How to configure and use it?

Supported Parameters

  • openai_api_key (str) - Set an OpenAI key for running the OpenAI Model. (required)

  • model_name (str) - Set which model of the OpenAI model you want to use. Defaults to gpt-3.5-turbo-16k

  • temperature (float) - The sampling temperature for text generation. Defaults to 0.

  • model_kwargs (Dict[str, Any]): Additional model parameters. (optional)

  • openai_api_base (Optional[str]): The base URL path for API requests (optional).

  • openai_organization (Optional[str]): The organization identifier (optional).

  • openai_proxy (Optional[str]): Proxy configuration for OpenAI (optional).

  • request_timeout (Optional[Union[float, Tuple[float, float]]]): Timeout for API requests (optional).

  • max_retries (int): Maximum number of retries for text generation. Defaults to 6. (optional)

  • streaming (bool): Whether to stream results. Defaults to False

  • n (int): Number of chat completions to generate for each prompt. Defaults to 1.

  • max_tokens (Optional[int]): Maximum number of tokens in the generated response (optional).

  • tiktoken_model_name (Optional[str]): Model name for token counting (optional).

Running in a Colab/Kaggle/Python scripts(s)

from genai_stack.model import OpenAIGpt35Model
from genai_stack.stack.stack import Stack

llm = OpenAIGpt35Model.from_kwargs(
    parameters={"openai_api_key": "sk-xxxx"} # Update with your OpenAI Key
Stack(model=llm)  # Initialize stack
model_response = llm.predict("How long AI has been around.")
  1. Import the model from genai_stack.model

  2. Instantiate the class with openai_api_key

  3. Call .predict() method and pass the query you want the model to answer to.

  4. Print the response. As the response is a dictionary, get the output only.

    • The response on predict() from the model includes output.

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