refactor(server): split oversized backend services
This commit is contained in:
caoxiaozhu
627
server/src/app/services/user_agent_knowledge.py
Normal file
627
server/src/app/services/user_agent_knowledge.py
Normal file
@@ -0,0 +1,627 @@
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from __future__ import annotations
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import re
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from typing import Any
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from app.schemas.user_agent import UserAgentCitation, UserAgentRequest
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from app.services.user_agent_knowledge_helpers import UserAgentKnowledgeHelpersMixin
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from app.services.user_agent_knowledge_constants import (
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KNOWLEDGE_ARTICLE_PATTERN,
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KNOWLEDGE_DIRECT_ANSWER_HINTS,
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KNOWLEDGE_LIST_ITEM_PATTERN,
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KNOWLEDGE_NUMBERED_ITEM_PATTERN,
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KNOWLEDGE_QUERY_STOPWORDS,
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KNOWLEDGE_SECTION_HEADING_PATTERN,
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MAX_KNOWLEDGE_DIRECT_EVIDENCE,
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MAX_KNOWLEDGE_MODEL_HITS,
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MAX_KNOWLEDGE_QUERY_TERMS,
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)
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class UserAgentKnowledgeMixin(UserAgentKnowledgeHelpersMixin):
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@staticmethod
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def _build_model_tool_payload(
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tool_payload: dict[str, Any],
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*,
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question: str | None = None,
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) -> dict[str, Any]:
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normalized = dict(tool_payload or {})
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hits = []
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for item in UserAgentKnowledgeMixin._select_knowledge_model_hits(
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tool_payload,
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question=question,
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):
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if not isinstance(item, dict):
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continue
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hits.append(
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{
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"title": str(item.get("title") or "").strip(),
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"document_name": str(item.get("document_name") or "").strip(),
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"excerpt": str(item.get("excerpt") or "").strip(),
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"content": str(item.get("content") or "").strip()[:1200],
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"tags": list(item.get("tags") or [])[:5],
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"evidence": list(item.get("evidence") or [])[:3],
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"code": str(item.get("code") or "").strip(),
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}
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)
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normalized["hits"] = hits
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return normalized
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@staticmethod
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def _build_knowledge_evidence_blocks(
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tool_payload: dict[str, Any],
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*,
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question: str | None = None,
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) -> str:
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blocks: list[str] = []
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for index, item in enumerate(
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UserAgentKnowledgeMixin._select_knowledge_model_hits(
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tool_payload,
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question=question,
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)[:3],
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start=1,
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):
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if not isinstance(item, dict):
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continue
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title = str(item.get("title") or item.get("document_name") or f"证据 {index}").strip()
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code = str(item.get("code") or "").strip()
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content = str(item.get("content") or "").strip()
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if not content:
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continue
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blocks.append(
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"\n".join(
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[
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f"[证据 {index}] {title}" + (f" ({code})" if code else ""),
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"```text",
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content[:1200],
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"```",
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]
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)
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)
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return "\n\n".join(blocks)
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def _build_fast_knowledge_answer(
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self,
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payload: UserAgentRequest,
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*,
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citations: list[UserAgentCitation],
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) -> str | None:
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if payload.ontology.scenario != "knowledge":
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return None
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if str(payload.tool_payload.get("result_type") or "").strip() != "knowledge_search":
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return None
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evidence_items = self._build_knowledge_answer_evidence(payload)
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if not evidence_items:
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return None
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question = self._resolve_knowledge_question(payload)
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if not self._should_use_direct_knowledge_answer(question, evidence_items):
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return None
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return self._render_knowledge_direct_answer(
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payload,
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citations=citations,
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evidence_items=evidence_items,
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)
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def _render_knowledge_direct_answer(
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self,
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payload: UserAgentRequest,
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*,
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citations: list[UserAgentCitation],
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evidence_items: list[dict[str, Any]],
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) -> str | None:
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if not evidence_items:
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return None
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title = str(
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(citations[0].title if citations else "")
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or evidence_items[0].get("title")
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or "相关制度"
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).strip()
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user_name = str(payload.context_json.get("name") or "").strip()
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question = self._resolve_knowledge_question(payload)
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query_terms = self._extract_knowledge_query_terms(question)
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ordered_evidence_items = self._prioritize_knowledge_evidence_items(question, evidence_items)
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primary_item = ordered_evidence_items[0]
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primary_heading = self._format_knowledge_heading_label(
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str(primary_item.get("heading") or "").strip()
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)
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primary_lines = self._collect_direct_knowledge_answer_lines(ordered_evidence_items)
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lines: list[str] = []
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if user_name:
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lines.append(f"{user_name}，您好。")
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source_prefix = f"根据《{title}》"
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if primary_heading:
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source_prefix = f"{source_prefix}（{primary_heading}）"
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if str(primary_item.get("kind") or "") == "table":
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lines.append(f"{source_prefix}，当前能直接确认的是：")
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lines.append(self._extract_relevant_table_preview(str(primary_item.get("content") or ""), query_terms))
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else:
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if not primary_lines:
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lines.append(
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f"{source_prefix}，当前能直接确认的是："
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f"{self._summarize_knowledge_evidence_content(primary_item, query_terms)}"
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)
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elif len(primary_lines) == 1:
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lines.append(f"{source_prefix}，当前能直接确认的是：{primary_lines[0].strip()}")
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else:
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lines.append(f"{source_prefix}，当前能直接确认的是：")
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lines.extend(primary_lines)
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notes: list[str] = []
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location_note = self._build_missing_location_grounding_note(question, evidence_items)
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if location_note:
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notes.append(location_note)
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if self._question_requires_explicit_condition(question) and not self._answer_evidence_has_numeric_or_condition(evidence_items):
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notes.append("当前命中的证据更偏规则说明或流程约束，还没有直接给出可立即套用的数值或完整条件。")
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if notes:
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lines.append("")
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lines.append("说明：")
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lines.extend(f"- {note}" for note in notes)
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return "\n".join(line for line in lines if line is not None).strip()
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@staticmethod
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def _resolve_knowledge_question(payload: UserAgentRequest) -> str:
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return str(payload.context_json.get("user_input_text") or payload.message or "").strip()
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@staticmethod
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def _looks_like_structured_knowledge_query(question: str) -> bool:
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normalized = str(question or "").strip()
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if not normalized:
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return False
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return any(keyword in normalized for keyword in KNOWLEDGE_DIRECT_ANSWER_HINTS)
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def _should_use_direct_knowledge_answer(
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self,
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question: str,
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evidence_items: list[dict[str, Any]],
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) -> bool:
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if not evidence_items:
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return False
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if self._looks_like_structured_knowledge_query(question):
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return True
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return str(evidence_items[0].get("kind") or "") in {"table", "kv", "list", "clause"}
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def _build_knowledge_answer_evidence(
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self,
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payload: UserAgentRequest,
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) -> list[dict[str, Any]]:
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question = self._resolve_knowledge_question(payload)
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query_terms = self._extract_knowledge_query_terms(question)
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candidates: list[dict[str, Any]] = []
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for hit in self._select_knowledge_model_hits(
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payload.tool_payload,
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question=question,
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):
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if not isinstance(hit, dict):
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continue
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candidates.extend(self._extract_knowledge_evidence_candidates(hit, query_terms))
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deduped: list[dict[str, Any]] = []
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seen: set[tuple[str, str, str]] = set()
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ranked_candidates = sorted(
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candidates,
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key=lambda value: (
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float(value.get("score") or 0),
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-len(str(value.get("content") or "")),
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),
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reverse=True,
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)
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top_score = float(ranked_candidates[0].get("score") or 0) if ranked_candidates else 0.0
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for item in ranked_candidates:
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score = float(item.get("score") or 0)
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if deduped and score < max(6.0, top_score - 14):
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continue
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key = (
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str(item.get("title") or "").strip(),
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str(item.get("heading") or "").strip(),
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self._clean_knowledge_segment_text(str(item.get("content") or ""))[:180],
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)
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if key in seen:
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continue
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seen.add(key)
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deduped.append(item)
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if len(deduped) >= MAX_KNOWLEDGE_DIRECT_EVIDENCE:
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break
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return deduped
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def _extract_knowledge_evidence_candidates(
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self,
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hit: dict[str, Any],
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query_terms: list[str],
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) -> list[dict[str, Any]]:
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title = str(hit.get("title") or hit.get("document_name") or "相关制度").strip()
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content = str(hit.get("content") or "").strip()
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if not content:
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return []
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raw_candidates = self._merge_knowledge_lead_in_segments(
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self._split_knowledge_hit_into_segments(content)
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)
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candidates: list[dict[str, Any]] = []
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for item in raw_candidates:
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score = self._score_knowledge_evidence_candidate(item, query_terms)
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if query_terms and score <= 0:
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continue
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normalized = dict(item)
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normalized["title"] = title
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normalized["score"] = score
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candidates.append(normalized)
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if candidates:
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return candidates
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fallback_text = str(hit.get("excerpt") or "").strip() or self._extract_excerpt(content)
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if not fallback_text:
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return []
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return [
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{
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"title": title,
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"heading": "",
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"kind": "paragraph",
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"content": fallback_text,
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"score": 1,
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}
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]
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def _merge_knowledge_lead_in_segments(
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self,
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segments: list[dict[str, str]],
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) -> list[dict[str, str]]:
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if not segments:
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return []
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merged: list[dict[str, str]] = []
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index = 0
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while index < len(segments):
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current = dict(segments[index])
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if not self._is_knowledge_lead_in_segment(current):
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merged.append(current)
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index += 1
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continue
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base_heading = str(current.get("heading") or "").strip()
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current_marker = self._extract_knowledge_marker_family(str(current.get("content") or ""))
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follow_segments: list[dict[str, str]] = []
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next_index = index + 1
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while next_index < len(segments):
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candidate = segments[next_index]
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if str(candidate.get("heading") or "").strip() != base_heading:
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break
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candidate_kind = str(candidate.get("kind") or "").strip()
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candidate_content = str(candidate.get("content") or "").strip()
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candidate_marker = self._extract_knowledge_marker_family(candidate_content)
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if not candidate_content or candidate_kind == "table":
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break
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if current_marker and candidate_marker == current_marker:
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break
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if self._is_knowledge_lead_in_segment(candidate) and follow_segments:
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break
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if candidate_kind not in {"list", "paragraph", "kv", "clause"}:
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break
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follow_segments.append(candidate)
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next_index += 1
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if len(follow_segments) >= 4:
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break
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if candidate_kind == "paragraph" and len(candidate_content) >= 200:
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break
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if follow_segments:
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current["content"] = "\n".join(
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[str(current.get("content") or "").strip()]
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+ [str(item.get("content") or "").strip() for item in follow_segments]
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)
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if any(str(item.get("kind") or "").strip() == "list" for item in follow_segments):
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current["kind"] = "list"
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merged.append(current)
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index = next_index
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continue
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merged.append(current)
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index += 1
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return merged
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def _split_knowledge_hit_into_segments(self, content: str) -> list[dict[str, str]]:
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segments: list[dict[str, str]] = []
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markdown_headings: list[str] = []
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section_heading = ""
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paragraph_lines: list[str] = []
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table_lines: list[str] = []
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def current_heading() -> str:
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heading_parts = [item for item in markdown_headings if item]
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if section_heading:
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heading_parts.append(section_heading)
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return " > ".join(heading_parts)
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def flush_paragraph() -> None:
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nonlocal paragraph_lines
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if not paragraph_lines:
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return
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merged = " ".join(line.strip() for line in paragraph_lines if line.strip()).strip()
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paragraph_lines = []
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if merged:
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segments.append(
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{
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"heading": current_heading(),
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"kind": "paragraph",
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"content": merged,
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}
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)
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def flush_table() -> None:
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nonlocal table_lines
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if not table_lines:
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return
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merged = "\n".join(line.rstrip() for line in table_lines if line.strip()).strip()
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table_lines = []
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if merged:
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segments.append(
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{
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"heading": current_heading(),
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"kind": "table",
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"content": merged,
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}
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)
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for raw_line in str(content or "").replace("\r\n", "\n").replace("\r", "\n").splitlines():
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line = raw_line.rstrip()
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stripped = line.strip()
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if not stripped:
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flush_paragraph()
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flush_table()
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continue
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markdown_heading_match = re.match(r"^(#{1,6})\s+(.+)$", stripped)
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if markdown_heading_match:
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flush_paragraph()
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flush_table()
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level = len(markdown_heading_match.group(1))
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heading_text = markdown_heading_match.group(2).strip()
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markdown_headings = markdown_headings[: max(0, level - 1)]
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markdown_headings.append(heading_text)
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section_heading = ""
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continue
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if KNOWLEDGE_SECTION_HEADING_PATTERN.match(stripped) and len(stripped) <= 90:
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flush_paragraph()
|
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flush_table()
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section_heading = stripped.lstrip("#").strip()
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continue
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|
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if stripped.count("|") >= 2 and "|" in stripped:
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flush_paragraph()
|
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table_lines.append(stripped)
|
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continue
|
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|
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flush_table()
|
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|
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if KNOWLEDGE_LIST_ITEM_PATTERN.match(stripped):
|
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flush_paragraph()
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segments.append(
|
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{
|
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"heading": current_heading(),
|
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"kind": "list",
|
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"content": stripped,
|
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}
|
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)
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continue
|
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|
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if KNOWLEDGE_NUMBERED_ITEM_PATTERN.match(stripped):
|
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flush_paragraph()
|
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segments.append(
|
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{
|
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"heading": current_heading(),
|
||||
"kind": "list",
|
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"content": stripped,
|
||||
}
|
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)
|
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continue
|
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|
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if KNOWLEDGE_ARTICLE_PATTERN.match(stripped):
|
||||
flush_paragraph()
|
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segments.append(
|
||||
{
|
||||
"heading": current_heading(),
|
||||
"kind": "clause",
|
||||
"content": stripped,
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
if ("：" in stripped or ":" in stripped) and len(stripped) <= 180:
|
||||
flush_paragraph()
|
||||
segments.append(
|
||||
{
|
||||
"heading": current_heading(),
|
||||
"kind": "kv",
|
||||
"content": stripped,
|
||||
}
|
||||
)
|
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continue
|
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|
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paragraph_lines.append(stripped)
|
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|
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flush_paragraph()
|
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flush_table()
|
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return segments
|
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|
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|
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def _render_knowledge_evidence_text(self, item: dict[str, Any]) -> str:
|
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lines = self._split_clean_knowledge_lines(
|
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str(item.get("content") or ""),
|
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preserve_marker=True,
|
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)
|
||||
if not lines:
|
||||
return ""
|
||||
if len(lines) == 1:
|
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return self._clean_knowledge_segment_text(lines[0])
|
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return "\n".join(f" {line}" for line in lines)
|
||||
|
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|
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def _collect_direct_knowledge_answer_lines(
|
||||
self,
|
||||
ordered_evidence_items: list[dict[str, Any]],
|
||||
) -> list[str]:
|
||||
if not ordered_evidence_items:
|
||||
return []
|
||||
|
||||
primary_item = ordered_evidence_items[0]
|
||||
primary_title = str(primary_item.get("title") or "").strip()
|
||||
primary_heading = str(primary_item.get("heading") or "").strip()
|
||||
primary_kind = str(primary_item.get("kind") or "").strip()
|
||||
|
||||
related_items = [primary_item]
|
||||
if primary_kind != "table":
|
||||
for item in ordered_evidence_items[1:]:
|
||||
if len(related_items) >= 3:
|
||||
break
|
||||
if str(item.get("kind") or "").strip() != primary_kind:
|
||||
continue
|
||||
if str(item.get("title") or "").strip() != primary_title:
|
||||
continue
|
||||
if str(item.get("heading") or "").strip() != primary_heading:
|
||||
continue
|
||||
related_items.append(item)
|
||||
|
||||
lines: list[str] = []
|
||||
seen: set[str] = set()
|
||||
for item in related_items:
|
||||
rendered = self._render_knowledge_evidence_text(item)
|
||||
for line in rendered.splitlines():
|
||||
normalized = str(line or "").strip()
|
||||
if not normalized or normalized in seen:
|
||||
continue
|
||||
seen.add(normalized)
|
||||
lines.append(line)
|
||||
return lines
|
||||
|
||||
|
||||
def _summarize_knowledge_evidence_content(
|
||||
self,
|
||||
item: dict[str, Any],
|
||||
query_terms: list[str],
|
||||
) -> str:
|
||||
kind = str(item.get("kind") or "").strip()
|
||||
content = str(item.get("content") or "").strip()
|
||||
if kind == "table":
|
||||
preview = self._extract_relevant_table_preview(content, query_terms)
|
||||
preview_rows = [line for line in preview.splitlines() if line.strip()][:4]
|
||||
if len(preview_rows) >= 3:
|
||||
return "当前命中的直接依据是一张与问题强相关的标准表，已摘出最相关的表头和行。"
|
||||
return "当前命中的直接依据是一张与问题强相关的标准表。"
|
||||
lines = self._split_clean_knowledge_lines(content, preserve_marker=True)
|
||||
if len(lines) >= 2:
|
||||
return self._clean_knowledge_segment_text(f"{lines[0]} {' '.join(lines[1:4])}")
|
||||
return self._clean_knowledge_segment_text(content)
|
||||
|
||||
|
||||
def _build_missing_location_grounding_note(
|
||||
self,
|
||||
question: str,
|
||||
evidence_items: list[dict[str, Any]],
|
||||
) -> str:
|
||||
location = self._extract_query_location(question)
|
||||
if not location:
|
||||
return ""
|
||||
|
||||
haystack = "\n".join(
|
||||
str(item.get("heading") or "") + "\n" + str(item.get("content") or "")
|
||||
for item in evidence_items
|
||||
)
|
||||
if location in haystack:
|
||||
return ""
|
||||
return (
|
||||
f"当前命中的制度依据没有直接写出“{location}”对应的地区档位或映射关系，"
|
||||
"因此不能直接把它套用到表格中的某一列。"
|
||||
)
|
||||
|
||||
|
||||
def _build_knowledge_search_answer(
|
||||
self,
|
||||
payload: UserAgentRequest,
|
||||
citations: list[UserAgentCitation],
|
||||
) -> str:
|
||||
hits = [item for item in list(payload.tool_payload.get("hits") or []) if isinstance(item, dict)]
|
||||
evidence_items = self._build_knowledge_answer_evidence(payload)
|
||||
primary_citation = citations[0] if citations else None
|
||||
title = str(
|
||||
(primary_citation.title if primary_citation else "")
|
||||
or (hits[0].get("title") if hits else "")
|
||||
or "相关制度"
|
||||
).strip()
|
||||
user_name = str(payload.context_json.get("name") or "").strip()
|
||||
prefix = f"{user_name}，您好。\n" if user_name else ""
|
||||
if not hits:
|
||||
return (
|
||||
f"{prefix}我已经从《{title}》中检索到与你这次问题相关的制度依据，"
|
||||
"但本次答案生成环节暂时没有成功返回。请稍后重试一次；如果仍然失败，"
|
||||
"建议先检查主对话模型的连通性。"
|
||||
)
|
||||
|
||||
evidence_lines: list[str] = []
|
||||
for item in evidence_items[:3]:
|
||||
heading = str(item.get("heading") or "").strip()
|
||||
heading_text = f" > {heading}" if heading else ""
|
||||
if str(item.get("kind") or "") == "table":
|
||||
preview = self._extract_relevant_table_preview(
|
||||
str(item.get("content") or ""),
|
||||
self._extract_knowledge_query_terms(self._resolve_knowledge_question(payload)),
|
||||
)
|
||||
evidence_lines.append(f"- 《{item.get('title') or title}》{heading_text}：\n{preview}")
|
||||
continue
|
||||
rendered = self._render_knowledge_evidence_text(item)
|
||||
if rendered:
|
||||
if "\n" in rendered:
|
||||
evidence_lines.append(f"- 《{item.get('title') or title}》{heading_text}：\n{rendered}")
|
||||
else:
|
||||
evidence_lines.append(f"- 《{item.get('title') or title}》{heading_text}：{rendered}")
|
||||
|
||||
if not evidence_lines:
|
||||
for item in hits[:2]:
|
||||
item_title = str(item.get("title") or item.get("document_name") or "相关制度").strip()
|
||||
excerpt = (
|
||||
str(item.get("excerpt") or "").strip()
|
||||
or self._extract_excerpt(str(item.get("content") or ""))
|
||||
)
|
||||
if not excerpt:
|
||||
continue
|
||||
evidence_lines.append(f"- 《{item_title}》：{excerpt}")
|
||||
|
||||
if not evidence_lines:
|
||||
return (
|
||||
f"{prefix}我已经从《{title}》中检索到与你这次问题相关的制度依据，"
|
||||
"但本次答案生成环节暂时没有成功返回。请稍后重试一次；如果仍然失败，"
|
||||
"建议先检查主对话模型的连通性。"
|
||||
)
|
||||
|
||||
return "\n".join(
|
||||
[
|
||||
f"{prefix}我已经命中与你这次问题最相关的制度依据，但答案整理阶段本轮没有及时返回。",
|
||||
"先给你当前最直接的依据：",
|
||||
*evidence_lines,
|
||||
"如果你希望我继续把这些依据整理成更完整的结论、步骤或对比说明，可以继续缩小问题范围后再问一次。",
|
||||
]
|
||||
).strip()
|
||||
|
||||
Reference in New Issue
Block a user