194 lines
8.3 KiB
Python
194 lines
8.3 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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base_skill: dict[str, Any] | None = None,
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) -> dict[str, Any]:
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return await self._synthesize(candidate, evidence_packet, provider, model, "revise", base_skill=base_skill)
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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", base_skill=None)
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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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base_skill: dict[str, Any] | None = None,
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) -> dict[str, Any]:
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return await self._synthesize(candidate, evidence_packet, provider, model, "merge", base_skill=base_skill)
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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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*,
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base_skill: dict[str, Any] | None,
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) -> dict[str, Any]:
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prompt = self._build_prompt(candidate, evidence_packet, action, base_skill=base_skill)
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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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"preserved_sections, changed_sections, dropped_sections."
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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=4096,
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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 self._normalize_payload(payload, evidence_packet)
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return self._fallback_payload(candidate, evidence_packet, action)
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@staticmethod
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def _build_prompt(
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candidate: SkillLearningCandidate,
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evidence_packet: EvidencePacket,
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action: str,
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base_skill: dict[str, Any] | None = None,
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) -> str:
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tool_names = _coerce_string_list(evidence_packet.metadata.get("tool_names"))
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tool_section = ", ".join(tool_names) if tool_names else "none observed"
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selected_tool_names = _coerce_string_list(evidence_packet.metadata.get("selected_tool_names"))
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selected_tool_section = ", ".join(selected_tool_names) if selected_tool_names else "none recorded"
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base_section = ""
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if base_skill:
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base_section = (
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"\n\nBase skill snapshot:\n"
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f"- skill_name: {base_skill.get('skill_name')}\n"
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f"- version: {base_skill.get('version')}\n"
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f"- frontmatter: {json.dumps(base_skill.get('frontmatter') or {}, ensure_ascii=False, sort_keys=True)}\n"
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f"- tool_hints: {base_skill.get('tool_hints') or []}\n"
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f"- summary: {base_skill.get('summary') or ''}\n"
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"Base skill content:\n"
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f"{base_skill.get('content') or ''}\n"
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"Preserve existing instructions unless the evidence requires a change. "
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"If any section is changed or dropped, explain it in changed_sections or dropped_sections."
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)
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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"Called tool names: {tool_section}\n"
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f"Run-selected tool names: {selected_tool_section}\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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+ base_section
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+ "\n\nReturn JSON only. The frontmatter object must include:"
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+ "\n- description: a concise skill description"
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+ "\n- tools: an explicit JSON array of exact tool names this skill needs. "
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+ "Prefer called tool names when the workflow depends on them; use run-selected tool names only when clearly required. "
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+ "Use [] only when no tool is required."
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+ "\nThe JSON may include preserved_sections, changed_sections, and dropped_sections arrays."
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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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"preserved_sections": _coerce_string_list(payload.get("preserved_sections")),
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"changed_sections": _coerce_string_list(payload.get("changed_sections")),
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"dropped_sections": _coerce_string_list(payload.get("dropped_sections")),
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}
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@staticmethod
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def _normalize_payload(payload: dict[str, Any], evidence_packet: EvidencePacket) -> dict[str, Any]:
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frontmatter = dict(payload.get("frontmatter") or {})
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tool_hints = _coerce_string_list(frontmatter.get("tools"))
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if not tool_hints:
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tool_hints = _coerce_string_list(evidence_packet.metadata.get("tool_names"))
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frontmatter["tools"] = tool_hints
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return {
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"frontmatter": frontmatter,
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"content": str(payload.get("content") or "").strip(),
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"change_reason": str(payload.get("change_reason") or ""),
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"preserved_sections": _coerce_string_list(payload.get("preserved_sections")),
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"changed_sections": _coerce_string_list(payload.get("changed_sections")),
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"dropped_sections": _coerce_string_list(payload.get("dropped_sections")),
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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": _coerce_string_list(evidence_packet.metadata.get("tool_names")),
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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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"preserved_sections": [],
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"changed_sections": [],
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"dropped_sections": [],
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}
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def _coerce_string_list(value: Any) -> list[str]:
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raw_items: list[Any]
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if isinstance(value, list):
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raw_items = value
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elif isinstance(value, str):
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raw_items = value.split(",")
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else:
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raw_items = []
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result: list[str] = []
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for item in raw_items:
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cleaned = str(item).strip()
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if cleaned and cleaned not in result:
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result.append(cleaned)
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return result
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