fix: add retry logic for OpenAI and Azure OpenAI (#315)

src/ragas/llms/openai.py

@@ -1,29 +1,104 @@
 from __future__ import annotations
 
+import asyncio
+import logging
 import os
 import typing as t
 from abc import abstractmethod
 from dataclasses import dataclass, field
 
+import openai
 from langchain.adapters.openai import convert_message_to_dict
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
 from langchain.schema import Generation, LLMResult
 from openai import AsyncAzureOpenAI, AsyncClient, AsyncOpenAI
+from tenacity import (
+    RetryCallState,
+    before_sleep_log,
+    retry,
+    retry_base,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
 
 from ragas.async_utils import run_async_tasks
 from ragas.exceptions import AzureOpenAIKeyNotFound, OpenAIKeyNotFound
 from ragas.llms.base import RagasLLM
 from ragas.llms.langchain import _compute_token_usage_langchain
-from ragas.utils import NO_KEY
+from ragas.utils import NO_KEY, get_debug_mode
 
 if t.TYPE_CHECKING:
     from langchain.callbacks.base import Callbacks
     from langchain.prompts import ChatPromptTemplate
 
+logger = logging.getLogger(__name__)
+
+errors = [
+    openai.APITimeoutError,
+    openai.APIConnectionError,
+    openai.RateLimitError,
+    openai.APIConnectionError,
+    openai.InternalServerError,
+]
+
+
+def create_base_retry_decorator(
+    error_types: t.List[t.Type[BaseException]],
+    max_retries: int = 1,
+    run_manager: t.Optional[
+        t.Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
+    ] = None,
+) -> t.Callable[[t.Any], t.Any]:
+    """Create a retry decorator for a given LLM and provided list of error types."""
+
+    log_level = logging.WARNING if get_debug_mode() else logging.DEBUG
+    _logging = before_sleep_log(logger, log_level)
+
+    def _before_sleep(retry_state: RetryCallState) -> None:
+        _logging(retry_state)
+        if run_manager:
+            if isinstance(run_manager, AsyncCallbackManagerForLLMRun):
+                coro = run_manager.on_retry(retry_state)
+                try:
+                    loop = asyncio.get_event_loop()
+                    if loop.is_running():
+                        loop.create_task(coro)
+                    else:
+                        asyncio.run(coro)
+                except Exception as e:
+                    logger.error(f"Error in on_retry: {e}")
+            else:
+                run_manager.on_retry(retry_state)
+        return None
+
+    min_seconds = 4
+    max_seconds = 10
+    # Wait 2^x * 1 second between each retry starting with
+    # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
+    retry_instance: "retry_base" = retry_if_exception_type(error_types[0])
+    for error in error_types[1:]:
+        retry_instance = retry_instance | retry_if_exception_type(error)
+    return retry(
+        reraise=True,
+        stop=stop_after_attempt(max_retries),
+        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
+        retry=retry_instance,
+        before_sleep=_before_sleep,
+    )
+
+
+retry_decorator = create_base_retry_decorator(errors, max_retries=4)
+
 
 class OpenAIBase(RagasLLM):
-    def __init__(self, model: str, _api_key_env_var: str) -> None:
+    def __init__(self, model: str, _api_key_env_var: str, timeout: int = 60) -> None:
         self.model = model
         self._api_key_env_var = _api_key_env_var
+        self.timeout = timeout
 
         # api key
         key_from_env = os.getenv(self._api_key_env_var, NO_KEY)
@@ -83,6 +158,7 @@ def generate(
         llm_output = _compute_token_usage_langchain(llm_results)
         return LLMResult(generations=generations, llm_output=llm_output)
 
+    @retry_decorator
     async def agenerate(
         self,
         prompt: ChatPromptTemplate,
@@ -112,9 +188,13 @@ def __post_init__(self):
         self._client_init()
 
     def _client_init(self):
-        self._client = AsyncOpenAI(api_key=self.api_key)
+        self._client = AsyncOpenAI(api_key=self.api_key, timeout=self.timeout)
 
     def validate_api_key(self):
+        # before validating, check if the api key is already set
+        api_key = os.getenv(self._api_key_env_var, NO_KEY)
+        if api_key != NO_KEY:
+            self._client.api_key = api_key
         if self.llm.api_key == NO_KEY:
             raise OpenAIKeyNotFound
 
@@ -136,6 +216,7 @@ def _client_init(self):
             api_version=self.api_version,
             azure_endpoint=self.azure_endpoint,
             api_key=self.api_key,
+            timeout=self.timeout,
         )
 
     def validate_api_key(self):

src/ragas/utils.py

@@ -12,7 +12,6 @@
 @lru_cache(maxsize=1)
 def get_debug_mode() -> bool:
     if os.environ.get(DEBUG_ENV_VAR, str(False)).lower() == "true":
-        logging.basicConfig(level=logging.DEBUG)
         return True
     else:
         return False