如何用利用deepseek的API能力来搭建属于自己的智能体-优雅草卓伊凡

import requests

api_key = "YOUR_DEEPSEEK_API_KEY"
url = "https://api.deepseek.com/v1/chat/completions"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

data = {
    "model": "deepseek-v3",
    "messages": [
        {"role": "user", "content": "你好，介绍一下你自己！"}
    ]
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "deepseek-ai/deepseek-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "如何构建一个 AI 智能体？"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import Tool
from langchain_community.llms import DeepSeek

# 初始化 DeepSeek LLM
llm = DeepSeek(api_key="YOUR_API_KEY")

# 定义工具（如网络搜索、计算器）
tools = [
    Tool(
        name="Search",
        func=lambda query: "搜索结果：" + query,
        description="用于搜索网络信息"
    )
]

# 构建智能体
agent = create_react_agent(llm, tools)
agent_executor = AgentExecutor(agent=agent, tools=tools)

# 运行智能体
response = agent_executor.invoke({"input": "2024年巴黎奥运会的举办时间？"})
print(response["output"])

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,
    verbose=True
)

agent_executor.invoke({"input": "我叫张三，记住我！"})
agent_executor.invoke({"input": "我是谁？"})  # 输出 "你是张三！"

import gradio as gr

def respond(message, history):
    response = agent_executor.invoke({"input": message})
    return response["output"]

gr.ChatInterface(respond).launch()