# OpenAI

### 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)

```python
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.")
print(model_response["output"])
```

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`.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://genaistack.aiplanet.com/components/llms/openai.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.