mirror of
https://github.com/dograh-hq/dograh.git
synced 2026-06-07 07:55:16 +02:00
700 lines
17 KiB
Python
700 lines
17 KiB
Python
import random
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from enum import Enum, auto
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from typing import Annotated, Dict, Literal, Type, TypeVar, Union
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from pydantic import BaseModel, Field, computed_field, field_validator
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class ServiceType(Enum):
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LLM = auto()
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TTS = auto()
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STT = auto()
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EMBEDDINGS = auto()
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class ServiceProviders(str, Enum):
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OPENAI = "openai"
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DEEPGRAM = "deepgram"
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GROQ = "groq"
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OPENROUTER = "openrouter"
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CARTESIA = "cartesia"
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# NEUPHONIC = "neuphonic"
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ELEVENLABS = "elevenlabs"
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GOOGLE = "google"
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AZURE = "azure"
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DOGRAH = "dograh"
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SARVAM = "sarvam"
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SPEECHMATICS = "speechmatics"
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CAMB = "camb"
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AWS_BEDROCK = "aws_bedrock"
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class BaseServiceConfiguration(BaseModel):
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provider: Literal[
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ServiceProviders.OPENAI,
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ServiceProviders.DEEPGRAM,
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ServiceProviders.GROQ,
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ServiceProviders.OPENROUTER,
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ServiceProviders.ELEVENLABS,
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ServiceProviders.GOOGLE,
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ServiceProviders.AZURE,
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ServiceProviders.DOGRAH,
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ServiceProviders.AWS_BEDROCK,
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# ServiceProviders.SARVAM,
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]
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api_key: str | list[str]
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@field_validator("api_key")
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@classmethod
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def validate_api_key(cls, v):
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if v is None:
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return v
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if isinstance(v, list) and len(v) == 0:
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raise ValueError("api_key list must not be empty")
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return v
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def __getattribute__(self, name: str):
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if name == "api_key":
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value = super().__getattribute__(name)
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if value is None:
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return value
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if isinstance(value, list):
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return random.choice(value)
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return value
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return super().__getattribute__(name)
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def get_all_api_keys(self) -> list[str]:
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"""Get all API keys as a list (bypasses random selection)."""
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value = super().__getattribute__("api_key")
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if value is None:
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return []
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if isinstance(value, list):
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return list(value)
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return [value]
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class BaseLLMConfiguration(BaseServiceConfiguration):
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model: str
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class BaseTTSConfiguration(BaseServiceConfiguration):
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model: str
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class BaseSTTConfiguration(BaseServiceConfiguration):
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model: str
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class BaseEmbeddingsConfiguration(BaseServiceConfiguration):
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model: str
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# Unified registry for all service types
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REGISTRY: Dict[ServiceType, Dict[str, Type[BaseServiceConfiguration]]] = {
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ServiceType.LLM: {},
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ServiceType.TTS: {},
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ServiceType.STT: {},
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ServiceType.EMBEDDINGS: {},
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}
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T = TypeVar("T", bound=BaseServiceConfiguration)
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def register_service(service_type: ServiceType):
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"""Generic decorator for registering service configurations"""
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def decorator(cls: Type[T]) -> Type[T]:
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# Get provider from class attributes or field defaults
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provider = getattr(cls, "provider", None)
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if provider is None:
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# Try to get from model fields
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provider = cls.model_fields.get("provider", None)
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if provider is not None:
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provider = provider.default
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if provider is None:
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raise ValueError(f"Provider not specified for {cls.__name__}")
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REGISTRY[service_type][provider] = cls
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return cls
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return decorator
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# Convenience decorators
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def register_llm(cls: Type[BaseLLMConfiguration]):
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return register_service(ServiceType.LLM)(cls)
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def register_tts(cls: Type[BaseTTSConfiguration]):
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return register_service(ServiceType.TTS)(cls)
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def register_stt(cls: Type[BaseSTTConfiguration]):
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return register_service(ServiceType.STT)(cls)
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def register_embeddings(cls: Type[BaseEmbeddingsConfiguration]):
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return register_service(ServiceType.EMBEDDINGS)(cls)
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###################################################### LLM ########################################################################
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# Suggested models for each provider (used for UI dropdown)
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OPENAI_MODELS = [
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"gpt-4.1",
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"gpt-4.1-mini",
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"gpt-4.1-nano",
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"gpt-5",
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"gpt-5-mini",
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"gpt-5-nano",
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"gpt-3.5-turbo",
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]
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GOOGLE_MODELS = [
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"gemini-2.0-flash",
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"gemini-2.0-flash-lite",
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"gemini-2.5-flash",
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"gemini-2.5-flash-lite",
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]
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GROQ_MODELS = [
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"llama-3.3-70b-versatile",
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"deepseek-r1-distill-llama-70b",
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"qwen-qwq-32b",
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"meta-llama/llama-4-scout-17b-16e-instruct",
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"meta-llama/llama-4-maverick-17b-128e-instruct",
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"gemma2-9b-it",
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"llama-3.1-8b-instant",
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"openai/gpt-oss-120b",
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]
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OPENROUTER_MODELS = [
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"openai/gpt-4.1",
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"openai/gpt-4.1-mini",
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"anthropic/claude-sonnet-4",
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"google/gemini-2.5-flash",
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"google/gemini-2.0-flash",
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"meta-llama/llama-3.3-70b-instruct",
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"deepseek/deepseek-chat-v3-0324",
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]
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AZURE_MODELS = ["gpt-4.1-mini"]
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DOGRAH_LLM_MODELS = ["default", "accurate", "fast", "lite", "zen"]
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AWS_BEDROCK_MODELS = [
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"us.amazon.nova-pro-v1:0",
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"us.amazon.nova-lite-v1:0",
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"us.amazon.nova-micro-v1:0",
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"us.anthropic.claude-sonnet-4-20250514-v1:0",
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"us.anthropic.claude-3-5-sonnet-20241022-v2:0",
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"us.anthropic.claude-haiku-4-5-20251001-v1:0",
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]
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@register_llm
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class OpenAILLMService(BaseLLMConfiguration):
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provider: Literal[ServiceProviders.OPENAI] = ServiceProviders.OPENAI
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model: str = Field(default="gpt-4.1", json_schema_extra={"examples": OPENAI_MODELS})
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@register_llm
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class GoogleLLMService(BaseLLMConfiguration):
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provider: Literal[ServiceProviders.GOOGLE] = ServiceProviders.GOOGLE
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model: str = Field(
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default="gemini-2.0-flash", json_schema_extra={"examples": GOOGLE_MODELS}
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)
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@register_llm
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class GroqLLMService(BaseLLMConfiguration):
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provider: Literal[ServiceProviders.GROQ] = ServiceProviders.GROQ
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model: str = Field(
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default="llama-3.3-70b-versatile", json_schema_extra={"examples": GROQ_MODELS}
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)
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@register_llm
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class OpenRouterLLMConfiguration(BaseLLMConfiguration):
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provider: Literal[ServiceProviders.OPENROUTER] = ServiceProviders.OPENROUTER
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model: str = Field(
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default="openai/gpt-4.1", json_schema_extra={"examples": OPENROUTER_MODELS}
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)
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base_url: str = Field(default="https://openrouter.ai/api/v1")
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@register_llm
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class AzureLLMService(BaseLLMConfiguration):
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provider: Literal[ServiceProviders.AZURE] = ServiceProviders.AZURE
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model: str = Field(
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default="gpt-4.1-mini", json_schema_extra={"examples": AZURE_MODELS}
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)
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endpoint: str
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@register_llm
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class DograhLLMService(BaseLLMConfiguration):
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provider: Literal[ServiceProviders.DOGRAH] = ServiceProviders.DOGRAH
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model: str = Field(
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default="default", json_schema_extra={"examples": DOGRAH_LLM_MODELS}
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)
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@register_llm
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class AWSBedrockLLMConfiguration(BaseLLMConfiguration):
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provider: Literal[ServiceProviders.AWS_BEDROCK] = ServiceProviders.AWS_BEDROCK
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model: str = Field(
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default="us.amazon.nova-pro-v1:0",
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json_schema_extra={"examples": AWS_BEDROCK_MODELS},
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)
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aws_access_key: str = Field(default="")
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aws_secret_key: str = Field(default="")
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aws_region: str = Field(default="us-east-1")
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api_key: str | list[str] | None = Field(default=None)
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LLMConfig = Annotated[
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Union[
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OpenAILLMService,
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GroqLLMService,
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OpenRouterLLMConfiguration,
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GoogleLLMService,
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AzureLLMService,
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DograhLLMService,
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AWSBedrockLLMConfiguration,
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],
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Field(discriminator="provider"),
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]
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###################################################### TTS ########################################################################
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@register_tts
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class DeepgramTTSConfiguration(BaseServiceConfiguration):
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provider: Literal[ServiceProviders.DEEPGRAM] = ServiceProviders.DEEPGRAM
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voice: str = "aura-2-helena-en"
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@computed_field
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@property
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def model(self) -> str:
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# Deepgram model's name is inferred using the voice name.
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# It can either contain aura-2 or aura-1
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if "aura-2" in self.voice:
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return "aura-2"
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elif "aura-1" in self.voice:
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return "aura-1"
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else:
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# Default fallback
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return "aura-2"
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ELEVENLABS_TTS_MODELS = ["eleven_flash_v2_5"]
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@register_tts
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class ElevenlabsTTSConfiguration(BaseServiceConfiguration):
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provider: Literal[ServiceProviders.ELEVENLABS] = ServiceProviders.ELEVENLABS
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voice: str = "21m00Tcm4TlvDq8ikWAM" # Rachel voice ID
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speed: float = Field(default=1.0, ge=0.1, le=2.0, description="Speed of the voice")
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model: str = Field(
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default="eleven_flash_v2_5",
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json_schema_extra={"examples": ELEVENLABS_TTS_MODELS},
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)
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OPENAI_TTS_MODELS = ["gpt-4o-mini-tts"]
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@register_tts
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class OpenAITTSService(BaseTTSConfiguration):
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provider: Literal[ServiceProviders.OPENAI] = ServiceProviders.OPENAI
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model: str = Field(
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default="gpt-4o-mini-tts", json_schema_extra={"examples": OPENAI_TTS_MODELS}
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)
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voice: str = "alloy"
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DOGRAH_TTS_MODELS = ["default"]
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@register_tts
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class DograhTTSService(BaseTTSConfiguration):
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provider: Literal[ServiceProviders.DOGRAH] = ServiceProviders.DOGRAH
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model: str = Field(
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default="default", json_schema_extra={"examples": DOGRAH_TTS_MODELS}
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)
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voice: str = "default"
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speed: float = Field(default=1.0, ge=0.5, le=2.0, description="Speed of the voice")
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CARTESIA_TTS_MODELS = ["sonic-3"]
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@register_tts
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class CartesiaTTSConfiguration(BaseTTSConfiguration):
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provider: Literal[ServiceProviders.CARTESIA] = ServiceProviders.CARTESIA
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model: str = Field(
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default="sonic-3", json_schema_extra={"examples": CARTESIA_TTS_MODELS}
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)
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voice: str = Field(default="3faa81ae-d3d8-4ab1-9e44-e50e46d33c30")
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speed: float = Field(default=1.0, ge=0.6, le=1.5, description="Speed of the voice")
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SARVAM_TTS_MODELS = ["bulbul:v2", "bulbul:v3"]
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SARVAM_V2_VOICES = [
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"anushka",
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"manisha",
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"vidya",
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"arya",
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"abhilash",
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"karun",
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"hitesh",
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]
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SARVAM_V3_VOICES = [
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"shubh",
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"aditya",
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"ritu",
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"priya",
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"neha",
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"rahul",
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"pooja",
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"rohan",
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"simran",
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"kavya",
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"amit",
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"dev",
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"ishita",
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"shreya",
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"ratan",
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"varun",
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"manan",
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"sumit",
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"roopa",
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"kabir",
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"aayan",
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"ashutosh",
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"advait",
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"amelia",
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"sophia",
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"anand",
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"tanya",
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"tarun",
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"sunny",
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"mani",
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"gokul",
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"vijay",
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"shruti",
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"suhani",
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"mohit",
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"kavitha",
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"rehan",
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"soham",
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"rupali",
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]
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SARVAM_LANGUAGES = [
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"bn-IN",
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"en-IN",
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"gu-IN",
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"hi-IN",
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"kn-IN",
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"ml-IN",
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"mr-IN",
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"od-IN",
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"pa-IN",
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"ta-IN",
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"te-IN",
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"as-IN",
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]
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@register_tts
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class SarvamTTSConfiguration(BaseTTSConfiguration):
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provider: Literal[ServiceProviders.SARVAM] = ServiceProviders.SARVAM
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model: str = Field(
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default="bulbul:v2", json_schema_extra={"examples": SARVAM_TTS_MODELS}
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)
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voice: str = Field(
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default="anushka",
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json_schema_extra={
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"examples": SARVAM_V2_VOICES,
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"model_options": {
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"bulbul:v2": SARVAM_V2_VOICES,
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"bulbul:v3": SARVAM_V3_VOICES,
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},
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},
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)
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language: str = Field(
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default="hi-IN", json_schema_extra={"examples": SARVAM_LANGUAGES}
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)
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CAMB_TTS_MODELS = ["mars-flash", "mars-pro", "mars-instruct"]
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@register_tts
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class CambTTSConfiguration(BaseTTSConfiguration):
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provider: Literal[ServiceProviders.CAMB] = ServiceProviders.CAMB
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model: str = Field(
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default="mars-flash", json_schema_extra={"examples": CAMB_TTS_MODELS}
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)
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voice: str = Field(default="147320", description="Camb.ai voice ID")
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language: str = Field(default="en-us", description="BCP-47 language code")
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TTSConfig = Annotated[
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Union[
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DeepgramTTSConfiguration,
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OpenAITTSService,
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ElevenlabsTTSConfiguration,
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CartesiaTTSConfiguration,
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DograhTTSService,
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SarvamTTSConfiguration,
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CambTTSConfiguration,
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],
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Field(discriminator="provider"),
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]
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###################################################### STT ########################################################################
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DEEPGRAM_STT_MODELS = ["nova-3-general", "flux-general-en"]
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DEEPGRAM_LANGUAGES = [
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"multi",
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"ar",
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"ar-AE",
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"ar-SA",
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"ar-QA",
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"ar-KW",
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"ar-SY",
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"ar-LB",
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"ar-PS",
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"ar-JO",
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"ar-EG",
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"ar-SD",
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"ar-TD",
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"ar-MA",
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"ar-DZ",
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"ar-TN",
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"ar-IQ",
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"ar-IR",
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"be",
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"bn",
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"bs",
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"bg",
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"ca",
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"cs",
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"da",
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"da-DK",
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"de",
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"de-CH",
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"el",
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"en",
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"en-US",
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"en-AU",
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"en-GB",
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"en-IN",
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"en-NZ",
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"es",
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"es-419",
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"et",
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"fa",
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"fi",
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"fr",
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"fr-CA",
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"he",
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"hi",
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"hr",
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"hu",
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"id",
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"it",
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"ja",
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"kn",
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"ko",
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"ko-KR",
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"lt",
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"lv",
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"mk",
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"mr",
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"ms",
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"nl",
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"nl-BE",
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"no",
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"pl",
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"pt",
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"pt-BR",
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"pt-PT",
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"ro",
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"ru",
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"sk",
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"sl",
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"sr",
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"sv",
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"sv-SE",
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"ta",
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"te",
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"th",
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"tl",
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"tr",
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"uk",
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"ur",
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"vi",
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"zh-CN",
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"zh-TW",
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]
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@register_stt
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class DeepgramSTTConfiguration(BaseSTTConfiguration):
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provider: Literal[ServiceProviders.DEEPGRAM] = ServiceProviders.DEEPGRAM
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model: str = Field(
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default="nova-3-general", json_schema_extra={"examples": DEEPGRAM_STT_MODELS}
|
|
)
|
|
language: str = Field(
|
|
default="multi",
|
|
json_schema_extra={
|
|
"examples": DEEPGRAM_LANGUAGES,
|
|
"model_options": {
|
|
"nova-3-general": DEEPGRAM_LANGUAGES,
|
|
"flux-general-en": ["en"],
|
|
},
|
|
},
|
|
)
|
|
|
|
|
|
CARTESIA_STT_MODELS = ["ink-whisper"]
|
|
|
|
|
|
@register_stt
|
|
class CartesiaSTTConfiguration(BaseSTTConfiguration):
|
|
provider: Literal[ServiceProviders.CARTESIA] = ServiceProviders.CARTESIA
|
|
model: str = Field(
|
|
default="ink-whisper", json_schema_extra={"examples": CARTESIA_STT_MODELS}
|
|
)
|
|
|
|
|
|
OPENAI_STT_MODELS = ["gpt-4o-transcribe"]
|
|
|
|
|
|
@register_stt
|
|
class OpenAISTTConfiguration(BaseSTTConfiguration):
|
|
provider: Literal[ServiceProviders.OPENAI] = ServiceProviders.OPENAI
|
|
model: str = Field(
|
|
default="gpt-4o-transcribe", json_schema_extra={"examples": OPENAI_STT_MODELS}
|
|
)
|
|
|
|
|
|
# Dograh STT Service
|
|
DOGRAH_STT_MODELS = ["default"]
|
|
DOGRAH_STT_LANGUAGES = DEEPGRAM_LANGUAGES
|
|
|
|
|
|
@register_stt
|
|
class DograhSTTService(BaseSTTConfiguration):
|
|
provider: Literal[ServiceProviders.DOGRAH] = ServiceProviders.DOGRAH
|
|
model: str = Field(
|
|
default="default", json_schema_extra={"examples": DOGRAH_STT_MODELS}
|
|
)
|
|
language: str = Field(
|
|
default="multi", json_schema_extra={"examples": DOGRAH_STT_LANGUAGES}
|
|
)
|
|
|
|
|
|
# Sarvam STT Service
|
|
SARVAM_STT_MODELS = ["saarika:v2.5", "saaras:v2"]
|
|
|
|
|
|
@register_stt
|
|
class SarvamSTTConfiguration(BaseSTTConfiguration):
|
|
provider: Literal[ServiceProviders.SARVAM] = ServiceProviders.SARVAM
|
|
model: str = Field(
|
|
default="saarika:v2.5", json_schema_extra={"examples": SARVAM_STT_MODELS}
|
|
)
|
|
language: str = Field(
|
|
default="hi-IN", json_schema_extra={"examples": SARVAM_LANGUAGES}
|
|
)
|
|
|
|
|
|
# Speechmatics STT Service
|
|
SPEECHMATICS_STT_LANGUAGES = [
|
|
"en",
|
|
"es",
|
|
"fr",
|
|
"de",
|
|
"it",
|
|
"pt",
|
|
"nl",
|
|
"ja",
|
|
"ko",
|
|
"zh",
|
|
"ru",
|
|
"ar",
|
|
"hi",
|
|
"pl",
|
|
"tr",
|
|
"vi",
|
|
"th",
|
|
"id",
|
|
"ms",
|
|
"sv",
|
|
"da",
|
|
"no",
|
|
"fi",
|
|
]
|
|
|
|
|
|
@register_stt
|
|
class SpeechmaticsSTTConfiguration(BaseSTTConfiguration):
|
|
provider: Literal[ServiceProviders.SPEECHMATICS] = ServiceProviders.SPEECHMATICS
|
|
model: str = Field(
|
|
default="enhanced", description="Operating point: standard or enhanced"
|
|
)
|
|
language: str = Field(
|
|
default="en", json_schema_extra={"examples": SPEECHMATICS_STT_LANGUAGES}
|
|
)
|
|
|
|
|
|
STTConfig = Annotated[
|
|
Union[
|
|
DeepgramSTTConfiguration,
|
|
CartesiaSTTConfiguration,
|
|
OpenAISTTConfiguration,
|
|
DograhSTTService,
|
|
SpeechmaticsSTTConfiguration,
|
|
SarvamSTTConfiguration,
|
|
],
|
|
Field(discriminator="provider"),
|
|
]
|
|
|
|
###################################################### EMBEDDINGS ########################################################################
|
|
|
|
OPENAI_EMBEDDING_MODELS = ["text-embedding-3-small"]
|
|
|
|
|
|
@register_embeddings
|
|
class OpenAIEmbeddingsConfiguration(BaseEmbeddingsConfiguration):
|
|
provider: Literal[ServiceProviders.OPENAI] = ServiceProviders.OPENAI
|
|
model: str = Field(
|
|
default="text-embedding-3-small",
|
|
json_schema_extra={"examples": OPENAI_EMBEDDING_MODELS},
|
|
)
|
|
|
|
|
|
OPENROUTER_EMBEDDING_MODELS = ["openai/text-embedding-3-small"]
|
|
|
|
|
|
@register_embeddings
|
|
class OpenRouterEmbeddingsConfiguration(BaseEmbeddingsConfiguration):
|
|
provider: Literal[ServiceProviders.OPENROUTER] = ServiceProviders.OPENROUTER
|
|
model: str = Field(
|
|
default="openai/text-embedding-3-small",
|
|
json_schema_extra={"examples": OPENROUTER_EMBEDDING_MODELS},
|
|
)
|
|
|
|
base_url: str = Field(default="https://openrouter.ai/api/v1")
|
|
|
|
|
|
EmbeddingsConfig = Annotated[
|
|
Union[OpenAIEmbeddingsConfiguration, OpenRouterEmbeddingsConfiguration],
|
|
Field(discriminator="provider"),
|
|
]
|
|
|
|
ServiceConfig = Annotated[
|
|
Union[LLMConfig, TTSConfig, STTConfig, EmbeddingsConfig],
|
|
Field(discriminator="provider"),
|
|
]
|