Diagram generation: ref-arch-driven procedure + spec validator + KB enrichment

The diagram path now follows a documented standard procedure (lookup the closest Oracle Architecture Center reference → confirm components → author absolute_layout → spec validator → render → visually verify) and ships persistent guardrails so layout regressions can't recur. Persistent procedure changes (apply to all users, all sessions): - tools/diagram_spec_validator.py — geometry checks (CONTAINER_TOO_THIN, CONTAINER_PADDING_VIOLATION, LABEL_OVERFLOW_PARENT) run BEFORE either renderer (drawio + PPTX). Catches the subnet-collapse / label-overflow bugs that the post-render drawio validator missed. - tools/oci_diagram_gen.py + tools/oci_pptx_diagram_gen.py — call the spec validator before emitting any output. Adds mysql / mysql_heatwave type aliases. - tools/archcenter_pattern_lookup.py — scores against cached page descriptions (not just the 1-line summary), supports --queries for multi-fragment composition, and applies synonym expansion via kb/architecture-center/synonyms.yaml so "LB HA cross AD" matches "load balancer high availability availability domain". - kb/architecture-center/synonyms.yaml — canonical synonym table (load balancer, autonomous database, data guard, …) used by the lookup scorer. KB enrichment: - tools/archcenter_description_fetcher.py + 121 cached _description.md under kb/diagram/assets/archcenter-refs/<slug>/. Removes the runtime dependency on docs.oracle.com when authoring specs and feeds the pattern-lookup scorer. - 110+ cached .drawio / .svg / .png references for offline reuse, plus the OCI Toolkit v24.2 import (kb/diagram/assets/oci-toolkit-drawio). Documentation: - docs/skill/output-formats.md — new "Standard diagram-generation procedure (MANDATORY)" + geometry rules + the new validator entry. - SKILL.md option 2 — references the mandatory procedure. - README.md — describes the spec validator, archcenter_pattern_lookup and description fetcher, and updates the KB-health table. Tooling that backs the procedure (cumulative across recent sessions): tools/archcenter_case_runner.py, archcenter_batch_driver.py, archcenter_zip_downloader.py, drawio_visual_validator.py, drawio_fidelity_eval.py, harvest_drawio_icon.py, import_oci_library.py, oci_pptx_diagram_gen.py, oci_pptx_render.py, refresh_pptx_icon_index.py. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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+# Stream fraud detection with NVIDIA Morpheus on Oracle Compute Cloud@Customer
+
+- Source: https://docs.oracle.com/en/solutions/fraud-detection-nvidia-morpheus-compute-cloud/index.html
+- Date: 2025-08
+- Type: built-deployed
+- Services: compute
+- Tags: ai-ml, security
+
+## Summary (catalog)
+
+Real-time fraud detection with NVIDIA Morpheus on Compute Cloud@Customer. GPU-accelerated ML pipeline for transaction monitoring. On-premises deployment for data sovereignty requirements.
+
+## Architecture (fetched from source)
+
+Learn About Streaming Fraud Detection 
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+Learn About Streaming Fraud
+ Detection
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+ Oracle Compute Cloud@Customer , a key component of the Oracle Roving Edge Infrastructure portfolio, provides organizations a scalable solution to process sensitive data
+ securely, with low latency—close to its source. 
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+In this solution playbook, you learn how to use the NVIDIA Morpheus cybersecurity framework to deploy real-time, AI-driven fraud detection using a
+ GPU-accelerated Morpheus pipeline, and publish the results using Compute Cloud@Customer . The solution enables instant fraud detection without waiting for batch jobs and
+ keeps your data secure by locally processing it at the edge.
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+Before You Begin
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+ Ensure you perform the deployment on a host machine with the
+ following environment settings: 
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+- Operating System : Ubuntu 24.04 LTS
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+- Platform : NVIDIA AI Enterprise on a single-node Oracle Compute Cloud@Customer instance equipped with an NVIDIA L40S GPU.
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+Before you begin, ensure the following tools are installed on the host machine:
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+- Docker and Docker Compose ( docker compose V2 )
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+- Git and Git LFS ( git-lfs )
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+- Python 3 and Pip 
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+Workflow
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+The workflow leverages the NVIDIA Morpheus cybersecurity framework to perform GPU-accelerated inference on a stream of financial
+ transaction data. 
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+The following diagram shows a workflow through three swimlanes; Host
+ environment, Apache Kafka , and the Morpheus pipeline:
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+
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+ Description of the illustration ai-driven-fraud-detection-workflow-arch.png 
+
+The pipeline ingests live transaction data through Apache Kafka , performs graph-based contextual analysis using a pre-trained Graph Sample and Aggregate ( GraphSAGE ) model, and executes final fraud classification using XGBoost . All stages are accelerated using NVIDIA 
+ RAPIDS libraries (cuDF, cuML), making the entire workflow GPU-optimized for high throughput.
+ The following is how the process flows:
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+- Transaction data (.csv) is produced by a Python producer.
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+- Data goes into Kafka topic INPUT stream.
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+- Data is processed through the Morpheus pipeline in steps: reading from Kafka source, deserializing, constructing a graph, interfacing with Graph Neural Network (GNN) interface ( GraphSAGE ), classifying with
+ XGBoost , serializing results, and writing to Kafka sink.
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+- Results are sent to Kafka topic OUTPUT stream.
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+- A Python consumer receives output and provides live fraud prediction.
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+ Model Provenance 
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+The inference pipeline at the core of this architecture uses two pre-trained
+ machine learning models: a GraphSAGE GNN and an XGBoost classifier. These models were generated using a separate training pipeline, which is
+ included in the Morpheus repository for reference.
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+- Training Script Location: 
+ examples/gnn_fraud_detection_pipeline/training.py .
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+- Process: The script processes a labeled historical dataset to
+ train the GNN on graph-based features and the XGBoost model on
+ the resulting embeddings.
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+In this solution, you don't have to run the training script becausethe
+ pre-trained models are already provided. Focus on deploying and running the real-time
+ inference pipeline.
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+This architecture supports the following components:
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+- Oracle Compute Cloud@Customer 
+ Oracle Compute Cloud@Customer is fully-managed, rack-scale infrastructure
+ that lets you use OCI Compute anywhere. Gain the benefits of cloud automation
+ and economics in your data center by running OCI Compute and GPU shapes with storage and networking
+ services on Compute Cloud@Customer . You can run applications and harness the power
+ of GenAI on cloud infrastructure in your data
+ center while helping address data residency,
+ security, and low-latency connections to local
+ resources and real-time operations.
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+- RAPIDS 
+ cuDF/cuML 
+ RAPIDS cuDF/cuML are a suite of GPU-accelerated libraries for high-performance data
+ manipulation and machine learning utilities within the Morpheus
+ pipeline.
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+- Docker + Conda 
+ Docker + Conda provide a layered approach to dependency management, using Docker for OS-level isolation and Conda for managing the complex Python environment inside the container.
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+Considerations for
+ Production
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+When implementing this solution in a production environment, consider the
+ scalability and resilience that Kubernetes can provide. You can migrate this solution to OCI Kubernetes Engine ( OKE ) by:
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+- Containerizing the producer and consumer helper scripts. 
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+- Deploying Kafka using a production-grade Kubernetes operator.
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+- Deploying the Morpheus pipeline as a Kubernetes job or deployment.
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+About Required Services and
+ Roles
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+This solution requires the following services and roles:
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+- Oracle Compute Cloud@Customer 
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+- NVIDIA AI Enterprise 6.0
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+Ubuntu Linux (or a compatible Linux distribution)
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+- Docker 
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+- NVIDIA Morpheus 25.02
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+These are the roles needed for each service.
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+ Service Name: Role 
+ Required to... 
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+ Oracle Compute Cloud@Customer : administrator 
+ Configure and deploy the NVIDIA AI Enterprise virtual machine instance, manage network resources, and ensure access
+ to the NVIDIA L40S GPU.
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+ Ubuntu Linux: root or user with sudo 
+ privileges
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+ Install prerequisite software ( Docker , Git), manage system services, and execute Docker commands.
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+ NVIDIA AI Enterprise : Account User
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+ Pull the required NVIDIA Morpheus container image from the NVIDIA GPU Cloud ( NGC ) catalog.
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+See Oracle Products, Solutions, and Services to get what you need.
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+ Title and Copyright Information 
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+Fraud detection with NVIDIA Morpheus on Compute Cloud@Customer and Private Cloud Appliance
+
+ 
+G38649-02
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+ 
+August 2025
+
+ 
+ Copyright © 2025, 
+
+Oracle and/or its affiliates.