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## Overview Implements complete Kubernetes/OpenShift deployment strategy with Helm charts, auto-scaling, high availability, and comprehensive documentation. ## What's New ### Kubernetes Manifests - ✅ Complete K8s manifests in `deployment/k8s/base/` - ✅ Namespace, ConfigMaps, and Secrets templates - ✅ StatefulSets for PostgreSQL, Milvus, MinIO, etcd - ✅ Deployments for Backend, Frontend, MLFlow - ✅ Services for all components - ✅ Ingress with TLS and OpenShift Routes - ✅ HorizontalPodAutoscaler for auto-scaling ### Helm Chart - ✅ Production-ready Helm chart in `deployment/helm/rag-modulo/` - ✅ Environment-specific values (dev, staging, prod) - ✅ Configurable resources and scaling policies - ✅ Support for multiple cloud providers ### Deployment Scripts - ✅ `deployment/scripts/deploy-k8s.sh` - Raw K8s deployment - ✅ `deployment/scripts/deploy-helm.sh` - Helm deployment - ✅ Environment validation and health checks - ✅ Automated deployment workflow ### Makefile Targets (40+ new commands) **Kubernetes:** - `make k8s-deploy-dev/staging/prod` - Deploy to K8s - `make k8s-status` - Show deployment status - `make k8s-logs-backend/frontend` - Stream logs - `make k8s-port-forward-*` - Port forwarding - `make k8s-shell-backend` - Open pod shell - `make k8s-cleanup` - Clean up resources **Helm:** - `make helm-install-dev/staging/prod` - Install chart - `make helm-upgrade-dev/staging/prod` - Upgrade release - `make helm-rollback` - Rollback release - `make helm-status` - Show release status - `make helm-uninstall` - Remove release **Cloud Providers:** - `make ibmcloud-deploy CLUSTER_NAME=<name>` - IBM Cloud - `make openshift-deploy` - OpenShift - Support for AWS EKS, Azure AKS, Google GKE **Documentation:** - `make docs-install` - Install MkDocs - `make docs-serve` - Serve docs locally - `make docs-build` - Build static site - `make docs-deploy` - Deploy to GitHub Pages ### CI/CD Workflows - ✅ `.github/workflows/k8s-deploy-production.yml` - Production deployment - ✅ `.github/workflows/k8s-deploy-staging.yml` - Staging/PR deployment - ✅ Automated build, push, and deploy pipeline - ✅ Health checks and verification ### Documentation (MkDocs) - ✅ Updated `mkdocs.yml` with complete navigation - ✅ `docs/deployment/QUICKSTART.md` - 5-minute quick start - ✅ `docs/deployment/kubernetes.md` - Complete K8s guide - ✅ `docs/deployment/index.md` - Deployment overview - ✅ `docs/README.md` - MkDocs writing guide - ✅ `docs/MKDOCS_SETUP.md` - Setup summary - ✅ Custom styling in `docs/stylesheets/extra.css` ## Key Features ### High Availability - Backend: 3 replicas with auto-scaling (2-10 pods) - Frontend: 2 replicas with auto-scaling (2-5 pods) - Rolling updates with zero downtime - Health probes (liveness, readiness, startup) ### Auto-Scaling - HPA based on CPU (70%) and Memory (80%) - Intelligent scale-up/down policies - Resource limits enforced ### Persistent Storage - PostgreSQL: 50Gi (prod), 10Gi (dev) - Milvus: 100Gi (prod), 20Gi (dev) - MinIO: 100Gi (prod), 20Gi (dev) - etcd: 10Gi (prod), 5Gi (dev) ### Security - Secrets management templates - TLS/SSL with cert-manager integration - OpenShift SCC support - Network policies ready ### Monitoring - Prometheus metrics endpoints - HPA metrics collection - Comprehensive logging ## Cloud Provider Support ### IBM Cloud Kubernetes Service ```bash make ibmcloud-deploy CLUSTER_NAME=<cluster-name> ``` ### OpenShift ```bash make openshift-deploy ``` ### AWS EKS / Azure AKS / Google GKE See docs/deployment/kubernetes.md for details ## Files Changed - Modified: Makefile, mkdocs.yml, docs/deployment/index.md - Added: 45+ new files for K8s, Helm, docs, scripts ## Testing - ✅ All pre-commit checks passed - ✅ Helm chart lints successfully - ✅ K8s manifests validate - ✅ Deployment scripts tested Closes #260
) Implements IBM Docling integration with AI-powered table extraction (TableFormer) and layout analysis (DocLayNet) to significantly improve document processing quality. Key Features: - DoclingProcessor with comprehensive text, table, and image extraction - Feature flag control (ENABLE_DOCLING) for transparent deployment - Automatic fallback to legacy processors on error - Support for PDF, DOCX, PPTX, HTML, and image formats - 313% improvement in chunk extraction vs legacy processors - Table detection: 3 tables vs 0 (legacy) - Image detection: 13 images vs 0 (legacy) Implementation: - New DoclingProcessor class with DocumentConverter integration - Enhanced metadata extraction with table/image counts - Page number tracking with new Docling API compatibility - Chunking strategy integration for optimal text segmentation - Type-safe implementation with mypy validation Testing: - 14 comprehensive unit tests (100% passing) - Real PDF comparison validation - Debug utilities for development - All critical code quality checks passing Technical Details: - Updated transformers to 4.56.2 for compatibility - Handled Docling API changes (tuple unpacking, page_no attribute) - Multiple text item types support (TextItem, SectionHeaderItem, ListItem, CodeItem) - Separate counters for tables, images, and chunks - Code quality: 9.64/10 (docling_processor.py), 9.84/10 (document_processor.py) Closes #255
Resolved conflicts: - backend/core/config.py: Combined Docling and hierarchical chunking settings - backend/pyproject.toml: Added both docling+transformers and pydub dependencies - backend/poetry.lock: Regenerated after dependency resolution - .linting-progress.json: Removed (deleted in main) All conflicts resolved and dependencies updated.
- Fix missing podcast router registration in main.py - Add missing RERANKING and COT_REASONING enum values to database - Create comprehensive integration tests for router registration - Add database enum validation tests - Document analysis and prevention measures Fixes: - Podcast generation endpoint 404 error - Database enum validation errors for RERANKING template type - Missing integration test coverage for router registration Files: - backend/main.py: Added podcast_router import and registration - backend/fix_enum_migration.py: Database enum migration script - backend/tests/integration/test_router_registration_integration.py: Comprehensive tests - backend/MERGE_FAILURES_ANALYSIS_AND_FIXES.md: Analysis and documentation
- Change prefix from '/podcasts' to '/api/podcasts' to match other routers - Ensures podcast endpoints are accessible at correct URLs - Fixes 404 errors for podcast generation endpoint
Implement a hierarchical AGENTS.md documentation system throughout the codebase to provide contextual information for AI development tools (Claude Code, GitHub Copilot) and human developers. ## What's Added ### Root & Infrastructure - Enhanced `/AGENTS.md` with AI agent context loading protocol - Added `.github/AGENTS_README.md` team guide for the system ### Backend Documentation (8 files) - `backend/AGENTS.md` - Backend architecture overview - `backend/rag_solution/AGENTS.md` - Main application package - `backend/rag_solution/services/AGENTS.md` - Service layer patterns (SearchService, ChainOfThoughtService, ConversationService, etc.) - `backend/rag_solution/models/AGENTS.md` - SQLAlchemy ORM models - `backend/rag_solution/schemas/AGENTS.md` - Pydantic validation schemas - `backend/rag_solution/router/AGENTS.md` - FastAPI endpoint handlers - `backend/rag_solution/repository/AGENTS.md` - Data access layer - `backend/rag_solution/repository/AGENTS.md` - Repository patterns ### Frontend Documentation (2 files) - `frontend/AGENTS.md` - React frontend architecture - `frontend/src/components/AGENTS.md` - React component patterns ## Key Features ### AI Agent Context Loading Protocol - Clear instructions for AI agents to load context from multiple files - File location map with descriptions - Context loading strategies for different task types - Example workflows for common development scenarios ### Comprehensive Module Documentation Each AGENTS.md file includes: - Module purpose and responsibilities - Key files and their roles - Common patterns with code examples - Best practices and conventions - Common pitfalls to avoid - Links to related AGENTS.md files ### Living Documentation Strategy - Version controlled (committed to Git) - Maintenance guidelines - Update triggers and templates - What to include/exclude ## Benefits ### For AI Development Tools - Better code suggestions following project patterns - Contextual understanding of architecture - Consistent code generation - Fewer mistakes and pattern violations ### For Human Developers - Faster onboarding to the codebase - Clear architectural patterns and conventions - Reference documentation for code reviews - Module responsibility documentation ### For the Project - Knowledge preservation of architectural decisions - Reduced technical debt through pattern enforcement - Improved maintainability and consistency - Easier refactoring with clear dependencies ## Usage AI agents should: 1. Read root `/AGENTS.md` for project overview 2. Read relevant module AGENTS.md files for specific patterns 3. Implement following documented patterns 4. Update AGENTS.md files when discovering new patterns Developers should: - Consult AGENTS.md files before working on modules - Follow documented patterns and conventions - Update files when patterns change or are discovered - Use as reference during code reviews 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
🚀 Development Environment OptionsThis repository supports Dev Containers for a consistent development environment. Option 1: GitHub Codespaces (Recommended)Create a cloud-based development environment:
Option 2: VS Code Dev Containers (Local)Use Dev Containers on your local machine:
Option 3: Traditional Local SetupSet up the development environment manually: # Clone the repository
git clone https://github.com/manavgup/rag_modulo.git
cd rag_modulo
git checkout docs/agents-md-documentation-system
# Initialize development environment
make dev-init
make dev-build
make dev-up
make dev-validate Available CommandsOnce in your development environment: make help # Show all available commands
make dev-validate # Validate environment setup
make test-atomic # Run atomic tests
make test-unit # Run unit tests
make lint # Run linting Services AvailableWhen running
This automated message helps reviewers quickly set up the development environment. |
PR Review: AGENTS.md Documentation System🎯 Overall AssessmentVerdict: ✅ Approve with Minor Recommendations This is an excellent documentation initiative that will significantly improve AI agent context and developer onboarding. The PR adds comprehensive AGENTS.md files throughout the codebase following a well-structured hierarchy. Impact: 📚 20,047 additions / 174 deletions across 11 new documentation files ✅ Strengths1. Excellent Documentation Structure
2. Comprehensive Coverage
3. Practical Guidance
4. AI-First Design
|
Pull Request Review: AGENTS.md Documentation SystemOverview
Strengths1. Excellent Documentation Architecture
2. Strong Team Guidance
3. Production-Ready Infrastructure
CRITICAL Issues1. Log Files Committed - BLOCKERlogs/rag_modulo.log.5 - 4,951 lines of log output This is a serious security and hygiene issue:
Action Required: Remove logs/rag_modulo.log.5 and add logs/ to .gitignore 2. Temporary Scripts Committed - BLOCKERThese files should not be in main branch:
Action Required: Remove or move to scripts/ directory 3. Scope Creep - BLOCKERPR title says documentation but includes:
Action Required: Split into separate focused PRs Other Issues4. Misplaced Documentationbackend/MERGE_FAILURES_ANALYSIS_AND_FIXES.md belongs in docs/ not backend code 5. Documentation VerbositySome AGENTS.md files are very long (500+ lines). Consider:
AssessmentCode Quality: 6/10 - Good structure, hygiene issues Recommendation: Request changes - address critical issues before merge Action Items
The AGENTS.md documentation system itself is excellent. The issues are around repository hygiene and PR scope - both fixable. |
Add disk space cleanup step before Docker builds in both staging and production K8s deployment workflows to prevent "No space left on device" errors during image builds. Removes .NET, GHC, Boost, and GitHub Actions tools (~14GB).
Pull Request Review: AGENTS.md Documentation SystemOverviewThis PR adds a comprehensive AGENTS.md documentation system throughout the codebase. While the documentation goals are commendable, there are significant concerns about the scope, mixed changes, and deviation from the stated purpose. Critical Issues1. Scope Creep - Mixed Concerns (HIGH SEVERITY)The PR description states this is pure documentation with no breaking changes, but the diff shows:
Files that should NOT be in a documentation PR:
Recommendation: Split into 4 separate PRs for documentation, Docling feature, K8s infrastructure, and Makefile changes. 2. Breaking Changes Mislabeled (MEDIUM)Claims no breaking changes but includes:
3. Test Coverage (MEDIUM)
Code QualityAGENTS.md Documentation (POSITIVE)Strengths:
Minor issues:
Docling Processor (CONCERNS)Positives: Good error handling, logging, type hints Issues:
K8s Deployment (CONCERNS)
Security
Best Practices Violations
What's Good
RecommendationsIMMEDIATE ACTIONS:
FINAL VERDICT: NOT READY TO MERGE Reasons:
Constructive FeedbackThe AGENTS.md documentation concept is EXCELLENT and will genuinely help both AI agents and developers. The execution needs improvement:
The team has strong technical skills - this feedback is about process improvement for safer deployments and easier reviews. Reviewer: Claude Code |
Closing PR #265 - Being Split into Focused PRsAfter review, this PR has several critical issues that prevent it from being merged: 🚨 Critical Issues
✅ ResolutionPR #322 - AGENTS.md Documentation has been created with just the documentation files, properly rebased on current main. For the other features (Docling, K8s), they need to be:
📋 Follow-up Actions
This follows our principle: smaller, focused PRs = safer deployments + easier reviews. Related: #322 (documentation split from this PR) |
Add comprehensive AGENTS.md files throughout codebase to provide contextual information for AI development tools and human developers. Includes 10 AGENTS.md files covering backend architecture, services, models, schemas, routers, repositories, frontend architecture, and React components. Split from PR #265 for focused review. All CI checks passing ✅
Summary
Implements a comprehensive AGENTS.md documentation system throughout the codebase to provide contextual information for AI development tools (Claude Code, GitHub Copilot) and human developers.
What's Added
📁 Files Created (11 total)
Root & Infrastructure
/AGENTS.md
with AI agent context loading protocol.github/AGENTS_README.md
- Team guide for using and maintaining the systemBackend Documentation (8 files)
backend/AGENTS.md
- Backend architecture overviewbackend/rag_solution/AGENTS.md
- Main application package guidebackend/rag_solution/services/AGENTS.md
- Service layer patterns (SearchService, CoT, Conversation, etc.)backend/rag_solution/models/AGENTS.md
- SQLAlchemy ORM modelsbackend/rag_solution/schemas/AGENTS.md
- Pydantic validation schemasbackend/rag_solution/router/AGENTS.md
- FastAPI endpoint handlersbackend/rag_solution/repository/AGENTS.md
- Data access layerFrontend Documentation (2 files)
frontend/AGENTS.md
- React frontend architecturefrontend/src/components/AGENTS.md
- React components guideKey Features
🎯 AI Agent Context Loading Protocol
AI agents now have clear instructions to:
/AGENTS.md
for project overview📚 Comprehensive Module Documentation
Each AGENTS.md file includes:
🔄 Living Documentation Strategy
Benefits
For AI Development Tools 🤖
For Human Developers 👥
For the Project 🚀
Usage Examples
Example 1: Adding a New Feature
Example 2: Working on Frontend
Documentation Structure
Testing
Team Guide
See
.github/AGENTS_README.md
for:Breaking Changes
None - This is pure documentation addition.
Next Steps After Merge
.github/AGENTS_README.md
with the team🤖 Generated with Claude Code