group_fqcd_jr/app/service/agent/bootstrap.py

import logging
from functools import lru_cache
from typing import Any, cast

from sqlalchemy.ext.asyncio import AsyncSession

from app.core.config import get_settings
from app.core.errors import RecoverableAgentError
from app.core.fund_contracts import FundQuoteQuery
from app.core.nl2sql_contracts import FinancialNL2SQLInput
from app.infrastructure.fund_quote_cache import FundQuoteCache
from app.infrastructure.memory_cache import MemoryCacheAdapter
from app.infrastructure.vector_memory import VectorMemoryAdapter
from app.service.agent.factory import AgentFactory
from app.service.agent.governance import PlatformGovernance
from app.service.agent.offsite_fund_agent import OffsiteFundAgent
from app.service.agent.promotion_material_agent import PromotionMaterialAgent
from app.service.financial_nl2sql_service import query_financial_data_tool
from app.service.fund_quote_service import query_fund_quote_tool
from app.service.intent_classifier import IntentClassifier
from app.service.memory_recall_service import MemoryRecallService
from app.service.model_gateway import (
    DatabaseModelEndpointResolver,
    DatabaseModelGateway,
    ModelDispatchService,
    ModelEmbeddingService,
    ModelGenerationService,
)
from app.service.runtime_config_service import load_active_intent_configs
from app.service.suitability_service import SuitabilityToolInput, suitability_tool_handler
from app.service.tool_executor import ToolDefinition, ToolExecutor, ToolRegistry

logger = logging.getLogger(__name__)


@lru_cache(maxsize=1)
def get_model_service() -> ModelGenerationService:
    """模型生成的统一装配入口，HTTP 与 Worker 共用。"""
    return ModelGenerationService(ModelDispatchService(DatabaseModelGateway()))


@lru_cache(maxsize=1)
def get_memory_cache_adapter() -> MemoryCacheAdapter | None:
    """Redis 不可用时降级为无缓存，不能阻塞业务调用。"""
    try:
        from redis.asyncio import Redis

        settings = get_settings()
        client = Redis.from_url(
            settings.redis_url,
            socket_connect_timeout=settings.redis_connect_timeout_seconds,
            socket_timeout=settings.redis_connect_timeout_seconds,
            decode_responses=True,
        )
        return MemoryCacheAdapter(client)
    except Exception:
        logger.warning("memory cache adapter unavailable; recall runs without cache",
                       exc_info=True)
        return None


@lru_cache(maxsize=1)
def get_fund_quote_cache() -> FundQuoteCache | None:
    """行情缓存与记忆缓存共用 Redis 连接配置，故障时透明降级。"""
    adapter = get_memory_cache_adapter()
    return FundQuoteCache(adapter) if adapter is not None else None


@lru_cache(maxsize=1)
def get_vector_memory_adapter() -> VectorMemoryAdapter | None:
    """Milvus 语义召回适配器不可用时关闭语义通道，保留结构化召回。"""
    try:
        from pymilvus import MilvusClient  # type: ignore[import-untyped]

        settings = get_settings()
        client = MilvusClient(uri=settings.milvus_uri, token=settings.milvus_token or None)
        return VectorMemoryAdapter(client, settings.milvus_collection)
    except Exception:
        logger.warning("vector memory adapter unavailable; semantic recall disabled",
                       exc_info=True)
        return None


@lru_cache(maxsize=1)
def get_memory_embedding_service() -> ModelEmbeddingService:
    return ModelEmbeddingService(ModelDispatchService(DatabaseModelGateway()))


async def _embed_text(text: str) -> list[float]:
    endpoints = await DatabaseModelEndpointResolver().resolve(
        agent_type="memory_recall", task_type="embedding"
    )
    if not endpoints:
        raise RecoverableAgentError("没有可用的 embedding 端点")
    return (await get_memory_embedding_service().embed(endpoints, text)).vector


def build_memory_recall_service(session: AsyncSession) -> MemoryRecallService:
    vector = get_vector_memory_adapter()
    return MemoryRecallService(
        session,
        vector=vector,
        cache=get_memory_cache_adapter(),
        embed=_embed_text if vector is not None else None,
    )


@lru_cache(maxsize=1)
def get_agent_factory() -> AgentFactory:
    """HTTP 与 Worker 共用的唯一底座依赖组装入口。"""
    registry = ToolRegistry()
    registry.register(ToolDefinition(
        name="check_suitability",
        input_model=SuitabilityToolInput,
        handler=cast(Any, suitability_tool_handler),
        required_permission="suitability:read",
        allowed_roles=("customer", "advisor", "operator", "admin"),
    ))
    registry.register(ToolDefinition(
        name="query_fund_quote",
        input_model=FundQuoteQuery,
        handler=cast(Any, query_fund_quote_tool),
        required_permission="fund:quote:read",
        allowed_roles=("customer", "advisor", "operator", "risk_operator", "admin"),
        timeout_seconds=15,
    ))
    registry.register(ToolDefinition(
        name="query_financial_data",
        input_model=FinancialNL2SQLInput,
        handler=cast(Any, query_financial_data_tool),
        required_permission="financial:nl2sql:read",
        allowed_roles=("advisor", "operator", "admin", "super_admin"),
        timeout_seconds=10,
    ))

    model_service = get_model_service()
    endpoint_resolver = DatabaseModelEndpointResolver()
    factory = AgentFactory(
        governance=PlatformGovernance(recall_factory=build_memory_recall_service),
        model_service=model_service,
        tool_executor=ToolExecutor(registry),
        intent_classifier=IntentClassifier(
            model_service, config_loader=load_active_intent_configs
        ),
        intent_endpoint_resolver=endpoint_resolver,
    )
    register_business_agents(factory)
    return factory


def register_business_agents(factory: AgentFactory) -> None:
    """业务 Agent 仍由本项目统一注册，基座只负责提供公共治理链。"""
    factory.register(
        OffsiteFundAgent.definition,
        lambda _context: OffsiteFundAgent(OffsiteFundAgent.definition),
    )
    factory.register(
        PromotionMaterialAgent.definition,
        lambda _context: PromotionMaterialAgent(PromotionMaterialAgent.definition),
    )