162 lines
8.7 KiB
Python
162 lines
8.7 KiB
Python
from app.contracts import ModelCapability, ProviderConfig, ProviderPreset, ProviderType
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from app.providers.base import ModelProvider
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from app.providers.credentials import CredentialResolver, ProviderCredentialResolver
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from app.providers.ollama import OllamaProvider
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from app.providers.openai_compatible import OpenAICompatibleProvider
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class UnsupportedProviderError(ValueError):
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pass
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class ProviderFactory:
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def __init__(self, credentials: CredentialResolver) -> None:
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# ProviderFactory 是所有可配置 Provider 的创建边界，在此统一禁止
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# Provider 借用 Plugin Secret 引用，避免调用方漏包安全 Resolver。
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self.credentials = ProviderCredentialResolver(credentials)
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def build(self, config: ProviderConfig) -> ModelProvider:
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adapter = self._build(config)
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adapter.provider_config = config.model_copy(deep=True)
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from app.services.usage_service import usage_context
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from contextlib import aclosing
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from uuid import uuid4
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from app.providers.context_budget import prepare_context
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from app.services.persona_settings import apply_global_persona
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from app.providers.base import ProviderError
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from app.contracts import ModelEvent, ModelEventType
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from datetime import datetime, timezone
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complete, stream = adapter.complete, adapter.stream
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async def complete_with_trace(request):
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token = usage_context.set({"request_id": uuid4().hex, "run_id": request.metadata.get("run_id")})
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try:
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request = await prepare_context(apply_global_persona(request), config, complete)
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return await complete(request)
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finally:
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usage_context.reset(token)
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async def stream_with_trace(request):
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sequence = 0
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token = usage_context.set({"request_id": uuid4().hex, "run_id": request.metadata.get("run_id")})
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try:
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original = request
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request = await prepare_context(apply_global_persona(request), config, complete, stream=True)
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if request.messages != original.messages:
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yield ModelEvent(event=ModelEventType.context_status, sequence=sequence, timestamp=datetime.now(timezone.utc), data={"message": "本次请求已压缩旧对话；原始记录保留，摘要生成计入用量。"})
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sequence += 1
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async with aclosing(stream(request)) as events:
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async for event in events:
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yield event.model_copy(update={"sequence": sequence})
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sequence += 1
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except ProviderError as exc:
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yield ModelEvent(event=ModelEventType.error, sequence=sequence, timestamp=datetime.now(timezone.utc), data={"code": exc.code, "message": exc.message})
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yield ModelEvent(event=ModelEventType.done, timestamp=datetime.now(timezone.utc), sequence=sequence + 1, data={"status": "failed"})
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finally:
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usage_context.reset(token)
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adapter.complete, adapter.stream = complete_with_trace, stream_with_trace
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return adapter
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def _build(self, config: ProviderConfig) -> ModelProvider:
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if config.provider_type == ProviderType.openai_responses:
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from app.providers.openai_responses import OpenAIResponsesProvider
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return OpenAIResponsesProvider(
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base_url=config.base_url or "https://api.openai.com/v1",
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credential_id=config.credential_id, credentials=self.credentials,
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)
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if config.provider_type == ProviderType.anthropic_messages:
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from app.providers.anthropic_messages import AnthropicMessagesProvider
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return AnthropicMessagesProvider(
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base_url=config.base_url or "https://api.anthropic.com/v1",
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credential_id=config.credential_id, credentials=self.credentials,
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)
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if config.provider_type in {
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ProviderType.openai_chat,
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ProviderType.openai_compatible,
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}:
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return OpenAICompatibleProvider(
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base_url=config.base_url or "https://api.openai.com/v1",
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credential_id=config.credential_id,
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credentials=self.credentials,
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)
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if config.provider_type == ProviderType.ollama:
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return OllamaProvider(config.base_url or "http://127.0.0.1:11434")
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raise UnsupportedProviderError(config.provider_type.value)
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@staticmethod
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def presets() -> list[ProviderPreset]:
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presets = [
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ProviderPreset(
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preset_id="openai",
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name="OpenAI",
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provider_type=ProviderType.openai_chat,
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base_url="https://api.openai.com/v1",
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default_credential_id="openai",
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),
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ProviderPreset(
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preset_id="deepseek",
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name="DeepSeek",
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provider_type=ProviderType.openai_compatible,
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base_url="https://api.deepseek.com",
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default_credential_id="deepseek",
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),
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ProviderPreset(
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preset_id="ollama",
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name="Ollama",
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provider_type=ProviderType.ollama,
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base_url="http://127.0.0.1:11434",
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requires_credential=False,
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),
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]
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# General API endpoints. Coding-plan endpoints and keys are separate products.
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domestic = [
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("kimi", "Kimi / 月之暗面", "https://api.moonshot.cn/v1", [], "长上下文对话；模型以账号权限为准。"),
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("qwen", "阿里云百炼", "https://dashscope.aliyuncs.com/compatible-mode/v1", [ModelCapability.embedding], "中国内地兼容接口；海外地域需修改地址。"),
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("zhipu", "智谱 GLM", "https://open.bigmodel.cn/api/paas/v4", [ModelCapability.embedding], "通用 API；Coding Plan 请使用其专用地址。"),
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("volcengine", "火山方舟 / 豆包", "https://ark.cn-beijing.volces.com/api/v3", [ModelCapability.embedding], "按账号填写模型 ID 或推理接入点 ID。"),
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("siliconflow", "硅基流动", "https://api.siliconflow.cn/v1", [ModelCapability.embedding, ModelCapability.transcription], "支持兼容 Embedding 和音频转写接口。"),
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("baidu", "百度千帆", "https://qianfan.baidubce.com/v2", [ModelCapability.embedding], "使用千帆 API Key；模型列表取决于账号。"),
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("hunyuan", "腾讯混元", "https://api.hunyuan.cloud.tencent.com/v1", [], "OpenAI 兼容对话接口。"),
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("minimax", "MiniMax", "https://api.minimaxi.com/v1", [], "文本对话兼容接口；其他媒体协议需独立适配。"),
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("stepfun", "阶跃星辰", "https://api.stepfun.com/v1", [], "通用 API；Step Plan 请使用其专用地址。"),
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]
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for preset_id, name, url, extra, description in domestic:
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presets.append(ProviderPreset(
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preset_id=preset_id, name=name, provider_type=ProviderType.openai_compatible,
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base_url=url, default_credential_id=preset_id, logo_id=preset_id,
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capabilities=[ModelCapability.chat, *extra], description=description,
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))
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presets.extend([
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ProviderPreset(preset_id="openai-responses", name="OpenAI Responses", provider_type=ProviderType.openai_responses,
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base_url="https://api.openai.com/v1", default_credential_id="openai", logo_id="openai"),
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ProviderPreset(preset_id="anthropic", name="Anthropic / Claude", provider_type=ProviderType.anthropic_messages,
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base_url="https://api.anthropic.com/v1", default_credential_id="anthropic", logo_id="anthropic"),
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])
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for preset in presets:
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if preset.logo_id == "custom":
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preset.logo_id = preset.preset_id
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if not preset.capabilities:
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preset.capabilities = [ModelCapability.chat]
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presets[0].capabilities += [ModelCapability.embedding, ModelCapability.transcription]
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return presets
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@staticmethod
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def capabilities(provider_type: ProviderType) -> list[ModelCapability]:
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if provider_type in {
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ProviderType.openai_chat,
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ProviderType.openai_compatible,
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ProviderType.openai_responses,
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ProviderType.anthropic_messages,
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}:
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return [
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ModelCapability.chat,
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ModelCapability.tool_calling,
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ModelCapability.streaming,
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ModelCapability.structured_output,
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]
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if provider_type == ProviderType.ollama:
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return [
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ModelCapability.chat,
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ModelCapability.tool_calling,
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ModelCapability.streaming,
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]
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return []
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