621 lines
27 KiB
Python
621 lines
27 KiB
Python
from __future__ import annotations
|
||
|
||
import re
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from uuid import uuid4
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from typing import Any
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from ...config.ai_prompts import (
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PROMPT_EXTERNAL_COMPARE_WITH_SOURCES_QA,
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PROMPT_EXTERNAL_DIRECT_FACT_QA,
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PROMPT_EXTERNAL_SIGNAL_SOURCE_RECOMMENDATION_PACKET,
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PROMPT_EXTERNAL_SIGNAL_SOURCE_RECOMMENDATION_QA,
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PROMPT_EXTERNAL_SUMMARY_WITH_SOURCES_QA,
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)
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from ..prompt_release import PromptReleaseService
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from .schemas import (
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ExternalContextPacket,
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SignalSourceRecommendationCandidate,
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SignalSourceRecommendationPacket,
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)
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class ExternalAnswerPresenter:
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_PROMPT_CONFIG = {
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"direct_fact": (
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"PROMPT_EXTERNAL_DIRECT_FACT_QA",
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PROMPT_EXTERNAL_DIRECT_FACT_QA,
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"external_direct_fact",
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),
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"summary_with_sources": (
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"PROMPT_EXTERNAL_SUMMARY_WITH_SOURCES_QA",
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PROMPT_EXTERNAL_SUMMARY_WITH_SOURCES_QA,
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"external_summary_with_sources",
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),
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"compare_with_sources": (
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"PROMPT_EXTERNAL_COMPARE_WITH_SOURCES_QA",
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PROMPT_EXTERNAL_COMPARE_WITH_SOURCES_QA,
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"external_compare_with_sources",
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),
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}
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def __init__(
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self,
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*,
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llm_service: Any,
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db: Any,
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tenant_id: int,
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) -> None:
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self.llm_service = llm_service
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self.db = db
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self.tenant_id = int(tenant_id or 1)
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@classmethod
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def _normalize_contract(cls, value: Any) -> str:
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normalized = str(value or "").strip().lower()
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if normalized in cls._PROMPT_CONFIG:
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return normalized
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return "summary_with_sources"
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@staticmethod
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def _build_evidence_blocks(packet: ExternalContextPacket) -> str:
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blocks: list[str] = []
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for idx, item in enumerate(list(packet.verified_evidence or [])[:5], start=1):
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source_label = str(item.source_label or item.title or item.target_url or "").strip()
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title = str(item.title or "").strip()
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snippet = str(item.snippet or "").strip()
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target_url = str(item.target_url or "").strip()
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block_lines = [f"[{idx}] 来源: {source_label or '外部来源'}"]
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if title and title != source_label:
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block_lines.append(f"标题: {title}")
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if snippet:
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block_lines.append(f"证据: {snippet}")
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if target_url:
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block_lines.append(f"链接: {target_url}")
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blocks.append("\n".join(block_lines))
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return "\n\n".join(blocks).strip() or "无"
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@staticmethod
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def _build_source_summaries(packet: ExternalContextPacket) -> str:
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labels: list[str] = []
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for item in list(packet.verified_evidence or [])[:5]:
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label = str(item.source_label or item.title or item.target_url or "").strip()
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if label and label not in labels:
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labels.append(label)
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return "；".join(labels[:5]) if labels else "无"
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@staticmethod
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def _build_plan_summary(packet: ExternalContextPacket) -> str:
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plan = packet.plan
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rows = [
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f"主题: {str(plan.topic or '').strip() or '未指定'}",
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f"回答合同: {str(plan.answer_contract or '').strip() or 'summary_with_sources'}",
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]
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if plan.time_range_hint:
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rows.append(f"时间范围: {plan.time_range_hint}")
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if plan.location_hint:
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rows.append(f"地点: {plan.location_hint}")
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if plan.subject_entities:
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rows.append(f"对象: {'、'.join([str(item).strip() for item in plan.subject_entities[:5] if str(item).strip()])}")
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if plan.signal_source_objectives:
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rows.append(
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"任务目标: "
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+ "；".join([str(item).strip() for item in plan.signal_source_objectives[:3] if str(item).strip()])
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)
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return "\n".join(rows)
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@classmethod
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def _is_signal_source_recommendation_request(
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cls,
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*,
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packet: ExternalContextPacket,
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) -> bool:
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interaction_level = str(packet.plan.interaction_level or "").strip().lower()
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if interaction_level and interaction_level != "recommendation":
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return False
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if interaction_level != "recommendation":
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return False
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recommendation_goal = str(packet.plan.recommendation_goal or "").strip()
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return bool(recommendation_goal or packet.plan.project_object_anchors or packet.plan.signal_source_objectives)
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@staticmethod
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def _build_recommendation_uncertainty_text(packet: ExternalContextPacket) -> str:
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retrieval_status = str(packet.retrieval_status or "").strip().lower()
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if retrieval_status == "verifier_blocked":
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return "当前外部公开资料还在校验，这里先给推荐，不把它当成已验证事实。"
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if retrieval_status == "no_relevant_results":
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return "当前外部公开资料还没找到直接证据，这里先按项目目标给推荐。"
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if retrieval_status == "retrieval_failed":
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return "这次外部检索失败，这里先按项目目标给推荐，后面还需要补核验。"
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return ""
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@staticmethod
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def _collect_signal_source_objectives(packet: ExternalContextPacket) -> list[str]:
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objectives: list[str] = []
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for item in list(packet.plan.signal_source_objectives or []):
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normalized = str(item or "").strip()
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if normalized and normalized not in objectives:
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objectives.append(normalized)
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if objectives:
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return objectives[:4]
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for item in list(packet.plan.project_object_anchors or []):
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normalized = str(item or "").strip()
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if normalized and normalized not in objectives:
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objectives.append(normalized)
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return objectives[:4]
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@staticmethod
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def _objective_label(value: Any) -> str:
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text = str(value or "").strip()
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if not text:
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return ""
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for separator in ("：", ":"):
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if separator in text:
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return str(text.split(separator, 1)[0] or "").strip() or text
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return text
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@classmethod
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def _extract_objective_parts(cls, value: Any) -> tuple[str, str]:
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text = str(value or "").strip()
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if not text:
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return "", ""
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for separator in ("：", ":"):
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if separator in text:
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label, detail = text.split(separator, 1)
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return str(label or "").strip(), str(detail or "").strip()
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return text, ""
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@staticmethod
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def _sanitize_recommendation_candidate_text(value: Any) -> str:
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text = re.sub(r"\s+", " ", str(value or "")).strip(" ，,。；;：:")
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text = re.sub(r"^(?:当前|建议|推荐)\s*", "", text)
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return text[:36]
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@classmethod
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def _objective_candidate_name(cls, *, label: str, detail: str) -> str:
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candidate = detail or label
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candidate = re.sub(r"^(?:编写脚本|收集|跟踪|监控|处理|实现|支持|自动|实时|用于|把|对|完成|生成并推送)\s*", "", candidate)
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candidate = re.sub(r"^(?:收到|基于|围绕|用于)\s*", "", candidate)
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candidate = re.sub(r"(?:后自动|并在.+)$", "", candidate).strip()
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candidate = cls._sanitize_recommendation_candidate_text(candidate)
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if candidate and candidate not in {"数据与阈值", "结果发给客户", "话题波动"}:
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return candidate
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return cls._sanitize_recommendation_candidate_text(label) or "外部变化线索"
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@classmethod
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def _objective_candidate_reason(cls, *, label: str, detail: str) -> str:
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detail_text = cls._sanitize_recommendation_candidate_text(detail)
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label_text = cls._sanitize_recommendation_candidate_text(label)
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if detail_text and label_text:
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return f"它最早支撑“{label_text}”，也更容易先看出这件事有没有开始偏。"
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if label_text:
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return f"它能更早暴露“{label_text}”是不是开始出问题。"
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return "它更容易先把问题从项目现状里提前暴露出来。"
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@classmethod
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def _build_signal_source_recommendation_fallback_packet(
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cls,
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*,
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packet: ExternalContextPacket,
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) -> SignalSourceRecommendationPacket:
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candidates: list[SignalSourceRecommendationCandidate] = []
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for raw in cls._collect_signal_source_objectives(packet)[:3]:
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label, detail = cls._extract_objective_parts(raw)
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name = cls._objective_candidate_name(label=label, detail=detail)
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reason = cls._objective_candidate_reason(label=label, detail=detail)
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if name and all(existing.name != name for existing in candidates):
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candidates.append(
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SignalSourceRecommendationCandidate(
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candidate_ref=f"cand_{uuid4().hex[:12]}",
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||
name=name,
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||
display_name=name,
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||
reason=reason,
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||
business_value=reason,
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||
why_now=reason,
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source_origin="recommendation",
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||
materialization_payload={
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||
"display_name": name,
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||
"source_origin": "recommendation",
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||
},
|
||
)
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||
)
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||
fallback_seed = [
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("最近异常反馈", "它能看出问题是不是已经开始影响到真实结果，而不是只停在内部波动。"),
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||
("上游变化记录", "它更容易解释这次波动到底是内部原因还是外部变化带出来的。"),
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||
("结果失败回执", "它能最早把真正失败的位置暴露出来，避免只看到表面症状。"),
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||
]
|
||
for name, reason in fallback_seed:
|
||
if len(candidates) >= 3:
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||
break
|
||
if all(existing.name != name for existing in candidates):
|
||
candidates.append(
|
||
SignalSourceRecommendationCandidate(
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||
candidate_ref=f"cand_{uuid4().hex[:12]}",
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||
name=name,
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||
display_name=name,
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||
reason=reason,
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||
business_value=reason,
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||
why_now=reason,
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||
source_origin="recommendation",
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||
materialization_payload={
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||
"display_name": name,
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||
"source_origin": "recommendation",
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||
},
|
||
)
|
||
)
|
||
return SignalSourceRecommendationPacket(
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candidate_set_id=f"recset_{uuid4().hex[:12]}",
|
||
resource_role="signal_source",
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||
source_kind="external",
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||
evidence_mode="verified" if str(packet.retrieval_status or "").strip().lower() == "evidence_found" else "advisory",
|
||
candidates=candidates[:3],
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caveat=cls._build_recommendation_uncertainty_text(packet)
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||
or "这些先算建议，不代表已经接入或验证完成。",
|
||
)
|
||
|
||
@staticmethod
|
||
def _ensure_recommendation_packet_defaults(
|
||
packet: SignalSourceRecommendationPacket,
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||
*,
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||
fallback_packet: SignalSourceRecommendationPacket,
|
||
) -> SignalSourceRecommendationPacket:
|
||
if not str(packet.candidate_set_id or "").strip():
|
||
packet.candidate_set_id = str(fallback_packet.candidate_set_id or "").strip() or f"recset_{uuid4().hex[:12]}"
|
||
packet.resource_role = "signal_source"
|
||
packet.source_kind = str(packet.source_kind or fallback_packet.source_kind or "external").strip() or "external"
|
||
packet.evidence_mode = (
|
||
str(packet.evidence_mode or fallback_packet.evidence_mode or "advisory").strip() or "advisory"
|
||
)
|
||
for item in list(packet.candidates or []):
|
||
if not str(item.candidate_ref or "").strip():
|
||
item.candidate_ref = f"cand_{uuid4().hex[:12]}"
|
||
payload = dict(item.materialization_payload or {})
|
||
payload.setdefault("display_name", str(item.display_name or item.name or "").strip())
|
||
payload.setdefault("candidate_ref", str(item.candidate_ref or "").strip())
|
||
payload.setdefault("source_origin", str(item.source_origin or "recommendation").strip() or "recommendation")
|
||
item.materialization_payload = payload
|
||
return packet
|
||
|
||
@classmethod
|
||
def _recommendation_packet_has_risky_or_weak_content(
|
||
cls,
|
||
*,
|
||
recommendation_packet: SignalSourceRecommendationPacket,
|
||
packet: ExternalContextPacket,
|
||
) -> bool:
|
||
if len(recommendation_packet.candidates) < 2:
|
||
return True
|
||
risky_markers = (
|
||
"%",
|
||
"已确认",
|
||
"无需额外对接验证",
|
||
"相关性达",
|
||
"普遍可采集",
|
||
"近一年",
|
||
"今年",
|
||
"本月",
|
||
"这一层",
|
||
"主链",
|
||
"packet",
|
||
"route",
|
||
"verifier",
|
||
)
|
||
for item in list(recommendation_packet.candidates or []):
|
||
text = f"{str(item.name or '')} {str(item.reason or '')}".strip()
|
||
if not text:
|
||
return True
|
||
if any(marker in text for marker in risky_markers):
|
||
return True
|
||
if str(packet.impact_status or "").strip().lower() == "direct_match":
|
||
return False
|
||
return False
|
||
|
||
@classmethod
|
||
def _merge_recommendation_packets(
|
||
cls,
|
||
*,
|
||
primary: SignalSourceRecommendationPacket,
|
||
fallback: SignalSourceRecommendationPacket,
|
||
) -> SignalSourceRecommendationPacket:
|
||
merged: list[SignalSourceRecommendationCandidate] = []
|
||
for item in list(primary.candidates or []) + list(fallback.candidates or []):
|
||
name = cls._sanitize_recommendation_candidate_text(item.display_name or item.name)
|
||
reason = str(item.reason or item.business_value or item.why_now or "").strip()
|
||
if not name or not reason:
|
||
continue
|
||
if any(existing.display_name == name for existing in merged):
|
||
continue
|
||
merged.append(
|
||
SignalSourceRecommendationCandidate(
|
||
candidate_ref=str(item.candidate_ref or "").strip() or f"cand_{uuid4().hex[:12]}",
|
||
name=name,
|
||
display_name=name,
|
||
reason=reason,
|
||
business_value=str(item.business_value or reason).strip(),
|
||
why_now=str(item.why_now or reason).strip(),
|
||
source_origin=str(item.source_origin or "recommendation").strip() or "recommendation",
|
||
materialization_payload=dict(item.materialization_payload or {}),
|
||
)
|
||
)
|
||
caveat = str(primary.caveat or "").strip() or str(fallback.caveat or "").strip()
|
||
packet = SignalSourceRecommendationPacket(
|
||
candidate_set_id=str(primary.candidate_set_id or fallback.candidate_set_id or "").strip() or f"recset_{uuid4().hex[:12]}",
|
||
project_binding=primary.project_binding or fallback.project_binding,
|
||
resource_role="signal_source",
|
||
source_kind=str(primary.source_kind or fallback.source_kind or "external").strip() or "external",
|
||
evidence_mode=str(primary.evidence_mode or fallback.evidence_mode or "advisory").strip() or "advisory",
|
||
candidates=merged[:3],
|
||
caveat=caveat,
|
||
)
|
||
return cls._ensure_recommendation_packet_defaults(packet, fallback_packet=fallback)
|
||
|
||
@classmethod
|
||
def _render_signal_source_recommendation_text(
|
||
cls,
|
||
*,
|
||
recommendation_packet: SignalSourceRecommendationPacket,
|
||
packet: ExternalContextPacket,
|
||
) -> str:
|
||
candidates = list(recommendation_packet.candidates or [])[:3]
|
||
if not candidates:
|
||
recommendation_packet = cls._build_signal_source_recommendation_fallback_packet(packet=packet)
|
||
candidates = list(recommendation_packet.candidates or [])[:3]
|
||
lines = ["如果只是先给建议，我会先看这 3 类外部信号："]
|
||
for index, item in enumerate(candidates, start=1):
|
||
name = cls._sanitize_recommendation_candidate_text(item.name)
|
||
reason = str(item.reason or "").strip().rstrip("。")
|
||
if reason.startswith("因为"):
|
||
lines.append(f"{index}. {name}，{reason}")
|
||
else:
|
||
lines.append(f"{index}. {name}，因为{reason}")
|
||
caveat = str(recommendation_packet.caveat or "").strip() or cls._build_recommendation_uncertainty_text(packet)
|
||
if caveat:
|
||
lines.append(caveat.rstrip("。") + "。")
|
||
return "\n".join(lines)
|
||
|
||
async def _build_signal_source_recommendation_packet(
|
||
self,
|
||
*,
|
||
question: str,
|
||
packet: ExternalContextPacket,
|
||
) -> SignalSourceRecommendationPacket:
|
||
fallback_packet = self._build_signal_source_recommendation_fallback_packet(packet=packet)
|
||
generator = getattr(self.llm_service, "generate_structured_object", None)
|
||
if not callable(generator):
|
||
return fallback_packet
|
||
|
||
plan_summary = self._build_plan_summary(packet)
|
||
fit_brief_summary = str(packet.plan.fit_brief_summary or "").strip() or "未提供"
|
||
recommendation_goal = str(packet.plan.recommendation_goal or "").strip() or "给当前项目推荐最值得先关注的外部信号源。"
|
||
project_object_anchors = "、".join(
|
||
[str(item).strip() for item in list(packet.plan.project_object_anchors or []) if str(item).strip()][:4]
|
||
) or "无"
|
||
signal_source_objectives = "；".join(
|
||
[str(item).strip() for item in list(packet.plan.signal_source_objectives or []) if str(item).strip()][:4]
|
||
) or "无"
|
||
prompt = await self._render_prompt(
|
||
prompt_key="PROMPT_EXTERNAL_SIGNAL_SOURCE_RECOMMENDATION_PACKET",
|
||
fallback_template=PROMPT_EXTERNAL_SIGNAL_SOURCE_RECOMMENDATION_PACKET,
|
||
question=str(question or "").strip(),
|
||
plan_summary=plan_summary,
|
||
fit_brief_summary=fit_brief_summary,
|
||
recommendation_goal=recommendation_goal,
|
||
project_object_anchors=project_object_anchors,
|
||
signal_source_objectives=signal_source_objectives,
|
||
retrieval_status=str(packet.retrieval_status or "").strip() or "unknown",
|
||
uncertainties="；".join([str(item).strip() for item in list(packet.uncertainties or []) if str(item).strip()]) or "无",
|
||
source_summaries=self._build_source_summaries(packet),
|
||
)
|
||
try:
|
||
structured = await generator(
|
||
prompt=prompt,
|
||
db=self.db,
|
||
tenant_id=self.tenant_id,
|
||
function_name="external_signal_source_recommendation_packet",
|
||
use_prompt_profile=False,
|
||
temperature=0.1,
|
||
response_mode="fast",
|
||
routing_meta={
|
||
"tier_alias": "low",
|
||
"response_contract": "structured",
|
||
"fallback_policy": "fast_local",
|
||
"execution_mode": "query",
|
||
},
|
||
)
|
||
payload = getattr(structured, "payload", None)
|
||
if payload is None and hasattr(structured, "model_dump"):
|
||
payload = structured.model_dump()
|
||
recommendation_packet = SignalSourceRecommendationPacket.model_validate(payload or {})
|
||
recommendation_packet = self._merge_recommendation_packets(
|
||
primary=recommendation_packet,
|
||
fallback=fallback_packet,
|
||
)
|
||
recommendation_packet = self._ensure_recommendation_packet_defaults(
|
||
recommendation_packet,
|
||
fallback_packet=fallback_packet,
|
||
)
|
||
if self._recommendation_packet_has_risky_or_weak_content(
|
||
recommendation_packet=recommendation_packet,
|
||
packet=packet,
|
||
):
|
||
return fallback_packet
|
||
return recommendation_packet
|
||
except Exception:
|
||
return fallback_packet
|
||
|
||
async def _build_signal_source_recommendation_response(
|
||
self,
|
||
*,
|
||
question: str,
|
||
packet: ExternalContextPacket,
|
||
thinking_mode: str,
|
||
answer_max_tokens: int | None = None,
|
||
) -> dict[str, str]:
|
||
_ = (thinking_mode, answer_max_tokens)
|
||
recommendation_packet = await self._build_signal_source_recommendation_packet(
|
||
question=question,
|
||
packet=packet,
|
||
)
|
||
text = self._render_signal_source_recommendation_text(
|
||
recommendation_packet=recommendation_packet,
|
||
packet=packet,
|
||
)
|
||
return {
|
||
"answer_text": text,
|
||
"prompt_variant_id": "external_signal_source_recommendation",
|
||
"recommendation_packet": recommendation_packet.model_dump(),
|
||
}
|
||
|
||
@staticmethod
|
||
def _build_failure_response(*, question: str, retrieval_status: str) -> dict[str, str]:
|
||
short_question = str(question or "").strip()[:80]
|
||
if retrieval_status == "verifier_blocked":
|
||
return {
|
||
"answer_text": (
|
||
f"关于“{short_question}”，我拿到过外部候选结果，但它们没有通过核验，"
|
||
"所以先不给你直接结论。你如果愿意，我可以继续按更明确的对象或范围再检一次。"
|
||
),
|
||
"prompt_variant_id": "external_verifier_blocked",
|
||
}
|
||
if retrieval_status == "no_relevant_results":
|
||
return {
|
||
"answer_text": (
|
||
f"关于“{short_question}”，外部检索已经执行完成，但没有找到足够相关的外部公开证据。"
|
||
"现阶段我不建议把它直接当成当前项目的有效外部变化。"
|
||
),
|
||
"prompt_variant_id": "external_no_relevant_results",
|
||
}
|
||
return {
|
||
"answer_text": (
|
||
f"关于“{short_question}”，外部检索这次执行失败，没有产出可用的外部公开证据。"
|
||
"你可以稍后重试，或把要核验的公开对象说得更具体一些。"
|
||
),
|
||
"prompt_variant_id": "external_retrieval_failed",
|
||
}
|
||
|
||
@classmethod
|
||
def _build_indirect_only_response(cls, *, question: str, packet: ExternalContextPacket) -> dict[str, str]:
|
||
short_question = str(question or "").strip()[:80]
|
||
source_summaries = cls._build_source_summaries(packet)
|
||
return {
|
||
"answer_text": (
|
||
f"关于“{short_question}”，当前外部证据只能说明它和当前项目有背景相关性，"
|
||
"不能直接说明它已经命中当前项目对象。"
|
||
f"现有来源：{source_summaries}。如果你要，我可以继续把它整理成一份映射验证清单。"
|
||
),
|
||
"prompt_variant_id": "external_indirect_only",
|
||
}
|
||
|
||
async def _render_prompt(
|
||
self,
|
||
*,
|
||
prompt_key: str,
|
||
fallback_template: str,
|
||
**kwargs: Any,
|
||
) -> str:
|
||
if not hasattr(self.db, "execute"):
|
||
return str(fallback_template).format(**kwargs).strip()
|
||
return await PromptReleaseService(self.db).render_prompt(
|
||
tenant_id=self.tenant_id,
|
||
prompt_key=prompt_key,
|
||
fallback_template=fallback_template,
|
||
**kwargs,
|
||
)
|
||
|
||
async def present(
|
||
self,
|
||
*,
|
||
question: str,
|
||
context_packet: dict[str, Any] | ExternalContextPacket,
|
||
thinking_mode: str,
|
||
answer_max_tokens: int | None = None,
|
||
) -> dict[str, str]:
|
||
packet = (
|
||
context_packet
|
||
if isinstance(context_packet, ExternalContextPacket)
|
||
else ExternalContextPacket.model_validate(context_packet)
|
||
)
|
||
if self._is_signal_source_recommendation_request(
|
||
packet=packet,
|
||
):
|
||
return await self._build_signal_source_recommendation_response(
|
||
question=question,
|
||
packet=packet,
|
||
thinking_mode=thinking_mode,
|
||
answer_max_tokens=answer_max_tokens,
|
||
)
|
||
retrieval_status = str(packet.retrieval_status or "").strip().lower() or "retrieval_failed"
|
||
impact_status = str(packet.impact_status or "").strip().lower() or "no_direct_impact"
|
||
if retrieval_status != "evidence_found":
|
||
return self._build_failure_response(
|
||
question=question,
|
||
retrieval_status=retrieval_status,
|
||
)
|
||
if impact_status == "indirect_only":
|
||
return self._build_indirect_only_response(
|
||
question=question,
|
||
packet=packet,
|
||
)
|
||
verifier = dict(packet.verifier or {})
|
||
if not packet.verified_evidence or not bool(verifier.get("ok")):
|
||
return {
|
||
"answer_text": (
|
||
f"当前关于“{str(question or '').strip()[:80]}”的外部证据不足。"
|
||
"请补充更明确的对象、范围或继续提供可核验来源后，我再继续分析。"
|
||
),
|
||
"prompt_variant_id": "external_clarify",
|
||
}
|
||
|
||
contract = self._normalize_contract(packet.plan.answer_contract)
|
||
prompt_key, fallback_template, prompt_variant_id = self._PROMPT_CONFIG[contract]
|
||
prompt = await self._render_prompt(
|
||
prompt_key=prompt_key,
|
||
fallback_template=fallback_template,
|
||
question=str(question or "").strip(),
|
||
plan_summary=self._build_plan_summary(packet),
|
||
evidence_blocks=self._build_evidence_blocks(packet),
|
||
source_summaries=self._build_source_summaries(packet),
|
||
time_range=str(packet.plan.time_range_hint or "").strip() or "未指定",
|
||
uncertainties="；".join([str(item).strip() for item in list(packet.uncertainties or []) if str(item).strip()]) or "无",
|
||
)
|
||
|
||
deep_thinking = str(thinking_mode or "").strip().lower() == "deep"
|
||
if deep_thinking:
|
||
prompt = (
|
||
f"{prompt}\n\n"
|
||
"补充要求（深入思考模式）：\n"
|
||
"1. 先给结论，再给证据链；\n"
|
||
"2. 明确区分已验证事实与推断；\n"
|
||
"3. 若存在不确定项，给出待确认清单。"
|
||
)
|
||
|
||
effective_max_tokens = max(256, min(int(answer_max_tokens or (1400 if deep_thinking else 900)), 3000))
|
||
routing_meta = None
|
||
function_name = "adaptive_rag_answer_deep" if deep_thinking else "adaptive_rag_answer_normal"
|
||
if deep_thinking:
|
||
routing_meta = {
|
||
"tier_alias": "low",
|
||
"planned_tier": "low",
|
||
"executed_tier": "low",
|
||
"response_contract": "complete",
|
||
"fallback_policy": "low_to_high",
|
||
"execution_mode": "query",
|
||
"routing_decision": "force_low_first",
|
||
}
|
||
answer = await self.llm_service.generate(
|
||
prompt,
|
||
db=self.db,
|
||
tenant_id=self.tenant_id,
|
||
function_name=function_name,
|
||
temperature=0.15 if deep_thinking else 0.2,
|
||
max_tokens=effective_max_tokens,
|
||
use_prompt_profile=False,
|
||
routing_meta=routing_meta,
|
||
)
|
||
text = str(answer or "").strip()
|
||
if not text:
|
||
raise ValueError("external_answer_presenter_empty_response")
|
||
return {
|
||
"answer_text": text,
|
||
"prompt_variant_id": prompt_variant_id,
|
||
}
|