Jonathan KirkwoodandClaude Fable 5
1d1074b625
- Pinned target's profitability flag now overrides the extractor's bucket guess - Extractor/target periods canonicalized so ledger forecast chaining matches - autoRunOnDrop no longer error-loops on ungradeable inbox content; failed batches count as seen - Ship 2 default graders (pipeline requires >=2 valid reports per deck) - UI styles 'failed' deck chips as errors; .markdown discoverable - Unknown adjudicator model disables adjudication loudly instead of silently - Reserved rids extractor/adjudicator; teardown also clears bm-grader-* containers Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
"""Config loading for the Boardroom Map orchestrator.
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Defaults mirror startos/file-models/config.ts. The StartOS actions only persist
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the fields the user actually touched, and Python (unlike the zod schema) does not
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auto-fill defaults — so we apply them here. Keep in sync with the zod schema.
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"""
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from __future__ import annotations
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import os
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import spark_client as sc
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DATA_DIR = os.environ.get("BM_DATA_DIR", "/data")
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HF_TOKEN_PATH = os.path.join(DATA_DIR, "secrets", "hf_token")
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BDEF_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "bdef.md")
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# Scoring weights (composite 0-100 = quant 60 + qual 40 - penalties).
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# Every knob the deterministic scorer uses lives here so the user can retune
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# without a rebuild. Keep flat: StartOS action inputs are flat number fields.
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WEIGHTS_DEFAULTS = {
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"profitabilityKpi": 30, # profitability KPI attainment bucket
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"otherKpi": 20, # non-profitability measurable KPI bucket
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"forecastIntegrity": 10, # deck N actuals vs deck N-1 stated targets
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"qualCategoryMax": 5, # each BDEF category A-H maxes at this (8x5=40)
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"redFlagCap": 15, # max total penalty
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"kpiCreditFloor": 0.5, # actual/target ratio below which credit = 0
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"droppedKpiPenalty": 2, # severity of a KPI that silently disappeared
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"droppedKpiMax": 3, # count at most this many dropped-KPI flags
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"evidenceFullCredit": 400, # quote chars for full qualitative weight
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"singleSourceFlagFactor": 0.5, # damping for flags raised by one source only
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}
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CONFIG_DEFAULTS = {
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# Spark connection
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"primarySparkHost": "",
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"primarySparkUser": "nvidia",
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"sshPort": 22,
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"secondarySparkHost": None,
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"useBothSparks": False,
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"headInternalHost": "127.0.0.1",
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"remoteWorkDir": "/home/nvidia/boardroom-map",
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# Images
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"servingImage": "boardroom-vllm:latest",
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"graderImage": "boardroom-grader:latest",
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# Serving
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"gpuMemoryUtilization": "0.85",
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"maxModelLen": 32768,
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"toolCallParser": "hermes",
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"proxyPort": 4000,
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"maxConcurrentModels": 1,
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"models": [
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{"alias": "grader-a", "hfModel": "Qwen/Qwen3-32B-FP8", "spark": "primary", "port": 8001},
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],
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# Grading panel (>= 2 required: every deck needs >= 2 valid grade reports)
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"graders": [
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{"name": "munger-lens", "model": "grader-a", "persona": "", "temperature": None},
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{"name": "girdley-operator", "model": "grader-a", "persona": "", "temperature": None},
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],
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# Which catalog model runs the stage-1 structured extractor ("" = first model)
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"extractorModel": "",
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# Grading job settings
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"bdefOverride": "", # non-empty replaces the baked-in bdef.md rubric
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"weights": dict(WEIGHTS_DEFAULTS),
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"networkMode": "airgapped",
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"searxngUrl": "",
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"adjudicatorEnabled": True,
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"adjudicatorModel": "",
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"adjudicatorPersona": "",
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"wipeRemoteDocs": True,
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"autoRunOnDrop": False,
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"networkName": "boardroom-net",
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# Portfolio companies (authoritative source of pinned targets / aliases).
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# pinnedTargets: [{kpi, target, unit, direction: gte|lte, profitability}]
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# kpiAliases: newline-separated "canonical=alias1;alias2" lines.
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"companies": [],
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# Flags
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"hfTokenSet": False,
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}
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def load() -> dict:
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"""Return the merged config (defaults <- saved), or just defaults if unset."""
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merged = dict(CONFIG_DEFAULTS)
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try:
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saved = sc.load_config()
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except FileNotFoundError:
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return merged
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merged.update({k: v for k, v in saved.items() if v is not None})
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# weights merge key-by-key so a partially-saved weights object keeps defaults
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w = dict(WEIGHTS_DEFAULTS)
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w.update({k: v for k, v in (merged.get("weights") or {}).items() if v is not None})
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merged["weights"] = w
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return merged
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def bdef_text(cfg: dict) -> str:
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"""The grading rubric: config override if set, else the baked-in spec."""
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override = (cfg.get("bdefOverride") or "").strip()
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if override:
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return override
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with open(BDEF_PATH, encoding="utf-8") as f:
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return f.read()
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def hf_token() -> str | None:
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if os.path.exists(HF_TOKEN_PATH):
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t = open(HF_TOKEN_PATH).read().strip()
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return t or None
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return None
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