53e0065e3e
配置 SSOT（TOML + .env） 统一错误契约 Auth 与事务边界 Redis / Celery 可靠性:业务 Redis（DB/0）与 Celery broker/backend（DB/1）显式拆分；连接池、sync client 可观测性（OpenTelemetry + LGTM）
110 lines
3.2 KiB
Python
110 lines
3.2 KiB
Python
"""Use-case oriented LLM gateway.
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This is a small compatibility layer over the existing provider and JSON helper
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functions. It gives new code a stable place to request model capabilities while
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older agents continue to use LangChain directly during the transition.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any, Callable, TypeVar
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from pydantic import BaseModel
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from app.core.dependencies import get_llm_provider, get_llm_provider_fast
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from app.core.llm_call import allm_json_call, llm_json_call
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T = TypeVar("T", bound=BaseModel)
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@dataclass(frozen=True)
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class LlmUseCase:
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name: str
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fast: bool = False
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max_tokens: int | None = None
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temperature: float | None = None
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model: str | None = None
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class LlmGateway:
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"""Facade for text and JSON LLM calls."""
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def provider_for(self, use_case: LlmUseCase | None = None):
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if use_case and use_case.fast:
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return get_llm_provider_fast()
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return get_llm_provider()
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def langchain_llm_for(self, use_case: LlmUseCase | None = None) -> Any | None:
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provider = self.provider_for(use_case)
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return getattr(provider, "langchain_llm", None)
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async def chat_text(
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self,
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messages: list[dict],
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*,
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use_case: LlmUseCase | None = None,
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temperature: float | None = None,
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model: str | None = None,
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max_tokens: int | None = None,
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) -> str:
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provider = self.provider_for(use_case)
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resolved_temperature = (
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temperature
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if temperature is not None
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else (
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use_case.temperature
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if use_case and use_case.temperature is not None
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else 0.7
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)
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)
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resolved_model = model if model is not None else (use_case.model if use_case else None)
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agent_name = use_case.name if use_case else "llm_gateway.chat"
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kwargs = dict(
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messages=messages,
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temperature=resolved_temperature,
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model=resolved_model,
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max_tokens=(
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max_tokens
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if max_tokens is not None
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else (use_case.max_tokens if use_case else None)
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),
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)
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return await provider.complete(**kwargs)
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async def json_object(
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self,
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prompt: str,
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schema: type[T],
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*,
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use_case: LlmUseCase,
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fallback_factory: Callable[[], T] | None = None,
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) -> T:
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return await allm_json_call(
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self.langchain_llm_for(use_case),
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prompt,
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schema,
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max_tokens=use_case.max_tokens or 1024,
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agent=use_case.name,
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fallback_factory=fallback_factory,
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)
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def sync_json_object(
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self,
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prompt: str,
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schema: type[T],
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*,
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use_case: LlmUseCase,
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fallback_factory: Callable[[], T] | None = None,
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) -> T:
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return llm_json_call(
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self.langchain_llm_for(use_case),
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prompt,
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schema,
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max_tokens=use_case.max_tokens or 1024,
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agent=use_case.name,
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fallback_factory=fallback_factory,
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)
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__all__ = ["LlmGateway", "LlmUseCase"]
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