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fix: fix configuration option

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This commit is contained in:
Abhishek Kumar 2025-12-25 19:37:00 +05:30
parent eabe6783ef
commit 74b069354b
1 changed files with 14 additions and 8 deletions
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22
api/services/pipecat/service_factory.py
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@ -179,11 +179,17 @@ def create_tts_service(user_config, audio_config: "AudioConfig"):
def create_llm_service(user_config):
"""Create and return appropriate LLM service based on user configuration"""
# Handle both enum and string values for model
model_value = (
user_config.llm.model.value
if hasattr(user_config.llm.model, "value")
else user_config.llm.model
)
if user_config.llm.provider == ServiceProviders.OPENAI.value:
if "gpt-5" in user_config.llm.model.value:
if "gpt-5" in model_value:
return OpenAILLMService(
api_key=user_config.llm.api_key,
model=user_config.llm.model.value,
model=model_value,
params=OpenAILLMService.InputParams(
reasoning_effort="minimal", verbosity="low"
),
@ -191,16 +197,16 @@ def create_llm_service(user_config):
else:
return OpenAILLMService(
api_key=user_config.llm.api_key,
model=user_config.llm.model.value,
model=model_value,
params=OpenAILLMService.InputParams(temperature=0.1),
)
elif user_config.llm.provider == ServiceProviders.GROQ.value:
print(
f"Creating Groq LLM service with API key: {user_config.llm.api_key} and model: {user_config.llm.model.value}"
f"Creating Groq LLM service with API key: {user_config.llm.api_key} and model: {model_value}"
)
return GroqLLMService(
api_key=user_config.llm.api_key,
model=user_config.llm.model.value,
model=model_value,
params=OpenAILLMService.InputParams(temperature=0.1),
)
elif user_config.llm.provider == ServiceProviders.GOOGLE.value:
@ -208,21 +214,21 @@ def create_llm_service(user_config):
# NOT_GIVEN sentinels that break Pydantic validation in GoogleLLMService.
return GoogleLLMService(
api_key=user_config.llm.api_key,
model=user_config.llm.model.value,
model=model_value,
params=GoogleLLMService.InputParams(temperature=0.1),
)
elif user_config.llm.provider == ServiceProviders.AZURE.value:
return AzureLLMService(
api_key=user_config.llm.api_key,
endpoint=user_config.llm.endpoint,
model=user_config.llm.model.value, # Azure uses deployment name as model
model=model_value, # Azure uses deployment name as model
params=AzureLLMService.InputParams(temperature=0.1),
)
elif user_config.llm.provider == ServiceProviders.DOGRAH.value:
return DograhLLMService(
base_url=f"{MPS_API_URL}/api/v1/llm",
api_key=user_config.llm.api_key,
model=user_config.llm.model.value,
model=model_value,
)
else:
raise HTTPException(status_code=400, detail="Invalid LLM provider")