mirror of
https://github.com/dograh-hq/dograh.git
synced 2026-06-07 07:55:16 +02:00
ef5b9e40a9
* feat: upload file and store embedding * feat: add documents in nodes * feat: add openai embedding service
472 lines
12 KiB
Python
472 lines
12 KiB
Python
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
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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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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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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.ELEVENLABS,
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ServiceProviders.GOOGLE,
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ServiceProviders.AZURE,
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ServiceProviders.DOGRAH,
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# ServiceProviders.SARVAM,
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]
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api_key: str
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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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AZURE_MODELS = ["gpt-4.1-mini"]
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DOGRAH_LLM_MODELS = ["default", "accurate", "fast", "lite", "zen"]
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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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api_key: str
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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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api_key: str
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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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api_key: str
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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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api_key: str
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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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api_key: str
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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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GoogleLLMService,
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AzureLLMService,
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DograhLLMService,
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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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api_key: str
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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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api_key: str
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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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api_key: str
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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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api_key: str
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SARVAM_TTS_MODELS = ["bulbul:v2", "bulbul:v3"]
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SARVAM_VOICES = ["anushka", "manisha", "vidya", "arya", "abhilash", "karun", "hitesh"]
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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(default="anushka", json_schema_extra={"examples": SARVAM_VOICES})
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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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api_key: str
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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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DograhTTSService,
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SarvamTTSConfiguration,
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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-2", "nova-3-general"]
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DEEPGRAM_LANGUAGES = [
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"multi",
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"en",
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"en-US",
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"en-GB",
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"en-AU",
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"en-IN",
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"es",
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"es-419",
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"fr",
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"fr-CA",
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"de",
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"it",
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"pt",
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"pt-BR",
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"nl",
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"hi",
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"ja",
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"ko",
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"zh-CN",
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"zh-TW",
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"ru",
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"pl",
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"tr",
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"uk",
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"vi",
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"sv",
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"da",
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"no",
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"fi",
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"id",
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"th",
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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}
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)
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language: str = Field(
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default="multi", json_schema_extra={"examples": DEEPGRAM_LANGUAGES}
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)
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api_key: str
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@register_stt
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class CartesiaSTTConfiguration(BaseSTTConfiguration):
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provider: Literal[ServiceProviders.CARTESIA] = ServiceProviders.CARTESIA
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api_key: str
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OPENAI_STT_MODELS = ["gpt-4o-transcribe"]
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@register_stt
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class OpenAISTTConfiguration(BaseSTTConfiguration):
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provider: Literal[ServiceProviders.OPENAI] = ServiceProviders.OPENAI
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model: str = Field(
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default="gpt-4o-transcribe", json_schema_extra={"examples": OPENAI_STT_MODELS}
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)
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api_key: str
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# Dograh STT Service
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DOGRAH_STT_MODELS = ["default"]
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@register_stt
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class DograhSTTService(BaseSTTConfiguration):
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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_STT_MODELS}
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)
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api_key: str
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# Sarvam STT Service
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SARVAM_STT_MODELS = ["saarika:v2.5", "saaras:v2"]
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@register_stt
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class SarvamSTTConfiguration(BaseSTTConfiguration):
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provider: Literal[ServiceProviders.SARVAM] = ServiceProviders.SARVAM
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model: str = Field(
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default="saarika:v2.5", json_schema_extra={"examples": SARVAM_STT_MODELS}
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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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api_key: str
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# Speechmatics STT Service
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SPEECHMATICS_STT_LANGUAGES = [
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"en",
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"es",
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"fr",
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"de",
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"it",
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"pt",
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"nl",
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"ja",
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"ko",
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"zh",
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"ru",
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"ar",
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"hi",
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"pl",
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"tr",
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"vi",
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"th",
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"id",
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"ms",
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"sv",
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"da",
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"no",
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"fi",
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]
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@register_stt
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class SpeechmaticsSTTConfiguration(BaseSTTConfiguration):
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provider: Literal[ServiceProviders.SPEECHMATICS] = ServiceProviders.SPEECHMATICS
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model: str = Field(
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default="enhanced", description="Operating point: standard or enhanced"
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)
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language: str = Field(
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default="en", json_schema_extra={"examples": SPEECHMATICS_STT_LANGUAGES}
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)
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api_key: str
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STTConfig = Annotated[
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Union[
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DeepgramSTTConfiguration,
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OpenAISTTConfiguration,
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DograhSTTService,
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SpeechmaticsSTTConfiguration,
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SarvamSTTConfiguration,
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],
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Field(discriminator="provider"),
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]
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###################################################### EMBEDDINGS ########################################################################
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OPENAI_EMBEDDING_MODELS = ["text-embedding-3-small"]
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@register_embeddings
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class OpenAIEmbeddingsConfiguration(BaseEmbeddingsConfiguration):
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provider: Literal[ServiceProviders.OPENAI] = ServiceProviders.OPENAI
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model: str = Field(
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default="text-embedding-3-small",
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json_schema_extra={"examples": OPENAI_EMBEDDING_MODELS},
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)
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api_key: str
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EmbeddingsConfig = Annotated[
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Union[OpenAIEmbeddingsConfiguration],
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Field(discriminator="provider"),
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]
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ServiceConfig = Annotated[
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Union[LLMConfig, TTSConfig, STTConfig, EmbeddingsConfig],
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Field(discriminator="provider"),
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]
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