steven_li
6e9e74d1ee
添加 RuntimeContext 类用于捕获模型运行时的日期时间信息， 包括UTC时间、本地时间和时区信息，并在系统提示中显示这些信息。 同时增加最大上下文消息数和工具迭代次数的配置选项， 将验证服务从引擎加载器中移除，并更新相关的数据结构和接口。 BREAKING CHANGE: 移除了验证服务，相关字段被替换为证据状态和接受状态。 - 添加 RuntimeContext 类和相关渲染方法 - 增加 max_context_messages 和 max_tool_iterations 配置 - 移除 ValidationService 相关代码 - 更新消息记录中的验证状态字段 - 添加原始工具调用检测和回退处理
438 lines
20 KiB
Python
438 lines
20 KiB
Python
"""Skill learning loop services."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta, timezone
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from itertools import combinations
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import re
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from typing import Any
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from uuid import uuid4
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from beaver.engine.providers import ProviderBundle
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from beaver.memory.runs.models import RunRecord, SkillEffectRecord
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from beaver.memory.runs.store import RunMemoryStore
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from beaver.memory.skills.models import SkillLearningCandidate, SkillPerformanceSnapshot
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from beaver.memory.skills.store import SkillLearningStore
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from beaver.skills.drafts.service import DraftService
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from beaver.skills.learning.evidence import EvidencePacket, EvidenceSelector
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from beaver.skills.learning.synthesizer import SkillDraftSynthesizer
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from beaver.skills.specs import SkillActivationReceipt
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@dataclass(slots=True)
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class RunReceiptContext:
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run_record: RunRecord
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effect_records: list[SkillEffectRecord] = field(default_factory=list)
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class SkillLearningService:
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def __init__(
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self,
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*,
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run_store: RunMemoryStore,
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learning_store: SkillLearningStore,
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draft_service: DraftService,
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evidence_selector: EvidenceSelector,
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synthesizer: SkillDraftSynthesizer | None = None,
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) -> None:
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self.run_store = run_store
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self.learning_store = learning_store
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self.draft_service = draft_service
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self.evidence_selector = evidence_selector
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self.synthesizer = synthesizer or SkillDraftSynthesizer()
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def collect_run_receipts(
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self,
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run_result_context: RunReceiptContext,
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*,
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generate_candidates: bool = True,
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) -> list[SkillLearningCandidate]:
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self.run_store.append_run_record(run_result_context.run_record)
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for effect in run_result_context.effect_records:
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self.run_store.append_skill_effect(effect)
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self.rescore_skill_versions()
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if not generate_candidates:
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return []
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return self.build_learning_candidates()
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def build_learning_candidates(self) -> list[SkillLearningCandidate]:
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candidates: list[SkillLearningCandidate] = []
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candidates.extend(self._build_revision_candidates())
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candidates.extend(self._build_new_skill_candidates())
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candidates.extend(self._build_merge_candidates())
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candidates.extend(self._build_retire_candidates())
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existing_ids = {item.candidate_id for item in self.learning_store.list_learning_candidates()}
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for candidate in candidates:
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if candidate.candidate_id not in existing_ids:
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self.learning_store.record_learning_candidate(candidate)
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existing_ids.add(candidate.candidate_id)
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return candidates
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def build_learning_candidates_for_task(
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self,
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task_id: str,
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*,
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final_accepted_run_id: str | None = None,
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trigger_run_id: str | None = None,
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) -> list[SkillLearningCandidate]:
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"""Build candidates from a user-accepted Task and all of its runs."""
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final_accepted_run_id = final_accepted_run_id or trigger_run_id
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if not final_accepted_run_id:
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return []
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runs = [record for record in self.run_store.list_runs() if record.task_id == task_id]
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final_run = next((record for record in runs if record.run_id == final_accepted_run_id), None)
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if final_run is None or not self._is_task_accepted_run(final_run):
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return []
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source_runs = sorted(runs, key=lambda item: (item.started_at, item.run_id))
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if not source_runs:
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return []
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candidates: list[SkillLearningCandidate] = []
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published_receipts = [
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receipt
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for record in source_runs
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for receipt in record.activated_skills
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if self._is_published_skill_receipt(receipt)
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]
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source_run_ids = [record.run_id for record in source_runs]
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source_session_ids = list(dict.fromkeys(record.session_id for record in source_runs))
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if not published_receipts:
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candidates.append(
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SkillLearningCandidate(
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candidate_id=f"new:task:{task_id}",
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kind="new_skill",
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source_run_ids=source_run_ids,
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source_session_ids=source_session_ids,
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related_skill_names=[],
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reason=f"Task {task_id} completed successfully without a published skill; consider extracting reusable guidance.",
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evidence={
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"task_id": task_id,
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"final_accepted_run_id": final_accepted_run_id,
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"source_run_ids": source_run_ids,
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"theme": self._task_theme(final_run.task_text),
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},
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status="open",
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priority=1,
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confidence=0.8,
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trigger_reason="task_accepted",
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)
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)
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else:
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seen: set[tuple[str, str]] = set()
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for receipt in published_receipts:
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key = (receipt.skill_name, receipt.skill_version)
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if key in seen:
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continue
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seen.add(key)
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skill_runs = [
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record
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for record in source_runs
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if any(
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item.skill_name == receipt.skill_name
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and item.skill_version == receipt.skill_version
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and self._is_published_skill_receipt(item)
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for item in record.activated_skills
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)
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]
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candidates.append(
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SkillLearningCandidate(
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candidate_id=f"revise:{receipt.skill_name}:{receipt.skill_version}:task:{task_id}",
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kind="revise_skill",
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source_run_ids=[record.run_id for record in skill_runs],
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source_session_ids=list(dict.fromkeys(record.session_id for record in skill_runs)),
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related_skill_names=[receipt.skill_name],
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reason=(
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f"Task {task_id} succeeded with published skill "
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f"{receipt.skill_name}/{receipt.skill_version}; consider whether the skill should capture this evidence."
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),
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evidence={
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"task_id": task_id,
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"final_accepted_run_id": final_accepted_run_id,
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"source_run_ids": source_run_ids,
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"skill_version": receipt.skill_version,
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},
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status="open",
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priority=1,
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confidence=0.7,
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trigger_reason="task_accepted",
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)
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)
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existing_ids = {item.candidate_id for item in self.learning_store.list_learning_candidates()}
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created: list[SkillLearningCandidate] = []
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for candidate in candidates:
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if candidate.candidate_id in existing_ids:
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continue
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self.learning_store.record_learning_candidate(candidate)
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existing_ids.add(candidate.candidate_id)
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created.append(candidate)
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return created
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async def synthesize_draft(self, candidate_id: str, provider_bundle: ProviderBundle) -> Any:
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candidates = {item.candidate_id: item for item in self.learning_store.list_learning_candidates()}
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candidate = candidates.get(candidate_id)
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if candidate is None:
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raise ValueError(f"Unknown learning candidate: {candidate_id}")
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if candidate.kind == "retire_skill":
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target_skill = candidate.related_skill_names[0]
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return self.draft_service.create_retire_proposal(
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skill_name=target_skill,
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base_version=candidate.evidence.get("skill_version"),
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created_by="learning-loop",
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reason=candidate.reason,
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evidence_refs=[{"run_id": item} for item in candidate.source_run_ids],
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)
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packet = self.evidence_selector.build_evidence_packet(candidate.source_run_ids, candidate.source_session_ids)
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provider = provider_bundle.auxiliary_provider or provider_bundle.main_provider
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model = (
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provider_bundle.auxiliary_runtime.model
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if provider_bundle.auxiliary_runtime is not None
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else provider_bundle.main_runtime.model
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)
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if candidate.kind == "new_skill":
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payload = await self.synthesizer.synthesize_new_skill(candidate, packet, provider, model)
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return self.draft_service.create_new_skill_draft(
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skill_name=self._suggest_skill_name(candidate, packet, payload.get("frontmatter")),
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proposed_content=payload["content"],
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proposed_frontmatter=payload["frontmatter"],
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created_by="learning-loop",
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reason=payload["change_reason"] or candidate.reason,
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evidence_refs=[{"run_id": item} for item in candidate.source_run_ids],
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)
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if candidate.kind == "merge_skills":
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target_name = self._suggest_skill_name(candidate, packet)
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payload = await self.synthesizer.synthesize_merge(candidate, packet, provider, model)
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return self.draft_service.create_merge_draft(
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skill_name=target_name,
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base_version=None,
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proposed_content=payload["content"],
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proposed_frontmatter=payload["frontmatter"],
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created_by="learning-loop",
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reason=payload["change_reason"] or candidate.reason,
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evidence_refs=[{"run_id": item} for item in candidate.source_run_ids],
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)
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target_skill = candidate.related_skill_names[0]
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base_version = candidate.evidence.get("skill_version")
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payload = await self.synthesizer.synthesize_revision(candidate, packet, provider, model)
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return self.draft_service.create_revision_draft(
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skill_name=target_skill,
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base_version=base_version,
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proposed_content=payload["content"],
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proposed_frontmatter=payload["frontmatter"],
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created_by="learning-loop",
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reason=payload["change_reason"] or candidate.reason,
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evidence_refs=[{"run_id": item} for item in candidate.source_run_ids],
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)
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def rescore_skill_versions(self) -> list[SkillPerformanceSnapshot]:
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snapshots: list[SkillPerformanceSnapshot] = []
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grouped: dict[tuple[str, str], list[SkillEffectRecord]] = {}
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for record in self.run_store.list_runs():
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for receipt in record.activated_skills:
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key = (receipt.skill_name, receipt.skill_version)
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grouped.setdefault(key, [])
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for effect in self._all_effects():
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grouped.setdefault((effect.skill_name, effect.skill_version), []).append(effect)
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for (skill_name, skill_version), effects in grouped.items():
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activation_count = len(effects)
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success_count = sum(1 for item in effects if item.success)
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failure_count = activation_count - success_count
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last_feedback = next((item.feedback_score for item in reversed(effects) if item.feedback_score is not None), None)
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latest_used = effects[-1].created_at if effects else ""
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snapshot = SkillPerformanceSnapshot(
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skill_name=skill_name,
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skill_version=skill_version,
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activation_count=activation_count,
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success_count=success_count,
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failure_count=failure_count,
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latest_used_at=latest_used,
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last_feedback_score=last_feedback,
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)
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self.learning_store.update_performance_snapshot(snapshot)
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snapshots.append(snapshot)
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return snapshots
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def _build_revision_candidates(self) -> list[SkillLearningCandidate]:
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candidates: list[SkillLearningCandidate] = []
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for snapshot in self.learning_store.list_low_performing_versions():
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runs = self.run_store.list_runs_by_skill(snapshot.skill_name, version=snapshot.skill_version, limit=5)
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if len(runs) < 2:
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continue
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candidate = SkillLearningCandidate(
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candidate_id=self._candidate_id("revise", snapshot.skill_name, snapshot.skill_version),
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kind="revise_skill",
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source_run_ids=[record.run_id for record in runs],
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source_session_ids=list(dict.fromkeys(record.session_id for record in runs)),
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related_skill_names=[snapshot.skill_name],
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reason=f"Skill version {snapshot.skill_name}/{snapshot.skill_version} is underperforming across repeated runs.",
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evidence={"skill_version": snapshot.skill_version},
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status="open",
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)
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candidates.append(candidate)
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return candidates
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def _build_new_skill_candidates(self) -> list[SkillLearningCandidate]:
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groups: dict[str, list[RunRecord]] = {}
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for record in self.run_store.list_runs():
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key = self._task_theme(record.task_text)
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if not key:
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continue
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groups.setdefault(key, []).append(record)
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candidates: list[SkillLearningCandidate] = []
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for theme, runs in groups.items():
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successful = [record for record in runs if self._is_task_accepted_run(record)]
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if len(successful) < 2:
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continue
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if any(record.activated_skills for record in successful):
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continue
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candidate = SkillLearningCandidate(
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candidate_id=self._candidate_id("new", theme, str(len(successful))),
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kind="new_skill",
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source_run_ids=[record.run_id for record in successful[-5:]],
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source_session_ids=list(dict.fromkeys(record.session_id for record in successful[-5:])),
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related_skill_names=[],
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reason=f"Repeated successful tasks around '{theme}' suggest a reusable skill should be created.",
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evidence={"theme": theme},
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status="open",
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)
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candidates.append(candidate)
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return candidates
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def _build_merge_candidates(self) -> list[SkillLearningCandidate]:
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pair_counts: dict[tuple[str, str], list[RunRecord]] = {}
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for record in self.run_store.list_runs():
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if not self._is_task_accepted_run(record):
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continue
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unique = sorted({receipt.skill_name for receipt in record.activated_skills})
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for pair in combinations(unique, 2):
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pair_counts.setdefault(pair, []).append(record)
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candidates: list[SkillLearningCandidate] = []
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for pair, runs in pair_counts.items():
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if len(runs) < 2:
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continue
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candidate = SkillLearningCandidate(
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candidate_id=self._candidate_id("merge", *pair),
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kind="merge_skills",
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source_run_ids=[record.run_id for record in runs[-5:]],
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source_session_ids=list(dict.fromkeys(record.session_id for record in runs[-5:])),
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related_skill_names=list(pair),
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reason=f"Skills {pair[0]} and {pair[1]} repeatedly co-activate and may benefit from consolidation.",
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evidence={"pair": list(pair)},
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status="open",
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)
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candidates.append(candidate)
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return candidates
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def _build_retire_candidates(self, *, stale_days: int = 30) -> list[SkillLearningCandidate]:
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candidates: list[SkillLearningCandidate] = []
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cutoff = datetime.now(timezone.utc) - timedelta(days=stale_days)
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for snapshot in self.learning_store.list_performance_snapshots():
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if snapshot.activation_count == 0 or not snapshot.latest_used_at:
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continue
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latest_used = self._parse_timestamp(snapshot.latest_used_at)
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if latest_used is None or latest_used > cutoff:
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continue
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runs = self.run_store.list_runs_by_skill(snapshot.skill_name, version=snapshot.skill_version, limit=3)
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candidate = SkillLearningCandidate(
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candidate_id=self._candidate_id("retire", snapshot.skill_name, snapshot.skill_version),
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kind="retire_skill",
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source_run_ids=[record.run_id for record in runs],
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source_session_ids=list(dict.fromkeys(record.session_id for record in runs)),
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related_skill_names=[snapshot.skill_name],
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reason=(
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f"Skill version {snapshot.skill_name}/{snapshot.skill_version} has been inactive "
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f"since {snapshot.latest_used_at} and may be ready for retirement."
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),
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evidence={"skill_version": snapshot.skill_version, "latest_used_at": snapshot.latest_used_at},
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status="open",
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)
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candidates.append(candidate)
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return candidates
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def _all_effects(self) -> list[SkillEffectRecord]:
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effects: list[SkillEffectRecord] = []
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for candidate in self.learning_store.list_performance_snapshots():
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effects.extend(self.run_store.list_skill_effects(candidate.skill_name, version=candidate.skill_version))
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if effects:
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return effects
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# Bootstrap from runs when there are no prior snapshots.
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for record in self.run_store.list_runs():
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for receipt in record.activated_skills:
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effects.extend(self.run_store.list_skill_effects(receipt.skill_name, version=receipt.skill_version))
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return effects
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@staticmethod
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def _is_task_accepted_run(record: RunRecord) -> bool:
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feedback = record.feedback or {}
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acceptance_type = feedback.get("acceptance_type")
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if acceptance_type is None and feedback.get("feedback_type") == "satisfied":
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acceptance_type = "accept"
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return (
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bool(record.success)
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and bool(record.task_id)
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and acceptance_type == "accept"
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)
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@staticmethod
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def _is_published_skill_receipt(receipt: SkillActivationReceipt) -> bool:
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return (
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not receipt.skill_name.startswith(("draft:", "ephemeral:"))
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and not receipt.skill_version.startswith(("draft:", "ephemeral:"))
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and receipt.activation_reason not in {"generated_missing_skill", "ephemeral_guidance"}
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)
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@staticmethod
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def _candidate_id(kind: str, *parts: str) -> str:
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return f"{kind}:{'|'.join(parts)}"
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@staticmethod
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def _task_theme(task_text: str) -> str:
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cleaned = re.sub(r"\s+", " ", task_text.strip().lower())
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if not cleaned:
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return ""
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words = cleaned.split(" ")
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return " ".join(words[:8]).strip()
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@staticmethod
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def _suggest_skill_name(
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candidate: SkillLearningCandidate,
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packet: EvidencePacket,
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frontmatter: dict[str, Any] | None = None,
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) -> str:
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if candidate.related_skill_names:
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return candidate.related_skill_names[0]
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if isinstance(frontmatter, dict):
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description = str(frontmatter.get("description") or "")
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seed = SkillLearningService._slugify_skill_name(description)
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if seed:
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return seed
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if packet.task_summaries:
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seed = SkillLearningService._slugify_skill_name(packet.task_summaries[0])
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if seed:
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return seed
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return f"generated-skill-{uuid4().hex[:8]}"
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@staticmethod
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def _slugify_skill_name(value: str) -> str:
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seed = re.sub(r"[^a-z0-9]+", "-", value.lower()).strip("-")
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seed = re.sub(r"-+", "-", seed)
|
|
if not seed or seed.isdigit() or len(seed) < 3:
|
|
return ""
|
|
words = [part for part in seed.split("-") if part and not part.isdigit()]
|
|
seed = "-".join(words) or seed
|
|
return seed[:48].strip("-")
|
|
|
|
@staticmethod
|
|
def _parse_timestamp(value: str) -> datetime | None:
|
|
try:
|
|
parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
|
|
except ValueError:
|
|
return None
|
|
if parsed.tzinfo is None:
|
|
return parsed.replace(tzinfo=timezone.utc)
|
|
return parsed.astimezone(timezone.utc)
|