330 lines
12 KiB
Python
330 lines
12 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 AdaptiveRAGAnswerPrecheckMixin:
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async def _maybe_return_meta_followup_response(
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self,
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*,
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meta_followup_payload: Optional[dict[str, Any]],
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started: float,
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clean_q: str,
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effective_q: str,
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query_type: str,
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asked_by: Optional[int],
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asked_via: str,
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project_id: Optional[int],
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session_id: Optional[int],
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turn_index: int,
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state_row: Optional[QASessionState],
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rewrite_meta: dict[str, Any],
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context_session_id: Optional[int],
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active_thinking_mode: str,
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) -> Optional[dict[str, Any]]:
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if not isinstance(meta_followup_payload, dict):
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return None
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answer = str(meta_followup_payload.get("answer") or "").strip()
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if not answer:
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return None
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reason_code = str(meta_followup_payload.get("reason_code") or "").strip() or "meta_followup_answered"
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anchor_kind = str(meta_followup_payload.get("anchor_kind") or "").strip() or None
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anchor_mode = str(meta_followup_payload.get("anchor_mode") or "").strip() or None
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meta_followup_kind = str(meta_followup_payload.get("meta_followup_kind") or "").strip() or None
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routing_snapshot = (
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meta_followup_payload.get("routing_snapshot")
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if isinstance(meta_followup_payload.get("routing_snapshot"), dict)
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else {}
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)
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elapsed = int((time.perf_counter() - started) * 1000)
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direct_sources = [
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{
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"source": "system",
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"label": "Followup Anchor",
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"score": 0.97,
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"metadata": {
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"mode": "meta_followup",
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"reason_code": reason_code,
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"anchor_kind": anchor_kind,
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"anchor_mode": anchor_mode,
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"meta_followup_kind": meta_followup_kind,
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},
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}
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]
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active_session_id = int(session_id) if session_id is not None else (
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int(context_session_id) if context_session_id is not None else None
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)
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qa = QARecord(
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tenant_id=self.tenant_id,
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question=clean_q,
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answer=answer,
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sources=direct_sources,
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intent="question",
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domains_used=["system_context"],
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response_time_ms=elapsed,
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asked_by=asked_by,
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asked_via=(asked_via or "pmflow")[:20],
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project_id=project_id,
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session_id=active_session_id,
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turn_index=turn_index,
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**self._build_qa_record_external_fields(
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anchor_kind=anchor_kind,
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anchor_ref=str(meta_followup_payload.get("anchor_ref") or "").strip() or None,
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routing_snapshot=routing_snapshot,
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),
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)
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self.db.add(qa)
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await self._update_session_state_after_turn(
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session_state=state_row,
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question=clean_q,
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effective_question=effective_q,
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answer=answer,
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query_type=query_type,
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ambiguity="clear",
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rewrite_meta=rewrite_meta,
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sources=direct_sources,
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)
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await self.db.commit()
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await self.db.refresh(qa)
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await self.context_cache.append_session_messages(
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tenant_id=self.tenant_id,
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session_id=context_session_id,
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question=clean_q,
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answer=answer,
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)
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return self._build_response_payload(
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answer=answer,
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sources=direct_sources,
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domains_used=["system_context"],
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response_time_ms=elapsed,
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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=int(qa.id),
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session_id=active_session_id,
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turn_index=int(turn_index),
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relevance_max=0.97,
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query_type=query_type,
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confidence=0.94,
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missing_fields=[],
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thinking_mode=active_thinking_mode,
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anchor_mode=anchor_mode,
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anchor_kind=anchor_kind,
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meta_followup_kind=meta_followup_kind,
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reason_code=reason_code,
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)
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async def _maybe_return_clarification_response(
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self,
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*,
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clarification_payload: Optional[dict[str, Any]],
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started: float,
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clean_q: str,
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effective_q: str,
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query_type: str,
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asked_by: Optional[int],
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asked_via: str,
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project_id: Optional[int],
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session_id: Optional[int],
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turn_index: int,
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state_row: Optional[QASessionState],
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rewrite_meta: dict[str, Any],
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context_session_id: Optional[int],
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active_thinking_mode: str,
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) -> Optional[dict[str, Any]]:
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if not isinstance(clarification_payload, dict):
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return None
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clarification_answer = str(clarification_payload.get("answer") or "").strip()
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if not clarification_answer:
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return None
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clarification_code = str(clarification_payload.get("code") or "no_anchor_found").strip() or "no_anchor_found"
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clarification_candidates = (
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clarification_payload.get("candidates")
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if isinstance(clarification_payload.get("candidates"), list)
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else []
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)
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missing_fields = [
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str(item)
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for item in (
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clarification_payload.get("missing_fields")
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if isinstance(clarification_payload.get("missing_fields"), list)
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else ["reply_anchor"]
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)
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if str(item).strip()
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]
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elapsed = int((time.perf_counter() - started) * 1000)
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clarify_sources = [
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{
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"source": "system",
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"label": "Session State",
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"score": 0.95,
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"metadata": {"mode": "clarification", "reason": clarification_code},
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}
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]
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active_session_id = int(session_id) if session_id is not None else (
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int(context_session_id) if context_session_id is not None else None
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)
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qa = QARecord(
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tenant_id=self.tenant_id,
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question=clean_q,
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answer=clarification_answer,
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sources=clarify_sources,
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intent="question",
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domains_used=["system_context"],
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response_time_ms=elapsed,
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asked_by=asked_by,
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asked_via=(asked_via or "pmflow")[:20],
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project_id=project_id,
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session_id=active_session_id,
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turn_index=turn_index,
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**self._build_qa_record_external_fields(),
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)
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self.db.add(qa)
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if clarification_code == "ambiguous_recent_reply" and clarification_candidates:
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previous = self._get_pending_clarification(session_state=state_row)
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previous_attempts = int(previous.get("attempts") or 0) if isinstance(previous, dict) else 0
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self._store_pending_clarification(
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session_state=state_row,
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original_question=clean_q,
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clarification_code=clarification_code,
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candidates=clarification_candidates,
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previous_attempts=previous_attempts,
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)
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else:
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self._clear_pending_clarification(session_state=state_row)
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await self._update_session_state_after_turn(
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session_state=state_row,
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question=clean_q,
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effective_question=effective_q,
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answer=clarification_answer,
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query_type=query_type,
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ambiguity="needs_clarification",
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rewrite_meta=rewrite_meta,
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sources=clarify_sources,
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)
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await self.db.commit()
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await self.db.refresh(qa)
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await self.context_cache.append_session_messages(
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tenant_id=self.tenant_id,
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session_id=context_session_id,
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question=clean_q,
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answer=clarification_answer,
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)
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return self._build_response_payload(
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answer=clarification_answer,
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sources=clarify_sources,
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domains_used=["system_context"],
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response_time_ms=elapsed,
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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=int(qa.id),
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session_id=active_session_id,
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turn_index=int(turn_index),
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relevance_max=0.95,
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query_type=query_type,
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confidence=0.92,
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missing_fields=missing_fields,
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thinking_mode=active_thinking_mode,
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clarification_code=clarification_code,
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clarification_candidates=clarification_candidates,
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reason_code=clarification_code,
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)
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async def _maybe_return_snapshot_direct_response(
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self,
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*,
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direct_answer: Optional[str],
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started: float,
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clean_q: str,
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effective_q: str,
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asked_by: Optional[int],
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asked_via: str,
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project_id: Optional[int],
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session_id: Optional[int],
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turn_index: int,
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state_row: Optional[QASessionState],
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rewrite_meta: dict[str, Any],
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context_session_id: Optional[int],
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active_thinking_mode: str,
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user_snapshot: Optional[dict[str, Any]],
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) -> Optional[dict[str, Any]]:
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if not direct_answer:
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return None
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elapsed = int((time.perf_counter() - started) * 1000)
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direct_sources = [
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{
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"source": "system",
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"label": "System Snapshot",
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"score": 0.99,
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"metadata": {"mode": "snapshot_direct", "topic": "identity_or_architecture"},
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}
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]
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if user_snapshot:
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direct_sources.append(
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{
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"source": "system",
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"label": "User Snapshot",
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"score": 0.98,
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"metadata": {"mode": "snapshot_direct", "topic": "current_user"},
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}
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)
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qa = QARecord(
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tenant_id=self.tenant_id,
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question=clean_q,
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answer=direct_answer,
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sources=direct_sources,
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intent="question",
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domains_used=["system_context"],
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response_time_ms=elapsed,
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asked_by=asked_by,
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asked_via=(asked_via or "pmflow")[:20],
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project_id=project_id,
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session_id=int(session_id) if session_id is not None else (int(context_session_id) if context_session_id is not None else None),
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turn_index=turn_index,
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**self._build_qa_record_external_fields(),
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)
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self.db.add(qa)
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await self._update_session_state_after_turn(
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session_state=state_row,
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question=clean_q,
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effective_question=effective_q,
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answer=direct_answer,
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query_type=QUERY_TYPE_EXPLAIN,
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ambiguity="clear",
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rewrite_meta=rewrite_meta,
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sources=direct_sources,
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)
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await self.db.commit()
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await self.db.refresh(qa)
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await self.context_cache.append_session_messages(
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tenant_id=self.tenant_id,
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session_id=context_session_id,
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question=clean_q,
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answer=direct_answer,
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)
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return self._build_response_payload(
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answer=direct_answer,
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sources=direct_sources,
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domains_used=["system_context"],
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response_time_ms=elapsed,
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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=int(qa.id),
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session_id=int(session_id) if session_id is not None else None,
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turn_index=int(turn_index),
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relevance_max=1.0,
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query_type=QUERY_TYPE_EXPLAIN,
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confidence=0.99,
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missing_fields=[],
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thinking_mode=active_thinking_mode,
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)
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