1. 使用到技术

OpenAI KEYSerper KEYBing Search

2. 原理解析

使用Google和Bing的搜搜结果交由OpenAI处理并给出回答。

3. 代码实现

<code>import requests

from lxml import etree

import os

from openai import OpenAI

# 从环境变量中加载 API 密钥

os.environ["OPENAI_API_KEY"] = "sk-xxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

os.environ["SERPER_API_KEY"] = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

# 确保在执行代码前已经设置了环境变量

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

SERPER_API_KEY = os.getenv("SERPER_API_KEY")

def search_bing(query):

headers = {

'Referer': 'https://www.bing.com/',

'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',

}

params = {

'q': query,

'mkt': 'zh-CN'

}

response = requests.get('https://www.bing.com/search', params=params, headers=headers)

html = etree.HTML(response.text)

li_list = html.xpath("//li[@class='b_algo']")code>

result = []

for index in range(len(li_list)):

title = ";".join(li_list[index].xpath("./h2/a/text()"))

link = li_list[index].xpath("./h2/a/@href")[0]

snippet = ";".join(li_list[index].xpath("./div/p/text()"))

position = index

print(title, link, snippet, position)

result.append({

'title': title,

'link': link,

'snippet': snippet,

'position': position,

})

return result

def search_serper(query):

"""使用Serper API进行搜索并返回结果。"""

url = "https://google.serper.dev/search"

headers = {

"X-API-KEY": SERPER_API_KEY,

"Content-Type": "application/json",

}

params = {

'q': query,

'gl': "cn",

'hl': "zh-cn",

}

try:

response = requests.post(url, headers=headers, json=params)

response.raise_for_status() # 检查HTTP请求状态

serper_data = response.json()

if not serper_data:

return "无法获取搜索结果", []

google_context = serper_data.get('organic', [])

google_other = serper_data.get('relatedSearches', [])

return google_context, google_other

except requests.RequestException as e:

print(f"请求失败: { e}")

return None

def search_openai(query, context):

"""利用OpenAI API回答问题并引用相关上下文，并使用流的方式输出。"""

context_template = (

"你是GinLynn构建的大型语言AI助手。给你一个用户问题，请正确、简洁、准确的讲述这个问题的答案。"

"你会得到一组与问题相关的上下文，其中每个对象都是一个json字符串，"

"'snippet'字段表示片段，'title'字段表示标题，'link'字段表示链接，'position'字段表示位置。"

"请使用这些上下文并在每个句子的末尾引用上下文(如果适用)。"

"你的答案必须是正确、准确的，由专家以公正和专业的语气撰写。请限制为2048token。"

"不要给出任何与问题无关的信息，也不要重复。如果给定的上下文没有提供足够的信息，"

"那么在相关主题后面加上“information is missing on”。请以[position]的格式注明出处和参考编号。"

"以下是一组上下文:"

)

client = OpenAI(api_key=OPENAI_API_KEY)

try:

completion = client.chat.completions.create(

model="gpt-4o",code>

messages=[

{ "role": "system", "content": context_template + context},

{ "role": "user", "content": query}

],

stream=True # 启用流式响应

)

# 逐条打印流式输出的结果

for chunk in completion:

if chunk.choices[0].delta.content is not None:

print(chunk.choices[0].delta.content, end="")code>

print() # 输出换行

return "完成输出"

except Exception as e:

print(f"OpenAI API request failed: { e}")

return "无法完成请求", []

if __name__ == '__main__':

query = input("请输入查询: ")

if query.strip() == "":

query = "最新俄乌局势信息"

print("正在搜索...")

serper_context, other_queries = search_serper(query)

bing_context = search_bing(query)

context = []

if bing_context:

context.extend(bing_context)

if serper_context:

# 为Serper上下文的每个条目重置 position 值，以防止重复

for index, item in enumerate(serper_context, start=len(bing_context)):

item['position'] = index # 从当前Bing结果的数量开始

context.extend(serper_context)

print("搜索结果:", context)

search_openai(query, str(context))

if other_queries:

print("相关搜索:", other_queries)

4. 运行结果