chatglm/README.md

# Zhipu AI

*author: Jael*

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## Description 描述

This operator is implemented with [ChatGLM services from Zhipu AI](https://open.bigmodel.cn).
It directly returns the original response in dictionary without parsing.
Please note you will need [API Key](https://open.bigmodel.cn/login?redirect=%2Fusercenter%2Fapikeys) to access the service.

LLM/ZhipuAI 利用了来自[智谱AI开放平台](https://open.bigmodel.cn)的大语言模型服务。该算子以字典的形式直接返回原始的模型回复。请注意，您需要[API Key](https://open.bigmodel.cn/login?redirect=%2Fusercenter%2Fapikeys)才能访问该服务。

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## Code Example 代码示例

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

```python
from towhee import pipe, ops

p = (
    pipe.input('messages')
        .map('messages', 'response', ops.LLM.ZhipuAI(
            api_key=ZHIPUAI_API_KEY,
            model_name='chatglm_130b',  # or 'chatglm_6b'
            temperature=0.5,
            max_tokens=50,
            ))
        .output('response')
)

messages=[
    {'system': '你是一个资深的软件工程师，善于回答关于科技项目的问题。'},
    {'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？'}
    ]
response = p(messages).get()[0]
answer = response['choices'][0]['content']
token_usage = response['usage']
```

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## Factory Constructor 接口说明

Create the operator via the following factory method:

***LLM.ZhipuAI(api_key: str, model_name: str, \*\*kwargs)***

**Parameters:**


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

The Zhipu AI API key in string, defaults to None. If None, it will use the environment variable `ZHIPUAI_API_KEY`.

***model_name***: *str='chatglm_130b'*

The model used in Zhipu AI service, defaults to 'chatglm_130b'. Visit Zhipu AI documentation for supported models.

***\*\*kwargs***

Other ChatGLM parameters such as temperature, etc.

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## Interface 使用说明

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?"}].
It also accepts the orignal ChatGLM message format like [{"role": "user", "content": "a question?"}, {"role": "assistant", "content": "an answer."}]

**Returns**:

*response: dict*

   The original llm response in dictionary, including next answer and token usage.

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