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 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.schema import HumanMessage
from langchain_community.chat_models.tongyi import ChatTongyi

chatLLM = ChatTongyi(
    streaming=True,
)
res = chatLLM.stream([HumanMessage(content="hi")], streaming=True)
for r in res:
    print("chat resp:", r)

chat resp: content='Hello! How' additional_kwargs={} example=False
chat resp: content=' can I assist you today?' additional_kwargs={} example=False

from langchain.schema 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)

AIMessageChunk(content="J'aime programmer.", additional_kwargs={}, example=False)