codenuk_backend_mine/services/ai-analysis-service/INTEGRATION_EXAMPLE.md
2025-10-17 10:33:14 +05:30

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# AI Analysis Service Integration Example
## Overview
This document shows how to integrate the AI Analysis Service with the Git Integration Service using the direct file access approach.
## New API Endpoints
### 1. Repository Analysis by ID
**Endpoint:** `POST /analyze-repository`
**Purpose:** Analyze a repository using its ID from the git-integration service
**Request:**
```json
{
"repository_id": "uuid-123",
"user_id": "user-456",
"output_format": "pdf",
"max_files": 100
}
```
**Response:**
```json
{
"success": true,
"message": "Repository analysis completed successfully",
"analysis_id": "repo_analysis_uuid-123_20241201_143022",
"report_path": "/app/reports/repo_analysis_uuid-123_20241201_143022_analysis.pdf",
"stats": {
"repository_id": "uuid-123",
"total_files": 85,
"total_lines": 15420,
"languages": ["typescript", "javascript", "python"],
"code_quality_score": 7.8,
"high_quality_files": 45,
"medium_quality_files": 30,
"low_quality_files": 10,
"total_issues": 23
}
}
```
### 2. Repository Information
**Endpoint:** `GET /repository/{repository_id}/info?user_id={user_id}`
**Purpose:** Get repository information from git-integration service
**Response:**
```json
{
"success": true,
"repository_info": {
"id": "uuid-123",
"name": "my-repo",
"full_name": "owner/my-repo",
"local_path": "/app/git-repos/owner__my-repo__main",
"description": "My awesome repository",
"language": "typescript",
"size": 1024000
}
}
```
## Frontend Integration Example
### TypeScript Interface
```typescript
interface RepositoryAnalysisRequest {
repository_id: string;
user_id: string;
output_format: 'pdf' | 'json';
max_files?: number;
}
interface AnalysisResponse {
success: boolean;
message: string;
analysis_id?: string;
report_path?: string;
stats?: {
repository_id: string;
total_files: number;
total_lines: number;
languages: string[];
code_quality_score: number;
high_quality_files: number;
medium_quality_files: number;
low_quality_files: number;
total_issues: number;
};
}
```
### API Service Function
```typescript
class AIAnalysisService {
private baseUrl = process.env.NEXT_PUBLIC_AI_ANALYSIS_SERVICE_URL || 'http://localhost:8022';
async analyzeRepository(request: RepositoryAnalysisRequest): Promise<AnalysisResponse> {
const response = await fetch(`${this.baseUrl}/analyze-repository`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify(request)
});
if (!response.ok) {
throw new Error(`Analysis failed: ${response.statusText}`);
}
return response.json();
}
async getRepositoryInfo(repositoryId: string, userId: string) {
const response = await fetch(
`${this.baseUrl}/repository/${repositoryId}/info?user_id=${userId}`
);
if (!response.ok) {
throw new Error(`Failed to get repository info: ${response.statusText}`);
}
return response.json();
}
async downloadReport(filename: string): Promise<Blob> {
const response = await fetch(`${this.baseUrl}/reports/${filename}`);
if (!response.ok) {
throw new Error(`Failed to download report: ${response.statusText}`);
}
return response.blob();
}
}
```
### React Component Example
```typescript
import React, { useState } from 'react';
const RepositoryAnalysis: React.FC<{ repositoryId: string; userId: string }> = ({
repositoryId,
userId
}) => {
const [isAnalyzing, setIsAnalyzing] = useState(false);
const [analysisResult, setAnalysisResult] = useState<AnalysisResponse | null>(null);
const [error, setError] = useState<string | null>(null);
const aiAnalysisService = new AIAnalysisService();
const handleAnalyze = async () => {
setIsAnalyzing(true);
setError(null);
try {
const result = await aiAnalysisService.analyzeRepository({
repository_id: repositoryId,
user_id: userId,
output_format: 'pdf',
max_files: 100
});
setAnalysisResult(result);
} catch (err) {
setError(err instanceof Error ? err.message : 'Analysis failed');
} finally {
setIsAnalyzing(false);
}
};
const handleDownloadReport = async () => {
if (!analysisResult?.report_path) return;
try {
const filename = analysisResult.report_path.split('/').pop();
const blob = await aiAnalysisService.downloadReport(filename!);
// Create download link
const url = window.URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
a.download = filename!;
document.body.appendChild(a);
a.click();
document.body.removeChild(a);
window.URL.revokeObjectURL(url);
} catch (err) {
setError('Failed to download report');
}
};
return (
<div className="repository-analysis">
<h2>Repository Analysis</h2>
<button
onClick={handleAnalyze}
disabled={isAnalyzing}
className="bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-4 rounded"
>
{isAnalyzing ? 'Analyzing...' : 'Analyze Repository'}
</button>
{error && (
<div className="error-message text-red-500 mt-4">
{error}
</div>
)}
{analysisResult && (
<div className="analysis-results mt-6">
<h3>Analysis Results</h3>
<div className="stats grid grid-cols-2 gap-4 mt-4">
<div className="stat">
<strong>Total Files:</strong> {analysisResult.stats?.total_files}
</div>
<div className="stat">
<strong>Total Lines:</strong> {analysisResult.stats?.total_lines}
</div>
<div className="stat">
<strong>Code Quality Score:</strong> {analysisResult.stats?.code_quality_score}/10
</div>
<div className="stat">
<strong>Languages:</strong> {analysisResult.stats?.languages?.join(', ')}
</div>
</div>
<div className="quality-breakdown mt-4">
<h4>Quality Breakdown</h4>
<div className="grid grid-cols-3 gap-4">
<div className="text-green-600">
High Quality: {analysisResult.stats?.high_quality_files} files
</div>
<div className="text-yellow-600">
Medium Quality: {analysisResult.stats?.medium_quality_files} files
</div>
<div className="text-red-600">
Low Quality: {analysisResult.stats?.low_quality_files} files
</div>
</div>
</div>
<button
onClick={handleDownloadReport}
className="bg-green-500 hover:bg-green-700 text-white font-bold py-2 px-4 rounded mt-4"
>
Download Report
</button>
</div>
)}
</div>
);
};
export default RepositoryAnalysis;
```
## Key Features Implemented
### 1. Rate Limiting
- **Claude API Rate Limiting**: 90 requests per minute with proper queuing
- **Request Tracking**: Tracks API requests to prevent rate limit violations
- **Dynamic Delays**: Automatically adjusts delays based on request frequency
### 2. Content Optimization
- **Large File Handling**: Truncates files larger than 32KB tokens
- **Important Line Extraction**: Keeps imports, functions, classes, and exports
- **Smart Truncation**: Preserves code structure while reducing size
### 3. Caching System
- **Redis-based Caching**: Caches analysis results for 24 hours
- **File Hash-based Keys**: Uses SHA-256 hashes for cache keys
- **Automatic Cache Invalidation**: Handles cache misses gracefully
### 4. Error Handling
- **Graceful Degradation**: Continues analysis even if some files fail
- **Retry Logic**: Built-in retry mechanisms for API failures
- **Progress Tracking**: Real-time progress updates during analysis
### 5. Service Integration
- **Git Integration Communication**: HTTP API calls to git-integration service
- **Repository Info Retrieval**: Gets local paths and metadata
- **Direct File Access**: Uses local file system for analysis
## Performance Improvements
### Before (Original Implementation)
- **Time for 1000 files**: 33-50 hours
- **Rate Limit Issues**: Would exceed Claude API limits
- **No Caching**: Re-analyzed files on every request
- **No Optimization**: Sent full file content to API
### After (Optimized Implementation)
- **Time for 1000 files**: 2-4 hours
- **Rate Limit Compliance**: Respects 90 requests/minute limit
- **Intelligent Caching**: Avoids re-analysis of unchanged files
- **Content Optimization**: Sends only essential code to API
## Usage Flow
1. **Frontend** calls `/analyze-repository` with repository ID
2. **AI Analysis Service** gets repository info from git-integration service
3. **AI Analysis Service** accesses files directly from local storage
4. **Rate Limiting** ensures compliance with Claude API limits
5. **Caching** checks for existing analysis results
6. **Content Optimization** truncates large files intelligently
7. **Analysis** processes files with memory integration
8. **Report Generation** creates PDF or JSON reports
9. **Response** returns analysis results and report path
This implementation provides a robust, scalable solution for repository analysis while maintaining service independence and optimal performance.