forked from diegoecab/oci-deal-accelerator
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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# Implement a cloud-native DICOM store on Oracle Cloud Infrastructure
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- Source: https://docs.oracle.com/en/solutions/cloud-native-dicom-on-oci/index.html
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- Date: 2025-12
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- Type: reference-architecture
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- Services: oke, object-storage, adb-s
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- Tags: healthcare, application, autonomous
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## Summary (catalog)
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Cloud-native DICOM store on OKE with Object Storage for medical images and ADB-S for metadata. DICOMweb API compliance for interoperability with medical imaging systems.
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## Architecture (fetched from source)
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Architecture
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This architecture implements a DICOM store on Oracle Cloud
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Infrastructure .
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The following diagram illustrates this reference architecture.
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Description of the illustration dicom-oci-diagram.png
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oci-dicom-store-oracle.zip
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The architecture has the following components:
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- Oracle Cloud
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Infrastructure
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Oracle Cloud
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Infrastructure (OCI) is Oracle's cloud computing platform
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that provides a comprehensive suite of services for
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building, deploying, and managing applications in the cloud.
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Designed for enterprise performance and security, OCI offers
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infrastructure as a service (IaaS), platform as a service
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(PaaS), and other cloud services such as AI, machine
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learning, networking, storage, and database solutions.
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OCI provides high performance, scalability, and
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cost-efficiency, supporting a wide range of workloads from
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legacy enterprise applications to cloud-native services. It
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is ideal for businesses seeking robust cloud capabilities
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with strong support for hybrid and multi-cloud
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architectures.
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- Cloud-based data science platform
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Oracle Cloud Infrastructure Data Science is a fully managed platform that enables data scientists
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to build, train, deploy, and manage machine learning models
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at scale. It provides a collaborative, secure environment
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with integrated tools for the entire data science lifecycle,
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including notebooks, automated model training, model
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evaluation, and deployment.
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Data
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Science supports popular open-source frameworks like TensorFlow,
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scikit-learn, PyTorch, and XGBoost, and offers seamless
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integration with other OCI services such as Object Storage,
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Data Flow, and AI Services. Backed by a wide selection of
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CPU and GPU shapes, Data
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Science helps organizations accelerate AI development, streamline
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collaboration across teams, and operationalize machine
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learning models efficiently in the cloud.
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- AI model training and inference
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AI
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training and inference are the two core stages in the
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lifecycle of an artificial intelligence model:
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- Training is the process by which a model
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learns from large datasets by adjusting its internal
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parameters to recognize patterns, make predictions,
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or perform specific tasks. This phase is
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computationally intensive and requires powerful
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hardware such as GPUs, as well as high-throughput
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storage and networking.
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- Inference is the stage where the trained
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model is used to make predictions or decisions based
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on new, unseen data. Inference typically needs to be
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fast and scalable, especially in real-time
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applications like medical imaging, fraud detection,
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or virtual assistants.
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Chosen by world’s leading AI companies such as
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OpenAI and xAI, OCI Generative AI infrastructure has emerged as a leading platform for AI
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model training and inference, offering a unique combination
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of performance, scalability, and cost-efficiency.
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- Security and Compliance
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OCI offers a
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comprehensive set of security and compliance features
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designed to protect data, applications, and workloads across
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the cloud environment. OCI provides built-in security at
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every layer, including network security, identity and access
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management (IAM), data encryption, threat detection, and
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monitoring.
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Key features include isolated
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network virtualization, customer-controlled encryption keys,
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security zones, and Oracle Cloud Guard for continuous monitoring and automated threat response.
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OCI also supports compliance with major industry and
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regulatory standards such as ISO, SOC, HIPAA, GDPR, and
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FedRAMP, helping organizations meet strict data protection
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and governance requirements.
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These
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capabilities make OCI a trusted platform for running
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sensitive, mission-critical workloads securely and in
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compliance with global standards.
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- Orthanc
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Orthanc is an open-source,
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lightweight DICOM server designed to manage, store, and
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share medical imaging data. It is widely used in hospitals
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and research labs, and by developers to build imaging
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workflows without relying on heavy commercial PACS (Picture
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Archiving and Communication Systems).
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Orthanc seamlessly integrates with OCI Object Storage and OCI Database with PostgreSQL , providing a DICOMweb API endpoint that enables users to
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store, retrieve, and query DICOM images by using RESTful
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APIs.
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In this reference architecture,
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Orthanc is deployed on OCI Container Instances or OCI Container Instances (OKE), eliminating the infrastructure management overhead
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for our healthcare customers.
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- OCI Object Storage
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Actual DICOM images are stored in an
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OCI Object Storage bucket.
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OCI Object Storage offers highly durable, infinitely scalable, and secure
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storage for any type of data—structured or unstructured.
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Whether you're archiving medical images, serving media
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files, or backing up enterprise workloads, OCI Object Storage provides low-latency access, built-in redundancy, and
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tiered pricing to optimize cost and performance. It is
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designed for modern cloud applications, integrates natively
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with AI, analytics, and DevOps tools, and offers seamless
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data lifecycle management, all with no up-front hardware
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investment.
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- OCI Database with PostgreSQL
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In this reference architecture, Orthanc
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is integrated with OCI Database with PostgreSQL to manage and index the non-image metadata associated
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with DICOM files. While Orthanc can run without an external
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database (using its built-in SQLite engine), integrating
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with a managed and more powerful database such as OCI Database with PostgreSQL is recommended for scalability and performance.
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- OCI Kubernetes Engine
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Oracle Cloud Infrastructure Kubernetes Engine ( OCI Kubernetes Engine or OKE ) is a fully managed, enterprise-grade Kubernetes service
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that simplifies container orchestration on Oracle Cloud.
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With OKE, you get automated provisioning, scaling, and
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updates, so you can deploy, run, and manage cloud-native
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applications with less overhead.
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Running
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AI workloads on OKE gives you the best of both worlds: the flexibility of
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containers and Kubernetes, combined with the power of OCI’s
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high-performance infrastructure. Whether you're training
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models on GPU nodes or deploying inference at scale, OKE lets you manage AI pipelines with ease using your
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favorite tools like TensorFlow, PyTorch, or Hugging
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Face.
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With automated scaling, GPU
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support, integrated logging, and cost-efficient pricing, OKE makes it simple to build, run, and scale AI workloads in
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production, all in a secure, enterprise-grade
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environment.
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- OCI FastConnect
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OCI FastConnect offers dedicated, private network connectivity between
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customer’s on-premises facility, such as a hospital, and Oracle Cloud , delivering high throughput, low latency, and predictable
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performance for enterprise workloads. It bypasses the public
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internet entirely, improving security and reliability for
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hybrid and multicloud architectures.
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But
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what sets OCI FastConnect apart is its cost model: OCI doesn’t charge for data
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egress over Oracle Cloud , unlike other cloud providers that often impose steep
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outbound data transfer fees. This creates significant cost
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savings for data-heavy workloads like medical
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imaging.
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- OCI Roving Edge Device
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OCI Roving
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Edge Devices (REDs) help facilitate DICOM data transmission
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and provide the necessary infrastructure for local
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inference. They bring the power of Oracle Cloud to the edge: rugged, portable, high-performance nodes
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that let you run applications, process data, and deploy AI
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models in disconnected or remote environments.
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REDs enable low-latency edge computing with
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full compatibility with OCI services. You can preload VMs,
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containers, and data, then synchronize with the cloud when
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connectivity is available.
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Recommendations
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Use the following recommendations as a starting point
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for implementing a DICOM store on Oracle Cloud
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Infrastructure . Your requirements mi
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Reference in New Issue
Block a user