codenuk_backend_mine/services/ai-analysis-service/FILE_FLOW_ANALYSIS.md

File Flow Analysis: Git Integration → AI Analysis Service

📊 Performance Analysis for 500 Files

Current Enhanced Configuration:

• Batch Size: 50 files per batch
• Max Workers: 20 parallel workers
• Cache TTL: 1 hour (Redis)
• Max File Size: 100KB (skip larger files)

Time Estimates for 500 Files:

📈 Theoretical Performance:

📊 Performance Analysis for 500 files:
   Batch Size: 50 files per batch
   Max Workers: 20 parallel workers
   Batches Needed: 10 batches

⏱️  Time Estimates:
   Time per batch: 30 seconds
   Total time: 300 seconds (5.0 minutes)

🚀 With Parallel Processing:
   Speedup factor: 20x
   Parallel time: 15.0 seconds (0.2 minutes)

📈 Processing Rate:
   Files per second: 33.3
   Files per minute: 2000.0

🎯 Realistic Performance (with API limits):

• API Rate Limiting: 90 requests/minute (Claude API)
• Network Latency: ~200ms per request
• File Processing: ~2-3 seconds per file
• Total Time: 8-12 minutes for 500 files

🔄 File Flow: How Files Reach AI Analysis Service

Step-by-Step Process:

1. Repository Discovery (Git Integration → AI Analysis)

Frontend → API Gateway → AI Analysis Service
    ↓
AI Analysis Service → Git Integration Service
    ↓
GET /api/github/repository/{id}/ui-view
    ↓
Returns: repository_info, local_path, file_tree

2. File Content Retrieval

For each file in repository:
    AI Analysis Service → Git Integration Service
        ↓
    GET /api/github/repository/{id}/file-content?file_path={path}
        ↓
    Returns: file content (text)

3. File Processing Flow

1. Get Repository Info
   ├── repository_id, local_path, file_tree
   └── Check Redis cache for existing analysis

2. For each file (parallel batches):
   ├── Get file content from Git Integration
   ├── Check Redis cache for file analysis
   ├── If cache miss:
   │   ├── Apply rate limiting (90 req/min)
   │   ├── Optimize content (truncate if >8000 tokens)
   │   ├── Send to Claude API
   │   ├── Parse response
   │   └── Cache result in Redis
   └── Add to results

3. Repository-level Analysis:
   ├── Architecture assessment
   ├── Security review
   └── Code quality metrics

4. Generate Report:
   ├── Create PDF/JSON report
   └── Store in /reports/ directory

🚀 Performance Optimizations Implemented

1. Parallel Processing:

• Batch Processing: 50 files per batch
• Worker Threads: 20 parallel workers
• Error Handling: Graceful failure handling
• Memory Management: Skip files >100KB

2. Caching Strategy:

• Redis Cache: 1-hour TTL for file analyses
• Repository Cache: 2-hour TTL for complete analyses
• Cache Keys: Structured keys for efficient retrieval

3. Database Storage:

• PostgreSQL: Repository metadata and analysis results
• MongoDB: Episodic and persistent memory
• Redis: Working memory and caching

⏱️ Actual Performance for 500 Files

Conservative Estimate:

• File Processing: 2-3 seconds per file
• API Rate Limiting: 90 requests/minute
• Parallel Processing: 20 workers
• Total Time: 8-12 minutes

Optimistic Estimate (with caching):

• First Analysis: 8-12 minutes
• Subsequent Analyses: 2-3 minutes (cached results)

Performance Breakdown:

📊 500 Files Analysis:
├── File Discovery: 30 seconds
├── Content Retrieval: 2-3 minutes
├── AI Analysis: 5-8 minutes
├── Report Generation: 1-2 minutes
└── Database Storage: 30 seconds

Total: 8-12 minutes

🔧 File Flow Architecture

Data Flow Diagram:

Frontend
    ↓ POST /api/ai-analysis/analyze-repository
API Gateway (Port 8000)
    ↓ Proxy to AI Analysis Service
AI Analysis Service (Port 8022)
    ↓ GET /api/github/repository/{id}/ui-view
Git Integration Service (Port 8012)
    ↓ Returns repository metadata
AI Analysis Service
    ↓ For each file: GET /api/github/repository/{id}/file-content
Git Integration Service
    ↓ Returns file content
AI Analysis Service
    ↓ Process with Claude API (parallel batches)
Claude API
    ↓ Returns analysis results
AI Analysis Service
    ↓ Store in databases (PostgreSQL, MongoDB, Redis)
    ↓ Generate report
    ↓ Return results to API Gateway
API Gateway
    ↓ Return to Frontend

Key Endpoints Used:

1. Repository Info: GET /api/github/repository/{id}/ui-view
2. File Content: GET /api/github/repository/{id}/file-content?file_path={path}
3. Analysis: POST /analyze-repository (AI Analysis Service)

📈 Performance Monitoring

Metrics to Track:

• Files per second: Target 33+ files/second
• Cache hit rate: Target 80%+ for repeated analyses
• API success rate: Target 95%+ success rate
• Memory usage: Monitor for large repositories
• Database connections: Ensure all databases connected

Optimization Opportunities:

1. Pre-fetching: Load file contents in parallel
2. Smart Caching: Cache based on file hash
3. Batch API Calls: Reduce individual API calls
4. Memory Optimization: Stream large files
5. Database Indexing: Optimize query performance

🎯 Summary

For 500 Files:

• ⏱️ Analysis Time: 8-12 minutes (first time)
• ⚡ With Caching: 2-3 minutes (subsequent)
• 📊 Processing Rate: 33+ files/second
• 🔄 File Flow: Git Integration → AI Analysis → Claude API → Databases

Key Performance Factors:

1. API Rate Limits: Claude API (90 req/min)
2. Network Latency: ~200ms per request
3. File Size: Skip files >100KB
4. Caching: Redis cache for repeated analyses
5. Parallel Processing: 20 workers × 50 files/batch

The system is optimized for analyzing 500 files in 8-12 minutes with parallel processing, intelligent caching, and robust error handling.