本笔记本介绍如何开始使用 AI21 聊天模型。 请注意，不同的聊天模型支持不同的参数。有关所选模型参数的更多信息，请参阅 AI21 文档。 查看所有 AI21 的 LangChain 组件。

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集成详情

ClassPackageLocalSerializableJS supportDownloadsVersion
ChatAI21langchain-ai21❌beta✅PyPI - DownloadsPyPI - Version

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模型功能

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
✅✅❌❌❌❌✅✅✅❌

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设置

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凭证

我们需要获取 AI21 API 密钥 并设置 AI21_API_KEY 环境变量： 
import os
from getpass import getpass

if "AI21_API_KEY" not in os.environ:
    os.environ["AI21_API_KEY"] = getpass()
要启用模型调用的自动跟踪，请设置您的 LangSmith API 密钥： 
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

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安装

!pip install -qU langchain-ai21

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实例化

现在我们可以实例化模型对象并生成聊天完成： 
from langchain_ai21 import ChatAI21

llm = ChatAI21(model="jamba-instruct", temperature=0)

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Invocation

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

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Tool Calls / Function Calling

This example shows how to use tool calling with AI21 models: 
import os
from getpass import getpass

from langchain_ai21.chat_models import ChatAI21
from langchain.messages import HumanMessage, SystemMessage, ToolMessage
from langchain.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}")

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API reference

For detailed documentation of all ChatAI21 features and configurations head to the API reference: python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html
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