Source code for langchain_community.llms.promptlayer_openai

import datetime
from typing import Any, List, Optional

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.outputs import LLMResult

from langchain_community.llms.openai import OpenAI, OpenAIChat


[docs]class PromptLayerOpenAI(OpenAI): """PromptLayer OpenAI large language models. 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 PromptLayerOpenAI LLM adds two 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.llms import PromptLayerOpenAI openai = PromptLayerOpenAI(model_name="gpt-3.5-turbo-instruct") """ pl_tags: Optional[List[str]] return_pl_id: Optional[bool] = False
[docs] @classmethod def is_lc_serializable(cls) -> bool: return False
def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Call OpenAI 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(prompts, stop, run_manager) request_end_time = datetime.datetime.now().timestamp() for i in range(len(prompts)): prompt = prompts[i] generation = generated_responses.generations[i][0] resp = { "text": generation.text, "llm_output": generated_responses.llm_output, } params = {**self._identifying_params, **kwargs} pl_request_id = promptlayer_api_request( "langchain.PromptLayerOpenAI", "langchain", [prompt], params, self.pl_tags, resp, 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, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: from promptlayer.utils import get_api_key, promptlayer_api_request_async request_start_time = datetime.datetime.now().timestamp() generated_responses = await super()._agenerate(prompts, stop, run_manager) request_end_time = datetime.datetime.now().timestamp() for i in range(len(prompts)): prompt = prompts[i] generation = generated_responses.generations[i][0] resp = { "text": generation.text, "llm_output": generated_responses.llm_output, } params = {**self._identifying_params, **kwargs} pl_request_id = await promptlayer_api_request_async( "langchain.PromptLayerOpenAI.async", "langchain", [prompt], params, self.pl_tags, resp, 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
[docs]class PromptLayerOpenAIChat(OpenAIChat): """PromptLayer OpenAI large language models. 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 OpenAIChat LLM can also be passed here. The PromptLayerOpenAIChat adds two 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.llms import PromptLayerOpenAIChat openaichat = PromptLayerOpenAIChat(model_name="gpt-3.5-turbo") """ pl_tags: Optional[List[str]] return_pl_id: Optional[bool] = False def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Call OpenAI 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(prompts, stop, run_manager) request_end_time = datetime.datetime.now().timestamp() for i in range(len(prompts)): prompt = prompts[i] generation = generated_responses.generations[i][0] resp = { "text": generation.text, "llm_output": generated_responses.llm_output, } params = {**self._identifying_params, **kwargs} pl_request_id = promptlayer_api_request( "langchain.PromptLayerOpenAIChat", "langchain", [prompt], params, self.pl_tags, resp, 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, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: from promptlayer.utils import get_api_key, promptlayer_api_request_async request_start_time = datetime.datetime.now().timestamp() generated_responses = await super()._agenerate(prompts, stop, run_manager) request_end_time = datetime.datetime.now().timestamp() for i in range(len(prompts)): prompt = prompts[i] generation = generated_responses.generations[i][0] resp = { "text": generation.text, "llm_output": generated_responses.llm_output, } params = {**self._identifying_params, **kwargs} pl_request_id = await promptlayer_api_request_async( "langchain.PromptLayerOpenAIChat.async", "langchain", [prompt], params, self.pl_tags, resp, 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