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Model Internet Search

🌐 Real-time Web Search: Breaking LLM Time Limitations for More Accurate and Reliable Outputs​

We've enhanced OpenAI and Gemini series models with the ability to access the latest information from the web, helping you:

  • ✅ Access Latest Information: Get real-time updates on current events, latest research, or live data
  • ✅ Eliminate Knowledge Gaps: Overcome the time limitations of LLM training data by accessing post-training information
  • ✅ Reduce Hallucinations: Provide fact-based answers through real-time web searches, significantly reducing AI confabulations
  • ✅ Improve Decision Quality: Make more confident decisions based on analysis and recommendations grounded in current facts

Supported Models​

Currently supporting OpenAI and Gemini model series with two integration methods:

1. Models with Native Search Capabilities​

Gemini Series (Ground with Google search):

  • gemini-2.0-pro-exp-02-05-search
  • gemini-2.0-flash-exp-search
  • gemini-2.0-flash-search

OpenAI Series (Bing search):

  • gpt-4o-search-preview
  • gpt-4o-mini-search-preview

2. Parameter-Based Support​

Simply add the parameter web_search_options={} to enable web connectivity for all Gemini and OpenAI models. The search fee for Gemini models is $3.5 per thousand searches.

3. Future Plans​

We're planning to enable Web Search through parameter suffixes, making it even easier for developers to implement. Stay tuned!

Usage Guide​

OpenAI SDK Support​

Before using, run pip install -U openai to upgrade the openai package.

Python Example​

from openai import OpenAI

client = OpenAI(
    api_key="sk-***",
    base_url="https://aihubmix.com/v1"
)

chat_completion = client.chat.completions.create(
    model="gemini-2.0-flash-exp",
    # 🌐 Enable search
    web_search_options={},
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Search for information about the AIhubmix LLM API platform, provide a brief introduction, and include relevant links."
                }
            ]
        }
    ]
)

print(chat_completion.choices[0].message.content)

TypeScript Example​

import OpenAI from 'openai';

const client = new OpenAI({
    apiKey: 'sk-***',
    baseURL: 'https://aihubmix.com/v1'
});

async function main() {
    const chatCompletion = await client.chat.completions.create({
        model: 'gemini-2.0-flash-exp',
        // 🌐 Enable search
        web_search_options: {},
        messages: [
            {
                role: 'user',
                content: [
                    {
                        type: 'text',
                        text: 'Search for information about the AIhubmix LLM API platform, provide a brief introduction, and include relevant links.'
                    }
                ]
            }
        ]
    });

    console.log(chatCompletion.choices[0].message.content);
}

main().catch(console.error);

CURL Example​

curl "https://aihubmix.com/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer xxx" \
    -d '{
    "model": "gemini-2.0-flash-exp",
    "web_search_options": {},
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Provide information about Van Gogh on the Google Arts & Culture website, with a brief introduction and relevant links."
                }
            ]
        }
    ],
    "stream": false
}'