233 lines
7.8 KiB
Python
233 lines
7.8 KiB
Python
import os
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import threading
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import ollama
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import yaml
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import logging.config
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import agno.utils.log # type: ignore
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from typing import Any, ClassVar
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from pydantic import BaseModel
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from agno.agent import Agent
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from agno.tools import Toolkit
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from agno.models.base import Model
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from agno.models.google import Gemini
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from agno.models.ollama import Ollama
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log = logging.getLogger(__name__)
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class AppModel(BaseModel):
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name: str = "gemini-2.0-flash"
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label: str = "Gemini"
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model: type[Model] | None = None
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def get_model(self, instructions: str) -> Model:
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"""
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Restituisce un'istanza del modello specificato.
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Args:
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instructions: istruzioni da passare al modello (system prompt).
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Returns:
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Un'istanza di BaseModel o una sua sottoclasse.
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Raise:
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ValueError se il modello non è supportato.
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"""
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if self.model is None:
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raise ValueError(f"Model class for '{self.name}' is not set.")
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return self.model(id=self.name, instructions=[instructions])
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def get_agent(self, instructions: str, name: str = "", output_schema: type[BaseModel] | None = None, tools: list[Toolkit] | None = None) -> Agent:
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"""
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Costruisce un agente con il modello e le istruzioni specificate.
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Args:
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instructions: istruzioni da passare al modello (system prompt)
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name: nome dell'agente (opzionale)
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output: schema di output opzionale (Pydantic BaseModel)
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tools: lista opzionale di strumenti (tools) da fornire all'agente
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Returns:
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Un'istanza di Agent.
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"""
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return Agent(
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model=self.get_model(instructions),
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name=name,
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retries=2,
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tools=tools,
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delay_between_retries=5, # seconds
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output_schema=output_schema
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)
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class APIConfig(BaseModel):
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retry_attempts: int = 3
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retry_delay_seconds: int = 2
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currency: str = "USD"
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class Strategy(BaseModel):
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name: str = "Conservative"
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label: str = "Conservative"
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description: str = "Focus on low-risk investments with steady returns."
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class ModelsConfig(BaseModel):
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gemini: list[AppModel] = [AppModel()]
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ollama: list[AppModel] = []
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@property
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def all_models(self) -> list[AppModel]:
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return self.gemini + self.ollama
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class AgentsConfigs(BaseModel):
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strategy: str = "Conservative"
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team_model: str = "gemini-2.0-flash"
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team_leader_model: str = "gemini-2.0-flash"
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predictor_model: str = "gemini-2.0-flash"
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class AppConfig(BaseModel):
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port: int = 8000
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gradio_share: bool = False
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logging_level: str = "INFO"
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api: APIConfig = APIConfig()
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strategies: list[Strategy] = [Strategy()]
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models: ModelsConfig = ModelsConfig()
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agents: AgentsConfigs = AgentsConfigs()
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__lock: ClassVar[threading.Lock] = threading.Lock()
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@classmethod
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def load(cls, file_path: str = "configs.yaml") -> 'AppConfig':
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"""
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Load the application configuration from a YAML file.
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Be sure to call load_dotenv() before if you use environment variables.
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Args:
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file_path: path to the YAML configuration file.
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Returns:
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An instance of AppConfig with the loaded settings.
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"""
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with open(file_path, 'r') as f:
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data = yaml.safe_load(f)
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configs = cls(**data)
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configs.set_logging_level()
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configs.validate_models()
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log.info(f"Loaded configuration from {file_path}")
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return configs
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def __new__(cls, *args: Any, **kwargs: Any) -> 'AppConfig':
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with cls.__lock:
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if not hasattr(cls, 'instance'):
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cls.instance = super(AppConfig, cls).__new__(cls)
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return cls.instance
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def get_model_by_name(self, name: str) -> AppModel:
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"""
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Retrieve a model configuration by its name.
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Args:
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name: the name of the model to retrieve.
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Returns:
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The AppModel instance if found.
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Raises:
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ValueError if no model with the specified name is found.
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"""
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for model in self.models.all_models:
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if model.name == name:
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return model
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raise ValueError(f"Model with name '{name}' not found.")
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def get_strategy_by_name(self, name: str) -> Strategy:
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"""
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Retrieve a strategy configuration by its name.
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Args:
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name: the name of the strategy to retrieve.
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Returns:
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The Strategy instance if found.
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Raises:
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ValueError if no strategy with the specified name is found.
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"""
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for strat in self.strategies:
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if strat.name == name:
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return strat
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raise ValueError(f"Strategy with name '{name}' not found.")
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def set_logging_level(self) -> None:
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"""
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Set the logging level based on the configuration.
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"""
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logging.config.dictConfig({
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'version': 1,
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'disable_existing_loggers': False, # Keep existing loggers (e.g. third-party loggers)
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'formatters': {
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'colored': {
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'()': 'colorlog.ColoredFormatter',
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'format': '%(log_color)s%(levelname)s%(reset)s [%(asctime)s] (%(name)s) - %(message)s'
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},
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},
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'handlers': {
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'console': {
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'class': 'logging.StreamHandler',
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'formatter': 'colored',
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'level': self.logging_level,
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},
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},
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'root': { # Configure the root logger
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'handlers': ['console'],
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'level': self.logging_level,
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},
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'loggers': {
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'httpx': {'level': 'WARNING'}, # Too much spam for INFO
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}
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})
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# Modify the agno loggers
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agno_logger_names = ["agno", "agno-team", "agno-workflow"]
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for logger_name in agno_logger_names:
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logger = logging.getLogger(logger_name)
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logger.handlers.clear()
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logger.propagate = True
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def validate_models(self) -> None:
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"""
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Validate the configured models for each provider.
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"""
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self.__validate_online_models("gemini", clazz=Gemini, key="GOOGLE_API_KEY")
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self.__validate_ollama_models()
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def __validate_online_models(self, provider: str, clazz: type[Model], key: str | None = None) -> None:
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"""
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Validate models for online providers like Gemini.
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Args:
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provider: name of the provider (e.g. "gemini")
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clazz: class of the model (e.g. Gemini)
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key: API key required for the provider (optional)
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"""
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if getattr(self.models, provider) is None:
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log.warning(f"No models configured for provider '{provider}'.")
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models: list[AppModel] = getattr(self.models, provider)
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if key and os.getenv(key) is None:
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log.warning(f"No {key} set in environment variables for {provider}.")
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models.clear()
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return
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for model in models:
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model.model = clazz
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def __validate_ollama_models(self) -> None:
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"""
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Validate models for the Ollama provider.
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"""
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try:
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models_list = ollama.list()
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availables = {model['model'] for model in models_list['models']}
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not_availables: list[str] = []
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for model in self.models.ollama:
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if model.name in availables:
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model.model = Ollama
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else:
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not_availables.append(model.name)
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if not_availables:
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log.warning(f"Ollama models not available: {not_availables}")
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self.models.ollama = [model for model in self.models.ollama if model.model]
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except Exception as e:
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log.warning(f"Ollama is not running or not reachable: {e}")
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