244 lines
9.5 KiB
Python
244 lines
9.5 KiB
Python
"""投顾 Agent 的 NL2SQL 数据查询适配层。"""
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from __future__ import annotations
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from dataclasses import asdict
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from typing import Any
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from uuid import uuid4
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from agent.advisor_agent.auth import ensure_customer_access
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from agent.data_query.agent import DataQueryAgent
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from common.common_const import CUSTOMER_REL_STATUS_SIGNED, CUSTOMER_REL_STATUS_UNSIGNED
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from config import database
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from config.settings import settings
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from nl2sql.contracts import DataQueryRequest, DataQueryResult
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from nl2sql.embedding import EmbeddingError
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from nl2sql.retrieval import retrieve_metadata
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from nl2sql.cache import cache_get, cache_set, build_question_cache_key
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from nl2sql.runtime_config import runtime_config
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from nl2sql.schema import load_authoritative_schema
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from repositories.customer_relation import CustomerRelationRepo
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from service.nl2sql.permission_service import load_query_permission
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from service.nl2sql.query_service import QueryServiceError
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from tool.llm import llm as default_llm
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from utils.exceptions import LLMFailError
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from repositories.fin_holdings import FinHoldingsRepo
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from repositories.fin_product import FinProductRepo
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def enrich_customer_rows(
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rows: list[dict[str, Any]],
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columns: list[str],
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name_by_id: dict[int, str],
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) -> dict[str, Any]:
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"""为查询结果补充客户姓名，并生成可直接展示的姓名摘要。"""
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id_keys = ("customer_id", "客户ID", "客户编号", "客户_id")
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enriched = [dict(row) for row in rows]
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names: list[str] = []
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for row in enriched:
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customer_id = next((row.get(key) for key in id_keys if row.get(key) is not None), None)
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try:
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customer_id = int(customer_id)
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except (TypeError, ValueError):
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continue
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name = name_by_id.get(customer_id)
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if name:
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row["客户姓名"] = name
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names.append(f"客户{customer_id}（{name}）")
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output_columns = list(columns)
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if any("客户姓名" in row for row in enriched) and "客户姓名" not in output_columns:
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insert_at = next(
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(index + 1 for index, column in enumerate(output_columns) if column in id_keys),
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len(output_columns),
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)
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output_columns.insert(insert_at, "客户姓名")
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return {"rows": enriched, "columns": output_columns, "name_summary": "、".join(dict.fromkeys(names))}
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def _is_current_holdings_query(question: str) -> bool:
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text = "".join((question or "").split())
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return any(term in text for term in ("当前持仓", "目前持仓", "现有持仓", "持仓明细", "持仓情况"))
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async def _query_current_holdings(
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db, *, customer_id: int, trace_id: str, customer_name: str | None = None
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) -> dict[str, Any]:
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holdings = await FinHoldingsRepo(db).list_by_customer(customer_id, status="持有中")
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product_repo = FinProductRepo(db)
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rows: list[dict[str, Any]] = []
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total_value = 0
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for holding in holdings:
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product = await product_repo.get(holding.product_id)
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rows.append(
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{
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"产品代码": product.product_code if product else None,
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"产品名称": product.product_name if product else None,
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"风险等级": product.risk_level if product else None,
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"持有份额": f"{holding.shares:.4f}",
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"成本金额": f"{holding.cost_amount:.2f}",
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"当前市值": f"{holding.current_value:.2f}",
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"盈亏": f"{holding.profit_loss:.2f}",
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"收益率": f"{holding.profit_ratio:.4f}",
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"状态": holding.status,
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}
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)
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total_value += holding.current_value
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names = [row["产品名称"] for row in rows if row["产品名称"]]
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return {
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"query_id": f"holdings-{uuid4().hex}",
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"trace_id": trace_id,
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"columns": list(rows[0].keys()) if rows else ["产品代码", "产品名称", "风险等级", "持有份额", "成本金额", "当前市值", "盈亏", "收益率", "状态"],
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"rows": rows,
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"row_count": len(rows),
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"truncated": False,
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"summary": f"当前持仓共 {len(rows)} 条记录。",
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"answer": (
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(f"客户{customer_id}（{customer_name}）" if customer_name else f"客户{customer_id}")
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+ f"当前持有 {len(rows)} 只基金，总市值约 {total_value:.2f} 元。"
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+ (f"包括：{'、'.join(names[:6])}。" if names else "")
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),
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"sql": None,
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}
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async def execute_advisor_data_query(
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db,
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*,
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advisor_id: int,
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customer_id: int | None,
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scope: str = "customer",
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question: str,
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trace_id: str,
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session_id: str | None = None,
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conversation_context: str = "",
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data_scope: dict[str, Any] | None = None,
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max_rows: int | None = None,
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page: int = 1,
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page_size: int = 100,
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sort_by: str | None = None,
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sort_order: str = "asc",
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milvus=None,
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redis=None,
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llm_client=None,
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query_agent=None,
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) -> dict[str, Any]:
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"""在当前投顾或选中客户范围内执行只读自然语言查询。
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``data_scope`` 即使由调用方传入也不会被信任，服务端始终覆盖为当前
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客户关系范围，避免投顾借助 NL2SQL 查询其他客户数据。
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"""
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if scope == "advisor":
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relations = await CustomerRelationRepo(db).list_by_advisor(advisor_id)
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customer_ids = [
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relation.customer_id
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for relation in relations
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if relation.status in {CUSTOMER_REL_STATUS_UNSIGNED, CUSTOMER_REL_STATUS_SIGNED}
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]
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if not customer_ids:
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raise QueryServiceError("当前投顾名下没有可查询客户")
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else:
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if customer_id is None:
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raise QueryServiceError("单客户查询需要明确客户范围")
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await ensure_customer_access(db, advisor_id=advisor_id, customer_id=customer_id)
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customer_ids = [customer_id]
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name_by_id: dict[int, str] = {}
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try:
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relation_rows = await CustomerRelationRepo(db).list_customer_rows(
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advisor_id=advisor_id, limit=max(len(customer_ids), 100)
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)
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name_by_id = {
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int(account.id): account.real_name
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for _relation, account, _profile in relation_rows
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if account.real_name
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}
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except Exception: # noqa: BLE001 姓名增强失败不阻断数据查询
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pass
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if scope == "customer" and _is_current_holdings_query(question):
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redis = redis or database.redis.client()
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return await _query_current_holdings(
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db,
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customer_id=customer_id,
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trace_id=trace_id,
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customer_name=name_by_id.get(customer_id),
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)
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permission = await load_query_permission(db, advisor_id)
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if not permission.get("can_query", False):
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raise QueryServiceError("当前投顾没有 NL2SQL 查询权限")
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milvus = milvus or database.milvus.client()
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redis = redis or database.redis.client()
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cache_key = build_question_cache_key(
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question,
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permission=permission,
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data_scope={"customer_ids": customer_ids},
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page=page,
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page_size=page_size,
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sort_by=sort_by,
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sort_order=sort_order,
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)
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cached = await cache_get(redis, cache_key)
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if cached is not None:
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cached["trace_id"] = trace_id
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cached["customer_id"] = customer_id
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cached["warnings"] = [*cached.get("warnings", []), "cache_hit"]
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return cached
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llm_client = llm_client or default_llm
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request = DataQueryRequest(
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question=question,
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user_id=advisor_id,
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trace_id=trace_id,
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session_id=session_id,
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caller_agent="advisor_agent",
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data_scope={"customer_ids": customer_ids},
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max_rows=min(max_rows or runtime_config.max_rows, runtime_config.max_rows),
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include_sql=False,
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page=page,
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page_size=page_size,
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sort_by=sort_by,
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sort_order=sort_order,
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)
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async def permission_loader(_user_id: int):
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return permission
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async def metadata_retriever(query: str):
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return await retrieve_metadata(
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query,
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milvus,
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top_k=runtime_config.retrieval_top_k,
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)
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async def schema_loader(table_names: set[str], _permission: dict):
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return await load_authoritative_schema(
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db,
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database=settings.mysql.database,
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candidate_tables=table_names,
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)
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try:
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result: DataQueryResult = await (query_agent or DataQueryAgent()).query(
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request,
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session=db,
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permission_loader=permission_loader,
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metadata_retriever=metadata_retriever,
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schema_loader=schema_loader,
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llm_client=llm_client,
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summary_llm=llm_client,
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masks=permission.get("masks"),
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conversation_context=conversation_context,
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)
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except (EmbeddingError, LLMFailError) as exc:
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raise QueryServiceError("投顾 Agent 依赖服务不可用，请检查 LLM/Embedding 服务连接") from exc
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payload = asdict(result)
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enriched = enrich_customer_rows(payload.get("rows", []), payload.get("columns", []), name_by_id)
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payload["rows"] = enriched["rows"]
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payload["columns"] = enriched["columns"]
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if enriched["name_summary"]:
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existing_answer = payload.get("answer") or payload.get("summary") or ""
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payload["answer"] = f"{existing_answer.rstrip('。')}。客户姓名：{enriched['name_summary']}。"
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payload["sql"] = None
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payload["customer_id"] = customer_id
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await cache_set(redis, cache_key, payload, ttl=runtime_config.cache_ttl)
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return payload
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