# OpenAI Chat Completion

*author: Jael*

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## Description

A LLM operator generates answer given prompt in messages using a large language model or service.
This operator is implemented with Chat Completion method from [OpenAI](https://platform.openai.com/docs/guides/chat).
Please note you need an [OpenAI API key](https://platform.openai.com/account/api-keys) to access OpenAI.

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## Code Example

Use the default model to continue the conversation from given messages.

*Write a pipeline with explicit inputs/outputs name specifications:*

```python
from towhee import pipe, ops

p = (
    pipe.input('messages')
        .map('messages', 'answer', ops.LLM.OpenAI(api_key=OPENAI_API_KEY))
        .output('messages', 'answer')
)

messages=[
        {'question': 'Who won the world series in 2020?', 'answer': 'The Los Angeles Dodgers won the World Series in 2020.'},
        {'question': 'Where was it played?'}
    ]
answer = p(messages)
```

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## Factory Constructor

Create the operator via the following factory method:

***chatbot.openai(model_name: str, api_key: str)***

**Parameters:**

***model_name***: *str*

The model name in string, defaults to 'gpt-3.5-turbo'. Supported model names:
- gpt-3.5-turbo
- pt-3.5-turbo-0301

***api_key***: *str=None*

The OpenAI API key in string, defaults to None.

***\*\*kwargs***

Other OpenAI parameters such as max_tokens, stream, temperature, etc.

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## Interface

The operator takes a piece of text in string as input.
It returns answer in json.

***\_\_call\_\_(txt)***

**Parameters:**

***messages***: *list*

​	A list of messages to set up chat.
Must be a list of dictionaries with key value from "system", "question", "answer". For example, [{"question": "a past question?", "answer": "a past answer."}, {"question": "current question?"}]

**Returns**:

*answer: str*

​	The next answer generated by role "assistant".

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