177 lines
6.5 KiB
Python
177 lines
6.5 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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HIGH_ACCURACY_CHANNELS = {
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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",
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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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@classmethod
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def _is_high_accuracy_channel(cls, asked_via: str) -> bool:
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return str(asked_via or "").strip().lower() in cls.HIGH_ACCURACY_CHANNELS
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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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return intent == "mixed"
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@staticmethod
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def _should_trigger_write_ack_gate(
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*,
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sync_requirement: bool,
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asked_via: str,
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effective_action_mode: str,
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message_intent: Optional[str] = None,
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) -> bool:
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intent = str(message_intent or "").strip().lower()
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if intent in {"mixed", "question"}:
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return False
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return (
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bool(sync_requirement)
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and str(asked_via or "").strip().lower() != "feishu"
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and str(effective_action_mode or "").strip().lower() in {ACTION_MODE_WRITE, ACTION_MODE_READ_WRITE}
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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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source_message_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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source_sender_open_id: Optional[str] = None,
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thread_context: Optional[dict[str, Any]] = 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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consultant_mode_preference: 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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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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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 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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runtime = await self._prepare_answer_runtime(
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clean_q=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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source_chat_id=source_chat_id,
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source_message_id=source_message_id,
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session_id=session_id,
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conversation_key=conversation_key,
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force_new_session=force_new_session,
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action_mode=action_mode,
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source_sender_open_id=source_sender_open_id,
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thread_context=thread_context,
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)
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clean_q = str(runtime.get("resolved_question") or clean_q).strip() or clean_q
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session_id = runtime["session_id"]
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turn_index = int(runtime["turn_index"] or 1)
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context_session_id = runtime["context_session_id"]
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conversation = runtime["conversation"]
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state_row = runtime["state_row"]
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effective_q = str(runtime["effective_q"] or "").strip()
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rewrite_meta = runtime["rewrite_meta"] if isinstance(runtime["rewrite_meta"], dict) else {}
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query_type = str(runtime["query_type"] or QUERY_TYPE_UNKNOWN)
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effective_action_mode = str(runtime["effective_action_mode"] or ACTION_MODE_AUTO).strip().lower()
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anchor_resolution = runtime["anchor_resolution"]
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if self._should_trigger_write_ack_gate(
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sync_requirement=sync_requirement,
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asked_via=asked_via,
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effective_action_mode=effective_action_mode,
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message_intent=message_intent,
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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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return await self._execute_base_answer_mainline(
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clean_q=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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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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effective_q=effective_q,
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rewrite_meta=rewrite_meta,
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query_type=query_type,
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thinking_mode=active_thinking_mode,
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active_thinking_mode=active_thinking_mode,
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deep_thinking=deep_thinking,
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enforce_scene_prompt_only=enforce_scene_prompt_only,
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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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started=started,
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message_intent=message_intent,
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mixed_signal_part=mixed_signal_part,
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anchor_resolution=anchor_resolution,
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consultant_mode_preference=consultant_mode_preference,
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)
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