Add Retry Events (#8053)

![image](https://github.com/hwchase17/langchain/assets/13333726/59a5c3b4-4367-47e6-9f58-5b6557576a8a)

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/langchain/langchain/callbacks/base.py

@@ -242,6 +242,11 @@ class BaseCallbackHandler(
         """Whether to ignore LLM callbacks."""
         return False
 
+    @property
+    def ignore_retry(self) -> bool:
+        """Whether to ignore retry callbacks."""
+        return False
+
     @property
     def ignore_chain(self) -> bool:
         """Whether to ignore chain callbacks."""

libs/langchain/langchain/callbacks/manager.py

@@ -23,6 +23,8 @@ from typing import (
 )
 from uuid import UUID
 
+from tenacity import RetryCallState
+
 import langchain
 from langchain.callbacks.base import (
     BaseCallbackHandler,
@@ -572,6 +574,22 @@ class CallbackManagerForLLMRun(RunManager, LLMManagerMixin):
             **kwargs,
         )
 
+    def on_retry(
+        self,
+        retry_state: RetryCallState,
+        **kwargs: Any,
+    ) -> None:
+        _handle_event(
+            self.handlers,
+            "on_retry",
+            "ignore_retry",
+            retry_state,
+            run_id=self.run_id,
+            parent_run_id=self.parent_run_id,
+            tags=self.tags,
+            **kwargs,
+        )
+
     def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         """Run when LLM ends running.
 
@@ -635,6 +653,22 @@ class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):
             **kwargs,
         )
 
+    async def on_retry(
+        self,
+        retry_state: RetryCallState,
+        **kwargs: Any,
+    ) -> None:
+        await _ahandle_event(
+            self.handlers,
+            "on_retry",
+            "ignore_retry",
+            retry_state,
+            run_id=self.run_id,
+            parent_run_id=self.parent_run_id,
+            tags=self.tags,
+            **kwargs,
+        )
+
     async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         """Run when LLM ends running.
 

libs/langchain/langchain/callbacks/tracers/base.py

@@ -7,6 +7,8 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional, Sequence, Union, cast
 from uuid import UUID
 
+from tenacity import RetryCallState
+
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.tracers.schemas import Run, RunTypeEnum
 from langchain.load.dump import dumpd
@@ -138,6 +140,41 @@ class BaseTracer(BaseCallbackHandler, ABC):
             },
         )
 
+    def on_retry(
+        self,
+        retry_state: RetryCallState,
+        *,
+        run_id: UUID,
+        **kwargs: Any,
+    ) -> None:
+        if not run_id:
+            raise TracerException("No run_id provided for on_retry callback.")
+        run_id_ = str(run_id)
+        llm_run = self.run_map.get(run_id_)
+        if llm_run is None or llm_run.run_type != RunTypeEnum.llm:
+            raise TracerException("No LLM Run found to be traced for on_retry")
+        retry_d: Dict[str, Any] = {
+            "slept": retry_state.idle_for,
+            "attempt": retry_state.attempt_number,
+        }
+        if retry_state.outcome is None:
+            retry_d["outcome"] = "N/A"
+        elif retry_state.outcome.failed:
+            retry_d["outcome"] = "failed"
+            exception = retry_state.outcome.exception()
+            retry_d["exception"] = str(exception)
+            retry_d["exception_type"] = exception.__class__.__name__
+        else:
+            retry_d["outcome"] = "success"
+            retry_d["result"] = str(retry_state.outcome.result())
+        llm_run.events.append(
+            {
+                "name": "retry",
+                "time": datetime.utcnow(),
+                "kwargs": retry_d,
+            },
+        )
+
     def on_llm_end(self, response: LLMResult, *, run_id: UUID, **kwargs: Any) -> None:
         """End a trace for an LLM run."""
         if not run_id:

libs/langchain/langchain/chat_models/openai.py

@@ -18,23 +18,14 @@ from typing import (
 )
 
 from pydantic import Field, root_validator
-from tenacity import (
-    before_sleep_log,
-    retry,
-    retry_if_exception_type,
-    stop_after_attempt,
-    wait_exponential,
-)
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.chat_models.base import BaseChatModel
-from langchain.schema import (
-    ChatGeneration,
-    ChatResult,
-)
+from langchain.llms.base import create_base_retry_decorator
+from langchain.schema import ChatGeneration, ChatResult
 from langchain.schema.messages import (
     AIMessage,
     AIMessageChunk,
@@ -70,31 +61,33 @@ def _import_tiktoken() -> Any:
     return tiktoken
 
 
-def _create_retry_decorator(llm: ChatOpenAI) -> Callable[[Any], Any]:
+def _create_retry_decorator(
+    llm: ChatOpenAI,
+    run_manager: Optional[
+        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
+    ] = None,
+) -> Callable[[Any], Any]:
     import openai
 
-    min_seconds = 1
-    max_seconds = 60
-    # Wait 2^x * 1 second between each retry starting with
-    # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
-    return retry(
-        reraise=True,
-        stop=stop_after_attempt(llm.max_retries),
-        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
-        retry=(
-            retry_if_exception_type(openai.error.Timeout)
-            | retry_if_exception_type(openai.error.APIError)
-            | retry_if_exception_type(openai.error.APIConnectionError)
-            | retry_if_exception_type(openai.error.RateLimitError)
-            | retry_if_exception_type(openai.error.ServiceUnavailableError)
-        ),
-        before_sleep=before_sleep_log(logger, logging.WARNING),
+    errors = [
+        openai.error.Timeout,
+        openai.error.APIError,
+        openai.error.APIConnectionError,
+        openai.error.RateLimitError,
+        openai.error.ServiceUnavailableError,
+    ]
+    return create_base_retry_decorator(
+        error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
     )
 
 
-async def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any:
+async def acompletion_with_retry(
+    llm: ChatOpenAI,
+    run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
     """Use tenacity to retry the async completion call."""
-    retry_decorator = _create_retry_decorator(llm)
+    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
 
     @retry_decorator
     async def _completion_with_retry(**kwargs: Any) -> Any:
@@ -322,9 +315,11 @@ class ChatOpenAI(BaseChatModel):
             **self.model_kwargs,
         }
 
-    def completion_with_retry(self, **kwargs: Any) -> Any:
+    def completion_with_retry(
+        self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
+    ) -> Any:
         """Use tenacity to retry the completion call."""
-        retry_decorator = _create_retry_decorator(self)
+        retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
 
         @retry_decorator
         def _completion_with_retry(**kwargs: Any) -> Any:
@@ -357,7 +352,9 @@ class ChatOpenAI(BaseChatModel):
         params = {**params, **kwargs, "stream": True}
 
         default_chunk_class = AIMessageChunk
-        for chunk in self.completion_with_retry(messages=message_dicts, **params):
+        for chunk in self.completion_with_retry(
+            messages=message_dicts, run_manager=run_manager, **params
+        ):
             if len(chunk["choices"]) == 0:
                 continue
             delta = chunk["choices"][0]["delta"]
@@ -388,7 +385,9 @@ class ChatOpenAI(BaseChatModel):
 
         message_dicts, params = self._create_message_dicts(messages, stop)
         params = {**params, **kwargs}
-        response = self.completion_with_retry(messages=message_dicts, **params)
+        response = self.completion_with_retry(
+            messages=message_dicts, run_manager=run_manager, **params
+        )
         return self._create_chat_result(response)
 
     def _create_message_dicts(
@@ -427,7 +426,7 @@ class ChatOpenAI(BaseChatModel):
 
         default_chunk_class = AIMessageChunk
         async for chunk in await acompletion_with_retry(
-            self, messages=message_dicts, **params
+            self, messages=message_dicts, run_manager=run_manager, **params
         ):
             if len(chunk["choices"]) == 0:
                 continue
@@ -459,7 +458,9 @@ class ChatOpenAI(BaseChatModel):
 
         message_dicts, params = self._create_message_dicts(messages, stop)
         params = {**params, **kwargs}
-        response = await acompletion_with_retry(self, messages=message_dicts, **params)
+        response = await acompletion_with_retry(
+            self, messages=message_dicts, run_manager=run_manager, **params
+        )
         return self._create_chat_result(response)
 
     @property

libs/langchain/langchain/llms/base.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import asyncio
+import functools
 import inspect
 import json
 import logging
@@ -28,6 +29,7 @@ from typing import (
 import yaml
 from pydantic import Field, root_validator, validator
 from tenacity import (
+    RetryCallState,
     before_sleep_log,
     retry,
     retry_base,
@@ -66,11 +68,36 @@ def _get_verbosity() -> bool:
     return langchain.verbose
 
 
+@functools.lru_cache
+def _log_error_once(msg: str) -> None:
+    """Log an error once."""
+    logger.error(msg)
+
+
 def create_base_retry_decorator(
-    error_types: List[Type[BaseException]], max_retries: int = 1
+    error_types: List[Type[BaseException]],
+    max_retries: int = 1,
+    run_manager: Optional[
+        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
+    ] = None,
 ) -> Callable[[Any], Any]:
     """Create a retry decorator for a given LLM and provided list of error types."""
 
+    _logging = before_sleep_log(logger, logging.WARNING)
+
+    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:
+                    asyncio.run(coro)
+                except Exception as e:
+                    _log_error_once(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
@@ -83,7 +110,7 @@ def create_base_retry_decorator(
         stop=stop_after_attempt(max_retries),
         wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
         retry=retry_instance,
-        before_sleep=before_sleep_log(logger, logging.WARNING),
+        before_sleep=_before_sleep,
     )
 
 

libs/langchain/langchain/llms/openai.py

@@ -80,7 +80,12 @@ def _streaming_response_template() -> Dict[str, Any]:
     }
 
 
-def _create_retry_decorator(llm: Union[BaseOpenAI, OpenAIChat]) -> Callable[[Any], Any]:
+def _create_retry_decorator(
+    llm: Union[BaseOpenAI, OpenAIChat],
+    run_manager: Optional[
+        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
+    ] = None,
+) -> Callable[[Any], Any]:
     import openai
 
     errors = [
@@ -90,12 +95,18 @@ def _create_retry_decorator(llm: Union[BaseOpenAI, OpenAIChat]) -> Callable[[Any
         openai.error.RateLimitError,
         openai.error.ServiceUnavailableError,
     ]
-    return create_base_retry_decorator(error_types=errors, max_retries=llm.max_retries)
+    return create_base_retry_decorator(
+        error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
+    )
 
 
-def completion_with_retry(llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any) -> Any:
+def completion_with_retry(
+    llm: Union[BaseOpenAI, OpenAIChat],
+    run_manager: Optional[CallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
     """Use tenacity to retry the completion call."""
-    retry_decorator = _create_retry_decorator(llm)
+    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
 
     @retry_decorator
     def _completion_with_retry(**kwargs: Any) -> Any:
@@ -105,10 +116,12 @@ def completion_with_retry(llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any) ->
 
 
 async def acompletion_with_retry(
-    llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any
+    llm: Union[BaseOpenAI, OpenAIChat],
+    run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    **kwargs: Any,
 ) -> Any:
     """Use tenacity to retry the async completion call."""
-    retry_decorator = _create_retry_decorator(llm)
+    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
 
     @retry_decorator
     async def _completion_with_retry(**kwargs: Any) -> Any:
@@ -291,8 +304,10 @@ class BaseOpenAI(BaseLLM):
         **kwargs: Any,
     ) -> Iterator[GenerationChunk]:
         params = {**self._invocation_params, **kwargs, "stream": True}
-        self.get_sub_prompts(params, [prompt], stop)  # this mutate params
-        for stream_resp in completion_with_retry(self, prompt=prompt, **params):
+        self.get_sub_prompts(params, [prompt], stop)  # this mutates params
+        for stream_resp in completion_with_retry(
+            self, prompt=prompt, run_manager=run_manager, **params
+        ):
             chunk = _stream_response_to_generation_chunk(stream_resp)
             yield chunk
             if run_manager:
@@ -314,7 +329,7 @@ class BaseOpenAI(BaseLLM):
         params = {**self._invocation_params, **kwargs, "stream": True}
         self.get_sub_prompts(params, [prompt], stop)  # this mutate params
         async for stream_resp in await acompletion_with_retry(
-            self, prompt=prompt, **params
+            self, prompt=prompt, run_manager=run_manager, **params
         ):
             chunk = _stream_response_to_generation_chunk(stream_resp)
             yield chunk
@@ -381,7 +396,9 @@ class BaseOpenAI(BaseLLM):
                     }
                 )
             else:
-                response = completion_with_retry(self, prompt=_prompts, **params)
+                response = completion_with_retry(
+                    self, prompt=_prompts, run_manager=run_manager, **params
+                )
                 choices.extend(response["choices"])
                 update_token_usage(_keys, response, token_usage)
         return self.create_llm_result(choices, prompts, token_usage)
@@ -428,7 +445,9 @@ class BaseOpenAI(BaseLLM):
                     }
                 )
             else:
-                response = await acompletion_with_retry(self, prompt=_prompts, **params)
+                response = await acompletion_with_retry(
+                    self, prompt=_prompts, run_manager=run_manager, **params
+                )
                 choices.extend(response["choices"])
                 update_token_usage(_keys, response, token_usage)
         return self.create_llm_result(choices, prompts, token_usage)
@@ -818,7 +837,9 @@ class OpenAIChat(BaseLLM):
     ) -> Iterator[GenerationChunk]:
         messages, params = self._get_chat_params([prompt], stop)
         params = {**params, **kwargs, "stream": True}
-        for stream_resp in completion_with_retry(self, messages=messages, **params):
+        for stream_resp in completion_with_retry(
+            self, messages=messages, run_manager=run_manager, **params
+        ):
             token = stream_resp["choices"][0]["delta"].get("content", "")
             yield GenerationChunk(text=token)
             if run_manager:
@@ -834,7 +855,7 @@ class OpenAIChat(BaseLLM):
         messages, params = self._get_chat_params([prompt], stop)
         params = {**params, **kwargs, "stream": True}
         async for stream_resp in await acompletion_with_retry(
-            self, messages=messages, **params
+            self, messages=messages, run_manager=run_manager, **params
         ):
             token = stream_resp["choices"][0]["delta"].get("content", "")
             yield GenerationChunk(text=token)
@@ -860,7 +881,9 @@ class OpenAIChat(BaseLLM):
 
         messages, params = self._get_chat_params(prompts, stop)
         params = {**params, **kwargs}
-        full_response = completion_with_retry(self, messages=messages, **params)
+        full_response = completion_with_retry(
+            self, messages=messages, run_manager=run_manager, **params
+        )
         llm_output = {
             "token_usage": full_response["usage"],
             "model_name": self.model_name,
@@ -891,7 +914,9 @@ class OpenAIChat(BaseLLM):
 
         messages, params = self._get_chat_params(prompts, stop)
         params = {**params, **kwargs}
-        full_response = await acompletion_with_retry(self, messages=messages, **params)
+        full_response = await acompletion_with_retry(
+            self, messages=messages, run_manager=run_manager, **params
+        )
         llm_output = {
             "token_usage": full_response["usage"],
             "model_name": self.model_name,

libs/langchain/tests/integration_tests/llms/test_openai.py

@@ -1,7 +1,7 @@
 """Test OpenAI API wrapper."""
-
 from pathlib import Path
-from typing import Generator
+from typing import Any, Generator
+from unittest.mock import MagicMock, patch
 
 import pytest
 
@@ -10,7 +10,10 @@ from langchain.chat_models.openai import ChatOpenAI
 from langchain.llms.loading import load_llm
 from langchain.llms.openai import OpenAI, OpenAIChat
 from langchain.schema import LLMResult
-from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
+from tests.unit_tests.callbacks.fake_callback_handler import (
+    FakeAsyncCallbackHandler,
+    FakeCallbackHandler,
+)
 
 
 def test_openai_call() -> None:
@@ -334,3 +337,77 @@ def test_chat_openai_get_num_tokens(model: str) -> None:
     """Test get_tokens."""
     llm = ChatOpenAI(model=model)
     assert llm.get_num_tokens("表情符号是\n🦜🔗") == _EXPECTED_NUM_TOKENS[model]
+
+
+@pytest.fixture
+def mock_completion() -> dict:
+    return {
+        "id": "cmpl-3evkmQda5Hu7fcZavknQda3SQ",
+        "object": "text_completion",
+        "created": 1689989000,
+        "model": "text-davinci-003",
+        "choices": [
+            {"text": "Bar Baz", "index": 0, "logprobs": None, "finish_reason": "length"}
+        ],
+        "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
+    }
+
+
+@pytest.mark.requires("openai")
+def test_openai_retries(mock_completion: dict) -> None:
+    llm = OpenAI()
+    mock_client = MagicMock()
+    completed = False
+    raised = False
+    import openai
+
+    def raise_once(*args: Any, **kwargs: Any) -> Any:
+        nonlocal completed, raised
+        if not raised:
+            raised = True
+            raise openai.error.APIError
+        completed = True
+        return mock_completion
+
+    mock_client.create = raise_once
+    callback_handler = FakeCallbackHandler()
+    with patch.object(
+        llm,
+        "client",
+        mock_client,
+    ):
+        res = llm.predict("bar", callbacks=[callback_handler])
+        assert res == "Bar Baz"
+    assert completed
+    assert raised
+    assert callback_handler.retries == 1
+
+
+@pytest.mark.requires("openai")
+async def test_openai_async_retries(mock_completion: dict) -> None:
+    llm = OpenAI()
+    mock_client = MagicMock()
+    completed = False
+    raised = False
+    import openai
+
+    def raise_once(*args: Any, **kwargs: Any) -> Any:
+        nonlocal completed, raised
+        if not raised:
+            raised = True
+            raise openai.error.APIError
+        completed = True
+        return mock_completion
+
+    mock_client.create = raise_once
+    callback_handler = FakeAsyncCallbackHandler()
+    with patch.object(
+        llm,
+        "client",
+        mock_client,
+    ):
+        res = llm.apredict("bar", callbacks=[callback_handler])
+        assert res == "Bar Baz"
+    assert completed
+    assert raised
+    assert callback_handler.retries == 1

libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py

@@ -39,6 +39,7 @@ class BaseFakeCallbackHandler(BaseModel):
     retriever_starts: int = 0
     retriever_ends: int = 0
     retriever_errors: int = 0
+    retries: int = 0
 
 
 class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
@@ -58,8 +59,10 @@ class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
     def on_llm_new_token_common(self) -> None:
         self.llm_streams += 1
 
+    def on_retry_common(self) -> None:
+        self.retries += 1
+
     def on_chain_start_common(self) -> None:
-        ("CHAIN START")
         self.chain_starts += 1
         self.starts += 1
 
@@ -82,7 +85,6 @@ class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
         self.errors += 1
 
     def on_agent_action_common(self) -> None:
-        print("AGENT ACTION")
         self.agent_actions += 1
         self.starts += 1
 
@@ -91,7 +93,6 @@ class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
         self.ends += 1
 
     def on_chat_model_start_common(self) -> None:
-        print("STARTING CHAT MODEL")
         self.chat_model_starts += 1
         self.starts += 1
 
@@ -162,6 +163,13 @@ class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
     ) -> Any:
         self.on_llm_error_common()
 
+    def on_retry(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> Any:
+        self.on_retry_common()
+
     def on_chain_start(
         self,
         *args: Any,

libs/langchain/tests/unit_tests/chat_models/test_openai.py

@@ -1,8 +1,12 @@
 """Test OpenAI Chat API wrapper."""
-
 import json
+from typing import Any
+from unittest.mock import MagicMock, patch
+
+import pytest
 
 from langchain.chat_models.openai import (
+    ChatOpenAI,
     _convert_dict_to_message,
 )
 from langchain.schema.messages import FunctionMessage
@@ -21,3 +25,67 @@ def test_function_message_dict_to_function_message() -> None:
     assert isinstance(result, FunctionMessage)
     assert result.name == name
     assert result.content == content
+
+
+@pytest.fixture
+def mock_completion() -> dict:
+    return {
+        "id": "chatcmpl-7fcZavknQda3SQ",
+        "object": "chat.completion",
+        "created": 1689989000,
+        "model": "gpt-3.5-turbo-0613",
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": "Bar Baz",
+                },
+                "finish_reason": "stop",
+            }
+        ],
+    }
+
+
+@pytest.mark.requires("openai")
+def test_openai_predict(mock_completion: dict) -> None:
+    llm = ChatOpenAI()
+    mock_client = MagicMock()
+    completed = False
+
+    def mock_create(*args: Any, **kwargs: Any) -> Any:
+        nonlocal completed
+        completed = True
+        return mock_completion
+
+    mock_client.create = mock_create
+    with patch.object(
+        llm,
+        "client",
+        mock_client,
+    ):
+        res = llm.predict("bar")
+        assert res == "Bar Baz"
+    assert completed
+
+
+@pytest.mark.requires("openai")
+async def test_openai_apredict(mock_completion: dict) -> None:
+    llm = ChatOpenAI()
+    mock_client = MagicMock()
+    completed = False
+
+    def mock_create(*args: Any, **kwargs: Any) -> Any:
+        nonlocal completed
+        completed = True
+        return mock_completion
+
+    mock_client.create = mock_create
+    with patch.object(
+        llm,
+        "client",
+        mock_client,
+    ):
+        res = llm.predict("bar")
+        assert res == "Bar Baz"
+    assert completed

libs/langchain/tests/unit_tests/llms/test_openai.py

@@ -1,4 +1,6 @@
 import os
+from typing import Any
+from unittest.mock import MagicMock, patch
 
 import pytest
 
@@ -26,3 +28,61 @@ def test_openai_incorrect_field() -> None:
     with pytest.warns(match="not default parameter"):
         llm = OpenAI(foo="bar")
     assert llm.model_kwargs == {"foo": "bar"}
+
+
+@pytest.fixture
+def mock_completion() -> dict:
+    return {
+        "id": "cmpl-3evkmQda5Hu7fcZavknQda3SQ",
+        "object": "text_completion",
+        "created": 1689989000,
+        "model": "text-davinci-003",
+        "choices": [
+            {"text": "Bar Baz", "index": 0, "logprobs": None, "finish_reason": "length"}
+        ],
+        "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
+    }
+
+
+@pytest.mark.requires("openai")
+def test_openai_calls(mock_completion: dict) -> None:
+    llm = OpenAI()
+    mock_client = MagicMock()
+    completed = False
+
+    def raise_once(*args: Any, **kwargs: Any) -> Any:
+        nonlocal completed
+        completed = True
+        return mock_completion
+
+    mock_client.create = raise_once
+    with patch.object(
+        llm,
+        "client",
+        mock_client,
+    ):
+        res = llm.predict("bar")
+        assert res == "Bar Baz"
+    assert completed
+
+
+@pytest.mark.requires("openai")
+async def test_openai_async_retries(mock_completion: dict) -> None:
+    llm = OpenAI()
+    mock_client = MagicMock()
+    completed = False
+
+    def raise_once(*args: Any, **kwargs: Any) -> Any:
+        nonlocal completed
+        completed = True
+        return mock_completion
+
+    mock_client.create = raise_once
+    with patch.object(
+        llm,
+        "client",
+        mock_client,
+    ):
+        res = llm.apredict("bar")
+        assert res == "Bar Baz"
+    assert completed