Denton Social
0db41241be
- dashboard.py: stdlib http.server + JSON API (tree/trace/dream/status, POST submit/relabel); static/ single-page Redux-DevTools-style inspector - trace.py: runs.jsonl lifecycle events keyed by run_id - scheduler/skills/skill: every decision carries run_id; navigation choices now logged (navigate:category/leaf); requeues stamp meta.parent_run - cli: 'dashboard' subcommand; config.json untracked per-machine (see config.example.json)
74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
"""The skill execution loop: observe -> predict -> act -> observe -> assess.
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Every SemIf decision made during a run is logged as a training row; the
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assessment outcome is kept on the row so the dream pass can weigh failed runs.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from .decisions import Request
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from .llm import Assessment, LLMClient
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from .log import DecisionLog
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from .skills import ActionContext, Prediction, Skill
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@dataclass
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class RunResult:
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skill: str
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success: bool
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summary: str
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action_log: str
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new_state: str
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updated_request: str | None = None
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decisions_logged: int = 0
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error: str | None = None
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class SkillRunner:
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def __init__(self, ctx: ActionContext, llm: LLMClient, log: DecisionLog):
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self.ctx = ctx
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self.llm = llm
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self.log = log
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def run(self, skill: Skill, request: Request) -> RunResult:
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baseline = request.text
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try:
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prediction = skill.predict(self.ctx, request) if skill.predict else Prediction(text="")
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action = skill.act(self.ctx, request, prediction)
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observed = action.new_state
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assessment: Assessment = self.llm.assess(skill.name, baseline, action.action_log)
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except Exception as exc:
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return RunResult(
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skill=skill.name,
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success=False,
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summary="",
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action_log="",
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new_state=baseline,
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error=str(exc),
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)
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run_ok = assessment.success
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decisions = getattr(prediction, "decisions", [])
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for decision, result in decisions:
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self.log.append(
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decision,
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result,
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extra={
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"phase": "predict",
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"skill": skill.name,
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"run_ok": run_ok,
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"run_id": request.id,
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},
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)
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return RunResult(
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skill=skill.name,
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success=assessment.success,
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summary=assessment.summary,
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action_log=action.action_log,
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new_state=observed,
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updated_request=assessment.updated_request,
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decisions_logged=len(decisions),
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) |