Remove ModelVariables (model_utils) from application & optimize Tenant
This commit is contained in:
Josako
@@ -139,142 +139,26 @@ def process_pdf():
|
||||
full_model_name = 'mistral-ocr-latest'
|
||||
|
||||
|
||||
def get_template(template_name: str, version: Optional[str] = "1.0") -> tuple[
|
||||
def get_template(template_name: str, version: Optional[str] = "1.0", temperature: float = 0.3) -> tuple[
|
||||
Any, BaseChatModel | None | ChatOpenAI | ChatMistralAI]:
|
||||
"""
|
||||
Get a prompt template
|
||||
"""
|
||||
prompt = cache_manager.prompts_config_cache.get_config(template_name, version)
|
||||
if "llm_model" in prompt:
|
||||
llm = get_embedding_llm(full_model_name=prompt["llm_model"])
|
||||
llm = get_embedding_llm(full_model_name=prompt["llm_model"], temperature=temperature)
|
||||
else:
|
||||
llm = get_embedding_llm()
|
||||
llm = get_embedding_llm(temperature=temperature)
|
||||
|
||||
return prompt["content"], llm
|
||||
|
||||
|
||||
class ModelVariables:
|
||||
"""Manages model-related variables and configurations"""
|
||||
|
||||
def __init__(self, tenant_id: int, variables: Dict[str, Any] = None):
|
||||
"""
|
||||
Initialize ModelVariables with tenant and optional template manager
|
||||
|
||||
Args:
|
||||
tenant_id: Tenant instance
|
||||
variables: Optional variables
|
||||
"""
|
||||
current_app.logger.info(f'Model variables initialized with tenant {tenant_id} and variables \n{variables}')
|
||||
self.tenant_id = tenant_id
|
||||
self._variables = variables if variables is not None else self._initialize_variables()
|
||||
current_app.logger.info(f'Model _variables initialized to {self._variables}')
|
||||
self._llm_instances = {}
|
||||
self.llm_metrics_handler = LLMMetricsHandler()
|
||||
self._transcription_model = None
|
||||
|
||||
def _initialize_variables(self) -> Dict[str, Any]:
|
||||
"""Initialize the variables dictionary"""
|
||||
variables = {}
|
||||
|
||||
tenant = Tenant.query.get(self.tenant_id)
|
||||
if not tenant:
|
||||
raise EveAITenantNotFound(self.tenant_id)
|
||||
|
||||
# Set model providers
|
||||
variables['llm_provider'], variables['llm_model'] = tenant.llm_model.split('.')
|
||||
variables['llm_full_model'] = tenant.llm_model
|
||||
|
||||
# Set model-specific configurations
|
||||
model_config = MODEL_CONFIG.get(variables['llm_provider'], {}).get(variables['llm_model'], {})
|
||||
variables.update(model_config)
|
||||
|
||||
# Additional configurations
|
||||
variables['annotation_chunk_length'] = current_app.config['ANNOTATION_TEXT_CHUNK_LENGTH'][tenant.llm_model]
|
||||
variables['max_compression_duration'] = current_app.config['MAX_COMPRESSION_DURATION']
|
||||
variables['max_transcription_duration'] = current_app.config['MAX_TRANSCRIPTION_DURATION']
|
||||
variables['compression_cpu_limit'] = current_app.config['COMPRESSION_CPU_LIMIT']
|
||||
variables['compression_process_delay'] = current_app.config['COMPRESSION_PROCESS_DELAY']
|
||||
|
||||
return variables
|
||||
|
||||
@property
|
||||
def annotation_chunk_length(self):
|
||||
return self._variables['annotation_chunk_length']
|
||||
|
||||
@property
|
||||
def max_compression_duration(self):
|
||||
return self._variables['max_compression_duration']
|
||||
|
||||
@property
|
||||
def max_transcription_duration(self):
|
||||
return self._variables['max_transcription_duration']
|
||||
|
||||
@property
|
||||
def compression_cpu_limit(self):
|
||||
return self._variables['compression_cpu_limit']
|
||||
|
||||
@property
|
||||
def compression_process_delay(self):
|
||||
return self._variables['compression_process_delay']
|
||||
|
||||
def get_llm(self, temperature: float = 0.3, **kwargs) -> Any:
|
||||
"""
|
||||
Get an LLM instance with specific configuration
|
||||
|
||||
Args:
|
||||
temperature: The temperature for the LLM
|
||||
**kwargs: Additional configuration parameters
|
||||
|
||||
Returns:
|
||||
An instance of the configured LLM
|
||||
"""
|
||||
cache_key = f"{temperature}_{hash(frozenset(kwargs.items()))}"
|
||||
|
||||
if cache_key not in self._llm_instances:
|
||||
provider = self._variables['llm_provider']
|
||||
model = self._variables['llm_model']
|
||||
|
||||
if provider == 'openai':
|
||||
self._llm_instances[cache_key] = ChatOpenAI(
|
||||
api_key=os.getenv('OPENAI_API_KEY'),
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
callbacks=[self.llm_metrics_handler],
|
||||
**kwargs
|
||||
)
|
||||
elif provider == 'anthropic':
|
||||
self._llm_instances[cache_key] = ChatAnthropic(
|
||||
api_key=os.getenv('ANTHROPIC_API_KEY'),
|
||||
model=current_app.config['ANTHROPIC_LLM_VERSIONS'][model],
|
||||
temperature=temperature,
|
||||
callbacks=[self.llm_metrics_handler],
|
||||
**kwargs
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported LLM provider: {provider}")
|
||||
|
||||
return self._llm_instances[cache_key]
|
||||
|
||||
@property
|
||||
def transcription_model(self) -> TrackedOpenAITranscription:
|
||||
"""Get the transcription model instance"""
|
||||
if self._transcription_model is None:
|
||||
api_key = os.getenv('OPENAI_API_KEY')
|
||||
self._transcription_model = TrackedOpenAITranscription(
|
||||
api_key=api_key,
|
||||
model='whisper-1'
|
||||
)
|
||||
return self._transcription_model
|
||||
|
||||
# Remove the old transcription-related methods since they're now handled by TrackedOpenAITranscription
|
||||
@property
|
||||
def transcription_client(self):
|
||||
raise DeprecationWarning("Use transcription_model instead")
|
||||
|
||||
def transcribe(self, *args, **kwargs):
|
||||
raise DeprecationWarning("Use transcription_model.transcribe() instead")
|
||||
|
||||
|
||||
# Helper function to get cached model variables
|
||||
def get_model_variables(tenant_id: int) -> ModelVariables:
|
||||
return ModelVariables(tenant_id=tenant_id)
|
||||
def get_transcription_model(model_name: str = "whisper-1") -> TrackedOpenAITranscription:
|
||||
"""
|
||||
Get a transcription model instance
|
||||
"""
|
||||
api_key = os.getenv('OPENAI_API_KEY')
|
||||
return TrackedOpenAITranscription(
|
||||
api_key=api_key,
|
||||
model=model_name
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user