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revisão e complementação da documentação do projeto
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@@ -2,7 +2,7 @@
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Every declarative config type in the RI (client profiles, capability manifests,
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policies, ...) is loaded through :func:`load_yaml_model`, so secrets never live
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as literal values in a committed YAML file (see docs/RI-PLANNING.md, EP-00-T05):
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as literal values in a committed YAML file (EP-00-T05):
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a value written as ``${VAR_NAME}`` or ``${VAR_NAME:-default}`` is resolved from
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the environment at load time, and a value that still looks like an unresolved
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placeholder after interpolation is treated as a configuration error.
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@@ -10,8 +10,8 @@ the `mcp` SDK's built-in `OpenTelemetryMiddleware` (on by default on every
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`Server`/`MCPServer`): it injects/extracts standard W3C `traceparent` into the
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JSON-RPC `_meta` field around every request, so a single trace_id spans both
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processes without any extra instrumentation here -- this is the concrete,
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checkable form of the M0 acceptance criterion in docs/RI-PLANNING.md ("um
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único trace_id conecta Gateway -> Fabric -> backend mock"), verified in
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checkable form of the M0 acceptance criterion ("um único trace_id conecta
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Gateway -> Fabric -> backend mock"), verified in
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tests/e2e/test_walking_skeleton.py.
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`setup_tracing` registers one *process-global* TracerProvider via
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@@ -2,7 +2,7 @@
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Maps an authenticated OAuth2 client identity (the `client_id`/`azp` claim a
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validated access token carries -- see EP-01-T03) to exactly one client
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profile (EP-04). This indirection is what lets `docs/RI-PLANNING.md`'s §18
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profile (EP-04). This indirection is what lets README.md's §18
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rule ("share a client only if all sharing agents can legitimately carry the
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same ceiling") be expressed as data instead of code: two client_ids pointing
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at the same profile are an explicit, auditable sharing decision.
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@@ -1,8 +1,8 @@
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"""Reference `NBADecisionModel` (EP-11-T03): deterministic rules/priority
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over `DecisionContext.filtered_offerings` -- not an AI model, not
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clustering. See `nba_model.py`'s module docstring for the pluggable
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interface a real ranking model would implement instead, and P7
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(docs/RI-PLANNING.md) for why this RI doesn't attempt "real" ranking without
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interface a real ranking model would implement instead, and
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`RI/docs/Assumptions.md` for why this RI doesn't attempt "real" ranking without
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a real dataset.
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"""
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@@ -2,8 +2,8 @@
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A deterministic, rules-over-synthetic-attributes stub -- not clustering, not
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a trained model, not a proprietary taxonomy (P7: this RI does not attempt
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"real" clustering without a real dataset, see docs/RI-PLANNING.md's gap
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resolution). `ProfileIntelligenceProvider` is a `Protocol` so a production
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"real" clustering without a real dataset, see `RI/docs/Assumptions.md`).
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`ProfileIntelligenceProvider` is a `Protocol` so a production
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fork can swap in a real ML-backed implementation without touching any
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consumer (the Offering Filter, EP-10) -- the contract is `ProfileView` in,
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`ProfileView` out.
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