What Even Is AI? (I Took a Break & Had to Relearn Everything)

from openai import OpenAI

client = OpenAI()

class SimpleAgent:
    def __init__(self, name, system_prompt):
        self.name = name
        self.system_prompt = system_prompt
        self.memory = []  # 记忆，记录历史对话

    def think(self, user_input):
        # 把历史记忆和新的用户输入拼在一起
        messages = [
            {"role": "system", "content": self.system_prompt}
        ] + self.memory + [
            {"role": "user", "content": user_input}
        ]

        # 调用LLM思考下一步做什么
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=[
                {
                    "type": "function",
                    "function": {
                        "name": "search_web",
                        "description": "搜索网页获取最新信息",
                        "parameters": {
                            "type": "object",
                            "properties": {
                                "query": {
                                    "type": "string",
                                    "description": "搜索关键词"
                                }
                            },
                            "required": ["query"]
                        }
                    }
                },
                {
                    "type": "function",
                    "function": {
                        "name": "send_email",
                        "description": "发送邮件给指定收件人",
                        "parameters": {
                            "type": "object",
                            "properties": {
                                "to": {
                                    "type": "string",
                                    "description": "收件人邮箱地址"
                                },
                                "subject": {
                                    "type": "string",
                                    "description": "邮件主题"
                                },
                                "content": {
                                    "type": "string",
                                    "description": "邮件内容"
                                }
                            },
                            "required": ["to", "subject", "content"]
                        }
                    }
                }
            ]
        )

        # 记录到记忆
        self.memory.append({"role": "user", "content": user_input})
        self.memory.append({"role": "assistant", "content": response.choices[0].message.content})

        # 如果需要调用工具，就调用工具
        if response.choices[0].message.tool_calls:
            for tool_call in response.choices[0].message.tool_calls:
                function_name = tool_call.function.name
                arguments = eval(tool_call.function.arguments)

                if function_name == "search_web":
                    result = self.search_web(arguments["query"])
                elif function_name == "send_email":
                    result = self.send_email(**arguments)

                # 把工具调用的结果再返回给LLM，让它继续思考
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "name": function_name,
                    "content": result
                })

                response = client.chat.completions.create(
                    model="gpt-4o",
                    messages=messages
                )

                self.memory.append({"role": "assistant", "content": response.choices[0].message.content})

        return response.choices[0].message.content

    def search_web(self, query):
        # 调用搜索引擎API获取结果
        return f"搜索 '{query}' 的结果：..."

    def send_email(self, to, subject, content):
        # 调用邮件发送API
        return f"已发送邮件给 {to}，主题：{subject}"

# 使用Agent
agent = SimpleAgent(
    name="我的助手",
    system_prompt="你是我的个人助手，可以帮我搜索信息和发送邮件。回答要简洁准确。"
)

response = agent.think("帮我搜索一下2026年5月的AI行业新闻，然后把摘要发送给xxx@example.com")
print(response)

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import RetrievalQA

# 1. 加载文档
loader = TextLoader("公司知识库.txt")
documents = loader.load()

# 2. 把文档切成小片段
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# 3. 把片段转换成向量，存在向量数据库里
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
db = Chroma.from_documents(texts, embeddings)

# 4. 创建检索器
retriever = db.as_retriever(search_kwargs={"k": 3})

# 5. 创建RAG问答链
qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o-mini"),
    chain_type="stuff",
    retriever=retriever,
    return_source_documents=True
)

# 6. 提问
result = qa.invoke({"query": "公司的年假制度是什么？"})
print(result["result"])