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ChatTongyi

Tongyi Qwen 是由阿里巴巴達摩院開發的大型語言模型。它能夠基於使用者以自然語言輸入的內容，透過自然語言理解和語義分析來理解使用者意圖。它在不同的領域和任務中為使用者提供服務和協助。透過提供清晰且詳細的指示，您可以獲得更符合您期望的結果。在本筆記本中，我們將介紹如何在 langchain 中使用 Tongyi，主要是在 Chat 中，對應於 langchain 中的套件 langchain/chat_models

# Install the package
%pip install --upgrade --quiet  dashscope
Note: you may need to restart the kernel to use updated packages.
# Get a new token: https://help.aliyun.com/document_detail/611472.html?spm=a2c4g.2399481.0.0
from getpass import getpass

DASHSCOPE_API_KEY = getpass()
import os

os.environ["DASHSCOPE_API_KEY"] = DASHSCOPE_API_KEY
from langchain_community.chat_models.tongyi import ChatTongyi
from langchain_core.messages import HumanMessage

chatLLM = ChatTongyi(
    streaming=True,
)
res = chatLLM.stream([HumanMessage(content="hi")], streaming=True)
for r in res:
    print("chat resp:", r)
API 參考：ChatTongyi | HumanMessage
chat resp: content='Hello' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'
chat resp: content='!' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'
chat resp: content=' How' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'
chat resp: content=' can I assist you today' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'
chat resp: content='?' id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'
chat resp: content='' response_metadata={'finish_reason': 'stop', 'request_id': '921db2c5-4d53-9a89-8e87-e4ad6a671237', 'token_usage': {'input_tokens': 20, 'output_tokens': 9, 'total_tokens': 29}} id='run-f2301962-6d46-423c-8afa-1e667bd11e2b'
from langchain_core.messages import HumanMessage, SystemMessage

messages = [
    SystemMessage(
        content="You are a helpful assistant that translates English to French."
    ),
    HumanMessage(
        content="Translate this sentence from English to French. I love programming."
    ),
]
chatLLM(messages)
/Users/cheese/PARA/Projects/langchain-contribution/langchain/libs/core/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseChatModel.__call__` was deprecated in langchain-core 0.1.7 and will be removed in 0.2.0. Use invoke instead.
  warn_deprecated(
AIMessage(content="J'adore programmer.", response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'stop', 'request_id': 'ae725086-0ffa-9728-8c72-b204c7bc7eeb', 'token_usage': {'input_tokens': 36, 'output_tokens': 6, 'total_tokens': 42}}, id='run-060cc103-ef5f-4c8a-af40-792ac7f40c26-0')

工具呼叫​

ChatTongyi 支援工具呼叫 API，可讓您描述工具及其參數，並讓模型傳回一個 JSON 物件，其中包含要調用的工具以及該工具的輸入。

搭配 bind_tools 使用​

from langchain_community.chat_models.tongyi import ChatTongyi
from langchain_core.tools import tool


@tool
def multiply(first_int: int, second_int: int) -> int:
    """Multiply two integers together."""
    return first_int * second_int


llm = ChatTongyi(model="qwen-turbo")

llm_with_tools = llm.bind_tools([multiply])

msg = llm_with_tools.invoke("What's 5 times forty two")

print(msg)
API 參考：ChatTongyi | tool
content='' additional_kwargs={'tool_calls': [{'function': {'name': 'multiply', 'arguments': '{"first_int": 5, "second_int": 42}'}, 'id': '', 'type': 'function'}]} response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'tool_calls', 'request_id': '4acf0e36-44af-987a-a0c0-8b5c5eaa1a8b', 'token_usage': {'input_tokens': 200, 'output_tokens': 25, 'total_tokens': 225}} id='run-0ecd0f09-1d20-4e55-a4f3-f14d1f710ae7-0' tool_calls=[{'name': 'multiply', 'args': {'first_int': 5, 'second_int': 42}, 'id': ''}]

手動建構參數​

from langchain_community.chat_models.tongyi import ChatTongyi
from langchain_core.messages import HumanMessage, SystemMessage

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "当你想知道现在的时间时非常有用。",
            "parameters": {},
        },
    },
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "当你想查询指定城市的天气时非常有用。",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                    }
                },
            },
            "required": ["location"],
        },
    },
]

messages = [
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="What is the weather like in San Francisco?"),
]
chatLLM = ChatTongyi()
llm_kwargs = {"tools": tools, "result_format": "message"}
ai_message = chatLLM.bind(**llm_kwargs).invoke(messages)
ai_message
AIMessage(content='', additional_kwargs={'tool_calls': [{'function': {'name': 'get_current_weather', 'arguments': '{"location": "San Francisco"}'}, 'id': '', 'type': 'function'}]}, response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'tool_calls', 'request_id': '87ef33d2-5c6b-9457-91e2-39faad7120eb', 'token_usage': {'input_tokens': 229, 'output_tokens': 19, 'total_tokens': 248}}, id='run-7939ba7f-e3f7-46f8-980b-30499b52723c-0', tool_calls=[{'name': 'get_current_weather', 'args': {'location': 'San Francisco'}, 'id': ''}])

Tongyi 視覺模型​

Qwen-VL (qwen-vl-plus/qwen-vl-max) 是可以處理圖像的模型。

from langchain_community.chat_models import ChatTongyi
from langchain_core.messages import HumanMessage

chatLLM = ChatTongyi(model_name="qwen-vl-max")
image_message = {
    "image": "https://lilianweng.github.io/posts/2023-06-23-agent/agent-overview.png",
}
text_message = {
    "text": "summarize this picture",
}
message = HumanMessage(content=[text_message, image_message])
chatLLM.invoke([message])
API 參考：ChatTongyi | HumanMessage
AIMessage(content=[{'text': 'The image presents a flowchart of an artificial intelligence system. The system is divided into two main components: short-term memory and long-term memory, which are connected to the "Memory" box.\n\nFrom the "Memory" box, there are three branches leading to different functionalities:\n\n1. "Tools" - This branch represents various tools that the AI system can utilize, including "Calendar()", "Calculator()", "CodeInterpreter()", "Search()" and others not explicitly listed.\n\n2. "Action" - This branch represents the action taken by the AI system based on its processing of information. It\'s connected to both the "Tools" and the "Agent" boxes.\n\n3. "Planning" - This branch represents the planning process of the AI system, which involves reflection, self-critics, chain of thoughts, subgoal decomposition, and other processes not shown.\n\nThe central component of the system is the "Agent" box, which seems to orchestrate the flow of information between the different components. The "Agent" interacts with the "Tools" and "Memory" boxes, suggesting it plays a crucial role in the AI\'s decision-making process. \n\nOverall, the image depicts a complex and interconnected artificial intelligence system, where different components work together to process information, make decisions, and take actions.'}], response_metadata={'model_name': 'qwen-vl-max', 'finish_reason': 'stop', 'request_id': '6a2b9e90-7c3b-960d-8a10-6a0cf9991ae5', 'token_usage': {'input_tokens': 1262, 'output_tokens': 260, 'image_tokens': 1232}}, id='run-fd030661-c734-4580-b977-b77d42680742-0')

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