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Header authentication of the form Bearer <token>

Embedding model configuration. This object specifies all the information necessary to access an embedding model to usage with Letta, except for secret keys.

Attributes: embedding_endpoint_type (str): The endpoint type for the model. embedding_endpoint (str): The endpoint for the model. embedding_model (str): The model for the embedding. embedding_dim (int): The dimension of the embedding. embedding_chunk_size (int): The chunk size of the embedding. azure_endpoint (:obj:str, optional): The Azure endpoint for the model (Azure only). azure_version (str): The Azure version for the model (Azure only). azure_deployment (str): The Azure deployment for the model (Azure only).

Configuration for a Language Model (LLM) model. This object specifies all the information necessary to access an LLM model to usage with Letta, except for secret keys.

Attributes: model (str): The name of the LLM model. model_endpoint_type (str): The endpoint type for the model. model_endpoint (str): The endpoint for the model. model_wrapper (str): The wrapper for the model. This is used to wrap additional text around the input/output of the model. This is useful for text-to-text completions, such as the Completions API in OpenAI. context_window (int): The context window size for the model. put_inner_thoughts_in_kwargs (bool): Puts inner_thoughts as a kwarg in the function call if this is set to True. This helps with function calling performance and also the generation of inner thoughts. temperature (float): The temperature to use when generating text with the model. A higher temperature will result in more random text. max_tokens (int): The maximum number of tokens to generate.