mirror of
https://github.com/dograh-hq/dograh.git
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265 lines
11 KiB
Python
265 lines
11 KiB
Python
import os
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from typing import TYPE_CHECKING
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from fastapi import HTTPException
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from loguru import logger
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from api.constants import MPS_API_URL
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from api.services.configuration.registry import ServiceProviders
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from pipecat.services.azure.llm import AzureLLMService
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from pipecat.services.cartesia.stt import CartesiaSTTService
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from pipecat.services.deepgram.stt import DeepgramSTTService, LiveOptions
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from pipecat.services.deepgram.tts import DeepgramTTSService
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from pipecat.services.dograh.llm import DograhLLMService
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from pipecat.services.dograh.stt import DograhSTTService
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from pipecat.services.dograh.tts import DograhTTSService
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from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
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from pipecat.services.google.llm import GoogleLLMService
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from pipecat.services.groq.llm import GroqLLMService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.services.openai.stt import OpenAISTTService
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from pipecat.services.openai.tts import OpenAITTSService
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from pipecat.services.sarvam.stt import SarvamSTTService
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from pipecat.services.sarvam.tts import SarvamTTSService
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from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
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from pipecat.transcriptions.language import Language
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from pipecat.utils.text.xml_function_tag_filter import XMLFunctionTagFilter
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if TYPE_CHECKING:
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from api.services.pipecat.audio_config import AudioConfig
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def create_stt_service(user_config):
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"""Create and return appropriate STT service based on user configuration"""
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logger.info(
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f"Creating STT service: provider={user_config.stt.provider}, model={user_config.stt.model}"
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)
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if user_config.stt.provider == ServiceProviders.DEEPGRAM.value:
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# Use language from user config, defaulting to "multi" for multilingual support
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language = getattr(user_config.stt, "language", None) or "multi"
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live_options = LiveOptions(
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language=language,
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profanity_filter=False,
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endpointing=100,
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model=user_config.stt.model,
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)
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logger.debug(f"Using DeepGram Model - {user_config.stt.model}")
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return DeepgramSTTService(
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live_options=live_options, api_key=user_config.stt.api_key
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)
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elif user_config.stt.provider == ServiceProviders.OPENAI.value:
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return OpenAISTTService(
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api_key=user_config.stt.api_key, model=user_config.stt.model
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)
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elif user_config.stt.provider == ServiceProviders.CARTESIA.value:
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return CartesiaSTTService(api_key=user_config.stt.api_key)
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elif user_config.stt.provider == ServiceProviders.DOGRAH.value:
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base_url = MPS_API_URL.replace("http://", "ws://").replace("https://", "wss://")
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return DograhSTTService(
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base_url=base_url,
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api_key=user_config.stt.api_key,
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model=user_config.stt.model,
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)
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elif user_config.stt.provider == ServiceProviders.SARVAM.value:
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# Map Sarvam language code to pipecat Language enum
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language_mapping = {
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"bn-IN": Language.BN_IN,
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"gu-IN": Language.GU_IN,
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"hi-IN": Language.HI_IN,
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"kn-IN": Language.KN_IN,
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"ml-IN": Language.ML_IN,
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"mr-IN": Language.MR_IN,
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"ta-IN": Language.TA_IN,
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"te-IN": Language.TE_IN,
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"pa-IN": Language.PA_IN,
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"od-IN": Language.OR_IN,
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"en-IN": Language.EN_IN,
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"as-IN": Language.AS_IN,
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}
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language = getattr(user_config.stt, "language", None)
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pipecat_language = language_mapping.get(language, Language.HI_IN)
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return SarvamSTTService(
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api_key=user_config.stt.api_key,
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model=user_config.stt.model,
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params=SarvamSTTService.InputParams(language=pipecat_language),
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)
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elif user_config.stt.provider == ServiceProviders.SPEECHMATICS.value:
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from pipecat.services.speechmatics.stt import OperatingPoint
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language = getattr(user_config.stt, "language", None) or "en"
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# Map model field to operating point (standard or enhanced)
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operating_point = (
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OperatingPoint.ENHANCED
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if user_config.stt.model == "enhanced"
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else OperatingPoint.STANDARD
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)
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return SpeechmaticsSTTService(
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api_key=user_config.stt.api_key,
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params=SpeechmaticsSTTService.InputParams(
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language=language,
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operating_point=operating_point,
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),
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)
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else:
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raise HTTPException(
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status_code=400, detail=f"Invalid STT provider {user_config.stt.provider}"
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)
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def create_tts_service(user_config, audio_config: "AudioConfig"):
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"""Create and return appropriate TTS service based on user configuration
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Args:
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user_config: User configuration containing TTS settings
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transport_type: Type of transport (e.g., 'stasis', 'twilio', 'webrtc')
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"""
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logger.info(
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f"Creating TTS service: provider={user_config.tts.provider}, model={user_config.tts.model}"
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)
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# Create function call filter to prevent TTS from speaking function call tags
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xml_function_tag_filter = XMLFunctionTagFilter()
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if user_config.tts.provider == ServiceProviders.DEEPGRAM.value:
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return DeepgramTTSService(
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api_key=user_config.tts.api_key,
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voice=user_config.tts.voice,
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text_filters=[xml_function_tag_filter],
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)
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elif user_config.tts.provider == ServiceProviders.OPENAI.value:
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return OpenAITTSService(
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api_key=user_config.tts.api_key,
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model=user_config.tts.model,
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text_filters=[xml_function_tag_filter],
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)
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elif user_config.tts.provider == ServiceProviders.ELEVENLABS.value:
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# Backward compatible with older configuration "Name - voice_id"
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try:
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voice_id = user_config.tts.voice.split(" - ")[1]
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except IndexError:
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voice_id = user_config.tts.voice
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return ElevenLabsTTSService(
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reconnect_on_error=False,
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api_key=user_config.tts.api_key,
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voice_id=voice_id,
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model=user_config.tts.model,
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params=ElevenLabsTTSService.InputParams(
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stability=0.8, speed=user_config.tts.speed, similarity_boost=0.75
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),
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text_filters=[xml_function_tag_filter],
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)
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elif user_config.tts.provider == ServiceProviders.DOGRAH.value:
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# Convert HTTP URL to WebSocket URL for TTS
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base_url = MPS_API_URL.replace("http://", "ws://").replace("https://", "wss://")
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return DograhTTSService(
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base_url=base_url,
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api_key=user_config.tts.api_key,
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model=user_config.tts.model,
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voice=user_config.tts.voice,
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params=DograhTTSService.InputParams(speed=user_config.tts.speed),
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text_filters=[xml_function_tag_filter],
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)
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elif user_config.tts.provider == ServiceProviders.SARVAM.value:
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# Map Sarvam language code to pipecat Language enum for TTS
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language_mapping = {
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"bn-IN": Language.BN,
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"en-IN": Language.EN,
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"gu-IN": Language.GU,
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"hi-IN": Language.HI,
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"kn-IN": Language.KN,
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"ml-IN": Language.ML,
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"mr-IN": Language.MR,
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"od-IN": Language.OR,
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"pa-IN": Language.PA,
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"ta-IN": Language.TA,
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"te-IN": Language.TE,
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}
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language = getattr(user_config.tts, "language", None)
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pipecat_language = language_mapping.get(language, Language.HI)
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voice = getattr(user_config.tts, "voice", None) or "anushka"
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return SarvamTTSService(
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api_key=user_config.tts.api_key,
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model=user_config.tts.model,
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voice_id=voice,
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params=SarvamTTSService.InputParams(language=pipecat_language),
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text_filters=[xml_function_tag_filter],
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)
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else:
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raise HTTPException(
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status_code=400, detail=f"Invalid TTS provider {user_config.tts.provider}"
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)
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def create_llm_service(user_config):
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"""Create and return appropriate LLM service based on user configuration"""
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model = user_config.llm.model
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logger.info(
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f"Creating LLM service: provider={user_config.llm.provider}, model={model}"
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)
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if user_config.llm.provider == ServiceProviders.OPENAI.value:
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if "gpt-5" in model:
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return OpenAILLMService(
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api_key=user_config.llm.api_key,
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model=model,
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params=OpenAILLMService.InputParams(
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reasoning_effort="minimal", verbosity="low"
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),
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)
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else:
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return OpenAILLMService(
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api_key=user_config.llm.api_key,
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model=model,
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params=OpenAILLMService.InputParams(temperature=0.1),
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)
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elif user_config.llm.provider == ServiceProviders.GROQ.value:
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print(
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f"Creating Groq LLM service with API key: {user_config.llm.api_key} and model: {model}"
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)
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return GroqLLMService(
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api_key=user_config.llm.api_key,
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model=model,
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params=OpenAILLMService.InputParams(temperature=0.1),
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)
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elif user_config.llm.provider == ServiceProviders.GOOGLE.value:
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# Use the correct InputParams class for Google to avoid propagating OpenAI-specific
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# NOT_GIVEN sentinels that break Pydantic validation in GoogleLLMService.
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return GoogleLLMService(
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api_key=user_config.llm.api_key,
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model=model,
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params=GoogleLLMService.InputParams(temperature=0.1),
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)
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elif user_config.llm.provider == ServiceProviders.AZURE.value:
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return AzureLLMService(
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api_key=user_config.llm.api_key,
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endpoint=user_config.llm.endpoint,
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model=model, # Azure uses deployment name as model
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params=AzureLLMService.InputParams(temperature=0.1),
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)
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elif user_config.llm.provider == ServiceProviders.DOGRAH.value:
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return DograhLLMService(
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base_url=f"{MPS_API_URL}/api/v1/llm",
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api_key=user_config.llm.api_key,
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model=model,
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)
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else:
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raise HTTPException(status_code=400, detail="Invalid LLM provider")
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def create_voicemail_classification_llm():
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"""Create a fast, lightweight LLM service for voicemail classification.
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Uses gpt-4o-mini which is fast and cost-effective for simple classification tasks.
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The model only needs to output "CONVERSATION" or "VOICEMAIL" based on transcriptions.
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Returns:
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OpenAILLMService instance, or None if OPENAI_API_KEY is not set.
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"""
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api_key = os.environ.get("OPENAI_API_KEY")
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if not api_key:
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logger.warning("OPENAI_API_KEY not set - voicemail detection will be disabled")
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return None
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return OpenAILLMService(
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api_key=api_key,
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model="gpt-4o",
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params=OpenAILLMService.InputParams(temperature=0.0),
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)
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