python --version
pip --version

git --version

python -m venv deepseek_env
deepseek_env\Scripts\activate

pip install deepseek-sdk --upgrade

{
    "api_key": "your_api_key_here",
    "model": "deepseek-chat",
    "temperature": 0.7,
    "max_tokens": 1000,
    "timeout": 30,
    "proxy": null,
    "log_level": "INFO"
}

import json
from deepseek import DeepSeekClient

# 加载配置
with open('config.json') as f:
    config = json.load(f)

# 初始化客户端
client = DeepSeekClient(config['api_key'])

# 简单查询示例
def simple_query(prompt):
    response = client.generate(
        model=config['model'],
        prompt=prompt,
        temperature=config['temperature'],
        max_tokens=config['max_tokens']
    )
    return response['choices'][0]['text']

# 测试查询
if __name__ == "__main__":
    question = "请用中文解释一下量子计算的基本原理"
    answer = simple_query(question)
    print("问题:", question)
    print("回答:", answer)

python deepseek_demo.py

import os
import json
from deepseek import DeepSeekClient, VectorStore

class RAGSystem:
    def __init__(self, config_path="config.json"):
        # Load configuration
        with open(config_path) as f:
            self.config = json.load(f)

        # Initialize clients and stores
        self.client = DeepSeekClient(self.config['api_key'])
        self.vs = VectorStore("my_vector_store")

        # Create knowledge base directory if not exists
        if not os.path.exists("knowledge_base"):
            os.makedirs("knowledge_base")

    def ingest_document(self, file_path):
        """Process and store a document"""
        with open(file_path, 'r', encoding='utf-8') as f:
            content = f.read()

        # Split document into chunks (simplified version)
        chunks = [content[i:i+500] for i in range(0, len(content), 500)]

        # Store each chunk with its embedding in vector store 
        for i, chunk in enumerate(chunks):
            embedding = self.client.get_embedding(chunk)
            doc_id = f"{os.path.basename(file_path)}_chunk_{i}"
            self.vs.add(doc_id, embedding, metadata={"text": chunk})

    def query(self, question):
        """Answer a question using RAG approach"""
        # Get question embedding for semantic search 
        q_embedding = self.client.get_embedding(question)

        # Find most relevant chunks from vector store (top_k=3)
        results = self.vs.search(q_embedding, top_k=3)

        # Build context from retrieved documents 
        context = "\n\n".join([r.metadata["text"] for r in results])

        # Generate answer using context 
        prompt = f"""基于以下上下文信息回答问题。如果无法从上下文中得到答案，请说明。

        上下文：
{context}

        问题：{question}
        回答："""

        response = self.client.generate(
            model=self.config['model'],
            prompt=prompt,
            temperature=self.config['temperature'],
            max_tokens=self.config['max_tokens']
        )

        return response['choices'][0]['text']

if __name__ == "__main__":
    # Initialize RAG system 
    rag_system = RAGSystem()

    # Ingest sample document (create a sample.txt first)
    if os.path.exists("sample.txt"):
        rag_system.ingest_document("sample.txt")

    # Interactive query loop 
    print("RAG系统已启动，输入'退出'结束对话")

    while True:
        question = input("\n请输入问题: ")

        if question.lower() in ['退出', 'exit', 'quit']:
            break

        answer = rag_system.query(question) 

        print("\n回答:")
        print(answer) 

{
    "python.pythonPath": "./deepseek_env/Scripts/python.exe",
    "python.linting.enabled": true,
    "python.linting.pylintEnabled": true,
    "python.formatting.provider": "autopep8",

}

{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.

}

FROM python:3.9-slim

WORKDIR /app 

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt 

COPY . .

CMD ["python", "./rag_demo.py"]

docker build -t deepseek-app .
docker run -it --rm -p5000:5000 deepseek-app 

# PowerShell管理员权限执行  
Set-ItemProperty -Path HKLM:\SYSTEM\CurrentControlSet\Control\Session Manager\Memory Management -Name PagedPoolSize -Value '0xFFFFFFFF'<br>
   

pip config set global.cert path\to\cert.pem  <br>
   

New-ItemProperty -Path HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem LongPathsEnabled -Value1  <br>
   

Set-Service WinDefend -StartupType Disabled  <br>
   

pip uninstall torch torchvision torchaudio   
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118   <br>
   

Get-WinEvent -LogName Application | Where {$_.Message-like "*DeepSeek*"} | Select TimeCreated, Message | Format-Table AutoSize   <br>
   

9.**后台运行保持**
```powershell   
Start-Process python .\rag_demo.py WindowStyle Hidden   

$WshShell=NewObject-comObject WScript.Shell $Shortcut=$WshShell.CreateShortcut "$env:APPDATA\\Microsoft\\Windows\\Start Menu\\Programs\\Startup\\DeepSeek.lnk") $Shortcut.TargetPath="C:\\path\\to\\python.exe" $Shortcut.Save()  

New-NetFirewallRule DisplayName"AllowDeepSeekPort5000" Direction Inbound LocalPort5000 ProtocolTCP Action Allow  

$Action=New-ScheduledTaskAction Execute'C:\path\to\python.exe'-Argument'. \rag_demo.py'$Trigger=New-ScheduledTaskTrigger Daily At9am Register-ScheduledTask TaskName"DailyDeepSeekRun" Action$Action Trigger$Trigger User"SYSTEM" RunLevel Highest  

pip install pyinstrument python m pyinstrument rag demo.py

pip install memory profiler python m memory profiler rag demo.py

pip install pyinstaller pyinstaller onefile windowed rag demo.py

nssm install DeepSeekService C:\path\to\python.exe C:\path\to\rag demo.py nssm start DeepSeekService