58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
import json
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import httpx
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import pytest
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from app.core.config import get_settings
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from app.services.llm_client import LLMClient
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@pytest.mark.asyncio
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async def test_openai_compatible_semantic_and_stream_protocol(monkeypatch: pytest.MonkeyPatch) -> None:
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get_settings.cache_clear()
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monkeypatch.setenv("RISK_LLM_API_KEY", "test-key")
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monkeypatch.setenv("RISK_LLM_BASE_URL", "https://llm.test/v1")
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monkeypatch.setenv("RISK_LLM_MODEL", "test-model")
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calls = 0
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async def handler(request: httpx.Request) -> httpx.Response:
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nonlocal calls
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calls += 1
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if calls == 1:
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return httpx.Response(
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200,
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json={
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"choices": [
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{
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"message": {
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"content": json.dumps(
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{"intent": "assessment", "category": "洗发水", "confidence": 0.92},
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ensure_ascii=False,
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)
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}
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}
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]
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},
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)
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return httpx.Response(
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200,
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headers={"content-type": "text/event-stream"},
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content=(
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'data: {"choices":[{"delta":{"content":"第一段"}}]}\n\n'
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'data: {"choices":[{"delta":{"content":"第二段"}}]}\n\n'
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"data: [DONE]\n\n"
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).encode(),
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)
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client = LLMClient(transport=httpx.MockTransport(handler))
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semantic = await client.extract_semantics("评估洗发水新品")
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chunks = [
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chunk
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async for chunk in client.stream_reply([{"role": "user", "content": "继续分析"}])
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]
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assert semantic["intent"] == "assessment"
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assert semantic["confidence"] == 0.92
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assert "".join(chunks) == "第一段第二段"
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assert calls == 2
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get_settings.cache_clear()
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