用 DuckDuckGo 的搜索 API 免费给大模型接入联网功能，实现类似 New Bing 的效果

项目概述

通过集成 DuckDuckGo 搜索 API，为普通大语言模型赋予类似 New Bing 的实时网络访问和智能问答能力。复用大模型现有的的 Function calling功能，实现高效灵活的查询处理和回答生成过程。

核心实现

系统采用了 Function calling 机制，实现了查询处理和回答生成的无缝集成：

1. 查询分析和搜索：使用 claude-3-haiku 或 gpt-3.5-turbo 等较简易模型生成优化后的搜索关键词并通过 function calling 调用搜索API。
2. 网络搜索：调用 DuckDuckGo API 执行实时信息检索。
3. 智能回答生成：使用 claude-3-5-sonnnet 或 gpt-4o 等较复杂模型结合搜索结果和原始问题，生成准确、全面的回答。

代码实现

1. 环境设置和函数定义

import os
import json
from openai import OpenAI
from duckduckgo_search import DDGS
API_KEY = os.getenv("OPENAI_API_KEY")
BASE_URL = os.getenv("OPENAI_BASE_URL")
client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
FUNCTIONS = [
{
"name": "search_duckduckgo",
"description": "使用DuckDuckGo搜索引擎查询信息。可以搜索最新新闻、文章、博客等内容。",
"parameters": {
"type": "object",
"properties": {
"keywords": {
"type": "array",
"items": {"type": "string"},
"description": "搜索的关键词列表。例如：['Python', '机器学习', '最新进展']。"
}
},
"required": ["keywords"]
}
}
]

2. 相关辅助函数

def search_duckduckgo(keywords):
search_term = " ".join(keywords)
with DDGS() as ddgs:
return list(ddgs.text(keywords=search_term, region="cn-zh", safesearch="on", max_results=5))
def print_search_results(results):
for result in results:
print(
f"标题: {result['title']}\n链接: {result['href']}\n摘要: {result['body']}\n---")
def get_openai_response(messages, model="gpt-3.5-turbo", functions=None, function_call=None):
try:
response = client.chat.completions.create(
model=model,
messages=messages,
functions=functions,
function_call=function_call
)
return response.choices[0].message
except Exception as e:
print(f"调用OpenAI API时出错: {str(e)}")
return None
def process_function_call(response_message):
function_name = response_message.function_call.name
function_args = json.loads(response_message.function_call.arguments)
print(f"\n模型选择调用函数: {function_name}")
if function_name == "search_duckduckgo":
keywords = function_args.get('keywords', [])
if not keywords:
print("错误：模型没有提供搜索关键词")
return None
print(f"关键词: {', '.join(keywords)}")
function_response = search_duckduckgo(keywords)
print("\nDuckDuckGo搜索返回结果:")
print_search_results(function_response)
return function_response
else:
print(f"未知的函数名称: {function_name}")
return None

3. 主处理函数

def main(question):
print(f"问题：{question}")
messages = [{"role": "user", "content": question}]
response_message = get_openai_response(
messages, functions=FUNCTIONS, function_call="auto")
if not response_message:
return
if response_message.function_call:
function_response = process_function_call(response_message)
if function_response:
messages.extend([
response_message.model_dump(),
{
"role": "function",
"name": response_message.function_call.name,
"content": json.dumps(function_response, ensure_ascii=False)
}
])
final_response = get_openai_response(messages, model="gpt-4o")
if final_response:
print("\n最终回答:")
print(final_response.content)
else:
print("\n模型直接回答:")
print(response_message.content)

实现效果

通过这种方法，可以让大模型能够回答各种问题，比如：

1. 地理信息：“珠三角是否包括佛山？”
2. 关于AI自身能力的问题：“你能做什么？”
3. 特定领域的问题：“植物大战僵尸杂交版作者是谁？”
4. 哲学思考：“人生就俩字，是哪两个字？”
5. 时事相关：“Jumping不等式是什么？”
6. 时间相关问题："现在的北京时间是多少？

完整代码

import os
import json
from openai import OpenAI
from duckduckgo_search import DDGS

API_KEY = os.getenv("OPENAI_API_KEY")
BASE_URL = os.getenv("OPENAI_BASE_URL")
client = OpenAI(api_key=API_KEY, base_url=BASE_URL)

FUNCTIONS = [
{
"name": "search_duckduckgo",
"description": "使用DuckDuckGo搜索引擎查询信息。可以搜索最新新闻、文章、博客等内容。",
"parameters": {
"type": "object",
"properties": {
"keywords": {
"type": "array",
"items": {"type": "string"},
"description": "搜索的关键词列表。例如：['Python', '机器学习', '最新进展']。"
}
},
"required": ["keywords"]
}
}
]

def search_duckduckgo(keywords):
search_term = " ".join(keywords)
with DDGS() as ddgs:
return list(ddgs.text(keywords=search_term, region="cn-zh", safesearch="on", max_results=5))

def print_search_results(results):
for result in results:
print(
f"标题: {result['title']}\n链接: {result['href']}\n摘要: {result['body']}\n---")

def get_openai_response(messages, model="gpt-3.5-turbo", functions=None, function_call=None):
try:
response = client.chat.completions.create(
model=model,
messages=messages,
functions=functions,
function_call=function_call
)
return response.choices[0].message
except Exception as e:
print(f"调用OpenAI API时出错: {str(e)}")
return None

def process_function_call(response_message):
function_name = response_message.function_call.name
function_args = json.loads(response_message.function_call.arguments)

print(f"\n模型选择调用函数: {function_name}")

if function_name == "search_duckduckgo":
keywords = function_args.get('keywords', [])

if not keywords:
print("错误：模型没有提供搜索关键词")
return None

print(f"关键词: {', '.join(keywords)}")

function_response = search_duckduckgo(keywords)
print("\nDuckDuckGo搜索返回结果:")
print_search_results(function_response)

return function_response
else:
print(f"未知的函数名称: {function_name}")
return None

def main(question):
print(f"问题：{question}")

messages = [{"role": "user", "content": question}]
response_message = get_openai_response(
messages, functions=FUNCTIONS, function_call="auto")

if not response_message:
return

if response_message.function_call:
if not response_message.content:
response_message.content = ""
function_response = process_function_call(response_message)
if function_response:
messages.extend([
response_message.model_dump(),
{
"role": "function",
"name": response_message.function_call.name,
"content": json.dumps(function_response, ensure_ascii=False)
}
])

final_response = get_openai_response(messages, model="gpt-4o")
if final_response:
print("\n最终回答:")
print(final_response.content)
else:
print("\n模型直接回答:")
print(response_message.content)

if __name__ == "__main__":
main("植物大战僵尸杂交版的作者是谁？他是怎么想到做出来这个游戏的？")

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