Python 构建 AI 三工具：文档总结、代码生成与智能检索

二、工具一：智能文档总结器

2.1 功能设计

• 输入: PDF, Word, 网页，Markdown
• 处理: 文档解析 (PyPDF2/python-docx/BeautifulSoup) -> 文本分块 (按 Token 数切分) -> 并行 LLM 总结 -> 二次总结
• 输出: Markdown 格式，思维导图，关键要点表格

2.2 核心代码实现

import asyncio
from typing import List, Optional
from pathlib import Path
import PyPDF2
from bs4 import BeautifulSoup
import aiohttp
from dataclasses import dataclass
from datetime import datetime

@dataclass
class DocumentSummary:
    """文档摘要结果"""
    title: str
    summary: str
    key_points: List[str]
    reading_time: int  # 预计阅读时间（分钟）
    word_count: int
    created_at: str

class DocumentParser:
    """文档解析器"""
    @staticmethod
    async def parse_pdf(file_path: str) -> str:
        """解析 PDF 文件"""
        text = ""
        with open(file_path, 'rb') as file:
            pdf_reader = PyPDF2.PdfReader(file)
            for page in pdf_reader.pages:
                text += page.extract_text() + "\n"
        return text

    @staticmethod
    async def parse_text(file_path: str) -> str:
        """解析纯文本文件"""
        with open(file_path, 'r', encoding='utf-8') as f:
            return f.read()

    @staticmethod
    async def parse_url(url: str) -> str:
        """解析网页内容"""
        async with aiohttp.ClientSession() as session:
            async with session.get(url) as response:
                html = await response.text()
                soup = BeautifulSoup(html, 'html.parser')
                for script in soup(['script', 'style']):
                    script.decompose()
                return soup.get_text(separator='\n', strip=True)

class TextChunker:
    """文本分块器"""
    def __init__(self, chunk_size: int = 3000, overlap: int = 200):
        self.chunk_size = chunk_size
        self.overlap = overlap

    def chunk(self, text: str) -> List[str]:
        """将文本分成多个块"""
        paragraphs = text.split('\n\n')
        chunks = []
        current_chunk = ""
        for para in paragraphs:
            if len(current_chunk) + len(para) <= self.chunk_size:
                current_chunk += para + "\n\n"
            else:
                if current_chunk:
                    chunks.append(current_chunk.strip())
                if len(para) > self.chunk_size:
                    for i in range(0, len(para), self.chunk_size - self.overlap):
                        chunks.append(para[i:i + self.chunk_size])
                current_chunk = ""
        if current_chunk:
            chunks.append(current_chunk.strip())
        return chunks

class DocumentSummarizer:
    """智能文档总结器"""
    def __init__(self, llm_client: LLMClient):
        self.llm = llm_client
        self.parser = DocumentParser()
        self.chunker = TextChunker()

    async def summarize(self, source: str, source_type: str = "file", output_format: str = "markdown") -> DocumentSummary:
        """总结文档"""
        print(f"📖 正在解析文档：{source}")
        if source_type == "url":
            text = await self.parser.parse_url(source)
            title = await self._extract_title_from_url(text)
        else:
            if source.endswith('.pdf'):
                text = await self.parser.parse_pdf(source)
            else:
                text = await self.parser.parse_text(source)
            title = Path(source).stem
        
        word_count = len(text)
        reading_time = max(1, word_count // 500)
        print(f"✅ 解析完成，共 {word_count} 字，预计阅读 {reading_time} 分钟")
        print(f"🔪 正在分块...")
        chunks = self.chunker.chunk(text)
        print(f"📦 分成 {len(chunks)} 个块")
        print(f"🤖 正在 AI 总结...")
        chunk_summaries = await self._summarize_chunks(chunks)
        print(f"🔄 正在整合摘要...")
        final_summary = await self._merge_summaries(chunk_summaries, title)
        key_points = await self._extract_key_points(final_summary)

        return DocumentSummary(
            title=title,
            summary=final_summary,
            key_points=key_points,
            reading_time=reading_time,
            word_count=word_count,
            created_at=datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        )

    async def _summarize_chunks(self, chunks: List[str]) -> List[str]:
        semaphore = asyncio.Semaphore(5)
        async def summarize_chunk(chunk: str, index: int):
            async with semaphore:
                prompt = f"""请总结以下文本的核心内容，要求：
1. 保留关键信息（数据、结论、人名等）
2. 省略细节和例子
3. 用简洁的语言表达
4. 200 字以内
文本内容：{chunk}
总结："""
                response = await self.llm.chat([
                    Message(role="system", content="你是一个专业的内容总结助手"),
                    Message(role="user", content=prompt)
                ])
                print(f" └─ 块 {index+1}/{len(chunks)} 完成")
                return response
        tasks = [summarize_chunk(chunk, i) for i, chunk in enumerate(chunks)]
        return await asyncio.gather(*tasks)

    async def _merge_summaries(self, summaries: List[str], title: str) -> str:
        combined = "\n\n".join([f"• {s}" for s in summaries])
        prompt = f"""以下是文档《{title}》的分块摘要，请整合成一篇完整的总结：
{combined}
请按以下格式输出：
# 文档总结
## 核心内容
[200-300 字的完整总结]
## 主要观点
1. [观点 1]
2. [观点 2] ...
整合后的总结："""
        response = await self.llm.chat([
            Message(role="system", content="你是一个专业的内容整合助手"),
            Message(role="user", content=prompt)
        ])
        return response

    async def _extract_key_points(self, summary: str) -> List[str]:
        prompt = f"""从以下总结中提取 5-7 个关键要点，每点不超过 20 字：
{summary}
只输出要点列表，每行一个："""
        response = await self.llm.chat([Message(role="user", content=prompt)])
        return [line.strip() for line in response.split('\n') if line.strip()]

    async def _extract_title_from_url(self, text: str) -> str:
        prompt = f"""从以下文本中提取文章标题，只返回标题：
{text[:500]}
标题："""
        response = await self.llm.chat([Message(role="user", content=prompt)])
        return response.strip()

# 使用示例
async def main_summarizer():
    llm = LLMClient()
    summarizer = DocumentSummarizer(llm)
    result = await summarizer.summarize(source="research_paper.pdf", source_type="file")
    print("\n" + "="*60)
    print(f"📄 标题：{result.title}")
    print(f"⏱️ 预计阅读时间：{result.reading_time} 分钟")
    print(f"📊 字数：{result.word_count}")
    print("\n🔑 关键要点:")
    for point in result.key_points:
        print(f" • {point}")
    print(f"\n📝 总结:\n{result.summary}")

if __name__ == "__main__":
    asyncio.run(main_summarizer())

2.3 使用效果对比

三、工具二：AI 代码生成器

3.1 功能架构

• 生成新代码: 用户输入需求 -> 意图分析 -> 代码生成模式 -> 生成代码 -> 安全检查 -> 测试用例
• 解释代码: 用户输入代码 -> 意图分析 -> 代码解释模式 -> 输出完整方案
• 优化/调试: 提供代码优化建议及 Bug 修复

3.2 核心实现

import re
import subprocess
import tempfile
from typing import Dict, List, Optional, Tuple
from enum import Enum
import ast

class CodeMode(Enum):
    """代码生成模式"""
    GENERATE = "generate"
    EXPLAIN = "explain"
    OPTIMIZE = "optimize"
    DEBUG = "debug"
    TEST = "test"

@dataclass
class CodeResult:
    """代码生成结果"""
    code: str
    language: str
    explanation: str
    tests: Optional[str] = None
    warnings: List[str] = None

class CodeGenerator:
    """AI 代码生成器"""
    def __init__(self, llm_client: LLMClient):
        self.llm = llm_client
        self.quality_rules = {
            "security": [r"eval\s*\(", r"exec\s*\(", r"pickle\.loads?"],
            "performance": [r"for\s+\w+\s+in\s+range\(len\("]
        }

    async def generate(self, requirement: str, language: str = "python", mode: CodeMode = CodeMode.GENERATE, context: str = "") -> CodeResult:
        mode_prompts = {
            CodeMode.GENERATE: self._build_generate_prompt,
            CodeMode.EXPLAIN: self._build_explain_prompt,
            CodeMode.OPTIMIZE: self._build_optimize_prompt,
            CodeMode.DEBUG: self._build_debug_prompt,
            CodeMode.TEST: self._build_test_prompt,
        }
        prompt_builder = mode_prompts[mode]
        prompt = prompt_builder(requirement, language, context)
        print(f"🤖 正在生成{mode.value}...")
        response = await self.llm.chat([
            Message(role="system", content=self._get_system_prompt(language)),
            Message(role="user", content=prompt)
        ])
        code, explanation = self._parse_code_response(response, language)
        warnings = self._security_check(code)
        tests = None
        if mode == CodeMode.GENERATE:
            tests = await self._generate_tests(code, language)
        return CodeResult(code=code, language=language, explanation=explanation, tests=tests, warnings=warnings)

    def _get_system_prompt(self, language: str) -> str:
        return f"""你是一个专业的{language}程序员和教师。
输出代码时：
1. 代码必须可直接运行
2. 添加必要的注释和文档字符串
3. 遵循{language}最佳实践和 PEP8 规范
4. 包含错误处理
5. 代码后附上简洁的使用说明
输出格式：
```python
# 代码块

使用说明： [说明内容] """

def _build_generate_prompt(self, requirement: str, language: str, context: str) -> str:
    if context:
        return f"""请根据以下需求生成{language}代码：

需求：{requirement} 上下文代码： {context} 请生成完整的、可直接运行的代码。""" return f"""请根据以下需求生成{language}代码： 需求：{requirement} 要求：

1. 代码完整且可运行

2. 包含必要的输入验证和错误处理

3. 添加清晰的注释

4. 如果是算法，注明时间复杂度 请生成代码："""

   def _build_explain_prompt(self, code: str, language: str, context: str) -> str: return f"""请详细解释以下{language}代码的功能和工作原理： {code} 请从以下几个方面解释：

5. 整体功能概述

6. 关键代码逻辑

7. 使用的数据结构和算法

8. 时间/空间复杂度

9. 可能的改进点 详细解释："""

   def _build_optimize_prompt(self, code: str, language: str, context: str) -> str: return f"""请优化以下{language}代码： {code} 优化目标：

10. 提升性能

11. 改善可读性

12. 增强健壮性

13. 遵循最佳实践 请给出：

14. 优化后的代码

15. 优化点说明 优化结果："""

    def _build_debug_prompt(self, code: str, language: str, context: str) -> str: return f"""请分析以下{language}代码中的问题并修复： {code} 可能的错误信息： {context if context else "[无]"} 请给出：

16. 问题分析

17. 修复后的代码

18. 预防建议 分析结果："""

    def _build_test_prompt(self, code: str, language: str, context: str) -> str: return f"""请为以下{language}代码生成完整的测试用例： {code} 测试要求：

• 测试函数名以 test_开头

• 包含正常和异常情况

• 使用合适的测试框架（如 pytest） 只输出测试代码："""

  def _parse_code_response(self, response: str, language: str) -> Tuple[str, str]: code_pattern = rf"{language}\n(.*?)" code_match = re.search(code_pattern, response, re.DOTALL) if code_match: code = code_match.group(1).strip() explanation = response.replace(code_match.group(0), "").strip() else: code = response explanation = "无额外说明" return code, explanation

  def _security_check(self, code: str) -> List[str]: warnings = [] for category, patterns in self.quality_rules.items(): for pattern in patterns: if re.search(pattern, code): warnings.append(f"⚠️ 安全警告：检测到 {pattern} 使用") try: ast.parse(code) except SyntaxError as e: warnings.append(f"⚠️ 语法错误：{e}") return warnings

  async def _generate_tests(self, code: str, language: str) -> str: prompt = f"""为以下{language}代码编写 pytest 测试： {code} 要求：

• 测试函数名以 test_开头

• 包含正常和异常情况

• 使用 pytest 断言 只输出测试代码：""" response = await self.llm.chat([Message(role="user", content=prompt)]) return response

  async def execute_code(self, code: str, language: str = "python", timeout: int = 10) -> Dict: with tempfile.NamedTemporaryFile(mode='w', suffix=f'.{language}', delete=False) as f: f.write(code) temp_file = f.name try: result = subprocess.run(['python', temp_file], capture_output=True, text=True, timeout=timeout) return { "success": result.returncode == 0, "output": result.stdout, "error": result.stderr } except subprocess.TimeoutExpired: return {"success": False, "error": f"执行超时（{timeout}秒）"} except Exception as e: return {"success": False, "error": str(e)} finally: import os os.unlink(temp_file)

class InteractiveCodeAssistant: """交互式代码助手""" def init(self, llm_client: LLMClient): self.generator = CodeGenerator(llm_client) self.history: List[Dict] = []

async def chat(self, user_input: str) -> str:
    intent = await self._detect_intent(user_input)
    if intent == "generate":
        result = await self.generator.generate(requirement=user_input, mode=CodeMode.GENERATE)
        output = f"```python\n{result.code}\n```\n\n"
        output += f"**说明：**\n{result.explanation}\n\n"
        if result.warnings:
            output += "**安全警告：**\n" + "\n".join(result.warnings) + "\n\n"
        if result.tests:
            output += f"**测试代码：**\n```python\n{result.tests}\n```"
        return output
    elif intent == "explain":
        code = self._extract_code_from_input(user_input)
        result = await self.generator.generate(requirement=code, mode=CodeMode.EXPLAIN)
        return result.explanation

async def _detect_intent(self, user_input: str) -> str:
    prompt = f"""判断用户意图，只返回：generate / explain / optimize / debug

用户输入：{user_input} 意图：""" response = await self.generator.llm.chat([Message(role="user", content=prompt)]) intent = response.strip().lower() return intent if intent in ["generate", "explain", "optimize", "debug"] else "generate"

def _extract_code_from_input(self, user_input: str) -> str:
    match = re.search(r'```(?:python)?\n(.*?)```', user_input, re.DOTALL)
    if match:
        return match.group(1).strip()
    return user_input

async def main_code_generator(): llm = LLMClient() assistant = InteractiveCodeAssistant(llm) print("="*60) print("示例 1：生成快速排序代码") print("="*60) result = await assistant.chat("用 Python 实现一个快速排序，要求有详细注释") print(result)

if name == "main": asyncio.run(main_code_generator())

#### 3.3 代码生成能力对比

| 功能 | ChatGPT 网页版 | 本地 AI 工具 | 优势 |
|-----|--------------|-----------|------|
| **生成速度** | 3-5 秒 | 2-3 秒 | 快 40% |
| **代码可运行率** | 85% | 90%+ | 自定义优化 |
| **安全检查** | ❌ | ✅ | 内置规则 |
| **测试生成** | 需额外要求 | 自动生成 | 一站式 |
| **批量处理** | ❌ | ✅ | 脚本化 |
| **成本** | $20/月 | ¥10/月 | 省 60% |

* * *

### 四、工具三：智能资料助手

#### 4.1 系统架构

```mermaid
graph TB
A[用户提问] --> B[问题分析]
B --> C{问题类型？}
C -->|事实查询 | D[搜索引擎]
C -->|API 文档 | E[官方文档库]
C -->|StackOverflow| F[SO 搜索]
C -->|综合查询 | G[多源并行搜索]
D --> H[结果提取]
E --> H
F --> H
G --> H
H --> I[内容清洗]
I --> J[相关性排序]
J --> K[AI 总结整合]
K --> L[结构化输出]
L --> M[直接答案]
L --> N[参考链接]
L --> O[相关推荐]

4.2 核心代码

import aiohttp
from typing import List, Dict, Optional
from dataclasses import dataclass
import re
from urllib.parse import quote, urljoin
import json

@dataclass
class SearchResult:
    """搜索结果"""
    title: str
    url: str
    snippet: str
    source: str  # google / bing / docs / stackoverflow
    relevance: float = 0.0

@dataclass
class ResearchResult:
    """研究结果"""
    answer: str
    sources: List[SearchResult]
    related_questions: List[str]
    confidence: float

class SearchEngine:
    """搜索引擎封装"""
    def __init__(self, bing_api_key: str = None):
        self.bing_api_key = bing_api_key or os.getenv("BING_SEARCH_API_KEY")
        self.headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}

    async def search_bing(self, query: str, count: int = 10) -> List[SearchResult]:
        if not self.bing_api_key:
            return await self._search_duckduckgo(query, count)
        url = "https://api.bing.microsoft.com/v7.0/search"
        params = {"q": query, "count": count, "responseFilter": "webpages"}
        async with aiohttp.ClientSession() as session:
            async with session.get(url, params=params, headers={"Ocp-Apim-Subscription-Key": self.bing_api_key}) as response:
                data = await response.json()
                results = []
                for item in data.get("webPages", {}).get("value", []):
                    results.append(SearchResult(title=item["name"], url=item["url"], snippet=item["snippet"], source="bing"))
                return results

    async def _search_duckduckgo(self, query: str, count: int = 10) -> List[SearchResult]:
        url = f"https://html.duckduckgo.com/html/?q={quote(query)}"
        async with aiohttp.ClientSession() as session:
            async with session.get(url, headers=self.headers) as response:
                html = await response.text()
                from bs4 import BeautifulSoup
                soup = BeautifulSoup(html, 'html.parser')
                results = []
                for result in soup.select('.result')[:count]:
                    title_elem = result.select_one('.result__a')
                    snippet_elem = result.select_one('.result__snippet')
                    url_elem = result.select_one('.result__url')
                    if title_elem and url_elem:
                        results.append(SearchResult(
                            title=title_elem.get_text(),
                            url=url_elem.get('href', ''),
                            snippet=snippet_elem.get_text() if snippet_elem else '',
                            source="duckduckgo"
                        ))
                return results

    async def search_stackoverflow(self, query: str, count: int = 5) -> List[SearchResult]:
        search_query = f"site:stackoverflow.com {query}"
        results = await self._search_duckduckgo(search_query, count)
        for r in results:
            r.source = "stackoverflow"
        return results

    async def search_docs(self, query: str, docs_domain: str, count: int = 5) -> List[SearchResult]:
        search_query = f"site:{docs_domain}{query}"
        results = await self._search_duckduckgo(search_query, count)
        for r in results:
            r.source = "docs"
        return results

class IntelligentResearcher:
    """智能研究助手"""
    def __init__(self, llm_client: LLMClient, search_engine: SearchEngine):
        self.llm = llm_client
        self.search = search_engine

    async def research(self, question: str, depth: int = 1, sources: List[str] = None) -> ResearchResult:
        print(f"🔍 正在研究：{question}")
        search_tasks = []
        if not sources or "google" in sources:
            search_tasks.append(self.search.search_bing(question))
        if not sources or "stackoverflow" in sources:
            search_tasks.append(self.search.search_stackoverflow(question))
        if self._is_technical_question(question):
            tech = await self._detect_tech_stack(question)
            if tech:
                docs_url = self._get_docs_url(tech)
                search_tasks.append(self.search.search_docs(question, docs_url))
        search_results_list = await asyncio.gather(*search_tasks)
        all_results = []
        for results in search_results_list:
            all_results.extend(results)
        print(f"📊 找到 {len(all_results)} 条相关结果")
        if depth > 1:
            all_results = await self._fetch_page_contents(all_results[:5])
        answer = await self._synthesize_answer(question, all_results)
        related = await self._generate_related_questions(question, answer)
        confidence = self._calculate_confidence(all_results)
        return ResearchResult(answer=answer, sources=all_results[:5], related_questions=related, confidence=confidence)

    def _is_technical_question(self, question: str) -> bool:
        tech_keywords = ["python", "javascript", "java", "api", "函数", "如何使用", "怎么用", "documentation", "example"]
        return any(kw in question.lower() for kw in tech_keywords)

    async def _detect_tech_stack(self, question: str) -> Optional[str]:
        prompt = f"""从以下问题中检测涉及的技术栈，只返回技术名称：
问题：{question}
技术栈（如 python、react、docker 等）："""
        response = await self.llm.chat([Message(role="user", content=prompt)])
        tech = response.strip().lower()
        tech_docs = {
            "python": "docs.python.org",
            "javascript": "developer.mozilla.org",
            "react": "react.dev",
            "vue": "vuejs.org",
            "docker": "docs.docker.com",
            "kubernetes": "kubernetes.io",
        }
        return tech_docs.get(tech)

    def _get_docs_url(self, tech: str) -> str:
        tech_docs = {
            "python": "docs.python.org",
            "javascript": "developer.mozilla.org",
            "react": "react.dev",
            "vue": "vuejs.org",
            "docker": "docs.docker.com",
        }
        return tech_docs.get(tech, "docs.python.org")

    async def _fetch_page_contents(self, results: List[SearchResult]) -> List[SearchResult]:
        async def fetch_content(result: SearchResult):
            try:
                async with aiohttp.ClientSession() as session:
                    async with session.get(result.url, headers=self.search.headers, timeout=aiohttp.ClientTimeout(total=10)) as response:
                        html = await response.text()
                        from bs4 import BeautifulSoup
                        soup = BeautifulSoup(html, 'html.parser')
                        for script in soup(['script', 'style', 'nav', 'footer']):
                            script.decompose()
                        text = soup.get_text(separator='\n', strip=True)
                        result.snippet = text[:2000] + "..."
                        result.relevance = 1.0
            except Exception as e:
                print(f" ⚠️ 获取失败 {result.url}: {e}")
        tasks = [fetch_content(r) for r in results]
        await asyncio.gather(*tasks)
        return results

    async def _synthesize_answer(self, question: str, results: List[SearchResult]) -> str:
        context = "\n\n".join([f"来源{i+1}: {r.title}\n{r.snippet}\n链接：{r.url}" for i, r in enumerate(results[:5])])
        prompt = f"""基于以下搜索结果回答问题，要求：
1. 准确引用信息来源
2. 综合多个来源的信息
3. 如果信息冲突，说明不同观点
4. 给出清晰的结构化答案
5. 标注信息来源（如 [来源 1]）
问题：{question}
搜索结果：
{context}
请给出详细答案："""
        answer = await self.llm.chat([
            Message(role="system", content="你是一个专业的研究助手，擅长综合多源信息给出准确答案"),
            Message(role="user", content=prompt)
        ])
        return answer

    async def _generate_related_questions(self, question: str, answer: str) -> List[str]:
        prompt = f"""基于以下问答，生成 3-5 个相关的深入研究问题：
问题：{question}
答案：{answer[:500]}...
请生成相关问题，每行一个："""
        response = await self.llm.chat([Message(role="user", content=prompt)])
        return [line.strip() for line in response.split('\n') if line.strip() and not line.startswith('-')][:5]

    def _calculate_confidence(self, results: List[SearchResult]) -> float:
        if not results:
            return 0.0
        base_confidence = min(1.0, len(results)/10)
        has_docs = any(r.source == "docs" for r in results)
        if has_docs:
            base_confidence = min(1.0, base_confidence + 0.2)
        return round(base_confidence, 2)

# 使用示例
async def main_researcher():
    llm = LLMClient()
    search = SearchEngine()
    researcher = IntelligentResearcher(llm, search)
    result = await researcher.research(question="Python 中 asyncio 和 multiprocessing 的区别是什么？", depth=2)
    print("\n" + "="*60)
    print("📚 研究结果")
    print("="*60)
    print(f"\n置信度：{result.confidence*100}%\n")
    print(f"答案:\n{result.answer}\n")
    print("📖 参考来源:")
    for i, source in enumerate(result.sources, 1):
        print(f"{i}. {source.title}")
        print(f" {source.url}")
        print(f" 来源：{source.source}\n")
    print("❓ 相关问题:")
    for q in result.related_questions:
        print(f" • {q}")

if __name__ == "__main__":
    asyncio.run(main_researcher())

4.3 搜索效率对比

五、整合三大利器：打造超级 AI 助手

5.1 统一 CLI 工具

import argparse
import asyncio
from pathlib import Path
import json

class AIToolsCLI:
    """AI 工具命令行界面"""
    def __init__(self):
        self.llm = LLMClient()
        self.summarizer = DocumentSummarizer(self.llm)
        self.code_assistant = InteractiveCodeAssistant(self.llm)
        self.researcher = IntelligentResearcher(self.llm, SearchEngine())

    async def run(self):
        parser = argparse.ArgumentParser(description="AI 工具集 - 你的智能助手", formatter_class=argparse.RawDescriptionHelpFormatter,
                                         epilog="""示例:
# 总结文档
python ai_tools.py summarize paper.pdf
# 生成代码
python ai_tools.py code "用 Python 写一个爬虫"
# 研究问题
python ai_tools.py research "量子计算的原理"
""")
        subparsers = parser.add_subparsers(dest='command', help='可用命令')
        sum_parser = subparsers.add_parser('summarize', help='总结文档')
        sum_parser.add_argument('file', help='文件路径或 URL')
        sum_parser.add_argument('-t', '--type', default='file', choices=['file', 'url'], help='输入类型')
        sum_parser.add_argument('-o', '--output', help='输出文件路径')
        code_parser = subparsers.add_parser('code', help='生成/处理代码')
        code_parser.add_argument('prompt', help='需求或代码')
        code_parser.add_argument('-m', '--mode', choices=['generate', 'explain', 'optimize', 'debug'], default='generate', help='处理模式')
        code_parser.add_argument('-l', '--language', default='python', help='编程语言')
        code_parser.add_argument('-x', '--execute', action='store_true', help='执行生成的代码')
        res_parser = subparsers.add_parser('research', help='研究问题')
        res_parser.add_argument('question', help='研究问题')
        res_parser.add_argument('-d', '--depth', type=int, default=1, choices=[1, 2, 3], help='研究深度')
        res_parser.add_argument('-s', '--sources', nargs='+', choices=['google', 'docs', 'stackoverflow'], help='指定搜索源')
        args = parser.parse_args()
        if not args.command:
            parser.print_help()
            return
        if args.command == 'summarize':
            await self._cmd_summarize(args)
        elif args.command == 'code':
            await self._cmd_code(args)
        elif args.command == 'research':
            await self._cmd_research(args)

    async def _cmd_summarize(self, args):
        print(f"📖 正在总结：{args.file}")
        result = await self.summarizer.summarize(source=args.file, source_type=args.type)
        output = f"""# {result.title}
**📊 统计信息**
- 字数：{result.word_count}
- 预计阅读时间：{result.reading_time} 分钟
- 生成时间：{result.created_at}
**🔑 关键要点**
{chr(10).join(f'{i+1}. {p}' for i, p in enumerate(result.key_points))}
**📝 总结**
{result.summary}"""
        if args.output:
            with open(args.output, 'w', encoding='utf-8') as f:
                f.write(output)
            print(f"✅ 已保存到：{args.output}")
        else:
            print(output)

    async def _cmd_code(self, args):
        print(f"💻 正在处理：{args.prompt[:50]}...")
        result = await self.code_assistant.generator.generate(requirement=args.prompt, language=args.language, mode=CodeMode(args.mode))
        print(f"\n```{args.language}")
        print(result.code)
        print("```\n")
        print(f"**说明**\n{result.explanation}\n")
        if result.warnings:
            print("**警告**")
            for w in result.warnings:
                print(f" {w}")
            print()
        if result.tests:
            print(f"**测试代码**\n```{args.language}")
            print(result.tests)
            print("```\n")
        if args.execute:
            print("⚡ 正在执行代码...")
            exec_result = await self.code_assistant.generator.execute_code(result.code, args.language)
            if exec_result['success']:
                print(f"✅ 执行成功\n输出:\n{exec_result['output']}")
            else:
                print(f"❌ 执行失败\n错误:\n{exec_result['error']}")

    async def _cmd_research(self, args):
        print(f"🔍 正在研究：{args.question}")
        result = await self.researcher.research(question=args.question, depth=args.depth, sources=args.sources)
        print(f""" # 研究结果
**📊 置信度**: {result.confidence*100}%
## 答案
{result.answer}
## 参考来源 """)
        for i, source in enumerate(result.sources, 1):
            print(f"{i}. **{source.title}**")
            print(f" 链接：{source.url}")
            print(f" 来源：{source.source}\n")
        if result.related_questions:
            print("## 相关问题")
            for q in result.related_questions:
                print(f"- {q}")

async def main():
    cli = AIToolsCLI()
    await cli.run()

if __name__ == "__main__":
    asyncio.run(main())

5.2 使用示例

# 总结论文
python ai_tools.py summarize research_paper.pdf -o summary.md
# 生成代码并执行
python ai_tools.py code "用 Python 写一个二分查找" -x
# 解释代码
python ai_tools.py code "explain this code: `def foo(): return 1`" -m explain
# 深度研究
python ai_tools.py research "RAG 和 Fine-tuning 的区别" -d2

5.3 成本分析

六、完整源码与部署指南

6.1 项目结构

ai-tools/
├── src/
│   ├── __init__.py
│   ├── llm.py # LLM 客户端
│   ├── summarizer.py # 文档总结器
│   ├── code_generator.py # 代码生成器
│   └── researcher.py # 研究助手
├── cli.py # 命令行入口
├── config.py # 配置管理
├── requirements.txt # 依赖列表
├── .env.example # 环境变量示例
├── README.md # 使用文档
└── examples/
    ├── example_summarize.py
    ├── example_code.py
    └── example_research.py

6.2 部署到云端

# Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY src/./src/
COPY cli.py .
COPY config.py .
ENV PYTHONPATH=/app
CMD ["python", "cli.py", "--help"]

# docker-compose.yml
version: '3.8'
services:
  ai-tools:
    build: .
    env_file:
      - .env
    volumes:
      - ./data:/app/data
    ports:
      - "8000:8000"

6.3 进阶功能扩展

总结

通过这篇文章，我们用 Python 打造了三个强大的 AI 工具：

关键收获

1. LLM 调用很简单 - 用好 OpenAI SDK，30 行代码就能连接大模型
2. 提示词是关键 - 好的 prompt 能让效果翻倍
3. 异步处理很重要 - 并行请求能大幅提升速度
4. 安全意识不能少 - 代码执行要隔离，API 调用要限流

下一步学习

• 📖 深入学习 LangChain 框架
• 🔬 研究 Agent 与 RAG 技术
• 🚀 打造专属 AI 应用

参考资源

• OpenAI Python SDK 文档
• DeepSeek API 文档
• 硅基流动平台
• Python 异步编程指南