4a57b290d3
- Introduce a new `RuntimeTool` model to encapsulate tool data for runtime sessions, including attributes like `id`, `name`, `function_name`, `type`, and `description`. - Update the `AssistantConfig` model to include a list of reusable tools, allowing for better management of tools within assistant configurations. - Modify the `config_resolver` service to fetch and resolve tools associated with assistants, ensuring they are available during runtime. - Refactor tool-related CRUD operations in the `tools` route to support the new runtime execution model, enhancing the overall tool management system. - Update documentation and comments to reflect changes in tool execution and configuration handling, improving clarity for future development.
196 lines
8.4 KiB
Python
196 lines
8.4 KiB
Python
"""assistant_id → 运行时配置(把真 key 在服务端组装好)。
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浏览器只传 assistant_id;真 key 在这里从 model_resources 取出注入。
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助手按 capability binding 引用资源;取不到则回退该能力默认资源。
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"""
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from db.models import (
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Assistant,
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AssistantModelBinding,
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AssistantToolBinding,
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ModelResource,
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Tool,
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)
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from models import AssistantConfig, RuntimeTool
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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async def _resource_for(
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session: AsyncSession,
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assistant_id: str,
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capability: str,
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) -> ModelResource | None:
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binding = await session.get(AssistantModelBinding, (assistant_id, capability))
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resource_id = binding.model_resource_id if binding else None
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resource = await session.get(ModelResource, resource_id) if resource_id else None
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if resource and resource.capability != capability:
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resource = None
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if resource is None:
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resource = (
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await session.execute(
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select(ModelResource)
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.where(ModelResource.capability == capability, ModelResource.enabled.is_(True))
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.order_by(ModelResource.is_default.desc(), ModelResource.id.asc())
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.limit(1)
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)
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).scalar_one_or_none()
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return resource
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async def _agent_resource_for(
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session: AsyncSession,
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assistant_id: str,
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interface_type: str,
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) -> ModelResource | None:
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if interface_type not in {"dify", "fastgpt", "opencode"}:
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return None
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binding = await session.get(AssistantModelBinding, (assistant_id, "Agent"))
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resource_id = binding.model_resource_id if binding else None
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resource = await session.get(ModelResource, resource_id) if resource_id else None
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if (
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resource
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and resource.capability == "Agent"
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and resource.interface_type == interface_type
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):
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return resource
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return (
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await session.execute(
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select(ModelResource)
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.where(
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ModelResource.capability == "Agent",
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ModelResource.interface_type == interface_type,
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ModelResource.enabled.is_(True),
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)
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.order_by(ModelResource.is_default.desc(), ModelResource.id.asc())
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.limit(1)
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)
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).scalar_one_or_none()
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def _value(resource: ModelResource | None, key: str, default=""):
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if not resource:
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return default
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value = (resource.values or {}).get(key, default)
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return default if value is None else value
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def _secret(resource: ModelResource | None, key: str, default: str = "") -> str:
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if not resource:
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return default
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return str((resource.secrets or {}).get(key) or default)
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async def _tools_for(session: AsyncSession, assistant: Assistant) -> list[RuntimeTool]:
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if assistant.type != "prompt":
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return []
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tools = (
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await session.execute(
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select(Tool)
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.join(AssistantToolBinding, AssistantToolBinding.tool_id == Tool.id)
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.where(
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AssistantToolBinding.assistant_id == assistant.id,
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Tool.status == "active",
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)
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.order_by(AssistantToolBinding.created_at, Tool.id)
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)
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).scalars().all()
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return [
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RuntimeTool(
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id=tool.id,
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name=tool.name,
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function_name=tool.function_name,
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type=tool.type,
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description=tool.description,
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definition=tool.definition or {},
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)
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for tool in tools
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]
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async def resolve_runtime_config(
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session: AsyncSession, assistant_id: str
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) -> AssistantConfig:
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"""加载助手 + 解析模型资源,产出可直接交给管线的运行时配置(含真 key)。"""
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assistant = await session.get(Assistant, assistant_id)
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if assistant is None:
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raise ValueError(f"助手不存在: {assistant_id}")
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llm_resource = await _resource_for(session, assistant.id, "LLM")
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stt_resource = await _resource_for(session, assistant.id, "ASR")
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tts_resource = await _resource_for(session, assistant.id, "TTS")
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realtime_resource = await _resource_for(session, assistant.id, "Realtime")
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agent_resource = await _agent_resource_for(session, assistant.id, assistant.type)
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vision_resource = (
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await session.get(ModelResource, assistant.vision_model_resource_id)
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if assistant.vision_model_resource_id
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else llm_resource
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)
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return AssistantConfig(
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name=assistant.name,
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type=assistant.type,
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greeting=assistant.greeting,
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# prompt 现在是真列;外部类型由其平台编排,这里给个兜底
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prompt=assistant.prompt or "你是一个有帮助的助手。",
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runtimeMode=assistant.runtime_mode, # type: ignore[arg-type]
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enableInterrupt=assistant.enable_interrupt,
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tools=await _tools_for(session, assistant),
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# workflow 图:仅 workflow 类型非空,引擎据此启用图驱动对话
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graph=(assistant.graph or {}) if assistant.type == "workflow" else {},
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# 外部托管类型连接信息(DB 存真 key,直接注入)
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fastgpt_api_url=str(_value(agent_resource, "apiUrl", assistant.api_url)),
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fastgpt_api_key=_secret(agent_resource, "apiKey", assistant.api_key),
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fastgpt_app_id=str(_value(agent_resource, "appId", assistant.app_id)),
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# 模型/音色:模型资源中的配置优先
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model=str(_value(llm_resource, "modelId", "")),
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asr=str(_value(stt_resource, "modelId", "")),
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tts_model=str(_value(tts_resource, "modelId", "")),
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voice=str(_value(tts_resource, "voice", "")),
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stt_language=str(_value(stt_resource, "language", "")),
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tts_speed=float(_value(tts_resource, "speed", 1.0)),
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llm_interface_type=(llm_resource.interface_type if llm_resource else "openai-llm"),
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stt_interface_type=(stt_resource.interface_type if stt_resource else "openai-asr"),
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tts_interface_type=(tts_resource.interface_type if tts_resource else "openai-tts"),
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realtimeModel=str(_value(realtime_resource, "modelId", "")),
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llm_values=(llm_resource.values or {}) if llm_resource else {},
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llm_secrets=(llm_resource.secrets or {}) if llm_resource else {},
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llm_support_image_input=(
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bool(llm_resource.support_image_input) if llm_resource else False
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),
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vision_enabled=assistant.vision_enabled,
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vision_model_resource_id=assistant.vision_model_resource_id,
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vision_model=str(_value(vision_resource, "modelId", "")),
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vision_llm_interface_type=(
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vision_resource.interface_type if vision_resource else "openai-llm"
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),
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vision_llm_values=(vision_resource.values or {}) if vision_resource else {},
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vision_llm_secrets=(vision_resource.secrets or {}) if vision_resource else {},
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vision_llm_support_image_input=(
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bool(vision_resource.support_image_input) if vision_resource else False
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),
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stt_values=(stt_resource.values or {}) if stt_resource else {},
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stt_secrets=(stt_resource.secrets or {}) if stt_resource else {},
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tts_values=(tts_resource.values or {}) if tts_resource else {},
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tts_secrets=(tts_resource.secrets or {}) if tts_resource else {},
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realtime_interface_type=(
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realtime_resource.interface_type if realtime_resource else ""
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),
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realtime_values=(realtime_resource.values or {}) if realtime_resource else {},
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realtime_secrets=(realtime_resource.secrets or {}) if realtime_resource else {},
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agent_interface_type=(agent_resource.interface_type if agent_resource else ""),
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agent_values=(agent_resource.values or {}) if agent_resource else {},
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agent_secrets=(agent_resource.secrets or {}) if agent_resource else {},
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# 运行时连接信息(真 key + url):来自模型资源,不再从 .env 兜底。
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llm_api_key=_secret(llm_resource, "apiKey"),
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llm_base_url=str(_value(llm_resource, "apiUrl")),
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vision_llm_api_key=_secret(vision_resource, "apiKey"),
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vision_llm_base_url=str(_value(vision_resource, "apiUrl")),
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stt_api_key=_secret(stt_resource, "apiKey"),
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stt_base_url=str(_value(stt_resource, "apiUrl")),
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tts_api_key=_secret(tts_resource, "apiKey"),
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tts_base_url=str(_value(tts_resource, "apiUrl")),
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realtime_api_key=_secret(realtime_resource, "apiKey", ""),
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realtime_base_url=str(_value(realtime_resource, "apiUrl", "")),
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)
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