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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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Improve search results with Oracle Generative AI Agents, Vector Search, and OCI OpenSearch
- Source: https://docs.oracle.com/en/solutions/genai-vector-opensearch/index.html
- Date: 2024-12
- Type: reference-architecture
- Services: genai, opensearch, adb-s
- Tags: ai-ml, autonomous
Summary (catalog)
Hybrid search combining vector similarity (ADB-S) with keyword search (OpenSearch). GenAI for query understanding and result re-ranking. Suitable for enterprise knowledge bases and document search.
Architecture (fetched from source)
Deploy an AI-powered enterprise search engine using Oracle Generative AI Agents, Oracle Database 23ai Vector, and OCI OpenSearch
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Improve search results with Oracle Generative AI Agents, Vector Search, and OCI OpenSearch
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Deploy an AI-powered enterprise search engine using Oracle Generative AI Agents, Oracle Database 23ai Vector, and OCI OpenSearch
Deploy an AI-powered enterprise search engine using Oracle Generative AI Agents, Oracle Database 23ai Vector, and OCI OpenSearch
Search powered by artificial intelligence (AI) is intended to help employees in every line of business get immediate answers to complex queries. But writing programmatic queries using keywords or semantic search can be extremely challenging if not impossible for anyone without technical expertise.
By using Oracle Generative AI services, anyone with a keyboard or voice command interface can:
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Quickly predict customer purchase behaviors by asking natural language questions
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Instantly retrieve information from a knowledge base, and provide contextually relevant solutions to complex problems
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Access and synthesize technical manuals and support forums to instantly retrieve step-by-step instructions
By combining Oracle Generative AI Retrieval-Augmented Generation (RAG) Agents with a supported knowledge base, cloud engineers and enterprise architects can quickly:
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Build an intelligent search system that supports hybrid search (keyword search and semantic search), advanced data retrieval, and reranking to provide the most precise and relevant information
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Provide a chat interface where any authorized user, with or without technical expertise, can use natural language to query enterprise data
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Provide a managed vector data store and automated data ingestion pipeline to enable efficient storage and retrieval of complex data
Replay the Webinar
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Title and Copyright Information
Improve search results with Oracle Generative AI Agents, Vector Search, and OCI OpenSearch
G19700-01
December 2024
Copyright © 2024,
Oracle and/or its affiliates.