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
# 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 Reference:
chat resp: content='Hello! How' additional_kwargs={} example=False
chat resp: content=' can I assist you today?' additional_kwargs={} example=False
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)
API Reference:
AIMessageChunk(content="J'aime programmer.", additional_kwargs={}, example=False)
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.
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
API Reference:
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': 'dae79197-8780-9b7e-8c15-6a83e2a53534', 'token_usage': {'input_tokens': 229, 'output_tokens': 19, 'total_tokens': 248}}, id='run-9e06f837-582b-473b-bb1f-5e99a68ecc10-0', tool_calls=[{'name': 'get_current_weather', 'args': {'location': 'San Francisco'}, 'id': ''}])