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ChatTongyi

Tongyi Qwen is a large language model developed by Alibaba's Damo Academy. It is capable of understanding user intent through natural language understanding and semantic analysis, based on user input in natural language. It provides services and assistance to users in different domains and tasks. By providing clear and detailed instructions, you can obtain results that better align with your expectations. In this notebook, we will introduce how to use langchain with Tongyi mainly in Chat corresponding to the package langchain/chat_models in langchain

# 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)
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')

Tool Calling​

ChatTongyi supports tool calling API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool.

Use with bind_tools​

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 Reference:

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': ''}]

Construct args manually​

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': ''}])

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