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>
This commit is contained in:
root
@@ -0,0 +1,267 @@
|
||||
# Implement a cloud-native DICOM store on Oracle Cloud Infrastructure
|
||||
|
||||
- Source: https://docs.oracle.com/en/solutions/cloud-native-dicom-on-oci/index.html
|
||||
- Date: 2025-12
|
||||
- Type: reference-architecture
|
||||
- Services: oke, object-storage, adb-s
|
||||
- Tags: healthcare, application, autonomous
|
||||
|
||||
## Summary (catalog)
|
||||
|
||||
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.
|
||||
|
||||
## Architecture (fetched from source)
|
||||
|
||||
Architecture
|
||||
|
||||
|
||||
|
||||
|
||||
This architecture implements a DICOM store on Oracle Cloud
|
||||
Infrastructure .
|
||||
|
||||
|
||||
|
||||
The following diagram illustrates this reference architecture.
|
||||
|
||||
|
||||
Description of the illustration dicom-oci-diagram.png
|
||||
|
||||
oci-dicom-store-oracle.zip
|
||||
|
||||
|
||||
The architecture has the following components:
|
||||
|
||||
|
||||
|
||||
- Oracle Cloud
|
||||
Infrastructure
|
||||
Oracle Cloud
|
||||
Infrastructure (OCI) is Oracle's cloud computing platform
|
||||
that provides a comprehensive suite of services for
|
||||
building, deploying, and managing applications in the cloud.
|
||||
Designed for enterprise performance and security, OCI offers
|
||||
infrastructure as a service (IaaS), platform as a service
|
||||
(PaaS), and other cloud services such as AI, machine
|
||||
learning, networking, storage, and database solutions.
|
||||
|
||||
|
||||
|
||||
OCI provides high performance, scalability, and
|
||||
cost-efficiency, supporting a wide range of workloads from
|
||||
legacy enterprise applications to cloud-native services. It
|
||||
is ideal for businesses seeking robust cloud capabilities
|
||||
with strong support for hybrid and multi-cloud
|
||||
architectures.
|
||||
|
||||
|
||||
|
||||
- Cloud-based data science platform
|
||||
Oracle Cloud Infrastructure Data Science is a fully managed platform that enables data scientists
|
||||
to build, train, deploy, and manage machine learning models
|
||||
at scale. It provides a collaborative, secure environment
|
||||
with integrated tools for the entire data science lifecycle,
|
||||
including notebooks, automated model training, model
|
||||
evaluation, and deployment.
|
||||
|
||||
|
||||
|
||||
Data
|
||||
Science supports popular open-source frameworks like TensorFlow,
|
||||
scikit-learn, PyTorch, and XGBoost, and offers seamless
|
||||
integration with other OCI services such as Object Storage,
|
||||
Data Flow, and AI Services. Backed by a wide selection of
|
||||
CPU and GPU shapes, Data
|
||||
Science helps organizations accelerate AI development, streamline
|
||||
collaboration across teams, and operationalize machine
|
||||
learning models efficiently in the cloud.
|
||||
|
||||
|
||||
|
||||
|
||||
- AI model training and inference
|
||||
AI
|
||||
training and inference are the two core stages in the
|
||||
lifecycle of an artificial intelligence model:
|
||||
|
||||
|
||||
|
||||
- Training is the process by which a model
|
||||
learns from large datasets by adjusting its internal
|
||||
parameters to recognize patterns, make predictions,
|
||||
or perform specific tasks. This phase is
|
||||
computationally intensive and requires powerful
|
||||
hardware such as GPUs, as well as high-throughput
|
||||
storage and networking.
|
||||
|
||||
- Inference is the stage where the trained
|
||||
model is used to make predictions or decisions based
|
||||
on new, unseen data. Inference typically needs to be
|
||||
fast and scalable, especially in real-time
|
||||
applications like medical imaging, fraud detection,
|
||||
or virtual assistants.
|
||||
|
||||
|
||||
Chosen by world’s leading AI companies such as
|
||||
OpenAI and xAI, OCI Generative AI infrastructure has emerged as a leading platform for AI
|
||||
model training and inference, offering a unique combination
|
||||
of performance, scalability, and cost-efficiency.
|
||||
|
||||
|
||||
|
||||
|
||||
- Security and Compliance
|
||||
OCI offers a
|
||||
comprehensive set of security and compliance features
|
||||
designed to protect data, applications, and workloads across
|
||||
the cloud environment. OCI provides built-in security at
|
||||
every layer, including network security, identity and access
|
||||
management (IAM), data encryption, threat detection, and
|
||||
monitoring.
|
||||
|
||||
|
||||
Key features include isolated
|
||||
network virtualization, customer-controlled encryption keys,
|
||||
security zones, and Oracle Cloud Guard for continuous monitoring and automated threat response.
|
||||
OCI also supports compliance with major industry and
|
||||
regulatory standards such as ISO, SOC, HIPAA, GDPR, and
|
||||
FedRAMP, helping organizations meet strict data protection
|
||||
and governance requirements.
|
||||
|
||||
|
||||
|
||||
These
|
||||
capabilities make OCI a trusted platform for running
|
||||
sensitive, mission-critical workloads securely and in
|
||||
compliance with global standards.
|
||||
|
||||
|
||||
|
||||
- Orthanc
|
||||
Orthanc is an open-source,
|
||||
lightweight DICOM server designed to manage, store, and
|
||||
share medical imaging data. It is widely used in hospitals
|
||||
and research labs, and by developers to build imaging
|
||||
workflows without relying on heavy commercial PACS (Picture
|
||||
Archiving and Communication Systems).
|
||||
|
||||
|
||||
Orthanc seamlessly integrates with OCI Object Storage and OCI Database with PostgreSQL , providing a DICOMweb API endpoint that enables users to
|
||||
store, retrieve, and query DICOM images by using RESTful
|
||||
APIs.
|
||||
|
||||
|
||||
|
||||
In this reference architecture,
|
||||
Orthanc is deployed on OCI Container Instances or OCI Container Instances (OKE), eliminating the infrastructure management overhead
|
||||
for our healthcare customers.
|
||||
|
||||
|
||||
|
||||
|
||||
- OCI Object Storage
|
||||
Actual DICOM images are stored in an
|
||||
OCI Object Storage bucket.
|
||||
|
||||
|
||||
|
||||
OCI Object Storage offers highly durable, infinitely scalable, and secure
|
||||
storage for any type of data—structured or unstructured.
|
||||
Whether you're archiving medical images, serving media
|
||||
files, or backing up enterprise workloads, OCI Object Storage provides low-latency access, built-in redundancy, and
|
||||
tiered pricing to optimize cost and performance. It is
|
||||
designed for modern cloud applications, integrates natively
|
||||
with AI, analytics, and DevOps tools, and offers seamless
|
||||
data lifecycle management, all with no up-front hardware
|
||||
investment.
|
||||
|
||||
|
||||
|
||||
|
||||
- OCI Database with PostgreSQL
|
||||
In this reference architecture, Orthanc
|
||||
is integrated with OCI Database with PostgreSQL to manage and index the non-image metadata associated
|
||||
with DICOM files. While Orthanc can run without an external
|
||||
database (using its built-in SQLite engine), integrating
|
||||
with a managed and more powerful database such as OCI Database with PostgreSQL is recommended for scalability and performance.
|
||||
|
||||
|
||||
|
||||
|
||||
- OCI Kubernetes Engine
|
||||
Oracle Cloud Infrastructure Kubernetes Engine ( OCI Kubernetes Engine or OKE ) is a fully managed, enterprise-grade Kubernetes service
|
||||
that simplifies container orchestration on Oracle Cloud.
|
||||
With OKE, you get automated provisioning, scaling, and
|
||||
updates, so you can deploy, run, and manage cloud-native
|
||||
applications with less overhead.
|
||||
|
||||
|
||||
|
||||
Running
|
||||
AI workloads on OKE gives you the best of both worlds: the flexibility of
|
||||
containers and Kubernetes, combined with the power of OCI’s
|
||||
high-performance infrastructure. Whether you're training
|
||||
models on GPU nodes or deploying inference at scale, OKE lets you manage AI pipelines with ease using your
|
||||
favorite tools like TensorFlow, PyTorch, or Hugging
|
||||
Face.
|
||||
|
||||
|
||||
|
||||
With automated scaling, GPU
|
||||
support, integrated logging, and cost-efficient pricing, OKE makes it simple to build, run, and scale AI workloads in
|
||||
production, all in a secure, enterprise-grade
|
||||
environment.
|
||||
|
||||
|
||||
|
||||
|
||||
- OCI FastConnect
|
||||
OCI FastConnect offers dedicated, private network connectivity between
|
||||
customer’s on-premises facility, such as a hospital, and Oracle Cloud , delivering high throughput, low latency, and predictable
|
||||
performance for enterprise workloads. It bypasses the public
|
||||
internet entirely, improving security and reliability for
|
||||
hybrid and multicloud architectures.
|
||||
|
||||
|
||||
|
||||
But
|
||||
what sets OCI FastConnect apart is its cost model: OCI doesn’t charge for data
|
||||
egress over Oracle Cloud , unlike other cloud providers that often impose steep
|
||||
outbound data transfer fees. This creates significant cost
|
||||
savings for data-heavy workloads like medical
|
||||
imaging.
|
||||
|
||||
|
||||
|
||||
|
||||
- OCI Roving Edge Device
|
||||
OCI Roving
|
||||
Edge Devices (REDs) help facilitate DICOM data transmission
|
||||
and provide the necessary infrastructure for local
|
||||
inference. They bring the power of Oracle Cloud to the edge: rugged, portable, high-performance nodes
|
||||
that let you run applications, process data, and deploy AI
|
||||
models in disconnected or remote environments.
|
||||
|
||||
|
||||
|
||||
REDs enable low-latency edge computing with
|
||||
full compatibility with OCI services. You can preload VMs,
|
||||
containers, and data, then synchronize with the cloud when
|
||||
connectivity is available.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Recommendations
|
||||
|
||||
|
||||
|
||||
|
||||
Use the following recommendations as a starting point
|
||||
for implementing a DICOM store on Oracle Cloud
|
||||
Infrastructure . Your requirements mi
|
||||
Reference in New Issue
Block a user