402 lines
17 KiB
Python
402 lines
17 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 AdaptiveRAGAnswerRetrievalMixin:
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async def _build_retrieval_response(
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self,
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*,
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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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source_chat_id: Optional[str],
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session_id: Optional[int],
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turn_index: int,
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context_session_id: Optional[int],
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conversation: list[dict[str, Any]],
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state_row: Optional[QASessionState],
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rewrite_meta: dict[str, Any],
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query_type: str,
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query_semantics: dict[str, Any],
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retrieval_domains: list[str],
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scene_semantics: dict[str, Any],
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thinking_mode: str,
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active_thinking_mode: str,
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deep_thinking: bool,
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forced_deep_reason: str,
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deep_retrieval_steps: int,
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deep_vector_top_k: int,
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system_snapshot: dict[str, Any],
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user_snapshot: Optional[dict[str, Any]],
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started: float,
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message_intent: Optional[str] = None,
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mixed_signal_part: Optional[str] = None,
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answer_mode: str = ANSWER_MODE_RETRIEVAL_NORMAL,
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consultant_mode_preference: Optional[str] = None,
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) -> dict[str, Any]:
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domains = [str(item).strip().lower() for item in retrieval_domains if str(item or "").strip()]
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if not domains:
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domains = ["db", "graph", "doc", "feishu", "pmflow", "url"]
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if str(asked_via or "").strip().lower() == "feishu" and source_chat_id and "feishu" not in domains:
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domains.append("feishu")
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retrieved, search_meta = await self._multi_step_search(
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question=effective_q,
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domains=domains,
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project_id=project_id,
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source_chat_id=source_chat_id,
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query_semantics=query_semantics,
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session_state=state_row,
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max_steps_override=deep_retrieval_steps if deep_thinking else None,
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vector_top_k=deep_vector_top_k if deep_thinking else None,
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)
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db_query_ms = int(search_meta.get("db_query_ms") or 0)
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vector_search_ms = int(search_meta.get("vector_search_ms") or 0)
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graph_query_ms = int(search_meta.get("graph_query_ms") or 0)
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db_timeout = bool(search_meta.get("db_timeout"))
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vector_timeout = bool(search_meta.get("vector_timeout"))
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graph_timeout = bool(search_meta.get("graph_timeout"))
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tool_trace = list(search_meta.get("tool_trace") or [])
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tool_trace.insert(
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0,
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{
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"step": 0,
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"tool": "thinking_mode",
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"mode": active_thinking_mode,
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"fast_path_bypassed": bool(deep_thinking),
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"reason": forced_deep_reason or (
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"manual_deep" if self._normalize_thinking_mode(thinking_mode) == THINKING_MODE_DEEP else "normal"
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),
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},
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)
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final_domains = [str(x) for x in (search_meta.get("domains_used") or domains)]
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prompt_variant_id = self._peek_answer_prompt_variant()
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max_score = retrieved[0].score if retrieved else 0.0
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answer_text = await self._generate_answer(
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effective_q,
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retrieved,
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conversation,
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project_id,
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system_snapshot=system_snapshot,
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user_snapshot=user_snapshot,
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relevance_max=max_score,
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thinking_mode=active_thinking_mode,
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context_block_limit=DEEP_CONTEXT_BLOCKS if deep_thinking else None,
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answer_max_tokens=DEEP_ANSWER_MAX_TOKENS if deep_thinking else None,
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asked_via=asked_via,
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message_intent=message_intent,
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mixed_signal_part=mixed_signal_part,
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query_type=query_type,
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consultant_mode_preference=consultant_mode_preference,
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scene_semantics=scene_semantics,
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query_semantics=query_semantics,
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)
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prompt_variant_id = self._peek_answer_prompt_variant()
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routing_snapshot = self._peek_last_answer_routing_snapshot()
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used_sources = self._dedupe_sources(retrieved)
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if not used_sources:
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used_sources = [
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{
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"source": "system",
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"label": "System Snapshot",
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"score": round(max(max_score, 0.6), 4),
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"metadata": {"mode": "snapshot_only", "relevance_max": round(max_score, 4)},
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}
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]
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if user_snapshot:
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used_sources.append(
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{
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"source": "system",
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"label": "User Snapshot",
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"score": round(max(max_score, 0.58), 4),
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"metadata": {"mode": "snapshot_only", "relevance_max": round(max_score, 4)},
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}
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)
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pre_retry_guard_semantics = self._evaluate_post_retrieval_guard(
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deep_intent=str((scene_semantics or {}).get("deep_intent") or "none"),
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sources=used_sources,
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relevance_max=max_score,
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)
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auto_retry_reason = ""
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if not deep_thinking:
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auto_retry_reason = str(pre_retry_guard_semantics.get("auto_retry_reason") or "").strip()
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if auto_retry_reason:
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deep_retrieved, deep_meta = await self._multi_step_search(
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question=effective_q,
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domains=domains,
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project_id=project_id,
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source_chat_id=source_chat_id,
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query_semantics=query_semantics,
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session_state=state_row,
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max_steps_override=deep_retrieval_steps,
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vector_top_k=deep_vector_top_k,
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)
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deep_max_score = deep_retrieved[0].score if deep_retrieved else 0.0
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deep_answer_text = await self._generate_answer(
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effective_q,
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deep_retrieved,
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conversation,
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project_id,
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system_snapshot=system_snapshot,
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user_snapshot=user_snapshot,
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relevance_max=deep_max_score,
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thinking_mode=THINKING_MODE_DEEP,
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context_block_limit=DEEP_CONTEXT_BLOCKS,
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answer_max_tokens=DEEP_ANSWER_MAX_TOKENS,
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asked_via=asked_via,
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message_intent=message_intent,
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mixed_signal_part=mixed_signal_part,
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query_type=query_type,
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consultant_mode_preference=consultant_mode_preference,
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scene_semantics=scene_semantics,
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query_semantics=query_semantics,
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)
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deep_sources = self._dedupe_sources(deep_retrieved)
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if not deep_sources:
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deep_sources = [
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{
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"source": "system",
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"label": "System Snapshot",
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"score": round(max(deep_max_score, 0.6), 4),
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"metadata": {"mode": "snapshot_only", "relevance_max": round(deep_max_score, 4)},
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}
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]
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if user_snapshot:
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deep_sources.append(
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{
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"source": "system",
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"label": "User Snapshot",
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"score": round(max(deep_max_score, 0.58), 4),
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"metadata": {"mode": "snapshot_only", "relevance_max": round(deep_max_score, 4)},
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}
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)
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guard_semantics = self._evaluate_post_retrieval_guard(
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deep_intent=str((scene_semantics or {}).get("deep_intent") or "none"),
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sources=deep_sources,
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relevance_max=deep_max_score,
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)
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merged_metrics = self._merge_search_metrics(
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{
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"db_query_ms": db_query_ms,
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"vector_search_ms": vector_search_ms,
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"graph_query_ms": graph_query_ms,
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"db_timeout": db_timeout,
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"vector_timeout": vector_timeout,
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"graph_timeout": graph_timeout,
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},
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deep_meta,
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)
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db_query_ms = int(merged_metrics.get("db_query_ms") or 0)
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vector_search_ms = int(merged_metrics.get("vector_search_ms") or 0)
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graph_query_ms = int(merged_metrics.get("graph_query_ms") or 0)
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db_timeout = bool(merged_metrics.get("db_timeout"))
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vector_timeout = bool(merged_metrics.get("vector_timeout"))
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graph_timeout = bool(merged_metrics.get("graph_timeout"))
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deep_trace = list(deep_meta.get("tool_trace") or [])
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tool_trace.append(
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{
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"step": 98,
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"tool": "auto_deep_retry",
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"reason": auto_retry_reason,
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"prev_relevance_max": round(float(max_score or 0.0), 4),
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"prev_snapshot_only": bool(pre_retry_guard_semantics.get("snapshot_only_sources")),
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}
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)
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for row in deep_trace:
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tool_trace.append(row)
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final_domains = [str(x) for x in (deep_meta.get("domains_used") or final_domains or domains)]
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max_score = deep_max_score
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answer_text = deep_answer_text
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used_sources = deep_sources
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deep_thinking = True
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active_thinking_mode = THINKING_MODE_DEEP
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answer_mode = ANSWER_MODE_RETRIEVAL_DEEP
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prompt_variant_id = self._peek_answer_prompt_variant()
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routing_snapshot = self._peek_last_answer_routing_snapshot()
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evidence_guard_triggered = False
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clarification_due_to_evidence = False
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if not evidence_guard_triggered and self._is_snapshot_only_sources(used_sources) and float(max_score or 0.0) < 0.45:
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clarification_due_to_evidence = True
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answer_text = (
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f"当前关于“{clean_q[:80]}”的证据不足，现阶段只命中系统快照。"
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"请补充更明确的对象、范围或文档入口，我再给你可核验的结论。"
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)
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used_sources = [
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{
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"source": "system",
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"label": "Evidence Guard",
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"score": 0.91,
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"metadata": {"mode": "clarification", "reason": "insufficient_evidence"},
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}
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]
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max_score = min(float(max_score or 0.0), 0.4)
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if "system_context" not in final_domains:
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final_domains.append("system_context")
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tool_trace.append(
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{
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"step": 97,
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"tool": "clarification_guard",
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"reason": "snapshot_only_low_relevance",
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}
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)
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answer_mode = ANSWER_MODE_CLARIFY
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guard_semantics = self._evaluate_post_retrieval_guard(
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deep_intent=str((scene_semantics or {}).get("deep_intent") or "none"),
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sources=used_sources,
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relevance_max=max_score,
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)
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evidence_guard_triggered = bool(guard_semantics.get("should_block_with_guard"))
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if evidence_guard_triggered:
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answer_text = self._build_evidence_guard_answer(
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clean_q,
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guard_focus=str(guard_semantics.get("guard_focus") or "call_chain"),
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)
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used_sources = [
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{
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"source": "system",
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"label": "Evidence Guard",
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"score": 0.99,
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"metadata": {"mode": "guard", "reason": "snapshot_only_process_query"},
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}
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]
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max_score = min(float(max_score or 0.0), 0.35)
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if "system_context" not in final_domains:
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final_domains.append("system_context")
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tool_trace.append(
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{
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"step": 99,
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"tool": "answer_guard",
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"reason": "insufficient_call_chain_evidence",
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}
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)
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answer_mode = ANSWER_MODE_BLOCKED
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elapsed = int((time.perf_counter() - started) * 1000)
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grounding_verdict = GroundingVerdict(
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status=GROUNDING_STATUS_GROUNDED,
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missing_fields=[],
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reason_code=None,
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)
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if evidence_guard_triggered:
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grounding_verdict = GroundingVerdict(
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status=GROUNDING_STATUS_BLOCKED,
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missing_fields=["call_chain_evidence", "target_scope"],
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reason_code="insufficient_evidence",
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)
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elif clarification_due_to_evidence:
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grounding_verdict = GroundingVerdict(
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status=GROUNDING_STATUS_INSUFFICIENT_EVIDENCE,
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missing_fields=["high_relevance_evidence"],
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reason_code="insufficient_evidence",
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)
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elif self._is_snapshot_only_sources(used_sources) or float(max_score or 0.0) < 0.45:
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grounding_verdict = GroundingVerdict(
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status=GROUNDING_STATUS_PARTIALLY_GROUNDED,
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missing_fields=["high_relevance_evidence"] if float(max_score or 0.0) < 0.45 else [],
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reason_code=None,
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)
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retrieval_metrics = {
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"db_query_ms": int(db_query_ms or 0),
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"vector_search_ms": int(vector_search_ms or 0),
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"graph_query_ms": int(graph_query_ms or 0),
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"db_timeout": bool(db_timeout),
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"vector_timeout": bool(vector_timeout),
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"graph_timeout": bool(graph_timeout),
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}
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runtime_fields = self._build_qa_record_runtime_fields(
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answer=answer_text,
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sources=used_sources,
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missing_fields=grounding_verdict.missing_fields,
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answer_mode=answer_mode,
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grounding_status=grounding_verdict.status,
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prompt_variant_id=prompt_variant_id,
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reason_code=grounding_verdict.reason_code,
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routing_snapshot=routing_snapshot,
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tool_trace=tool_trace,
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retrieval_metrics=retrieval_metrics,
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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_text,
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sources=used_sources,
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intent="question",
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domains_used=final_domains,
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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=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=None,
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anchor_ref=None,
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routing_snapshot=routing_snapshot,
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),
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**runtime_fields,
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)
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self.db.add(qa)
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confidence = round(max(0.2, min(1.0, max_score if max_score > 0 else 0.55)), 4)
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if evidence_guard_triggered:
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confidence = min(confidence, 0.35)
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missing_fields = list(grounding_verdict.missing_fields)
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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_text,
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query_type=query_type if query_type != QUERY_TYPE_UNKNOWN else QUERY_TYPE_EXPLAIN,
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ambiguity="clear" if not missing_fields else "low_evidence",
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rewrite_meta=rewrite_meta,
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sources=used_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_text,
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)
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return self._build_response_payload(
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answer=answer_text,
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sources=used_sources,
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domains_used=final_domains,
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response_time_ms=elapsed,
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db_query_ms=db_query_ms,
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vector_search_ms=vector_search_ms,
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graph_query_ms=graph_query_ms,
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db_timeout=db_timeout,
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vector_timeout=vector_timeout,
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graph_timeout=graph_timeout,
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tool_trace=runtime_fields["tool_trace"],
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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=round(max_score, 4),
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query_type=query_type if query_type != QUERY_TYPE_UNKNOWN else QUERY_TYPE_EXPLAIN,
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confidence=confidence,
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missing_fields=missing_fields,
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thinking_mode=active_thinking_mode,
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reason_code=grounding_verdict.reason_code or ("answer_generated" if answer_mode not in {ANSWER_MODE_CLARIFY, ANSWER_MODE_BLOCKED} else "insufficient_evidence"),
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answer_mode=answer_mode,
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grounding_status=grounding_verdict.status,
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policy_version=runtime_fields["policy_version"],
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citations=runtime_fields["citations"],
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sources_used=runtime_fields["sources_used"],
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evidence_pack=runtime_fields["evidence_pack"],
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prompt_variant_id=prompt_variant_id,
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)
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