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from google import genai
import os
import logging
import json
import requests
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, encoding='utf-8')
file_handler.setFormatter(
logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
)
logger.addHandler(file_handler)
# Simple cache configuration
cache_file = "llm_cache.json"
def load_cache():
try:
with open(cache_file, 'r') as f:
return json.load(f)
except:
logger.warning(f"Failed to load cache.")
return {}
def save_cache(cache):
try:
with open(cache_file, 'w') as f:
json.dump(cache, f)
except:
logger.warning(f"Failed to save cache")
def get_llm_provider():
provider = os.getenv("LLM_PROVIDER")
if not provider and (os.getenv("GEMINI_PROJECT_ID") or os.getenv("GEMINI_API_KEY")):
provider = "GEMINI"
# if necessary, add ANTHROPIC/OPENAI
return provider
def _call_llm_provider(prompt: str) -> str:
"""
Call an LLM provider based on environment variables.
Environment variables:
- LLM_PROVIDER: "OLLAMA" or "XAI"
- <provider>_MODEL: Model name (e.g., OLLAMA_MODEL, XAI_MODEL)
- <provider>_BASE_URL: Base URL without endpoint (e.g., OLLAMA_BASE_URL, XAI_BASE_URL)
- <provider>_API_KEY: API key (e.g., OLLAMA_API_KEY, XAI_API_KEY; optional for providers that don't require it)
The endpoint /v1/chat/completions will be appended to the base URL.
"""
logger.info(f"PROMPT: {prompt}") # log the prompt
# Read the provider from environment variable
provider = os.environ.get("LLM_PROVIDER")
if not provider:
raise ValueError("LLM_PROVIDER environment variable is required")
# Construct the names of the other environment variables
model_var = f"{provider}_MODEL"
base_url_var = f"{provider}_BASE_URL"
api_key_var = f"{provider}_API_KEY"
# Read the provider-specific variables
model = os.environ.get(model_var)
base_url = os.environ.get(base_url_var)
api_key = os.environ.get(api_key_var, "") # API key is optional, default to empty string
# Validate required variables
if not model:
raise ValueError(f"{model_var} environment variable is required")
if not base_url:
raise ValueError(f"{base_url_var} environment variable is required")
# Append the endpoint to the base URL
url = f"{base_url.rstrip('/')}/v1/chat/completions"
# Configure headers and payload based on provider
headers = {
"Content-Type": "application/json",
}
if api_key: # Only add Authorization header if API key is provided
headers["Authorization"] = f"Bearer {api_key}"
payload = {
"model": model,
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.7,
}
try:
response = requests.post(url, headers=headers, json=payload)
response_json = response.json() # Log the response
logger.info("RESPONSE:\n%s", json.dumps(response_json, indent=2))
#logger.info(f"RESPONSE: {response.json()}")
response.raise_for_status()
return response.json()["choices"][0]["message"]["content"]
except requests.exceptions.HTTPError as e:
error_message = f"HTTP error occurred: {e}"
try:
error_details = response.json().get("error", "No additional details")
error_message += f" (Details: {error_details})"
except:
pass
raise Exception(error_message)
except requests.exceptions.ConnectionError:
raise Exception(f"Failed to connect to {provider} API. Check your network connection.")
except requests.exceptions.Timeout:
raise Exception(f"Request to {provider} API timed out.")
except requests.exceptions.RequestException as e:
raise Exception(f"An error occurred while making the request to {provider}: {e}")
except ValueError:
raise Exception(f"Failed to parse response as JSON from {provider}. The server might have returned an invalid response.")
# 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 = load_cache()
# Return from cache if exists
if prompt in cache:
logger.info(f"RESPONSE: {cache[prompt]}")
return cache[prompt]
provider = get_llm_provider()
if provider == "GEMINI":
response_text = _call_llm_gemini(prompt)
else: # generic method using a URL that is OpenAI compatible API (Ollama, ...)
response_text = _call_llm_provider(prompt)
# Log the response
logger.info(f"RESPONSE: {response_text}")
# Update cache if enabled
if use_cache:
# Load cache again to avoid overwrites
cache = load_cache()
# Add to cache and save
cache[prompt] = response_text
save_cache(cache)
return response_text
def _call_llm_gemini(prompt: str) -> str:
if os.getenv("GEMINI_PROJECT_ID"):
client = genai.Client(
vertexai=True,
project=os.getenv("GEMINI_PROJECT_ID"),
location=os.getenv("GEMINI_LOCATION", "us-central1")
)
elif os.getenv("GEMINI_API_KEY"):
client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
else:
raise ValueError("Either GEMINI_PROJECT_ID or GEMINI_API_KEY must be set in the environment")
model = os.getenv("GEMINI_MODEL", "gemini-2.5-pro-exp-03-25")
response = client.models.generate_content(
model=model,
contents=[prompt]
)
return response.text
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}")