chore: create Multica handoff checkpoint
This commit is contained in:
leefer
@@ -15,6 +15,11 @@ class MentorAgentError(RuntimeError):
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pass
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FOLLOW_UP_START = "<XIAOBAI_FOLLOW_UPS>"
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FOLLOW_UP_END = "</XIAOBAI_FOLLOW_UPS>"
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MAX_FOLLOW_UP_LENGTH = 80
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@dataclass(frozen=True)
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class MentorSkill:
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skill_id: str
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@@ -185,6 +190,8 @@ def stream_with_mentor(
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base_url: str,
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model: str,
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timeout: int = 90,
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*,
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follow_ups: list[str] | None = None,
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) -> Iterator[str]:
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if not api_key or not model:
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raise MentorAgentError("LLM API Key 或模型尚未配置。")
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@@ -193,8 +200,10 @@ def stream_with_mentor(
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messages = [{"role": "system", "content": system_prompt}]
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messages.extend(history[-10:])
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messages.append({"role": "user", "content": question})
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if follow_ups is not None:
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follow_ups.clear()
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try:
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yield from llm_transport.stream_chat_completion(
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upstream = llm_transport.stream_chat_completion(
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api_key=api_key,
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base_url=base_url,
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model=model,
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@@ -202,6 +211,7 @@ def stream_with_mentor(
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timeout=timeout,
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user_agent="XiaobaiReviewWeb/0.6",
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)
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yield from _stream_answer_and_collect_follow_ups(upstream, follow_ups)
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except llm_transport.OpenAIEmptyResponseError as exc:
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raise MentorAgentError("问师模型未返回有效内容。") from exc
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except llm_transport.OpenAIHTTPError as exc:
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@@ -223,6 +233,10 @@ def _build_system_prompt(skill: MentorSkill, market_context: dict[str, Any]) ->
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5. 优先回答用户真正的问题。市场分析通常按“判断、数据依据、思维模型下的应对、失效条件”组织；纯交易心理或方法问题可以自然回答，不强制套模板。
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6. 保留该 Skill 的核心心智模型和表达节奏，但不要复述身份履历，不要宣称自己就是真人，不攻击或贬低用户。
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7. 使用中文，信息密度高，避免空泛口号。引用数字时标明数据日期。
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8. 正文结束后必须输出2至3条与本轮问题和正文直接相关的追问。追问用于帮助用户继续核实条件、风险或失效边界，不得引入正文没有依据的新事实，不得给出无条件买卖指令。严格使用以下机器结构，不要放进Markdown代码块，结束标签后不要再输出文字：
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<XIAOBAI_FOLLOW_UPS>
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["追问一？","追问二？","追问三？"]
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</XIAOBAI_FOLLOW_UPS>
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网页市场数据：
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{context_json}
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@@ -233,6 +247,67 @@ def _build_system_prompt(skill: MentorSkill, market_context: dict[str, Any]) ->
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""".strip()
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def _stream_answer_and_collect_follow_ups(
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chunks: Iterator[str], follow_ups: list[str] | None
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) -> Iterator[str]:
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buffer = ""
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collecting = False
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for raw_chunk in chunks:
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chunk = str(raw_chunk or "")
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if not chunk:
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continue
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buffer += chunk
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if collecting:
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continue
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marker_index = buffer.find(FOLLOW_UP_START)
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if marker_index >= 0:
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if marker_index:
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yield buffer[:marker_index]
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buffer = buffer[marker_index + len(FOLLOW_UP_START):]
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collecting = True
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continue
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overlap = _marker_prefix_overlap(buffer, FOLLOW_UP_START)
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emit_length = len(buffer) - overlap
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if emit_length:
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yield buffer[:emit_length]
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buffer = buffer[emit_length:]
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if not collecting:
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if buffer:
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yield buffer
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return
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raw_follow_ups = buffer.split(FOLLOW_UP_END, 1)[0].strip()
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parsed = _parse_follow_ups(raw_follow_ups)
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if follow_ups is not None and len(parsed) >= 2:
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follow_ups.extend(parsed)
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def _marker_prefix_overlap(value: str, marker: str) -> int:
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max_length = min(len(value), len(marker) - 1)
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for length in range(max_length, 0, -1):
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if value.endswith(marker[:length]):
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return length
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return 0
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def _parse_follow_ups(payload: str) -> list[str]:
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try:
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values = json.loads(payload)
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except (TypeError, json.JSONDecodeError):
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return []
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if not isinstance(values, list):
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return []
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result: list[str] = []
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for value in values:
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question = re.sub(r"\s+", " ", str(value or "")).strip()
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if not question or len(question) > MAX_FOLLOW_UP_LENGTH or question in result:
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continue
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result.append(question)
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if len(result) == 3:
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break
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return result
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def _parse_frontmatter(content: str) -> dict[str, str]:
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if not content.startswith("---"):
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return {}
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@@ -129,9 +129,10 @@ class MentorServiceMixin:
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def generate():
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answer_parts: list[str] = []
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follow_ups: list[str] = []
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events = self.llm_gateway.stream(
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"mentor",
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f"mentor-skill-v1:{skill.skill_id}",
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f"mentor-skill-v2:{skill.skill_id}",
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lambda profile: stream_with_mentor(
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skill,
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context,
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@@ -140,6 +141,7 @@ class MentorServiceMixin:
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profile.api_key,
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profile.base_url,
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profile.model,
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follow_ups=follow_ups,
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),
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(MentorAgentError,),
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)
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@@ -160,6 +162,7 @@ class MentorServiceMixin:
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yield {
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"type": "meta",
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"data_trade_date": context["data_trade_date"],
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"follow_ups": follow_ups or self._mentor_follow_up_fallback(question),
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"notice": "智能解读已自动切换可用服务。"
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if event.role == "fallback"
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else "",
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@@ -167,6 +170,27 @@ class MentorServiceMixin:
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return generate()
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@staticmethod
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def _mentor_follow_up_fallback(question: str) -> list[str]:
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normalized = question.strip()
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if any(keyword in normalized for keyword in ("风险", "亏损", "回撤", "止损")):
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return [
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"这些风险最早会从哪些信号中暴露？",
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"哪些变化会让当前风险判断失效？",
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"如果风险继续扩大，仓位预案应如何调整？",
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]
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if any(keyword in normalized for keyword in ("股票", "个股", "代码", "怎么看")):
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return [
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"这个判断最关键的确认信号是什么？",
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"哪些变化会让当前结论失效？",
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"明日盘中应该优先观察哪些数据？",
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]
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return [
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"这个判断最关键的确认依据是什么？",
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"哪些变化会让当前结论失效？",
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"下一步应该优先观察什么？",
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]
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def mentor_messages(self, mentor_id: str, trade_date: str) -> list[dict[str, Any]]:
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mentor_id = validate_text(mentor_id, "问师角色", 100, required=True)
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trade_date = normalize_date(trade_date)
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