bugfix
This commit is contained in:
parent
7fbf7f7a31
commit
6dda573aed
@ -5,6 +5,7 @@ from dataclasses import dataclass, field
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from pydantic import BaseModel, Field, ValidationError
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from typing import Literal
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from appPublic.worker import awaitify
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from appPublic.streamhttpclient import StreamHttpClient, liner
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from .skillkit_wrapper import SkillkitWrapper
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# ---------------------------
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@ -12,32 +13,32 @@ from .skillkit_wrapper import SkillkitWrapper
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# ---------------------------
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@dataclass
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class SkillDecision:
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skill: str
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params: dict
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reason: Optional[str] = None
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skill: str
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params: dict
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reason: Optional[str] = None
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@dataclass
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class PlanState:
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user_intent: str
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skill: str
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user_intent: str
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skill: str
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script: str
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params: dict
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missing: List[str] = field(default_factory=list)
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params: dict
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missing: List[str] = field(default_factory=list)
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# ---------------------------
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# 自定义异常
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# ---------------------------
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class MissingParams(Exception):
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def __init__(self, skill: str, fields: List[str]):
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self.skill = skill
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self.fields = fields
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def __init__(self, skill: str, fields: List[str]):
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self.skill = skill
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self.fields = fields
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# ---------------------------
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# LLM 接口（可替换为你的模型）
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# ---------------------------
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class LLM:
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async def complete(self, prompt: str) -> str:
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raise NotImplementedError
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async def complete(self, prompt: str) -> str:
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raise NotImplementedError
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# ---------------------------
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# DummyLLM 示例（测试用）
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@ -45,38 +46,39 @@ class LLM:
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class DummyLLM(LLM):
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def __init__(self, llmid, apikey):
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self.llmid = llmid
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self.akikey = apikey
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self.apikey = apikey
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async def complete(self, prompt: str) -> str:
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async def complete(self, prompt: str) -> str:
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hc = StreamHttpClient()
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headers = {
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'Authorization': f'Bearer {self.apikey}',
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'Content-Type': 'application/json'
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}
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headers = {
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'Authorization': f'Bearer {self.apikey}',
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'Content-Type': 'application/json'
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}
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d = {
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'llmid': self.llmid,
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'prompt': prompt
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}
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'llmid': self.llmid,
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'prompt': prompt
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}
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url = 'https://opencomputing.ai/v1/llm'
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reco = hc('POST', url, headers=headers, data=json.dumps(d))
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doc = ''
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async for chunk in liner(reco):
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try:
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d = json.loads(chunk)
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except Exception as e:
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print(f'****{chunk=} error {e} {format_exc()}')
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continue
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async for chunk in liner(reco):
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try:
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d = json.loads(chunk)
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except Exception as e:
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print(f'****{chunk=} error {e} {format_exc()}')
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continue
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if d.get('content'):
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doc = f'{doc}{d["content"]}'
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else:
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print(f'{f}:{d} error')
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return json.loads(doc)
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doc = f'{doc}{d["content"]}'
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else:
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print(f'{d} error')
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return doc
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# ---------------------------
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# Agent 实现
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# ---------------------------
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class Agent:
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def __init__(self, llm: LLM, skillkit):
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self.llm = llm
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def __init__(self, llm: LLM, skillkit):
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self.llm = llm
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self.skillkit = skillkit
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self.skills = None
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self.loaded = False
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@ -88,30 +90,30 @@ class Agent:
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for s in self.skills:
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self.skillkit.load_skill(s.name)
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# ---------------------------
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# plan: 多 skill 候选 + 参数抽取
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# ---------------------------
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async def plan(self, user_text: str):
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# ---------------------------
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# plan: 多 skill 候选 + 参数抽取
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# ---------------------------
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async def plan(self, user_text: str):
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self.load_skills()
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candidates = await self._candidate_skills(user_text)
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decision = await self._plan_with_candidates(user_text, candidates)
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try:
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validated_params = self._validate_params(decision)
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except MissingParams as e:
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question = await self._ask_user_for_params(user_text, decision.skill, e.fields)
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state = PlanState(
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user_intent=user_text,
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skill=decision.skill,
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candidates = await self._candidate_skills(user_text)
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decision = await self._plan_with_candidates(user_text, candidates)
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try:
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validated_params = self._validate_params(decision)
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except MissingParams as e:
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question = await self._ask_user_for_params(user_text, decision.skill, e.fields)
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state = PlanState(
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user_intent=user_text,
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skill=decision.skill,
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script=decision.script,
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params=decision.params,
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missing=e.fields
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)
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return {
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params=decision.params,
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missing=e.fields
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)
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return {
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"type": "clarification",
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"state": state,
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"question": question
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}
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return {
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return {
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"type": "script_call",
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"script": decision.script,
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"skill": decision.skill,
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@ -122,15 +124,15 @@ class Agent:
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def get_scripts(self, skillname):
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return self.skillkit.get_skill_scripts(skillname)
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# ---------------------------
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# resume: 补 missing 参数
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# ---------------------------
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async def resume(self, state: PlanState, user_reply: str):
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skill_spec = next(s for s in self.skills if s.name == state.skill)
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schema_fields = next(s.params for s in skill.scripts if s.name==state.script)
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# ---------------------------
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# resume: 补 missing 参数
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# ---------------------------
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async def resume(self, state: PlanState, user_reply: str):
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skill_spec = next(s for s in self.skills if s.name == state.skill)
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schema_fields = self.skillkit.get_script_params(state.skill, state.script)
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if schema_fields is None:
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schema_fields = []
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prompt = f"""
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prompt = f"""
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You are an agent helping a user fill parameters for a skill.
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Skill name: {state.skill}
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@ -155,35 +157,38 @@ Task:
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- All output must match the skill parameter schema.
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- Output JSON only with the missing parameters.
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"""
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raw = await self.llm.complete(prompt)
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new_params = json.loads(raw)
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raw = await self.llm.complete(prompt)
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new_params = json.loads(raw)
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state.params.update(new_params)
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state.params.update(new_params)
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# 校验 schema
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try:
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validated = self._validate_params(SkillDecision(skill=state.skill, params=state.params))
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except MissingParams as e:
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state.missing = e.fields
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question = await self._ask_user_for_params(state.user_intent, state.skill, e.fields)
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return {"type": "clarification", "state": state, "question": question}
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# 校验 schema
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try:
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validated = self._validate_params(SkillDecision(skill=state.skill, params=state.params))
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except MissingParams as e:
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state.missing = e.fields
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question = await self._ask_user_for_params(state.user_intent, state.skill, e.fields)
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return {"type": "clarification", "state": state, "question": question}
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# 参数完整，返回可直接调用 skill
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return {"type": "skill_call", "skill": state.skill, "params": validated}
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# 参数完整，返回可直接调用 skill
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return {"type": "skill_call", "skill": state.skill, "params": validated}
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# ---------------------------
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# 内部方法
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# ---------------------------
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# ---------------------------
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# 内部方法
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# ---------------------------
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def scripts_info(self, skill):
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def scripts_info(self, skill_name):
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d = []
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skill = self.skillkit.load_skill(skill_name)
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for s in skill.scripts:
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d.append( f'name:{s.name}, description:{s.description}, params:{str(s.params}'
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return "Scripts: '::'.join(d)
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params = self.skillkit.get_script_params(skill_name, s.name)
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print(f'{params=}')
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d.append( f'name:{s.name}, description:{s.description}, params:{str(params)}')
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return "Scripts: " + '::'.join(d)
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async def _candidate_skills(self, user_text: str):
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skill_list = "\n".join(f"- skillname:{s.name}({s.description}): {self.scripts_info(s)}" for s in self.skills)
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prompt = f"""
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async def _candidate_skills(self, user_text: str):
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skill_list = "\n".join(f"- skillname:{s.name}({s.description}): {self.scripts_info(s.name)}" for s in self.skills)
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prompt = f"""
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User request:
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\"\"\"{user_text}\"\"\"
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@ -195,15 +200,15 @@ Select up to 3 most relevant skill's scripts.
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Output JSON list only.
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"""
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raw = await self.llm.complete(prompt)
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return json.loads(raw)
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raw = await self.llm.complete(prompt)
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return json.loads(raw)
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async def _plan_with_candidates(self, user_text: str, candidates: list[str]):
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specs = [s for s in self.skills if s.name in candidates]
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spec_desc = "\n".join(
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f"- {s.name}: inputs={list(s.schema.model_fields.keys())}" for s in specs if s.schema
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)
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prompt = f"""
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async def _plan_with_candidates(self, user_text: str, candidates: list[str]):
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specs = [s for s in self.skills if s.name in candidates]
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spec_desc = "\n".join(
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f"- {s.name}: inputs={list(s.schema.model_fields.keys())}" for s in specs if s.schema
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)
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prompt = f"""
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User request:
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\"\"\"{user_text}\"\"\"
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@ -226,23 +231,23 @@ Output:
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"reason": "..."
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}}
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"""
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raw = await self.llm.complete(prompt)
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return SkillDecision(**json.loads(raw))
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raw = await self.llm.complete(prompt)
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return SkillDecision(**json.loads(raw))
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def _validate_params(self, decision: SkillDecision):
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spec = next(s for s in self.skills if s.name == decision.skill)
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if not spec.schema:
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return decision.params
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try:
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return spec.schema(**decision.params).dict()
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except ValidationError as e:
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missing = [err["loc"][0] for err in e.errors() if err["type"] == "missing"]
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if missing:
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raise MissingParams(decision.skill, missing)
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raise
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def _validate_params(self, decision: SkillDecision):
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spec = next(s for s in self.skills if s.name == decision.skill)
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if not spec.schema:
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return decision.params
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try:
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return spec.schema(**decision.params).dict()
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except ValidationError as e:
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missing = [err["loc"][0] for err in e.errors() if err["type"] == "missing"]
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if missing:
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raise MissingParams(decision.skill, missing)
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raise
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async def _ask_user_for_params(self, user_text: str, skill: str, fields: List[str]):
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prompt = f"""
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async def _ask_user_for_params(self, user_text: str, skill: str, fields: List[str]):
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prompt = f"""
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User request:
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\"\"\"{user_text}\"\"\"
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@ -251,15 +256,15 @@ The script "{script} in skill "{skill}" requires the following missing parameter
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Ask the user a concise clarification question.
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"""
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return await self.llm.complete(prompt)
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return await self.llm.complete(prompt)
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# ---------------------------
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# 测试运行
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# ---------------------------
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async def skillagent(llm, apikey, user_skillroot, sys_skillroot):
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llm = DummyLLM('8L4hFJ4QpSMyu1UP03Juo', 'eYgNuD6sVQgbj-khOOUNU')
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skillkit = SkillKitWrapper(skill_rootpath)
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agent = Agent(llm, skillkit)
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async def skillagent(llm, apikey, user_skillroot, sys_skillroot=None):
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llm = DummyLLM('8L4hFJ4QpSMyu1UP03Juo', 'eYgNuD6sVQgbj-khOOUNU')
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skillkit = SkillkitWrapper(user_skillroot)
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agent = Agent(llm, skillkit)
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while True:
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print('What you want to do?')
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@ -3,6 +3,7 @@ from skillkit import SkillManager
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import yaml
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from pathlib import Path
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from typing import Dict, Any
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from appPublic.dictObject import DictObject
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def find_missing_params(
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input_schema: Dict[str, Any],
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@ -19,7 +20,7 @@ def find_missing_params(
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return missing
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def load_schemas(yaml_path: str) -> Dict[str, Any]:
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def load_schemas(path) -> Dict[str, Any]:
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"""
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从 YAML 文件中读取 script 输入参数定义
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@ -32,47 +33,46 @@ def load_schemas(yaml_path: str) -> Dict[str, Any]:
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}
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}
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"""
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path = Path(yaml_path)
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if not path.exists():
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raise FileNotFoundError(f"Script yaml not found: {yaml_path}")
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with path.open("r", encoding="utf-8") as f:
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data = yaml.safe_load(f)
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if "script" not in data or "inputs" not in data:
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raise ValueError("Invalid script yaml format")
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return {
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"script": data["script"],
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"description": data.get("description", ""),
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"inputs": data["inputs"],
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}
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return DictObject(data)
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class SkillkitWrapper:
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def __init__(self, user_skillsroot, sys_skillsroot=None):
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self.client = SkillManager(project_skill_dir=skillroot,
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self.client = SkillManager(project_skill_dir=user_skillsroot,
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anthropic_config_dir=sys_skillsroot)
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self.client.discover()
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self.schemas = {}
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def list_skills(self):
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return self.client.list_skills()
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def load_skill(self, skillname):
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def load_skill(self, skill_name):
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skill = self.client.load_skill(skill_name)
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if not hasattr(skill, 'schemas'):
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fp = os.path.join(skill.base_dir, 'schemas.yaml')
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if os.path.exists(fp):
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data = load_schema(fp)
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skill.schemas = data
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for s in skill.scripts:
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s.params = next(sch.inputs for sch in skill.schemas if sch.script==script_name)
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print(skill, dir(skill))
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schemaspath = skill.base_directory / 'schemas.yaml'
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if schemaspath.exists():
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if not self.schemas.get(skill_name):
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data = load_schemas(schemaspath)
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self.schemas[skill_name] = data
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print(f'{data=}, {str(schemaspath)}')
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return skill
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def get_script_params(self, skill_name, script_name):
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skill = self.load_skill(skill_name)
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return next(s.params for s in skill.scripts if s.name==script_name)
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d = self.schemas.get('skill_name')
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if not d:
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return []
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m = d.scripts.get(script_name)
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if not m:
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return []
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return m.inputs
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def get_skill_scripts(self, skill_name):
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skill = self.load_skill(skill_name)
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