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oci-deal-accelerator/kb/diagram/assets/archcenter-refs/oci-genai-enterprise/_description.md
root b30a4f0d32 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>
2026-04-25 21:15:21 -03:00

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Build an enterprise level Generative AI stack on Oracle Cloud Infrastructure

Summary (catalog)

Enterprise GenAI stack with model fine-tuning, RAG, and inference. OCI AI Infrastructure for GPU compute, Object Storage for model artifacts, OKE for scalable inference serving.

Architecture (fetched from source)

Architecture

This reference architecture describes a four layer AI stack and all the different components that are needed to implement an enterprise grade Generative AI solution within an enterprise setting.

  • Application Layer

  • Access Layer

  • Logging and Monitoring across the solution

  • AI layer consisting of the following five modules:

  • AI integration

  • LLM

  • AI Development

  • Data Integration

  • Context and Data Catalog

The hypothetical flow considered for this reference architecture is described in the following section:

  • A request will come in from the application to the API and Access layer.

  • The layer is protected by WAF and the request is checked for authentication using OCI Identity and Access Management and authorization policies.

  • The API gateway then takes the request to the integration layer, this layer includes LangChain which is used for AI abstraction and orchestration. This layer also includes the prompts repository that have been whitelisted and mapped to the proper authorization and the LLM model version.

  • The request is sent to the LLM that matches the request class and prompt.

  • Context and consumer history is loaded from the context database.

  • The location of any data that needs to be enriched is accessed from the data catalog.

  • Let us say some data is still missing, The Data integration layer will first check if the data has been cached and if not it will be queried from the customer's data.

  • LLM will respond through integration.

  • The response will pass through the Hallucination checker, the Hallucination checker will then run adversarial AI to validate whether the response is meaningful.

  • Finally it goes through the API gateway back to the application.

The following diagram illustrates this reference architecture.

Description of the illustration oci-genai-enterprise-arch.png

oci-genai-enterprise-arch-oracle.zip

Let us go through the building blocks that make each block layer:

AI Layer

  • Mix and match LLMs within the LLM module with each LLM used for the area it is the best fit for.

  • Context should be maintained per customer and across different conversations, Data Catalog helps the different LLMs know where to find the required data.

  • Data Integration layer accesses the customer data and provides it at speed to AI, this includes the required data caching as well as integration.

  • AI integration module, maintains the prompts Repo, LangChain to abstract LLMs, and Oracle Integration for integration.

  • AI development layer allows for model versioning and storage as well as the DevOps needed to evolve the solution.

Logging and Monitoring Layer

  • Hallucination checker runs adversarial AI to run the output of the LLM output to validate its veracity.

  • Application Performance Monitoring tracks the performance SLA.

  • Logging and auditing track how the Generative AI solution is being used to observe the system and identify potential issues.

API and Access Layer

  • API gateway allows controlled access to the AI Stack.

  • The policies are maintained centrally to manage access to the LLM stack.

  • WAF protects the environment from potential attack vectors.

  • Access tokens and control are managed with OCI Identity and Access Management .

The architecture has the following components:

  • OCI Generative AI Agents OCI Generative AI Agents is a fully managed service that combines the power of large language models (LLMs) with an intelligent retrieval system to create contextually relevant answers by searching your knowledge base, making your AI applications smart and efficient.OCI Generative AI Agents supports several ways to onboard your data and then allows you and your customers to interact with your data using a chat interface or API.

  • Generative AI Oracle Cloud Infrastructure Generative AI is a fully managed OCI service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases for text generation, summarization, semantic search, and more. Use the playground to try out the ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters.

  • Integration Oracle Integration is a fully managed, preconfigured environment that allows you to integrate cloud and on-premises applications, automate business processes, and develop visual applications. It uses an SFTP-compliant file server to store and retrieve files and allows you to exchange documents with business-to-business trading partners by using a portfolio of hundreds of adapters and recipes to connect with Oracle and third-party applications.

  • API Gateway Oracle Cloud Infrastructure API Gateway enables you to publish APIs with private endpoints that are accessible from within your network, and which you can expose to the public internet if required. The endpoints support API validation, request and response transformation, CORS, authentication and authorization, and request limiting.

  • OCI Data Integration Oracle Cloud Infrastructure Data Integration is a fully managed, serverless, cloud-native service that extracts, loads, transforms, cleanses, and reshapes data from a variety of data sources into target Oracle Cloud Infrastructure services, such as Autonomous Data Warehouse and Oracle Cloud Infrastructure Object Storage. ETL (extract transform load) leverages fully-managed scale-out processing on Spark, and ELT (extract load transform) leverages full SQL push-down capabilities of the Autonomous Data Warehouse in order to minimize data movement and to improve the time to value for newly ingested data. Users design data integration processes using an intuitive, codeless user interface that optimizes integration flows to generate the most efficient engine and orchestration, automatically allocating and scaling the execution environment. Oracle Cloud Infrastructure Data Integration provides interactive exploration and data preparation and helps data engineers protect against schema drift by defining rules to handle schema changes.

  • Oracle Exadata Database Service Oracle Exadata Database Service enables you to leverage the power of Exadata in the cloud. Oracle Exadata Database Service delivers proven Oracle Database capabilities on purpose-built, optimized Oracle Exadata infrastructure in the public cloud and on Cloud@Customer. Built-in cloud automation, elastic resource scaling, security, and fast performance for all Oracle Database workloads helps you simplify management and reduce costs.

  • Identity and Access Management (IAM) Oracle Cloud Infrastructure Identity and Access Management (IAM) is the access control plane for Oracle Cloud Infrastructure (OCI) and Oracle Cloud Applications. The IAM API and the user interface enable you to manage identity domains and the resources within the identity domain. Each OCI IAM identity domain represents a standalone identity and access management solution or a different user population.

Recommendations

Use the following recommendations as a starting point. Your requirements might differ from the architecture described here.

  • Oracle Cloud Infrastructure + Generative AI Generative AI can drive innovation, improve processes, and help companies accomplish more than ever before, but it requires the right approach. Oracle continues to make the best of AI available to enterprises everywhere, with a unique focus on high performing models, embedding Generative AI across the stack, and data management, security and privacy. By embedding AI throughout the entire technology stack - from the infrastructure that businesses run on, through to applications for every line of business from finance to supply
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