刘金宝
109 lines
4.4 KiB
Python
109 lines
4.4 KiB
Python
import time
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from fastapi import APIRouter, Depends
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from app.agents.llm_adapter import OpenAICompatibleLLMClient
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from app.core.config import settings
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from app.core.exceptions import AppError
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from app.core.response import ApiResponse, ok
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from app.core.user_context import UserContext, get_user_context
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from app.schemas.llm import LLMTestRequest, LLMTestResponse
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router = APIRouter()
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@router.post("/deepseek-fast", response_model=ApiResponse[LLMTestResponse])
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async def test_deepseek_fast(
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payload: LLMTestRequest,
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_: UserContext = Depends(get_user_context),
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):
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"""Fast 模型测试：验证快速模型的非流式响应耗时。"""
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client = OpenAICompatibleLLMClient()
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response = await client.chat(
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[{"role": "user", "content": payload.message}],
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settings.llm_fast_model,
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thinking_enabled=settings.llm_fast_thinking_enabled,
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max_tokens=min(settings.llm_fast_max_tokens, 256),
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)
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return ok(
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LLMTestResponse(
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model=response.model,
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total_latency_ms=response.latency_ms,
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stream=False,
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mock_mode=client.is_mock_mode,
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fallback_used=response.model.startswith("mock-fallback"),
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thinking_enabled=settings.llm_fast_thinking_enabled,
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)
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)
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@router.post("/deepseek-reason", response_model=ApiResponse[LLMTestResponse])
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async def test_deepseek_reason(
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payload: LLMTestRequest,
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_: UserContext = Depends(get_user_context),
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):
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"""Reason 模型测试：优先验证流式耗时，流式不兼容时降级为真实非流式测试。"""
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client = OpenAICompatibleLLMClient()
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messages = [{"role": "user", "content": payload.message}]
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first_token_ms = None
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start = time.perf_counter()
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try:
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async for chunk in client.stream_chat(
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messages,
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settings.llm_reason_model,
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thinking_enabled=settings.llm_reason_thinking_enabled,
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reasoning_effort=settings.llm_reasoning_effort if settings.llm_reason_thinking_enabled else None,
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max_tokens=min(settings.llm_fast_max_tokens, 256),
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):
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if first_token_ms is None and chunk.first_token_ms is not None:
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first_token_ms = chunk.first_token_ms
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if chunk.done:
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return ok(
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LLMTestResponse(
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model=chunk.model or (settings.llm_reason_model if not client.is_mock_mode else f"mock-{settings.llm_reason_model}"),
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first_token_ms=first_token_ms,
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total_latency_ms=chunk.total_latency_ms or int((time.perf_counter() - start) * 1000),
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stream=True,
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mock_mode=client.is_mock_mode,
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fallback_used=chunk.fallback_used,
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thinking_enabled=settings.llm_reason_thinking_enabled,
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reasoning_effort=settings.llm_reasoning_effort if settings.llm_reason_thinking_enabled else None,
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)
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)
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except AppError as exc:
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if exc.code != "LLM_STREAM_FAILED":
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raise
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response = await client.chat(
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messages,
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settings.llm_reason_model,
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thinking_enabled=settings.llm_reason_thinking_enabled,
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reasoning_effort=settings.llm_reasoning_effort if settings.llm_reason_thinking_enabled else None,
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max_tokens=min(settings.llm_fast_max_tokens, 256),
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)
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return ok(
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LLMTestResponse(
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model=response.model,
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first_token_ms=None,
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total_latency_ms=response.latency_ms,
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stream=False,
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mock_mode=client.is_mock_mode,
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fallback_used=response.model.startswith("mock-fallback"),
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thinking_enabled=settings.llm_reason_thinking_enabled,
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reasoning_effort=settings.llm_reasoning_effort if settings.llm_reason_thinking_enabled else None,
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)
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)
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return ok(
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LLMTestResponse(
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model=settings.llm_reason_model,
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first_token_ms=first_token_ms,
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total_latency_ms=int((time.perf_counter() - start) * 1000),
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stream=True,
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mock_mode=client.is_mock_mode,
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fallback_used=False,
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thinking_enabled=settings.llm_reason_thinking_enabled,
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reasoning_effort=settings.llm_reasoning_effort if settings.llm_reason_thinking_enabled else None,
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)
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)
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