import os import requests import boto3 import litellm import openai from litellm import acompletion from tenacity import retry, retry_if_exception_type, stop_after_attempt from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler from pr_agent.config_loader import get_settings from pr_agent.log import get_logger OPENAI_RETRIES = 5 class LiteLLMAIHandler(BaseAiHandler): """ This class handles interactions with the OpenAI API for chat completions. It initializes the API key and other settings from a configuration file, and provides a method for performing chat completions using the OpenAI ChatCompletion API. """ def __init__(self): """ Initializes the OpenAI API key and other settings from a configuration file. Raises a ValueError if the OpenAI key is missing. """ self.azure = False self.api_base = None self.repetition_penalty = None if get_settings().get("OPENAI.KEY", None): openai.api_key = get_settings().openai.key litellm.openai_key = get_settings().openai.key elif 'OPENAI_API_KEY' not in os.environ: litellm.api_key = "dummy_key" if get_settings().get("aws.AWS_ACCESS_KEY_ID"): assert get_settings().aws.AWS_SECRET_ACCESS_KEY and get_settings().aws.AWS_REGION_NAME, "AWS credentials are incomplete" os.environ["AWS_ACCESS_KEY_ID"] = get_settings().aws.AWS_ACCESS_KEY_ID os.environ["AWS_SECRET_ACCESS_KEY"] = get_settings().aws.AWS_SECRET_ACCESS_KEY os.environ["AWS_REGION_NAME"] = get_settings().aws.AWS_REGION_NAME if get_settings().get("litellm.use_client"): litellm_token = get_settings().get("litellm.LITELLM_TOKEN") assert litellm_token, "LITELLM_TOKEN is required" os.environ["LITELLM_TOKEN"] = litellm_token litellm.use_client = True if get_settings().get("LITELLM.DROP_PARAMS", None): litellm.drop_params = get_settings().litellm.drop_params if get_settings().get("OPENAI.ORG", None): litellm.organization = get_settings().openai.org if get_settings().get("OPENAI.API_TYPE", None): if get_settings().openai.api_type == "azure": self.azure = True litellm.azure_key = get_settings().openai.key if get_settings().get("OPENAI.API_VERSION", None): litellm.api_version = get_settings().openai.api_version if get_settings().get("OPENAI.API_BASE", None): litellm.api_base = get_settings().openai.api_base if get_settings().get("ANTHROPIC.KEY", None): litellm.anthropic_key = get_settings().anthropic.key if get_settings().get("COHERE.KEY", None): litellm.cohere_key = get_settings().cohere.key if get_settings().get("GROQ.KEY", None): litellm.api_key = get_settings().groq.key if get_settings().get("REPLICATE.KEY", None): litellm.replicate_key = get_settings().replicate.key if get_settings().get("HUGGINGFACE.KEY", None): litellm.huggingface_key = get_settings().huggingface.key if get_settings().get("HUGGINGFACE.API_BASE", None) and 'huggingface' in get_settings().config.model: litellm.api_base = get_settings().huggingface.api_base self.api_base = get_settings().huggingface.api_base if get_settings().get("OLLAMA.API_BASE", None): litellm.api_base = get_settings().ollama.api_base self.api_base = get_settings().ollama.api_base if get_settings().get("HUGGINGFACE.REPETITION_PENALTY", None): self.repetition_penalty = float(get_settings().huggingface.repetition_penalty) if get_settings().get("VERTEXAI.VERTEX_PROJECT", None): litellm.vertex_project = get_settings().vertexai.vertex_project litellm.vertex_location = get_settings().get( "VERTEXAI.VERTEX_LOCATION", None ) def prepare_logs(self, response, system, user, resp, finish_reason): response_log = response.dict().copy() response_log['system'] = system response_log['user'] = user response_log['output'] = resp response_log['finish_reason'] = finish_reason if hasattr(self, 'main_pr_language'): response_log['main_pr_language'] = self.main_pr_language else: response_log['main_pr_language'] = 'unknown' return response_log def add_callbacks(selfs, kwargs): pr_metadata = [] def capture_logs(message): # Parsing the log message and context record = message.record log_entry = {} if record.get('extra', {}).get('command', None) is not None: log_entry.update({"command": record['extra']["command"]}) if record.get('extra', {}).get('pr_url', None) is not None: log_entry.update({"pr_url": record['extra']["pr_url"]}) # Append the log entry to the captured_logs list pr_metadata.append(log_entry) # Adding the custom sink to Loguru handler_id = get_logger().add(capture_logs) get_logger().debug("Capturing logs for litellm callbacks") get_logger().remove(handler_id) # Adding the captured logs to the kwargs kwargs["metadata"] = pr_metadata return kwargs @property def deployment_id(self): """ Returns the deployment ID for the OpenAI API. """ return get_settings().get("OPENAI.DEPLOYMENT_ID", None) @retry( retry=retry_if_exception_type((openai.APIError, openai.APIConnectionError, openai.APITimeoutError)), # No retry on RateLimitError stop=stop_after_attempt(OPENAI_RETRIES) ) async def chat_completion(self, model: str, system: str, user: str, temperature: float = 0.2, img_path: str = None): try: resp, finish_reason = None, None deployment_id = self.deployment_id if self.azure: model = 'azure/' + model if 'claude' in model and not system: system = "\n" get_logger().warning( "Empty system prompt for claude model. Adding a newline character to prevent OpenAI API error.") messages = [{"role": "system", "content": system}, {"role": "user", "content": user}] if img_path: try: # check if the image link is alive r = requests.head(img_path, allow_redirects=True) if r.status_code == 404: error_msg = f"The image link is not [alive](img_path).\nPlease repost the original image as a comment, and send the question again with 'quote reply' (see [instructions](https://pr-agent-docs.codium.ai/tools/ask/#ask-on-images-using-the-pr-code-as-context))." get_logger().error(error_msg) return f"{error_msg}", "error" except Exception as e: get_logger().error(f"Error fetching image: {img_path}", e) return f"Error fetching image: {img_path}", "error" messages[1]["content"] = [{"type": "text", "text": messages[1]["content"]}, {"type": "image_url", "image_url": {"url": img_path}}] kwargs = { "model": model, "deployment_id": deployment_id, "messages": messages, "temperature": temperature, "force_timeout": get_settings().config.ai_timeout, "api_base": self.api_base, } if get_settings().litellm.get("enable_callbacks", False): kwargs = self.add_callbacks(kwargs) seed = get_settings().config.get("seed", -1) if temperature > 0 and seed >= 0: raise ValueError(f"Seed ({seed}) is not supported with temperature ({temperature}) > 0") elif seed >= 0: get_logger().info(f"Using fixed seed of {seed}") kwargs["seed"] = seed if self.repetition_penalty: kwargs["repetition_penalty"] = self.repetition_penalty get_logger().debug("Prompts", artifact={"system": system, "user": user}) if get_settings().config.verbosity_level >= 2: get_logger().info(f"\nSystem prompt:\n{system}") get_logger().info(f"\nUser prompt:\n{user}") response = await acompletion(**kwargs) except (openai.APIError, openai.APITimeoutError) as e: get_logger().warning("Error during OpenAI inference: ", e) raise except (openai.RateLimitError) as e: get_logger().error("Rate limit error during OpenAI inference: ", e) raise except (Exception) as e: get_logger().warning("Unknown error during OpenAI inference: ", e) raise openai.APIError from e if response is None or len(response["choices"]) == 0: raise openai.APIError else: resp = response["choices"][0]['message']['content'] finish_reason = response["choices"][0]["finish_reason"] get_logger().debug(f"\nAI response:\n{resp}") # log the full response for debugging response_log = self.prepare_logs(response, system, user, resp, finish_reason) get_logger().debug("Full_response", artifact=response_log) # for CLI debugging if get_settings().config.verbosity_level >= 2: get_logger().info(f"\nAI response:\n{resp}") return resp, finish_reason