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Merge develop into feature/agent-hq
- Resolved template.yaml conflicts by updating ARN references from 'arn:aws:' to 'arn:${AWS::Partition}:' - Kept ExternalMCPAgentsSecret permissions from feature branch - Updated table references to use AnalyticsTable (renamed from AgentTable) - All other files merged automatically
2 parents 5ebd1d8 + 29ae0fa commit d865875

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CHANGELOG.md

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@@ -7,6 +7,15 @@ SPDX-License-Identifier: MIT-0
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### Added
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- **AWS GovCloud Support with Automated Template Generation**
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- Added GovCloud compatibility through `scripts/generate_govcloud_template.py` script
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- **ARN Partition Compatibility**: All templates updated to use `arn:${AWS::Partition}:` for both commercial and GovCloud regions
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- **Headless Operation**: Automatically removes UI-related resources (CloudFront, AppSync, Cognito, WAF) for GovCloud deployment
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- **Core Functionality Preserved**: All 3 processing patterns and complete 6-step pipeline (OCR, Classification, Extraction, Assessment, Summarization, Evaluation) remain fully functional
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- **Automated Workflow**: Single script orchestrates build + GovCloud template generation + S3 upload with deployment URLs
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- **Enterprise Ready**: Enables headless document processing for government and enterprise environments requiring GovCloud compliance
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- **Documentation**: New `docs/govcloud-deployment.md` with deployment guide, architecture differences, and access methods
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- **Pattern-2 and Pattern-3 Assessment now generate geometry (bounding boxes) for visualization in UI 'Visual Editor' (parity with Pattern-1)**
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- Added comprehensive spatial localization capabilities to both regular and granular assessment services
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- **Automatic Processing**: When LLM provides bbox coordinates, automatically converts to UI-compatible (Visual Edit) geometry format without any configuration
@@ -31,6 +40,8 @@ SPDX-License-Identifier: MIT-0
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- **Reverted to python3.12 runtime to resolve build package dependency problems**
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### Fixed
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- **Improved Visual Edit bounding box position when using image zoom or pan**
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Makefile

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@@ -14,7 +14,7 @@ test:
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$(MAKE) -C lib/idp_common_pkg test
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# Run both linting and formatting in one command
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lint: ruff-lint format
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lint: ruff-lint format check-arn-partitions
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# Run linting checks and fix issues automatically
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ruff-lint:
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lint-cicd:
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@echo "Running code quality checks..."
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@if ! ruff check; then \
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echo "$(RED)ERROR: Ruff linting failed!$(NC)"; \
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echo "$(YELLOW)Please run 'make ruff-lint' locally to fix these issues.$(NC)"; \
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echo -e "$(RED)ERROR: Ruff linting failed!$(NC)"; \
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echo -e "$(YELLOW)Please run 'make ruff-lint' locally to fix these issues.$(NC)"; \
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exit 1; \
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fi
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@if ! ruff format --check; then \
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echo "$(RED)ERROR: Code formatting check failed!$(NC)"; \
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echo "$(YELLOW)Please run 'make format' locally to fix these issues.$(NC)"; \
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echo -e "$(RED)ERROR: Code formatting check failed!$(NC)"; \
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echo -e "$(YELLOW)Please run 'make format' locally to fix these issues.$(NC)"; \
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exit 1; \
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fi
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@echo -e "$(GREEN)All code quality checks passed!$(NC)"
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# Check CloudFormation templates for hardcoded AWS partition ARNs
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check-arn-partitions:
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@echo "Checking CloudFormation templates for hardcoded ARN partitions..."
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@FOUND_ISSUES=0; \
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for template in template.yaml patterns/*/template.yaml patterns/*/sagemaker_classifier_endpoint.yaml options/*/template.yaml; do \
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if [ -f "$$template" ]; then \
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echo "Checking $$template..."; \
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MATCHES=$$(grep -n "arn:aws:" "$$template" | grep -v "arn:\$${AWS::Partition}:" || true); \
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if [ -n "$$MATCHES" ]; then \
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echo -e "$(RED)ERROR: Found hardcoded 'arn:aws:' references in $$template:$(NC)"; \
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echo "$$MATCHES" | sed 's/^/ /'; \
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echo -e "$(YELLOW) These should use 'arn:\$${AWS::Partition}:' instead for GovCloud compatibility$(NC)"; \
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FOUND_ISSUES=1; \
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fi; \
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fi; \
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done; \
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if [ $$FOUND_ISSUES -eq 0 ]; then \
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echo -e "$(GREEN)✅ No hardcoded ARN partition references found!$(NC)"; \
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else \
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echo -e "$(RED)❌ Found hardcoded ARN partition references that need to be fixed$(NC)"; \
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exit 1; \
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fi
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@echo "$(GREEN)All code quality checks passed!$(NC)"
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# A convenience Makefile target that runs
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commit: lint test

docs/configuration.md

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Use the included script to check document processing status via CLI:
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```bash
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bash scripts/lookup_file_status.sh <STACK_NAME> <DOCUMENT_KEY>
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```
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or directly using the Lambda function:
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```bash
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aws lambda invoke \
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--function-name <STACK_NAME>-LookupFunction \
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--payload '{"document_key":"<DOCUMENT_KEY>"}' \
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output.json && cat output.json | jq
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bash scripts/lookup_file_status.sh <DOCUMENT_KEY> <STACK_NAME>
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```
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### Response Format

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