294 lines
11 KiB
Python
294 lines
11 KiB
Python
"""Adaptive RAG mixin module."""
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from __future__ import annotations
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from .shared import *
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class AdaptiveRAGAnswerEntryMixin:
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@staticmethod
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def _should_enforce_scene_prompt_only(
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*,
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asked_via: str,
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message_intent: Optional[str],
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) -> bool:
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intent = str(message_intent or "").strip().lower()
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if intent == "mixed":
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return True
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channel = str(asked_via or "").strip().lower()
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return channel in {
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"feishu",
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"lark",
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"feishu_group",
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"feishu_dm",
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"pmflow_manual",
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"/ai-pmo/process-text",
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"ai-pmo-process-text",
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"ai_pmo",
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"ai_pmo_ui",
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}
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async def answer_question(
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self,
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*,
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question: str,
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asked_by: Optional[int],
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asked_via: str,
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project_id: Optional[int] = None,
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source_chat_id: Optional[str] = None,
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session_id: Optional[int] = None,
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conversation_key: Optional[str] = None,
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force_new_session: bool = False,
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thinking_mode: str = THINKING_MODE_NORMAL,
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action_mode: str = ACTION_MODE_AUTO,
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sync_requirement: bool = True,
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message_intent: Optional[str] = None,
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mixed_signal_part: Optional[str] = None,
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) -> dict[str, Any]:
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started = time.perf_counter()
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clean_q = str(question or "").strip()
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external_conversation_key = self._build_external_conversation_key(
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asked_via=asked_via,
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source_chat_id=source_chat_id,
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conversation_key=conversation_key,
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)
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active_thinking_mode = self._normalize_thinking_mode(thinking_mode)
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deep_thinking = active_thinking_mode == THINKING_MODE_DEEP
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asked_via_lower = str(asked_via or "").strip().lower()
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enforce_scene_prompt_only = self._should_enforce_scene_prompt_only(
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asked_via=asked_via,
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message_intent=message_intent,
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)
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deep_retrieval_steps = DEEP_RETRIEVAL_STEPS
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deep_vector_top_k = DEEP_VECTOR_TOP_K
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if asked_via_lower == "feishu":
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# 飞书链路优先低时延，保留 deep 能力但收紧预算。
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deep_retrieval_steps = min(DEEP_RETRIEVAL_STEPS, 3)
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deep_vector_top_k = min(DEEP_VECTOR_TOP_K, 8)
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if not clean_q:
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return self._build_response_payload(
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answer="问题为空，无法检索。",
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sources=[],
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domains_used=[],
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response_time_ms=0,
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db_query_ms=0,
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vector_search_ms=0,
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db_timeout=False,
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vector_timeout=False,
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qa_id=None,
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session_id=None,
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turn_index=1,
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relevance_max=0.0,
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query_type=QUERY_TYPE_UNKNOWN,
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confidence=0.0,
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missing_fields=["question"],
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thinking_mode=active_thinking_mode,
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)
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session_id, turn_index = await self._touch_session_for_new_record(
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asked_by=asked_by,
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asked_via=asked_via,
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project_id=project_id,
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question=clean_q,
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preferred_session_id=session_id,
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force_new_session=bool(force_new_session),
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)
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context_session_id: Optional[int]
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if session_id is not None:
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context_session_id = int(session_id)
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elif asked_by is None:
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context_session_id = self._external_session_id_from_key(external_conversation_key)
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else:
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context_session_id = None
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conversation = await self._load_recent_conversation(
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asked_by=asked_by,
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asked_via=asked_via,
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project_id=project_id,
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session_id=session_id,
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external_session_id=context_session_id,
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limit=10,
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)
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state_row = await self._ensure_session_state(session_id=session_id)
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effective_q, rewrite_meta = self._rewrite_question_with_state(
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question=clean_q,
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conversation=conversation,
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session_state=state_row,
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)
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query_type = self._classify_query_type(effective_q)
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requested_action_mode = self._normalize_action_mode(action_mode)
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effective_action_mode = self._resolve_action_mode(
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requested_mode=requested_action_mode,
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question=clean_q,
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effective_question=effective_q,
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)
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if (
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bool(sync_requirement)
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and asked_via_lower != "feishu"
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and effective_action_mode in {ACTION_MODE_WRITE, ACTION_MODE_READ_WRITE}
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):
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ack_result = await self._handle_ack_first_requirement(
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question=clean_q,
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effective_question=effective_q,
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asked_by=asked_by,
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asked_via=asked_via,
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project_id=project_id,
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session_id=session_id,
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turn_index=turn_index,
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state_row=state_row,
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rewrite_meta=rewrite_meta,
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context_session_id=context_session_id,
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query_type=query_type,
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action_mode=effective_action_mode,
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started_at=started,
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)
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if ack_result is not None:
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return ack_result
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forced_deep_reason = ""
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if not deep_thinking:
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forced_deep_reason = self._forced_deep_reason(
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question=clean_q,
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effective_question=effective_q,
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conversation=conversation,
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)
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if forced_deep_reason and self._should_force_deep_for_channel(
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asked_via=asked_via,
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question=clean_q,
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effective_question=effective_q,
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reason=forced_deep_reason,
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):
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deep_thinking = True
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active_thinking_mode = THINKING_MODE_DEEP
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else:
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forced_deep_reason = ""
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clarification_answer = self._build_clarification_question(
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original_question=clean_q,
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effective_question=effective_q,
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session_state=state_row,
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)
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clarification_result = await self._maybe_return_clarification_response(
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clarification_answer=clarification_answer,
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started=started,
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clean_q=clean_q,
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effective_q=effective_q,
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query_type=query_type,
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asked_by=asked_by,
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asked_via=asked_via,
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project_id=project_id,
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session_id=session_id,
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turn_index=turn_index,
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state_row=state_row,
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rewrite_meta=rewrite_meta,
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context_session_id=context_session_id,
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active_thinking_mode=active_thinking_mode,
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)
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if clarification_result is not None:
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return clarification_result
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system_snapshot = await self.context_cache.get_system_snapshot(
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db=self.db,
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tenant_id=self.tenant_id,
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)
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user_snapshot = await self.context_cache.get_user_snapshot(
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db=self.db,
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tenant_id=self.tenant_id,
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user_id=asked_by,
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)
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if not deep_thinking and not enforce_scene_prompt_only:
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deterministic_result = await self._run_deterministic_query(
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question=effective_q,
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project_id=project_id,
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query_type=query_type,
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)
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if deterministic_result is not None:
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return await self._persist_fast_answer(
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question=clean_q,
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asked_by=asked_by,
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asked_via=asked_via,
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project_id=project_id,
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fast_result=deterministic_result,
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session_id=session_id,
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external_session_id=context_session_id,
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turn_index=turn_index,
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query_type=str(deterministic_result.get("query_type") or query_type),
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missing_fields=list(deterministic_result.get("missing_fields") or []),
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state_row=state_row,
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effective_question=effective_q,
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rewrite_meta=rewrite_meta,
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thinking_mode=active_thinking_mode,
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)
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direct_answer = None
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if not deep_thinking and not enforce_scene_prompt_only:
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direct_answer = self._build_snapshot_direct_answer(
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effective_q,
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system_snapshot=system_snapshot,
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user_snapshot=user_snapshot,
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)
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direct_result = await self._maybe_return_snapshot_direct_response(
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direct_answer=direct_answer,
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started=started,
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clean_q=clean_q,
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effective_q=effective_q,
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asked_by=asked_by,
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asked_via=asked_via,
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project_id=project_id,
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session_id=session_id,
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turn_index=turn_index,
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state_row=state_row,
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rewrite_meta=rewrite_meta,
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context_session_id=context_session_id,
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active_thinking_mode=active_thinking_mode,
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user_snapshot=user_snapshot,
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)
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if direct_result is not None:
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return direct_result
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is_snapshot_direct_question = self._is_snapshot_direct_question(effective_q)
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if not deep_thinking and not enforce_scene_prompt_only and not is_snapshot_direct_question:
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glm_semantic_result = await self._try_glm_project_semantic_answer(clean_q=effective_q, project_id=project_id)
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if glm_semantic_result is not None:
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return await self._persist_fast_answer(
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question=clean_q,
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asked_by=asked_by,
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asked_via=asked_via,
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project_id=project_id,
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fast_result=glm_semantic_result,
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session_id=session_id,
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external_session_id=context_session_id,
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turn_index=turn_index,
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query_type=query_type if query_type != QUERY_TYPE_UNKNOWN else QUERY_TYPE_EXPLAIN,
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missing_fields=[],
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state_row=state_row,
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effective_question=effective_q,
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rewrite_meta=rewrite_meta,
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thinking_mode=active_thinking_mode,
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)
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return await self._build_retrieval_response(
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clean_q=clean_q,
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effective_q=effective_q,
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asked_by=asked_by,
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asked_via=asked_via,
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project_id=project_id,
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source_chat_id=source_chat_id,
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session_id=session_id,
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turn_index=turn_index,
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context_session_id=context_session_id,
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conversation=conversation,
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state_row=state_row,
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rewrite_meta=rewrite_meta,
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query_type=query_type,
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thinking_mode=thinking_mode,
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active_thinking_mode=active_thinking_mode,
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deep_thinking=deep_thinking,
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forced_deep_reason=forced_deep_reason,
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deep_retrieval_steps=deep_retrieval_steps,
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deep_vector_top_k=deep_vector_top_k,
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system_snapshot=system_snapshot,
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user_snapshot=user_snapshot,
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started=started,
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message_intent=message_intent,
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mixed_signal_part=mixed_signal_part,
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)
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