CIAgent
925ab096fb
Tasks 6-1-01..04 (REQ-3-007): Lab consumes the LIVE trace digest (compute_digest over
TraceStore events; empty trace coaches the baseline); Assessor renders coaching FROM
the stored GradeRecord (it never invents scores — the grading engine owns that;
evaluate endpoint re-grounded: 404 without a grade); Proctor consumes digest +
DefenseStore long-pause signals + variant seed cross-check. Corpus telemetry/artifacts
DORMANT (headers + AST dormancy test: zero production importers; learner_context stays
active; retained as Phase-3 calibration history). lifespan now adopts a pre-set
provider (state-injection pattern).
v0.2 corpus-path endpoint tests updated honestly to the live contract (learner_id+task_id).
392 tests green; ruff clean.
---ci---
phase: 6
milestone: v0.3
status: execute
requirements: {covered: [REQ-3-007], partial: []}
---/ci---
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
"""LabAgent — in-flow feedback over LIVE sandbox telemetry (REQ-3-007).
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v0.3 re-grounding: consumes a TraceDigest computed from the learner's real
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trace (grading/features.compute_digest over TraceStore events) — the v0.2
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corpus scenarios are retired from this path (corpus dormancy, Task 6-1-04).
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No session chat — each request is one live-trace read.
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"""
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from collections.abc import AsyncIterator
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from typing import TYPE_CHECKING
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from ..config import Settings
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from ..corpus.learner_context import LearnerContext, get_learner_context
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from ..grading.features import TraceDigest
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from ..llm.base import LLMProvider
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from ..prompts.lab import SYSTEM_PROMPT, render_context, render_digest_timeline
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from .base import BaseAgent
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if TYPE_CHECKING: # pragma: no cover
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pass
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class LabAgent(BaseAgent):
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name = "lab"
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def __init__(self, provider: LLMProvider, settings: Settings) -> None:
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super().__init__(provider, settings)
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def system_prompt(self, learner_context: LearnerContext | None = None) -> str:
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ctx = learner_context or get_learner_context()
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return SYSTEM_PROMPT.format_map(render_context(ctx))
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async def stream_feedback(
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self,
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digest: TraceDigest | None,
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learner_context: LearnerContext | None = None,
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) -> AsyncIterator[str]:
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"""Feedback grounded in the learner's live trace digest."""
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timeline = render_digest_timeline(digest)
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async for token in self.stream_reply(
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history=None, user_input=timeline, learner_context=learner_context
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):
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yield token
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