Langchain构建聊天机器人

1、Langchain调用LLM

#!/usr/bin/env pythonfrom langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage
from langchain_core.messages import HumanMessage
import os# from langchain.chat_models import ChatOpenAI
# llm = ChatOpenAI(openai_api_base="https://api.crond.dev/v1", openai_api_key="sk-aTU1v09zvzfZLJ6oCzhIxilgri7sFYZ0Xf1lItmqKCGgI2Mt", model="gpt-3.5-turbo")
# res = llm.predict("hello")
# print(res)os.environ['http_proxy'] = '127.0.0.1:7890'
os.environ['https_proxy'] = '127.0.0.1:7890'os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = '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'  #langsmith监控key
os.environ["OPENAI_API_KEY"] = 'aHP78iUOsuamufjyc2lkt0KD0iOFRKfly8fQ74QcdWrPbyrm‌'  #openai的keymodel = ChatOpenAI(model="gpt-4-turbo")msg = [SystemMessage(content='请将一下的内容翻译成意大利语') ,HumanMessage(content='你好,请问你要去哪里?')]result = model.invoke(msg)
print(result)

响应的结果：

langsmith监控能查看大模型的响应过程：

直接将content内容解析出来：

待完善。。。。。。