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Args: modelname: The modelname we want to know the context size for. Returns: The maximum context size Example: .. code-block:: python max_tokens = openai.modelname_to_contextsize("gpt-3.5-turbo-instruct") ii iii@iiiiAi)z gpt-4o-minizgpt-4ozgpt-4o-2024-05-13zgpt-4z gpt-4-0314z gpt-4-0613z gpt-4-32kzgpt-4-32k-0314zgpt-4-32k-0613z gpt-3.5-turbozgpt-3.5-turbo-0301zgpt-3.5-turbo-0613zgpt-3.5-turbo-16kzgpt-3.5-turbo-16k-0613r<z text-ada-001adaztext-babbage-001Zbabbageztext-curie-001ZcurieZdavinciztext-davinci-003ztext-davinci-002zcode-davinci-002zcode-davinci-001zcode-cushman-002zcode-cushman-001zft-:rNzUnknown model: z=. Please provide a valid OpenAI model name.Known models are: z, )splitr5rpjoinr!)rZmodel_token_mappingZ context_sizer(r(r)modelname_to_contextsizesN    z#BaseOpenAI.modelname_to_contextsizecCs ||jS)z$Get max context size for this model.)rr@rr(r(r)max_context_size7szBaseOpenAI.max_context_size)rvr$cCs||}|j|S)aoCalculate the maximum number of tokens possible to generate for a prompt. Args: prompt: The prompt to pass into the model. Returns: The maximum number of tokens to generate for a prompt. Example: .. code-block:: python max_tokens = openai.max_token_for_prompt("Tell me a joke.") )rr)rsrvZ num_tokensr(r(r)r<s z BaseOpenAI.max_tokens_for_prompt)NN)NN)NN)NN)N)?__name__ __module__ __qualname____doc__rr:__annotations__r;r@rBrErGrHrIrJrKr4rMrrPrrRrTrUrWrYrZr\r]r0r_setr`rbrcrdrerfrgrhrZ model_configr classmethodrmrtpropertyrurrrrrrr~rrr staticmethodrrr __classcell__r(r(rr)r7@s                          S<;r7csreZdZdZeddddZeddddZed dfd d Zed dd dZ ed dddZ Z S)rru OpenAI completion model integration. Setup: Install ``langchain-openai`` and set environment variable ``OPENAI_API_KEY``. .. code-block:: bash pip install -U langchain-openai export OPENAI_API_KEY="your-api-key" Key init args — completion params: model: str Name of OpenAI model to use. temperature: float Sampling temperature. max_tokens: Optional[int] Max number of tokens to generate. logprobs: Optional[bool] Whether to return logprobs. stream_options: Dict Configure streaming outputs, like whether to return token usage when streaming (``{"include_usage": True}``). Key init args — client params: timeout: Union[float, Tuple[float, float], Any, None] Timeout for requests. max_retries: int Max number of retries. api_key: Optional[str] OpenAI API key. If not passed in will be read from env var OPENAI_API_KEY. base_url: Optional[str] Base URL for API requests. Only specify if using a proxy or service emulator. organization: Optional[str] OpenAI organization ID. If not passed in will be read from env var OPENAI_ORG_ID. See full list of supported init args and their descriptions in the params section. Instantiate: .. code-block:: python from langchain_openai import OpenAI llm = OpenAI( model="gpt-3.5-turbo-instruct", temperature=0, max_retries=2, # api_key="...", # base_url="...", # organization="...", # other params... ) Invoke: .. code-block:: python input_text = "The meaning of life is " llm.invoke(input_text) .. code-block:: none "a philosophical question that has been debated by thinkers and scholars for centuries." Stream: .. code-block:: python for chunk in llm.stream(input_text): print(chunk, end="|") .. code-block:: none a| philosophical| question| that| has| been| debated| by| thinkers| and| scholars| for| centuries|. .. code-block:: python "".join(llm.stream(input_text)) .. code-block:: none "a philosophical question that has been debated by thinkers and scholars for centuries." Async: .. code-block:: python await llm.ainvoke(input_text) # stream: # async for chunk in (await llm.astream(input_text)): # print(chunk) # batch: # await llm.abatch([input_text]) .. code-block:: none "a philosophical question that has been debated by thinkers and scholars for centuries." rrocCsgdS)z*Get the namespace of the langchain object.)Z langchainZllmsrqr(rlr(r(r)get_lc_namespaceszOpenAI.get_lc_namespacer^cCsdS)z9Return whether this model can be serialized by Langchain.Tr(rr(r(r)is_lc_serializableszOpenAI.is_lc_serializablercsid|jitjS)Nr=)r@rr~rrr(r)r~szOpenAI._invocation_paramszDict[str, str]cCsddiS)NrPrOr(rr(r(r) lc_secretsszOpenAI.lc_secretscCs8i}|jr|j|d<|jr$|j|d<|jr4|j|d<|S)NrRrTrU)rRrTrU)rs attributesr(r(r) lc_attributess   zOpenAI.lc_attributes) rrrrrrrrr~rrrr(r(rr)rrNsdrr)1 __future__rloggingrtypingrrrrrrr r r r r rrrqrZlangchain_core.callbacksrrZ#langchain_core.language_models.llmsrZlangchain_core.outputsrrrZlangchain_core.utilsrZlangchain_core.utils.utilsrrrZpydanticrrrrZtyping_extensionsr getLoggerrloggerr*r6r7rrr(r(r(r)s* <