From ad2c27d7933f9062a78f2ec191c3ebb0b7a7b3bd Mon Sep 17 00:00:00 2001 From: Pradeep Date: Thu, 13 Nov 2025 09:07:54 +0530 Subject: [PATCH] implemented KG DB --- ...-c95b2815aac1_20251110_032129_analysis.pdf | 1352 +++++++++++++++++ ...-c95b2815aac1_20251110_055924_analysis.pdf | 1251 +++++++++++++++ docker-compose.yml | 55 +- services/ai-analysis-service/__init__.py | 8 + services/ai-analysis-service/ai-analyze.py | 1035 ++++++------- services/ai-analysis-service/env.example | 12 +- .../knowledge_graph/__init__.py | 9 + .../knowledge_graph/neo4j_client.py | 328 ++++ .../knowledge_graph/operations.py | 214 +++ .../ai-analysis-service/progress_manager.py | 10 + services/ai-analysis-service/pyproject.toml | 32 + services/ai-analysis-service/requirements.txt | 1 + services/ai-analysis-service/server.py | 711 ++++----- .../ai-analysis-service/test_data_storage.py | 183 --- .../test_db_connections.py | 106 -- .../test_frontend_compatibility.py | 271 ---- .../test_intelligent_chunking.py | 318 ---- .../test_knowledge_graph_operations.py | 103 ++ .../test_multi_level_report.py | 244 --- .../test_progressive_context.py | 309 ---- 20 files changed, 4080 insertions(+), 2472 deletions(-) create mode 100644 ai-analysis-reports/repo_analysis_e395ea2c-ea3b-43e0-94af-c95b2815aac1_20251110_032129_analysis.pdf create mode 100644 ai-analysis-reports/repo_analysis_e395ea2c-ea3b-43e0-94af-c95b2815aac1_20251110_055924_analysis.pdf create mode 100644 services/ai-analysis-service/__init__.py create mode 100644 services/ai-analysis-service/knowledge_graph/__init__.py create mode 100644 services/ai-analysis-service/knowledge_graph/neo4j_client.py create mode 100644 services/ai-analysis-service/knowledge_graph/operations.py create mode 100644 services/ai-analysis-service/pyproject.toml delete mode 100644 services/ai-analysis-service/test_data_storage.py delete mode 100644 services/ai-analysis-service/test_db_connections.py delete mode 100755 services/ai-analysis-service/test_frontend_compatibility.py delete mode 100644 services/ai-analysis-service/test_intelligent_chunking.py create mode 100644 services/ai-analysis-service/test_knowledge_graph_operations.py delete mode 100755 services/ai-analysis-service/test_multi_level_report.py delete mode 100644 services/ai-analysis-service/test_progressive_context.py diff --git a/ai-analysis-reports/repo_analysis_e395ea2c-ea3b-43e0-94af-c95b2815aac1_20251110_032129_analysis.pdf b/ai-analysis-reports/repo_analysis_e395ea2c-ea3b-43e0-94af-c95b2815aac1_20251110_032129_analysis.pdf new file mode 100644 index 0000000..880c9f2 --- /dev/null +++ b/ai-analysis-reports/repo_analysis_e395ea2c-ea3b-43e0-94af-c95b2815aac1_20251110_032129_analysis.pdf @@ -0,0 +1,1352 @@ +%PDF-1.4 +%“Œ‹ž ReportLab Generated PDF document http://www.reportlab.com +1 0 obj +<< +/F1 2 0 R /F2 3 0 R /F3 5 0 R /F4 7 0 R /F5 17 0 R +>> +endobj +2 0 obj +<< +/BaseFont /Helvetica /Encoding /WinAnsiEncoding /Name /F1 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build: ./services/ai-analysis-service - container_name: pipeline_ai_analysis_service + build: + context: ./services/ai-analysis-service + dockerfile: Dockerfile + container_name: pipeline_ai_analysis ports: - "8022:8022" environment: - PORT=8022 - HOST=0.0.0.0 - ANTHROPIC_API_KEY=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 - - POSTGRES_HOST=postgres + + # Neo4j Configuration + - USE_NEO4J_KG=true + - NEO4J_URI=bolt://neo4j:7687 + - NEO4J_USER=neo4j + - NEO4J_PASSWORD=password + - NEO4J_DATABASE=neo4j + + # Report Configuration + - REPORT_TECHNICAL_ONLY=false + + # Existing database configurations + - POSTGRES_HOST=pipeline_postgres - POSTGRES_PORT=5432 - POSTGRES_DB=dev_pipeline - POSTGRES_USER=pipeline_admin - POSTGRES_PASSWORD=secure_pipeline_2024 - - REDIS_HOST=redis + + - MONGODB_URL=mongodb://pipeline_admin:mongo_secure_2024@pipeline_mongodb:27017/ + - MONGODB_DB=repo_analyzer + + - REDIS_HOST=pipeline_redis - REDIS_PORT=6379 - REDIS_PASSWORD=redis_secure_2024 - - MONGODB_URL=mongodb://pipeline_admin:mongo_secure_2024@mongodb:27017/ - - MONGODB_DB=repo_analyzer - - GIT_INTEGRATION_SERVICE_URL=http://git-integration:8012 - - CLAUDE_REQUESTS_PER_MINUTE=90 - - MAX_FILES_DEFAULT=100 - - CACHE_TTL_SECONDS=86400 - - CONTENT_MAX_TOKENS=8000 - - ENHANCED_PROCESSING_ENABLED=true - - ENHANCED_BATCH_PROCESSING=true - - ENHANCED_SMART_CHUNKING=true - - ENHANCED_RATE_LIMIT=120 - - ENHANCED_BATCH_DELAY=0.05 - - ENHANCED_SMALL_FILE_DELAY=0.02 - - ENHANCED_MEDIUM_FILE_DELAY=0.05 - - ENHANCED_LARGE_FILE_DELAY=0.1 - volumes: - - ai_analysis_logs:/app/logs - - ./ai-analysis-reports:/app/reports - - ai_analysis_temp:/app/temp + depends_on: + - neo4j + - postgres + - mongodb + - redis networks: - pipeline_network - depends_on: - - postgres - - redis - - mongodb - - git-integration deploy: resources: limits: diff --git a/services/ai-analysis-service/__init__.py b/services/ai-analysis-service/__init__.py new file mode 100644 index 0000000..2e29b52 --- /dev/null +++ b/services/ai-analysis-service/__init__.py @@ -0,0 +1,8 @@ +# Service initialization +# This file helps Python treat the directory as a package +# and can be used to set up any service-wide configurations + +from .server import app # Import the FastAPI app +from .ai_analyze import analyze_repository # Import key functions + +__all__ = ['app', 'analyze_repository'] diff --git a/services/ai-analysis-service/ai-analyze.py b/services/ai-analysis-service/ai-analyze.py index f141107..e178f9c 100644 --- a/services/ai-analysis-service/ai-analyze.py +++ b/services/ai-analysis-service/ai-analyze.py @@ -19,13 +19,14 @@ Example: import os import asyncio +import copy import hashlib import json import uuid from pathlib import Path from typing import Dict, List, Optional, Tuple, Any from datetime import datetime, timedelta -from dataclasses import dataclass, asdict, field +from dataclasses import dataclass, asdict, field, is_dataclass from collections import defaultdict, Counter import logging import tempfile @@ -2358,91 +2359,38 @@ Focus on business outcomes, financial impact, and competitive implications. Use ts_files = [fa for fa in direct_frontend_check if fa.path.lower().endswith(('.ts', '.tsx'))] ai_analysis_text = f""" -**1. FRONTEND OVERVIEW - WHAT IS THE FRONTEND?** +**1. FRONTEND OVERVIEW** +This repository contains {len(direct_frontend_check)} frontend-focused files totaling {actual_total_lines:,} lines of code that render the browser experience. -The frontend is the part of the application that users see and interact with in their web browser. Think of it like the visible part of an iceberg - what users see on their screen. +**2. FILE TYPE SUMMARY** +- HTML Files ({len(html_files)}): define document structure, semantics, and mount points for client-side scripts. +- CSS Files ({len(css_files)}): enforce layout rules, typography, spacing, and responsive behaviour. +- JavaScript Files ({len(js_files)}): implement interaction logic, event handling, API connectivity, and state coordination. +- TypeScript Files ({len(ts_files)}): offer typed JavaScript sources that compile into the runtime JavaScript bundle. -This repository contains {len(direct_frontend_check)} frontend files with a total of {actual_total_lines:,} lines of code that create the user interface. +**3. RUNTIME EXECUTION FLOW** +1. Browsers parse HTML and construct the DOM tree. +2. Linked CSS stylesheets apply presentation and responsive breakpoints. +3. JavaScript or TypeScript modules initialise, attach event listeners, and hydrate components. +4. State transitions trigger DOM updates through framework abstractions or direct DOM manipulation. -**2. FRONTEND FILE TYPES - WHAT EACH TYPE DOES** +**4. USER INTERACTION PIPELINE** +- Button handlers execute JavaScript callbacks that drive navigation, trigger side effects, or fetch data. +- Form flows validate input, submit HTTP requests, handle responses, and update the interface accordingly. -**HTML Files ({len(html_files)} files):** -- HTML files are like the skeleton or framework of a building -- They define WHAT appears on the page (headings, buttons, forms, text, images) -- Think of HTML as the structure - like the walls and rooms of a house -- These files create the basic layout and content structure +**5. DATA FLOW** +1. User actions invoke JavaScript functions that compose HTTP requests. +2. Backend endpoints respond with JSON or HTML payloads. +3. The frontend updates local state and re-renders affected DOM nodes. +4. CSS keeps presentation consistent after dynamic content updates. -**CSS Files ({len(css_files)} files):** -- CSS files are like the paint, decoration, and interior design -- They control HOW things look (colors, sizes, spacing, fonts, layouts) -- Think of CSS as the styling - making the house look beautiful -- These files make the page visually appealing and organized +**6. STRUCTURE AND ORGANISATION** +- Markup, styling, and behavioural concerns are separated across HTML, CSS, and script assets for maintainability. +- JavaScript modules coordinate data loading, state transitions, and rendering routines. +- Styling sources encapsulate visual design without coupling to business logic. -**JavaScript Files ({len(js_files)} files):** -- JavaScript files are like the electrical system and appliances -- They add INTERACTIVITY and FUNCTIONALITY (clicking buttons, submitting forms, loading data) -- Think of JavaScript as the "smarts" - making things work when you click them -- These files make the page dynamic and responsive to user actions - -**TypeScript Files ({len(ts_files)} files):** -- TypeScript files are enhanced JavaScript files with better error checking -- They work the same as JavaScript but with additional safety features -- Think of TypeScript as JavaScript with better quality control - -**3. HOW THE FRONTEND WORKS - STEP-BY-STEP EXPLANATION** - -**Step 1: Loading the Page** -When a user opens the website, the browser reads the HTML file first. This tells the browser what elements to display (like a blueprint tells builders what to build). - -**Step 2: Styling the Page** -Next, the browser reads the CSS files. These tell the browser how to style each element - what colors to use, how big things should be, where to place them (like interior designers telling builders how to decorate). - -**Step 3: Making It Interactive** -Finally, the browser runs the JavaScript/TypeScript files. These add the "brain" - making buttons clickable, forms submittable, and data loadable (like installing electrical systems and appliances). - -**4. USER INTERACTION FLOW** - -**When a User Clicks a Button:** -1. The HTML defines where the button is -2. The CSS makes it look like a button (colored, styled) -3. The JavaScript detects the click -4. The JavaScript performs the action (like sending data to the server) -5. The page updates to show the result - -**When a User Fills a Form:** -1. The HTML creates the form structure (input fields, labels) -2. The CSS styles the form (makes it look nice) -3. The JavaScript validates the input (checks if it's correct) -4. The JavaScript sends the data to the server -5. The page shows a success or error message - -**5. DATA FLOW - HOW INFORMATION MOVES** - -**Getting Data from Server:** -1. User clicks a button or loads a page -2. JavaScript sends a request to the server (like ordering food) -3. Server processes the request and sends back data (like the kitchen preparing food) -4. JavaScript receives the data (like receiving the food) -5. JavaScript updates the HTML to show the data (like displaying it on the plate) -6. CSS styles the data display (like arranging the food nicely) - -**6. STRUCTURE AND ORGANIZATION** - -The frontend files are organized in a way that makes them easy to maintain: -- HTML files define the structure -- CSS files control the appearance -- JavaScript files add the functionality - -They all work together like parts of a machine - each part has a specific job, but they all need to work together for the machine to function properly. - -**7. FRONTEND ARCHITECTURE SUMMARY** - -This frontend uses a traditional web architecture: -- HTML provides the foundation (structure) -- CSS provides the styling (appearance) -- JavaScript provides the behavior (functionality) - -Together, these files create a complete, interactive web application that users can see, use, and interact with in their web browsers. +**7. SUMMARY** +This fallback section originates from static inspection because AI synthesis was unavailable. Re-run the analysis with the AI service enabled for a richer narrative tied to code evidence. """ if ai_analysis_text: @@ -3870,6 +3818,46 @@ modelBuilder.Entity() borderColor=colors.HexColor('#d1d5db'), borderPadding=6 ) + + def _is_truthy(value: Optional[str], default: bool = False) -> bool: + if value is None: + return default + return str(value).strip().lower() in ("1", "true", "yes", "on") + + technical_only = _is_truthy(os.getenv("REPORT_TECHNICAL_ONLY"), default=True) + self._report_technical_only = technical_only + include_nontechnical = not technical_only + + def append_markdown_block(markdown_text: str, paragraph_style: ParagraphStyle) -> bool: + if not markdown_text or not markdown_text.strip(): + return False + elements = self._convert_markdown_to_pdf_elements( + markdown_text, + styles, + section_style, + subsection_style, + code_style, + paragraph_style + ) + story.extend(elements) + return True + + def render_markdown_pair( + nontech_md: str, + tech_md: str, + spacer_after_nontech: float = 10, + add_page_break: bool = False + ) -> None: + section_has_content = False + if include_nontechnical: + if append_markdown_block(nontech_md, nontech_style): + section_has_content = True + if spacer_after_nontech: + story.append(Spacer(1, spacer_after_nontech)) + if append_markdown_block(tech_md, tech_style): + section_has_content = True + if add_page_break and section_has_content: + story.append(PageBreak()) # Extract context data module_analyses = comprehensive_context.get('module_analyses', []) @@ -3919,18 +3907,12 @@ modelBuilder.Entity() progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - exec_summary_elements = self._convert_markdown_to_pdf_elements( - exec_summary_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + exec_summary_nontech, + exec_summary_tech, + spacer_after_nontech=10, + add_page_break=True ) - story.extend(exec_summary_elements) - story.append(Spacer(1, 10)) - - exec_summary_tech_elements = self._convert_markdown_to_pdf_elements( - exec_summary_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(exec_summary_tech_elements) - story.append(PageBreak()) # SECTION 3: PROJECT OVERVIEW (Multi-Level) if progress_mgr: @@ -3951,18 +3933,12 @@ modelBuilder.Entity() progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - project_overview_elements = self._convert_markdown_to_pdf_elements( - project_overview_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + project_overview_nontech, + project_overview_tech, + spacer_after_nontech=15, + add_page_break=True ) - story.extend(project_overview_elements) - story.append(Spacer(1, 15)) - - project_overview_tech_elements = self._convert_markdown_to_pdf_elements( - project_overview_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(project_overview_tech_elements) - story.append(PageBreak()) # SECTION 4: ARCHITECTURE ANALYSIS (Multi-Level with Frontend, Backend, Database, APIs) print(f" šŸ“ SECTION 3: ARCHITECTURE ANALYSIS") @@ -3984,18 +3960,12 @@ modelBuilder.Entity() synthesis_analysis=synthesis_analysis, progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - frontend_elements = self._convert_markdown_to_pdf_elements( - frontend_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + frontend_nontech, + frontend_tech, + spacer_after_nontech=10, + add_page_break=True ) - story.extend(frontend_elements) - story.append(Spacer(1, 10)) - - frontend_tech_elements = self._convert_markdown_to_pdf_elements( - frontend_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(frontend_tech_elements) - story.append(PageBreak()) # 4.2 Backend Architecture story.append(Paragraph("3.2 Backend Architecture", subsection_style)) @@ -4007,18 +3977,12 @@ modelBuilder.Entity() synthesis_analysis=synthesis_analysis, progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - backend_elements = self._convert_markdown_to_pdf_elements( - backend_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + backend_nontech, + backend_tech, + spacer_after_nontech=10, + add_page_break=True ) - story.extend(backend_elements) - story.append(Spacer(1, 10)) - - backend_tech_elements = self._convert_markdown_to_pdf_elements( - backend_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(backend_tech_elements) - story.append(PageBreak()) # 4.3 Database Architecture story.append(Paragraph("3.3 Database Architecture", subsection_style)) @@ -4030,18 +3994,12 @@ modelBuilder.Entity() synthesis_analysis=synthesis_analysis, progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - database_elements = self._convert_markdown_to_pdf_elements( - database_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + database_nontech, + database_tech, + spacer_after_nontech=10, + add_page_break=True ) - story.extend(database_elements) - story.append(Spacer(1, 10)) - - database_tech_elements = self._convert_markdown_to_pdf_elements( - database_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(database_tech_elements) - story.append(PageBreak()) # 4.4 API Architecture story.append(Paragraph("3.4 API Architecture", subsection_style)) @@ -4053,18 +4011,12 @@ modelBuilder.Entity() synthesis_analysis=synthesis_analysis, progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - api_elements = self._convert_markdown_to_pdf_elements( - api_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + api_nontech, + api_tech, + spacer_after_nontech=10, + add_page_break=True ) - story.extend(api_elements) - story.append(Spacer(1, 10)) - - api_tech_elements = self._convert_markdown_to_pdf_elements( - api_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(api_tech_elements) - story.append(PageBreak()) # SECTION 5: SECURITY ASSESSMENT (Multi-Level) print(f" šŸ“ SECTION 4: SECURITY ASSESSMENT") @@ -4087,18 +4039,12 @@ modelBuilder.Entity() progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - security_elements = self._convert_markdown_to_pdf_elements( - security_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + security_nontech, + security_tech, + spacer_after_nontech=15, + add_page_break=True ) - story.extend(security_elements) - story.append(Spacer(1, 15)) - - security_tech_elements = self._convert_markdown_to_pdf_elements( - security_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(security_tech_elements) - story.append(PageBreak()) # SECTION 6: MODULE DEEP DIVES (One per module) print(f" šŸ“ SECTION 5: MODULE DEEP DIVES") @@ -4168,18 +4114,12 @@ modelBuilder.Entity() progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - module_elements = self._convert_markdown_to_pdf_elements( - module_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + module_nontech, + module_tech, + spacer_after_nontech=10, + add_page_break=True ) - story.extend(module_elements) - story.append(Spacer(1, 10)) - - module_tech_elements = self._convert_markdown_to_pdf_elements( - module_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(module_tech_elements) - story.append(PageBreak()) else: # No file analyses either - generate minimal section story.append(Paragraph("No modules found in analysis. Please check the analysis logs.", tech_style)) @@ -4203,18 +4143,12 @@ modelBuilder.Entity() progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - module_elements = self._convert_markdown_to_pdf_elements( - module_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + module_nontech, + module_tech, + spacer_after_nontech=10, + add_page_break=True ) - story.extend(module_elements) - story.append(Spacer(1, 10)) - - module_tech_elements = self._convert_markdown_to_pdf_elements( - module_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(module_tech_elements) - story.append(PageBreak()) # SECTION 7: CRITICAL ISSUES & RECOMMENDATIONS (Multi-Level) print(f" šŸ“ SECTION 6: CRITICAL ISSUES & RECOMMENDATIONS") @@ -4236,67 +4170,15 @@ modelBuilder.Entity() progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - issues_elements = self._convert_markdown_to_pdf_elements( - issues_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + issues_nontech, + issues_tech, + spacer_after_nontech=15, + add_page_break=True ) - story.extend(issues_elements) - story.append(Spacer(1, 15)) - issues_tech_elements = self._convert_markdown_to_pdf_elements( - issues_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(issues_tech_elements) - story.append(PageBreak()) - - # SECTION 7: CODE EVIDENCE & PROOF (NEW) - print(f" šŸ“ SECTION 7: CODE EVIDENCE & PROOF") - if progress_mgr: - await progress_mgr.emit_event("report_progress", { - "message": "Generating code evidence section", - "percent": 78 - }) - - story.append(Paragraph("SECTION 7: CODE EVIDENCE & PROOF", section_style)) - - # Get all file analyses from comprehensive context - all_file_analyses = [] - if 'file_analyses' in comprehensive_context: - all_file_analyses = comprehensive_context['file_analyses'] - elif module_analyses: - # Extract file analyses from module analyses - for module in module_analyses: - if 'file_analyses' in module: - all_file_analyses.extend(module['file_analyses']) - - # Extract code evidence - code_evidence = self._extract_code_evidence_for_report(all_file_analyses) - - if code_evidence: - # Generate code evidence sections - evidence_nontech, evidence_tech = await self._generate_code_evidence_section( - code_evidence=code_evidence, - progress_mgr=progress_mgr - ) - - # Convert markdown to properly formatted PDF elements - evidence_elements = self._convert_markdown_to_pdf_elements( - evidence_nontech, styles, section_style, subsection_style, code_style, nontech_style - ) - story.extend(evidence_elements) - story.append(Spacer(1, 15)) - - evidence_tech_elements = self._convert_markdown_to_pdf_elements( - evidence_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(evidence_tech_elements) - else: - story.append(Paragraph("No specific code evidence available. File analyses may not contain detailed issue information.", tech_style)) - - story.append(PageBreak()) - - # SECTION 8: SYSTEM-LEVEL INSIGHTS (Multi-Level) - print(f" šŸ“ SECTION 8: SYSTEM-LEVEL INSIGHTS") + # SECTION 7: SYSTEM-LEVEL INSIGHTS (Multi-Level) + print(f" šŸ“ SECTION 7: SYSTEM-LEVEL INSIGHTS") if progress_mgr: await progress_mgr.emit_event("report_progress", { "message": "Generating system-level insights", @@ -4315,18 +4197,12 @@ modelBuilder.Entity() progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - system_elements = self._convert_markdown_to_pdf_elements( - system_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + system_nontech, + system_tech, + spacer_after_nontech=15, + add_page_break=True ) - story.extend(system_elements) - story.append(Spacer(1, 15)) - - system_tech_elements = self._convert_markdown_to_pdf_elements( - system_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(system_tech_elements) - story.append(PageBreak()) # SECTION 9: JUNIOR DEVELOPER ONBOARDING GUIDE (Technical Only) if progress_mgr: @@ -4370,17 +4246,12 @@ modelBuilder.Entity() progress_mgr=progress_mgr ) - # Convert markdown to properly formatted PDF elements - conclusion_elements = self._convert_markdown_to_pdf_elements( - conclusion_nontech, styles, section_style, subsection_style, code_style, nontech_style + render_markdown_pair( + conclusion_nontech, + conclusion_tech, + spacer_after_nontech=15, + add_page_break=False ) - story.extend(conclusion_elements) - story.append(Spacer(1, 15)) - - conclusion_tech_elements = self._convert_markdown_to_pdf_elements( - conclusion_tech, styles, section_style, subsection_style, code_style, tech_style - ) - story.extend(conclusion_tech_elements) # Build PDF try: @@ -4406,6 +4277,132 @@ modelBuilder.Entity() print(f" Output Path: {output_path}") print(f"{'='*80}\n") raise + finally: + self._report_technical_only = None + + def _prepare_llm_payload(self, section_name: str, section_data: Dict[str, Any]) -> Dict[str, Any]: + """Trim large fields from section data to keep prompts below token limits.""" + if not isinstance(section_data, dict): + return section_data + + safe_payload = copy.deepcopy(section_data) + + modules = safe_payload.get("module_analyses") + if isinstance(modules, list): + max_modules = 8 if section_name.lower().startswith("module") else 12 + safe_payload["module_analyses"] = modules[:max_modules] + + allowed_module_names = { + m.get("module_name") + for m in safe_payload.get("module_analyses", []) + if isinstance(m, dict) + } + + metrics_by_module = safe_payload.get("metrics_by_module") + if isinstance(metrics_by_module, dict) and allowed_module_names: + safe_payload["metrics_by_module"] = { + name: metrics_by_module[name] + for name in allowed_module_names + if name in metrics_by_module + } + + findings_by_module = safe_payload.get("findings_by_module") + if isinstance(findings_by_module, dict) and allowed_module_names: + safe_payload["findings_by_module"] = { + name: findings_by_module[name] + for name in allowed_module_names + if name in findings_by_module + } + + max_string_length = 1500 + max_top_list = 20 + max_nested_list = 10 + max_dict_items = 40 + + exclude_exact = { + "detailed_analysis", + "full_response", + "raw_content", + "raw_text", + "raw_chunks", + "content", + "code", + "code_snippet", + "file_content", + "nontechnical_version", + "technical_version", + "non_technical_version", + "markdown", + "html", + "prompt", + "raw_prompt", + "claude_response", + "pdf_data", + } + exclude_substrings = [ + "raw_", + "_raw", + "nontechnical", + "non-technical", + "technical_version", + "_html", + "markdown", + "html", + "prompt", + "claude", + ] + + def truncate(value: Any, depth: int = 0) -> Any: + if is_dataclass(value): + value = asdict(value) + + if isinstance(value, str): + if len(value) > max_string_length: + return ( + value[:max_string_length] + + f"... [truncated {len(value) - max_string_length} chars]" + ) + return value + + if isinstance(value, (int, float, bool)) or value is None: + return value + + if isinstance(value, list): + limit = max_top_list if depth == 0 else max_nested_list + trimmed = value[:limit] + result = [truncate(item, depth + 1) for item in trimmed] + if len(value) > limit: + result.append( + f"... {len(value) - limit} additional items truncated ..." + ) + return result + + if isinstance(value, dict): + result = {} + items = list(value.items()) + truncated_count = 0 + if len(items) > max_dict_items: + truncated_count = len(items) - max_dict_items + items = items[:max_dict_items] + + for key, item in items: + key_str = str(key) + if key_str in exclude_exact: + continue + key_lower = key_str.lower() + if any(sub in key_lower for sub in exclude_substrings): + continue + result[key] = truncate(item, depth + 1) + + if truncated_count: + result["__truncated_keys__"] = ( + f"{truncated_count} additional keys removed" + ) + return result + + return str(value) + + return truncate(safe_payload) async def _generate_section_multi_level( self, @@ -4418,21 +4415,45 @@ modelBuilder.Entity() Returns: (non_technical_version, technical_version) """ try: - prompt = f""" + technical_only_default = True + section_technical_only = getattr(self, "_report_technical_only", None) + if section_technical_only is None: + technical_only_env = os.getenv("REPORT_TECHNICAL_ONLY") + if technical_only_env is None: + section_technical_only = technical_only_default + else: + section_technical_only = technical_only_env.lower() in ("1", "true", "yes", "on") + + sanitized_section_data = self._prepare_llm_payload(section_name, section_data) + + if section_technical_only: + prompt = f""" +You are a senior software architect with 30+ years of experience. Produce a strictly technical analysis for the section "{section_name}". + +CONSTRAINTS: +- Focus exclusively on code, architecture, dependencies, security, performance, and testing. +- Do not include business analogies, executive summaries, emojis, or non-technical commentary. +- Reference files, modules, and metrics when possible. +- Respond in GitHub-flavoured Markdown with clear headings and bullet lists. + +SECTION DATA: +{json.dumps(sanitized_section_data, indent=2, default=str)} +""" + else: + prompt = f""" You are a senior software architect with 30+ years of experience. Generate a comprehensive analysis for the section: "{section_name}". SECTION DATA: -{json.dumps(section_data, indent=2, default=str)} +{json.dumps(sanitized_section_data, indent=2, default=str)} Generate TWO versions of this section: 1. NON-TECHNICAL VERSION: - - Use analogies (restaurant, building, car, city) - - No jargon - explain in plain English - - Focus on business impact and implications - - Use emojis for ratings (ā­ā­ā­ for good, āš ļø for warnings, āŒ for critical) - - Explain what this means for stakeholders - - Keep it accessible to executives and non-technical managers + - Use clear, plain language without analogies, metaphors, or storytelling. + - Avoid jargon while grounding statements in observable evidence. + - Focus on business impact and stakeholder implications derived from the section data. + - Do not use emojis or decorative symbols. + - Keep the structure concise and easy for executives to follow. 2. TECHNICAL VERSION: - Full technical details with code examples @@ -4463,6 +4484,12 @@ Output format: response_text = await loop.run_in_executor(None, call_claude) + if section_technical_only: + technical = response_text.strip() + if not technical: + technical = "Technical analysis generation failed." + return "", technical + # Parse response nontech_match = re.search(r'\[NON-TECHNICAL\](.*?)(?=\[TECHNICAL\]|$)', response_text, re.DOTALL) tech_match = re.search(r'\[TECHNICAL\](.*?)$', response_text, re.DOTALL) @@ -4819,6 +4846,59 @@ Output format: try: if not code_evidence: return "No specific code evidence found.", "No code evidence available." + + technical_only_default = True + section_technical_only = getattr(self, "_report_technical_only", None) + if section_technical_only is None: + technical_only_env = os.getenv("REPORT_TECHNICAL_ONLY") + if technical_only_env is None: + section_technical_only = technical_only_default + else: + section_technical_only = technical_only_env.lower() in ("1", "true", "yes", "on") + + high_count = len([e for e in code_evidence if e['severity'] == 'HIGH']) + medium_count = len([e for e in code_evidence if e['severity'] == 'MEDIUM']) + unique_files = len(set(e['file'] for e in code_evidence)) + + tech_content = f""" + šŸ”§ DETAILED CODE EVIDENCE ANALYSIS

+ + šŸ“‹ COMPREHENSIVE FINDINGS:
+ Total Issues Found: {len(code_evidence)}
+ Files Analyzed: {unique_files}
+ High Severity: {high_count}
+ Medium Severity: {medium_count}

+ """ + + for idx, evidence in enumerate(code_evidence[:10], 1): # Top 10 detailed findings + tech_content += f""" + FINDING #{idx} - {evidence['severity']} PRIORITY
+ File: {evidence['file']}
+ Issue: {evidence['issue']}
+ Line: {evidence['line_number']}

+ + Code Evidence:
+ 
+{evidence['code_snippet'][:300]}{"..." if len(evidence['code_snippet']) > 300 else ""}
+
+ + Recommended Fix:
+ {evidence['recommendation']}

+ + {'ā”€' * 60}
+ """ + + tech_content += """ + šŸ”§ IMPLEMENTATION NOTES:
+ ā€¢ Focus on HIGH severity issues first
+ ā€¢ Test all fixes in staging environment
+ ā€¢ Use code review process for all changes
+ ā€¢ Update documentation after fixes
+ ā€¢ Consider automated testing for fixed issues + """ + + if section_technical_only: + return "", tech_content # Non-technical version (for managers) nontech_content = f""" @@ -4827,9 +4907,9 @@ Output format: Our automated code review identified {len(code_evidence)} specific issues with actual code examples as proof.

šŸ“Š ISSUE BREAKDOWN:
- ā€¢ High Priority Issues: {len([e for e in code_evidence if e['severity'] == 'HIGH'])}
- ā€¢ Medium Priority Issues: {len([e for e in code_evidence if e['severity'] == 'MEDIUM'])}
- ā€¢ Files with Evidence: {len(set(e['file'] for e in code_evidence))}

+ ā€¢ High Priority Issues: {high_count}
+ ā€¢ Medium Priority Issues: {medium_count}
+ ā€¢ Files with Evidence: {unique_files}

šŸŽÆ TOP CRITICAL FINDINGS:
""" @@ -4853,46 +4933,6 @@ Output format: āš” IMMEDIATE ACTION:
Assign developers to fix high-priority issues within 1-2 weeks to prevent system degradation. """ - - # Technical version (for developers) - tech_content = f""" - šŸ”§ DETAILED CODE EVIDENCE ANALYSIS

- - šŸ“‹ COMPREHENSIVE FINDINGS:
- Total Issues Found: {len(code_evidence)}
- Files Analyzed: {len(set(e['file'] for e in code_evidence))}
- High Severity: {len([e for e in code_evidence if e['severity'] == 'HIGH'])}
- Medium Severity: {len([e for e in code_evidence if e['severity'] == 'MEDIUM'])}

- """ - - # Add detailed code evidence for developers - for idx, evidence in enumerate(code_evidence[:10], 1): # Top 10 detailed findings - tech_content += f""" - FINDING #{idx} - {evidence['severity']} PRIORITY
- File: {evidence['file']}
- Issue: {evidence['issue']}
- Line: {evidence['line_number']}

- - Code Evidence:
- 
-{evidence['code_snippet'][:300]}{"..." if len(evidence['code_snippet']) > 300 else ""}
-
- - Recommended Fix:
- {evidence['recommendation']}

- - {'ā”€' * 60}
- """ - - tech_content += """ - šŸ”§ IMPLEMENTATION NOTES:
- ā€¢ Focus on HIGH severity issues first
- ā€¢ Test all fixes in staging environment
- ā€¢ Use code review process for all changes
- ā€¢ Update documentation after fixes
- ā€¢ Consider automated testing for fixed issues - """ - return nontech_content, tech_content except Exception as e: @@ -7054,18 +7094,23 @@ async def main(): js_files = [fa for fa in frontend_files if fa.path.lower().endswith(('.js', '.jsx', '.mjs', '.cjs'))] ts_files = [fa for fa in frontend_files if fa.path.lower().endswith(('.ts', '.tsx'))] - frontend_analysis_prompt = f""" -You are a Senior Frontend Architect and Technical Writer with 20+ years of experience. Your task is to analyze this frontend codebase and create a COMPREHENSIVE, DETAILED explanation that even a non-technical person can understand. + front_end_prompt = f""" +You are a Senior Frontend Architect and Technical Writer with 20+ years of experience. Analyze this frontend codebase and produce a comprehensive, technically precise report. The audience includes senior engineers and stakeholders who expect evidence-based, objective findings. -CRITICAL: Write in SIMPLE, CLEAR language. Use analogies and real-world examples. Avoid jargon. Explain everything as if talking to someone who has never coded before. +STRICT STYLE RULES: +- Use professional, technical language only. Do not use analogies, metaphors, storytelling, or colloquial comparisons. +- Ground every statement in repository evidence. Reference file paths, components, functions, metrics, or configurations explicitly. +- When assumptions are necessary, label them clearly and mark for validation. +- Follow the outline exactly; preserve headings, numbering, and bullet structure. +- Provide 2000-3000 words of detailed insight. FRONTEND FILES SUMMARY: - Total Frontend Files: {len(frontend_files)} - Total Frontend Lines: {sum(fa.lines_of_code for fa in frontend_files):,} -- HTML Files: {len(html_files)} files -- CSS/Styling Files: {len(css_files)} files -- JavaScript Files: {len(js_files)} files -- TypeScript Files: {len(ts_files)} files +- HTML Files: {len(html_files)} +- CSS/Styling Files: {len(css_files)} +- JavaScript Files: {len(js_files)} +- TypeScript Files: {len(ts_files)} - Component Files: {len(component_files)} - Routing Files: {len(routing_files)} - State Management Files: {len(state_files)} @@ -7085,159 +7130,85 @@ STATE MANAGEMENT FILES: SAMPLE FRONTEND FILES: {frontend_files_text} -Provide a COMPREHENSIVE and EXTREMELY DETAILED frontend architecture analysis following this EXACT structure. Write at least 2000-3000 words. Be very thorough and detailed: +Produce the analysis using this structure: **1. FRONTEND FRAMEWORK DETECTION:** -- Identify the frontend framework(s) used (React, Vue, Angular, Svelte, Next.js, Nuxt, Remix, Astro, Qwik, SolidJS, Ember, Backbone, or vanilla JS) -- Detect framework versions from package.json or config files -- Identify any meta-frameworks (Next.js, Nuxt, Remix, etc.) -- Note any version-specific issues or outdated versions +- Identify framework(s) and versions (from package.json, lockfiles, config files). +- Note meta-framework usage (Next.js, Nuxt, Remix, etc.). +- Highlight version-specific issues or EOL dependencies. **2. TECHNOLOGY STACK ANALYSIS:** -- List all frontend dependencies and their purposes -- Identify any outdated or vulnerable dependencies -- Detect duplicate libraries (e.g., multiple date libraries, multiple state management libraries) -- Security issues in dependencies -- Build tools detected (Webpack, Vite, Rollup, Parcel, etc.) -- Testing frameworks and tools +- Catalogue all dependencies with purpose and usage context. +- Flag outdated/vulnerable packages (cite version/date where possible). +- Identify redundant or overlapping libraries. +- Summarize build tooling, testing frameworks, bundlers. **3. COMPONENT ARCHITECTURE (GRANULAR ANALYSIS):** -For each major component identified, provide: -- Component name and purpose (what it does in simple terms) -- Component location and file path -- Props/inputs it receives -- State it manages internally -- Dependencies on other components -- Side effects (API calls, browser storage, etc.) -- Component hierarchy (parent-child relationships) -- Component reusability assessment +For each major component: +- Name, responsibility, and file path. +- Props/inputs, outputs, and state managed. +- Dependencies and side effects (API calls, browser APIs, storage). +- Hierarchy position (parent/child relationships) and reusability assessment. **4. NAVIGATION & ROUTING ANALYSIS:** -- Routing system used (React Router, Vue Router, Angular Router, Next.js file-based routing, etc.) -- All route definitions and their purposes -- Navigation flow (how users move between pages) -- Route mapping (URL ā†’ Component mapping) -- Route guards or middleware (authentication, authorization) -- Dynamic routes and parameters -- Route structure and organization +- Routing mechanism and configuration files. +- Route definitions, dynamic parameters, guards/middleware. +- Navigation flow between major views. +- Lazy loading/split points tied to routing. **5. STATE MANAGEMENT ANALYSIS:** -- State management pattern used (Context API, Redux, Zustand, Pinia, Vuex, NgRx, MobX, Jotai, Recoil, etc.) -- Global state vs local state strategy -- State flow diagram description (how data flows through the app) -- State management issues or improvements needed -- Data fetching patterns (React Query, SWR, Apollo, etc.) +- State management paradigm (Context, Redux, Zustand, etc.). +- Global vs local state boundaries. +- Data flow patterns, caching strategies, synchronization points. +- Known issues or improvement opportunities. -**6. FRONTEND ARCHITECTURE FLOW (NON-CODER FRIENDLY - VERY DETAILED):** -This is the MOST IMPORTANT section. Explain in EXTREMELY SIMPLE language that a non-technical person can understand: +**6. FRONTEND ARCHITECTURE FLOW (TECHNICAL WALKTHROUGH):** 6.1. WHAT IS THE FRONTEND? -- Explain what frontend means in simple terms (like the part of a website users see and interact with) -- What files make up the frontend in this repository (HTML, CSS, JavaScript) -- How these files work together (like ingredients in a recipe) +- Define the UI layer in this repository. +- Enumerate file types and roles (HTML templating, styling, interactivity). -6.2. HOW DOES THE FRONTEND WORK? (STEP-BY-STEP EXPLANATION) -- User Action Flow: When a user clicks a button or types something, explain step-by-step what happens: - * What happens when the user clicks? - * Which file handles the click? - * How does the page respond? - * What information is sent to the server (if any)? - * How does the page update? -- Component Interaction: Explain how different parts of the website communicate: - * Like how different departments in a company work together - * Which parts talk to each other? - * How do they share information? -- Data Flow: Explain how data moves through the system: - * Where does data come from? (user, server, database) - * How does it travel through the frontend? - * Where does it get stored temporarily? - * How does it appear on the screen? - * Use a simple analogy like a postal system or delivery service -- Navigation Flow: Explain how users move between pages: - * How does clicking a link work? - * What happens when you go to a new page? - * How does the browser know which page to show? - * Use an analogy like navigating a building with different rooms -- State Updates: Explain when and how the screen updates: - * What triggers a screen update? - * How quickly does it update? - * What information changes on the screen? - * Use an analogy like updating a dashboard or scoreboard +6.2. HOW DOES THE FRONTEND WORK? (STEP-BY-STEP FLOW) +- User Action Flow: trace representative interactions through handlers, state updates, and rendering. +- Component Interaction: describe data exchange, context usage, dependencies. +- Data Flow: document sources (APIs, constants), transformation, caching, rendering. +- Navigation Flow: explain route resolution, dynamic segments, transition handling. +- State Updates: detail triggers, asynchronous coordination, batching/debouncing behavior. -6.3. VISUAL MAPPING (IN WORDS) -- Describe the structure like a map of a building: - * What are the main "rooms" (pages)? - * How are they connected? - * What's in each room? -- Describe the component hierarchy like a family tree: - * Which components are parents? - * Which are children? - * How do they relate to each other? +6.3. VISUAL MAPPING (TEXTUAL DESCRIPTION) +- Provide a textual map of main views/pages and relationships. +- Describe component hierarchy like a dependency graph (parent/child/data links). -6.4. REAL-WORLD ANALOGY -- Compare the frontend to something familiar: - * Like a restaurant (HTML = menu, CSS = decoration, JavaScript = waiters) - * Or a car (HTML = body, CSS = paint, JavaScript = engine) - * Or a house (HTML = structure, CSS = interior design, JavaScript = electrical system) +6.4. TECHNICAL SUMMARY +- Summarize strengths, weaknesses, and priorities in concise technical terms only. -**7. FRONTEND PERFORMANCE ANALYSIS:** -- Bundle size analysis and optimization opportunities -- Code splitting strategy -- Lazy loading implementation -- Image optimization -- Performance bottlenecks -- Memory usage concerns -- Load time estimation +**7. PERFORMANCE ANALYSIS:** +- Bundle size estimates (computed or inferred). +- Code splitting/lazy loading evaluation. +- Runtime performance considerations (render hotspots, expensive operations). +- Optimization opportunities with expected impact. -**8. FRONTEND TESTING ANALYSIS:** -- Test files found and their coverage -- Testing frameworks used -- Test coverage percentage -- Missing test areas -- Testing best practices adherence +**8. UI/UX REVIEW (STRUCTURED OBSERVATIONS):** +- Layout architecture, design system usage, spacing/typography consistency. +- Accessibility findings (ARIA usage, keyboard navigation, color contrast). +- Responsiveness across breakpoints/devices. +- Usability issues grounded in code evidence. -**9. CODE QUALITY & ISSUES:** -- Large files (>500 lines) that need refactoring -- Code duplication issues -- Component complexity issues -- Security vulnerabilities in frontend code -- Best practices violations +**9. RISK & MATURITY ASSESSMENT:** +- Architecture risk score (1-10) with technical justification. +- Maintainability risk score (comment on complexity, coupling). +- Business risk commentary (security exposure, performance degradation, compliance concerns). +- Identify technical debt hotspots with file/component references. -**10. RECOMMENDATIONS:** -- Top 5-10 specific improvements needed -- Priority order for changes -- Framework upgrade recommendations -- Architecture improvements -- Performance optimizations +**10. PRIORITIZED RECOMMENDATIONS:** +- List improvements grouped into short-term (next sprint), medium-term (1-3 months), long-term (3-6 months). +- For each recommendation provide action, expected outcome, and risk mitigated. -IMPORTANT: -- Write in clear, simple language that non-coders can understand -- Use specific examples from the codebase -- Be detailed and comprehensive -- No hardcoded or generic responses - analyze the actual code provided -- Focus on actionable insights - -Keep the response comprehensive but well-structured. Use markdown formatting for better readability. - -CRITICAL REQUIREMENTS: -1. Write at least 2000-3000 words total -2. Use simple analogies throughout (like explaining to a child or a friend who has never coded) -3. Explain EVERY technical term in simple language when first used -4. Use real-world examples and comparisons -5. Break down complex concepts into step-by-step explanations -6. Use bullet points and numbered lists for clarity -7. Include specific examples from the actual codebase provided -8. Make it so clear that even a business person can understand how the frontend works -9. Be extremely detailed - don't skip any important aspect -10. Use visual descriptions where helpful (like describing a building, a restaurant, a car, etc.) - -Remember: The goal is to make a non-technical person understand: -- What frontend files exist in this repository -- How they work together -- How the frontend functions from a user's perspective -- How data flows through the system -- How users interact with the frontend -- The complete architecture in simple terms +FINAL REQUIREMENTS: +- Keep tone technical and objective; no analogies, metaphors, or storytelling. +- Use markdown formatting as requested (bold headings, bullet lists). +- Cite file paths, components, and metrics wherever possible. +- Ensure total length between 2000-3000 words. """ try: @@ -7249,7 +7220,7 @@ Remember: The goal is to make a non-technical person understand: model=os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest"), max_tokens=8000, # Increased from 6000 to 8000 for more detailed analysis temperature=0.1, - messages=[{"role": "user", "content": frontend_analysis_prompt}] + messages=[{"role": "user", "content": front_end_prompt}] ) ai_analysis = message.content[0].text.strip() @@ -7259,7 +7230,7 @@ Remember: The goal is to make a non-technical person understand: if not ai_analysis or len(ai_analysis) < 100: print("āš ļø [FRONTEND AI] AI analysis too short, regenerating...") # Retry with more emphasis on detail - retry_prompt = frontend_analysis_prompt + "\n\nIMPORTANT: Provide a VERY DETAILED analysis. The previous response was too short. Please provide at least 2000 words of detailed explanation." + retry_prompt = front_end_prompt + "\n\nIMPORTANT: Provide a VERY DETAILED analysis. The previous response was too short. Please provide at least 2000 words of detailed explanation." message = self.client.messages.create( model=os.getenv("CLAUDE_MODEL", "claude-3-5-haiku-latest"), max_tokens=8000, @@ -7301,7 +7272,12 @@ Remember: The goal is to make a non-technical person understand: try: # Create a simpler, more focused prompt that's more likely to succeed simple_prompt = f""" -You are explaining a frontend codebase to a non-technical person. Be VERY DETAILED and use simple language. +You are a senior frontend engineer. The previous request failed; provide a concise but technically accurate overview of this frontend codebase. + +STYLE: +- Use professional technical language only. Do not use analogies, metaphors, or storytelling. +- Reference specific file paths, components, and observable behavior from the repository. +- Mark any assumptions clearly as assumptions. FRONTEND FILES DETECTED: - Total Frontend Files: {len(frontend_files)} @@ -7312,14 +7288,14 @@ FRONTEND FILES DETECTED: SAMPLE FRONTEND FILES: {frontend_files_text[:5000]} -Provide a COMPREHENSIVE explanation covering: -1. What frontend files are present and what each type does (HTML, CSS, JavaScript) -2. How the frontend works step-by-step (like explaining to someone who has never seen code) -3. How users interact with the frontend -4. How data flows through the system -5. The structure and organization of the frontend files +Deliver a structured explanation covering: +1. File types present and the responsibilities they implement. +2. Step-by-step technical description of rendering, event handling, and data flow. +3. Component/module organization and how information moves between them. +4. Routing or navigation behavior, if applicable. +5. Key risks, limitations, or improvement areas supported by code evidence. -Write at least 1500 words. Use simple analogies and real-world examples. Be extremely detailed. +Write at least 1000 words and adhere strictly to the technical style. """ retry_message = self.client.messages.create( @@ -7342,24 +7318,28 @@ Write at least 1500 words. Use simple analogies and real-world examples. Be extr basic_analysis = f""" **FRONTEND ARCHITECTURE ANALYSIS** -**Overview:** -This repository contains {len(frontend_files)} frontend files with a total of {total_frontend_lines:,} lines of code. +**Overview** +This repository contains {len(frontend_files)} frontend files with a total of {total_frontend_lines:,} lines of code. The following summary is generated without AI assistance. -**Frontend File Types Detected:** -- HTML Files: {len(html_files)} files - These are the structure/skeleton of web pages (like the framework of a house) -- CSS Files: {len(css_files)} files - These define the styling and appearance (like paint and decoration) -- JavaScript Files: {len(js_files)} files - These add interactivity and functionality (like electrical systems and appliances) -- TypeScript Files: {len(ts_files)} files - These are enhanced JavaScript files with type checking +**Frontend File Types Detected** +- HTML Files: {len(html_files)} files - define the DOM structure and static content for each view. +- CSS Files: {len(css_files)} files - implement styling rules, layout definitions, and responsive behavior. +- JavaScript Files: {len(js_files)} files - provide interaction handlers, client-side logic, and integration with backend services. +- TypeScript Files: {len(ts_files)} files - supply strongly typed client logic compiled to JavaScript. -**How the Frontend Works (Simple Explanation):** -1. HTML files create the structure - they define what elements appear on the page (like headings, buttons, forms) -2. CSS files style these elements - they control colors, sizes, layouts, and visual appearance -3. JavaScript/TypeScript files add behavior - they make things happen when users interact (clicking buttons, submitting forms, loading data) +**Execution Flow** +1. HTML documents load in the browser and reference the required CSS and JavaScript bundles. +2. CSS stylesheets apply layout, typography, and responsive rules to the rendered DOM. +3. JavaScript or TypeScript modules register event listeners, hydrate components, and coordinate data retrieval. +4. State updates trigger DOM mutations through the frontend framework, refreshing the visible UI. -**Frontend Structure:** -The frontend is organized into different files that work together to create a complete web application. +**Structure Summary** +- Frontend source files are grouped by feature or component to deliver the user interface. +- Styling assets are maintained separately to control presentation concerns. +- Interactive behavior is encapsulated within JavaScript or TypeScript modules. -**Note:** Detailed AI analysis was not available, but the frontend files have been detected and analyzed. +**Note** +Detailed AI analysis was unavailable; this summary is derived from static inspection of the repository contents. """ ai_analysis = basic_analysis @@ -7467,95 +7447,40 @@ The frontend is organized into different files that work together to create a co js_files = [fa for fa in frontend_files_detected if fa.path.lower().endswith(('.js', '.jsx', '.mjs', '.cjs'))] ts_files = [fa for fa in frontend_files_detected if fa.path.lower().endswith(('.ts', '.tsx'))] - # Generate comprehensive basic analysis for non-technical audience + # Generate comprehensive basic analysis (technical fallback) basic_analysis = f""" -**1. FRONTEND OVERVIEW - WHAT IS THE FRONTEND?** +**1. FRONTEND OVERVIEW** +Detected {len(frontend_files_detected)} frontend-related files totaling {total_frontend_lines:,} lines of client-side code. -The frontend is the part of the application that users see and interact with in their web browser. Think of it like the visible part of an iceberg - what users see on their screen. +**2. FILE TYPE SUMMARY** +- HTML Files ({len(html_files)}): define document structure, semantic layout, and script mount points. +- CSS Files ({len(css_files)}): manage layout, typography, spacing, and responsive behaviour. +- JavaScript Files ({len(js_files)}): provide interaction handlers, API connectivity, and state management. +- TypeScript Files ({len(ts_files)}): supply typed sources that compile to JavaScript for runtime execution. -This repository contains {len(frontend_files_detected)} frontend files with a total of {total_frontend_lines:,} lines of code that create the user interface. +**3. RUNTIME EXECUTION FLOW** +1. Browsers load HTML documents and create the DOM tree. +2. CSS stylesheets apply presentation rules across breakpoints. +3. JavaScript or TypeScript modules initialise, attach event listeners, and hydrate UI components. +4. State changes trigger DOM updates via framework abstractions or direct DOM APIs. -**2. FRONTEND FILE TYPES - WHAT EACH TYPE DOES** +**4. USER INTERACTION PIPELINE** +- Button callbacks execute JavaScript routines to drive navigation, trigger side effects, or fetch data. +- Form workflows validate input locally, issue HTTP requests, and update the interface with success or error states. -**HTML Files ({len(html_files)} files):** -- HTML files are like the skeleton or framework of a building -- They define WHAT appears on the page (headings, buttons, forms, text, images) -- Think of HTML as the structure - like the walls and rooms of a house -- These files create the basic layout and content structure +**5. DATA FLOW** +1. User actions invoke JavaScript functions that compose network requests. +2. Backend endpoints respond with structured payloads (typically JSON or HTML). +3. The frontend updates local state and rerenders affected DOM nodes. +4. CSS keeps presentation consistent after dynamic updates. -**CSS Files ({len(css_files)} files):** -- CSS files are like the paint, decoration, and interior design -- They control HOW things look (colors, sizes, spacing, fonts, layouts) -- Think of CSS as the styling - making the house look beautiful -- These files make the page visually appealing and organized +**6. STRUCTURE AND ORGANISATION** +- Markup, styling, and behavioural concerns are separated for maintainability. +- Script modules coordinate data loading, state transitions, and rendering logic. +- Styling assets encapsulate layout concerns independently of business logic. -**JavaScript Files ({len(js_files)} files):** -- JavaScript files are like the electrical system and appliances -- They add INTERACTIVITY and FUNCTIONALITY (clicking buttons, submitting forms, loading data) -- Think of JavaScript as the "smarts" - making things work when you click them -- These files make the page dynamic and responsive to user actions - -**TypeScript Files ({len(ts_files)} files):** -- TypeScript files are enhanced JavaScript files with better error checking -- They work the same as JavaScript but with additional safety features -- Think of TypeScript as JavaScript with better quality control - -**3. HOW THE FRONTEND WORKS - STEP-BY-STEP EXPLANATION** - -**Step 1: Loading the Page** -When a user opens the website, the browser reads the HTML file first. This tells the browser what elements to display (like a blueprint tells builders what to build). - -**Step 2: Styling the Page** -Next, the browser reads the CSS files. These tell the browser how to style each element - what colors to use, how big things should be, where to place them (like interior designers telling builders how to decorate). - -**Step 3: Making It Interactive** -Finally, the browser runs the JavaScript/TypeScript files. These add the "brain" - making buttons clickable, forms submittable, and data loadable (like installing electrical systems and appliances). - -**4. USER INTERACTION FLOW** - -**When a User Clicks a Button:** -1. The HTML defines where the button is -2. The CSS makes it look like a button (colored, styled) -3. The JavaScript detects the click -4. The JavaScript performs the action (like sending data to the server) -5. The page updates to show the result - -**When a User Fills a Form:** -1. The HTML creates the form structure (input fields, labels) -2. The CSS styles the form (makes it look nice) -3. The JavaScript validates the input (checks if it's correct) -4. The JavaScript sends the data to the server -5. The page shows a success or error message - -**5. DATA FLOW - HOW INFORMATION MOVES** - -**Getting Data from Server:** -1. User clicks a button or loads a page -2. JavaScript sends a request to the server (like ordering food) -3. Server processes the request and sends back data (like the kitchen preparing food) -4. JavaScript receives the data (like receiving the food) -5. JavaScript updates the HTML to show the data (like displaying it on the plate) -6. CSS styles the data display (like arranging the food nicely) - -**6. STRUCTURE AND ORGANIZATION** - -The frontend files are organized in a way that makes them easy to maintain: -- HTML files define the structure -- CSS files control the appearance -- JavaScript files add the functionality - -They all work together like parts of a machine - each part has a specific job, but they all need to work together for the machine to function properly. - -**7. FRONTEND ARCHITECTURE SUMMARY** - -This frontend uses a traditional web architecture: -- HTML provides the foundation (structure) -- CSS provides the styling (appearance) -- JavaScript provides the behavior (functionality) - -Together, these files create a complete, interactive web application that users can see, use, and interact with in their web browsers. - -**Note:** Detailed AI analysis encountered an error, but all frontend files have been successfully detected and analyzed. The frontend is fully functional and ready for use. +**7. SUMMARY** +This fallback summary is produced from static inspection because AI synthesis was unavailable during the run. Re-run the automated analysis when the AI service is reachable for richer insight. """ return { diff --git a/services/ai-analysis-service/env.example b/services/ai-analysis-service/env.example index 2b8c19d..d6095cf 100644 --- a/services/ai-analysis-service/env.example +++ b/services/ai-analysis-service/env.example @@ -6,7 +6,17 @@ HOST=0.0.0.0 NODE_ENV=development # AI API Keys -ANTHROPIC_API_KEY=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 +ANTHROPIC_API_KEY=your_anthropic_api_key + +# Neo4j Knowledge Graph Configuration +USE_NEO4J_KG=true +NEO4J_URI=bolt://localhost:7687 +NEO4J_USER=neo4j +NEO4J_PASSWORD=secure_neo4j_2024 +NEO4J_DATABASE=neo4j + +# Report Configuration +REPORT_TECHNICAL_ONLY=false # Database Configuration POSTGRES_HOST=localhost diff --git a/services/ai-analysis-service/knowledge_graph/__init__.py b/services/ai-analysis-service/knowledge_graph/__init__.py new file mode 100644 index 0000000..b4bc108 --- /dev/null +++ b/services/ai-analysis-service/knowledge_graph/__init__.py @@ -0,0 +1,9 @@ +""" +Knowledge graph utilities for the AI Analysis Service. + +This package provides the Neo4j client and high-level helpers used to +persist and query analysis results in a graph representation. +""" + +from .neo4j_client import Neo4jGraphClient # noqa: F401 + diff --git a/services/ai-analysis-service/knowledge_graph/neo4j_client.py b/services/ai-analysis-service/knowledge_graph/neo4j_client.py new file mode 100644 index 0000000..d3790ae --- /dev/null +++ b/services/ai-analysis-service/knowledge_graph/neo4j_client.py @@ -0,0 +1,328 @@ +""" +Neo4j client helpers for the AI Analysis Service. + +This module wraps the official Neo4j async driver and exposes a minimal +set of convenience methods that we can reuse across the service without +sprinkling Cypher execution boilerplate everywhere. +""" + +from __future__ import annotations + +import json +from contextlib import asynccontextmanager +from dataclasses import dataclass +from datetime import datetime +from typing import Any, AsyncIterator, Dict, List, Optional, Sequence + +from neo4j import AsyncGraphDatabase + +try: + from neo4j import AsyncResult, AsyncSession # type: ignore +except ImportError: # pragma: no cover - fallback for older/newer driver versions + AsyncResult = Any # type: ignore + AsyncSession = Any # type: ignore + + +def _json_dumps(value: Any) -> str: + """Serialize complex values so we can persist them as strings safely.""" + if value is None: + return "" + if isinstance(value, (str, int, float, bool)): + return str(value) + try: + return json.dumps(value, default=str) + except Exception: + return str(value) + + +@dataclass +class Neo4jConfig: + uri: str + user: str + password: str + database: Optional[str] = None + fetch_size: int = 1000 + + +class Neo4jGraphClient: + """ + Thin wrapper around the Neo4j async driver that provides helpers for + writing analysis artefacts into the graph and querying them back. + """ + + def __init__(self, config: Neo4jConfig) -> None: + self._config = config + self._driver = AsyncGraphDatabase.driver( + config.uri, + auth=(config.user, config.password), + # Allow long running operations while the analysis progresses + max_connection_lifetime=3600, + ) + + async def close(self) -> None: + if self._driver: + await self._driver.close() + + @asynccontextmanager + async def session(self) -> AsyncIterator[AsyncSession]: + kwargs: Dict[str, Any] = {} + if self._config.database: + kwargs["database"] = self._config.database + if self._config.fetch_size: + kwargs["fetch_size"] = self._config.fetch_size + async with self._driver.session(**kwargs) as session: + yield session + + async def _run_write(self, query: str, **params: Any) -> None: + async with self.session() as session: + async def _write(tx): + result = await tx.run(query, **params) + await result.consume() + await session.execute_write(_write) + + async def _run_read(self, query: str, **params: Any) -> List[Dict[str, Any]]: + async with self.session() as session: + result: AsyncResult = await session.run(query, **params) + records = await result.data() + return records + + # ------------------------------------------------------------------ # + # Write helpers + # ------------------------------------------------------------------ # + + async def upsert_run(self, run_id: str, repository_id: str) -> None: + await self._run_write( + """ + MERGE (r:Run {run_id: $run_id}) + ON CREATE SET + r.repository_id = $repository_id, + r.created_at = datetime(), + r.updated_at = datetime() + ON MATCH SET + r.repository_id = $repository_id, + r.updated_at = datetime() + """, + run_id=run_id, + repository_id=repository_id, + ) + + async def clear_run(self, run_id: str) -> None: + await self._run_write( + """ + MATCH (r:Run {run_id: $run_id}) + OPTIONAL MATCH (r)-[rel]-() + DETACH DELETE r + """, + run_id=run_id, + ) + + async def upsert_module_graph( + self, + run_id: str, + repository_id: str, + module_props: Dict[str, Any], + files: Sequence[Dict[str, Any]], + findings: Sequence[Dict[str, Any]], + dependencies: Sequence[Dict[str, Any]], + ) -> None: + """ + Persist module level artefacts in a single transaction. + """ + # Ensure strings + module_props = {k: _json_dumps(v) if isinstance(v, (dict, list, tuple, set)) else v for k, v in module_props.items()} + files_payload = [ + { + "path": item["path"], + "props": { + key: _json_dumps(value) if isinstance(value, (dict, list, tuple, set)) else value + for key, value in item.get("props", {}).items() + }, + } + for item in files + ] + findings_payload = [ + { + "id": item["id"], + "props": { + key: _json_dumps(value) if isinstance(value, (dict, list, tuple, set)) else value + for key, value in item.get("props", {}).items() + }, + "file_path": item.get("file_path"), + } + for item in findings + ] + dependencies_payload = [ + { + "target": dependency.get("target"), + "kind": dependency.get("kind", "depends_on"), + "metadata": _json_dumps(dependency.get("metadata", {})), + } + for dependency in dependencies + ] + + await self._run_write( + """ + MERGE (r:Run {run_id: $run_id}) + ON CREATE SET + r.repository_id = $repository_id, + r.created_at = datetime(), + r.updated_at = datetime() + ON MATCH SET + r.repository_id = $repository_id, + r.updated_at = datetime() + + MERGE (m:Module {run_id: $run_id, name: $module_name}) + SET m += $module_props, + m.updated_at = datetime() + + MERGE (r)-[:RUN_HAS_MODULE]->(m) + + WITH m + UNWIND $files AS file_data + MERGE (f:File {run_id: $run_id, path: file_data.path}) + SET f += file_data.props, + f.updated_at = datetime() + MERGE (m)-[:MODULE_INCLUDES_FILE]->(f) + + WITH m + UNWIND $findings AS finding_data + MERGE (fd:Finding {run_id: $run_id, finding_id: finding_data.id}) + SET fd += finding_data.props, + fd.updated_at = datetime() + MERGE (m)-[:MODULE_HAS_FINDING]->(fd) + FOREACH (fp IN CASE WHEN finding_data.file_path IS NULL THEN [] ELSE [finding_data.file_path] END | + MERGE (ff:File {run_id: $run_id, path: fp}) + MERGE (fd)-[:FINDING_TOUCHES_FILE]->(ff) + ) + + WITH m + UNWIND $dependencies AS dependency + FOREACH (_ IN CASE WHEN dependency.target IS NULL THEN [] ELSE [1] END | + MERGE (dep:Module {run_id: $run_id, name: dependency.target}) + MERGE (m)-[rel:MODULE_DEPENDENCY {kind: dependency.kind}]->(dep) + SET rel.metadata = dependency.metadata, + rel.updated_at = datetime() + ) + """, + run_id=run_id, + repository_id=repository_id, + module_name=module_props.get("name"), + module_props=module_props, + files=files_payload, + findings=findings_payload, + dependencies=dependencies_payload, + ) + + async def upsert_run_state(self, run_id: str, state: Dict[str, Any]) -> None: + await self._run_write( + """ + MERGE (r:Run {run_id: $run_id}) + SET r.analysis_state = $state, + r.state_updated_at = datetime() + """, + run_id=run_id, + state=_json_dumps(state), + ) + + async def upsert_synthesis(self, run_id: str, synthesis: Dict[str, Any]) -> None: + await self._run_write( + """ + MERGE (r:Run {run_id: $run_id}) + SET r.synthesis_analysis = $synthesis, + r.synthesis_updated_at = datetime() + """, + run_id=run_id, + synthesis=_json_dumps(synthesis), + ) + + # ------------------------------------------------------------------ # + # Read helpers + # ------------------------------------------------------------------ # + + async def fetch_modules(self, run_id: str) -> List[Dict[str, Any]]: + records = await self._run_read( + """ + MATCH (r:Run {run_id: $run_id})-[:RUN_HAS_MODULE]->(m:Module) + OPTIONAL MATCH (m)-[:MODULE_INCLUDES_FILE]->(f:File) + OPTIONAL MATCH (m)-[:MODULE_HAS_FINDING]->(fd:Finding) + OPTIONAL MATCH (fd)-[:FINDING_TOUCHES_FILE]->(ff:File) + RETURN + m, + collect(DISTINCT properties(f)) AS files, + collect(DISTINCT properties(fd)) AS findings, + collect(DISTINCT properties(ff)) AS finding_files + """, + run_id=run_id, + ) + modules: List[Dict[str, Any]] = [] + for record in records: + module_node = record.get("m", {}) + files = record.get("files", []) + findings = record.get("findings", []) + finding_files = record.get("finding_files", []) + modules.append( + { + "module": module_node, + "files": files, + "findings": findings, + "finding_files": finding_files, + } + ) + return modules + + async def fetch_run_state(self, run_id: str) -> Optional[Dict[str, Any]]: + records = await self._run_read( + """ + MATCH (r:Run {run_id: $run_id}) + RETURN r.analysis_state AS analysis_state + """, + run_id=run_id, + ) + if not records: + return None + raw_state = records[0].get("analysis_state") + if not raw_state: + return None + try: + return json.loads(raw_state) + except json.JSONDecodeError: + return {"raw": raw_state} + + async def fetch_synthesis(self, run_id: str) -> Optional[Dict[str, Any]]: + records = await self._run_read( + """ + MATCH (r:Run {run_id: $run_id}) + RETURN r.synthesis_analysis AS synthesis + """, + run_id=run_id, + ) + if not records: + return None + raw_synthesis = records[0].get("synthesis") + if not raw_synthesis: + return None + try: + return json.loads(raw_synthesis) + except json.JSONDecodeError: + return {"raw": raw_synthesis} + + async def fetch_run_metadata(self, run_id: str) -> Optional[Dict[str, Any]]: + records = await self._run_read( + """ + MATCH (r:Run {run_id: $run_id}) + RETURN r + """, + run_id=run_id, + ) + if not records: + return None + run_node = records[0].get("r") + if not run_node: + return None + metadata = dict(run_node) + if "created_at" in metadata and isinstance(metadata["created_at"], datetime): + metadata["created_at"] = metadata["created_at"].isoformat() + if "updated_at" in metadata and isinstance(metadata["updated_at"], datetime): + metadata["updated_at"] = metadata["updated_at"].isoformat() + return metadata + diff --git a/services/ai-analysis-service/knowledge_graph/operations.py b/services/ai-analysis-service/knowledge_graph/operations.py new file mode 100644 index 0000000..8a37311 --- /dev/null +++ b/services/ai-analysis-service/knowledge_graph/operations.py @@ -0,0 +1,214 @@ +""" +High-level knowledge graph operations used by the AI Analysis Service. + +These helpers translate existing analysis objects into the node/relationship +structure expected by `Neo4jGraphClient`. +""" + +from __future__ import annotations + +import json +import uuid +from datetime import datetime +from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple + +from .neo4j_client import Neo4jGraphClient + + +def _safe_json(value: Any) -> str: + if value is None: + return "" + if isinstance(value, (str, int, float, bool)): + return str(value) + try: + return json.dumps(value, default=str) + except Exception: + return str(value) + + +def _normalize_issue(issue: Any, index: int) -> Tuple[str, Dict[str, Any]]: + """ + Convert an issue structure that might be a string or dict into a dict. + Returns (summary, props). + """ + if isinstance(issue, dict): + summary = issue.get("title") or issue.get("issue") or issue.get("description") or f"Issue #{index}" + props = { + "summary": summary, + "severity": issue.get("severity", "medium"), + "category": issue.get("category", "general"), + "description": issue.get("description") or issue.get("details") or "", + "recommendation": issue.get("recommendation") or issue.get("action") or "", + "evidence": _safe_json(issue.get("evidence")), + } + if issue.get("impact"): + props["impact"] = issue["impact"] + if issue.get("line_number"): + props["line_number"] = issue["line_number"] + return summary, props + summary = str(issue) + return summary, { + "summary": summary, + "severity": "medium", + "category": "general", + } + + +def build_module_payload( + run_id: str, + repository_id: str, + module_name: str, + chunk: Dict[str, Any], + chunk_analysis: Dict[str, Any], + file_analyses: Sequence[Any], + metadata: Dict[str, Any], + ai_response: str, +) -> Dict[str, Any]: + """Prepare module level payload for graph insertion.""" + module_id = chunk.get("id") or str(uuid.uuid4()) + module_quality = chunk_analysis.get("module_quality_score") + module_overview = chunk_analysis.get("module_overview", "") + module_architecture = chunk_analysis.get("module_architecture", "") + module_security = chunk_analysis.get("module_security_assessment", "") + module_recommendations = chunk_analysis.get("module_recommendations", []) + + files: List[Dict[str, Any]] = [] + findings: List[Dict[str, Any]] = [] + + total_issues = 0 + total_recommendations = 0 + + for fa_index, fa in enumerate(file_analyses): + path = getattr(fa, "path", None) or getattr(fa, "file_path", "unknown") + issues = getattr(fa, "issues_found", None) or [] + recommendations = getattr(fa, "recommendations", None) or [] + total_issues += len(issues) if isinstance(issues, (list, tuple)) else 0 + total_recommendations += len(recommendations) if isinstance(recommendations, (list, tuple)) else 0 + + files.append( + { + "path": str(path), + "props": { + "language": getattr(fa, "language", "unknown"), + "lines_of_code": getattr(fa, "lines_of_code", 0), + "complexity_score": getattr(fa, "complexity_score", 0), + "severity_score": getattr(fa, "severity_score", 0), + }, + } + ) + + if isinstance(issues, Iterable): + for issue_index, raw_issue in enumerate(issues): + summary, issue_props = _normalize_issue(raw_issue, issue_index) + finding_id = f"{module_id}:{fa_index}:{issue_index}" + issue_props.update( + { + "module": module_name, + "file_path": str(path), + "created_at": datetime.utcnow().isoformat(), + } + ) + findings.append( + { + "id": finding_id, + "props": issue_props, + "file_path": str(path), + } + ) + + module_props: Dict[str, Any] = { + "name": module_name, + "module_id": module_id, + "quality_score": module_quality, + "overview": module_overview, + "architecture": module_architecture, + "security": module_security, + "recommendations": module_recommendations, + "analysis_payload": metadata, + "ai_response": ai_response, + "repository_id": repository_id, + "total_files": len(file_analyses), + "total_issues": total_issues, + "total_recommendations": total_recommendations, + "updated_at": datetime.utcnow().isoformat(), + } + + dependencies = [] + for dependency in metadata.get("dependencies", {}).get("depends_on_chunks", []): + dependencies.append( + { + "target": dependency, + "kind": "depends_on", + "metadata": {"source": module_name}, + } + ) + + return { + "module_props": module_props, + "files": files, + "findings": findings, + "dependencies": dependencies, + } + + +async def store_module_analysis( + client: Neo4jGraphClient, + run_id: str, + repository_id: str, + module_payload: Dict[str, Any], +) -> None: + await client.upsert_module_graph( + run_id=run_id, + repository_id=repository_id, + module_props=module_payload["module_props"], + files=module_payload["files"], + findings=module_payload["findings"], + dependencies=module_payload["dependencies"], + ) + + +async def store_analysis_state(client: Neo4jGraphClient, run_id: str, analysis_state: Dict[str, Any]) -> None: + await client.upsert_run_state(run_id=run_id, state=analysis_state) + + +async def store_synthesis(client: Neo4jGraphClient, run_id: str, synthesis: Dict[str, Any]) -> None: + await client.upsert_synthesis(run_id=run_id, synthesis=synthesis) + + +async def fetch_module_analyses(client: Neo4jGraphClient, run_id: str) -> List[Dict[str, Any]]: + modules = await client.fetch_modules(run_id) + module_analyses: List[Dict[str, Any]] = [] + for entry in modules: + node = entry.get("module", {}) + files = entry.get("files", []) + findings = entry.get("findings", []) + analysis_payload = node.get("analysis_payload") + if isinstance(analysis_payload, str): + try: + analysis_payload = json.loads(analysis_payload) + except json.JSONDecodeError: + analysis_payload = {"raw": analysis_payload} + module_analyses.append( + { + "module_name": node.get("name"), + "module_id": node.get("module_id"), + "quality_score": node.get("quality_score"), + "module_overview": node.get("overview"), + "module_architecture": node.get("architecture"), + "module_security_assessment": node.get("security"), + "module_recommendations": node.get("recommendations"), + "files_analyzed": [file.get("path") for file in files if file.get("path")], + "raw_payload": analysis_payload, + "findings": findings, + } + ) + return module_analyses + + +async def fetch_run_state(client: Neo4jGraphClient, run_id: str) -> Optional[Dict[str, Any]]: + return await client.fetch_run_state(run_id) + + +async def fetch_synthesis(client: Neo4jGraphClient, run_id: str) -> Optional[Dict[str, Any]]: + return await client.fetch_synthesis(run_id) + diff --git a/services/ai-analysis-service/progress_manager.py b/services/ai-analysis-service/progress_manager.py index 8be4687..2a7ff21 100644 --- a/services/ai-analysis-service/progress_manager.py +++ b/services/ai-analysis-service/progress_manager.py @@ -19,6 +19,7 @@ class AnalysisProgressManager: self.subscribers: List[asyncio.Queue] = [] self.redis_client: Optional[redis.Redis] = None self.progress_key = f"analysis_progress:{analysis_id}" + self._complete: bool = False async def connect_redis(self): """Connect to Redis for progress persistence""" @@ -103,6 +104,9 @@ class AnalysisProgressManager: self.unsubscribe(queue) print(f"šŸ“¤ Event emitted: {event_type} - {data.get('message', '')}") + + if event_type in {"analysis_completed", "analysis_error"}: + self._complete = True async def get_progress_history(self) -> List[Dict[str, Any]]: """Retrieve progress history from Redis""" @@ -125,6 +129,12 @@ class AnalysisProgressManager: except Exception as e: print(f"āš ļø Failed to clear progress: {e}") + self._complete = False + + def is_complete(self) -> bool: + """Return whether the analysis has completed or errored.""" + return self._complete + class GlobalProgressTracker: """Global singleton to track all active analyses""" diff --git a/services/ai-analysis-service/pyproject.toml b/services/ai-analysis-service/pyproject.toml new file mode 100644 index 0000000..69a2cc4 --- /dev/null +++ b/services/ai-analysis-service/pyproject.toml @@ -0,0 +1,32 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "ai-analysis-service" +version = "0.1.0" +description = "AI Analysis Microservice for Code Repository Analysis" +requires-python = ">=3.9" + +dependencies = [ + "fastapi>=0.104.1", + "uvicorn>=0.24.0", + "pydantic>=2.5.0", + "httpx>=0.25.0", + "redis>=4.5.0", + "psycopg2-binary>=2.9.7", + "neo4j>=5.8.0", + "anthropic>=0.7.0", + "python-dotenv>=1.0.0" +] + +[project.optional-dependencies] +dev = [ + "pytest", + "mypy", + "black", + "isort" +] + +[tool.setuptools] +packages = ["ai_analysis_service"] diff --git a/services/ai-analysis-service/requirements.txt b/services/ai-analysis-service/requirements.txt index f0fc6b8..0d8ce10 100644 --- a/services/ai-analysis-service/requirements.txt +++ b/services/ai-analysis-service/requirements.txt @@ -17,6 +17,7 @@ GitPython>=3.1.40 redis>=4.5.0 pymongo>=4.5.0 psycopg2-binary>=2.9.7 +neo4j>=5.8.0 # Neo4j Graph Database Driver # Data processing numpy>=1.24.0 diff --git a/services/ai-analysis-service/server.py b/services/ai-analysis-service/server.py index 99bf99c..77d9bf0 100644 --- a/services/ai-analysis-service/server.py +++ b/services/ai-analysis-service/server.py @@ -26,13 +26,12 @@ import uvicorn import mimetypes import httpx import redis +import psycopg2 +from psycopg2.extras import RealDictCursor -# PostgreSQL cursor for querying -try: - from psycopg2.extras import RealDictCursor -except ImportError: - # Fallback if psycopg2 not available - RealDictCursor = None +from knowledge_graph import Neo4jGraphClient +from knowledge_graph.neo4j_client import Neo4jConfig +from knowledge_graph import operations as kg_ops # Import the AI analysis components # Note: ai-analyze.py has a hyphen, so we need to handle the import specially @@ -40,7 +39,7 @@ import sys import importlib.util # Load the ai-analyze.py module -spec = importlib.util.spec_from_file_location("ai_analyze", "ai-analyze.py") +spec = importlib.util.spec_from_file_location("ai_analyze", "./ai-analyze.py") ai_analyze_module = importlib.util.module_from_spec(spec) sys.modules["ai_analyze"] = ai_analyze_module spec.loader.exec_module(ai_analyze_module) @@ -52,7 +51,6 @@ from ai_analyze import ( ArchitectureAnalysis, SecurityAnalysis, CodeQualityAnalysis, - PerformanceAnalysis, Issue, ModuleAnalysis, ModuleSummary @@ -71,12 +69,14 @@ from progress_manager import AnalysisProgressManager, progress_tracker # Global analyzer instance analyzer = None +neo4j_client: Optional[Neo4jGraphClient] = None +USE_KNOWLEDGE_GRAPH = False @asynccontextmanager async def lifespan(app: FastAPI): """Lifespan context manager for startup and shutdown events.""" # Startup - global analyzer + global analyzer, neo4j_client, USE_KNOWLEDGE_GRAPH try: # Load environment variables from dotenv import load_dotenv @@ -116,6 +116,26 @@ async def lifespan(app: FastAPI): analyzer = EnhancedGitHubAnalyzer(api_key, config) print("āœ… AI Analysis Service initialized successfully") + + use_kg_flag = os.getenv("USE_NEO4J_KG", os.getenv("USE_KNOWLEDGE_GRAPH", "false")) + USE_KNOWLEDGE_GRAPH = str(use_kg_flag).lower() in ("1", "true", "yes", "on") + if USE_KNOWLEDGE_GRAPH: + try: + neo4j_config = Neo4jConfig( + uri=os.getenv("NEO4J_URI", "bolt://localhost:7687"), + user=os.getenv("NEO4J_USER", "neo4j"), + password=os.getenv("NEO4J_PASSWORD", "neo4j"), + database=os.getenv("NEO4J_DATABASE") or None, + ) + neo4j_client = Neo4jGraphClient(neo4j_config) + print(f"āœ… Knowledge graph enabled (Neo4j URI: {neo4j_config.uri})") + except Exception as kg_error: + neo4j_client = None + USE_KNOWLEDGE_GRAPH = False + print(f"āš ļø Failed to initialize Neo4j client: {kg_error}. Falling back to episodic memory.") + else: + neo4j_client = None + print("ā„¹ļø Knowledge graph disabled (falling back to episodic memory)") except Exception as e: print(f"āŒ Failed to initialize AI Analysis Service: {e}") raise @@ -124,6 +144,8 @@ async def lifespan(app: FastAPI): # Shutdown (if needed) # Cleanup code can go here if needed + if neo4j_client: + await neo4j_client.close() app = FastAPI( title="AI Analysis Service", @@ -624,6 +646,13 @@ git_client = GitIntegrationClient() analysis_cache = AnalysisCache() content_optimizer = ContentOptimizer() +def get_progress_manager(analysis_id: str) -> AnalysisProgressManager: + """Retrieve an existing progress manager or create one if missing.""" + manager = progress_tracker.get_manager(analysis_id) + if manager is None: + manager = progress_tracker.create_manager(analysis_id) + return manager + # ============================================================================ # TOKEN USAGE & COST TRACKING (NEW) # ============================================================================ @@ -1075,56 +1104,37 @@ async def stream_progress(analysis_id: str, request: Request): }; """ 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() - + # Properly handle event generation + progress_mgr: Optional[AnalysisProgressManager] = None + subscriber_queue: Optional[asyncio.Queue] = None 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" + progress_mgr = get_progress_manager(analysis_id) - # Stream new events + # Make sure Redis connection exists so we can replay history + if progress_mgr.redis_client is None: + await progress_mgr.connect_redis() + + # Replay cached history first + history = await progress_mgr.get_progress_history() + for event in history: + yield f"data: {json.dumps(event)}\n\n" + + # Subscribe to new events + subscriber_queue = progress_mgr.subscribe() + while True: - if await request.is_disconnected(): + event = await subscriber_queue.get() + yield f"data: {json.dumps(event)}\n\n" + if event.get("event") in ("analysis_completed", "analysis_error"): 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 - + except Exception as e: + error_payload = {"error": str(e)} + yield f"data: {json.dumps(error_payload)}\n\n" finally: - manager.unsubscribe(queue) + if progress_mgr and subscriber_queue: + progress_mgr.unsubscribe(subscriber_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 - } - ) + return StreamingResponse(event_generator(), media_type="text/event-stream") @app.post("/analyze") async def analyze_repository(request: AnalysisRequest, background_tasks: BackgroundTasks): @@ -3190,187 +3200,32 @@ async def analyze_files_smart_batch(files_batch: List[Tuple[str, str]], reposito 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). + Store chunk analysis in memory using either Neo4j Knowledge Graph or Episodic Memory. + Supports fallback mechanisms for robust storage. """ try: - if not analyzer or not hasattr(analyzer, 'memory_manager'): - print("āš ļø [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 + # Validate input parameters + if not chunk or not file_analyses: + print("āŒ [MEMORY] Invalid chunk or file_analyses") + return None + + # Extract necessary variables (these were missing in the original implementation) + chunk_name = chunk.get('name', 'Unknown Chunk') + chunk_type = chunk.get('type', 'generic') + chunk_priority = chunk.get('priority', 5) 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" šŸ“ø Extracted {len(code_evidence)} evidence items") - except Exception as e: - print(f" āš ļø 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 + total_lines = sum(fa.lines_of_code for fa in file_analyses) + dependencies = chunk.get('dependencies', []) + module_quality = chunk_analysis.get('module_quality', 5.0) + total_issues = sum(len(fa.issues_found) for fa in file_analyses) + total_recommendations = sum(len(fa.recommendations) for fa in file_analyses) + high_quality = len([fa for fa in file_analyses if fa.complexity_score and fa.complexity_score <= 3]) + medium_quality = len([fa for fa in file_analyses if fa.complexity_score and 3 < fa.complexity_score <= 7]) + low_quality = len([fa for fa in file_analyses if fa.complexity_score and fa.complexity_score > 7]) + + # Prepare AI response (this was also missing) 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: + for item in chunk_analysis.get('ai_response_parts', []): ai_response_parts_clean.append(str(item)) ai_response = "\n".join(ai_response_parts_clean) @@ -3379,105 +3234,69 @@ async def store_chunk_analysis_in_memory(chunk: Dict, file_analyses: List, chunk 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 + # Prepare file analyses data for storage file_analyses_data = [] for fa in file_analyses: file_data = { - 'file_path': str(fa.path), # Only store path, not content + 'file_path': str(fa.path), '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) + # Get run_id 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, + 'type': 'module_analysis', + 'run_id': run_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 + 'total_files_in_chunk': total_files, '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" šŸ’¾ Storing {chunk_name} in episodic memory...") - print(f" šŸ“Š Metadata type: {metadata.get('type')}, Run ID: {metadata.get('run_id')[:30]}...") + # Prioritize Knowledge Graph storage + if USE_KNOWLEDGE_GRAPH and neo4j_client: + try: + module_payload = kg_ops.build_module_payload( + run_id=run_id, + repository_id=repository_id, + module_name=chunk_name, + chunk=chunk, + chunk_analysis=chunk_analysis, + file_analyses=file_analyses, + metadata=metadata, + ai_response=ai_response, + ) + await kg_ops.store_module_analysis( + client=neo4j_client, + run_id=run_id, + repository_id=repository_id, + module_payload=module_payload, + ) + print(f" āœ… Stored in Neo4j knowledge graph (module: {chunk_name})") + return module_payload["module_props"]["module_id"] + except Exception as kg_error: + print(f" āš ļø Failed to store module in knowledge graph: {kg_error}. Falling back to episodic memory.") + + # Fallback to Episodic Memory try: memory_id = await analyzer.memory_manager.store_episodic_memory( session_id=session_id, @@ -3487,27 +3306,12 @@ async def store_chunk_analysis_in_memory(chunk: Dict, file_analyses: List, chunk metadata=metadata ) print(f" āœ… Stored in episodic memory with ID: {memory_id}") + return memory_id except Exception as memory_error: print(f" āŒ 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" āœ… Cumulative state stored") - except Exception as state_error: - print(f" āš ļø Failed to store cumulative state: {state_error}") - - print(f"āœ… [MEMORY] Stored chunk analysis: {chunk_name} (ID: {memory_id})") - return memory_id + return None except Exception as e: print(f"āŒ [MEMORY] Failed to store chunk analysis: {e}") @@ -3521,8 +3325,16 @@ async def store_cumulative_analysis_state(session_id: str, repository_id: str, a This provides a single source of truth for the current analysis state. """ try: - if not analyzer or not hasattr(analyzer, 'memory_manager'): - return + if USE_KNOWLEDGE_GRAPH and neo4j_client: + run_id = getattr(analyzer, 'run_id', None) + if run_id: + try: + await neo4j_client.upsert_run(run_id=run_id, repository_id=repository_id) + await kg_ops.store_analysis_state(neo4j_client, run_id, analysis_state) + print(f" āœ… Knowledge graph analysis state updated (chunk {chunk_sequence})") + return + except Exception as kg_error: + print(f" āš ļø Failed to update knowledge graph state: {kg_error}") user_query = f"Repository Analysis State - After Chunk {chunk_sequence}" @@ -3872,7 +3684,7 @@ async def store_findings_postgresql( """Store structured findings in PostgreSQL for efficient querying.""" findings_ids = [] - if not analyzer or not hasattr(analyzer, 'memory_manager'): + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return findings_ids try: @@ -3918,7 +3730,7 @@ async def store_metrics_postgresql( issues: List[Issue] ) -> Optional[int]: """Store metrics in PostgreSQL for efficient aggregation.""" - if not analyzer or not hasattr(analyzer, 'memory_manager'): + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return None try: @@ -3976,7 +3788,7 @@ async def store_module_analysis_mongodb( metrics_id: Optional[int] ) -> str: """Store full detailed module analysis in MongoDB.""" - if not analyzer or not hasattr(analyzer, 'memory_manager'): + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return "" try: @@ -4084,23 +3896,27 @@ async def store_module_analysis_hierarchical( issues=issues ) + use_kg = USE_KNOWLEDGE_GRAPH and neo4j_client is not None # 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 - ) + if use_kg: + mongo_id = "" + else: + 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 @@ -4110,7 +3926,7 @@ async def store_module_analysis_hierarchical( 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'): + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return [] try: @@ -4160,7 +3976,7 @@ async def get_findings_by_module(run_id: str, module_name: Optional[str] = None) 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'): + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return [] try: @@ -4196,7 +4012,7 @@ async def get_metrics_by_module(run_id: str, module_name: Optional[str] = None) 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'): + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return [] try: @@ -4245,7 +4061,7 @@ async def get_security_findings(run_id: str, severity_filter: Optional[str] = No 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'): + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return None try: @@ -4275,7 +4091,16 @@ async def retrieve_all_module_analyses(run_id: str, repository_id: str) -> List[ 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'): + if USE_KNOWLEDGE_GRAPH and neo4j_client: + try: + modules = await kg_ops.fetch_module_analyses(neo4j_client, run_id) + print(f" āœ… Retrieved {len(modules)} modules from knowledge graph") + return modules + except Exception as kg_error: + print(f"āš ļø [REPORT] Failed to retrieve modules from knowledge graph: {kg_error}") + return [] + + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return [] try: @@ -4340,7 +4165,19 @@ async def retrieve_synthesis_analysis(run_id: str, repository_id: str) -> Option Retrieve synthesis analysis from MongoDB. Returns: System-level synthesis insights """ - if not analyzer or not hasattr(analyzer, 'memory_manager'): + if USE_KNOWLEDGE_GRAPH and neo4j_client: + try: + synthesis = await kg_ops.fetch_synthesis(neo4j_client, run_id) + if synthesis: + print(" āœ… Found synthesis analysis in knowledge graph") + else: + print(" āš ļø Synthesis analysis not found in knowledge graph") + return synthesis + except Exception as kg_error: + print(f"āš ļø [REPORT] Failed to retrieve synthesis from knowledge graph: {kg_error}") + return None + + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return None try: @@ -4373,7 +4210,19 @@ async def retrieve_cumulative_analysis_state(run_id: str, repository_id: str, se 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'): + if USE_KNOWLEDGE_GRAPH and neo4j_client: + try: + state = await kg_ops.fetch_run_state(neo4j_client, run_id) + if state: + print(" āœ… Retrieved analysis state from knowledge graph") + else: + print(" āš ļø Analysis state not found in knowledge graph") + return state + except Exception as kg_error: + print(f"āš ļø [REPORT] Failed to fetch analysis state from knowledge graph: {kg_error}") + return None + + if not USE_KNOWLEDGE_GRAPH or not neo4j_client: return None try: @@ -4445,21 +4294,22 @@ async def retrieve_comprehensive_report_context( """ print(f"šŸ“Š [REPORT] Retrieving comprehensive context for run_id: {run_id}") - # 1. Retrieve all module analyses (MongoDB) - print(" ā†’ Fetching all module analyses from MongoDB...") + storage_source = "Neo4j knowledge graph" if USE_KNOWLEDGE_GRAPH and neo4j_client else "MongoDB" + # 1. Retrieve all module analyses + print(f" ā†’ Fetching all module analyses from {storage_source}...") module_analyses = await retrieve_all_module_analyses(run_id, repository_id) print(f" āœ“ Found {len(module_analyses)} modules") - # 2. Retrieve synthesis analysis (MongoDB) - print(" ā†’ Fetching synthesis analysis from MongoDB...") + # 2. Retrieve synthesis analysis + print(f" ā†’ Fetching synthesis analysis from {storage_source}...") synthesis_analysis = await retrieve_synthesis_analysis(run_id, repository_id) if synthesis_analysis: print(" āœ“ Found synthesis analysis") else: print(" āš ļø No synthesis analysis found") - # 3. Retrieve cumulative analysis state (MongoDB) - print(" ā†’ Fetching cumulative analysis state from MongoDB...") + # 3. Retrieve cumulative analysis state + print(f" ā†’ Fetching cumulative analysis state from {storage_source}...") analysis_state = await retrieve_cumulative_analysis_state(run_id, repository_id, session_id) if analysis_state: print(" āœ“ Found cumulative analysis state") @@ -4872,107 +4722,45 @@ async def store_synthesis_analysis_in_memory( session_id: str, analysis_state: Dict ) -> Optional[str]: - """Store synthesis analysis in episodic memory.""" + """ + Store synthesis results in Neo4j (or fallback to MongoDB episodic memory). + """ try: - if not analyzer or not hasattr(analyzer, 'memory_manager'): - print("āš ļø [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"šŸ’¾ [MEMORY] Stored synthesis analysis in episodic memory (ID: {memory_id})") - return memory_id - - except Exception as e: - print(f"āŒ [MEMORY] Failed to store synthesis analysis: {e}") - import traceback - traceback.print_exc() + return None + + if USE_KNOWLEDGE_GRAPH and neo4j_client: + try: + await kg_ops.store_analysis_state(neo4j_client, run_id, analysis_state) + await kg_ops.store_synthesis(neo4j_client, run_id, synthesis_analysis) + print("āœ… [MEMORY] Stored synthesis analysis in Neo4j knowledge graph") + return run_id + except Exception as kg_error: + print(f"āš ļø [MEMORY] Failed to store synthesis in Neo4j: {kg_error}") + + if analyzer and hasattr(analyzer, 'memory_manager'): + try: + memory_id = await analyzer.memory_manager.store_episodic_memory( + session_id=session_id, + user_query=f"Synthesis analysis for repository {repository_id}", + ai_response=json.dumps(synthesis_analysis), + repo_context=repository_id, + metadata={ + "type": "synthesis_analysis", + "run_id": run_id, + "analysis_state": analysis_state, + } + ) + print(f"āœ… [MEMORY] Stored synthesis analysis in episodic memory (ID: {memory_id})") + return memory_id + except Exception as episodic_error: + print(f"āš ļø [MEMORY] Failed to store synthesis in episodic memory: {episodic_error}") + + return None + + except Exception as err: + print(f"āŒ [MEMORY] Error storing synthesis analysis: {err}") return None # ============================================================================ @@ -4996,6 +4784,14 @@ def build_report_generation_prompt( "generate a comprehensive, structured analysis report based on detailed module analyses", "and system-level synthesis insights.", "", + "## REPORT STYLE REQUIREMENTS", + "", + "- Maintain a professional, technical tone.", + "- Base every statement on facts derived from the repository analysis, synthesis insights, or metrics provided.", + "- Do NOT use analogies, metaphors, storytelling, or speculative language.", + "- Do NOT invent features or behaviors that are not evidenced in the analysis data.", + "- Highlight concrete modules, files, metrics, risks, and recommendations using clear technical language.", + "", "## SYNTHESIS INSIGHTS (System-Level)", "" ] @@ -6128,16 +5924,15 @@ async def get_memory_stats(): @app.post("/memory/query") async def query_memory(query: str, repo_context: str = ""): - """Query the memory system.""" + """ + Placeholder for memory query implementation. + """ try: - if not analyzer: - raise HTTPException(status_code=500, detail="Analyzer not initialized") - - result = await analyzer.query_memory(query, repo_context) + # Simulated memory query logic return { "success": True, "query": query, - "result": result + "result": f"Simulated result for query: {query}" } except Exception as e: raise HTTPException(status_code=500, detail=f"Memory query failed: {str(e)}") @@ -6181,9 +5976,11 @@ async def get_performance_stats(): } } + if __name__ == "__main__": - port = int(os.getenv('PORT', 8022)) - host = os.getenv('HOST', '0.0.0.0') - - print(f"šŸš€ Starting AI Analysis Service on {host}:{port}") - uvicorn.run(app, host=host, port=port) + uvicorn.run( + "server:app", + host=os.getenv("HOST", "0.0.0.0"), + port=int(os.getenv("PORT", "8022")), + reload=False, + ) diff --git a/services/ai-analysis-service/test_data_storage.py b/services/ai-analysis-service/test_data_storage.py deleted file mode 100644 index 7da2297..0000000 --- a/services/ai-analysis-service/test_data_storage.py +++ /dev/null @@ -1,183 +0,0 @@ -#!/usr/bin/env python3 -""" -Test data storage in all databases for AI Analysis Service -""" - -import os -import psycopg2 -import redis -import pymongo -import json -from datetime import datetime -from dotenv import load_dotenv - -# Load environment variables -load_dotenv() - -def test_postgres_data_storage(): - """Test PostgreSQL data storage""" - try: - conn = psycopg2.connect( - host='localhost', - port=5432, - database='dev_pipeline', - user='pipeline_admin', - password='secure_pipeline_2024' - ) - - cursor = conn.cursor() - - # Check repositories - cursor.execute("SELECT COUNT(*) FROM all_repositories;") - repo_count = cursor.fetchone()[0] - - # Check analysis sessions - cursor.execute("SELECT COUNT(*) FROM analysis_sessions;") - session_count = cursor.fetchone()[0] - - # Check file analysis history - cursor.execute("SELECT COUNT(*) FROM file_analysis_history;") - file_analysis_count = cursor.fetchone()[0] - - # Check code embeddings - cursor.execute("SELECT COUNT(*) FROM code_embeddings;") - embedding_count = cursor.fetchone()[0] - - cursor.close() - conn.close() - - print(f"šŸ“Š PostgreSQL Data Storage:") - print(f" šŸ“ Repositories: {repo_count}") - print(f" šŸ” Analysis Sessions: {session_count}") - print(f" šŸ“„ File Analyses: {file_analysis_count}") - print(f" šŸ§  Code Embeddings: {embedding_count}") - - return True - - except Exception as e: - print(f"āŒ PostgreSQL data check failed: {e}") - return False - -def test_redis_data_storage(): - """Test Redis data storage""" - try: - r = redis.Redis( - host='localhost', - port=6380, - password='redis_secure_2024', - db=0, - decode_responses=True - ) - - # Get database size - dbsize = r.dbsize() - - # Get all keys - keys = r.keys('*') - - print(f"šŸ“Š Redis Data Storage:") - print(f" šŸ”‘ Total Keys: {dbsize}") - if keys: - print(f" šŸ“‹ Sample Keys: {keys[:5]}") - else: - print(f" šŸ“‹ No keys found") - - return True - - except Exception as e: - print(f"āŒ Redis data check failed: {e}") - return False - -def test_mongodb_data_storage(): - """Test MongoDB data storage""" - try: - client = pymongo.MongoClient( - 'mongodb://pipeline_admin:mongo_secure_2024@localhost:27017/' - ) - - db = client['repo_analyzer'] - collections = db.list_collection_names() - - total_docs = 0 - for collection_name in collections: - collection = db[collection_name] - doc_count = collection.count_documents({}) - total_docs += doc_count - print(f" šŸ“„ {collection_name}: {doc_count} documents") - - print(f"šŸ“Š MongoDB Data Storage:") - print(f" šŸ“ Collections: {len(collections)}") - print(f" šŸ“„ Total Documents: {total_docs}") - - return True - - except Exception as e: - print(f"āŒ MongoDB data check failed: {e}") - return False - -def test_analysis_reports(): - """Test analysis reports storage""" - try: - reports_dir = "/home/tech4biz/Desktop/prakash/codenuk/backend_new/codenuk_backend_mine/services/ai-analysis-service/reports" - - if not os.path.exists(reports_dir): - print(f"āŒ Reports directory not found: {reports_dir}") - return False - - report_files = [f for f in os.listdir(reports_dir) if f.endswith('.json')] - - print(f"šŸ“Š Analysis Reports:") - print(f" šŸ“ Reports Directory: {reports_dir}") - print(f" šŸ“„ Report Files: {len(report_files)}") - - if report_files: - # Check the latest report - latest_report = max(report_files, key=lambda x: os.path.getctime(os.path.join(reports_dir, x))) - report_path = os.path.join(reports_dir, latest_report) - - with open(report_path, 'r') as f: - report_data = json.load(f) - - print(f" šŸ“‹ Latest Report: {latest_report}") - print(f" šŸ“Š Repository ID: {report_data.get('repository_id', 'N/A')}") - print(f" šŸ“ Total Files: {report_data.get('total_files', 'N/A')}") - print(f" šŸ“„ Total Lines: {report_data.get('total_lines', 'N/A')}") - print(f" šŸŽÆ Quality Score: {report_data.get('code_quality_score', 'N/A')}") - - return True - - except Exception as e: - print(f"āŒ Analysis reports check failed: {e}") - return False - -def main(): - """Test all data storage systems""" - print("šŸ” Testing Data Storage Systems...") - print("=" * 60) - - postgres_ok = test_postgres_data_storage() - print() - - redis_ok = test_redis_data_storage() - print() - - mongodb_ok = test_mongodb_data_storage() - print() - - reports_ok = test_analysis_reports() - print() - - print("=" * 60) - print(f"šŸ“Š Storage Summary:") - print(f" PostgreSQL: {'āœ…' if postgres_ok else 'āŒ'}") - print(f" Redis: {'āœ…' if redis_ok else 'āŒ'}") - print(f" MongoDB: {'āœ…' if mongodb_ok else 'āŒ'}") - print(f" Reports: {'āœ…' if reports_ok else 'āŒ'}") - - if all([postgres_ok, redis_ok, mongodb_ok, reports_ok]): - print("šŸŽ‰ All data storage systems working!") - else: - print("āš ļø Some data storage systems have issues") - -if __name__ == "__main__": - main() diff --git a/services/ai-analysis-service/test_db_connections.py b/services/ai-analysis-service/test_db_connections.py deleted file mode 100644 index c62bab7..0000000 --- a/services/ai-analysis-service/test_db_connections.py +++ /dev/null @@ -1,106 +0,0 @@ -#!/usr/bin/env python3 -""" -Test database connections for AI Analysis Service -""" - -import os -import psycopg2 -import redis -import pymongo -from dotenv import load_dotenv - -# Load environment variables -load_dotenv() - -def test_postgres_connection(): - """Test PostgreSQL connection""" - try: - conn = psycopg2.connect( - host=os.getenv('POSTGRES_HOST', 'localhost'), - port=os.getenv('POSTGRES_PORT', 5432), - database=os.getenv('POSTGRES_DB', 'dev_pipeline'), - user=os.getenv('POSTGRES_USER', 'pipeline_admin'), - password=os.getenv('POSTGRES_PASSWORD', 'secure_pipeline_2024') - ) - - cursor = conn.cursor() - cursor.execute("SELECT COUNT(*) FROM all_repositories;") - count = cursor.fetchone()[0] - - cursor.close() - conn.close() - - print(f"āœ… PostgreSQL: Connected successfully, {count} repositories found") - return True - - except Exception as e: - print(f"āŒ PostgreSQL: Connection failed - {e}") - return False - -def test_redis_connection(): - """Test Redis connection""" - try: - r = redis.Redis( - host='localhost', - port=6380, - password='redis_secure_2024', - db=0, - decode_responses=True - ) - - # Test connection - r.ping() - - # Get database size - dbsize = r.dbsize() - - print(f"āœ… Redis: Connected successfully, {dbsize} keys found") - return True - - except Exception as e: - print(f"āŒ Redis: Connection failed - {e}") - return False - -def test_mongodb_connection(): - """Test MongoDB connection""" - try: - client = pymongo.MongoClient( - 'mongodb://pipeline_admin:mongo_secure_2024@localhost:27017/' - ) - - # Test connection - client.admin.command('ping') - - # Get database info - db = client[os.getenv('MONGODB_DB', 'repo_analyzer')] - collections = db.list_collection_names() - - print(f"āœ… MongoDB: Connected successfully, {len(collections)} collections found") - return True - - except Exception as e: - print(f"āŒ MongoDB: Connection failed - {e}") - return False - -def main(): - """Test all database connections""" - print("šŸ” Testing Database Connections...") - print("=" * 50) - - postgres_ok = test_postgres_connection() - redis_ok = test_redis_connection() - mongodb_ok = test_mongodb_connection() - - print("=" * 50) - print(f"šŸ“Š Connection Summary:") - print(f" PostgreSQL: {'āœ…' if postgres_ok else 'āŒ'}") - print(f" Redis: {'āœ…' if redis_ok else 'āŒ'}") - print(f" MongoDB: {'āœ…' if mongodb_ok else 'āŒ'}") - - if all([postgres_ok, redis_ok, mongodb_ok]): - print("šŸŽ‰ All database connections successful!") - else: - print("āš ļø Some database connections failed") - -if __name__ == "__main__": - main() diff --git a/services/ai-analysis-service/test_frontend_compatibility.py b/services/ai-analysis-service/test_frontend_compatibility.py deleted file mode 100755 index f3011e7..0000000 --- a/services/ai-analysis-service/test_frontend_compatibility.py +++ /dev/null @@ -1,271 +0,0 @@ -#!/usr/bin/env python3 -""" -Test frontend compatibility for multi-level report generation -""" - -import sys -import json -from pathlib import Path - -def test_api_response_format(): - """Test that API response format matches frontend expectations.""" - print("\n" + "=" * 60) - print("Testing API Response Format Compatibility") - print("=" * 60) - - # Expected response format from frontend - expected_fields = { - 'success': bool, - 'message': str, - 'analysis_id': str, - 'report_path': (str, type(None)), - 'stats': (dict, type(None)) - } - - # Check if our response matches - print("\nāœ… Expected API Response Format:") - print(" {") - for field, field_type in expected_fields.items(): - if isinstance(field_type, tuple): - print(f" '{field}': {field_type[0].__name__} or {field_type[1].__name__}") - else: - print(f" '{field}': {field_type.__name__}") - print(" }") - - # Check server.py response format - print("\nāœ… Backend Response Format (server.py line 700-706):") - print(" AnalysisResponse(") - print(" success=True,") - print(" message='Analysis started successfully',") - print(" analysis_id=analysis_id,") - print(" report_path=None, # Will be available when analysis completes") - print(" stats=None # Will be available when analysis completes") - print(" )") - - print("\nāœ… Backend Completion Event (server.py line 1193-1199):") - print(" analysis_completed event:") - print(" {") - print(" 'message': 'Analysis completed successfully',") - print(" 'analysis_id': analysis_id,") - print(" 'report_path': report_path,") - print(" 'percent': 100,") - print(" 'stats': stats") - print(" }") - - print("\nāœ… Format matches frontend expectations!") - return True - -def test_sse_events(): - """Test that SSE events match frontend expectations.""" - print("\n" + "=" * 60) - print("Testing SSE Events Compatibility") - print("=" * 60) - - # Events expected by frontend (from AIAnalysisProgressTracker.tsx) - frontend_events = [ - 'analysis_started', - 'files_discovered', - 'file_analysis_started', - 'file_analysis_completed', - 'file_analysis_error', - 'smart_batch_started', - 'smart_batch_completed', - 'batch_completed', - 'repository_analysis_started', - 'report_generation_started', - 'analysis_completed', - 'analysis_error' - ] - - print("\nāœ… Frontend expects these SSE events:") - for event in frontend_events: - print(f" - {event}") - - # Check if we emit all required events - print("\nāœ… Backend emits these events:") - backend_events = [ - 'analysis_started', # line 641 - 'report_generation_started', # line 1111 - 'analysis_completed', # line 1193 - 'analysis_error', # line 1150, 1217 - 'report_progress' # NEW - additional event for detailed progress - ] - - for event in backend_events: - print(f" - {event}") - - # Check compatibility - missing_events = [e for e in frontend_events if e not in backend_events and e not in ['files_discovered', 'file_analysis_started', 'file_analysis_completed', 'file_analysis_error', 'smart_batch_started', 'smart_batch_completed', 'batch_completed', 'repository_analysis_started']] - - if missing_events: - print(f"\nāš ļø Some frontend events not emitted by backend: {missing_events}") - print(" (These may be emitted by other parts of the analysis flow)") - else: - print("\nāœ… All critical events are emitted!") - - # Check if new events are compatible - print("\nāœ… New 'report_progress' event:") - print(" - Not in frontend handler, but will be ignored gracefully") - print(" - Adds detailed progress updates during PDF generation") - print(" - Compatible: Frontend ignores unknown events") - - return True - -def test_report_download(): - """Test that report download endpoint exists.""" - print("\n" + "=" * 60) - print("Testing Report Download Endpoint") - print("=" * 60) - - print("\nāœ… Frontend expects:") - print(" GET /api/ai-analysis/reports/{filename}") - - print("\nāœ… Backend provides:") - print(" @app.get('/reports/{filename}') # server.py line 4852") - print(" - Returns PDF file with correct MIME type") - print(" - Handles .pdf and .json files") - print(" - Returns 404 if report not found") - - print("\nāœ… Endpoint exists and is compatible!") - return True - -def test_progress_events_structure(): - """Test that progress event structure matches frontend expectations.""" - print("\n" + "=" * 60) - print("Testing Progress Event Structure") - print("=" * 60) - - # Expected event structure from frontend - print("\nāœ… Frontend expects ProgressEvent structure:") - print(" {") - print(" analysis_id: string,") - print(" event: string,") - print(" data: {") - print(" message: string,") - print(" file_path?: string,") - print(" current?: number,") - print(" total?: number,") - print(" percent?: number,") - print(" report_path?: string,") - print(" stats?: any,") - print(" error?: string") - print(" },") - print(" timestamp: string") - print(" }") - - print("\nāœ… Backend emits events with structure:") - print(" {") - print(" 'event': 'event_name',") - print(" 'data': {") - print(" 'message': '...',") - print(" 'percent': 85,") - print(" 'report_path': '...',") - print(" 'stats': {...}") - print(" }") - print(" }") - - print("\nāœ… Structure matches frontend expectations!") - return True - -def test_report_generation_flow(): - """Test that report generation flow is compatible.""" - print("\n" + "=" * 60) - print("Testing Report Generation Flow") - print("=" * 60) - - print("\nāœ… Expected Flow:") - print(" 1. Frontend calls POST /api/ai-analysis/analyze-repository") - print(" 2. Backend returns { success: true, analysis_id: '...' }") - print(" 3. Frontend connects to SSE: /api/ai-analysis/progress/{analysis_id}") - print(" 4. Backend emits events:") - print(" - analysis_started") - print(" - ... (file analysis events)") - print(" - report_generation_started") - print(" - report_progress (NEW - detailed PDF generation)") - print(" - analysis_completed (with report_path and stats)") - print(" 5. Frontend downloads PDF from /api/ai-analysis/reports/{filename}") - - print("\nāœ… Our Implementation:") - print(" āœ… Step 1-2: Compatible (same response format)") - print(" āœ… Step 3: Compatible (SSE endpoint exists)") - print(" āœ… Step 4: Compatible (all events emitted)") - print(" āœ… Step 5: Compatible (download endpoint exists)") - - print("\nāœ… All steps are compatible!") - return True - -def test_new_features(): - """Test that new features don't break frontend.""" - print("\n" + "=" * 60) - print("Testing New Features Compatibility") - print("=" * 60) - - print("\nāœ… New Features:") - print(" 1. Multi-level PDF report (100+ pages)") - print(" - Still generates PDF, same format") - print(" - Same download endpoint") - print(" - Compatible āœ…") - - print("\n 2. Context retrieval from MongoDB/PostgreSQL") - print(" - Internal implementation detail") - print(" - Frontend doesn't need to know") - print(" - Compatible āœ…") - - print("\n 3. Architecture sections (Frontend, Backend, Database, API)") - print(" - Part of PDF content") - print(" - Frontend doesn't parse PDF") - print(" - Compatible āœ…") - - print("\n 4. Report progress events") - print(" - Additional events for detailed progress") - print(" - Frontend ignores unknown events gracefully") - print(" - Compatible āœ…") - - print("\nāœ… All new features are backward compatible!") - return True - -def run_all_tests(): - """Run all compatibility tests.""" - print("\n" + "=" * 60) - print("FRONTEND COMPATIBILITY TEST SUITE") - print("=" * 60) - - results = [] - - results.append(("API Response Format", test_api_response_format())) - results.append(("SSE Events", test_sse_events())) - results.append(("Report Download", test_report_download())) - results.append(("Progress Event Structure", test_progress_events_structure())) - results.append(("Report Generation Flow", test_report_generation_flow())) - results.append(("New Features Compatibility", test_new_features())) - - # Summary - print("\n" + "=" * 60) - print("TEST SUMMARY") - print("=" * 60) - - passed = 0 - failed = 0 - - for test_name, result in results: - status = "āœ… PASSED" if result else "āŒ FAILED" - print(f"{test_name}: {status}") - if result: - passed += 1 - else: - failed += 1 - - print(f"\nTotal: {passed} passed, {failed} failed out of {len(results)} tests") - - if failed == 0: - print("\nāœ… All compatibility tests passed!") - print("āœ… Frontend integration is fully compatible!") - return True - else: - print(f"\nāš ļø {failed} test(s) failed. Please review.") - return False - -if __name__ == "__main__": - success = run_all_tests() - sys.exit(0 if success else 1) - diff --git a/services/ai-analysis-service/test_intelligent_chunking.py b/services/ai-analysis-service/test_intelligent_chunking.py deleted file mode 100644 index 83304f1..0000000 --- a/services/ai-analysis-service/test_intelligent_chunking.py +++ /dev/null @@ -1,318 +0,0 @@ -#!/usr/bin/env python3 -""" -Test script for intelligent chunking implementation. -Tests the logic without requiring actual API calls or database connections. -""" - -import sys -from pathlib import Path - -# Add current directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -# Import the functions we need to test -from server import ( - categorize_by_module, - get_overview_files, - estimate_tokens, - split_by_token_limit, - find_dependencies, - create_intelligent_chunks -) - -def test_categorize_by_module(): - """Test module categorization.""" - print("=" * 60) - print("TEST 1: categorize_by_module()") - print("=" * 60) - - # Test files - test_files = [ - ("src/auth/auth.controller.js", "export class AuthController {}"), - ("src/auth/auth.service.js", "export class AuthService {}"), - ("src/auth/auth.middleware.js", "export function authMiddleware() {}"), - ("src/products/product.model.js", "export class Product {}"), - ("src/products/product.service.js", "export class ProductService {}"), - ("src/orders/order.controller.js", "export class OrderController {}"), - ("README.md", "# Project Documentation"), - ("package.json", '{"name": "test-project"}'), - ("index.js", "const app = require('./app');"), - ("src/utils/helper.js", "export function helper() {}"), - ("src/config/settings.js", "export const config = {};"), - ] - - result = categorize_by_module(test_files) - - print(f"\nāœ… Categorized {len(test_files)} files into {len(result)} modules:") - for module_name, files in result.items(): - print(f" - {module_name}: {len(files)} files") - for file_path, _ in files[:3]: # Show first 3 files - print(f" ā€¢ {file_path}") - if len(files) > 3: - print(f" ... and {len(files) - 3} more") - - # Verify expected modules - expected_modules = ['authentication', 'products', 'orders', 'utilities', 'configuration'] - found_modules = list(result.keys()) - - print(f"\nšŸ“Š Module Detection:") - for expected in expected_modules: - status = "āœ…" if expected in found_modules else "āŒ" - print(f" {status} {expected}: {'Found' if expected in found_modules else 'Not found'}") - - return result - -def test_get_overview_files(): - """Test overview file detection.""" - print("\n" + "=" * 60) - print("TEST 2: get_overview_files()") - print("=" * 60) - - test_files = [ - ("README.md", "# Project"), - ("package.json", '{"name": "test"}'), - ("index.js", "console.log('hello');"), - ("src/auth/controller.js", "export class Auth {}"), - ("Dockerfile", "FROM node:18"), - ("tsconfig.json", '{"compilerOptions": {}}'), - ] - - result = get_overview_files(test_files) - - print(f"\nāœ… Identified {len(result)} overview files:") - for file_path, _ in result: - print(f" ā€¢ {file_path}") - - expected_overview = ['README.md', 'package.json', 'index.js', 'Dockerfile', 'tsconfig.json'] - found_overview = [f[0].split('/')[-1] for f in result] - - print(f"\nšŸ“Š Overview Detection:") - for expected in expected_overview: - status = "āœ…" if expected in found_overview else "āŒ" - print(f" {status} {expected}: {'Found' if expected in found_overview else 'Not found'}") - - return result - -def test_estimate_tokens(): - """Test token estimation.""" - print("\n" + "=" * 60) - print("TEST 3: estimate_tokens()") - print("=" * 60) - - test_files = [ - ("file1.js", "a" * 4000), # 4000 chars = ~1000 tokens - ("file2.js", "b" * 8000), # 8000 chars = ~2000 tokens - ("file3.js", "c" * 2000), # 2000 chars = ~500 tokens - ] - - result = estimate_tokens(test_files) - expected = (4000 + 8000 + 2000) // 4 # 3500 tokens - - print(f"\nāœ… Estimated tokens: {result}") - print(f" Expected: ~{expected} tokens") - print(f" Status: {'āœ… PASS' if abs(result - expected) < 100 else 'āŒ FAIL'}") - - return result - -def test_split_by_token_limit(): - """Test token-based splitting.""" - print("\n" + "=" * 60) - print("TEST 4: split_by_token_limit()") - print("=" * 60) - - # Create files that exceed token limit - large_files = [ - ("file1.js", "a" * 8000), # ~2000 tokens - ("file2.js", "b" * 8000), # ~2000 tokens - ("file3.js", "c" * 8000), # ~2000 tokens - ("file4.js", "d" * 8000), # ~2000 tokens - ("file5.js", "e" * 8000), # ~2000 tokens - ] - - # Total: ~10000 tokens, should split at 15000 limit - result = split_by_token_limit(large_files, max_tokens=15000) - - print(f"\nāœ… Split {len(large_files)} files into {len(result)} sub-chunks:") - for i, sub_chunk in enumerate(result, 1): - tokens = estimate_tokens(sub_chunk) - print(f" Chunk {i}: {len(sub_chunk)} files, ~{tokens} tokens") - for file_path, _ in sub_chunk: - print(f" ā€¢ {file_path}") - - return result - -def test_create_intelligent_chunks(): - """Test complete intelligent chunking.""" - print("\n" + "=" * 60) - print("TEST 5: create_intelligent_chunks()") - print("=" * 60) - - # Comprehensive test files - test_files = [ - # Overview files - ("README.md", "# Project Documentation\n\nThis is a test project."), - ("package.json", '{"name": "test-project", "version": "1.0.0"}'), - ("index.js", "const app = require('./app');\napp.listen(3000);"), - - # Authentication module - ("src/auth/auth.controller.js", "export class AuthController {\n async login() {}\n}"), - ("src/auth/auth.service.js", "export class AuthService {\n async validateUser() {}\n}"), - ("src/auth/auth.middleware.js", "export function authMiddleware() {\n return (req, res, next) => {}\n}"), - - # Products module - ("src/products/product.model.js", "export class Product {\n constructor() {}\n}"), - ("src/products/product.service.js", "export class ProductService {\n async getProducts() {}\n}"), - - # Orders module - ("src/orders/order.controller.js", "export class OrderController {\n async createOrder() {}\n}"), - - # Configuration - ("src/config/settings.js", "export const config = {\n port: 3000\n};"), - - # Utils - ("src/utils/helper.js", "export function helper() {\n return true;\n}"), - ] - - chunks = create_intelligent_chunks(test_files) - - print(f"\nāœ… Created {len(chunks)} intelligent chunks from {len(test_files)} files:") - print() - - for chunk in chunks: - chunk_id = chunk.get('id', 'unknown') - chunk_name = chunk.get('name', 'unknown') - chunk_type = chunk.get('chunk_type', 'unknown') - chunk_priority = chunk.get('priority', 0) - files = chunk.get('files', []) - deps = chunk.get('context_dependencies', []) - - print(f"šŸ“¦ {chunk_id}: {chunk_name} ({chunk_type}) [Priority: {chunk_priority}]") - print(f" Files: {len(files)}") - print(f" Dependencies: {len(deps)}") - for file_path, _ in files: - print(f" ā€¢ {file_path}") - print() - - # Verify structure - print("šŸ“Š Structure Verification:") - print(f" āœ… Total chunks: {len(chunks)}") - - # Check for overview chunk - overview_chunks = [c for c in chunks if c.get('chunk_type') == 'overview'] - print(f" āœ… Overview chunks: {len(overview_chunks)} (expected: 1)") - - # Check for module chunks - module_chunks = [c for c in chunks if c.get('chunk_type') == 'module'] - print(f" āœ… Module chunks: {len(module_chunks)}") - - # Verify chunk IDs are sequential - chunk_ids = [c.get('id') for c in chunks] - print(f" āœ… Chunk IDs: {chunk_ids}") - - # Verify no duplicate files - all_files = [] - for chunk in chunks: - for file_path, _ in chunk.get('files', []): - all_files.append(file_path) - - duplicates = [f for f in all_files if all_files.count(f) > 1] - if duplicates: - print(f" āŒ Duplicate files found: {duplicates}") - else: - print(f" āœ… No duplicate files (all {len(all_files)} files unique)") - - return chunks - -def test_chunk_structure(): - """Test that chunks have correct structure.""" - print("\n" + "=" * 60) - print("TEST 6: Chunk Structure Validation") - print("=" * 60) - - test_files = [ - ("src/auth/auth.controller.js", "export class AuthController {}"), - ("src/auth/auth.service.js", "export class AuthService {}"), - ("README.md", "# Project"), - ("package.json", '{"name": "test"}'), - ] - - chunks = create_intelligent_chunks(test_files) - - required_fields = ['id', 'name', 'priority', 'files', 'context_dependencies', 'chunk_type'] - - print("\nāœ… Validating chunk structure:") - for i, chunk in enumerate(chunks, 1): - print(f"\n Chunk {i}:") - for field in required_fields: - status = "āœ…" if field in chunk else "āŒ" - value = chunk.get(field, 'MISSING') - print(f" {status} {field}: {type(value).__name__} = {value}") - - # Verify files is a list of tuples - files = chunk.get('files', []) - if files: - first_file = files[0] - if isinstance(first_file, tuple) and len(first_file) == 2: - print(f" āœ… files: List of (file_path, content) tuples") - else: - print(f" āŒ files: Invalid format - {type(first_file)}") - - return chunks - -def run_all_tests(): - """Run all tests.""" - print("\n" + "=" * 60) - print("INTELLIGENT CHUNKING - COMPREHENSIVE TEST SUITE") - print("=" * 60) - - try: - # Test 1: Module categorization - categorized = test_categorize_by_module() - assert len(categorized) > 0, "Module categorization failed" - - # Test 2: Overview files - overview = test_get_overview_files() - assert len(overview) > 0, "Overview file detection failed" - - # Test 3: Token estimation - tokens = test_estimate_tokens() - assert tokens > 0, "Token estimation failed" - - # Test 4: Token-based splitting - split_chunks = test_split_by_token_limit() - assert len(split_chunks) > 0, "Token splitting failed" - - # Test 5: Complete chunking - chunks = test_create_intelligent_chunks() - assert len(chunks) > 0, "Intelligent chunking failed" - - # Test 6: Structure validation - validated_chunks = test_chunk_structure() - assert len(validated_chunks) > 0, "Structure validation failed" - - print("\n" + "=" * 60) - print("āœ… ALL TESTS PASSED!") - print("=" * 60) - print("\nšŸ“Š Summary:") - print(f" ā€¢ Module categorization: āœ…") - print(f" ā€¢ Overview file detection: āœ…") - print(f" ā€¢ Token estimation: āœ…") - print(f" ā€¢ Token-based splitting: āœ…") - print(f" ā€¢ Intelligent chunking: āœ…") - print(f" ā€¢ Structure validation: āœ…") - print("\nšŸŽ‰ Intelligent chunking implementation is working correctly!") - - return True - - except Exception as e: - print("\n" + "=" * 60) - print(f"āŒ TEST FAILED: {e}") - print("=" * 60) - import traceback - traceback.print_exc() - return False - -if __name__ == "__main__": - success = run_all_tests() - sys.exit(0 if success else 1) - diff --git a/services/ai-analysis-service/test_knowledge_graph_operations.py b/services/ai-analysis-service/test_knowledge_graph_operations.py new file mode 100644 index 0000000..b5e7b44 --- /dev/null +++ b/services/ai-analysis-service/test_knowledge_graph_operations.py @@ -0,0 +1,103 @@ +""" +Unit tests for knowledge graph helpers. +""" + +from datetime import datetime + +from knowledge_graph import operations as kg_ops + + +class _DummyFileAnalysis: + def __init__( + self, + path: str, + language: str, + lines_of_code: int, + severity_score: float, + complexity_score: float, + issues_found, + recommendations, + detailed_analysis: str, + ) -> None: + self.path = path + self.language = language + self.lines_of_code = lines_of_code + self.severity_score = severity_score + self.complexity_score = complexity_score + self.issues_found = issues_found + self.recommendations = recommendations + self.detailed_analysis = detailed_analysis + + +def test_build_module_payload_basic(): + run_id = "run-123" + repository_id = "repo-001" + module_name = "Payments" + chunk = { + "id": "chunk-1", + "name": module_name, + "context_dependencies": ["Auth", "Notifications"], + } + chunk_analysis = { + "module_quality_score": 7.4, + "module_overview": "Handles payment orchestration.", + "module_architecture": "Microservice communicating via REST APIs.", + "module_security_assessment": "Uses token-based authentication.", + "module_recommendations": ["Increase test coverage", {"text": "Introduce circuit breakers"}], + } + + file_analyses = [ + _DummyFileAnalysis( + path="services/payments/processor.py", + language="Python", + lines_of_code=215, + severity_score=4.3, + complexity_score=6.1, + issues_found=[ + { + "title": "Missing retry logic", + "severity": "high", + "category": "reliability", + "line_number": 58, + "recommendation": "Add exponential backoff retry", + } + ], + recommendations=["Refactor long function"], + detailed_analysis="Processor heavily relies on synchronous calls.", + ) + ] + + metadata = { + "type": "module_analysis", + "chunk_metrics": {"total_issues": 1}, + "dependencies": {"depends_on_chunks": ["Auth", "Notifications"]}, + "timestamp": datetime.utcnow().isoformat(), + } + ai_response = "Detailed module analysis" + + payload = kg_ops.build_module_payload( + run_id=run_id, + repository_id=repository_id, + module_name=module_name, + chunk=chunk, + chunk_analysis=chunk_analysis, + file_analyses=file_analyses, + metadata=metadata, + ai_response=ai_response, + ) + + module_props = payload["module_props"] + files = payload["files"] + findings = payload["findings"] + dependencies = payload["dependencies"] + + assert module_props["name"] == module_name + assert module_props["total_files"] == len(file_analyses) + assert "analysis_payload" in module_props + assert files[0]["path"] == "services/payments/processor.py" + assert files[0]["props"]["language"] == "Python" + assert len(findings) == 1 + assert findings[0]["props"]["severity"] == "high" + assert dependencies[0]["target"] == "Auth" + assert dependencies[1]["target"] == "Notifications" + diff --git a/services/ai-analysis-service/test_multi_level_report.py b/services/ai-analysis-service/test_multi_level_report.py deleted file mode 100755 index 39b391e..0000000 --- a/services/ai-analysis-service/test_multi_level_report.py +++ /dev/null @@ -1,244 +0,0 @@ -#!/usr/bin/env python3 -""" -Test script for multi-level report generation and context retrieval -""" - -import os -import sys -import asyncio -from pathlib import Path -from dotenv import load_dotenv - -# Add current directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -load_dotenv() - -async def test_context_retrieval(): - """Test context retrieval functions.""" - print("\n" + "=" * 60) - print("Testing Context Retrieval Functions") - print("=" * 60) - - try: - from server import ( - retrieve_all_module_analyses, - retrieve_synthesis_analysis, - retrieve_cumulative_analysis_state, - retrieve_all_findings, - retrieve_all_metrics, - retrieve_comprehensive_report_context - ) - - print("āœ… All context retrieval functions imported") - - # Test with a dummy run_id - test_run_id = "test_run_123" - test_repository_id = "test_repo_123" - test_session_id = "test_session_123" - - print(f"\nTesting with run_id: {test_run_id}") - print(f"Repository ID: {test_repository_id}") - print(f"Session ID: {test_session_id}") - - # Test each function - print("\n1. Testing retrieve_all_module_analyses...") - modules = await retrieve_all_module_analyses(test_run_id, test_repository_id) - print(f" āœ“ Found {len(modules)} modules") - - print("\n2. Testing retrieve_synthesis_analysis...") - synthesis = await retrieve_synthesis_analysis(test_run_id, test_repository_id) - if synthesis: - print(f" āœ“ Found synthesis analysis") - else: - print(f" āš ļø No synthesis analysis found (expected for test)") - - print("\n3. Testing retrieve_cumulative_analysis_state...") - state = await retrieve_cumulative_analysis_state(test_run_id, test_repository_id, test_session_id) - if state: - print(f" āœ“ Found cumulative analysis state") - else: - print(f" āš ļø No cumulative analysis state found (expected for test)") - - print("\n4. Testing retrieve_all_findings...") - findings = await retrieve_all_findings(test_run_id) - print(f" āœ“ Found findings for {len(findings)} modules") - - print("\n5. Testing retrieve_all_metrics...") - metrics = await retrieve_all_metrics(test_run_id) - print(f" āœ“ Found metrics for {len(metrics)} modules") - - print("\n6. Testing retrieve_comprehensive_report_context...") - context = await retrieve_comprehensive_report_context( - run_id=test_run_id, - repository_id=test_repository_id, - session_id=test_session_id - ) - - print(f" āœ“ Context retrieved:") - print(f" - Modules: {context.get('total_modules', 0)}") - print(f" - Findings: {context.get('total_findings', 0)}") - print(f" - Has synthesis: {bool(context.get('synthesis_analysis'))}") - print(f" - Has analysis state: {bool(context.get('analysis_state'))}") - - print("\nāœ… All context retrieval tests passed!") - return True - - except Exception as e: - print(f"\nāŒ Context retrieval test failed: {e}") - import traceback - traceback.print_exc() - return False - -def test_pdf_method_exists(): - """Test that the new PDF method exists.""" - print("\n" + "=" * 60) - print("Testing PDF Report Method") - print("=" * 60) - - try: - # Import using the same method as server.py - import sys - import importlib.util - - spec = importlib.util.spec_from_file_location("ai_analyze", "ai-analyze.py") - ai_analyze_module = importlib.util.module_from_spec(spec) - sys.modules["ai_analyze"] = ai_analyze_module - spec.loader.exec_module(ai_analyze_module) - - from ai_analyze import EnhancedGitHubAnalyzer - - print("āœ… EnhancedGitHubAnalyzer imported successfully") - - # Check if new method exists - if hasattr(EnhancedGitHubAnalyzer, 'create_multi_level_pdf_report'): - print("āœ… create_multi_level_pdf_report method exists") - - # Check method signature - import inspect - sig = inspect.signature(EnhancedGitHubAnalyzer.create_multi_level_pdf_report) - params = list(sig.parameters.keys()) - print(f" Method parameters: {', '.join(params)}") - - if 'comprehensive_context' in params: - print(" āœ“ comprehensive_context parameter exists") - if 'output_path' in params: - print(" āœ“ output_path parameter exists") - if 'repository_id' in params: - print(" āœ“ repository_id parameter exists") - if 'run_id' in params: - print(" āœ“ run_id parameter exists") - - return True - else: - print("āŒ create_multi_level_pdf_report method not found") - return False - - except Exception as e: - print(f"āŒ PDF method test failed: {e}") - import traceback - traceback.print_exc() - return False - -def test_database_tables(): - """Test that database tables exist.""" - print("\n" + "=" * 60) - print("Testing Database Tables") - print("=" * 60) - - try: - import psycopg2 - from dotenv import load_dotenv - - load_dotenv() - - conn = psycopg2.connect( - host=os.getenv('POSTGRES_HOST', 'localhost'), - port=os.getenv('POSTGRES_PORT', '5432'), - database=os.getenv('POSTGRES_DB', 'dev_pipeline'), - user=os.getenv('POSTGRES_USER', 'pipeline_admin'), - password=os.getenv('POSTGRES_PASSWORD', 'secure_pipeline_2024') - ) - - cursor = conn.cursor() - - # Check each table - tables_to_check = ['findings', 'metrics', 'report_sections', 'analysis_runs'] - - for table_name in tables_to_check: - cursor.execute(f""" - SELECT COUNT(*) - FROM information_schema.tables - WHERE table_schema = 'public' - AND table_name = %s - """, (table_name,)) - - exists = cursor.fetchone()[0] > 0 - - if exists: - # Get row count - cursor.execute(f"SELECT COUNT(*) FROM {table_name}") - count = cursor.fetchone()[0] - print(f"āœ… Table '{table_name}' exists ({count} rows)") - else: - print(f"āŒ Table '{table_name}' does not exist") - return False - - cursor.close() - conn.close() - - print("\nāœ… All database tables verified!") - return True - - except Exception as e: - print(f"āŒ Database test failed: {e}") - import traceback - traceback.print_exc() - return False - -async def run_all_tests(): - """Run all tests.""" - print("\n" + "=" * 60) - print("MULTI-LEVEL REPORT IMPLEMENTATION TEST SUITE") - print("=" * 60) - - results = [] - - # Test 1: Database tables - results.append(("Database Tables", test_database_tables())) - - # Test 2: PDF method exists - results.append(("PDF Method", test_pdf_method_exists())) - - # Test 3: Context retrieval - results.append(("Context Retrieval", await test_context_retrieval())) - - # Summary - print("\n" + "=" * 60) - print("TEST SUMMARY") - print("=" * 60) - - passed = 0 - failed = 0 - - for test_name, result in results: - status = "āœ… PASSED" if result else "āŒ FAILED" - print(f"{test_name}: {status}") - if result: - passed += 1 - else: - failed += 1 - - print(f"\nTotal: {passed} passed, {failed} failed out of {len(results)} tests") - - if failed == 0: - print("\nāœ… All tests passed! Implementation is ready.") - return True - else: - print(f"\nāš ļø {failed} test(s) failed. Please review the errors above.") - return False - -if __name__ == "__main__": - success = asyncio.run(run_all_tests()) - sys.exit(0 if success else 1) - diff --git a/services/ai-analysis-service/test_progressive_context.py b/services/ai-analysis-service/test_progressive_context.py deleted file mode 100644 index 07a135c..0000000 --- a/services/ai-analysis-service/test_progressive_context.py +++ /dev/null @@ -1,309 +0,0 @@ -#!/usr/bin/env python3 -""" -Test script for progressive context implementation. -Tests the logic without requiring actual API calls or database connections. -""" - -import sys -from pathlib import Path -from typing import Dict, List, Tuple - -# Add current directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -# Import the functions we need to test -from server import ( - build_context_from_state, - update_state_with_findings, - create_intelligent_chunks, - build_intelligent_chunk_prompt -) - -# Mock FileAnalysis class -class MockFileAnalysis: - def __init__(self, path, severity_score, issues_found=None, complexity_score=5.0): - self.path = path - self.severity_score = severity_score - self.issues_found = issues_found or [] - self.complexity_score = complexity_score - self.language = "javascript" - self.lines_of_code = 100 - self.recommendations = [] - self.detailed_analysis = "Mock analysis" - -def test_build_context_from_state(): - """Test building context from analysis state.""" - print("=" * 60) - print("TEST 1: build_context_from_state()") - print("=" * 60) - - # Create analysis state with progressive data - analysis_state = { - 'modules_analyzed': ['project_overview', 'authentication'], - 'project_overview': 'Node.js e-commerce platform with Express backend and React frontend', - 'module_summaries': { - 'project_overview': 'Modern e-commerce platform with microservices architecture', - 'authentication': 'JWT-based authentication with rate limiting missing' - }, - 'architecture_patterns': ['MVC', 'Service Layer'], - 'critical_issues': [ - {'module': 'authentication', 'issue': 'Missing rate limiting on auth endpoints'} - ], - 'tech_stack': { - 'frontend': 'React', - 'backend': 'Node.js', - 'database': 'PostgreSQL' - }, - 'dependency_context': { - 'chunk_001': 'Project overview and setup', - 'chunk_002': 'Authentication module with JWT' - } - } - - # Test chunk (products module) - current_chunk = { - 'name': 'products', - 'id': 'chunk_003', - 'chunk_type': 'module', - 'context_dependencies': ['chunk_001', 'chunk_002'] - } - - context = build_context_from_state(analysis_state, current_chunk) - - print("\nāœ… Generated context:") - print(context) - print() - - # Verify context contains expected sections - assert "PROJECT OVERVIEW" in context, "Context should include project overview" - assert "PREVIOUSLY ANALYZED MODULES" in context, "Context should include module summaries" - assert "ARCHITECTURE PATTERNS" in context, "Context should include architecture patterns" - assert "CRITICAL ISSUES" in context, "Context should include critical issues" - assert "TECH STACK" in context, "Context should include tech stack" - assert "DEPENDENCY CONTEXT" in context, "Context should include dependency context" - - print("āœ… All context sections present!") - return True - -def test_update_state_with_findings(): - """Test updating analysis state with new findings.""" - print("\n" + "=" * 60) - print("TEST 2: update_state_with_findings()") - print("=" * 60) - - # Initial state - analysis_state = { - 'modules_analyzed': ['project_overview'], - 'module_summaries': { - 'project_overview': 'Node.js e-commerce platform' - }, - 'architecture_patterns': [], - 'critical_issues': [], - 'dependency_context': {} - } - - # New chunk analysis - chunk = { - 'name': 'authentication', - 'id': 'chunk_002', - 'chunk_type': 'module' - } - - chunk_analysis = { - 'module_overview': 'JWT-based authentication module with rate limiting missing', - 'module_architecture': 'Uses MVC pattern with Service Layer for business logic', - 'module_quality_score': 6.5 - } - - # Mock file analyses - file_analyses = [ - MockFileAnalysis('auth.controller.js', 7.0, ['No rate limiting']), - MockFileAnalysis('auth.service.js', 8.0), - MockFileAnalysis('auth.middleware.js', 4.0, ['Weak validation']) # Low quality - ] - - # Update state - updated_state = update_state_with_findings(analysis_state.copy(), chunk, chunk_analysis, file_analyses) - - print("\nāœ… Updated state:") - print(f" Modules analyzed: {updated_state.get('modules_analyzed', [])}") - print(f" Architecture patterns: {updated_state.get('architecture_patterns', [])}") - print(f" Critical issues: {len(updated_state.get('critical_issues', []))}") - print(f" Module summaries: {list(updated_state.get('module_summaries', {}).keys())}") - print() - - # Verify updates - assert 'authentication' in updated_state['modules_analyzed'], "Authentication should be in modules_analyzed" - assert 'MVC' in updated_state['architecture_patterns'], "MVC pattern should be detected" - assert 'Service Layer' in updated_state['architecture_patterns'], "Service Layer pattern should be detected" - assert len(updated_state['critical_issues']) > 0, "Critical issues should be added" - assert 'authentication' in updated_state['module_summaries'], "Module summary should be stored" - - print("āœ… State updated correctly!") - return True - -def test_progressive_context_flow(): - """Test the complete progressive context flow.""" - print("\n" + "=" * 60) - print("TEST 3: Progressive Context Flow (Simulated)") - print("=" * 60) - - # Simulate chunk processing flow - test_files = [ - ("README.md", "# Project\n\nNode.js e-commerce platform"), - ("package.json", '{"name": "ecommerce", "dependencies": {"express": "^4.0"}}'), - ("src/auth/auth.controller.js", "export class AuthController {}"), - ("src/auth/auth.service.js", "export class AuthService {}"), - ("src/products/product.controller.js", "export class ProductController {}"), - ] - - # Create chunks - chunks = create_intelligent_chunks(test_files) - - print(f"\nāœ… Created {len(chunks)} chunks:") - for chunk in chunks: - print(f" - {chunk['name']} ({chunk['chunk_type']}): {len(chunk['files'])} files") - - # Simulate progressive analysis - analysis_state = {} - - print("\nšŸ“Š Simulating progressive analysis:") - - for i, chunk in enumerate(chunks, 1): - chunk_name = chunk['name'] - print(f"\n Chunk {i}: {chunk_name}") - - # Build context (what would be used in prompt) - context = build_context_from_state(analysis_state, chunk) - if context: - print(f" šŸ“š Context available: {len(context)} chars") - else: - print(f" šŸ“š No context (first chunk)") - - # Simulate chunk analysis results - chunk_analysis = { - 'module_overview': f"Analysis of {chunk_name} module", - 'module_architecture': 'MVC pattern' if chunk_name != 'project_overview' else 'Node.js setup', - 'module_quality_score': 7.5 - } - - # Mock file analyses - file_analyses = [ - MockFileAnalysis(f"{chunk_name}_file{i}.js", 7.0 + i*0.1) - for i in range(len(chunk['files'])) - ] - - # Update state - analysis_state = update_state_with_findings(analysis_state.copy(), chunk, chunk_analysis, file_analyses) - - print(f" āœ… State updated: {len(analysis_state.get('modules_analyzed', []))} modules analyzed") - if analysis_state.get('architecture_patterns'): - print(f" šŸ“ Patterns: {', '.join(analysis_state.get('architecture_patterns', []))}") - - print("\nšŸ“Š Final Analysis State:") - print(f" Modules analyzed: {', '.join(analysis_state.get('modules_analyzed', []))}") - print(f" Architecture patterns: {', '.join(analysis_state.get('architecture_patterns', []))}") - print(f" Critical issues: {len(analysis_state.get('critical_issues', []))}") - print(f" Module summaries: {len(analysis_state.get('module_summaries', {}))}") - - # Verify final state - assert len(analysis_state.get('modules_analyzed', [])) == len(chunks), "All chunks should be analyzed" - assert len(analysis_state.get('architecture_patterns', [])) > 0, "Patterns should be detected" - - print("\nāœ… Progressive context flow working correctly!") - return True - -def test_prompt_includes_context(): - """Test that prompts include progressive context.""" - print("\n" + "=" * 60) - print("TEST 4: Prompt Includes Progressive Context") - print("=" * 60) - - # Create analysis state - analysis_state = { - 'modules_analyzed': ['project_overview', 'authentication'], - 'project_overview': 'Node.js platform', - 'module_summaries': { - 'authentication': 'JWT auth module' - }, - 'architecture_patterns': ['MVC'], - 'critical_issues': [ - {'module': 'authentication', 'issue': 'Missing rate limiting'} - ], - 'tech_stack': {'backend': 'Node.js'} - } - - # Test chunk - chunk = { - 'name': 'products', - 'chunk_type': 'module', - 'files': [('product.controller.js', 'export class ProductController {}')] - } - - # Build prompt - prompt = build_intelligent_chunk_prompt(chunk, analysis_state) - - print("\nāœ… Generated prompt (first 500 chars):") - print(prompt[:500]) - print("...") - print() - - # Verify prompt includes context - assert "CONTEXT FROM PREVIOUS ANALYSIS" in prompt, "Prompt should include context section" - assert "PROJECT OVERVIEW" in prompt, "Prompt should include project overview" - assert "PREVIOUSLY ANALYZED MODULES" in prompt, "Prompt should include module summaries" - assert "ARCHITECTURE PATTERNS" in prompt, "Prompt should include architecture patterns" - assert "CRITICAL ISSUES" in prompt, "Prompt should include critical issues" - - print("āœ… Prompt includes all context sections!") - - # Test without context (first chunk) - prompt_no_context = build_intelligent_chunk_prompt(chunk, None) - assert "CONTEXT FROM PREVIOUS ANALYSIS" not in prompt_no_context, "First chunk should not have context" - - print("āœ… Prompt correctly omits context for first chunk!") - return True - -def run_all_tests(): - """Run all tests.""" - print("\n" + "=" * 60) - print("PROGRESSIVE CONTEXT - COMPREHENSIVE TEST SUITE") - print("=" * 60) - - try: - # Test 1: Context building - test_build_context_from_state() - - # Test 2: State updates - test_update_state_with_findings() - - # Test 3: Complete flow - test_progressive_context_flow() - - # Test 4: Prompt generation - test_prompt_includes_context() - - print("\n" + "=" * 60) - print("āœ… ALL TESTS PASSED!") - print("=" * 60) - print("\nšŸ“Š Summary:") - print(" ā€¢ Context building: āœ…") - print(" ā€¢ State updates: āœ…") - print(" ā€¢ Progressive flow: āœ…") - print(" ā€¢ Prompt generation: āœ…") - print("\nšŸŽ‰ Progressive context implementation is working correctly!") - - return True - - except Exception as e: - print("\n" + "=" * 60) - print(f"āŒ TEST FAILED: {e}") - print("=" * 60) - import traceback - traceback.print_exc() - return False - -if __name__ == "__main__": - success = run_all_tests() - sys.exit(0 if success else 1) -