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ChatAI21

總覽​

本筆記本涵蓋如何開始使用 AI21 聊天模型。請注意，不同的聊天模型支援不同的參數。請參閱 AI21 文件以瞭解有關您選擇模型中參數的更多資訊。查看所有 AI21 的 LangChain 組件。

整合詳細資訊​

類別套件本地可序列化JS 支援套件下載次數套件最新版本
ChatAI21langchain-ai21❌beta✅PyPI - DownloadsPyPI - Version

模型功能​

工具呼叫結構化輸出JSON 模式圖像輸入音訊輸入影片輸入Token 層級串流原生非同步Token 使用量Logprobs
✅✅❌❌❌❌✅✅✅❌

設定​

憑證​

我們需要取得 AI21 API 金鑰 並設定 AI21_API_KEY 環境變數

import os
from getpass import getpass

if "AI21_API_KEY" not in os.environ:
    os.environ["AI21_API_KEY"] = getpass()

如果您想要取得模型呼叫的自動追蹤，您也可以透過取消註解下方內容來設定您的 LangSmith API 金鑰

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

安裝​

!pip install -qU langchain-ai21

例項化​

現在我們可以例項化我們的模型物件並產生聊天完成

from langchain_ai21 import ChatAI21

llm = ChatAI21(model="jamba-instruct", temperature=0)
API 參考文檔：ChatAI21

調用​

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

串鏈​

我們可以像這樣使用提示範本串鏈我們的模型

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)
API 參考文檔：ChatPromptTemplate

工具呼叫 / 函數呼叫

此範例示範如何將工具呼叫與 AI21 模型搭配使用

import os
from getpass import getpass

from langchain_ai21.chat_models import ChatAI21
from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage
from langchain_core.tools import tool
from langchain_core.utils.function_calling import convert_to_openai_tool

if "AI21_API_KEY" not in os.environ:
    os.environ["AI21_API_KEY"] = getpass()


@tool
def get_weather(location: str, date: str) -> str:
    """“Provide the weather for the specified location on the given date.”"""
    if location == "New York" and date == "2024-12-05":
        return "25 celsius"
    elif location == "New York" and date == "2024-12-06":
        return "27 celsius"
    elif location == "London" and date == "2024-12-05":
        return "22 celsius"
    return "32 celsius"


llm = ChatAI21(model="jamba-1.5-mini")

llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])

chat_messages = [
    SystemMessage(
        content="You are a helpful assistant. You can use the provided tools "
        "to assist with various tasks and provide accurate information"
    )
]

human_messages = [
    HumanMessage(
        content="What is the forecast for the weather in New York on December 5, 2024?"
    ),
    HumanMessage(content="And what about the 2024-12-06?"),
    HumanMessage(content="OK, thank you."),
    HumanMessage(content="What is the expected weather in London on December 5, 2024?"),
]


for human_message in human_messages:
    print(f"User: {human_message.content}")
    chat_messages.append(human_message)
    response = llm_with_tools.invoke(chat_messages)
    chat_messages.append(response)
    if response.tool_calls:
        tool_call = response.tool_calls[0]
        if tool_call["name"] == "get_weather":
            weather = get_weather.invoke(
                {
                    "location": tool_call["args"]["location"],
                    "date": tool_call["args"]["date"],
                }
            )
            chat_messages.append(
                ToolMessage(content=weather, tool_call_id=tool_call["id"])
            )
            llm_answer = llm_with_tools.invoke(chat_messages)
            print(f"Assistant: {llm_answer.content}")
    else:
        print(f"Assistant: {response.content}")

API 參考文檔​

如需所有 ChatAI21 功能和組態的詳細文件，請前往 API 參考文檔： https://langchain-python.dev.org.tw/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html

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