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from google import genai

import os

import logging

import json

from datetime import datetime

# Configure logging

log_directory = os.getenv("LOG_DIR", "logs")

os.makedirs(log_directory, exist_ok=True)

log_file = os.path.join(log_directory, f"llm_calls_{datetime.now().strftime('%Y%m%d')}.log")

# Set up logger

logger = logging.getLogger("llm_logger")

logger.setLevel(logging.INFO)

logger.propagate = False # Prevent propagation to root logger

file_handler = logging.FileHandler(log_file)

file_handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))

logger.addHandler(file_handler)

# Simple cache configuration

cache_file = "llm_cache.json"

# By default, we Google Gemini 2.5 pro, as it shows great performance for code understanding

def call_llm(prompt: str, use_cache: bool = True) -> str:

# Log the prompt

logger.info(f"PROMPT: {prompt}")

# Check cache if enabled

if use_cache:

# Load cache from disk

cache = {}

if os.path.exists(cache_file):

try:

with open(cache_file, 'r') as f:

cache = json.load(f)

except:

logger.warning(f"Failed to load cache, starting with empty cache")

# Return from cache if exists

if prompt in cache:

logger.info(f"RESPONSE: {cache[prompt]}")

return cache[prompt]

# Call the LLM if not in cache or cache disabled

client = genai.Client(

vertexai=True,

# TODO: change to your own project id and location

project=os.getenv("GEMINI_PROJECT_ID", "your-project-id"),

location=os.getenv("GEMINI_LOCATION", "us-central1")

)

# You can comment the previous line and use the AI Studio key instead:

# client = genai.Client(

# api_key=os.getenv("GEMINI_API_KEY", "your-api_key"),

# )

model = os.getenv("GEMINI_MODEL", "gemini-2.5-pro-exp-03-25")

response = client.models.generate_content(

model=model,

contents=[prompt]

)

response_text = response.text

# Log the response

logger.info(f"RESPONSE: {response_text}")

# Update cache if enabled

if use_cache:

# Load cache again to avoid overwrites

cache = {}

if os.path.exists(cache_file):

try:

with open(cache_file, 'r') as f:

cache = json.load(f)

except:

pass

# Add to cache and save

cache[prompt] = response_text

try:

with open(cache_file, 'w') as f:

json.dump(cache, f)

except Exception as e:

logger.error(f"Failed to save cache: {e}")

return response_text

# # Use Anthropic Claude 3.7 Sonnet Extended Thinking

# def call_llm(prompt, use_cache: bool = True):

# from anthropic import Anthropic

# client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY", "your-api-key"))

# response = client.messages.create(

# model="claude-3-7-sonnet-20250219",

# max_tokens=21000,

# thinking={

# "type": "enabled",

# "budget_tokens": 20000

# },

# messages=[

# {"role": "user", "content": prompt}

# ]

# )

# return response.content[1].text

# # Use OpenAI o1

# def call_llm(prompt, use_cache: bool = True):

# from openai import OpenAI

# client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "your-api-key"))

# r = client.chat.completions.create(

# model="o1",

# messages=[{"role": "user", "content": prompt}],

# response_format={

# "type": "text"

# },

# reasoning_effort="medium",

# store=False

# )

# return r.choices[0].message.content

if __name__ == "__main__":

test_prompt = "Hello, how are you?"

# First call - should hit the API

print("Making call...")

response1 = call_llm(test_prompt, use_cache=False)

print(f"Response: {response1}")