DeepSeek 模型提供强大的推理能力，但缺乏内置的网络搜索访问。当用户询问时事、定价或最新版本时，DeepSeek 要么拒绝回答，要么产生过时的信息。搜索基础通过获取相关 SERP 数据并将其注入到 DeepSeek 生成响应之前的提示上下文中来解决此问题。本教程展示如何使用 Scavio API 以每次搜索 0.005 美元的价格向任何基于 DeepSeek 的代理添加搜索基础。
前置条件
- 已安装 Python 3.9+
- 安装了 openai 库（DeepSeek 使用 OpenAI 兼容的 API）
- 请求已安装库
- DeepSeek API 密钥和 Scavio API 密钥
操作指南
步骤 1: 设置 DeepSeek 客户端
DeepSeek 使用 OpenAI 兼容的 API，因此您可以使用带有自定义基本 URL 的 OpenAI Python 库。设置 DeepSeek 和搜索客户端。
Python
from openai import OpenAI
import requests, os
# DeepSeek client
deepseek = OpenAI(
api_key=os.environ['DEEPSEEK_API_KEY'],
base_url='https://api.deepseek.com'
)
# Search client
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
def web_search(query: str, max_results: int = 5) -> str:
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': query, 'country_code': 'us'})
results = resp.json().get('organic_results', [])[:max_results]
return '\n\n'.join(
f'Source: {r["link"]}\nTitle: {r["title"]}\n{r.get("snippet", "")}'
for r in results
)
print('DeepSeek + Search grounding ready')步骤 2: 检测何时需要接地
并非每个查询都需要搜索。构建一个分类器，根据查询内容决定是否进行搜索或让 DeepSeek 从其训练数据中进行回答。
Python
def needs_grounding(user_message: str) -> bool:
grounding_signals = [
'latest', 'current', 'today', 'now', '2025', '2026',
'price', 'cost', 'how much', 'release', 'version',
'news', 'update', 'recent', 'best', 'top', 'compare'
]
message_lower = user_message.lower()
return any(signal in message_lower for signal in grounding_signals)
# Test:
for q in ['explain quantum computing', 'best Python IDE 2026', 'what is recursion']:
print(f'Ground: {needs_grounding(q):5} | {q}')步骤 3: 构建接地气的DeepSeek聊天功能
创建主函数，该函数可以选择搜索、构建上下文，并使用接地或非接地提示调用 DeepSeek。
Python
def ask_deepseek(user_message: str, ground: bool = None) -> str:
should_ground = ground if ground is not None else needs_grounding(user_message)
messages = []
if should_ground:
search_context = web_search(user_message)
system_prompt = f"""You are a helpful assistant with access to current web search results.
Use the search results below to provide accurate, up-to-date answers.
Cite sources with URLs when referencing specific facts.
If the search results do not contain relevant information, say so.
Search Results:
{search_context}"""
messages.append({'role': 'system', 'content': system_prompt})
else:
messages.append({'role': 'system',
'content': 'You are a helpful assistant. Answer from your knowledge.'})
messages.append({'role': 'user', 'content': user_message})
response = deepseek.chat.completions.create(
model='deepseek-chat',
messages=messages,
max_tokens=1000,
temperature=0.7
)
return response.choices[0].message.content
# Test grounded query:
answer = ask_deepseek('What are the top Python web frameworks in 2026?')
print(answer)步骤 4: 添加多轮对话支持
对于对话，请保留消息历史记录，并且仅在当前回合需要时才接地。以前的接地背景已经成为历史。
Python
class GroundedDeepSeek:
def __init__(self):
self.history = []
self.search_count = 0
def chat(self, user_message: str) -> str:
self.history.append({'role': 'user', 'content': user_message})
if needs_grounding(user_message):
context = web_search(user_message)
self.search_count += 1
system = f'Use these search results:\n{context}'
messages = [{'role': 'system', 'content': system}] + self.history
else:
messages = self.history.copy()
response = deepseek.chat.completions.create(
model='deepseek-chat',
messages=messages,
max_tokens=1000
)
answer = response.choices[0].message.content
self.history.append({'role': 'assistant', 'content': answer})
return answer
def cost_so_far(self) -> str:
return f'{self.search_count} searches = ${self.search_count * 0.005:.3f}'
agent = GroundedDeepSeek()
print(agent.chat('What is the latest DeepSeek model?'))
print(f'Search cost: {agent.cost_so_far()}')Python 示例
Python
from openai import OpenAI
import os, requests
deepseek = OpenAI(api_key=os.environ['DEEPSEEK_API_KEY'], base_url='https://api.deepseek.com')
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
def search(query, k=5):
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': query, 'country_code': 'us'})
return '\n'.join(f'{r["title"]}: {r.get("snippet", "")}'
for r in resp.json().get('organic_results', [])[:k])
def ask(question):
ctx = search(question)
resp = deepseek.chat.completions.create(
model='deepseek-chat',
messages=[
{'role': 'system', 'content': f'Use search results:\n{ctx}'},
{'role': 'user', 'content': question}
], max_tokens=500)
return resp.choices[0].message.content
print(ask('Best Python web frameworks 2026'))JavaScript 示例
JavaScript
import OpenAI from 'openai';
const deepseek = new OpenAI({
apiKey: process.env.DEEPSEEK_API_KEY,
baseURL: 'https://api.deepseek.com'
});
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function search(query) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query, country_code: 'us' })
});
const data = await resp.json();
return (data.organic_results || []).slice(0, 5)
.map(r => `${r.title}: ${r.snippet || ''}`).join('\n');
}
async function ask(question) {
const ctx = await search(question);
const resp = await deepseek.chat.completions.create({
model: 'deepseek-chat',
messages: [
{ role: 'system', content: `Search results:\n${ctx}` },
{ role: 'user', content: question }
], max_tokens: 500
});
console.log(resp.choices[0].message.content);
}
ask('Best Python frameworks 2026');预期输出
JSON
Ground: False | explain quantum computing
Ground: True | best Python IDE 2026
Ground: False | what is recursion
Based on current search results, the top Python web frameworks in 2026 are:
1. FastAPI - async-first, ideal for APIs (Source: https://...)
2. Django - full-featured with built-in admin (Source: https://...)
3. Flask - lightweight and flexible (Source: https://...)
Search cost: 1 searches = $0.005