BaseOpenAI

Model name to use.

What sampling temperature to use.

The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size.

Total probability mass of tokens to consider at each step.

Penalizes repeated tokens according to frequency.

Penalizes repeated tokens.

How many completions to generate for each prompt.

Generates best_of completions server-side and returns the "best".

Holds any model parameters valid for create call not explicitly specified.

Automatically inferred from env var OPENAI_API_KEY if not provided.

Base URL path for API requests, leave blank if not using a proxy or service emulator.

Automatically inferred from env var OPENAI_ORG_ID if not provided.

Batch size to use when passing multiple documents to generate.

Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or None.

Adjust the probability of specific tokens being generated.

Maximum number of retries to make when generating.

Whether to stream the results or not.

Set of special tokens that are allowed。

Set of special tokens that are not allowed。

The model name to pass to tiktoken when using this class. Tiktoken is used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name. However, there are some cases where you may want to use this Embedding class with a model name not supported by tiktoken. This can include when using Azure embeddings or when using one of the many model providers that expose an OpenAI-like API but with different models. In those cases, in order to avoid erroring when tiktoken is called, you can specify a model name to use here.

Optional httpx.Client.

Get max context size for this model.

Get the namespace of the langchain object.

Build extra kwargs from additional params that were passed in.

Validate that api key and python package exists in environment.

Get the sub prompts for llm call.

Create the LLMResult from the choices and prompts.

Get the token IDs using the tiktoken package.

Calculate the maximum number of tokens possible to generate for a model.

Calculate the maximum number of tokens possible to generate for a prompt.