Wang Wei 7205bfdd00 feat: 1. Add system parameters, 2. Align with the QianfanChatEndpoint for function calling (#14275) ...

- **Description:** 
1. Add system parameters to the ERNIE LLM API to set the role of the
LLM.
2. Add support for the ERNIE-Bot-turbo-AI model according from the
document https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Alp0kdm0n.
3. For the function call of ErnieBotChat, align with the
QianfanChatEndpoint.

With this PR, the `QianfanChatEndpoint()` can use the `function calling`
ability with `create_ernie_fn_chain()`. The example is as the following:

```
from langchain.prompts import ChatPromptTemplate
import json
from langchain.prompts.chat import (
    ChatPromptTemplate,
)

from langchain.chat_models import QianfanChatEndpoint
from langchain.chains.ernie_functions import (
    create_ernie_fn_chain,
)

def get_current_news(location: str) -> str:
    """Get the current news based on the location.'

    Args:
        location (str): The location to query.
    
    Returs:
        str: Current news based on the location.
    """

    news_info = {
        "location": location,
        "news": [
            "I have a Book.",
            "It's a nice day, today."
        ]
    }

    return json.dumps(news_info)

def get_current_weather(location: str, unit: str="celsius") -> str:
    """Get the current weather in a given location

    Args:
        location (str): location of the weather.
        unit (str): unit of the tempuature.
    
    Returns:
        str: weather in the given location.
    """

    weather_info = {
        "location": location,
        "temperature": "27",
        "unit": unit,
        "forecast": ["sunny", "windy"],
    }
    return json.dumps(weather_info)

template = ChatPromptTemplate.from_messages([
    ("user", "{user_input}"),
])

chat = QianfanChatEndpoint(model="ERNIE-Bot-4")
chain = create_ernie_fn_chain([get_current_weather, get_current_news], chat, template, verbose=True)
res = chain.run("北京今天的新闻是什么？")
print(res)
```

The result of the above code:
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 北京今天的新闻是什么？
> Finished chain.
{'name': 'get_current_news', 'arguments': {'location': '北京'}}
```

For the `ErnieBotChat`, now can use the `system` parameter to set the
role of the LLM.

```
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
from langchain.chat_models import ErnieBotChat

llm = ErnieBotChat(model_name="ERNIE-Bot-turbo-AI", system="你是一个能力很强的机器人，你的名字叫 小叮当。无论问你什么问题，你都可以给出答案。")
prompt = ChatPromptTemplate.from_messages(
    [
        ("human", "{query}"),
    ]
)
chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
res = chain.run(query="你是谁？")
print(res)
```

The result of the above code:

```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 你是谁？
> Finished chain.
我是小叮当，一个智能机器人。我可以为你提供各种服务，包括回答问题、提供信息、进行计算等。如果你需要任何帮助，请随时告诉我，我会尽力为你提供最好的服务。
```

2023-12-05 17:28:31 -08:00

..

cli

Library Licenses (#13300)

2023-11-28 17:34:27 -08:00

core

Adding BaseChatMessageHistory.__str__ (#14311)

2023-12-05 16:22:31 -05:00

experimental

Multi-modal RAG template (#14186)

2023-12-05 13:36:38 -08:00

langchain

feat: 1. Add system parameters, 2. Align with the QianfanChatEndpoint for function calling (#14275)

2023-12-05 17:28:31 -08:00