Source code for langchain_community.chat_models.promptlayer_openai

"""PromptLayer wrapper."""
import datetime
from typing import Any, Dict, List, Optional

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatResult

from langchain_community.chat_models import ChatOpenAI


[docs]class PromptLayerChatOpenAI(ChatOpenAI): """`PromptLayer` and `OpenAI` Chat large language models API. To use, you should have the ``openai`` and ``promptlayer`` python package installed, and the environment variable ``OPENAI_API_KEY`` and ``PROMPTLAYER_API_KEY`` set with your openAI API key and promptlayer key respectively. All parameters that can be passed to the OpenAI LLM can also be passed here. The PromptLayerChatOpenAI adds to optional parameters: ``pl_tags``: List of strings to tag the request with. ``return_pl_id``: If True, the PromptLayer request ID will be returned in the ``generation_info`` field of the ``Generation`` object. Example: .. code-block:: python from langchain_community.chat_models import PromptLayerChatOpenAI openai = PromptLayerChatOpenAI(model="gpt-3.5-turbo") """ pl_tags: Optional[List[str]] return_pl_id: Optional[bool] = False
[docs] @classmethod def is_lc_serializable(cls) -> bool: return False
def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, stream: Optional[bool] = None, **kwargs: Any, ) -> ChatResult: """Call ChatOpenAI generate and then call PromptLayer API to log the request.""" from promptlayer.utils import get_api_key, promptlayer_api_request request_start_time = datetime.datetime.now().timestamp() generated_responses = super()._generate( messages, stop, run_manager, stream=stream, **kwargs ) request_end_time = datetime.datetime.now().timestamp() message_dicts, params = super()._create_message_dicts(messages, stop) for i, generation in enumerate(generated_responses.generations): response_dict, params = super()._create_message_dicts( [generation.message], stop ) params = {**params, **kwargs} pl_request_id = promptlayer_api_request( "langchain.PromptLayerChatOpenAI", "langchain", message_dicts, params, self.pl_tags, response_dict, request_start_time, request_end_time, get_api_key(), return_pl_id=self.return_pl_id, ) if self.return_pl_id: if generation.generation_info is None or not isinstance( generation.generation_info, dict ): generation.generation_info = {} generation.generation_info["pl_request_id"] = pl_request_id return generated_responses async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, stream: Optional[bool] = None, **kwargs: Any, ) -> ChatResult: """Call ChatOpenAI agenerate and then call PromptLayer to log.""" from promptlayer.utils import get_api_key, promptlayer_api_request_async request_start_time = datetime.datetime.now().timestamp() generated_responses = await super()._agenerate( messages, stop, run_manager, stream=stream, **kwargs ) request_end_time = datetime.datetime.now().timestamp() message_dicts, params = super()._create_message_dicts(messages, stop) for i, generation in enumerate(generated_responses.generations): response_dict, params = super()._create_message_dicts( [generation.message], stop ) params = {**params, **kwargs} pl_request_id = await promptlayer_api_request_async( "langchain.PromptLayerChatOpenAI.async", "langchain", message_dicts, params, self.pl_tags, response_dict, request_start_time, request_end_time, get_api_key(), return_pl_id=self.return_pl_id, ) if self.return_pl_id: if generation.generation_info is None or not isinstance( generation.generation_info, dict ): generation.generation_info = {} generation.generation_info["pl_request_id"] = pl_request_id return generated_responses @property def _llm_type(self) -> str: return "promptlayer-openai-chat" @property def _identifying_params(self) -> Dict[str, Any]: return { **super()._identifying_params, "pl_tags": self.pl_tags, "return_pl_id": self.return_pl_id, }