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>

kb/diagram/assets/archcenter-refs/implement-rag-oci/_description.md

@@ -0,0 +1,330 @@
+# Implement retrieval augmented generation by using Oracle Integration
+
+- Source: https://docs.oracle.com/en/solutions/implement-rag-oci/index.html
+- Date: 2024-09
+- Type: reference-architecture
+- Services: genai, oic, adb-s
+- Tags: ai-ml, integration, autonomous
+
+## Summary (catalog)
+
+RAG implementation using OIC for document ingestion pipeline. Pre-built OIC adapters for SaaS data sources, ADB-S for vector storage, GenAI for embedding and generation.
+
+## Architecture (fetched from source)
+
+Architecture
+
+ 
+
+ 
+This reference architecture shows how you can implement a RAG framework using
+ semantic search technique to answer a user query on a corporate data using low-code or
+ no-code integration platform such as Oracle Integration (OIC) services.
+ 
+
+ 
+In this architecture, Oracle Cloud Infrastructure Generative AI is used to create embeddings and generates optimized or helpful answers/responses
+ based on the context specific corporate data. Oracle Autonomous Database 23ai is used to store the vector embeddings, create indexes and allows to do a
+ semantic search based on the similarity or distance instead of keyword-based search. OCI Functions is used to perform a chunking of corporate documents or data using the standard
+ LangChain python packages. OIC services handles the entire orchestration and automation
+ process from receiving the corporate data to storing/querying those as vector embeddings
+ and generate the optimized and creative context specific answers for the user queries in
+ real or near-time fashion. 
+ 
+
+ 
+The following diagrams illustrate two processes supported by this reference
+ architecture:
+ 
+ 
+- Retrieval process: 
+
+ 
+ 
+ Description of the illustration rag-oic.png 
+
+ 
+ 
+
+ 
+
+ rag-oic-oracle.zip 
+ 
+ 
+
+ 
+In this process, the following occurs:
+
+ 
+ 
+- Corporate or company data is received to Oracle Integration Retriever service in various formats such as PDF, TXT, CSV, XML,
+ JSON,and so on by way of REST, File or sFTP, or any other
+ protocols.
+ 
+ 
+- The Retriever service chunks the documents or data by using
+ OCI Functions .
+ 
+ 
+- The Retriever service then gets the vector embeddings for
+ each chunk of data by calling the OCI Generative AI Embedding service by using embedding models such as Cohere or
+ others.
+ 
+ 
+- Finally, the Retriever service stores these embeddings in
+ the Oracle Autonomous Database 23ai along with the chunked data.
+ 
+ 
+ 
+ 
+- Augmentation and Generation process: 
+
+ 
+ 
+ Description of the illustration rag-oic-aug-gen.png 
+
+ 
+ 
+
+ 
+
+ rag-oic-aug-gen-oracle.zip 
+ 
+ 
+
+ 
+In this process, the following occurs:
+
+ 
+ 
+- Corporate or company users through front end applications
+ ask queries or questions about company data, such as polices, HR, sales,
+ purchase history, financial reports, issues, and so on. 
+ 
+- OIC's Generate service receives the query data and invokes
+ its local integration's Augment service to get the context for that
+ query. 
+ 
+- OIC's Augment service, once invoked, calls OCI Generative AI 's Embedding service to get the vector embeddings of the query data. 
+ 
+ 
+- OIC's Augment service gets the context stored in the Oracle Autonomous Database 23ai, based on the semantic search of the query data vector
+ embeddings. Retrieved context is sent back as a response to the Generate
+ service. 
+ 
+ 
+- The Generate service, with the received context and query,
+ invokes the OCI Generative AI Generation service to generate the appropriate response. 
+ 
+ 
+- Finally, the Generate service replies with the generated
+ response to the user. 
+ 
+ 
+ 
+ 
+
+ 
+OIC helps customers automate the end-to-end RAG process. Customers or
+ companies can benefit from using a low-code, no-code integration platform to implement
+ RAG on their corporate data. Building RAG by using a low-code, no-code platform enables
+ development and go-to-market within hours or days rather than months.
+
+ 
+The architecture has the following components:
+
+ 
+ 
+- Autonomous
+ Database 
+
+ Oracle Autonomous Database is a fully managed, preconfigured database
+ environments that you can use for transaction
+ processing and data warehousing workloads. You do
+ not need to configure or manage any hardware, or
+ install any software. Oracle Cloud
+ Infrastructure handles creating the database, as well as
+ backing up, patching, upgrading, and tuning the
+ database.
+ 
+
+ 
+ 
+- Autonomous Transaction
+ Processing 
+ Oracle Autonomous Transaction
+ Processing is a self-driving, self-securing,
+ self-repairing database service that is optimized
+ for transaction processing workloads. You do not
+ need to configure or manage any hardware, or
+ install any software. Oracle Cloud
+ Infrastructure handles creating the database, as well as
+ backing up, patching, upgrading, and tuning the
+ database. 
+ 
+
+ 
+ 
+- Functions 
+ Oracle Cloud Infrastructure
+ Functions is a fully managed, multitenant, highly
+ scalable, on-demand, Functions-as-a-Service (FaaS)
+ platform. It is powered by the Fn Project open
+ source engine. Functions enable you to deploy your
+ code, and either call it directly or trigger it in
+ response to events. Oracle Functions uses Docker
+ containers hosted in Oracle Cloud Infrastructure
+ Registry .
+ 
+
+ 
+ 
+- Integration 
+ Oracle Integration is a fully managed service that allows you to
+ integrate your applications, automate processes,
+ gain insight into your business processes, and
+ create visual applications.
+ 
+
+ 
+ 
+- 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.
+
+ 
+ 
+- Oracle Database 23ai 
+Oracle Database 23ai is the next long-term support
+ release of Oracle Database. It includes over 300 new features with a focus on
+ artificial intelligence (AI) and developer productivity. Features such as AI
+ Vector Search enable you to leverage a new generation of AI models to generate
+ and store vectors of documents, images, sound, and so on; index them and quickly
+ look for similarity while leveraging the existing analytical capabilities of
+ Oracle Database. This combined with the already extensive set of Machine
+ Learning algorithms enables you to quickly create sophisticated AI-enabled
+ applications. Oracle Database 23ai also uses AI to optimize many of the key
+ database functions to make more accurate estimates on timings and resource
+ costings.
+
+ 
+ 
+ 
+
+ 
+
+ 
+ 
+Explore More
+
+ 
+
+ 
+Learn more about implementing RAG by using Oracle Integration.
+
+ 
+Review these additional resources: 
+
+ 
+ 
+- Best practices framework
+ for Oracle Cloud Infrastructure 
+ 
+- Oracle Cloud
+ Infrastructure Documentation 
+ 
+- Oracle
+ Integration 
+ 
+- OCI
+ Functions 
+ 
+- About Oracle
+ Database 23ai 
+ 
+- Deploy generative AI models to
+ OCI 
+ 
+ 
+Review these OCI Generative AI resources: 
+ 
+ 
+- Generative AI Documentation Home 
+ 
+ 
+ 
+- Generative AI capabilities 
+ 
+ 
+ 
+- Press release: Oracle Introduces Integrated Vector Database to Augment
+ Generative AI and Dramatically Increase Developer
+ Productivity 
+ 
+ 
+ 
+- Using
+ the Large Language Models (LLMs) in Generative AI (Using the
+ Playground)
+ 
+ 
+- About
+ the Generation Models in Generative AI 
+ 
+- About the Summarization Models in Generative AI 
+ 
+- About the
+ Embedding Models in Generative AI 
+ 
+ 
+
+ 
+
+ 
+
+ 
+ 
+Acknowledgments
+
+ 
+
+ 
+ Author: Pavan Rajalbandi
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+
+ 
+ Title and Copyright Information 
+
+ 
+
+ 
+Implement retrieval augmented generation by using Oracle Integration
+
+ 
+G14623-01
+
+ 
+September 2024
+
+ 
+ Copyright © 2024, 
+
+Oracle and/or its affiliates.

kb/diagram/assets/archcenter-refs/implement-rag-oci/readme.txt

@@ -0,0 +1,7 @@
+The diagram you downloaded is available in these formats:
+- DRAWIO
+- SVG
+
+You can customize them for your organization using the associated tools:
+- For DRAWIO format, use draw.io for Confluence, online at diagrams.net, or the desktop app. Go to diagrams.net for more information.
+- For SVG format, use an SVG editor such as Inkscape or Sketsa SVG Editor, which are free and available for Windows, macOS, Linux.