原课程代码是用Anthropic写的，下面代码是用OpenAI改写的，模型则用阿里巴巴的模型做测试
.env 文件为：

OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
OPENAI_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1

完整代码

import arxiv
import json
import os
from typing import List
from dotenv import load_dotenv
import openaiPAPER_DIR = "papers"def search_papers(topic: str, max_results: int = 5) -> List[str]:"""Search for papers on arXiv based on a topic and store their information.Args:topic: The topic to search formax_results: Maximum number of results to retrieve (default: 5)Returns:List of paper IDs found in the search"""# Use arxiv to find the papersclient = arxiv.Client()# Search for the most relevant articles matching the queried topicsearch = arxiv.Search(query = topic,max_results = max_results,sort_by = arxiv.SortCriterion.Relevance)papers = client.results(search)# Create directory for this topicpath = os.path.join(PAPER_DIR, topic.lower().replace(" ", "_"))os.makedirs(path, exist_ok=True)file_path = os.path.join(path, "papers_info.json")# Try to load existing papers infotry:with open(file_path, "r") as json_file:papers_info = json.load(json_file)except (FileNotFoundError, json.JSONDecodeError):papers_info = {}# Process each paper and add to papers_infopaper_ids = []for paper in papers:paper_ids.append(paper.get_short_id())paper_info = {'title': paper.title,'authors': [author.name for author in paper.authors],'summary': paper.summary,'pdf_url': paper.pdf_url,'published': str(paper.published.date())}papers_info[paper.get_short_id()] = paper_info# Save updated papers_info to json filewith open(file_path, "w") as json_file:json.dump(papers_info, json_file, indent=2)print(f"Results are saved in: {file_path}")return paper_idsdef extract_info(paper_id: str) -> str:"""Search for information about a specific paper across all topic directories.Args:paper_id: The ID of the paper to look forReturns:JSON string with paper information if found, error message if not found"""for item in os.listdir(PAPER_DIR):item_path = os.path.join(PAPER_DIR, item)if os.path.isdir(item_path):file_path = os.path.join(item_path, "papers_info.json")if os.path.isfile(file_path):try:with open(file_path, "r") as json_file:papers_info = json.load(json_file)if paper_id in papers_info:return json.dumps(papers_info[paper_id], indent=2)except (FileNotFoundError, json.JSONDecodeError) as e:print(f"Error reading {file_path}: {str(e)}")continuereturn f"There's no saved information related to paper {paper_id}."tools = [{"type": "function","function": {"name": "search_papers","description": "Search for papers on arXiv based on a topic and store their information","parameters": {"type": "object","properties": {"topic": {"type": "string","description": "The topic to search for"},"max_results": {"type": "integer","description": "Maximum number of results to retrieve","default": 5}},"required": ["topic"]}}},{"type": "function","function": {"name": "extract_info","description": "Search for information about a specific paper across all topic directories","parameters": {"type": "object","properties": {"paper_id": {"type": "string","description": "The ID of the paper to look for"}},"required": ["paper_id"]}}}
]mapping_tool_function = {"search_papers": search_papers,"extract_info": extract_info
}def execute_tool(tool_name, tool_args):result = mapping_tool_function[tool_name](**tool_args)if result is None:result = "The operation completed but didn't return any results."elif isinstance(result, list):result = ', '.join(result)elif isinstance(result, dict):# Convert dictionaries to formatted JSON stringsresult = json.dumps(result, indent=2)else:# For any other type, convert using str()result = str(result)return resultload_dotenv()
client = openai.OpenAI(api_key = os.getenv("OPENAI_API_KEY"),base_url= os.getenv("OPENAI_API_BASE")
) def process_query(query):messages = [{"role": "user", "content": query}]response = client.chat.completions.create(model="qwen-turbo",  # 或其他OpenAI模型max_tokens=2024,tools=tools,messages=messages)process_query = Truewhile process_query:# 获取助手的回复message = response.choices[0].message# 检查是否有普通文本内容if message.content:print(message.content)process_query = False# 检查是否有工具调用elif message.tool_calls:# 添加助手消息到历史messages.append({"role": "assistant", "content": None,"tool_calls": message.tool_calls})# 处理每个工具调用for tool_call in message.tool_calls:tool_id = tool_call.idtool_name = tool_call.function.nametool_args = json.loads(tool_call.function.arguments)print(f"Calling tool {tool_name} with args {tool_args}")# 执行工具调用result = execute_tool(tool_name, tool_args)# 添加工具结果到消息历史messages.append({"role": "tool","tool_call_id": tool_id,"content": result})# 获取下一个回复response = client.chat.completions.create(model="qwen-turbo",  # 或其他OpenAI模型max_tokens=2024,tools=tools,messages=messages)# 如果只有文本回复，则结束处理if response.choices[0].message.content and not response.choices[0].message.tool_calls:print(response.choices[0].message.content)process_query = Falsedef chat_loop():print("Type your queries or 'quit' to exit.")while True:try:query = input("\nQuery: ").strip()if query.lower() == 'quit':breakprocess_query(query)print("\n")except Exception as e:print(f"\nError: {str(e)}")if __name__ == "__main__":chat_loop()

代码解释

导入模块

import arxiv        # 用于访问arXiv API搜索论文
import json         # 处理JSON数据
import os           # 操作系统功能，如文件路径处理
from typing import List  # 类型提示
from dotenv import load_dotenv  # 加载环境变量
import openai       # OpenAI API客户端

核心功能函数

1. search_papers 函数

这个函数用于在arXiv上搜索特定主题的论文并保存信息：

def search_papers(topic: str, max_results: int = 5) -> List[str]:

• 参数：
  • topic: 要搜索的主题
  • max_results: 最大结果数量（默认5个）
• 返回值：找到的论文ID列表

功能流程：

1. 创建arXiv客户端
2. 按相关性搜索主题相关论文
3. 为该主题创建目录（如papers/machine_learning）
4. 尝试加载已有的论文信息（如果存在）
5. 处理每篇论文，提取标题、作者、摘要等信息
6. 将论文信息保存到JSON文件中
7. 返回论文ID列表

2. extract_info 函数

这个函数用于在所有主题目录中搜索特定论文的信息：

def extract_info(paper_id: str) -> str:

• 参数：paper_id - 要查找的论文ID
• 返回值：包含论文信息的JSON字符串（如果找到），否则返回错误信息

功能流程：

1. 遍历papers目录下的所有子目录
2. 在每个子目录中查找papers_info.json文件
3. 如果找到文件，检查是否包含指定的论文ID
4. 如果找到论文信息，返回格式化的JSON字符串
5. 如果未找到，返回未找到的提示信息

工具定义

tools = [...]

定义了两个函数工具，用于OpenAI API的工具调用：

1. search_papers - 搜索论文
2. extract_info - 提取论文信息

每个工具都定义了名称、描述和参数规范。

工具执行函数

def execute_tool(tool_name, tool_args):

这个函数负责执行指定的工具函数，并处理返回结果：

• 将None结果转换为提示信息
• 将列表结果转换为逗号分隔的字符串
• 将字典结果转换为格式化的JSON字符串
• 其他类型转换为字符串

OpenAI客户端初始化

load_dotenv()
client = openai.OpenAI(api_key = os.getenv("OPENAI_API_KEY"),base_url= os.getenv("OPENAI_API_BASE")
)

从环境变量加载API密钥和基础URL，初始化OpenAI客户端。

查询处理函数

def process_query(query):

这个函数处理用户的查询：

1. 创建包含用户查询的消息列表
2. 调用OpenAI API创建聊天完成
3. 处理助手的回复：
   • 如果有普通文本内容，直接打印
   • 如果有工具调用，执行工具并将结果添加到消息历史
4. 如果执行了工具调用，获取下一个回复
5. 如果最终回复只有文本，打印并结束处理

聊天循环函数

def chat_loop():

这个函数实现了一个简单的聊天循环：

1. 提示用户输入查询或输入’quit’退出
2. 处理用户的查询
3. 捕获并显示任何错误

主程序

if __name__ == "__main__":chat_loop()

当脚本直接运行时，启动聊天循环。

总结

这个脚本实现了一个基于OpenAI API的聊天机器人，它可以：

1. 搜索arXiv上的论文并保存信息
2. 提取已保存的论文信息
3. 通过OpenAI API处理用户查询
4. 支持工具调用功能，实现与arXiv的交互

运行示例

目录结构

运行结果