b48d155c1b
6 CLI improvements from MCP integration testing + deck bug fixes: - kb_cli.py: --json on search, new arch-search and ecal-score subcommands, _load_yaml handles multi-document YAML (fixes health command on 32 files) - findings_cli.py: --json on search/list/stats, filter flags on search (--product, --severity, --tag, --client, --status, --category) - validate-architecture.py: --profile optional, --output defaults to stdout - common-objections.yaml + ecal-artefacts-catalog.yaml: merge front matter - oci_deck_gen.py: fix slide 9 cost_notes char iteration, fix slide 12 migration field mapping (duration/milestones), dynamic phase spacing - proposal-spec.yaml: replace real name with fictional Carlos Mendoza All 14 skill options validated with generated deliverables. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
87 lines
2.8 KiB
JSON
87 lines
2.8 KiB
JSON
[
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{
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"feature": "AI Vector Search (HNSW)",
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"category": "developer",
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"compatibility": [
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{
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"deployment": "ADB-S Serverless",
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"version": "23ai",
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"status": "GA_CAVEAT",
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"notes": "HNSW indexes are node-local on multi-node RAC. Works well up to\n~50M vectors on single node (\u226424 ECPUs). At 100M+ on multi-node\n(\u226564 ECPUs), requires hash-partitioned vector table with LOCAL\nHNSW index for <50ms P95. See FF-202603-008.\n"
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},
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{
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"deployment": "ADB-S Serverless",
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"version": "26ai",
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"status": "UNTESTED",
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"notes": "Check if distributed HNSW is added in 26ai. Expected to remain node-local."
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},
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{
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"deployment": "ADB-S Elastic Pool",
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"version": "23ai",
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"status": "GA_CAVEAT",
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"notes": "Same node-local limitation as ADB-S Serverless."
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},
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{
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"deployment": "ADB on Dedicated Exadata",
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"version": "23ai",
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"status": "GA_CAVEAT",
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"notes": "Same limitation. Dedicated infra doesn't change HNSW locality."
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},
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{
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"deployment": "Exadata Cloud Service",
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"version": "23ai",
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"status": "GA_CAVEAT",
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"notes": "RAC with HNSW has same node-local constraint. Partitioning workaround applies."
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},
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{
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"deployment": "Base DB Service (EE)",
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"version": "23ai",
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"status": "NOT_AVAIL",
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"notes": "AI Vector Search requires 23ai. Available in EE but HNSW performance limited without Exadata storage."
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}
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]
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},
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{
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"feature": "AI Vector Search (IVF)",
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"category": "developer",
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"compatibility": [
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{
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"deployment": "ADB-S Serverless",
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"version": "23ai",
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"status": "GA",
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"notes": "IVF (Inverted File) indexes ARE distributed across RAC nodes.\nWorks at 100M+ scale without partitioning. However, IVF has\nlower recall than HNSW at same latency budget. Trade-off:\nIVF for scale without redesign, HNSW+partitioning for best recall.\n"
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},
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{
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"deployment": "ADB-S Serverless",
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"version": "26ai",
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"status": "UNTESTED",
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"notes": ""
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},
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{
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"deployment": "ADB-S Elastic Pool",
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"version": "23ai",
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"status": "GA",
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"notes": "Same as ADB-S Serverless."
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},
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{
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"deployment": "ADB on Dedicated Exadata",
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"version": "23ai",
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"status": "GA",
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"notes": ""
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},
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{
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"deployment": "Exadata Cloud Service",
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"version": "23ai",
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"status": "GA",
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"notes": "IVF distributed across RAC nodes. Exadata storage accelerates scans."
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},
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{
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"deployment": "Base DB Service (EE)",
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"version": "23ai",
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"status": "NOT_AVAIL",
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"notes": "AI Vector Search requires 23ai. Available in EE but performance limited without Exadata."
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}
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]
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}
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]
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