初次体验

  1. 获取api

    在智谱注册后，在https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys获得api.

  2. 运行代码体验

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    from zhipuai import ZhipuAI
    client = ZhipuAI(api_key="")  # 请填写您自己的APIKey
    response = client.chat.completions.create(
        model="glm-4-plus",  # 请填写您要调用的模型名称
        messages=[
            {"role": "user", "content": "作为一名营销专家，请为我的产品创作一个吸引人的口号"},
            {"role": "assistant", "content": "当然，要创作一个吸引人的口号，请告诉我一些关于您产品的信息"},
            {"role": "user", "content": "智谱AI开放平台"},
            {"role": "assistant", "content": "点燃未来，智谱AI绘制无限，让创新触手可及！"},
            {"role": "user", "content": "创作一个更精准且吸引人的口号"}
        ],
    )
    print(response.choices[0].message)

和langchain框架使用体验

具体看这里 https://www.bigmodel.cn/dev/api/thirdparty-frame/langchain-sdk

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# !pip uninstall langchain_community
# !pip uninstall langchain langchain_openai langchain_core httpx_sse langchainhub langchain_community
!pip install langchain==0.1.12. 
##注意要这个版本，因为这个langchain更新比较频繁
!pip install  langchain_openai langchain_core httpx_sse langchainhub langchain_community
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!pip install langchain langchainhub httpx_sse

# ! pip install langchain_openai
import os

from langchain_openai import ChatOpenAI
from langchain.prompts import (
    ChatPromptTemplate,
    MessagesPlaceholder,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory

llm = ChatOpenAI(
    temperature=0.95,
    model="glm-4",
    openai_api_key="", ##!you api
    openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)
prompt = ChatPromptTemplate(
    messages=[
        SystemMessagePromptTemplate.from_template(
            "You are a nice chatbot having a conversation with a human."
        ),
        MessagesPlaceholder(variable_name="chat_history"),
        HumanMessagePromptTemplate.from_template("{question}")
    ]
)

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=True,
    memory=memory
)
conversation.invoke({"question": "tell me a joke"})

tavily的api从这里获取：https://app.tavily.com/home

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# !pip install langchain_core
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools.tavily_search import TavilySearchResults

os.environ["TAVILY_API_KEY"] = "api" ##换成自己的ai
tools = [TavilySearchResults(max_results=1)]
prompt = hub.pull("hwchase17/react")

# # Choose the LLM to use
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is LangChain?"})