From 1a869d0ef27d013ae1a6bb63fc4d0ebee7cefdf6 Mon Sep 17 00:00:00 2001 From: Bagatur Date: Fri, 13 Oct 2023 15:44:11 -0700 Subject: [PATCH] more --- .../langchain/chat_models/jinachat.py | 10 ++++---- libs/langchain/langchain/chat_models/konko.py | 10 ++++---- .../langchain/chat_models/litellm.py | 10 ++++---- .../langchain/langchain/chat_models/openai.py | 10 ++++---- .../langchain/langchain/chat_models/tongyi.py | 16 ++++++------- libs/langchain/langchain/llms/anyscale.py | 12 +++++----- libs/langchain/langchain/llms/openai.py | 24 +++++++++---------- .../langchain/llms/symblai_nebula.py | 8 +++---- libs/langchain/langchain/llms/vertexai.py | 20 ++++++++-------- 9 files changed, 60 insertions(+), 60 deletions(-) diff --git a/libs/langchain/langchain/chat_models/jinachat.py b/libs/langchain/langchain/chat_models/jinachat.py index 38510fb6ba7..dfe650cce6b 100644 --- a/libs/langchain/langchain/chat_models/jinachat.py +++ b/libs/langchain/langchain/chat_models/jinachat.py @@ -274,15 +274,15 @@ class JinaChat(BaseChatModel): before_sleep=before_sleep_log(logger, logging.WARNING), ) - def completion_with_retry(self, **kwargs: Any) -> Any: + def _completion_with_retry(self, **kwargs: Any) -> Any: """Use tenacity to retry the completion call.""" retry_decorator = self._create_retry_decorator() @retry_decorator - def _completion_with_retry(**kwargs: Any) -> Any: + def __completion_with_retry(**kwargs: Any) -> Any: return self.client.create(**kwargs) - return _completion_with_retry(**kwargs) + return __completion_with_retry(**kwargs) def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: overall_token_usage: dict = {} @@ -309,7 +309,7 @@ class JinaChat(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, **params): delta = chunk["choices"][0]["delta"] chunk = _convert_delta_to_message_chunk(delta, default_chunk_class) default_chunk_class = chunk.__class__ @@ -332,7 +332,7 @@ class JinaChat(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, **params) return self._create_chat_result(response) def _create_message_dicts( diff --git a/libs/langchain/langchain/chat_models/konko.py b/libs/langchain/langchain/chat_models/konko.py index daf0306e3f1..d5c95e83b3a 100644 --- a/libs/langchain/langchain/chat_models/konko.py +++ b/libs/langchain/langchain/chat_models/konko.py @@ -165,13 +165,13 @@ class ChatKonko(ChatOpenAI): return {model["id"] for model in models_response.json()["data"]} - def completion_with_retry( + def _completion_with_retry( self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any ) -> Any: - def _completion_with_retry(**kwargs: Any) -> Any: + def __completion_with_retry(**kwargs: Any) -> Any: return self.client.create(**kwargs) - return _completion_with_retry(**kwargs) + return __completion_with_retry(**kwargs) def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: overall_token_usage: dict = {} @@ -198,7 +198,7 @@ class ChatKonko(ChatOpenAI): params = {**params, **kwargs, "stream": True} default_chunk_class = AIMessageChunk - for chunk in self.completion_with_retry( + for chunk in self._completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ): if len(chunk["choices"]) == 0: @@ -233,7 +233,7 @@ class ChatKonko(ChatOpenAI): message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs} - response = self.completion_with_retry( + response = self._completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ) return self._create_chat_result(response) diff --git a/libs/langchain/langchain/chat_models/litellm.py b/libs/langchain/langchain/chat_models/litellm.py index 2590c7ab59f..d524ae81147 100644 --- a/libs/langchain/langchain/chat_models/litellm.py +++ b/libs/langchain/langchain/chat_models/litellm.py @@ -225,17 +225,17 @@ class ChatLiteLLM(BaseChatModel): } return {**self._default_params, **creds} - def completion_with_retry( + 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, run_manager=run_manager) @retry_decorator - def _completion_with_retry(**kwargs: Any) -> Any: + def __completion_with_retry(**kwargs: Any) -> Any: return self.client.completion(**kwargs) - return _completion_with_retry(**kwargs) + return __completion_with_retry(**kwargs) @root_validator() def validate_environment(cls, values: Dict) -> Dict: @@ -302,7 +302,7 @@ class ChatLiteLLM(BaseChatModel): message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs} - response = self.completion_with_retry( + response = self._completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ) return self._create_chat_result(response) @@ -345,7 +345,7 @@ class ChatLiteLLM(BaseChatModel): params = {**params, **kwargs, "stream": True} default_chunk_class = AIMessageChunk - for chunk in self.completion_with_retry( + for chunk in self._completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ): if len(chunk["choices"]) == 0: diff --git a/libs/langchain/langchain/chat_models/openai.py b/libs/langchain/langchain/chat_models/openai.py index 1e475f41b37..3bf770eaf2b 100644 --- a/libs/langchain/langchain/chat_models/openai.py +++ b/libs/langchain/langchain/chat_models/openai.py @@ -286,17 +286,17 @@ class ChatOpenAI(BaseChatModel): **self.model_kwargs, } - def completion_with_retry( + 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, run_manager=run_manager) @retry_decorator - def _completion_with_retry(**kwargs: Any) -> Any: + def __completion_with_retry(**kwargs: Any) -> Any: return self.client.create(**kwargs) - return _completion_with_retry(**kwargs) + return __completion_with_retry(**kwargs) def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: overall_token_usage: dict = {} @@ -323,7 +323,7 @@ class ChatOpenAI(BaseChatModel): params = {**params, **kwargs, "stream": True} default_chunk_class = AIMessageChunk - for chunk in self.completion_with_retry( + for chunk in self._completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ): if len(chunk["choices"]) == 0: @@ -357,7 +357,7 @@ class ChatOpenAI(BaseChatModel): return _generate_from_stream(stream_iter) message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs} - response = self.completion_with_retry( + response = self._completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ) return self._create_chat_result(response) diff --git a/libs/langchain/langchain/chat_models/tongyi.py b/libs/langchain/langchain/chat_models/tongyi.py index 754ea91dccc..5a40506e017 100644 --- a/libs/langchain/langchain/chat_models/tongyi.py +++ b/libs/langchain/langchain/chat_models/tongyi.py @@ -266,14 +266,14 @@ class ChatTongyi(BaseChatModel): **self.model_kwargs, } - def completion_with_retry( + 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, run_manager=run_manager) @retry_decorator - def _completion_with_retry(**_kwargs: Any) -> Any: + def __completion_with_retry(**_kwargs: Any) -> Any: resp = self.client.call(**_kwargs) if resp.status_code == 200: return resp @@ -289,19 +289,19 @@ class ChatTongyi(BaseChatModel): response=resp, ) - return _completion_with_retry(**kwargs) + return __completion_with_retry(**kwargs) - def stream_completion_with_retry( + def _stream_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, run_manager=run_manager) @retry_decorator - def _stream_completion_with_retry(**_kwargs: Any) -> Any: + def __stream_completion_with_retry(**_kwargs: Any) -> Any: return self.client.call(**_kwargs) - return _stream_completion_with_retry(**kwargs) + return __stream_completion_with_retry(**kwargs) def _generate( self, @@ -320,7 +320,7 @@ class ChatTongyi(BaseChatModel): message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs} - response = self.completion_with_retry( + response = self._completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ) return self._create_chat_result(response) @@ -337,7 +337,7 @@ class ChatTongyi(BaseChatModel): # Mark current chunk total length length = 0 default_chunk_class = AIMessageChunk - for chunk in self.stream_completion_with_retry( + for chunk in self._stream_completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ): if len(chunk["output"]["choices"]) == 0: diff --git a/libs/langchain/langchain/llms/anyscale.py b/libs/langchain/langchain/llms/anyscale.py index 65e4a9aded3..e088848018c 100644 --- a/libs/langchain/langchain/llms/anyscale.py +++ b/libs/langchain/langchain/llms/anyscale.py @@ -17,8 +17,8 @@ from langchain.callbacks.manager import ( ) from langchain.llms.openai import ( BaseOpenAI, - acompletion_with_retry, - completion_with_retry, + _acompletion_with_retry, + _completion_with_retry, ) from langchain.pydantic_v1 import Field, root_validator from langchain.schema import Generation, LLMResult @@ -162,7 +162,7 @@ class Anyscale(BaseOpenAI): ) -> Iterator[GenerationChunk]: messages, params = self._get_chat_messages([prompt], stop) params = {**params, **kwargs, "stream": True} - for stream_resp in completion_with_retry( + for stream_resp in _completion_with_retry( self, messages=messages, run_manager=run_manager, **params ): token = stream_resp["choices"][0]["delta"].get("content", "") @@ -180,7 +180,7 @@ class Anyscale(BaseOpenAI): ) -> AsyncIterator[GenerationChunk]: messages, params = self._get_chat_messages([prompt], stop) params = {**params, **kwargs, "stream": True} - async for stream_resp in await acompletion_with_retry( + async for stream_resp in await _acompletion_with_retry( self, messages=messages, run_manager=run_manager, **params ): token = stream_resp["choices"][0]["delta"].get("content", "") @@ -223,7 +223,7 @@ class Anyscale(BaseOpenAI): else: messages, params = self._get_chat_messages([prompt], stop) params = {**params, **kwargs} - response = completion_with_retry( + response = _completion_with_retry( self, messages=messages, run_manager=run_manager, **params ) choices.extend(response["choices"]) @@ -264,7 +264,7 @@ class Anyscale(BaseOpenAI): else: messages, params = self._get_chat_messages([prompt], stop) params = {**params, **kwargs} - response = await acompletion_with_retry( + response = await _acompletion_with_retry( self, messages=messages, run_manager=run_manager, **params ) choices.extend(response["choices"]) diff --git a/libs/langchain/langchain/llms/openai.py b/libs/langchain/langchain/llms/openai.py index 7ff51f442cf..8a9136f36f4 100644 --- a/libs/langchain/langchain/llms/openai.py +++ b/libs/langchain/langchain/llms/openai.py @@ -100,7 +100,7 @@ def _create_retry_decorator( ) -def completion_with_retry( +def _completion_with_retry( llm: Union[BaseOpenAI, OpenAIChat], run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, @@ -109,13 +109,13 @@ def completion_with_retry( retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @retry_decorator - def _completion_with_retry(**kwargs: Any) -> Any: + def __completion_with_retry(**kwargs: Any) -> Any: return llm.client.create(**kwargs) - return _completion_with_retry(**kwargs) + return __completion_with_retry(**kwargs) -async def acompletion_with_retry( +async def _acompletion_with_retry( llm: Union[BaseOpenAI, OpenAIChat], run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, @@ -305,7 +305,7 @@ class BaseOpenAI(BaseLLM): ) -> Iterator[GenerationChunk]: params = {**self._invocation_params, **kwargs, "stream": True} self.get_sub_prompts(params, [prompt], stop) # this mutates params - for stream_resp in completion_with_retry( + for stream_resp in _completion_with_retry( self, prompt=prompt, run_manager=run_manager, **params ): chunk = _stream_response_to_generation_chunk(stream_resp) @@ -329,7 +329,7 @@ class BaseOpenAI(BaseLLM): ) -> AsyncIterator[GenerationChunk]: 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( + async for stream_resp in await _acompletion_with_retry( self, prompt=prompt, run_manager=run_manager, **params ): chunk = _stream_response_to_generation_chunk(stream_resp) @@ -398,7 +398,7 @@ class BaseOpenAI(BaseLLM): } ) else: - response = completion_with_retry( + response = _completion_with_retry( self, prompt=_prompts, run_manager=run_manager, **params ) choices.extend(response["choices"]) @@ -447,7 +447,7 @@ class BaseOpenAI(BaseLLM): } ) else: - response = await acompletion_with_retry( + response = await _acompletion_with_retry( self, prompt=_prompts, run_manager=run_manager, **params ) choices.extend(response["choices"]) @@ -847,7 +847,7 @@ 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( + for stream_resp in _completion_with_retry( self, messages=messages, run_manager=run_manager, **params ): token = stream_resp["choices"][0]["delta"].get("content", "") @@ -865,7 +865,7 @@ class OpenAIChat(BaseLLM): ) -> AsyncIterator[GenerationChunk]: messages, params = self._get_chat_params([prompt], stop) params = {**params, **kwargs, "stream": True} - async for stream_resp in await acompletion_with_retry( + async for stream_resp in await _acompletion_with_retry( self, messages=messages, run_manager=run_manager, **params ): token = stream_resp["choices"][0]["delta"].get("content", "") @@ -893,7 +893,7 @@ class OpenAIChat(BaseLLM): messages, params = self._get_chat_params(prompts, stop) params = {**params, **kwargs} - full_response = completion_with_retry( + full_response = _completion_with_retry( self, messages=messages, run_manager=run_manager, **params ) llm_output = { @@ -926,7 +926,7 @@ class OpenAIChat(BaseLLM): messages, params = self._get_chat_params(prompts, stop) params = {**params, **kwargs} - full_response = await acompletion_with_retry( + full_response = await _acompletion_with_retry( self, messages=messages, run_manager=run_manager, **params ) llm_output = { diff --git a/libs/langchain/langchain/llms/symblai_nebula.py b/libs/langchain/langchain/llms/symblai_nebula.py index 8d33e1a42c0..8f61083ce1b 100644 --- a/libs/langchain/langchain/llms/symblai_nebula.py +++ b/libs/langchain/langchain/llms/symblai_nebula.py @@ -167,7 +167,7 @@ class Nebula(LLM): else: raise ValueError("Prompt must contain instruction and conversation.") - response = completion_with_retry( + response = _completion_with_retry( self, instruction=instruction, conversation=conversation, @@ -232,12 +232,12 @@ def _create_retry_decorator(llm: Nebula) -> Callable[[Any], Any]: ) -def completion_with_retry(llm: Nebula, **kwargs: Any) -> Any: +def _completion_with_retry(llm: Nebula, **kwargs: Any) -> Any: """Use tenacity to retry the completion call.""" retry_decorator = _create_retry_decorator(llm) @retry_decorator - def _completion_with_retry(**_kwargs: Any) -> Any: + def __completion_with_retry(**_kwargs: Any) -> Any: return make_request(llm, **_kwargs) - return _completion_with_retry(**kwargs) + return __completion_with_retry(**kwargs) diff --git a/libs/langchain/langchain/llms/vertexai.py b/libs/langchain/langchain/llms/vertexai.py index a2dc147edff..aa29d9b5069 100644 --- a/libs/langchain/langchain/llms/vertexai.py +++ b/libs/langchain/langchain/llms/vertexai.py @@ -86,7 +86,7 @@ def _create_retry_decorator( return decorator -def completion_with_retry( +def _completion_with_retry( llm: VertexAI, *args: Any, run_manager: Optional[CallbackManagerForLLMRun] = None, @@ -96,13 +96,13 @@ def completion_with_retry( retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @retry_decorator - def _completion_with_retry(*args: Any, **kwargs: Any) -> Any: + def __completion_with_retry(*args: Any, **kwargs: Any) -> Any: return llm.client.predict(*args, **kwargs) - return _completion_with_retry(*args, **kwargs) + return __completion_with_retry(*args, **kwargs) -def stream_completion_with_retry( +def _stream_completion_with_retry( llm: VertexAI, *args: Any, run_manager: Optional[CallbackManagerForLLMRun] = None, @@ -118,7 +118,7 @@ def stream_completion_with_retry( return _completion_with_retry(*args, **kwargs) -async def acompletion_with_retry( +async def _acompletion_with_retry( llm: VertexAI, *args: Any, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, @@ -128,10 +128,10 @@ async def acompletion_with_retry( retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @retry_decorator - async def _acompletion_with_retry(*args: Any, **kwargs: Any) -> Any: + async def __acompletion_with_retry(*args: Any, **kwargs: Any) -> Any: return await llm.client.predict_async(*args, **kwargs) - return await _acompletion_with_retry(*args, **kwargs) + return await __acompletion_with_retry(*args, **kwargs) class _VertexAIBase(BaseModel): @@ -295,7 +295,7 @@ class VertexAI(_VertexAICommon, BaseLLM): generation += chunk generations.append([generation]) else: - res = completion_with_retry( + res = _completion_with_retry( self, prompt, run_manager=run_manager, **params ) generations.append([_response_to_generation(r) for r in res.candidates]) @@ -311,7 +311,7 @@ class VertexAI(_VertexAICommon, BaseLLM): params = self._prepare_params(stop=stop, **kwargs) generations = [] for prompt in prompts: - res = await acompletion_with_retry( + res = await _acompletion_with_retry( self, prompt, run_manager=run_manager, **params ) generations.append([_response_to_generation(r) for r in res.candidates]) @@ -325,7 +325,7 @@ class VertexAI(_VertexAICommon, BaseLLM): **kwargs: Any, ) -> Iterator[GenerationChunk]: params = self._prepare_params(stop=stop, stream=True, **kwargs) - for stream_resp in stream_completion_with_retry( + for stream_resp in _stream_completion_with_retry( self, prompt, run_manager=run_manager, **params ): chunk = _response_to_generation(stream_resp)