用 Python + Ollama 搭一个本地代码问答助手，随时随地问编程问题

curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5:7b

pip install ollama rich

#!/usr/bin/env python3
"""本地代码问答助手"""

import ollama
import rich
from rich.console import Console
from rich.markdown import Markdown
from rich.syntax import Syntax
import glob
import os

console = Console()

def read_project_context(project_dir="."):
    """读取当前目录下的代码文件，作为上下文"""
    contexts = []
    extensions = ["*.py", "*.js", "*.ts", "*.go", "*.java", "*.rs", "*.md", "*.json", "*.yaml", "*.yml", "*.sh"]
    
    for ext in extensions:
        for filepath in glob.glob(os.path.join(project_dir, "**", ext), recursive=True):
            # 排除 node_modules、.git 等目录
            if any(skip in filepath for skip in ["node_modules", ".git", "__pycache__", ".venv", "venv"]):
                continue
            try:
                with open(filepath, "r", encoding="utf-8", errors="ignore") as f:
                    content = f.read()
                contexts.append(f"文件: {filepath}\n```\n{content}\n```")
            except Exception:
                continue
    
    return "\n\n".join(contexts[:50000])  # 限制上下文长度


def get_user_question():
    """获取用户提问"""
    console.print("\n[bold cyan]💡 你可以问我任何编程问题，输入 /exit 退出，/clear 清空历史[/bold cyan]")
    return console.input("[bold green]你: [/bold green]")


def chat(question, conversation_history, project_context):
    """发送问题到 Ollama，返回回答"""
    system_prompt = """你是一个专业的编程助手。你的任务是帮助开发者解决编程问题。

规则：
1. 用中文回答
2. 代码片段要用 markdown 代码块包裹，并标注语言
3. 解释要简洁明了，不要啰嗦
4. 如果问题涉及具体项目代码，结合上下文回答
5. 不确定时坦诚说明，不要编造"""

    # 构建消息
    messages = [{"role": "system", "content": system_prompt}]
    
    # 添加项目上下文（首次对话时）
    if conversation_history == [] and project_context:
        messages.append({
            "role": "system",
            "content": f"以下是当前项目的部分代码上下文，供参考：\n{project_context}"
        })
    
    # 添加对话历史
    messages.extend(conversation_history)
    messages.append({"role": "user", "content": question})
    
    # 调用 Ollama
    response = ollama.chat(model="qwen2.5:7b", messages=messages)
    answer = response["message"]["content"]
    
    return answer


def main():
    console.print("[bold blue]🤖 本地代码问答助手[/bold blue]")
    console.print("[dim]基于 Ollama + qwen2.5，完全离线运行[/dim]\n")
    
    # 读取项目上下文
    console.print("[dim]📂 正在读取项目代码上下文...[/dim]")
    project_context = read_project_context(".")
    if project_context:
        console.print(f"[dim]✅ 已加载项目上下文，覆盖 {project_context.count('文件:')} 个文件[/dim]")
    else:
        console.print("[dim]⚠️ 未找到代码文件，将以通用模式运行[/dim]")
    
    conversation_history = []
    
    while True:
        question = get_user_question().strip()
        
        if not question:
            continue
        
        # 处理特殊命令
        if question.lower() == "/exit" or question.lower() == "/quit":
            console.print("[dim]👋 再见！[/dim]")
            break
        
        if question.lower() == "/clear":
            conversation_history = []
            console.print("[dim]🗑️ 对话历史已清空[/dim]")
            continue
        
        if question.lower() == "/context":
            console.print(f"[dim]📊 项目上下文长度: {len(project_context)} 字符[/dim]")
            continue
        
        # 显示思考中
        console.print("[dim]🤔 思考中...[/dim]")
        
        try:
            answer = chat(question, conversation_history, project_context)
            
            # 更新对话历史
            conversation_history.append({"role": "user", "content": question})
            conversation_history.append({"role": "assistant", "content": answer})
            
            # 控制历史长度，避免上下文过长
            if len(conversation_history) > 20:
                conversation_history = conversation_history[-20:]
            
            # 渲染回答
            console.print("\n[bold magenta]🤖 助手:[/bold magenta]")
            console.print(Markdown(answer))
            
        except Exception as e:
            console.print(f"[red]❌ 错误: {e}[/red]")
            console.print("[dim]请检查 Ollama 是否正在运行: ollama list[/dim]")


if __name__ == "__main__":
    main()

python code_assistant.py

from collections import OrderedDict

class LRUCache:
    def __init__(self, capacity: int):
        self.cache = OrderedDict()
        self.capacity = capacity

    def get(self, key: str) -> int:
        if key not in self.cache:
            return -1
        self.cache.move_to_end(key)
        return self.cache[key]

    def put(self, key: str, value: int) -> None:
        if key in self.cache:
            self.cache.move_to_end(key)
        self.cache[key] = value
        if len(self.cache) > self.capacity:
            self.cache.popitem(last=False)