c64b7dcf99
- Introduce new fields for voice, speed, and language in the AssistantConfig and ProviderCredential models to support TTS and ASR configurations. - Update the database schema and seeding script to accommodate the new fields, ensuring backward compatibility. - Implement credential testing endpoints and logic to validate OpenAI-compatible credentials, enhancing user experience and reliability. - Modify frontend components to include new fields in the credential forms and improve connection testing feedback. - Refactor related services and API interactions to support the new credential testing feature.
125 lines
4.4 KiB
Python
125 lines
4.4 KiB
Python
"""OpenAI 兼容模型凭证的最小连通测试。"""
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from __future__ import annotations
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import io
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import time
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import wave
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import httpx
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from schemas import CredentialTestRequest, CredentialTestResult
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TEST_TIMEOUT_SECONDS = 10.0
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def _endpoint(base_url: str, path: str) -> str:
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return f"{base_url.rstrip('/')}/{path.lstrip('/')}"
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def _silent_wav() -> bytes:
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buffer = io.BytesIO()
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with wave.open(buffer, "wb") as wav:
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wav.setnchannels(1)
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wav.setsampwidth(2)
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wav.setframerate(16_000)
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wav.writeframes(b"\x00\x00" * 1_600)
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return buffer.getvalue()
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def _error_detail(response: httpx.Response, api_key: str) -> str:
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try:
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body = response.json()
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detail = (
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body.get("error", {}).get("message")
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if isinstance(body, dict) and isinstance(body.get("error"), dict)
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else body.get("detail") if isinstance(body, dict) else None
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)
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except ValueError:
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detail = None
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text = str(detail or response.text or response.reason_phrase).strip()
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return text.replace(api_key, "***")[:300]
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async def test_openai_credential(
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config: CredentialTestRequest,
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) -> CredentialTestResult:
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started = time.perf_counter()
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headers = {"Authorization": f"Bearer {config.api_key}"}
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try:
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async with httpx.AsyncClient(timeout=TEST_TIMEOUT_SECONDS) as client:
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if config.type == "LLM":
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response = await client.post(
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_endpoint(config.api_url, "chat/completions"),
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headers=headers,
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json={
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"model": config.model_id,
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"messages": [{"role": "user", "content": "Reply with OK."}],
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"max_tokens": 1,
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"stream": False,
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},
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)
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elif config.type == "Embedding":
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response = await client.post(
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_endpoint(config.api_url, "embeddings"),
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headers=headers,
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json={"model": config.model_id, "input": "ping"},
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)
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elif config.type == "ASR":
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response = await client.post(
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_endpoint(config.api_url, "audio/transcriptions"),
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headers=headers,
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data={
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"model": config.model_id,
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**({"language": config.language} if config.language else {}),
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},
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files={"file": ("test.wav", _silent_wav(), "audio/wav")},
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)
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elif config.type == "TTS":
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response = await client.post(
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_endpoint(config.api_url, "audio/speech"),
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headers=headers,
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json={
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"model": config.model_id,
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"input": "测试",
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"voice": config.voice,
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"speed": config.speed,
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},
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)
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else:
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return CredentialTestResult(
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ok=False,
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message="暂不支持该资源类型的连通测试",
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detail=f"当前仅支持 LLM、Embedding、ASR、TTS，收到 {config.type}",
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)
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latency_ms = round((time.perf_counter() - started) * 1000)
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if response.is_success:
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return CredentialTestResult(
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ok=True,
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latency_ms=latency_ms,
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message="连接成功",
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detail=f"OpenAI 兼容接口响应正常（HTTP {response.status_code}）",
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)
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return CredentialTestResult(
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ok=False,
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latency_ms=latency_ms,
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message=f"连接失败（HTTP {response.status_code}）",
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detail=_error_detail(response, config.api_key),
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)
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except httpx.TimeoutException:
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return CredentialTestResult(
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ok=False,
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latency_ms=round((time.perf_counter() - started) * 1000),
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message="连接超时",
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detail=f"服务未在 {TEST_TIMEOUT_SECONDS:g} 秒内响应",
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)
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except httpx.RequestError as exc:
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return CredentialTestResult(
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ok=False,
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latency_ms=round((time.perf_counter() - started) * 1000),
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message="无法连接到模型服务",
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detail=str(exc)[:300],
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)
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