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fix: overwrite stream for ChatOpenAI in runtime (#8288)
<!-- Thank you for contributing to LangChain! Replace this comment with: - Description: a description of the change, - Issue: the issue # it fixes (if applicable), - Dependencies: any dependencies required for this change, - Tag maintainer: for a quicker response, tag the relevant maintainer (see below), - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out! Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @baskaryan - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @baskaryan - Memory: @hwchase17 - Agents / Tools / Toolkits: @hinthornw - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> @hwchase17, @baskaryan --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Nuno Campos <nuno@boringbits.io>
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@ -381,9 +381,10 @@ class ChatOpenAI(BaseChatModel):
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messages: List[BaseMessage],
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any,
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**kwargs: Any,
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) -> ChatResult:
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) -> ChatResult:
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if self.streaming:
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if stream if stream is not None else self.streaming:
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generation: Optional[ChatGenerationChunk] = None
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generation: Optional[ChatGenerationChunk] = None
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for chunk in self._stream(
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for chunk in self._stream(
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messages=messages, stop=stop, run_manager=run_manager, **kwargs
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messages=messages, stop=stop, run_manager=run_manager, **kwargs
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@ -454,9 +455,10 @@ class ChatOpenAI(BaseChatModel):
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messages: List[BaseMessage],
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any,
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**kwargs: Any,
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) -> ChatResult:
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) -> ChatResult:
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if self.streaming:
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if stream if stream is not None else self.streaming:
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generation: Optional[ChatGenerationChunk] = None
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generation: Optional[ChatGenerationChunk] = None
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async for chunk in self._astream(
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async for chunk in self._astream(
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messages=messages, stop=stop, run_manager=run_manager, **kwargs
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messages=messages, stop=stop, run_manager=run_manager, **kwargs
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@ -43,13 +43,16 @@ class PromptLayerChatOpenAI(ChatOpenAI):
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messages: List[BaseMessage],
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any
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**kwargs: Any
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) -> ChatResult:
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) -> ChatResult:
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"""Call ChatOpenAI generate and then call PromptLayer API to log the request."""
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"""Call ChatOpenAI generate and then call PromptLayer API to log the request."""
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from promptlayer.utils import get_api_key, promptlayer_api_request
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from promptlayer.utils import get_api_key, promptlayer_api_request
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request_start_time = datetime.datetime.now().timestamp()
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request_start_time = datetime.datetime.now().timestamp()
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generated_responses = super()._generate(messages, stop, run_manager, **kwargs)
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generated_responses = super()._generate(
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messages, stop, run_manager, stream=stream, **kwargs
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)
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request_end_time = datetime.datetime.now().timestamp()
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request_end_time = datetime.datetime.now().timestamp()
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message_dicts, params = super()._create_message_dicts(messages, stop)
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message_dicts, params = super()._create_message_dicts(messages, stop)
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for i, generation in enumerate(generated_responses.generations):
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for i, generation in enumerate(generated_responses.generations):
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@ -82,13 +85,16 @@ class PromptLayerChatOpenAI(ChatOpenAI):
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messages: List[BaseMessage],
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any
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**kwargs: Any
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) -> ChatResult:
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) -> ChatResult:
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"""Call ChatOpenAI agenerate and then call PromptLayer to log."""
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"""Call ChatOpenAI agenerate and then call PromptLayer to log."""
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from promptlayer.utils import get_api_key, promptlayer_api_request_async
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from promptlayer.utils import get_api_key, promptlayer_api_request_async
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request_start_time = datetime.datetime.now().timestamp()
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request_start_time = datetime.datetime.now().timestamp()
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generated_responses = await super()._agenerate(messages, stop, run_manager)
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generated_responses = await super()._agenerate(
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messages, stop, run_manager, stream=stream, **kwargs
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)
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request_end_time = datetime.datetime.now().timestamp()
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request_end_time = datetime.datetime.now().timestamp()
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message_dicts, params = super()._create_message_dicts(messages, stop)
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message_dicts, params = super()._create_message_dicts(messages, stop)
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for i, generation in enumerate(generated_responses.generations):
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for i, generation in enumerate(generated_responses.generations):
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