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1️⃣ 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.

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Usage Guide

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

Example:

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # Replace with the key you generated in AiHubMix
    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)

​
2️⃣ Smart Surfing: Allowing AI to Explore the Internet Freely

By appending :surfing to the model id, any large language model can be equipped with search capabilities.

  • Simply append the suffix, no complex integration is required
  • This method will default to forwarding the user’s request to the Tavily search service, and the LLM will reference the search results for response
  • Search fee: $0.006 per search
  • The fee is currently deducted directly from the “credit change”, and the “log detail” does not list the search fee yet, but will be shown in the future

The model id can be copied from the model gallery.

Example:

import requests
import json
import os

try:
    response = requests.post(
        url="https://aihubmix.com/v1/chat/completions",
        headers={
            "Authorization": f"Bearer {os.environ.get('AIHUBMIX_API_KEY')}",
            "Content-Type": "application/json",
        },
        data=json.dumps({
            "model": "gpt-4o-mini:surfing", # Append :surfing to the model id to support searching
            "messages": [
                {
                    "role": "user",
                    "content": "Search the last fact about ChatGPT memory feature, return with the URL"
                }
            ]
        })
    )

    result = response.json()
    print("API response:", json.dumps(result, ensure_ascii=False, indent=2))

except requests.exceptions.RequestException as e:
    print(f"Request error: {e}")
except json.JSONDecodeError as e:
    print(f"JSON decode error: {e}")
except Exception as e:
    print(f"Other error: {e}")

API Response Example:

{
  "id": "chatcmpl-BLN21dGcrv8MrbeHfForjY4bYZHBF",
  "model": "gpt-4o-mini-2024-07-18",
  "object": "chat.completion",
  "created": 1744433121,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The latest update about ChatGPT's memory feature indicates that it now has expanded memory capabilities that allow it to recall information in two ways: through “saved memories” that users manually ask it to remember, and “reference chat history” for improving future interactions. For more details, you can visit the URL: [The Verge Article](https://www.theverge.com/news/646968/openai-chatgpt-long-term-memory-upgrade)."
      },
      "finish_reason": "stop"
    }
  ],
  "system_fingerprint": "fp_ded0d14823",
  "usage": {
    "prompt_tokens": 604,
    "completion_tokens": 89,
    "total_tokens": 693,
    "prompt_tokens_details": {
      "audio_tokens": 0,
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "accepted_prediction_tokens": 0,
      "audio_tokens": 0,
      "reasoning_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  }
}