文心一言

author: Jael

Description

A LLM operator generates answer given prompt in messages using a large language model or service. This operator is implemented with Ernie Bot from Baidu. Please note you will need Ernie API key & Secret key to access the service.

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.Ernie(api_key=ERNIE_API_KEY, secret_key=ERNIE_SECRET_KEY))
        .output('messages', 'answer')
)

messages=[
        {'question': 'Zilliz Cloud 是什么？', 'answer': 'Zilliz Cloud 是一种全托管的向量检索服务。'},
        {'question': '它和 Milvus 的关系是什么？'}
    ]
answer = p(messages).get()[0]

Factory Constructor

Create the operator via the following factory method:

LLM.Ernie(api_key: str, secret_key: str)

Parameters:

api_key: str=None

The Ernie API key in string, defaults to None. If None, it will use the environment variable ERNIE_API_KEY.

secret_key: str=None

The Ernie Secret key in string, defaults to None. If None, it will use the environment variable ERNIE_SECRET_KEY.

**kwargs

Other OpenAI parameters such as 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 "question", "answer". For example, [{"question": "a past question?", "answer": "a past answer."}, {"question": "current question?"}]. It also accepts the orignal Ernie message format like [{"role": "user", "content": "a question?"}, {"role": "assistant", "content": "an answer."}]

Returns:

answer: str

​ The next answer generated by role "assistant".