OpenAI Chat Completion

author: Jael

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. Please note you need an OpenAI API key to access OpenAI.

Code Example

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

Write a pipeline with explicit inputs/outputs name specifications:

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)

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.

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".