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

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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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# Deploy an AI-powered chatbot
- Source: https://docs.oracle.com/en/solutions/deploy-ai-powered-chatbot/index.html
- Date: 2025-08
- Type: reference-architecture
- Services: genai, functions, adb-s
- Tags: ai-ml, application, autonomous
## Summary (catalog)
AI chatbot with OCI GenAI and Functions. RAG for knowledge-grounded responses, ADB-S for conversation history and vector search. Serverless architecture with OCI Functions for cost efficiency.
## Architecture (fetched from source)
Architecture
This reference architecture outlines how to configure and deploy an
AI-powered chatbot on your own OCI tenancy. This chatbot can generate or summarize
content, answer questions, translate languages, and more.
This AI-powered chatbot leverages Oracle Digital Assistant and OCI Generative AI large language models (LLMs). Oracle Visual Builder is used to embed the chatbot in a web application. Users can interact with OCI Generative AI through natural language questions and receive responses from the LLM by using the
chatbot interface.
The following diagram illustrates this reference architecture.
Description of the illustration deploy-ai-chatbot-arch.png
deploy-ai-chatbot-arch.zip
The flow for users and developers using this architecture resembles:
- Developers and chatbot users authenticate with OCI Identity and Access Management .
- Users access the chatbot using the Oracle Visual Builder app where it is embedded. Developers can configure the app from the Oracle Visual Builder service home page.
- Developers configure the chatbot using the Oracle Digital Assistant service console.
- Developers access OCI Generative AI LLMs using APIs.
The architecture has the following components:
- Region
An Oracle Cloud
Infrastructure region is a localized geographic area that contains one or more data centers, hosting availability domains. Regions are independent of other regions, and vast distances can separate them (across countries or even continents).
- Identity and Access Management
Oracle Cloud Infrastructure Identity
and Access Management (IAM) provides user access control 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 them. Each OCI IAM identity domain represents a standalone identity and access management solution or a different user population.
- Oracle Visual Builder
Oracle Visual Builder is an intuitive development experience on top of a development and hosting
platform that empowers you to create engaging responsive applications. Focusing
on ease of use and a visual development approach, it provides an easy way for
you to create applications that are hosted in Oracle’s secure and scalable cloud
platform.
- Digital Assistant
Oracle Digital Assistant is a platform that allows you to create and deploy digital assistants for your users. With Oracle Digital Assistant , you can create AI-driven interfaces (or chatbots) for business applications through text, chat, and voice interfaces. Each digital assistant has a collection of one or more specialized skills to help users complete a variety of tasks in natural language conversations. For example, an individual digital assistant might have skills that focus on specific types of tasks such as tracking inventory, submitting time cards, and creating expense reports.
- 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.
Considerations
When deploying this AI-powered chatbot, consider the following.
- Region Availability
Oracle hosts its OCI services in regions
and availability domains. A region is a localized geographic area, and an
availability domain is one or more data centers in that region. AI services are
not always available in all regions. To learn more, see Regions with
Generative AI in the Explore More section.
- Document Processing
The document processing feature used in this
architecture is intended for smaller documents. For solutions that analyze
larger documents, see the other LiveLabs in the Explore More
section.
Deploy
To deploy this architecture, follow the instructions in this Live
Lab:
Deploy ATOM (AI Powered) Chatbot
Explore More
Learn more about deploying an AI-powered chatbot.
Review these additional resources:
- Deploy an ODA Chatbot powered by Generative
AI Agents (LiveLab)
- Deploy a Chatbot powered by
Generative AI Agents using 23ai Vector DB
(LiveLab)
- OCI Generative
AI
- Overview of Digital Assistants and Skills
- Regions with Generative AI
- Well-architected framework
for Oracle Cloud Infrastructure
Acknowledgments
- Authors : Luke Farley, Abhinav Jain
- Contributor : Kaushik Kundu
Title and Copyright Information
Deploy an AI-powered chatbot
G27457-02
August 2025
Copyright © 2025,
Oracle and/or its affiliates.