张胜宇
e239eb778b
1) 客服 Agent 四份交付文档 + 构建脚手架：品牌由包装占位 XX科技 / 旧名 南方财富 统一为南方基金（热线 400-889-8899 / 官网 nffund.com），系统名改为「智能服务系统」； 同步追加 §0.4 修订记录行，工程记录行保留原占位字面以支撑硬编码扫描验收。 2) 开发文档：清理 28 份已作废/残留文档（14 份移出归档 + 14 份仓库副本）， 新增《文档规整方案与开发前待决事项-2026-09-17》。 3) 客服agent 四份交付文档首次纳入本分支。
101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
import json
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from datetime import UTC, datetime
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.sql.elements import ColumnElement
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from app.core.knowledge_contracts import KnowledgeHit, KnowledgeQuery
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from app.model.knowledge import FinKnowledgeMeta
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class KnowledgeMysqlAuthority:
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def __init__(self, session: AsyncSession) -> None:
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self._session = session
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async def filter_published(self, hits: tuple[KnowledgeHit, ...]) -> list[KnowledgeHit]:
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ids = tuple(int(hit.knowledge_id) for hit in hits if hit.knowledge_id.isdecimal())
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if not ids:
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return []
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rows = await self._session.scalars(
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select(FinKnowledgeMeta).where(
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FinKnowledgeMeta.id.in_(ids), *self._published_filters()
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)
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)
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approved = {str(row.id): row for row in rows}
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result: list[KnowledgeHit] = []
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for hit in hits:
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row = approved.get(hit.knowledge_id)
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if row is None:
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continue
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answer = self.extract_answer(row.content_text).strip()
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if answer:
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result.append(hit.model_copy(update={
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"answer": answer, "version": row.version, "title": row.title,
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}))
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return result
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async def search_keyword(
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self, query: KnowledgeQuery, collections: tuple[str, ...], top_k: int
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) -> list[KnowledgeHit]:
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"""向量服务不可用时，在原授权集合内执行受限的只读关键词检索。"""
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keyword = self._keyword(query.query)
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if not collections or not keyword:
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return []
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# 显式转义 LIKE 通配符，避免用户输入扩大关键词降级的匹配范围。
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escaped_keyword = keyword.replace("\\", "\\\\").replace("%", "\\%").replace("_", "\\_")
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rows = await self._session.scalars(
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select(FinKnowledgeMeta)
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.where(
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FinKnowledgeMeta.milvus_collection.in_(collections),
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*self._published_filters(),
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FinKnowledgeMeta.content_text.like(f"%{escaped_keyword}%", escape="\\"),
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)
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.order_by(FinKnowledgeMeta.id.desc())
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.limit(top_k)
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)
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result: list[KnowledgeHit] = []
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for row in rows:
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answer = self.extract_answer(row.content_text).strip()
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if not answer:
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continue
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result.append(
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KnowledgeHit(
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knowledge_id=str(row.id),
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collection=row.milvus_collection,
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title=row.title,
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snippet=answer[:300],
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answer=answer,
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version=row.version,
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)
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)
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return result
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@staticmethod
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def _keyword(query: str) -> str:
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"""压缩空白并限制关键词长度，避免降级查询承载无界输入。"""
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return "".join(query.split())[:64]
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@staticmethod
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def _published_filters() -> tuple[ColumnElement[bool], ...]:
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today = datetime.now(UTC).date()
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return (
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FinKnowledgeMeta.review_status == "published",
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FinKnowledgeMeta.status == "active",
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(FinKnowledgeMeta.effective_date.is_(None))
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| (FinKnowledgeMeta.effective_date <= today),
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(FinKnowledgeMeta.expire_date.is_(None))
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| (FinKnowledgeMeta.expire_date > today),
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)
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@staticmethod
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def extract_answer(content_text: str) -> str:
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try:
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payload = json.loads(content_text)
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except json.JSONDecodeError:
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return content_text
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answer = payload.get("answer") if isinstance(payload, dict) else None
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if isinstance(answer, str):
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return answer
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return content_text
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