c62156af53
B1: 项目脚手架 + 数据模型 + 租户管理 - Task 1.1: FastAPI 项目脚手架、SQLite + async SQLAlchemy - Task 1.2: 7 个数据模型 (Tenant, TenantConfig, DigitalEmployee, Conversation, Message, KnowledgeBase, Document) - Task 1.3: 租户 CRUD API + LLM 配置（含 API Key AES 加密） B2: 数字员工配置 + LLM Provider 抽象层 - Task 2.1: 数字员工 CRUD API（关联知识库） - Task 2.2: BaseLLMProvider 抽象接口 + OpenAI/Qwen Provider - Task 2.3: Provider 动态实例化 + test-provider 端点 验证: 26 个测试全部通过 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
"""OpenAI Provider"""
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from openai import AsyncOpenAI
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from app.providers.base import BaseLLMProvider, LLMMessage, LLMResponse
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class OpenAIProvider(BaseLLMProvider):
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def __init__(self, api_key: str, model: str = "gpt-4", base_url: str | None = None):
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self.api_key = api_key
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self.model = model
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self.client = AsyncOpenAI(api_key=api_key, base_url=base_url)
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async def chat(self, messages: list[LLMMessage]) -> LLMResponse:
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[{"role": m.role, "content": m.content} for m in messages],
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)
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return LLMResponse(
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content=response.choices[0].message.content or "",
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model=response.model,
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usage={
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"total_tokens": response.usage.total_tokens,
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"prompt_tokens": response.usage.prompt_tokens,
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"completion_tokens": response.usage.completion_tokens,
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},
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)
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async def chat_stream(self, messages: list[LLMMessage]) -> str:
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stream = await self.client.chat.completions.create(
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model=self.model,
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messages=[{"role": m.role, "content": m.content} for m in messages],
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stream=True,
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)
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async for chunk in stream:
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if chunk.choices[0].delta.content:
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yield chunk.choices[0].delta.content
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async def embed(self, texts: list[str]) -> list[list[float]]:
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response = await self.client.embeddings.create(
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model=self.model,
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input=texts,
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)
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return [item.embedding for item in response.data] |