steven_li
8a12c30141
新增内部Task系统，包括验证、反馈门控机制，实现自动质量验证 (通过率>=0.75)和用户反馈闭环(satisfied/revise/abandon)。 实现Agent Team v1协调器，支持sequence/parallel/dag执行策略， sub-agent复用主AgentLoop，每个run使用独立memory snapshot。 建立Skill学习pipeline，包含draft/审核/发布/回滚完整生命周期， 通过Task验证通过且用户满意才生成学习候选。 重构目录结构，移除third_party依赖，建立统一engine内核， 所有agent共享运行时基础组件。 更新ContextBuilder清理provider消息字段，增强SkillContext版本管理， 集成TaskExecutionPlanner和TaskSkillResolver实现技能解析机制。
119 lines
4.4 KiB
Python
119 lines
4.4 KiB
Python
"""LLM-backed draft synthesis for skill learning."""
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from __future__ import annotations
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import json
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from typing import Any
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from beaver.engine.providers.base import LLMProvider
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from beaver.skills.learning.evidence import EvidencePacket
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from beaver.memory.skills.models import SkillLearningCandidate
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class SkillDraftSynthesizer:
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async def synthesize_revision(
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self,
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candidate: SkillLearningCandidate,
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evidence_packet: EvidencePacket,
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provider: LLMProvider,
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model: str,
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) -> dict[str, Any]:
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return await self._synthesize(candidate, evidence_packet, provider, model, "revise")
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async def synthesize_new_skill(
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self,
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candidate: SkillLearningCandidate,
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evidence_packet: EvidencePacket,
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provider: LLMProvider,
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model: str,
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) -> dict[str, Any]:
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return await self._synthesize(candidate, evidence_packet, provider, model, "new")
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async def synthesize_merge(
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self,
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candidate: SkillLearningCandidate,
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evidence_packet: EvidencePacket,
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provider: LLMProvider,
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model: str,
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) -> dict[str, Any]:
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return await self._synthesize(candidate, evidence_packet, provider, model, "merge")
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async def _synthesize(
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self,
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candidate: SkillLearningCandidate,
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evidence_packet: EvidencePacket,
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provider: LLMProvider,
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model: str,
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action: str,
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) -> dict[str, Any]:
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prompt = self._build_prompt(candidate, evidence_packet, action)
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response = await provider.chat(
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messages=[
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{
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"role": "system",
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"content": (
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"You synthesize Beaver skill drafts from execution evidence. "
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"Return only JSON with keys: frontmatter, content, change_reason."
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),
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},
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{"role": "user", "content": prompt},
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],
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tools=None,
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model=model,
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max_tokens=1500,
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temperature=0,
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)
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payload = self._parse_payload(response.content or "")
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if payload:
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return payload
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return self._fallback_payload(candidate, evidence_packet, action)
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@staticmethod
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def _build_prompt(candidate: SkillLearningCandidate, evidence_packet: EvidencePacket, action: str) -> str:
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return (
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f"Action: {action}\n"
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f"Candidate kind: {candidate.kind}\n"
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f"Reason: {candidate.reason}\n"
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f"Related skills: {candidate.related_skill_names}\n"
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f"Task summaries:\n- " + "\n- ".join(evidence_packet.task_summaries)
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+ "\n\nSession excerpts:\n" + "\n\n".join(evidence_packet.session_excerpts)
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+ "\n\nReturn JSON only."
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)
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@staticmethod
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def _parse_payload(content: str) -> dict[str, Any]:
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cleaned = content.strip()
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if cleaned.startswith("```"):
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lines = cleaned.splitlines()
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if len(lines) >= 3 and lines[0].startswith("```") and lines[-1].startswith("```"):
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cleaned = "\n".join(lines[1:-1]).strip()
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try:
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payload = json.loads(cleaned)
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except json.JSONDecodeError:
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return {}
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if not isinstance(payload, dict):
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return {}
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frontmatter = payload.get("frontmatter")
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content_value = payload.get("content")
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if not isinstance(frontmatter, dict) or not isinstance(content_value, str):
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return {}
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return {
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"frontmatter": frontmatter,
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"content": content_value.strip(),
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"change_reason": str(payload.get("change_reason") or ""),
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}
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@staticmethod
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def _fallback_payload(candidate: SkillLearningCandidate, evidence_packet: EvidencePacket, action: str) -> dict[str, Any]:
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related = candidate.related_skill_names[0] if candidate.related_skill_names else "generated-skill"
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title = related.replace("_", "-")
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content = "\n".join(f"- {item}" for item in evidence_packet.task_summaries[:5]) or "- No evidence captured."
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return {
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"frontmatter": {
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"description": candidate.reason or f"Auto-generated {action} draft for {title}.",
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"tools": [],
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},
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"content": f"# {title}\n\n## Evidence\n\n{content}\n",
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"change_reason": candidate.reason or f"Fallback {action} synthesis.",
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}
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