98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
"""投顾意图结果到草稿与事件的编排。"""
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from __future__ import annotations
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from decimal import Decimal
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import json
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from agent.advisor_agent.intent.draft_generation import (
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build_rebalance_draft,
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build_recommendation_draft,
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)
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from common.common_const import EVENT_ADVISOR_REBALANCE_DRAFT_CREATED
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from service.advisor_agent.draft import create_draft
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from agent.advisor_agent.llm import generate_text
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async def generate_rebalance_draft(
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*,
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draft_repo,
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publish,
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customer_id: int,
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advisor_id: int,
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customer_risk: str,
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relation_status: str,
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holdings: list[dict],
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target_allocation: dict[str, int | float | Decimal],
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threshold: Decimal,
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candidates: list[dict],
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trace_id: str,
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) -> dict | None:
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draft_data = build_rebalance_draft(
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customer_id=customer_id,
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advisor_id=advisor_id,
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customer_risk=customer_risk,
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relation_status=relation_status,
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holdings=holdings,
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target_allocation=target_allocation,
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threshold=threshold,
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candidates=candidates,
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)
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if draft_data is None:
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return None
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draft = await create_draft(draft_repo, draft_data)
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event_id = await publish(
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event_name=EVENT_ADVISOR_REBALANCE_DRAFT_CREATED,
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trace_id=trace_id,
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trigger_user_id=advisor_id,
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customer_id=customer_id,
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payload={
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"draft_id": draft.draft_id,
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"customer_id": customer_id,
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"advisor_id": advisor_id,
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"deviation": float(draft.deviation or 0),
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"created_at": draft.create_time.isoformat()
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if draft.create_time
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else None,
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},
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)
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return {"draft": draft, "event_id": event_id}
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async def generate_recommendation_draft(
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*,
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draft_repo,
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customer_id: int,
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advisor_id: int,
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customer_risk: str,
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relation_status: str,
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candidates: list[dict],
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trace_id: str,
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memories: list[dict] | None = None,
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llm_client=None,
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llm_timeout: float = 5.0,
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):
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draft_data = build_recommendation_draft(
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customer_id=customer_id,
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advisor_id=advisor_id,
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customer_risk=customer_risk,
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candidates=candidates,
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relation_status=relation_status,
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memories=memories,
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)
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if llm_client is not None:
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draft_data["content"] = await generate_text(
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llm_client,
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system_prompt="你是基金投顾助手，只生成内部投顾草稿说明，不下单、不承诺收益。",
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user_prompt=(
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"请根据以下候选基金和客户记忆生成简洁推荐说明："
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+ json.dumps(
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{"candidates": candidates, "memories": memories or []},
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ensure_ascii=False,
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)
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),
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fallback=lambda: draft_data["content"],
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timeout=llm_timeout,
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)
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return await create_draft(draft_repo, draft_data)
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