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diff --git a/docker-compose.yml b/docker-compose.yml
index 75e6646..f75182b 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -721,48 +721,47 @@ services:
   # =====================================
   
   ai-analysis-service:
-    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<Costing>()
             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<Costing>()
             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<Costing>()
             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<Costing>()
             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<Costing>()
             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<Costing>()
             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<Costing>()
             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<Costing>()
             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<Costing>()
                         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<Costing>()
                     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<Costing>()
             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<Costing>()
             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<Costing>()
             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<Costing>()
             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<Costing>()
         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"""
+            <b>šŸ”§ DETAILED CODE EVIDENCE ANALYSIS</b><br/><br/>
+            
+            <b>šŸ“‹ COMPREHENSIVE FINDINGS:</b><br/>
+            Total Issues Found: {len(code_evidence)}<br/>
+            Files Analyzed: {unique_files}<br/>
+            High Severity: {high_count}<br/>
+            Medium Severity: {medium_count}<br/><br/>
+            """
+
+            for idx, evidence in enumerate(code_evidence[:10], 1):  # Top 10 detailed findings
+                tech_content += f"""
+                <b>FINDING #{idx} - {evidence['severity']} PRIORITY</b><br/>
+                File: <code>{evidence['file']}</code><br/>
+                Issue: {evidence['issue']}<br/>
+                Line: {evidence['line_number']}<br/><br/>
+                
+                <b>Code Evidence:</b><br/>
+                <pre style="background-color: #f5f5f5; padding: 10px; border-left: 4px solid #ff6b6b;">
+{evidence['code_snippet'][:300]}{"..." if len(evidence['code_snippet']) > 300 else ""}
+                </pre>
+                
+                <b>Recommended Fix:</b><br/>
+                {evidence['recommendation']}<br/><br/>
+                
+                {'ā”€' * 60}<br/>
+                """
+
+            tech_content += """
+            <b>šŸ”§ IMPLEMENTATION NOTES:</b><br/>
+            ā€¢ Focus on HIGH severity issues first<br/>
+            ā€¢ Test all fixes in staging environment<br/>
+            ā€¢ Use code review process for all changes<br/>
+            ā€¢ Update documentation after fixes<br/>
+            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 <b>{len(code_evidence)} specific issues</b> with actual code examples as proof.<br/><br/>
             
             <b>šŸ“Š ISSUE BREAKDOWN:</b><br/>
-            ā€¢ High Priority Issues: {len([e for e in code_evidence if e['severity'] == 'HIGH'])}<br/>
-            ā€¢ Medium Priority Issues: {len([e for e in code_evidence if e['severity'] == 'MEDIUM'])}<br/>
-            ā€¢ Files with Evidence: {len(set(e['file'] for e in code_evidence))}<br/><br/>
+            ā€¢ High Priority Issues: {high_count}<br/>
+            ā€¢ Medium Priority Issues: {medium_count}<br/>
+            ā€¢ Files with Evidence: {unique_files}<br/><br/>
             
             <b>šŸŽÆ TOP CRITICAL FINDINGS:</b><br/>
             """
@@ -4853,46 +4933,6 @@ Output format:
             <b>āš” IMMEDIATE ACTION:</b><br/>
             Assign developers to fix high-priority issues within 1-2 weeks to prevent system degradation.
             """
-            
-            # Technical version (for developers)
-            tech_content = f"""
-            <b>šŸ”§ DETAILED CODE EVIDENCE ANALYSIS</b><br/><br/>
-            
-            <b>šŸ“‹ COMPREHENSIVE FINDINGS:</b><br/>
-            Total Issues Found: {len(code_evidence)}<br/>
-            Files Analyzed: {len(set(e['file'] for e in code_evidence))}<br/>
-            High Severity: {len([e for e in code_evidence if e['severity'] == 'HIGH'])}<br/>
-            Medium Severity: {len([e for e in code_evidence if e['severity'] == 'MEDIUM'])}<br/><br/>
-            """
-            
-            # Add detailed code evidence for developers
-            for idx, evidence in enumerate(code_evidence[:10], 1):  # Top 10 detailed findings
-                tech_content += f"""
-                <b>FINDING #{idx} - {evidence['severity']} PRIORITY</b><br/>
-                File: <code>{evidence['file']}</code><br/>
-                Issue: {evidence['issue']}<br/>
-                Line: {evidence['line_number']}<br/><br/>
-                
-                <b>Code Evidence:</b><br/>
-                <pre style="background-color: #f5f5f5; padding: 10px; border-left: 4px solid #ff6b6b;">
-{evidence['code_snippet'][:300]}{"..." if len(evidence['code_snippet']) > 300 else ""}
-                </pre>
-                
-                <b>Recommended Fix:</b><br/>
-                {evidence['recommendation']}<br/><br/>
-                
-                {'ā”€' * 60}<br/>
-                """
-            
-            tech_content += """
-            <b>šŸ”§ IMPLEMENTATION NOTES:</b><br/>
-            ā€¢ Focus on HIGH severity issues first<br/>
-            ā€¢ Test all fixes in staging environment<br/>
-            ā€¢ Use code review process for all changes<br/>
-            ā€¢ Update documentation after fixes<br/>
-            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)
-