ChatTongyi
通义千问是阿里巴巴达摩院开发的大型语言模型。它能够通过自然语言理解和语义分析理解用户意图，并根据用户以自然语言输入的信息提供服务和帮助。通过提供清晰详细的指令，您可以获得更符合您预期的结果。在本笔记本中，我们将介绍如何在 langchain 中使用 通义，主要是在 langchain 中 langchain/chat_models 包对应的 Chat 中。
# 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 参考：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)
API 参考：HumanMessage | SystemMessage
/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')
工具调用
ChatTongyi 支持工具调用 API，该 API 允许您描述工具及其参数，并让模型返回一个 JSON 对象，其中包含要调用的工具以及该工具的输入。
与 bind_tools 一起使用
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 参考：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': ''}]
手动构建参数
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': ''}])
通义与视觉
Qwen-VL(qwen-vl-plus/qwen-vl-max) 是可以处理图像的模型。
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 参考：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')