = result.path if hasattr(result, 'path') else files_batch[i][0] if i < len(files_batch) else 'unknown'
|
||
await progress_mgr.emit_event("file_analysis_completed", {
|
||
"message": f"Completed {file_path}",
|
||
"file_path": file_path,
|
||
"quality_score": result.severity_score,
|
||
"issues_count": len(result.issues_found) if hasattr(result, 'issues_found') else 0,
|
||
"chunk_processed": True,
|
||
"chunk_name": chunk_name
|
||
})
|
||
|
||
print(f"✅ [INTELLIGENT CHUNK] Completed chunk {chunk_name} - {len(file_analyses)} files analyzed")
|
||
print(f" 📊 State updated: {len(updated_state.get('modules_analyzed', []))} modules analyzed, {len(updated_state.get('architecture_patterns', []))} patterns found")
|
||
return file_analyses, chunk_analysis, updated_state
|
||
|
||
except Exception as e:
|
||
print(f"❌ [INTELLIGENT CHUNK] Error processing chunk {chunk_name}: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
# Fallback to individual analysis
|
||
print("🔄 [INTELLIGENT CHUNK] Falling back to individual analysis")
|
||
file_analyses = []
|
||
chunk_analysis = {
|
||
'module_overview': f"Analysis of {chunk_name} module (fallback)",
|
||
'module_quality_score': 5.0,
|
||
'module_architecture': 'Analysis in progress',
|
||
'module_security_assessment': 'Security assessment pending',
|
||
'module_recommendations': ['Review module structure']
|
||
}
|
||
|
||
for file_path, content in files_batch:
|
||
try:
|
||
result = await analyze_single_file_parallel(file_path, content, repository_id, progress_mgr=progress_mgr)
|
||
file_analyses.append(result)
|
||
except Exception as file_error:
|
||
print(f"❌ Error analyzing {file_path}: {file_error}")
|
||
from ai_analyze import FileAnalysis
|
||
failed_analysis = FileAnalysis(
|
||
path=file_path,
|
||
language="Unknown",
|
||
lines_of_code=len(content.splitlines()) if content else 0,
|
||
severity_score=5.0,
|
||
issues_found=[f"Analysis failed: {str(file_error)}"],
|
||
recommendations=["Review this file manually"],
|
||
detailed_analysis=f"Error analyzing {file_path}: {str(file_error)}",
|
||
complexity_score=5.0,
|
||
content=content
|
||
)
|
||
file_analyses.append(failed_analysis)
|
||
|
||
# Update state even with fallback
|
||
updated_state = update_state_with_findings(analysis_state.copy(), chunk, chunk_analysis, file_analyses)
|
||
return file_analyses, chunk_analysis, updated_state
|
||
|
||
async def analyze_files_smart_batch(files_batch: List[Tuple[str, str]], repository_id: str, progress_mgr: Optional[AnalysisProgressManager] = None):
|
||
"""Legacy function: Process batch (backward compatibility)."""
|
||
print(f"🚀 [SMART BATCH] Processing {len(files_batch)} files in single API call")
|
||
|
||
try:
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("smart_batch_started", {
|
||
"message": f"Processing {len(files_batch)} files in smart batch",
|
||
"files": [f[0] for f in files_batch],
|
||
"batch_size": len(files_batch)
|
||
})
|
||
|
||
combined_prompt = build_smart_batch_prompt(files_batch)
|
||
await rate_limiter.wait_if_needed()
|
||
|
||
if hasattr(analyzer, 'analyze_files_batch'):
|
||
response = await analyzer.analyze_files_batch(combined_prompt)
|
||
else:
|
||
print("⚠️ Batch method not available, falling back to individual analysis")
|
||
results = []
|
||
for file_path, content in files_batch:
|
||
result = await analyze_single_file_parallel(file_path, content, repository_id, progress_mgr=progress_mgr)
|
||
results.append(result)
|
||
return results
|
||
|
||
results = parse_smart_batch_response(response, files_batch)
|
||
|
||
for i, result in enumerate(results):
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("file_analysis_completed", {
|
||
"message": f"Completed {files_batch[i][0]}",
|
||
"file_path": files_batch[i][0],
|
||
"quality_score": result.severity_score,
|
||
"issues_count": len(result.issues_found) if hasattr(result, 'issues_found') else 0,
|
||
"batch_processed": True
|
||
})
|
||
|
||
print(f"✅ [SMART BATCH] Completed {len(results)} files in single API call")
|
||
return results
|
||
|
||
except Exception as e:
|
||
print(f"❌ [SMART BATCH] Error processing batch: {e}")
|
||
print("🔄 [SMART BATCH] Falling back to individual analysis")
|
||
results = []
|
||
for file_path, content in files_batch:
|
||
try:
|
||
result = await analyze_single_file_parallel(file_path, content, repository_id, progress_mgr=progress_mgr)
|
||
results.append(result)
|
||
except Exception as file_error:
|
||
print(f"❌ Error analyzing {file_path}: {file_error}")
|
||
from ai_analyze import FileAnalysis
|
||
failed_analysis = FileAnalysis(
|
||
path=file_path,
|
||
language="Unknown",
|
||
lines_of_code=len(content.splitlines()) if content else 0,
|
||
severity_score=5.0,
|
||
issues_found=[f"Analysis failed: {str(file_error)}"],
|
||
recommendations=["Review this file manually"],
|
||
detailed_analysis=f"Error analyzing {file_path}: {str(file_error)}",
|
||
complexity_score=5.0,
|
||
content=content
|
||
)
|
||
results.append(failed_analysis)
|
||
return results
|
||
|
||
# ============================================================================
|
||
# EPISODIC MEMORY STORAGE FOR INTELLIGENT CHUNKS
|
||
# ============================================================================
|
||
|
||
async def store_chunk_analysis_in_memory(chunk: Dict, file_analyses: List, chunk_analysis: Dict, repository_id: str, session_id: str = None, analysis_state: Optional[Dict] = None):
|
||
"""
|
||
Store detailed chunk-level analysis in episodic memory (MongoDB).
|
||
Creates one record per chunk with comprehensive analysis data.
|
||
Now includes progressive context (Option 3: Hybrid Approach).
|
||
"""
|
||
try:
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
print("⚠️ [MEMORY] Memory manager not available, skipping chunk storage")
|
||
return
|
||
|
||
# Get session ID from analyzer
|
||
if not session_id:
|
||
session_id = getattr(analyzer, 'session_id', str(uuid.uuid4()))
|
||
|
||
chunk_id = chunk.get('id', 'unknown')
|
||
chunk_name = chunk.get('name', 'unknown')
|
||
chunk_type = chunk.get('chunk_type', 'module')
|
||
chunk_priority = chunk.get('priority', 2)
|
||
dependencies = chunk.get('context_dependencies', [])
|
||
|
||
# Calculate chunk metrics
|
||
total_files = len(file_analyses)
|
||
total_lines = sum(fa.lines_of_code for fa in file_analyses if fa.lines_of_code is not None)
|
||
total_issues = sum(len(fa.issues_found) if isinstance(fa.issues_found, (list, tuple)) else 0 for fa in file_analyses)
|
||
total_recommendations = sum(len(fa.recommendations) if isinstance(fa.recommendations, (list, tuple)) else 0 for fa in file_analyses)
|
||
|
||
# Calculate quality distribution
|
||
high_quality = len([fa for fa in file_analyses if fa.severity_score >= 8])
|
||
medium_quality = len([fa for fa in file_analyses if 5 <= fa.severity_score < 8])
|
||
low_quality = len([fa for fa in file_analyses if fa.severity_score < 5])
|
||
|
||
# Get module quality score from chunk_analysis or calculate from files
|
||
module_quality = chunk_analysis.get('module_quality_score',
|
||
sum(fa.severity_score for fa in file_analyses if fa.severity_score is not None) / total_files if total_files > 0 else 5.0)
|
||
|
||
# Build comprehensive AI response text with CODE EVIDENCE
|
||
# FIX: Convert all values to strings immediately to prevent TypeError
|
||
module_overview = chunk_analysis.get('module_overview', f"Analysis of {chunk_name} module")
|
||
if isinstance(module_overview, dict):
|
||
module_overview = json.dumps(module_overview, indent=2)
|
||
else:
|
||
module_overview = str(module_overview)
|
||
|
||
# Extract code evidence from file analyses for concrete proof in reports
|
||
try:
|
||
code_evidence = extract_code_evidence_from_files(file_analyses)
|
||
print(f" 📸 Extracted {len(code_evidence)} evidence items")
|
||
except Exception as e:
|
||
print(f" ⚠️ Code evidence extraction failed: {e}")
|
||
code_evidence = []
|
||
|
||
module_architecture = chunk_analysis.get('module_architecture', 'Architecture analysis in progress')
|
||
if isinstance(module_architecture, dict):
|
||
module_architecture = json.dumps(module_architecture, indent=2)
|
||
else:
|
||
module_architecture = str(module_architecture)
|
||
|
||
module_security = chunk_analysis.get('module_security_assessment', 'Security assessment in progress')
|
||
if isinstance(module_security, dict):
|
||
module_security = json.dumps(module_security, indent=2)
|
||
else:
|
||
module_security = str(module_security)
|
||
|
||
ai_response_parts = [
|
||
f"# COMPREHENSIVE ANALYSIS: {chunk_name.upper()}",
|
||
f"Chunk ID: {chunk_id}",
|
||
f"Chunk Type: {chunk_type}",
|
||
"",
|
||
f"## MODULE OVERVIEW",
|
||
module_overview,
|
||
"",
|
||
f"## MODULE METRICS",
|
||
f"- Module Quality Score: {module_quality:.1f}/10",
|
||
f"- Total Files: {total_files}",
|
||
f"- Total Lines of Code: {total_lines:,}",
|
||
f"- Total Issues: {total_issues}",
|
||
f"- Total Recommendations: {total_recommendations}",
|
||
f"- High Quality Files (Score >= 8): {high_quality}",
|
||
f"- Medium Quality Files (Score 5-7): {medium_quality}",
|
||
f"- Low Quality Files (Score < 5): {low_quality}",
|
||
"",
|
||
f"## ARCHITECTURE ASSESSMENT",
|
||
module_architecture,
|
||
"",
|
||
f"## SECURITY ASSESSMENT",
|
||
module_security,
|
||
"",
|
||
f"## MODULE RECOMMENDATIONS",
|
||
]
|
||
|
||
module_recs = chunk_analysis.get('module_recommendations', [])
|
||
if module_recs:
|
||
for rec in module_recs:
|
||
# Handle both string and dict recommendations
|
||
if isinstance(rec, dict):
|
||
rec_text = rec.get('text', str(rec.get('recommendation', '')))[:200]
|
||
else:
|
||
rec_text = str(rec)
|
||
ai_response_parts.append(f"- {rec_text}")
|
||
else:
|
||
ai_response_parts.append("- Review module structure")
|
||
|
||
ai_response_parts.extend([
|
||
"",
|
||
"## CODE EVIDENCE & FINDINGS",
|
||
""
|
||
])
|
||
|
||
# Add code evidence section
|
||
if code_evidence:
|
||
ai_response_parts.append("### SPECIFIC CODE ISSUES WITH EVIDENCE:")
|
||
for evidence in code_evidence[:10]: # Top 10 most critical
|
||
ai_response_parts.extend([
|
||
f"**File:** {evidence['file']}",
|
||
f"**Issue:** {evidence['issue']}",
|
||
f"**Line {evidence['line_number']}:**",
|
||
"```" + evidence['language'],
|
||
evidence['code_snippet'],
|
||
"```",
|
||
f"**Recommendation:** {evidence['recommendation']}",
|
||
""
|
||
])
|
||
|
||
ai_response_parts.extend([
|
||
"",
|
||
"## FILE-LEVEL ANALYSIS SUMMARY",
|
||
""
|
||
])
|
||
|
||
# Add detailed file analyses
|
||
for fa in file_analyses:
|
||
ai_response_parts.extend([
|
||
f"### {fa.path}",
|
||
f"- Language: {fa.language}",
|
||
f"- Lines of Code: {fa.lines_of_code}",
|
||
f"- Quality Score: {fa.severity_score:.1f}/10",
|
||
f"- Complexity Score: {fa.complexity_score:.1f}/10",
|
||
f"- Issues: {len(fa.issues_found) if isinstance(fa.issues_found, (list, tuple)) else 0}",
|
||
""
|
||
])
|
||
|
||
if fa.issues_found:
|
||
ai_response_parts.append("**Issues Found:**")
|
||
for issue in fa.issues_found[:5]: # Top 5 issues
|
||
# Handle both string and dict issues
|
||
if isinstance(issue, dict):
|
||
issue_text = issue.get('title', str(issue.get('description', '')))[:200]
|
||
else:
|
||
issue_text = str(issue)
|
||
ai_response_parts.append(f"- {issue_text}")
|
||
ai_response_parts.append("")
|
||
|
||
if fa.recommendations:
|
||
ai_response_parts.append("**Recommendations:**")
|
||
for rec in fa.recommendations[:5]: # Top 5 recommendations
|
||
# Handle both string and dict recommendations
|
||
if isinstance(rec, dict):
|
||
rec_text = rec.get('text', str(rec.get('recommendation', '')))[:200]
|
||
else:
|
||
rec_text = str(rec)
|
||
ai_response_parts.append(f"- {rec_text}")
|
||
ai_response_parts.append("")
|
||
|
||
if fa.detailed_analysis:
|
||
# Ensure detailed_analysis is a string, not a dict
|
||
detailed_analysis_text = str(fa.detailed_analysis) if not isinstance(fa.detailed_analysis, str) else fa.detailed_analysis
|
||
ai_response_parts.extend([
|
||
"**Detailed Analysis:**",
|
||
detailed_analysis_text,
|
||
""
|
||
])
|
||
|
||
# Final safety check: Convert all items to strings before joining
|
||
ai_response_parts_clean = []
|
||
for item in ai_response_parts:
|
||
if isinstance(item, dict):
|
||
# Convert dict to JSON string (json is already imported at module level)
|
||
ai_response_parts_clean.append(json.dumps(item, indent=2))
|
||
elif isinstance(item, (list, tuple)):
|
||
# Convert list/tuple to string representation
|
||
ai_response_parts_clean.append(str(item))
|
||
else:
|
||
ai_response_parts_clean.append(str(item))
|
||
|
||
ai_response = "\n".join(ai_response_parts_clean)
|
||
|
||
# Build user query
|
||
file_names = [fa.path for fa in file_analyses]
|
||
user_query = f"Analysis of chunk: {chunk_name} ({chunk_type}) - {total_files} files: {', '.join(file_names[:5])}{'...' if len(file_names) > 5 else ''}"
|
||
|
||
# Prepare file analyses data for storage (OPTIMIZATION: Store only paths, not content)
|
||
# IMPORTANT: Never store file content in episodic memory to save storage space
|
||
file_analyses_data = []
|
||
for fa in file_analyses:
|
||
file_data = {
|
||
'file_path': str(fa.path), # Only store path, not content
|
||
'language': fa.language,
|
||
'lines_of_code': fa.lines_of_code,
|
||
# EXPLICITLY EXCLUDE 'content' field - never store file content in database
|
||
'complexity_score': fa.complexity_score,
|
||
'severity_score': fa.severity_score,
|
||
'issues_found': fa.issues_found if isinstance(fa.issues_found, (list, tuple)) else [],
|
||
'recommendations': fa.recommendations if isinstance(fa.recommendations, (list, tuple)) else [],
|
||
'detailed_analysis': fa.detailed_analysis,
|
||
# NOTE: 'content' field explicitly NOT included to save storage space
|
||
# File content can be retrieved from repository if needed
|
||
}
|
||
# Explicitly ensure content is NOT in the dict
|
||
if 'content' in file_data:
|
||
del file_data['content']
|
||
file_analyses_data.append(file_data)
|
||
|
||
# Build progressive context metadata (Option 3: Hybrid Approach)
|
||
progressive_context = {}
|
||
if analysis_state:
|
||
# OPTIMIZATION: Limit context to last 5 modules for faster processing
|
||
all_module_summaries = analysis_state.get('module_summaries', {})
|
||
modules_analyzed = analysis_state.get('modules_analyzed', [])
|
||
last_5_modules = modules_analyzed[-5:] if len(modules_analyzed) > 5 else modules_analyzed
|
||
|
||
progressive_context = {
|
||
'modules_analyzed_before': last_5_modules[:-1] if last_5_modules else [], # Only last 5 modules
|
||
'project_overview_summary': analysis_state.get('project_overview', '')[:300] if analysis_state.get('project_overview') else '', # Reduced from 500
|
||
'architecture_patterns_found_so_far': analysis_state.get('architecture_patterns', []),
|
||
'critical_issues_found_so_far': analysis_state.get('critical_issues', [])[:5], # Reduced from 10 to 5
|
||
'tech_stack_discovered': analysis_state.get('tech_stack', {}),
|
||
'previous_module_summaries': {
|
||
k: v[:100] for k, v in all_module_summaries.items() # Reduced from 200 to 100 chars
|
||
if k != chunk_name and k in last_5_modules # Only last 5 modules
|
||
}
|
||
}
|
||
|
||
# Get run_id from analyzer if available (for hierarchical storage compatibility)
|
||
run_id = getattr(analyzer, 'run_id', None)
|
||
if not run_id:
|
||
# Try to extract from session_id or generate
|
||
run_id = f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||
|
||
# Build comprehensive metadata
|
||
metadata = {
|
||
'type': 'module_analysis', # IMPORTANT: Mark as module_analysis for retrieval
|
||
'run_id': run_id, # IMPORTANT: Include run_id for retrieval
|
||
'chunk_id': chunk_id,
|
||
'chunk_name': chunk_name,
|
||
'chunk_type': chunk_type,
|
||
'chunk_priority': chunk_priority,
|
||
'module_name': chunk_name if chunk_type == 'module' else None,
|
||
'total_files_in_chunk': total_files,
|
||
'total_lines_in_chunk': total_lines,
|
||
'chunk_token_count': estimate_tokens(chunk.get('files', [])),
|
||
'context_dependencies': dependencies,
|
||
'repository_id': repository_id,
|
||
'analysis_type': 'intelligent_chunking',
|
||
|
||
# NEW: Progressive Context (Option 3)
|
||
'progressive_context': progressive_context,
|
||
|
||
# Chunk metrics
|
||
'chunk_metrics': {
|
||
'average_quality_score': module_quality,
|
||
'total_issues': total_issues,
|
||
'total_recommendations': total_recommendations,
|
||
'average_complexity': sum(fa.complexity_score for fa in file_analyses if fa.complexity_score is not None) / total_files if total_files > 0 else 5.0,
|
||
'high_quality_files': high_quality,
|
||
'medium_quality_files': medium_quality,
|
||
'low_quality_files': low_quality
|
||
},
|
||
|
||
# Module-level analysis
|
||
'module_analysis': {
|
||
'module_overview': chunk_analysis.get('module_overview', ''),
|
||
'module_architecture': chunk_analysis.get('module_architecture', ''),
|
||
'module_security_assessment': chunk_analysis.get('module_security_assessment', ''),
|
||
'module_recommendations': chunk_analysis.get('module_recommendations', [])
|
||
},
|
||
|
||
# Dependencies
|
||
'dependencies': {
|
||
'depends_on_chunks': dependencies,
|
||
'imports_from': [] # Can be enhanced with actual import analysis
|
||
},
|
||
|
||
# File analyses (detailed)
|
||
'file_analyses': file_analyses_data
|
||
}
|
||
|
||
# Store in episodic memory
|
||
print(f" 💾 Storing {chunk_name} in episodic memory...")
|
||
print(f" 📊 Metadata type: {metadata.get('type')}, Run ID: {metadata.get('run_id')[:30]}...")
|
||
try:
|
||
memory_id = await analyzer.memory_manager.store_episodic_memory(
|
||
session_id=session_id,
|
||
user_query=user_query,
|
||
ai_response=ai_response,
|
||
repo_context=repository_id,
|
||
metadata=metadata
|
||
)
|
||
print(f" ✅ Stored in episodic memory with ID: {memory_id}")
|
||
except Exception as memory_error:
|
||
print(f" ❌ Failed to store in episodic memory: {memory_error}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
raise
|
||
|
||
# Option 3: Also store/update cumulative analysis_state record
|
||
if analysis_state:
|
||
try:
|
||
await store_cumulative_analysis_state(
|
||
session_id=session_id,
|
||
repository_id=repository_id,
|
||
analysis_state=analysis_state,
|
||
chunk_sequence=len(analysis_state.get('modules_analyzed', []))
|
||
)
|
||
print(f" ✅ Cumulative state stored")
|
||
except Exception as state_error:
|
||
print(f" ⚠️ Failed to store cumulative state: {state_error}")
|
||
|
||
print(f"✅ [MEMORY] Stored chunk analysis: {chunk_name} (ID: {memory_id})")
|
||
return memory_id
|
||
|
||
except Exception as e:
|
||
print(f"❌ [MEMORY] Failed to store chunk analysis: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return None
|
||
|
||
async def store_cumulative_analysis_state(session_id: str, repository_id: str, analysis_state: Dict, chunk_sequence: int):
|
||
"""
|
||
Store or update cumulative analysis_state in episodic memory (Option 3: Hybrid Approach).
|
||
This provides a single source of truth for the current analysis state.
|
||
"""
|
||
try:
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return
|
||
|
||
user_query = f"Repository Analysis State - After Chunk {chunk_sequence}"
|
||
|
||
# Format cumulative state as readable text
|
||
state_parts = [
|
||
"# CUMULATIVE ANALYSIS STATE",
|
||
"",
|
||
f"## Modules Analyzed ({len(analysis_state.get('modules_analyzed', []))})",
|
||
", ".join(analysis_state.get('modules_analyzed', [])) or "None",
|
||
""
|
||
]
|
||
|
||
if analysis_state.get('project_overview'):
|
||
state_parts.extend([
|
||
"## Project Overview",
|
||
analysis_state.get('project_overview', '')[:1000],
|
||
""
|
||
])
|
||
|
||
if analysis_state.get('architecture_patterns'):
|
||
state_parts.extend([
|
||
"## Architecture Patterns Identified",
|
||
", ".join(analysis_state.get('architecture_patterns', [])),
|
||
""
|
||
])
|
||
|
||
if analysis_state.get('critical_issues'):
|
||
state_parts.extend([
|
||
"## Critical Issues Found",
|
||
])
|
||
for issue in analysis_state.get('critical_issues', [])[:10]:
|
||
module = issue.get('module', 'unknown')
|
||
issue_text = issue.get('issue', '')
|
||
state_parts.append(f"- {module}: {issue_text}")
|
||
state_parts.append("")
|
||
|
||
if analysis_state.get('tech_stack'):
|
||
state_parts.extend([
|
||
"## Tech Stack Discovered",
|
||
])
|
||
for key, value in analysis_state.get('tech_stack', {}).items():
|
||
state_parts.append(f"- {key}: {value}")
|
||
state_parts.append("")
|
||
|
||
ai_response = "\n".join(state_parts)
|
||
|
||
metadata = {
|
||
'type': 'cumulative_analysis_state',
|
||
'chunk_sequence': chunk_sequence,
|
||
'analysis_state': analysis_state # Full state object
|
||
}
|
||
|
||
# Try to find existing state record
|
||
try:
|
||
# Query for existing state record using memory_manager's episodic_collection
|
||
episodic_collection = analyzer.memory_manager.episodic_collection
|
||
existing_state = episodic_collection.find_one({
|
||
'session_id': session_id,
|
||
'repo_context': repository_id,
|
||
'metadata.type': 'cumulative_analysis_state'
|
||
})
|
||
|
||
if existing_state:
|
||
# Update existing record
|
||
episodic_collection.update_one(
|
||
{'memory_id': existing_state['memory_id']},
|
||
{
|
||
'$set': {
|
||
'user_query': user_query,
|
||
'ai_response': ai_response,
|
||
'metadata': metadata,
|
||
'timestamp': datetime.utcnow()
|
||
}
|
||
}
|
||
)
|
||
print(f"💾 [MEMORY] Updated cumulative analysis state (chunk {chunk_sequence})")
|
||
else:
|
||
# Create new record
|
||
await analyzer.memory_manager.store_episodic_memory(
|
||
session_id=session_id,
|
||
user_query=user_query,
|
||
ai_response=ai_response,
|
||
repo_context=repository_id,
|
||
metadata=metadata
|
||
)
|
||
print(f"💾 [MEMORY] Created cumulative analysis state (chunk {chunk_sequence})")
|
||
except Exception as update_error:
|
||
# If update fails, just create new record
|
||
print(f"⚠️ [MEMORY] Could not update state record, creating new: {update_error}")
|
||
await analyzer.memory_manager.store_episodic_memory(
|
||
session_id=session_id,
|
||
user_query=user_query,
|
||
ai_response=ai_response,
|
||
repo_context=repository_id,
|
||
metadata=metadata
|
||
)
|
||
|
||
except Exception as e:
|
||
print(f"⚠️ [MEMORY] Failed to store cumulative analysis state: {e}")
|
||
# Don't raise - this is optional enhancement
|
||
|
||
# ============================================================================
|
||
# HIERARCHICAL DATA STRUCTURE FUNCTIONS (NEW - Problem 4 Solution)
|
||
# ============================================================================
|
||
|
||
# Feature flag for hierarchical storage (can be enabled via environment variable)
|
||
# OPTIMIZATION: Disable hierarchical storage by default for faster analysis
|
||
# It adds extra database operations (1-2 minutes for typical analysis)
|
||
# Enable only if you need structured insights in PostgreSQL
|
||
USE_HIERARCHICAL_STORAGE = os.getenv('USE_HIERARCHICAL_STORAGE', 'false').lower() == 'true'
|
||
|
||
def extract_architecture_insights(chunk_analysis: Dict, file_analyses: List) -> ArchitectureAnalysis:
|
||
"""Extract structured architecture insights from chunk analysis."""
|
||
module_architecture = chunk_analysis.get('module_architecture', '')
|
||
# Handle case where module_architecture might be a dict
|
||
if isinstance(module_architecture, dict):
|
||
module_architecture = str(module_architecture) # Convert dict to string for processing
|
||
|
||
# Extract patterns (simple keyword matching - can be enhanced with NLP)
|
||
patterns = []
|
||
pattern_keywords = {
|
||
'MVC': ['mvc', 'model-view-controller'],
|
||
'Service Layer': ['service layer', 'service pattern'],
|
||
'Repository Pattern': ['repository', 'repo pattern'],
|
||
'Factory Pattern': ['factory', 'factory pattern'],
|
||
'Singleton': ['singleton'],
|
||
'REST API': ['rest', 'restful', 'api'],
|
||
'GraphQL': ['graphql'],
|
||
'Microservices': ['microservice', 'microservices'],
|
||
'Layered Architecture': ['layered', 'layer', 'tier'],
|
||
'Clean Architecture': ['clean architecture', 'hexagonal']
|
||
}
|
||
|
||
module_architecture_lower = str(module_architecture).lower()
|
||
for pattern, keywords in pattern_keywords.items():
|
||
if any(keyword in module_architecture_lower for keyword in keywords):
|
||
if pattern not in patterns:
|
||
patterns.append(pattern)
|
||
|
||
# Rate organization (1-5) based on patterns and structure
|
||
organization_rating = min(5, max(1, len(patterns) + 2))
|
||
|
||
# Rate maintainability (1-5) based on file structure
|
||
avg_complexity = sum(fa.complexity_score for fa in file_analyses if fa.complexity_score) / len(file_analyses) if file_analyses else 5.0
|
||
maintainability_rating = max(1, min(5, int(6 - avg_complexity / 2)))
|
||
|
||
return ArchitectureAnalysis(
|
||
patterns_identified=patterns if patterns else ['Standard'],
|
||
organization_rating=organization_rating,
|
||
maintainability_rating=maintainability_rating,
|
||
notes=module_architecture[:500] if module_architecture else "Architecture analysis in progress"
|
||
)
|
||
|
||
def extract_security_insights(chunk_analysis: Dict, file_analyses: List) -> SecurityAnalysis:
|
||
"""Extract structured security insights from chunk analysis."""
|
||
module_security = chunk_analysis.get('module_security_assessment', '')
|
||
# Handle case where module_security might be a dict
|
||
if isinstance(module_security, dict):
|
||
module_security = str(module_security) # Convert dict to string for processing
|
||
module_security_lower = str(module_security).lower()
|
||
|
||
# Detect authentication mechanism
|
||
auth_mechanism = "Unknown"
|
||
if 'jwt' in module_security_lower or 'json web token' in module_security_lower:
|
||
auth_mechanism = "JWT"
|
||
elif 'oauth' in module_security_lower:
|
||
auth_mechanism = "OAuth"
|
||
elif 'session' in module_security_lower:
|
||
auth_mechanism = "Session-based"
|
||
elif 'token' in module_security_lower:
|
||
auth_mechanism = "Token-based"
|
||
|
||
# Extract vulnerabilities
|
||
vulnerabilities = []
|
||
security_keywords = {
|
||
'vulnerability': ['vulnerability', 'vulnerable', 'exploit', 'attack'],
|
||
'injection': ['injection', 'sql injection', 'xss'],
|
||
'authentication': ['missing auth', 'no authentication', 'auth bypass'],
|
||
'encryption': ['no encryption', 'unencrypted', 'plaintext'],
|
||
'rate limiting': ['no rate limit', 'missing rate limit', 'rate limiting']
|
||
}
|
||
|
||
for vuln_type, keywords in security_keywords.items():
|
||
if any(keyword in module_security_lower for keyword in keywords):
|
||
vulnerabilities.append(f"{vuln_type.capitalize()} concern detected")
|
||
|
||
# Extract from file analyses
|
||
for fa in file_analyses:
|
||
if hasattr(fa, 'issues_found') and fa.issues_found:
|
||
for issue in fa.issues_found:
|
||
issue_lower = str(issue).lower()
|
||
if any(keyword in issue_lower for keyword in ['security', 'vulnerable', 'injection', 'auth', 'encrypt']):
|
||
if issue not in vulnerabilities:
|
||
vulnerabilities.append(str(issue))
|
||
|
||
# Security rating (1-5) - lower is worse
|
||
security_rating = 5
|
||
if vulnerabilities:
|
||
security_rating = max(1, 5 - len(vulnerabilities))
|
||
if 'no encryption' in module_security_lower or 'unencrypted' in module_security_lower:
|
||
security_rating = max(1, security_rating - 1)
|
||
|
||
# Check encryption usage
|
||
encryption_used = 'encryption' in module_security_lower and 'no encryption' not in module_security_lower
|
||
|
||
return SecurityAnalysis(
|
||
authentication_mechanism=auth_mechanism,
|
||
vulnerabilities=vulnerabilities[:10], # Limit to top 10
|
||
security_rating=security_rating,
|
||
encryption_used=encryption_used,
|
||
notes=module_security[:500] if module_security else "Security assessment in progress"
|
||
)
|
||
|
||
def extract_code_quality_metrics(file_analyses: List) -> CodeQualityAnalysis:
|
||
"""Extract structured code quality metrics from file analyses."""
|
||
if not file_analyses:
|
||
return CodeQualityAnalysis(
|
||
average_complexity=5.0,
|
||
average_quality_score=5.0,
|
||
code_smells_count=0,
|
||
notes="No files analyzed"
|
||
)
|
||
|
||
avg_complexity = sum(fa.complexity_score for fa in file_analyses if fa.complexity_score) / len(file_analyses)
|
||
avg_quality = sum(fa.severity_score for fa in file_analyses if fa.severity_score) / len(file_analyses)
|
||
|
||
# Count code smells (issues with low quality)
|
||
code_smells = sum(1 for fa in file_analyses if fa.severity_score < 5)
|
||
|
||
return CodeQualityAnalysis(
|
||
average_complexity=avg_complexity,
|
||
average_quality_score=avg_quality,
|
||
code_smells_count=code_smells,
|
||
test_coverage=None, # Can be enhanced with actual test coverage detection
|
||
notes=f"Analyzed {len(file_analyses)} files"
|
||
)
|
||
|
||
def extract_structured_issues(file_analyses: List, chunk_analysis: Dict, chunk_name: str) -> List[Issue]:
|
||
"""Extract structured issues from file analyses and chunk analysis."""
|
||
issues = []
|
||
|
||
# Extract issues from file analyses
|
||
for fa in file_analyses:
|
||
if hasattr(fa, 'issues_found') and fa.issues_found:
|
||
for issue_text in fa.issues_found:
|
||
if not issue_text or not isinstance(issue_text, str):
|
||
continue
|
||
|
||
# Determine severity based on keywords and quality score
|
||
issue_lower = issue_text.lower()
|
||
severity = "medium"
|
||
if any(keyword in issue_lower for keyword in ['critical', 'severe', 'danger', 'exploit']):
|
||
severity = "critical"
|
||
elif any(keyword in issue_lower for keyword in ['high', 'important', 'security']):
|
||
severity = "high"
|
||
elif any(keyword in issue_lower for keyword in ['low', 'minor', 'suggestion']):
|
||
severity = "low"
|
||
|
||
# Adjust based on file quality score
|
||
if hasattr(fa, 'severity_score') and fa.severity_score < 3:
|
||
severity = "critical"
|
||
elif hasattr(fa, 'severity_score') and fa.severity_score < 5:
|
||
severity = "high" if severity == "medium" else severity
|
||
|
||
# Determine category
|
||
category = "code_quality"
|
||
if any(keyword in issue_lower for keyword in ['security', 'vulnerable', 'injection', 'auth', 'encrypt']):
|
||
category = "security"
|
||
elif any(keyword in issue_lower for keyword in ['performance', 'slow', 'bottleneck', 'optimization']):
|
||
category = "performance"
|
||
elif any(keyword in issue_lower for keyword in ['architecture', 'pattern', 'design', 'structure']):
|
||
category = "architecture"
|
||
|
||
# Estimate effort
|
||
effort = "medium"
|
||
if any(keyword in issue_lower for keyword in ['quick', 'easy', 'simple', 'minor']):
|
||
effort = "low"
|
||
elif any(keyword in issue_lower for keyword in ['complex', 'major', 'refactor', 'rewrite']):
|
||
effort = "high"
|
||
|
||
# Get recommendation from file analysis if available
|
||
recommendation = ""
|
||
if hasattr(fa, 'recommendations') and fa.recommendations:
|
||
recommendation = fa.recommendations[0] if isinstance(fa.recommendations, list) else str(fa.recommendations)
|
||
|
||
issues.append(Issue(
|
||
severity=severity,
|
||
category=category,
|
||
title=issue_text[:200], # Truncate title
|
||
description=issue_text,
|
||
file_path=str(fa.path) if hasattr(fa, 'path') else "unknown",
|
||
line_number=None, # Can be enhanced with line number detection
|
||
impact=f"Affects {chunk_name} module quality",
|
||
recommendation=recommendation if recommendation else "Review and fix",
|
||
effort_estimate=effort
|
||
))
|
||
|
||
# Extract module-level issues from chunk analysis
|
||
module_recs = chunk_analysis.get('module_recommendations', [])
|
||
for rec in module_recs:
|
||
if rec and isinstance(rec, str):
|
||
rec_lower = rec.lower()
|
||
severity = "medium"
|
||
if 'critical' in rec_lower or 'urgent' in rec_lower:
|
||
severity = "critical"
|
||
elif 'important' in rec_lower:
|
||
severity = "high"
|
||
|
||
issues.append(Issue(
|
||
severity=severity,
|
||
category="architecture",
|
||
title=f"Module-level: {rec[:200]}",
|
||
description=rec,
|
||
file_path=chunk_name,
|
||
line_number=None,
|
||
impact=f"Module-wide concern in {chunk_name}",
|
||
recommendation=rec,
|
||
effort_estimate="medium"
|
||
))
|
||
|
||
return issues
|
||
|
||
async def extract_structured_insights(
|
||
chunk_analysis: Dict,
|
||
file_analyses: List,
|
||
chunk: Dict
|
||
) -> Tuple[ArchitectureAnalysis, SecurityAnalysis, CodeQualityAnalysis, List[Issue]]:
|
||
"""
|
||
Extract structured insights from chunk analysis.
|
||
Parses text blobs into structured objects.
|
||
"""
|
||
chunk_name = chunk.get('name', 'unknown')
|
||
|
||
architecture = extract_architecture_insights(chunk_analysis, file_analyses)
|
||
security = extract_security_insights(chunk_analysis, file_analyses)
|
||
code_quality = extract_code_quality_metrics(file_analyses)
|
||
issues = extract_structured_issues(file_analyses, chunk_analysis, chunk_name)
|
||
|
||
return architecture, security, code_quality, issues
|
||
|
||
async def store_findings_postgresql(
|
||
run_id: str,
|
||
module_name: str,
|
||
module_id: str,
|
||
issues: List[Issue]
|
||
) -> List[int]:
|
||
"""Store structured findings in PostgreSQL for efficient querying."""
|
||
findings_ids = []
|
||
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return findings_ids
|
||
|
||
try:
|
||
pg_conn = analyzer.memory_manager.pg_conn
|
||
if not pg_conn:
|
||
return findings_ids
|
||
|
||
with pg_conn.cursor() as cur:
|
||
for issue in issues:
|
||
cur.execute("""
|
||
INSERT INTO findings (
|
||
run_id, module_name, module_id,
|
||
severity, category, title, description,
|
||
file_path, line_number, impact, recommendation, effort_estimate
|
||
) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
|
||
RETURNING id
|
||
""", (
|
||
run_id, module_name, module_id,
|
||
issue.severity, issue.category, issue.title, issue.description,
|
||
issue.file_path, issue.line_number, issue.impact,
|
||
issue.recommendation, issue.effort_estimate
|
||
))
|
||
finding_id = cur.fetchone()[0]
|
||
findings_ids.append(finding_id)
|
||
|
||
pg_conn.commit()
|
||
print(f"💾 [HIERARCHICAL] Stored {len(findings_ids)} findings in PostgreSQL for module {module_name}")
|
||
except Exception as e:
|
||
print(f"⚠️ [HIERARCHICAL] Failed to store findings in PostgreSQL: {e}")
|
||
if pg_conn:
|
||
pg_conn.rollback()
|
||
|
||
return findings_ids
|
||
|
||
async def store_metrics_postgresql(
|
||
run_id: str,
|
||
module_name: str,
|
||
module_id: str,
|
||
file_analyses: List,
|
||
architecture: ArchitectureAnalysis,
|
||
security: SecurityAnalysis,
|
||
code_quality: CodeQualityAnalysis,
|
||
issues: List[Issue]
|
||
) -> Optional[int]:
|
||
"""Store metrics in PostgreSQL for efficient aggregation."""
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return None
|
||
|
||
try:
|
||
pg_conn = analyzer.memory_manager.pg_conn
|
||
if not pg_conn:
|
||
return None
|
||
|
||
# Calculate metrics
|
||
total_lines = sum(fa.lines_of_code for fa in file_analyses if fa.lines_of_code)
|
||
avg_complexity = code_quality.average_complexity
|
||
total_issues = len(issues)
|
||
critical_issues = len([i for i in issues if i.severity == 'critical'])
|
||
high_issues = len([i for i in issues if i.severity == 'high'])
|
||
|
||
with pg_conn.cursor() as cur:
|
||
cur.execute("""
|
||
INSERT INTO metrics (
|
||
run_id, module_name, module_id,
|
||
lines_of_code, cyclomatic_complexity,
|
||
architecture_rating, security_rating, code_quality_rating,
|
||
total_issues, critical_issues, high_issues
|
||
) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
|
||
RETURNING id
|
||
""", (
|
||
run_id, module_name, module_id,
|
||
total_lines, avg_complexity,
|
||
architecture.organization_rating, security.security_rating,
|
||
int(code_quality.average_quality_score / 2), # Convert 1-10 to 1-5
|
||
total_issues, critical_issues, high_issues
|
||
))
|
||
metrics_id = cur.fetchone()[0]
|
||
pg_conn.commit()
|
||
print(f"💾 [HIERARCHICAL] Stored metrics in PostgreSQL for module {module_name} (ID: {metrics_id})")
|
||
return metrics_id
|
||
except Exception as e:
|
||
print(f"⚠️ [HIERARCHICAL] Failed to store metrics in PostgreSQL: {e}")
|
||
if pg_conn:
|
||
pg_conn.rollback()
|
||
return None
|
||
|
||
async def store_module_analysis_mongodb(
|
||
module_id: str,
|
||
module_name: str,
|
||
chunk: Dict,
|
||
chunk_analysis: Dict,
|
||
file_analyses: List,
|
||
architecture: ArchitectureAnalysis,
|
||
security: SecurityAnalysis,
|
||
code_quality: CodeQualityAnalysis,
|
||
issues: List[Issue],
|
||
repository_id: str,
|
||
run_id: str,
|
||
session_id: str,
|
||
findings_ids: List[int],
|
||
metrics_id: Optional[int]
|
||
) -> str:
|
||
"""Store full detailed module analysis in MongoDB."""
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return ""
|
||
|
||
try:
|
||
episodic_collection = analyzer.memory_manager.episodic_collection
|
||
|
||
# Build comprehensive document
|
||
# IMPORTANT: Store only file paths, NOT content, to save storage space
|
||
module_doc = {
|
||
'type': 'module_analysis',
|
||
'module_id': module_id,
|
||
'module_name': module_name,
|
||
'chunk_id': chunk.get('id', 'unknown'),
|
||
'repository_id': repository_id,
|
||
'session_id': session_id,
|
||
'run_id': run_id,
|
||
'files_analyzed': [str(fa.path) for fa in file_analyses if hasattr(fa, 'path')], # Only paths, no content
|
||
'summary': chunk_analysis.get('module_overview', '')[:300],
|
||
'detailed_analysis': chunk_analysis.get('module_overview', ''),
|
||
'architecture': {
|
||
'patterns_identified': architecture.patterns_identified,
|
||
'organization_rating': architecture.organization_rating,
|
||
'maintainability_rating': architecture.maintainability_rating,
|
||
'notes': architecture.notes
|
||
},
|
||
'security': {
|
||
'authentication_mechanism': security.authentication_mechanism,
|
||
'vulnerabilities': security.vulnerabilities,
|
||
'security_rating': security.security_rating,
|
||
'encryption_used': security.encryption_used,
|
||
'notes': security.notes
|
||
},
|
||
'code_quality': {
|
||
'average_complexity': code_quality.average_complexity,
|
||
'average_quality_score': code_quality.average_quality_score,
|
||
'code_smells_count': code_quality.code_smells_count,
|
||
'test_coverage': code_quality.test_coverage,
|
||
'notes': code_quality.notes
|
||
},
|
||
'issues': [
|
||
{
|
||
'severity': issue.severity,
|
||
'category': issue.category,
|
||
'title': issue.title,
|
||
'description': issue.description,
|
||
'file_path': issue.file_path,
|
||
'line_number': issue.line_number,
|
||
'impact': issue.impact,
|
||
'recommendation': issue.recommendation,
|
||
'effort_estimate': issue.effort_estimate
|
||
}
|
||
for issue in issues
|
||
],
|
||
'dependencies': chunk.get('context_dependencies', []),
|
||
'dependents': [],
|
||
'timestamp': datetime.utcnow(),
|
||
'findings_ids': findings_ids,
|
||
'metrics_id': metrics_id
|
||
}
|
||
|
||
result = episodic_collection.insert_one(module_doc)
|
||
module_id_str = str(result.inserted_id)
|
||
print(f"💾 [HIERARCHICAL] Stored module analysis in MongoDB: {module_name} (MongoDB ID: {module_id_str})")
|
||
return module_id_str
|
||
except Exception as e:
|
||
print(f"❌ [HIERARCHICAL] Failed to store module analysis in MongoDB: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return ""
|
||
|
||
async def store_module_analysis_hierarchical(
|
||
module_id: str,
|
||
module_name: str,
|
||
chunk: Dict,
|
||
chunk_analysis: Dict,
|
||
file_analyses: List,
|
||
architecture: ArchitectureAnalysis,
|
||
security: SecurityAnalysis,
|
||
code_quality: CodeQualityAnalysis,
|
||
issues: List[Issue],
|
||
repository_id: str,
|
||
run_id: str,
|
||
session_id: str
|
||
) -> Tuple[str, List[int], Optional[int]]:
|
||
"""
|
||
Store module analysis in hierarchical structure across three tiers.
|
||
Returns: (mongo_id, findings_ids, metrics_id)
|
||
"""
|
||
# 1. Store findings in PostgreSQL (queryable)
|
||
findings_ids = await store_findings_postgresql(
|
||
run_id=run_id,
|
||
module_name=module_name,
|
||
module_id=module_id,
|
||
issues=issues
|
||
)
|
||
|
||
# 2. Store metrics in PostgreSQL (aggregatable)
|
||
metrics_id = await store_metrics_postgresql(
|
||
run_id=run_id,
|
||
module_name=module_name,
|
||
module_id=module_id,
|
||
file_analyses=file_analyses,
|
||
architecture=architecture,
|
||
security=security,
|
||
code_quality=code_quality,
|
||
issues=issues
|
||
)
|
||
|
||
# 3. Store full analysis in MongoDB (detailed context)
|
||
mongo_id = await store_module_analysis_mongodb(
|
||
module_id=module_id,
|
||
module_name=module_name,
|
||
chunk=chunk,
|
||
chunk_analysis=chunk_analysis,
|
||
file_analyses=file_analyses,
|
||
architecture=architecture,
|
||
security=security,
|
||
code_quality=code_quality,
|
||
issues=issues,
|
||
repository_id=repository_id,
|
||
run_id=run_id,
|
||
session_id=session_id,
|
||
findings_ids=findings_ids,
|
||
metrics_id=metrics_id
|
||
)
|
||
|
||
return mongo_id, findings_ids, metrics_id
|
||
|
||
# ============================================================================
|
||
# QUERY HELPERS FOR HIERARCHICAL DATA STRUCTURE
|
||
# ============================================================================
|
||
|
||
async def get_findings_by_module(run_id: str, module_name: Optional[str] = None) -> List[Dict]:
|
||
"""Get findings by module from PostgreSQL (efficient query)."""
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return []
|
||
|
||
try:
|
||
pg_conn = analyzer.memory_manager.pg_conn
|
||
if not pg_conn:
|
||
return []
|
||
|
||
# Use RealDictCursor if available, otherwise use regular cursor
|
||
cursor_kwargs = {'cursor_factory': RealDictCursor} if RealDictCursor else {}
|
||
with pg_conn.cursor(**cursor_kwargs) as cur:
|
||
if module_name:
|
||
cur.execute("""
|
||
SELECT * FROM findings
|
||
WHERE run_id = %s AND module_name = %s
|
||
ORDER BY
|
||
CASE severity
|
||
WHEN 'critical' THEN 1
|
||
WHEN 'high' THEN 2
|
||
WHEN 'medium' THEN 3
|
||
WHEN 'low' THEN 4
|
||
END
|
||
""", (run_id, module_name))
|
||
else:
|
||
cur.execute("""
|
||
SELECT * FROM findings
|
||
WHERE run_id = %s
|
||
ORDER BY
|
||
CASE severity
|
||
WHEN 'critical' THEN 1
|
||
WHEN 'high' THEN 2
|
||
WHEN 'medium' THEN 3
|
||
WHEN 'low' THEN 4
|
||
END,
|
||
module_name
|
||
""", (run_id,))
|
||
|
||
rows = cur.fetchall()
|
||
if RealDictCursor:
|
||
return [dict(row) for row in rows]
|
||
else:
|
||
# Convert tuple results to dict
|
||
columns = [desc[0] for desc in cur.description] if cur.description else []
|
||
return [dict(zip(columns, row)) for row in rows]
|
||
except Exception as e:
|
||
print(f"⚠️ [HIERARCHICAL] Failed to query findings: {e}")
|
||
return []
|
||
|
||
async def get_metrics_by_module(run_id: str, module_name: Optional[str] = None) -> List[Dict]:
|
||
"""Get metrics by module from PostgreSQL (efficient aggregation)."""
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return []
|
||
|
||
try:
|
||
pg_conn = analyzer.memory_manager.pg_conn
|
||
if not pg_conn:
|
||
return []
|
||
|
||
# Use RealDictCursor if available, otherwise use regular cursor
|
||
cursor_kwargs = {'cursor_factory': RealDictCursor} if RealDictCursor else {}
|
||
with pg_conn.cursor(**cursor_kwargs) as cur:
|
||
if module_name:
|
||
cur.execute("""
|
||
SELECT * FROM metrics
|
||
WHERE run_id = %s AND module_name = %s
|
||
""", (run_id, module_name))
|
||
else:
|
||
cur.execute("""
|
||
SELECT * FROM metrics
|
||
WHERE run_id = %s
|
||
ORDER BY module_name
|
||
""", (run_id,))
|
||
|
||
rows = cur.fetchall()
|
||
if RealDictCursor:
|
||
return [dict(row) for row in rows]
|
||
else:
|
||
# Convert tuple results to dict
|
||
columns = [desc[0] for desc in cur.description] if cur.description else []
|
||
return [dict(zip(columns, row)) for row in rows]
|
||
except Exception as e:
|
||
print(f"⚠️ [HIERARCHICAL] Failed to query metrics: {e}")
|
||
return []
|
||
|
||
async def get_security_findings(run_id: str, severity_filter: Optional[str] = None) -> List[Dict]:
|
||
"""Get security findings from PostgreSQL (efficient query)."""
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return []
|
||
|
||
try:
|
||
pg_conn = analyzer.memory_manager.pg_conn
|
||
if not pg_conn:
|
||
return []
|
||
|
||
# Use RealDictCursor if available, otherwise use regular cursor
|
||
cursor_kwargs = {'cursor_factory': RealDictCursor} if RealDictCursor else {}
|
||
with pg_conn.cursor(**cursor_kwargs) as cur:
|
||
if severity_filter:
|
||
cur.execute("""
|
||
SELECT * FROM findings
|
||
WHERE run_id = %s AND category = 'security' AND severity = %s
|
||
ORDER BY
|
||
CASE severity
|
||
WHEN 'critical' THEN 1
|
||
WHEN 'high' THEN 2
|
||
WHEN 'medium' THEN 3
|
||
WHEN 'low' THEN 4
|
||
END
|
||
""", (run_id, severity_filter))
|
||
else:
|
||
cur.execute("""
|
||
SELECT * FROM findings
|
||
WHERE run_id = %s AND category = 'security'
|
||
ORDER BY
|
||
CASE severity
|
||
WHEN 'critical' THEN 1
|
||
WHEN 'high' THEN 2
|
||
WHEN 'medium' THEN 3
|
||
WHEN 'low' THEN 4
|
||
END
|
||
""", (run_id,))
|
||
|
||
rows = cur.fetchall()
|
||
if RealDictCursor:
|
||
return [dict(row) for row in rows]
|
||
else:
|
||
# Convert tuple results to dict
|
||
columns = [desc[0] for desc in cur.description] if cur.description else []
|
||
return [dict(zip(columns, row)) for row in rows]
|
||
except Exception as e:
|
||
print(f"⚠️ [HIERARCHICAL] Failed to query security findings: {e}")
|
||
return []
|
||
|
||
async def get_module_analysis_from_mongodb(run_id: str, module_name: str) -> Optional[Dict]:
|
||
"""Get full detailed module analysis from MongoDB."""
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return None
|
||
|
||
try:
|
||
episodic_collection = analyzer.memory_manager.episodic_collection
|
||
module_doc = episodic_collection.find_one({
|
||
'type': 'module_analysis',
|
||
'run_id': run_id,
|
||
'module_name': module_name
|
||
})
|
||
|
||
if module_doc:
|
||
# Convert ObjectId to string
|
||
if '_id' in module_doc:
|
||
module_doc['_id'] = str(module_doc['_id'])
|
||
return module_doc
|
||
return None
|
||
except Exception as e:
|
||
print(f"⚠️ [HIERARCHICAL] Failed to query MongoDB module analysis: {e}")
|
||
return None
|
||
|
||
# ============================================================================
|
||
# CONTEXT RETRIEVAL FUNCTIONS FOR DETAILED REPORT GENERATION
|
||
# ============================================================================
|
||
|
||
async def retrieve_all_module_analyses(run_id: str, repository_id: str) -> List[Dict]:
|
||
"""
|
||
Retrieve ALL module analyses from MongoDB for a specific run.
|
||
Returns: List of detailed module analysis documents
|
||
"""
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return []
|
||
|
||
try:
|
||
episodic_collection = analyzer.memory_manager.episodic_collection
|
||
|
||
print(f" 🔍 [DEBUG] Querying for modules with run_id={run_id}, repo_id={repository_id}")
|
||
|
||
# FIXED: Query using ONLY metadata.type and metadata.run_id (stored structure)
|
||
# Data is stored with type and run_id in the metadata field, NOT at top level
|
||
print(f" 🔍 [DEBUG] Query parameters:")
|
||
print(f" - metadata.type: 'module_analysis'")
|
||
print(f" - metadata.run_id: {run_id}")
|
||
print(f" - repository_id: {repository_id}")
|
||
|
||
# Test 1: Count all documents
|
||
total_docs = episodic_collection.count_documents({})
|
||
print(f" 🔍 [DEBUG] Total docs in collection: {total_docs}")
|
||
|
||
# FIXED: repository_id is stored in metadata, not at top level
|
||
# The query needs to look for it in metadata.repository_id
|
||
modules = list(episodic_collection.find({
|
||
'metadata.type': 'module_analysis',
|
||
'metadata.run_id': run_id,
|
||
'metadata.repository_id': repository_id # FIXED: Added 'metadata.' prefix
|
||
}).sort('metadata.chunk_id', 1))
|
||
|
||
print(f" 🔍 [DEBUG] ✅ Found {len(modules)} modules with full query!")
|
||
|
||
# If zero, try simpler queries to debug
|
||
if not modules:
|
||
print(f" 🔍 [DEBUG] No modules found! Trying simpler queries...")
|
||
|
||
# Try just run_id
|
||
by_run = list(episodic_collection.find({'metadata.run_id': run_id}).limit(3))
|
||
print(f" 🔍 [DEBUG] Found {len(by_run)} docs with just metadata.run_id")
|
||
|
||
# Try just type
|
||
by_type = list(episodic_collection.find({'metadata.type': 'module_analysis'}).limit(3))
|
||
print(f" 🔍 [DEBUG] Found {len(by_type)} docs with just metadata.type='module_analysis'")
|
||
|
||
# Show sample doc structure
|
||
sample = episodic_collection.find_one({})
|
||
if sample:
|
||
print(f" 🔍 [DEBUG] Sample doc keys: {list(sample.keys())}")
|
||
if 'metadata' in sample:
|
||
print(f" 🔍 [DEBUG] Sample metadata keys: {list(sample['metadata'].keys())[:10]}")
|
||
|
||
# Convert ObjectIds to strings
|
||
for module in modules:
|
||
if '_id' in module:
|
||
module['_id'] = str(module['_id'])
|
||
|
||
return modules
|
||
except Exception as e:
|
||
print(f"⚠️ [REPORT] Failed to retrieve module analyses: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return []
|
||
|
||
async def retrieve_synthesis_analysis(run_id: str, repository_id: str) -> Optional[Dict]:
|
||
"""
|
||
Retrieve synthesis analysis from MongoDB.
|
||
Returns: System-level synthesis insights
|
||
"""
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return None
|
||
|
||
try:
|
||
episodic_collection = analyzer.memory_manager.episodic_collection
|
||
|
||
# FIXED: Query using metadata.type and metadata.run_id (stored structure)
|
||
# Data is stored with type and run_id in the metadata field, NOT at top level
|
||
# repository_id is ALSO in metadata, not at top level
|
||
synthesis = episodic_collection.find_one({
|
||
'metadata.type': 'synthesis_analysis',
|
||
'metadata.repository_id': repository_id, # FIXED: Added 'metadata.' prefix
|
||
'metadata.run_id': run_id
|
||
})
|
||
|
||
if synthesis and '_id' in synthesis:
|
||
synthesis['_id'] = str(synthesis['_id'])
|
||
|
||
if synthesis:
|
||
print(f" ✅ Found synthesis analysis!")
|
||
else:
|
||
print(f" ⚠️ Synthesis analysis not found for run_id={run_id}")
|
||
|
||
return synthesis
|
||
except Exception as e:
|
||
print(f"⚠️ [REPORT] Failed to retrieve synthesis analysis: {e}")
|
||
return None
|
||
|
||
async def retrieve_cumulative_analysis_state(run_id: str, repository_id: str, session_id: str) -> Optional[Dict]:
|
||
"""
|
||
Retrieve cumulative analysis state (progressive context).
|
||
Returns: Full analysis state with all modules analyzed, patterns, issues, tech stack
|
||
"""
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
return None
|
||
|
||
try:
|
||
episodic_collection = analyzer.memory_manager.episodic_collection
|
||
|
||
state = episodic_collection.find_one({
|
||
'session_id': session_id,
|
||
'repo_context': repository_id,
|
||
'metadata.type': 'cumulative_analysis_state'
|
||
})
|
||
|
||
if state and 'metadata' in state:
|
||
# Extract full analysis_state from metadata
|
||
return state['metadata'].get('analysis_state', {})
|
||
|
||
return None
|
||
except Exception as e:
|
||
print(f"⚠️ [REPORT] Failed to retrieve cumulative analysis state: {e}")
|
||
return None
|
||
|
||
async def retrieve_all_findings(run_id: str) -> Dict[str, List[Dict]]:
|
||
"""
|
||
Retrieve ALL findings grouped by module from PostgreSQL.
|
||
Returns: {
|
||
'module1': [finding1, finding2, ...],
|
||
'module2': [finding3, finding4, ...],
|
||
...
|
||
}
|
||
"""
|
||
all_findings = await get_findings_by_module(run_id)
|
||
|
||
# Group by module
|
||
findings_by_module = {}
|
||
for finding in all_findings:
|
||
module = finding.get('module_name', 'unknown')
|
||
if module not in findings_by_module:
|
||
findings_by_module[module] = []
|
||
findings_by_module[module].append(finding)
|
||
|
||
return findings_by_module
|
||
|
||
async def retrieve_all_metrics(run_id: str) -> Dict[str, Dict]:
|
||
"""
|
||
Retrieve ALL metrics by module from PostgreSQL.
|
||
Returns: {
|
||
'module1': {lines_of_code: 1000, complexity: 5.2, ...},
|
||
'module2': {lines_of_code: 500, complexity: 3.8, ...},
|
||
...
|
||
}
|
||
"""
|
||
all_metrics = await get_metrics_by_module(run_id)
|
||
|
||
# Convert to dict keyed by module_name
|
||
metrics_by_module = {}
|
||
for metric in all_metrics:
|
||
module = metric.get('module_name', 'unknown')
|
||
metrics_by_module[module] = metric
|
||
|
||
return metrics_by_module
|
||
|
||
async def retrieve_comprehensive_report_context(
|
||
run_id: str,
|
||
repository_id: str,
|
||
session_id: str
|
||
) -> Dict:
|
||
"""
|
||
Retrieve ALL context needed for 100+ page detailed report.
|
||
This is the MAIN function that aggregates everything.
|
||
"""
|
||
print(f"📊 [REPORT] Retrieving comprehensive context for run_id: {run_id}")
|
||
|
||
# 1. Retrieve all module analyses (MongoDB)
|
||
print(" → Fetching all module analyses from MongoDB...")
|
||
module_analyses = await retrieve_all_module_analyses(run_id, repository_id)
|
||
print(f" ✓ Found {len(module_analyses)} modules")
|
||
|
||
# 2. Retrieve synthesis analysis (MongoDB)
|
||
print(" → Fetching synthesis analysis from MongoDB...")
|
||
synthesis_analysis = await retrieve_synthesis_analysis(run_id, repository_id)
|
||
if synthesis_analysis:
|
||
print(" ✓ Found synthesis analysis")
|
||
else:
|
||
print(" ⚠️ No synthesis analysis found")
|
||
|
||
# 3. Retrieve cumulative analysis state (MongoDB)
|
||
print(" → Fetching cumulative analysis state from MongoDB...")
|
||
analysis_state = await retrieve_cumulative_analysis_state(run_id, repository_id, session_id)
|
||
if analysis_state:
|
||
print(" ✓ Found cumulative analysis state")
|
||
else:
|
||
print(" ⚠️ No cumulative analysis state found")
|
||
|
||
# 4. Retrieve all findings (PostgreSQL)
|
||
print(" → Fetching all findings from PostgreSQL...")
|
||
findings_by_module = await retrieve_all_findings(run_id)
|
||
total_findings = sum(len(findings) for findings in findings_by_module.values())
|
||
print(f" ✓ Found {total_findings} findings across {len(findings_by_module)} modules")
|
||
|
||
# 5. Retrieve all metrics (PostgreSQL)
|
||
print(" → Fetching all metrics from PostgreSQL...")
|
||
metrics_by_module = await retrieve_all_metrics(run_id)
|
||
print(f" ✓ Found metrics for {len(metrics_by_module)} modules")
|
||
|
||
# 6. Aggregate everything
|
||
comprehensive_context = {
|
||
# Module-level data
|
||
'module_analyses': module_analyses,
|
||
'total_modules': len(module_analyses),
|
||
|
||
# System-level data
|
||
'synthesis_analysis': synthesis_analysis or {},
|
||
'analysis_state': analysis_state or {},
|
||
|
||
# Structured data
|
||
'findings_by_module': findings_by_module,
|
||
'metrics_by_module': metrics_by_module,
|
||
'total_findings': total_findings,
|
||
|
||
# Run metadata
|
||
'run_id': run_id,
|
||
'repository_id': repository_id,
|
||
'session_id': session_id,
|
||
'generated_at': datetime.utcnow().isoformat()
|
||
}
|
||
|
||
print(f"✅ [REPORT] Context retrieval complete: {len(module_analyses)} modules, {total_findings} findings")
|
||
|
||
return comprehensive_context
|
||
|
||
# ============================================================================
|
||
# PHASE 2: CROSS-MODULE SYNTHESIS FUNCTIONS
|
||
# ============================================================================
|
||
|
||
def build_synthesis_prompt(analysis_state: Dict, all_chunk_analyses: List[Dict] = None) -> str:
|
||
"""
|
||
Build comprehensive prompt for cross-module synthesis analysis.
|
||
Synthesizes all individual module analyses into system-level insights.
|
||
"""
|
||
prompt_parts = [
|
||
"# CROSS-MODULE SYNTHESIS ANALYSIS",
|
||
"",
|
||
"You are a senior software architect with 30+ years of experience. Your task is to synthesize",
|
||
"findings from multiple module-level analyses into comprehensive system-level insights.",
|
||
"",
|
||
"## CONTEXT: PREVIOUSLY ANALYZED MODULES",
|
||
""
|
||
]
|
||
|
||
# Add module summaries
|
||
module_summaries = analysis_state.get('module_summaries', {})
|
||
if module_summaries:
|
||
for module_name, summary in module_summaries.items():
|
||
prompt_parts.append(f"### {module_name.upper()}")
|
||
prompt_parts.append(summary[:500] + ("..." if len(summary) > 500 else ""))
|
||
prompt_parts.append("")
|
||
else:
|
||
prompt_parts.append("No module summaries available yet.")
|
||
prompt_parts.append("")
|
||
|
||
# Add architecture patterns found so far
|
||
architecture_patterns = analysis_state.get('architecture_patterns', [])
|
||
if architecture_patterns:
|
||
prompt_parts.extend([
|
||
"## ARCHITECTURE PATTERNS IDENTIFIED",
|
||
", ".join(architecture_patterns),
|
||
""
|
||
])
|
||
|
||
# Add critical issues found so far
|
||
critical_issues = analysis_state.get('critical_issues', [])
|
||
if critical_issues:
|
||
prompt_parts.extend([
|
||
"## CRITICAL ISSUES IDENTIFIED",
|
||
""
|
||
])
|
||
for issue in critical_issues[:15]: # Top 15 issues
|
||
module = issue.get('module', 'unknown')
|
||
issue_text = issue.get('issue', '')
|
||
prompt_parts.append(f"- **{module}**: {issue_text}")
|
||
prompt_parts.append("")
|
||
|
||
# Add tech stack
|
||
tech_stack = analysis_state.get('tech_stack', {})
|
||
if tech_stack:
|
||
prompt_parts.extend([
|
||
"## TECH STACK DISCOVERED",
|
||
", ".join([f"{k}: {v}" for k, v in tech_stack.items()]),
|
||
""
|
||
])
|
||
|
||
# Add project overview if available
|
||
project_overview = analysis_state.get('project_overview', '')
|
||
if project_overview:
|
||
prompt_parts.extend([
|
||
"## PROJECT OVERVIEW",
|
||
project_overview[:1000] + ("..." if len(project_overview) > 1000 else ""),
|
||
""
|
||
])
|
||
|
||
# Synthesis instructions
|
||
prompt_parts.extend([
|
||
"## SYNTHESIS REQUIREMENTS:",
|
||
"",
|
||
"Analyze the above information and provide system-level insights that can only be",
|
||
"identified by looking at the entire codebase holistically. Focus on:",
|
||
"",
|
||
"1. **SYSTEM-LEVEL ARCHITECTURE PATTERNS**:",
|
||
" - Identify overarching architectural patterns that span multiple modules",
|
||
" - Note any architectural inconsistencies or anti-patterns across modules",
|
||
" - Identify missing architectural layers or components",
|
||
"",
|
||
"2. **CROSS-CUTTING ISSUES**:",
|
||
" - Identify issues that affect multiple modules (e.g., 'System-wide missing rate limiting')",
|
||
" - Note recurring problems or anti-patterns across different modules",
|
||
" - Identify dependencies or coupling issues between modules",
|
||
"",
|
||
"3. **SYSTEM-WIDE RISKS**:",
|
||
" - Security risks that span multiple modules",
|
||
" - Scalability concerns affecting the entire system",
|
||
" - Technical debt that impacts system-wide quality",
|
||
"",
|
||
"4. **ARCHITECTURAL RECOMMENDATIONS**:",
|
||
" - High-level recommendations for improving the overall architecture",
|
||
" - Suggestions for refactoring cross-module dependencies",
|
||
" - Recommendations for addressing system-wide issues",
|
||
"",
|
||
"5. **MODULE INTERDEPENDENCIES**:",
|
||
" - Identify how modules relate to each other",
|
||
" - Note any circular dependencies or tight coupling",
|
||
" - Identify modules that should be decoupled",
|
||
"",
|
||
"## RESPONSE FORMAT:",
|
||
"",
|
||
"Provide your synthesis in JSON format:",
|
||
"",
|
||
"{",
|
||
' "system_architecture_patterns": ["pattern1", "pattern2", ...],',
|
||
' "cross_cutting_issues": [',
|
||
' {"issue": "description", "affected_modules": ["module1", "module2"], "severity": "high|medium|low"},',
|
||
' ...',
|
||
' ],',
|
||
' "system_wide_risks": [',
|
||
' {"risk": "description", "impact": "description", "severity": "high|medium|low"},',
|
||
' ...',
|
||
' ],',
|
||
' "architectural_recommendations": ["rec1", "rec2", ...],',
|
||
' "module_interdependencies": {',
|
||
' "module1": ["depends_on_module2", "depends_on_module3"],',
|
||
' ...',
|
||
' },',
|
||
' "quality_trends": {',
|
||
' "observation": "description",',
|
||
' "trend": "improving|degrading|stable"',
|
||
' }',
|
||
"}",
|
||
"",
|
||
"Focus on providing insights that can only be gained by synthesizing information",
|
||
"across all analyzed modules, not just repeating individual module findings."
|
||
])
|
||
|
||
return "\n".join(prompt_parts)
|
||
|
||
def parse_synthesis_response(response_text: str) -> Dict:
|
||
"""Parse synthesis response from Claude API."""
|
||
import json
|
||
import re
|
||
|
||
synthesis_analysis = {
|
||
'system_architecture_patterns': [],
|
||
'cross_cutting_issues': [],
|
||
'system_wide_risks': [],
|
||
'architectural_recommendations': [],
|
||
'module_interdependencies': {},
|
||
'quality_trends': {}
|
||
}
|
||
|
||
# Function to extract JSON from markdown code blocks
|
||
def extract_json_from_text(text: str) -> Optional[str]:
|
||
text = text.strip()
|
||
json_block_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', text, re.DOTALL)
|
||
if json_block_match:
|
||
return json_block_match.group(1)
|
||
|
||
brace_count = 0
|
||
start_idx = -1
|
||
for i, char in enumerate(text):
|
||
if char == '{':
|
||
if brace_count == 0:
|
||
start_idx = i
|
||
brace_count += 1
|
||
elif char == '}':
|
||
brace_count -= 1
|
||
if brace_count == 0 and start_idx >= 0:
|
||
return text[start_idx:i+1]
|
||
return None
|
||
|
||
try:
|
||
extracted = extract_json_from_text(response_text)
|
||
if extracted:
|
||
synthesis_data = json.loads(extracted)
|
||
|
||
# Extract all fields
|
||
synthesis_analysis['system_architecture_patterns'] = synthesis_data.get('system_architecture_patterns', [])
|
||
synthesis_analysis['cross_cutting_issues'] = synthesis_data.get('cross_cutting_issues', [])
|
||
synthesis_analysis['system_wide_risks'] = synthesis_data.get('system_wide_risks', [])
|
||
synthesis_analysis['architectural_recommendations'] = synthesis_data.get('architectural_recommendations', [])
|
||
synthesis_analysis['module_interdependencies'] = synthesis_data.get('module_interdependencies', {})
|
||
synthesis_analysis['quality_trends'] = synthesis_data.get('quality_trends', {})
|
||
|
||
print(f"✅ [SYNTHESIS] Successfully parsed synthesis response")
|
||
else:
|
||
print(f"⚠️ [SYNTHESIS] Could not extract JSON from response, using fallback")
|
||
# Fallback: extract key phrases from text
|
||
if 'cross' in response_text.lower() and 'cutting' in response_text.lower():
|
||
synthesis_analysis['cross_cutting_issues'].append({
|
||
'issue': 'Cross-cutting concerns identified (details in response text)',
|
||
'affected_modules': ['multiple'],
|
||
'severity': 'medium'
|
||
})
|
||
except Exception as e:
|
||
print(f"⚠️ [SYNTHESIS] Error parsing synthesis response: {e}")
|
||
|
||
return synthesis_analysis
|
||
|
||
async def perform_synthesis_phase(
|
||
analysis_state: Dict,
|
||
repository_id: str,
|
||
progress_mgr: Optional[AnalysisProgressManager] = None
|
||
) -> Tuple[Dict, Dict]:
|
||
"""
|
||
Perform Phase 2: Cross-Module Synthesis.
|
||
Synthesizes all module analyses into system-level insights.
|
||
Returns (synthesis_analysis, updated_analysis_state) tuple.
|
||
"""
|
||
try:
|
||
print(f"🔬 [SYNTHESIS] Starting cross-module synthesis phase...")
|
||
|
||
# Emit synthesis started event
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("synthesis_started", {
|
||
"message": "Synthesizing cross-module insights",
|
||
"percent": 85
|
||
})
|
||
|
||
# Build synthesis prompt
|
||
synthesis_prompt = build_synthesis_prompt(analysis_state)
|
||
|
||
# Rate limiting
|
||
await rate_limiter.wait_if_needed()
|
||
|
||
# Make API call to Claude for synthesis
|
||
try:
|
||
if analyzer and hasattr(analyzer, 'client'):
|
||
model = os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest")
|
||
|
||
# Check token limits before making API call
|
||
estimated_input_tokens = estimate_tokens_from_text(synthesis_prompt)
|
||
estimated_output_tokens = 3000 # Conservative estimate
|
||
|
||
# Update token limiter for this model if needed
|
||
global token_usage_limiter
|
||
if token_usage_limiter.model != model:
|
||
token_usage_limiter = TokenUsageRateLimiter(model=model)
|
||
|
||
# Check if we can proceed with this request
|
||
can_proceed, wait_time = await token_usage_limiter.check_token_limits(
|
||
estimated_input_tokens, estimated_output_tokens
|
||
)
|
||
|
||
if not can_proceed:
|
||
print(f"⏳ [TOKEN LIMIT] Waiting {wait_time:.2f} seconds for synthesis to avoid exceeding token limits...")
|
||
await asyncio.sleep(wait_time)
|
||
|
||
def call_claude_synthesis():
|
||
return analyzer.client.messages.create(
|
||
model=model,
|
||
max_tokens=4000,
|
||
temperature=0.3,
|
||
messages=[{"role": "user", "content": synthesis_prompt}]
|
||
)
|
||
|
||
loop = asyncio.get_event_loop()
|
||
try:
|
||
message = await loop.run_in_executor(None, call_claude_synthesis)
|
||
response_text = message.content[0].text if message.content else ""
|
||
|
||
# Record actual token usage in limiter (after API call completes)
|
||
if message and hasattr(message, 'usage'):
|
||
actual_input = message.usage.input_tokens if hasattr(message.usage, 'input_tokens') else estimated_input_tokens
|
||
actual_output = message.usage.output_tokens if hasattr(message.usage, 'output_tokens') else estimated_output_tokens
|
||
# Update limiter with actual usage (replace estimate with actual)
|
||
await token_usage_limiter.record_token_usage(actual_input, actual_output)
|
||
|
||
# Log token usage and cost
|
||
run_id = getattr(analyzer, 'run_id', f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
|
||
log_token_usage(
|
||
run_id=run_id,
|
||
request_type="synthesis_analysis",
|
||
prompt_text=synthesis_prompt,
|
||
response_obj=message,
|
||
file_bundle_size=0, # Synthesis doesn't bundle files
|
||
model=model
|
||
)
|
||
except Exception as api_error:
|
||
# Log error with token tracking
|
||
run_id = getattr(analyzer, 'run_id', f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
|
||
log_token_usage(
|
||
run_id=run_id,
|
||
request_type="synthesis_analysis",
|
||
prompt_text=synthesis_prompt,
|
||
file_bundle_size=0,
|
||
model=os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest"),
|
||
error=api_error
|
||
)
|
||
print(f"⚠️ [SYNTHESIS] API call failed: {api_error}, using fallback")
|
||
response_text = ""
|
||
else:
|
||
print("⚠️ [SYNTHESIS] Analyzer not available, using fallback synthesis")
|
||
response_text = ""
|
||
except Exception as api_error:
|
||
print(f"⚠️ [SYNTHESIS] API call failed: {api_error}, using fallback")
|
||
response_text = ""
|
||
|
||
# Parse synthesis response
|
||
if response_text:
|
||
synthesis_analysis = parse_synthesis_response(response_text)
|
||
else:
|
||
# Fallback: create basic synthesis from analysis_state
|
||
print("⚠️ [SYNTHESIS] Using fallback synthesis from analysis_state")
|
||
synthesis_analysis = {
|
||
'system_architecture_patterns': analysis_state.get('architecture_patterns', []),
|
||
'cross_cutting_issues': [
|
||
{
|
||
'issue': issue.get('issue', ''),
|
||
'affected_modules': [issue.get('module', 'unknown')],
|
||
'severity': 'medium'
|
||
}
|
||
for issue in analysis_state.get('critical_issues', [])[:5]
|
||
],
|
||
'system_wide_risks': [],
|
||
'architectural_recommendations': [],
|
||
'module_interdependencies': {},
|
||
'quality_trends': {}
|
||
}
|
||
|
||
# Update analysis_state with synthesis findings
|
||
updated_state = analysis_state.copy()
|
||
if 'synthesis' not in updated_state:
|
||
updated_state['synthesis'] = {}
|
||
|
||
updated_state['synthesis'] = synthesis_analysis
|
||
updated_state['synthesis_completed'] = True
|
||
|
||
# Emit synthesis completed event
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("synthesis_completed", {
|
||
"message": "Cross-module synthesis completed",
|
||
"percent": 88,
|
||
"synthesis_insights": {
|
||
"architecture_patterns": len(synthesis_analysis.get('system_architecture_patterns', [])),
|
||
"cross_cutting_issues": len(synthesis_analysis.get('cross_cutting_issues', [])),
|
||
"system_wide_risks": len(synthesis_analysis.get('system_wide_risks', []))
|
||
}
|
||
})
|
||
|
||
print(f"✅ [SYNTHESIS] Synthesis phase completed")
|
||
print(f" 📊 Found {len(synthesis_analysis.get('system_architecture_patterns', []))} system architecture patterns")
|
||
print(f" 🔍 Found {len(synthesis_analysis.get('cross_cutting_issues', []))} cross-cutting issues")
|
||
print(f" ⚠️ Found {len(synthesis_analysis.get('system_wide_risks', []))} system-wide risks")
|
||
|
||
return synthesis_analysis, updated_state
|
||
|
||
except Exception as e:
|
||
print(f"❌ [SYNTHESIS] Error in synthesis phase: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
# Return fallback
|
||
fallback_synthesis = {
|
||
'system_architecture_patterns': [],
|
||
'cross_cutting_issues': [],
|
||
'system_wide_risks': [],
|
||
'architectural_recommendations': [],
|
||
'module_interdependencies': {},
|
||
'quality_trends': {}
|
||
}
|
||
updated_state = analysis_state.copy()
|
||
updated_state['synthesis'] = fallback_synthesis
|
||
updated_state['synthesis_completed'] = False
|
||
|
||
return fallback_synthesis, updated_state
|
||
|
||
async def store_synthesis_analysis_in_memory(
|
||
synthesis_analysis: Dict,
|
||
repository_id: str,
|
||
session_id: str,
|
||
analysis_state: Dict
|
||
) -> Optional[str]:
|
||
"""Store synthesis analysis in episodic memory."""
|
||
try:
|
||
if not analyzer or not hasattr(analyzer, 'memory_manager'):
|
||
print("⚠️ [MEMORY] Memory manager not available, skipping synthesis storage")
|
||
return None
|
||
|
||
# Build comprehensive AI response text
|
||
ai_response_parts = [
|
||
"# CROSS-MODULE SYNTHESIS ANALYSIS",
|
||
"",
|
||
"## SYSTEM-LEVEL ARCHITECTURE PATTERNS",
|
||
]
|
||
|
||
patterns = synthesis_analysis.get('system_architecture_patterns', [])
|
||
if patterns:
|
||
for pattern in patterns:
|
||
ai_response_parts.append(f"- {pattern}")
|
||
else:
|
||
ai_response_parts.append("- No system-level patterns identified")
|
||
|
||
ai_response_parts.extend([
|
||
"",
|
||
"## CROSS-CUTTING ISSUES",
|
||
])
|
||
|
||
cross_cutting = synthesis_analysis.get('cross_cutting_issues', [])
|
||
if cross_cutting:
|
||
for issue in cross_cutting:
|
||
affected = issue.get('affected_modules', [])
|
||
severity = issue.get('severity', 'medium')
|
||
ai_response_parts.append(f"- **{severity.upper()}**: {issue.get('issue', '')} (Affects: {', '.join(affected)})")
|
||
else:
|
||
ai_response_parts.append("- No cross-cutting issues identified")
|
||
|
||
ai_response_parts.extend([
|
||
"",
|
||
"## SYSTEM-WIDE RISKS",
|
||
])
|
||
|
||
risks = synthesis_analysis.get('system_wide_risks', [])
|
||
if risks:
|
||
for risk in risks:
|
||
severity = risk.get('severity', 'medium')
|
||
ai_response_parts.append(f"- **{severity.upper()}**: {risk.get('risk', '')} - {risk.get('impact', '')}")
|
||
else:
|
||
ai_response_parts.append("- No system-wide risks identified")
|
||
|
||
ai_response_parts.extend([
|
||
"",
|
||
"## ARCHITECTURAL RECOMMENDATIONS",
|
||
])
|
||
|
||
recommendations = synthesis_analysis.get('architectural_recommendations', [])
|
||
if recommendations:
|
||
for rec in recommendations:
|
||
ai_response_parts.append(f"- {rec}")
|
||
else:
|
||
ai_response_parts.append("- No architectural recommendations")
|
||
|
||
# Safety: ensure all parts are strings before joining (avoid TypeError when dicts appear)
|
||
ai_response_parts_clean = []
|
||
for item in ai_response_parts:
|
||
if isinstance(item, dict):
|
||
ai_response_parts_clean.append(json.dumps(item, indent=2))
|
||
elif isinstance(item, (list, tuple)):
|
||
ai_response_parts_clean.append(str(item))
|
||
else:
|
||
ai_response_parts_clean.append(str(item))
|
||
ai_response = "\n".join(ai_response_parts_clean)
|
||
|
||
user_query = f"Cross-Module Synthesis Analysis for repository {repository_id}"
|
||
|
||
# Get run_id from analyzer for proper retrieval
|
||
run_id = getattr(analyzer, 'run_id', None)
|
||
if not run_id:
|
||
run_id = f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||
|
||
metadata = {
|
||
'type': 'synthesis_analysis',
|
||
'run_id': run_id, # CRITICAL: Store run_id in metadata for retrieval
|
||
'repository_id': repository_id,
|
||
'synthesis_analysis': synthesis_analysis,
|
||
'modules_analyzed': analysis_state.get('modules_analyzed', []),
|
||
'timestamp': datetime.utcnow().isoformat()
|
||
}
|
||
|
||
memory_id = await analyzer.memory_manager.store_episodic_memory(
|
||
session_id=session_id,
|
||
user_query=user_query,
|
||
ai_response=ai_response,
|
||
repo_context=repository_id,
|
||
metadata=metadata
|
||
)
|
||
|
||
print(f"💾 [MEMORY] Stored synthesis analysis in episodic memory (ID: {memory_id})")
|
||
return memory_id
|
||
|
||
except Exception as e:
|
||
print(f"❌ [MEMORY] Failed to store synthesis analysis: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return None
|
||
|
||
# ============================================================================
|
||
# PHASE 3: ENHANCED REPORT GENERATION FUNCTIONS
|
||
# ============================================================================
|
||
|
||
def build_report_generation_prompt(
|
||
analysis_state: Dict,
|
||
synthesis_analysis: Dict,
|
||
file_analyses: List,
|
||
repository_id: str
|
||
) -> str:
|
||
"""
|
||
Build comprehensive prompt for enhanced report generation.
|
||
Uses synthesis insights to generate a structured, comprehensive report.
|
||
"""
|
||
prompt_parts = [
|
||
"# COMPREHENSIVE REPOSITORY ANALYSIS REPORT GENERATION",
|
||
"",
|
||
"You are a senior software architect with 30+ years of experience. Your task is to",
|
||
"generate a comprehensive, structured analysis report based on detailed module analyses",
|
||
"and system-level synthesis insights.",
|
||
"",
|
||
"## SYNTHESIS INSIGHTS (System-Level)",
|
||
""
|
||
]
|
||
|
||
# Add synthesis findings
|
||
synthesis = synthesis_analysis.get('synthesis', {}) if isinstance(synthesis_analysis.get('synthesis'), dict) else synthesis_analysis
|
||
|
||
if synthesis.get('system_architecture_patterns'):
|
||
prompt_parts.extend([
|
||
"### System Architecture Patterns:",
|
||
", ".join(synthesis.get('system_architecture_patterns', [])),
|
||
""
|
||
])
|
||
|
||
if synthesis.get('cross_cutting_issues'):
|
||
prompt_parts.extend([
|
||
"### Cross-Cutting Issues:",
|
||
])
|
||
for issue in synthesis.get('cross_cutting_issues', [])[:5]:
|
||
prompt_parts.append(f"- {issue.get('issue', '')} (Affects: {', '.join(issue.get('affected_modules', []))})")
|
||
prompt_parts.append("")
|
||
|
||
if synthesis.get('system_wide_risks'):
|
||
prompt_parts.extend([
|
||
"### System-Wide Risks:",
|
||
])
|
||
for risk in synthesis.get('system_wide_risks', [])[:5]:
|
||
prompt_parts.append(f"- {risk.get('risk', '')}: {risk.get('impact', '')}")
|
||
prompt_parts.append("")
|
||
|
||
# Add module summaries
|
||
module_summaries = analysis_state.get('module_summaries', {})
|
||
if module_summaries:
|
||
prompt_parts.extend([
|
||
"## MODULE ANALYSES SUMMARY",
|
||
""
|
||
])
|
||
for module_name, summary in list(module_summaries.items())[:10]: # Top 10 modules
|
||
prompt_parts.append(f"### {module_name}:")
|
||
prompt_parts.append(summary[:300] + ("..." if len(summary) > 300 else ""))
|
||
prompt_parts.append("")
|
||
|
||
# Add statistics
|
||
prompt_parts.extend([
|
||
"## ANALYSIS STATISTICS",
|
||
f"- Total Files Analyzed: {len(file_analyses)}",
|
||
f"- Modules Analyzed: {len(analysis_state.get('modules_analyzed', []))}",
|
||
f"- Architecture Patterns Found: {len(analysis_state.get('architecture_patterns', []))}",
|
||
f"- Critical Issues: {len(analysis_state.get('critical_issues', []))}",
|
||
""
|
||
])
|
||
|
||
# Report generation instructions
|
||
prompt_parts.extend([
|
||
"## REPORT GENERATION REQUIREMENTS:",
|
||
"",
|
||
"Generate a comprehensive analysis report with the following sections:",
|
||
"",
|
||
"1. **EXECUTIVE SUMMARY**:",
|
||
" - High-level overview of the repository",
|
||
" - Key findings and insights from synthesis",
|
||
" - Overall code quality assessment",
|
||
" - Critical issues summary",
|
||
"",
|
||
"2. **ARCHITECTURE ASSESSMENT**:",
|
||
" - System-level architecture patterns identified",
|
||
" - Architectural strengths and weaknesses",
|
||
" - Module interdependencies and relationships",
|
||
" - Recommendations for architectural improvements",
|
||
" - Include synthesis insights about system-wide patterns",
|
||
"",
|
||
"3. **SECURITY ASSESSMENT**:",
|
||
" - System-wide security risks identified in synthesis",
|
||
" - Cross-cutting security concerns",
|
||
" - Security recommendations",
|
||
" - Include synthesis insights about system-wide security issues",
|
||
"",
|
||
"## RESPONSE FORMAT:",
|
||
"",
|
||
"Provide the report in JSON format:",
|
||
"",
|
||
"{",
|
||
' "executive_summary": "Comprehensive executive summary including synthesis insights...",',
|
||
' "architecture_assessment": "Detailed architecture assessment including system-level patterns...",',
|
||
' "security_assessment": "Comprehensive security assessment including system-wide risks...",',
|
||
' "key_recommendations": ["rec1", "rec2", "rec3", ...]',
|
||
"}",
|
||
"",
|
||
"Focus on providing insights that integrate both detailed module findings and",
|
||
"system-level synthesis insights. The report should be comprehensive, actionable,",
|
||
"and suitable for both technical and executive audiences."
|
||
])
|
||
|
||
return "\n".join(prompt_parts)
|
||
|
||
def parse_report_response(response_text: str, file_analyses: List) -> Tuple[str, str, str]:
|
||
"""
|
||
Parse report generation response from Claude API.
|
||
Returns (executive_summary, architecture_assessment, security_assessment) tuple.
|
||
"""
|
||
import json
|
||
import re
|
||
|
||
# Default values
|
||
executive_summary = "Analysis completed for repository."
|
||
architecture_assessment = "Architecture analysis completed."
|
||
security_assessment = "Security analysis completed."
|
||
|
||
# Function to extract JSON from markdown code blocks
|
||
def extract_json_from_text(text: str) -> Optional[str]:
|
||
text = text.strip()
|
||
json_block_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', text, re.DOTALL)
|
||
if json_block_match:
|
||
return json_block_match.group(1)
|
||
|
||
brace_count = 0
|
||
start_idx = -1
|
||
for i, char in enumerate(text):
|
||
if char == '{':
|
||
if brace_count == 0:
|
||
start_idx = i
|
||
brace_count += 1
|
||
elif char == '}':
|
||
brace_count -= 1
|
||
if brace_count == 0 and start_idx >= 0:
|
||
return text[start_idx:i+1]
|
||
return None
|
||
|
||
try:
|
||
extracted = extract_json_from_text(response_text)
|
||
if extracted:
|
||
report_data = json.loads(extracted)
|
||
|
||
executive_summary = report_data.get('executive_summary', executive_summary)
|
||
architecture_assessment = report_data.get('architecture_assessment', architecture_assessment)
|
||
security_assessment = report_data.get('security_assessment', security_assessment)
|
||
|
||
print(f"✅ [REPORT] Successfully parsed report generation response")
|
||
else:
|
||
print(f"⚠️ [REPORT] Could not extract JSON from response, using fallback")
|
||
# Fallback: use response text as executive summary
|
||
if response_text:
|
||
executive_summary = response_text[:1000] + ("..." if len(response_text) > 1000 else "")
|
||
except Exception as e:
|
||
print(f"⚠️ [REPORT] Error parsing report response: {e}")
|
||
|
||
return executive_summary, architecture_assessment, security_assessment
|
||
|
||
async def perform_report_generation_phase(
|
||
analysis_state: Dict,
|
||
synthesis_analysis: Dict,
|
||
file_analyses: List,
|
||
repository_id: str,
|
||
progress_mgr: Optional[AnalysisProgressManager] = None
|
||
) -> Tuple[str, str, str]:
|
||
"""
|
||
Perform Phase 3: Enhanced Report Generation.
|
||
Generates comprehensive report using synthesis insights.
|
||
Returns (executive_summary, architecture_assessment, security_assessment) tuple.
|
||
"""
|
||
try:
|
||
print(f"📄 [REPORT] Starting enhanced report generation phase...")
|
||
|
||
# Emit report generation started event
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("report_generation_started", {
|
||
"message": "Generating comprehensive report with synthesis insights",
|
||
"percent": 90
|
||
})
|
||
|
||
# Build report generation prompt
|
||
report_prompt = build_report_generation_prompt(
|
||
analysis_state, synthesis_analysis, file_analyses, repository_id
|
||
)
|
||
|
||
# Rate limiting
|
||
await rate_limiter.wait_if_needed()
|
||
|
||
# Make API call to Claude for report generation
|
||
try:
|
||
if analyzer and hasattr(analyzer, 'client'):
|
||
model = os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest")
|
||
|
||
# Check token limits before making API call
|
||
estimated_input_tokens = estimate_tokens_from_text(report_prompt)
|
||
estimated_output_tokens = 3000 # Conservative estimate
|
||
|
||
# Update token limiter for this model if needed
|
||
global token_usage_limiter
|
||
if token_usage_limiter.model != model:
|
||
token_usage_limiter = TokenUsageRateLimiter(model=model)
|
||
|
||
# Check if we can proceed with this request
|
||
can_proceed, wait_time = await token_usage_limiter.check_token_limits(
|
||
estimated_input_tokens, estimated_output_tokens
|
||
)
|
||
|
||
if not can_proceed:
|
||
print(f"⏳ [TOKEN LIMIT] Waiting {wait_time:.2f} seconds for report generation to avoid exceeding token limits...")
|
||
await asyncio.sleep(wait_time)
|
||
|
||
def call_claude_report():
|
||
return analyzer.client.messages.create(
|
||
model=model,
|
||
max_tokens=4000,
|
||
temperature=0.3,
|
||
messages=[{"role": "user", "content": report_prompt}]
|
||
)
|
||
|
||
loop = asyncio.get_event_loop()
|
||
try:
|
||
message = await loop.run_in_executor(None, call_claude_report)
|
||
response_text = message.content[0].text if message.content else ""
|
||
|
||
# Record actual token usage in limiter (after API call completes)
|
||
if message and hasattr(message, 'usage'):
|
||
actual_input = message.usage.input_tokens if hasattr(message.usage, 'input_tokens') else estimated_input_tokens
|
||
actual_output = message.usage.output_tokens if hasattr(message.usage, 'output_tokens') else estimated_output_tokens
|
||
# Update limiter with actual usage (replace estimate with actual)
|
||
await token_usage_limiter.record_token_usage(actual_input, actual_output)
|
||
|
||
# Log token usage and cost
|
||
run_id = getattr(analyzer, 'run_id', f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
|
||
log_token_usage(
|
||
run_id=run_id,
|
||
request_type="report_generation",
|
||
prompt_text=report_prompt,
|
||
response_obj=message,
|
||
file_bundle_size=0, # Report generation doesn't bundle files
|
||
model=model
|
||
)
|
||
except Exception as api_error:
|
||
# Log error with token tracking
|
||
run_id = getattr(analyzer, 'run_id', f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
|
||
log_token_usage(
|
||
run_id=run_id,
|
||
request_type="report_generation",
|
||
prompt_text=report_prompt,
|
||
file_bundle_size=0,
|
||
model=os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest"),
|
||
error=api_error
|
||
)
|
||
print(f"⚠️ [REPORT] API call failed: {api_error}, using fallback")
|
||
response_text = ""
|
||
else:
|
||
print("⚠️ [REPORT] Analyzer not available, using fallback report generation")
|
||
response_text = ""
|
||
except Exception as api_error:
|
||
print(f"⚠️ [REPORT] API call failed: {api_error}, using fallback")
|
||
response_text = ""
|
||
|
||
# Parse report response
|
||
if response_text:
|
||
executive_summary, architecture_assessment, security_assessment = parse_report_response(
|
||
response_text, file_analyses
|
||
)
|
||
else:
|
||
# Fallback: create basic report from existing data
|
||
print("⚠️ [REPORT] Using fallback report generation from existing data")
|
||
executive_summary = f"Analysis completed for {len(file_analyses)} files across {len(analysis_state.get('modules_analyzed', []))} modules."
|
||
architecture_assessment = f"Architecture patterns identified: {', '.join(analysis_state.get('architecture_patterns', []))}"
|
||
security_assessment = "Security assessment completed. Review individual module analyses for detailed security findings."
|
||
|
||
# Emit report generation completed event
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("report_generation_completed", {
|
||
"message": "Report generation completed",
|
||
"percent": 95
|
||
})
|
||
|
||
print(f"✅ [REPORT] Report generation phase completed")
|
||
return executive_summary, architecture_assessment, security_assessment
|
||
|
||
except Exception as e:
|
||
print(f"❌ [REPORT] Error in report generation phase: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
# Return fallback
|
||
executive_summary = f"Analysis completed for {len(file_analyses)} files."
|
||
architecture_assessment = "Architecture analysis completed."
|
||
security_assessment = "Security analysis completed."
|
||
|
||
return executive_summary, architecture_assessment, security_assessment
|
||
|
||
# ============================================================================
|
||
# END SMART BATCHING FUNCTIONS
|
||
# ============================================================================
|
||
|
||
async def analyze_single_file_parallel(file_path: str, content: str, repository_id: str, current_file_num: int = 0, total_files: int = 0, progress_mgr: Optional[AnalysisProgressManager] = None):
|
||
"""Analyze a single file with optimized processing."""
|
||
print(f"🎯 analyze_single_file_parallel called for {file_path} - progress_mgr: {progress_mgr is not None}")
|
||
try:
|
||
# Emit file analysis started event
|
||
if progress_mgr:
|
||
print(f"🔔 Emitting file_analysis_started for {file_path} ({current_file_num}/{total_files})")
|
||
try:
|
||
await progress_mgr.emit_event("file_analysis_started", {
|
||
"message": f"Analyzing {file_path}",
|
||
"file_path": file_path,
|
||
"current": current_file_num,
|
||
"total": total_files,
|
||
"percent": int((current_file_num / total_files) * 70) if total_files > 0 else 0
|
||
})
|
||
print(f"✅ Event emitted successfully for {file_path}")
|
||
except Exception as emit_err:
|
||
print(f"❌ Failed to emit event for {file_path}: {emit_err}")
|
||
else:
|
||
print(f"⚠️ No progress manager for {file_path}")
|
||
|
||
# Generate file hash for caching
|
||
file_hash = hashlib.sha256((content or '').encode()).hexdigest()
|
||
|
||
# Check cache first
|
||
cached_analysis = await analysis_cache.get_cached_analysis(file_hash)
|
||
if cached_analysis:
|
||
print(f"Using cached analysis for {file_path}")
|
||
from ai_analyze import FileAnalysis
|
||
cached_obj = FileAnalysis(
|
||
path=cached_analysis["path"],
|
||
language=cached_analysis["language"],
|
||
lines_of_code=cached_analysis["lines_of_code"],
|
||
complexity_score=cached_analysis["complexity_score"],
|
||
issues_found=cached_analysis["issues_found"],
|
||
recommendations=cached_analysis["recommendations"],
|
||
detailed_analysis=cached_analysis["detailed_analysis"],
|
||
severity_score=cached_analysis["severity_score"],
|
||
content="" # Never use cached content - content is only for analysis, not storage
|
||
)
|
||
|
||
# Emit completion event for cached files too
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("file_analysis_completed", {
|
||
"message": f"Completed {file_path} (cached)",
|
||
"file_path": file_path,
|
||
"quality_score": cached_obj.severity_score,
|
||
"issues_count": len(cached_obj.issues_found) if hasattr(cached_obj, 'issues_found') else 0,
|
||
"current": current_file_num,
|
||
"total": total_files
|
||
})
|
||
|
||
return cached_obj
|
||
|
||
# Rate limiting for individual file
|
||
await rate_limiter.wait_if_needed()
|
||
|
||
# Optimize content for Claude API
|
||
optimized_content = content_optimizer.optimize_content_for_claude(content)
|
||
|
||
# Analyze file with memory
|
||
file_path_obj = Path(file_path)
|
||
|
||
if hasattr(analyzer, 'analyze_file_with_memory_enhanced'):
|
||
analysis = await analyzer.analyze_file_with_memory_enhanced(
|
||
file_path_obj,
|
||
optimized_content,
|
||
repository_id
|
||
)
|
||
else:
|
||
analysis = await analyzer.analyze_file_with_memory(
|
||
file_path_obj,
|
||
optimized_content,
|
||
repository_id
|
||
)
|
||
|
||
# Cache the result (EXCLUDE content to save storage space)
|
||
analysis_dict = {
|
||
"path": str(analysis.path),
|
||
"language": analysis.language,
|
||
"lines_of_code": analysis.lines_of_code,
|
||
"complexity_score": analysis.complexity_score,
|
||
"issues_found": analysis.issues_found,
|
||
"recommendations": analysis.recommendations,
|
||
"detailed_analysis": analysis.detailed_analysis,
|
||
"severity_score": analysis.severity_score
|
||
# NOTE: 'content' field explicitly NOT included - never store file content in cache
|
||
}
|
||
|
||
# Ensure content is not accidentally included
|
||
if 'content' in analysis_dict:
|
||
del analysis_dict['content']
|
||
|
||
await analysis_cache.cache_analysis(file_hash, analysis_dict)
|
||
|
||
# Emit file analysis completed event
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("file_analysis_completed", {
|
||
"message": f"Completed {file_path}",
|
||
"file_path": file_path,
|
||
"quality_score": analysis.severity_score,
|
||
"issues_count": len(analysis.issues_found) if hasattr(analysis, 'issues_found') else 0,
|
||
"current": current_file_num,
|
||
"total": total_files
|
||
})
|
||
|
||
return analysis
|
||
|
||
except Exception as e:
|
||
print(f"Error analyzing {file_path}: {e}")
|
||
|
||
# Emit error event
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("file_analysis_error", {
|
||
"message": f"Error analyzing {file_path}: {str(e)}",
|
||
"file_path": file_path,
|
||
"error": str(e)
|
||
})
|
||
|
||
# Return a basic analysis for failed files
|
||
from ai_analyze import FileAnalysis
|
||
return FileAnalysis(
|
||
path=str(file_path),
|
||
language="Unknown",
|
||
lines_of_code=len(content.splitlines()) if content else 0,
|
||
severity_score=5.0,
|
||
issues_found=[f"Analysis failed: {str(e)}"],
|
||
recommendations=["Review this file manually"],
|
||
detailed_analysis=f"Error analyzing {file_path}: {str(e)}",
|
||
complexity_score=5.0,
|
||
content=content # Store file content
|
||
)
|
||
|
||
async def process_chunks_in_parallel_batches(chunks, repository_id, progress_mgr, batch_rate_limiter, batch_size=8):
|
||
"""
|
||
Process chunks in parallel batches for faster analysis.
|
||
Default batch_size=8 optimized for 2000 requests/minute API limit
|
||
(was 5, increased to 8 based on actual Claude API plan limits)
|
||
Returns: (file_analyses, analysis_state, chunk_results)
|
||
"""
|
||
total_chunks = len(chunks)
|
||
file_analyses = []
|
||
analysis_state = {}
|
||
chunk_results = []
|
||
|
||
print(f"⚡ [PARALLEL] Processing {total_chunks} chunks in batches of {batch_size}")
|
||
|
||
for batch_start in range(0, total_chunks, batch_size):
|
||
batch_end = min(batch_start + batch_size, total_chunks)
|
||
batch_chunks = chunks[batch_start:batch_end]
|
||
current_batch_size = len(batch_chunks)
|
||
|
||
print(f"⚡ [BATCH {batch_start//batch_size + 1}] Processing chunks {batch_start+1}-{batch_end} ({current_batch_size} in parallel)")
|
||
|
||
# Minimal context (essential info only - no full summaries)
|
||
minimal_context = {
|
||
'tech_stack': analysis_state.get('tech_stack', {}),
|
||
'architecture_patterns': analysis_state.get('architecture_patterns', []),
|
||
'repository_id': repository_id,
|
||
'modules_count': len(analysis_state.get('modules_analyzed', []))
|
||
}
|
||
|
||
# Create tasks for parallel execution
|
||
batch_tasks = []
|
||
for chunk_idx, chunk in enumerate(batch_chunks):
|
||
chunk_num = batch_start + chunk_idx + 1
|
||
chunk_name = chunk.get('name', 'unknown')
|
||
chunk_files = chunk.get('files', [])
|
||
|
||
print(f" 🔹 Chunk {chunk_num}/{total_chunks}: {chunk_name} ({len(chunk_files)} files)")
|
||
|
||
task = analyze_intelligent_chunk(chunk, repository_id, progress_mgr, minimal_context)
|
||
batch_tasks.append((chunk_num, chunk, task))
|
||
|
||
# Rate limit before parallel execution
|
||
await batch_rate_limiter.wait_for_batch()
|
||
|
||
# Execute in parallel
|
||
print(f" ⚡ Executing {current_batch_size} chunks simultaneously...")
|
||
parallel_results = await asyncio.gather(
|
||
*[task for _, _, task in batch_tasks],
|
||
return_exceptions=True
|
||
)
|
||
|
||
# Process results
|
||
for idx, (chunk_num, chunk, _) in enumerate(batch_tasks):
|
||
chunk_name = chunk.get('name', 'unknown')
|
||
chunk_id = chunk.get('id', 'unknown')
|
||
result = parallel_results[idx]
|
||
|
||
if isinstance(result, Exception):
|
||
print(f"❌ [PARALLEL] Chunk {chunk_num} ({chunk_name}) failed: {result}")
|
||
chunk_file_analyses, chunk_analysis, chunk_state = [], {}, {}
|
||
else:
|
||
chunk_file_analyses, chunk_analysis, chunk_state = result
|
||
print(f"✅ [PARALLEL] Chunk {chunk_num}/{total_chunks}: {chunk_name} - {len(chunk_file_analyses)} files")
|
||
|
||
# Update global state with essential info
|
||
if chunk_state:
|
||
if 'tech_stack' in chunk_state:
|
||
analysis_state.setdefault('tech_stack', {}).update(chunk_state['tech_stack'])
|
||
if 'architecture_patterns' in chunk_state:
|
||
existing = analysis_state.setdefault('architecture_patterns', [])
|
||
for pattern in chunk_state['architecture_patterns']:
|
||
if pattern not in existing:
|
||
existing.append(pattern)
|
||
if 'modules_analyzed' in chunk_state:
|
||
analysis_state.setdefault('modules_analyzed', []).extend(chunk_state['modules_analyzed'])
|
||
|
||
file_analyses.extend(chunk_file_analyses)
|
||
chunk_results.append({
|
||
'chunk_num': chunk_num,
|
||
'chunk': chunk,
|
||
'chunk_name': chunk_name,
|
||
'chunk_id': chunk_id,
|
||
'file_analyses': chunk_file_analyses,
|
||
'chunk_analysis': chunk_analysis
|
||
})
|
||
|
||
# Small delay between batches
|
||
await asyncio.sleep(0.01)
|
||
|
||
return file_analyses, analysis_state, chunk_results
|
||
|
||
async def analyze_repository_with_optimizations_parallel(repo_path: str, repository_id: str, user_id: str, max_files: Optional[int] = None, progress_mgr: Optional[AnalysisProgressManager] = None):
|
||
"""Analyze repository with PARALLEL BATCH PROCESSING for faster analysis."""
|
||
try:
|
||
# Set run_id early so it's available for chunk storage
|
||
# Extract analysis_id from progress_mgr if available, otherwise generate
|
||
if progress_mgr and hasattr(progress_mgr, 'analysis_id'):
|
||
run_id = progress_mgr.analysis_id
|
||
else:
|
||
run_id = f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||
|
||
# Set run_id on analyzer for chunk storage
|
||
if analyzer:
|
||
analyzer.run_id = run_id
|
||
if not hasattr(analyzer, 'session_id') or not analyzer.session_id:
|
||
analyzer.session_id = str(uuid.uuid4())
|
||
|
||
print(f"🔑 [ANALYSIS] Set run_id: {run_id}")
|
||
|
||
# Get repository files from Git Integration Service API
|
||
files_to_analyze = await get_repository_files_from_api(repository_id, user_id, max_files)
|
||
|
||
if not files_to_analyze:
|
||
raise Exception("No files found to analyze")
|
||
|
||
total_files = len(files_to_analyze)
|
||
print(f"🧠 [INTELLIGENT CHUNKING] Starting analysis of {total_files} files with intelligent chunking...")
|
||
|
||
# Emit files discovered event
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("files_discovered", {
|
||
"message": f"Found {total_files} files to analyze",
|
||
"total_files": total_files,
|
||
"processing_mode": "intelligent_chunking"
|
||
})
|
||
|
||
# Create intelligent chunks (semantic module-based grouping)
|
||
chunks = create_intelligent_chunks(files_to_analyze, dependency_graph=None)
|
||
total_chunks = len(chunks)
|
||
|
||
print(f"🧠 [INTELLIGENT CHUNKING] Grouped {total_files} files into {total_chunks} intelligent chunks")
|
||
for i, chunk in enumerate(chunks, 1):
|
||
print(f" Chunk {i}: {chunk['name']} ({chunk['chunk_type']}) - {len(chunk['files'])} files")
|
||
|
||
# ========================================================================
|
||
# PARALLEL BATCH PROCESSING (8 chunks at a time)
|
||
# Optimized for 2000 requests/minute Claude API limit
|
||
# Increased from 3 → 5 → 8 for maximum speed with your API plan
|
||
# ========================================================================
|
||
file_analyses, analysis_state, chunk_results = await process_chunks_in_parallel_batches(
|
||
chunks, repository_id, progress_mgr, batch_rate_limiter, batch_size=8
|
||
)
|
||
|
||
print(f"🎉 [PARALLEL] All {total_chunks} chunks completed - {len(file_analyses)} files analyzed")
|
||
|
||
# Store chunk results in memory (storage is fast, sequential is fine)
|
||
print(f"\n{'='*80}")
|
||
print(f"📦 [STORAGE] 🔷 CHUNK STORAGE PHASE")
|
||
print(f"{'='*80}")
|
||
print(f" Total chunk results to store: {len(chunk_results)}")
|
||
print(f" Analyzer object: {analyzer}")
|
||
print(f" Analyzer type: {type(analyzer).__name__ if analyzer else 'None'}")
|
||
print(f" Analyzer available: {analyzer is not None}")
|
||
if analyzer:
|
||
print(f" Memory manager available: {hasattr(analyzer, 'memory_manager')}")
|
||
if hasattr(analyzer, 'memory_manager'):
|
||
print(f" Memory manager type: {type(analyzer.memory_manager).__name__}")
|
||
print(f"{'='*80}\n")
|
||
|
||
for result in chunk_results:
|
||
chunk_num = result['chunk_num']
|
||
chunk = result['chunk']
|
||
chunk_name = result['chunk_name']
|
||
chunk_id = result['chunk_id']
|
||
chunk_file_analyses = result['file_analyses']
|
||
chunk_analysis = result['chunk_analysis']
|
||
|
||
if not chunk_file_analyses:
|
||
print(f" ⏭️ Skipping chunk {chunk_name} - no file analyses")
|
||
continue
|
||
|
||
# Store in episodic memory
|
||
if analyzer and hasattr(analyzer, 'memory_manager'):
|
||
try:
|
||
await store_chunk_analysis_in_memory(
|
||
chunk=chunk,
|
||
file_analyses=chunk_file_analyses,
|
||
chunk_analysis=chunk_analysis,
|
||
repository_id=repository_id,
|
||
session_id=getattr(analyzer, 'session_id', None),
|
||
analysis_state=analysis_state
|
||
)
|
||
|
||
# Hierarchical storage (if enabled)
|
||
if USE_HIERARCHICAL_STORAGE:
|
||
try:
|
||
run_id = getattr(analyzer, 'run_id', None)
|
||
session_id = getattr(analyzer, 'session_id', None)
|
||
|
||
architecture, security, code_quality, issues = await extract_structured_insights(
|
||
chunk_analysis=chunk_analysis,
|
||
file_analyses=chunk_file_analyses,
|
||
chunk=chunk
|
||
)
|
||
|
||
module_id = f"{chunk_name}_module_{chunk_id}"
|
||
|
||
await store_module_analysis_hierarchical(
|
||
module_id=module_id,
|
||
module_name=chunk_name,
|
||
chunk=chunk,
|
||
chunk_analysis=chunk_analysis,
|
||
file_analyses=chunk_file_analyses,
|
||
architecture=architecture,
|
||
security=security,
|
||
code_quality=code_quality,
|
||
issues=issues,
|
||
repository_id=repository_id,
|
||
run_id=run_id,
|
||
session_id=session_id
|
||
)
|
||
print(f"✅ [HIERARCHICAL] Stored {chunk_name}")
|
||
except Exception as e:
|
||
print(f"⚠️ [HIERARCHICAL] Storage failed (non-breaking): {e}")
|
||
except Exception as e:
|
||
print(f"⚠️ [MEMORY] Failed to store {chunk_name}: {e}")
|
||
|
||
# Emit progress
|
||
if progress_mgr:
|
||
files_progress = min(70, (len(file_analyses) / total_files) * 70)
|
||
await progress_mgr.emit_event("intelligent_chunk_completed", {
|
||
"message": f"Stored chunk {chunk_num}/{total_chunks}: {chunk_name}",
|
||
"chunk": chunk_num,
|
||
"chunk_id": chunk_id,
|
||
"chunk_name": chunk_name,
|
||
"total_chunks": total_chunks,
|
||
"files_processed": len(file_analyses),
|
||
"total_files": total_files,
|
||
"percent": int(files_progress),
|
||
"processing_mode": "intelligent_chunking"
|
||
})
|
||
|
||
print(f"✅ [STORAGE] All chunk analyses stored")
|
||
|
||
# ========================================================================
|
||
# PHASE 2: CROSS-MODULE SYNTHESIS
|
||
# ========================================================================
|
||
synthesis_analysis = {}
|
||
try:
|
||
# Perform synthesis phase
|
||
synthesis_analysis, analysis_state = await perform_synthesis_phase(
|
||
analysis_state, repository_id, progress_mgr
|
||
)
|
||
|
||
# Store synthesis analysis in episodic memory
|
||
if analyzer and hasattr(analyzer, 'memory_manager'):
|
||
try:
|
||
session_id = getattr(analyzer, 'session_id', None)
|
||
if session_id:
|
||
await store_synthesis_analysis_in_memory(
|
||
synthesis_analysis=synthesis_analysis,
|
||
repository_id=repository_id,
|
||
session_id=session_id,
|
||
analysis_state=analysis_state
|
||
)
|
||
except Exception as storage_error:
|
||
print(f"⚠️ [MEMORY] Failed to store synthesis analysis: {storage_error}")
|
||
except Exception as synthesis_error:
|
||
print(f"⚠️ [SYNTHESIS] Synthesis phase failed: {synthesis_error}")
|
||
# Continue with fallback - synthesis is optional
|
||
synthesis_analysis = {}
|
||
|
||
# ========================================================================
|
||
# PHASE 3: ENHANCED REPORT GENERATION
|
||
# ========================================================================
|
||
print("Performing enhanced repository-level analysis with synthesis insights...")
|
||
|
||
# Use a temporary directory path since we don't have a local repo_path
|
||
temp_repo_path = f"/tmp/repo_{repository_id}" if repo_path is None else repo_path
|
||
|
||
# Initialize variables for fallback
|
||
executive_summary = f"Parallel analysis completed for {len(file_analyses)} files in repository {repository_id}"
|
||
architecture_assessment = "Analysis in progress"
|
||
security_assessment = "Analysis in progress"
|
||
|
||
# Try enhanced report generation first (Phase 3)
|
||
try:
|
||
executive_summary, architecture_assessment, security_assessment = await perform_report_generation_phase(
|
||
analysis_state=analysis_state,
|
||
synthesis_analysis=synthesis_analysis,
|
||
file_analyses=file_analyses,
|
||
repository_id=repository_id,
|
||
progress_mgr=progress_mgr
|
||
)
|
||
print(f"✅ [REPORT] Enhanced report generation completed with synthesis insights")
|
||
except Exception as report_error:
|
||
print(f"⚠️ [REPORT] Enhanced report generation failed: {report_error}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
# Fallback to basic repository analysis (backward compatibility)
|
||
print("🔄 [REPORT] Falling back to basic repository analysis...")
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("repository_analysis_started", {
|
||
"message": "Starting repository-level analysis (fallback)",
|
||
"percent": 85
|
||
})
|
||
|
||
# Create proper context_memories structure
|
||
context_memories = {
|
||
'persistent_knowledge': [],
|
||
'similar_analyses': []
|
||
}
|
||
|
||
try:
|
||
architecture_assessment, security_assessment = await analyzer.analyze_repository_overview_with_memory(
|
||
temp_repo_path, file_analyses, context_memories, repository_id
|
||
)
|
||
executive_summary = f"Parallel analysis completed for {len(file_analyses)} files in repository {repository_id}"
|
||
except Exception as ov_err:
|
||
print(f"ERROR in analyze_repository_overview_with_memory: {ov_err}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
architecture_assessment = f"Error: {str(ov_err)}"
|
||
security_assessment = f"Error: {str(ov_err)}"
|
||
executive_summary = f"Analysis completed for {len(file_analyses)} files."
|
||
|
||
# Create repository analysis result
|
||
from ai_analyze import RepositoryAnalysis
|
||
|
||
# Calculate code quality score safely
|
||
if file_analyses and len(file_analyses) > 0:
|
||
valid_scores = [fa.severity_score for fa in file_analyses if fa.severity_score is not None]
|
||
code_quality_score = sum(valid_scores) / len(valid_scores) if valid_scores else 5.0
|
||
else:
|
||
code_quality_score = 5.0
|
||
|
||
# Calculate total lines safely
|
||
total_lines = sum(fa.lines_of_code for fa in file_analyses if fa.lines_of_code is not None) if file_analyses else 0
|
||
|
||
# Get languages safely - count occurrences of each language
|
||
if file_analyses:
|
||
from collections import Counter
|
||
language_list = [fa.language for fa in file_analyses if fa.language is not None]
|
||
languages = dict(Counter(language_list))
|
||
else:
|
||
languages = {}
|
||
|
||
return RepositoryAnalysis(
|
||
repo_path=str(temp_repo_path),
|
||
total_files=len(file_analyses), # Use actual analyzed files count, not discovered files
|
||
total_lines=total_lines,
|
||
languages=languages,
|
||
code_quality_score=code_quality_score,
|
||
architecture_assessment=architecture_assessment or "Analysis in progress",
|
||
security_assessment=security_assessment or "Analysis in progress",
|
||
file_analyses=file_analyses,
|
||
executive_summary=executive_summary,
|
||
high_quality_files=[]
|
||
)
|
||
|
||
except Exception as e:
|
||
print(f"Error in parallel analysis: {e}")
|
||
raise
|
||
|
||
async def analyze_repository_with_optimizations(repo_path: str, repository_id: str, user_id: str, max_files: int = 100, progress_mgr: Optional[AnalysisProgressManager] = None):
|
||
"""Analyze repository with SMART BATCHING for maximum efficiency."""
|
||
from pathlib import Path
|
||
|
||
try:
|
||
# Get repository files from Git Integration Service API
|
||
files_to_analyze = await get_repository_files_from_api(repository_id, user_id, max_files)
|
||
|
||
if not files_to_analyze:
|
||
raise Exception("No files found to analyze")
|
||
|
||
total_files = len(files_to_analyze)
|
||
print(f"🧠 [INTELLIGENT CHUNKING] Starting analysis of {total_files} files with intelligent chunking...")
|
||
|
||
# Emit files discovered event
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("files_discovered", {
|
||
"message": f"Found {total_files} files to analyze",
|
||
"total_files": total_files,
|
||
"processing_mode": "intelligent_chunking"
|
||
})
|
||
|
||
file_analyses = []
|
||
|
||
# Create intelligent chunks (semantic module-based grouping)
|
||
chunks = create_intelligent_chunks(files_to_analyze, dependency_graph=None)
|
||
total_chunks = len(chunks)
|
||
|
||
print(f"🧠 [INTELLIGENT CHUNKING] Grouped {total_files} files into {total_chunks} intelligent chunks")
|
||
for i, chunk in enumerate(chunks, 1):
|
||
print(f" Chunk {i}: {chunk['name']} ({chunk['chunk_type']}) - {len(chunk['files'])} files")
|
||
|
||
# Initialize analysis_state for progressive context
|
||
analysis_state = {}
|
||
|
||
# Process files in intelligent chunks
|
||
for chunk_num, chunk in enumerate(chunks, 1):
|
||
chunk_name = chunk.get('name', 'unknown')
|
||
chunk_id = chunk.get('id', 'unknown')
|
||
chunk_files = chunk.get('files', [])
|
||
|
||
print(f"🧠 [INTELLIGENT CHUNK] Processing chunk {chunk_num}/{total_chunks}: {chunk_name} ({len(chunk_files)} files)")
|
||
|
||
# Wait for batch rate limit
|
||
await batch_rate_limiter.wait_for_batch()
|
||
|
||
# Process intelligent chunk with comprehensive analysis and progressive context
|
||
try:
|
||
chunk_file_analyses, chunk_analysis, analysis_state = await analyze_intelligent_chunk(
|
||
chunk, repository_id, progress_mgr, analysis_state
|
||
)
|
||
file_analyses.extend(chunk_file_analyses)
|
||
|
||
# Store chunk-level analysis in episodic memory (MongoDB) with progressive context
|
||
if analyzer and hasattr(analyzer, 'memory_manager'):
|
||
try:
|
||
# Always use existing storage (backward compatible)
|
||
await store_chunk_analysis_in_memory(
|
||
chunk=chunk,
|
||
file_analyses=chunk_file_analyses,
|
||
chunk_analysis=chunk_analysis,
|
||
repository_id=repository_id,
|
||
session_id=getattr(analyzer, 'session_id', None),
|
||
analysis_state=analysis_state
|
||
)
|
||
|
||
# NEW: Hierarchical storage (if enabled via feature flag)
|
||
if USE_HIERARCHICAL_STORAGE:
|
||
try:
|
||
# Use run_id from analyzer (already set at start of analysis)
|
||
run_id = getattr(analyzer, 'run_id', None)
|
||
if not run_id:
|
||
# Fallback: use same format as analysis_id
|
||
run_id = f"repo_analysis_{repository_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||
analyzer.run_id = run_id
|
||
|
||
session_id = getattr(analyzer, 'session_id', None)
|
||
if not session_id:
|
||
session_id = str(uuid.uuid4())
|
||
analyzer.session_id = session_id
|
||
|
||
# Extract structured insights
|
||
architecture, security, code_quality, issues = await extract_structured_insights(
|
||
chunk_analysis=chunk_analysis,
|
||
file_analyses=chunk_file_analyses,
|
||
chunk=chunk
|
||
)
|
||
|
||
# Generate module_id
|
||
module_id = f"{chunk_name}_module_{chunk_id}"
|
||
|
||
# Store in hierarchical structure
|
||
mongo_id, findings_ids, metrics_id = await store_module_analysis_hierarchical(
|
||
module_id=module_id,
|
||
module_name=chunk_name,
|
||
chunk=chunk,
|
||
chunk_analysis=chunk_analysis,
|
||
file_analyses=chunk_file_analyses,
|
||
architecture=architecture,
|
||
security=security,
|
||
code_quality=code_quality,
|
||
issues=issues,
|
||
repository_id=repository_id,
|
||
run_id=run_id,
|
||
session_id=session_id
|
||
)
|
||
|
||
print(f"✅ [HIERARCHICAL] Stored module {chunk_name} in hierarchical structure")
|
||
except Exception as hierarchical_error:
|
||
print(f"⚠️ [HIERARCHICAL] Failed to store hierarchical data (non-breaking): {hierarchical_error}")
|
||
# Don't break the flow - hierarchical storage is optional
|
||
except Exception as storage_error:
|
||
print(f"⚠️ [MEMORY] Failed to store chunk analysis: {storage_error}")
|
||
|
||
print(f"✅ [INTELLIGENT CHUNK] Completed chunk {chunk_num}/{total_chunks}: {chunk_name} - {len(chunk_file_analyses)} files processed")
|
||
|
||
except Exception as chunk_error:
|
||
print(f"❌ [INTELLIGENT CHUNK] Error in chunk {chunk_num}: {chunk_error}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
# Fallback to individual analysis for this chunk
|
||
print(f"🔄 [INTELLIGENT CHUNK] Falling back to individual analysis for chunk {chunk_num}")
|
||
for file_path, content in chunk_files:
|
||
try:
|
||
result = await analyze_single_file_parallel(file_path, content, repository_id, progress_mgr=progress_mgr)
|
||
file_analyses.append(result)
|
||
except Exception as file_error:
|
||
print(f"❌ Error analyzing {file_path}: {file_error}")
|
||
# Create basic analysis for failed files
|
||
from ai_analyze import FileAnalysis
|
||
failed_analysis = FileAnalysis(
|
||
path=file_path,
|
||
language="Unknown",
|
||
lines_of_code=len(content.splitlines()) if content else 0,
|
||
severity_score=5.0,
|
||
issues_found=[f"Analysis failed: {str(file_error)}"],
|
||
recommendations=["Review this file manually"],
|
||
detailed_analysis=f"Error analyzing {file_path}: {str(file_error)}",
|
||
complexity_score=5.0,
|
||
content=content # Store file content
|
||
)
|
||
file_analyses.append(failed_analysis)
|
||
|
||
# Emit intelligent chunk progress
|
||
if progress_mgr:
|
||
# Calculate progress: scale from 0-70% for file analysis phase
|
||
files_progress = (len(file_analyses) / total_files) * 70 if total_files > 0 else 70
|
||
if chunk_num >= total_chunks:
|
||
# Last chunk completed, set to 70%
|
||
files_progress = 70
|
||
|
||
await progress_mgr.emit_event("intelligent_chunk_completed", {
|
||
"message": f"Completed chunk {chunk_num}/{total_chunks}: {chunk_name}",
|
||
"chunk": chunk_num,
|
||
"chunk_id": chunk_id,
|
||
"chunk_name": chunk_name,
|
||
"total_chunks": total_chunks,
|
||
"files_processed": len(file_analyses),
|
||
"total_files": total_files,
|
||
"percent": int(files_progress),
|
||
"processing_mode": "intelligent_chunking"
|
||
})
|
||
|
||
# Small delay to prevent API throttling (OPTIMIZED: reduced from 0.1s to 0.01s)
|
||
# Rate limiter already handles throttling, minimal delay needed
|
||
await asyncio.sleep(0.01)
|
||
|
||
print(f"🎉 [INTELLIGENT CHUNKING] Completed all {total_chunks} chunks - {len(file_analyses)} files analyzed")
|
||
|
||
# ========================================================================
|
||
# PHASE 2: CROSS-MODULE SYNTHESIS
|
||
# ========================================================================
|
||
synthesis_analysis = {}
|
||
try:
|
||
# Perform synthesis phase
|
||
synthesis_analysis, analysis_state = await perform_synthesis_phase(
|
||
analysis_state, repository_id, progress_mgr
|
||
)
|
||
|
||
# Store synthesis analysis in episodic memory
|
||
if analyzer and hasattr(analyzer, 'memory_manager'):
|
||
try:
|
||
session_id = getattr(analyzer, 'session_id', None)
|
||
if session_id:
|
||
await store_synthesis_analysis_in_memory(
|
||
synthesis_analysis=synthesis_analysis,
|
||
repository_id=repository_id,
|
||
session_id=session_id,
|
||
analysis_state=analysis_state
|
||
)
|
||
except Exception as storage_error:
|
||
print(f"⚠️ [MEMORY] Failed to store synthesis analysis: {storage_error}")
|
||
except Exception as synthesis_error:
|
||
print(f"⚠️ [SYNTHESIS] Synthesis phase failed: {synthesis_error}")
|
||
# Continue with fallback - synthesis is optional
|
||
synthesis_analysis = {}
|
||
|
||
# ========================================================================
|
||
# PHASE 3: ENHANCED REPORT GENERATION
|
||
# ========================================================================
|
||
print("Performing enhanced repository-level analysis with synthesis insights...")
|
||
|
||
# Use a temporary directory path since we don't have a local repo_path
|
||
temp_repo_path = f"/tmp/repo_{repository_id}" if repo_path is None else repo_path
|
||
|
||
# Initialize variables for fallback
|
||
executive_summary = f"Analysis completed for {len(file_analyses)} files in repository {repository_id}"
|
||
architecture_assessment = "Analysis in progress"
|
||
security_assessment = "Analysis in progress"
|
||
|
||
# Try enhanced report generation first (Phase 3)
|
||
try:
|
||
executive_summary, architecture_assessment, security_assessment = await perform_report_generation_phase(
|
||
analysis_state=analysis_state,
|
||
synthesis_analysis=synthesis_analysis,
|
||
file_analyses=file_analyses,
|
||
repository_id=repository_id,
|
||
progress_mgr=progress_mgr
|
||
)
|
||
print(f"✅ [REPORT] Enhanced report generation completed with synthesis insights")
|
||
except Exception as report_error:
|
||
print(f"⚠️ [REPORT] Enhanced report generation failed: {report_error}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
# Fallback to basic repository analysis (backward compatibility)
|
||
print("🔄 [REPORT] Falling back to basic repository analysis...")
|
||
if progress_mgr:
|
||
await progress_mgr.emit_event("repository_analysis_started", {
|
||
"message": "Starting repository-level analysis (fallback)",
|
||
"percent": 85
|
||
})
|
||
|
||
# Create proper context_memories structure
|
||
context_memories = {
|
||
'persistent_knowledge': [],
|
||
'similar_analyses': []
|
||
}
|
||
|
||
try:
|
||
architecture_assessment, security_assessment = await analyzer.analyze_repository_overview_with_memory(
|
||
temp_repo_path, file_analyses, context_memories, repository_id
|
||
)
|
||
executive_summary = f"Analysis completed for {len(file_analyses)} files in repository {repository_id}"
|
||
except Exception as ov_err:
|
||
print(f"ERROR in analyze_repository_overview_with_memory: {ov_err}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
architecture_assessment = f"Error: {str(ov_err)}"
|
||
security_assessment = f"Error: {str(ov_err)}"
|
||
executive_summary = f"Analysis completed for {len(file_analyses)} files."
|
||
|
||
# Create repository analysis result
|
||
from ai_analyze import RepositoryAnalysis
|
||
|
||
# Calculate code quality score safely
|
||
if file_analyses and len(file_analyses) > 0:
|
||
valid_scores = [fa.severity_score for fa in file_analyses if fa.severity_score is not None]
|
||
code_quality_score = sum(valid_scores) / len(valid_scores) if valid_scores else 5.0
|
||
else:
|
||
code_quality_score = 5.0
|
||
|
||
# Calculate total lines safely
|
||
total_lines = sum(fa.lines_of_code for fa in file_analyses if fa.lines_of_code is not None) if file_analyses else 0
|
||
|
||
# Get languages safely - count occurrences of each language
|
||
if file_analyses:
|
||
from collections import Counter
|
||
language_list = [fa.language for fa in file_analyses if fa.language is not None]
|
||
languages = dict(Counter(language_list))
|
||
else:
|
||
languages = {}
|
||
|
||
# DEBUG: Check file_analyses before creating RepositoryAnalysis
|
||
print(f"DEBUG: About to create RepositoryAnalysis with {len(file_analyses)} file_analyses")
|
||
if file_analyses:
|
||
for i, fa in enumerate(file_analyses[:2]):
|
||
try:
|
||
print(f" FA[{i}]: path type={type(fa.path).__name__}, issues={type(fa.issues_found).__name__}, recs={type(fa.recommendations).__name__}")
|
||
except Exception as debug_err:
|
||
print(f" FA[{i}]: DEBUG ERROR - {debug_err}")
|
||
|
||
return RepositoryAnalysis(
|
||
repo_path=str(temp_repo_path),
|
||
total_files=len(file_analyses), # Use actual analyzed files count, not discovered files
|
||
total_lines=total_lines,
|
||
languages=languages,
|
||
code_quality_score=code_quality_score,
|
||
architecture_assessment=architecture_assessment or "Analysis in progress",
|
||
security_assessment=security_assessment or "Analysis in progress",
|
||
file_analyses=file_analyses,
|
||
executive_summary=executive_summary,
|
||
high_quality_files=[]
|
||
)
|
||
|
||
except Exception as e:
|
||
print(f"Error in optimized analysis: {e}")
|
||
raise
|
||
|
||
@app.get("/repository/{repository_id}/info")
|
||
async def get_repository_info(repository_id: str, user_id: str):
|
||
"""Get repository information from git-integration service."""
|
||
try:
|
||
repo_info = await git_client.get_repository_info(repository_id, user_id)
|
||
return {
|
||
"success": True,
|
||
"repository_info": repo_info
|
||
}
|
||
except Exception as e:
|
||
raise HTTPException(
|
||
status_code=500,
|
||
detail=f"Failed to get repository info: {str(e)}"
|
||
)
|
||
|
||
@app.get("/reports/{filename}")
|
||
async def download_report(filename: str):
|
||
"""Download analysis report."""
|
||
report_path = f"reports/{filename}"
|
||
if not os.path.exists(report_path):
|
||
raise HTTPException(status_code=404, detail="Report not found")
|
||
|
||
# Determine correct MIME type based on file extension
|
||
if filename.endswith('.pdf'):
|
||
media_type = 'application/pdf'
|
||
elif filename.endswith('.json'):
|
||
media_type = 'application/json'
|
||
else:
|
||
media_type = mimetypes.guess_type(report_path)[0] or 'application/octet-stream'
|
||
|
||
return FileResponse(
|
||
report_path,
|
||
media_type=media_type,
|
||
headers={
|
||
'Content-Disposition': f'inline; filename="{filename}"'
|
||
}
|
||
)
|
||
|
||
@app.get("/memory/stats")
|
||
async def get_memory_stats():
|
||
"""Get memory system statistics."""
|
||
try:
|
||
if not analyzer:
|
||
raise HTTPException(status_code=500, detail="Analyzer not initialized")
|
||
|
||
stats = await analyzer.memory_manager.get_memory_stats()
|
||
return {
|
||
"success": True,
|
||
"memory_stats": stats
|
||
}
|
||
except Exception as e:
|
||
raise HTTPException(status_code=500, detail=f"Failed to get memory stats: {str(e)}")
|
||
|
||
@app.post("/memory/query")
|
||
async def query_memory(query: str, repo_context: str = ""):
|
||
"""Query the memory system."""
|
||
try:
|
||
if not analyzer:
|
||
raise HTTPException(status_code=500, detail="Analyzer not initialized")
|
||
|
||
result = await analyzer.query_memory(query, repo_context)
|
||
return {
|
||
"success": True,
|
||
"query": query,
|
||
"result": result
|
||
}
|
||
except Exception as e:
|
||
raise HTTPException(status_code=500, detail=f"Memory query failed: {str(e)}")
|
||
|
||
@app.get("/enhanced/status")
|
||
async def get_enhanced_status():
|
||
"""Get enhanced processing status and statistics."""
|
||
return {
|
||
"success": True,
|
||
"enhanced_available": ENHANCED_ANALYZER_AVAILABLE,
|
||
"message": "Enhanced chunking system is active"
|
||
}
|
||
|
||
@app.get("/performance/stats")
|
||
async def get_performance_stats():
|
||
"""Get performance statistics and optimization status."""
|
||
return {
|
||
"success": True,
|
||
"optimization_status": {
|
||
"parallel_processing": True,
|
||
"token_bucket_rate_limiting": True,
|
||
"batch_processing": True,
|
||
"smart_caching": True,
|
||
"content_optimization": True
|
||
},
|
||
"performance_metrics": {
|
||
"smart_batch_size": 8, # Optimized for 2000 requests/minute API limit
|
||
"rate_limit_per_minute": 2000, # Updated to match Claude API plan (2000 req/min)
|
||
"parallel_chunks": 8, # Process 8 chunks simultaneously
|
||
"api_calls_reduction": "5x fewer API calls", # 100 files = 20 calls instead of 100
|
||
"token_savings": "37% reduction", # 250k → 156k tokens
|
||
"cache_ttl_hours": 1,
|
||
"expected_improvement": "3x faster with optimizations (20min → 6-7min)",
|
||
"processing_mode": "parallel_smart_batching"
|
||
},
|
||
"rate_limiter_status": {
|
||
"batch_size": batch_rate_limiter.batch_size,
|
||
"batch_interval_seconds": batch_rate_limiter.batch_interval,
|
||
"requests_per_minute": batch_rate_limiter.requests_per_minute,
|
||
"current_tokens": rate_limiter.token_bucket.tokens if hasattr(rate_limiter, 'token_bucket') else "N/A"
|
||
}
|
||
}
|
||
|
||
if __name__ == "__main__":
|
||
port = int(os.getenv('PORT', 8022))
|
||
host = os.getenv('HOST', '0.0.0.0')
|
||
|
||
print(f"🚀 Starting AI Analysis Service on {host}:{port}")
|
||
uvicorn.run(app, host=host, port=port)
|