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Introduce docs/diagrams/ with the README index and 8 versioned Mermaid sources that reflect the v3.8.0 platform: - request-pipeline.mmd /v1/chat/completions request pipeline - auto-combo-9factor.mmd Auto-Combo 9-factor scoring weights - resilience-3layers.mmd Provider breaker / cooldown / model lockout - i18n-flow.mmd Hash-based incremental doc translation - mcp-tools-37.mmd MCP Server tool inventory by category - cloud-agent-flow.mmd Codex / Devin / Jules task lifecycle - authz-pipeline.mmd 3-class authz pipeline (PUBLIC/CLIENT_API/MANAGEMENT) - db-schema-overview.mmd Selected core SQLite relations Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
24 lines
800 B
Plaintext
24 lines
800 B
Plaintext
%% Auto-Combo 9-factor scoring
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%% Reflects: open-sse/services/autoCombo/scoring.ts (DEFAULT_WEIGHTS, sum = 1.0)
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%% v3.8.0
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flowchart TB
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Request["Incoming request"] --> Candidates["Eligible candidates<br/>(provider × model × account)"]
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Candidates --> Score["Compute composite score<br/>per candidate"]
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subgraph Factors["9-factor scoring weights (sum = 1.0)"]
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f1["health (0.22)"]
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f2["quota (0.17)"]
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f3["costInv (0.17)"]
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f4["latencyInv (0.13)"]
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f5["taskFit (0.08)"]
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f6["specificityMatch (0.08)"]
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f7["stability (0.05)"]
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f8["tierPriority (0.05)"]
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f9["tierAffinity (0.05)"]
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end
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Score --> Factors
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Factors --> Final["Sort by score<br/>(desc)"]
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Final --> Top["Pick top-N targets"]
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Top --> Dispatch["Dispatch sequentially<br/>(short-circuit on success)"]
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