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import os
import logging
from llm.utils.hf_interface import HFInterface
from langchain_google_genai import GoogleGenerativeAI
from abc import ABC
logger = logging.getLogger(__name__)
logger.setLevel(logging.ERROR)
file_handler = logging.FileHandler(
"logs/chelsea_llm_gemini.log") # for all modules here template for logs file is "llm/logs/chelsea_{module_name}_{entity}.log"
logger.setLevel(logging.INFO) # informed
formatted = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
file_handler.setFormatter(formatted)
logger.addHandler(file_handler)
logger.info("Getting information from apimodel module")
# 429 - You've exceeded the rate limit.
# 400 - The request body is malformed.
# 403 - Your API key doesn't have the required permissions.
# 404 - The requested resource wasn't found.
# 500 - An unexpected error occurred on Google's side.
# 503 - The service may be temporarily overloaded or down.
# Якщо трапиться одна з цих помилок , то слід перемкнутися на HF , якщо якась модель занадто повільна
# перемкнутися на іншу після закінчення виконання текущої, якщо трабли з HF перемкнутися на локальну,
# якщо ж у користувача відсутнє інтернет зʼєднання, то нічим не зарадиш,
# хіба що пропонувати скачати репозиторій.
_api = os.environ.get("GEMINI_API_TOKEN")
class Gemini(HFInterface, ABC):
"""
This class represents a Gemini large language model interface.
It inherits from `HFInterface` (likely an interface from a Hugging Face library)
and `ABC` (for abstract base class) to enforce specific functionalities.
"""
def __init__(self):
"""
Initializer for the Gemini class.
- Raises a `ValueError` if the provided API key is None or an empty string.
- Creates an instance of `GoogleGenerativeAI` using the specified model name
("gemini-1.5-flash") and the stored API key.
"""
if not _api:
raise ValueError(f"Your api is None or empty string {_api}, please provide a Gemini API")
#{
# 'model': 'gemini-1.5-flash', 'temperature': 0.7, 'top_p': None,
# 'top_k': None, 'max_output_tokens': None, 'candidate_count': 1
#}
self.llm = GoogleGenerativeAI(model="gemini-1.5-flash", google_api_key=_api)
def execution(self) -> GoogleGenerativeAI:
"""
This method attempts to return the underlying `llm` (likely a language model object).
It wraps the retrieval in a `try-except` block to catch potential exceptions.
On success, it returns the `llm` object.
On failure, it logs an error message with the exception details using a logger
(assumed to be available elsewhere).
"""
try:
return self.llm
except Exception as e:
logger.error("Something wrong with Gemini api", exc_info=e)
print(f"Something wrong with Gemini api: {e}")
def model_name(self):
"""
Simple method that returns the hardcoded model name ("gemini-1.5-flash").
This can be useful for identifying the specific model being used.
"""
return "gemini-1.5-flash"
def __str__(self):
"""
Defines the string representation of the Gemini object for human readability.
It returns a string indicating that it's a "Gemini model" and appends the model name
obtained from the `model_name` method.
"""
return f"Gemini model: {self.model_name()}"
def __repr__(self):
"""
Defines the representation of the Gemini object for debugging purposes.
It uses `hasattr` to check if the `llm` attribute is set.
- If `llm` exists, it returns a string like `Gemini(llm=GoogleGenerativeAI(...))`,
showing the class name and the `llm` object information.
- If `llm` is not yet set (during initialization), it returns
`Gemini(llm=not initialized)`, indicating the state.
"""
llm_info = f"llm={self.llm}" if hasattr(self, 'llm') else 'llm=not initialized'
return f"{self.__class__.__name__}({llm_info})"
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