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The Role of Real-Time Data for ChatGPT/LLM Models: A Basic Introduction to SerpAPI

Learn how real-time data boosts ChatGPT/LLMs’ accuracy and freshness using SerpApi. This beginner guide covers API keys, search parameters, pagination, rate limits, pricing, and caching, with Python/Node examples, prompt patterns, and RAG integration. Build production workflows for news, finance, SEO, and ecommerce monitoring, plus logging, retries, and compliance.

2025-12-01

Early versions of ChatGPT lacked internet connectivity. The first-generation ChatGPT models (such as GPT-3.5) were built on offline training data, with their knowledge base cut off at September 2021.

This means a lack of information timeliness. For news events, policy changes, and technological advancements after 2021 (such as the Russia-Ukraine conflict in 2022 and AI regulatory policies in 2023), the model cannot provide real-time data support.

Then there is the difficulty in relying on dynamic content: in scenarios involving real-time updates such as stock quotes, flight information, and weather, early models could only generate simulated data based on historical patterns rather than real results.

Technological Breakthroughs and Challenges in Internet Connectivity

OpenAI gradually introduced internet connectivity capabilities in 2023, a process that underwent multiple technical iterations:

  1. Initial Testing and Withdrawal (May 2023)Initially, internet connectivity was achieved by integrating Microsoft Bing’s search API. However, due to users bypassing paywalls to access content and security risks (such as accessing malicious websites), the feature was disabled just two months after launch.
  2. Feature Optimization and Re-launch (September 2023)The improved version added the following mechanisms:
  1. Gradual Opening StrategyThe internet connectivity feature was first made available to ChatGPT Plus subscribers (requiring the selection of the GPT-4 model), then expanded to the enterprise version, with plans to cover free users in the future. This process reflects OpenAI’s balance between risk control and user experience.

Core Value of Internet Connectivity

The introduction of internet connectivity has significantly enhanced ChatGPT’s practicality, specifically in the following aspects:

  1. A Leap in Information AccuracyFor example, after October 2023, users could directly ask about “the latest Nobel Prize winners” or “the Federal Reserve’s latest interest rate decisions,” and the model could fetch real-time information from authoritative sources with reference links.
  2. Scenario Expansion and Specialization
  1. Reducing Hallucinations and ErrorsBy citing external data, the model can verify the authenticity of generated content, reducing the probability of “fabricating facts.” For instance, when answering technical questions, it attaches links to relevant papers or news as evidence.

But does the internet connectivity feature mean that ChatGPT’s backend deploys hundreds of millions of crawlers to obtain real-time data from the internet? The answer is no.

After all, with the massive amount of data on the internet, it would be impractical for ChatGPT to deploy its own crawlers to fetch data for model analysis. Firstly, there is too much internet data, and crawlers may not be universally applicable to all. Additionally, data cleaning is a problem. Developing all crawlers in-house would be like reinventing the wheel—building a search engine like Google or Bing.

Common Issues with Web Scraping APIs

  1. Technical Issues
  1. Performance Issues
  1. Data Issues
  1. Legal and Ethical Issues
  1. API Limitations
  1. Solutions and Tools
  1. Error Handling
  1. Interface Design
  1. Documentation and Support
  1. CAPTCHA Issues
    • CAPTCHA challenges: Websites may use CAPTCHAs to block automated scraping. Specialized CAPTCHA-solving services can be used to address this.

Therefore, ChatGPT or LLM models generally obtain real-time search data by calling SerpAPI, which saves a lot of unnecessary trouble. After all, since there are ready-made tools available, there is no need to reinvent the wheel.

So, What is SerpAPI?

SerpAPI is a powerful search API that can retrieve results from multiple search engines. It supports platforms such as Google, Bing, and Yahoo, and can be switched through simple configurations.

SerpApi is a real-time API that provides access to Google search results. It handles proxies, solves CAPTCHAs for clients, and parses all rich structured data. This API allows users to easily obtain Google search results without directly interacting with Google Search or dealing with complex crawlers and CAPTCHA issues. Through SerpApi, users can quickly get the search results they need, which are provided in a structured format for further processing and analysis. Currently, several common SERP API providers on the market include:

Here is an example of how to use SerpAPI to obtain search engine data.

First, register an account on the website https://serpapi.com/ to get an API key.

It allows querying with different parameters on the webpage.

Selecting different parameters will output different search results. For example, if the region is set to the United States, the first result for searching “iPhone 17” will be a link to an Amazon store. If the region is set to China, the first website in the search results may be a link to a Chinese e-commerce platform like JD.com.

Installation

Install the serpapi library using pip:

pip install google-search-results

Basic Usage

api_key is the key obtained from the registered website mentioned earlier, and engine specifies which search engine to use for results.

params = {
  "api_key": "8f6b88f0aece8ee19f6673eba5fc5f7a4e5114b74567521e86ca83c46d9de8a7",
  "engine": "google",  # Search engine to use
  "q": "Iphone 17", # Search keyword
  "location": "Austin, Texas, United States", # Simulated region
  "google_domain": "google.com",
  "gl": "us",
  "hl": "en"
}

search = GoogleSearch(params)
results = search.get_dict()
print(results)

The printed search results are as follows:

The output structured data corresponds to the structured data from Google Search results.

The “raw_html_file” field contains the original HTML data. When opened and rendered in a browser, it is identical to the Google Search results.

It comes from the stored data on the SerpAPI site.

More detailed parameters can be configured during the query to get granular results.

params = {
  "q": "coffee",
  "location": "Location Requested", 
  "device": "desktop|mobile|tablet",
  "hl": "Google UI Language",
  "gl": "Google Country",
  "safe": "Safe Search Flag",
  "num": "Number of Results",
  "start": "Pagination Offset",
  "api_key": "Your SerpApi Key", 
  # To be match
  "tbm": "nws|isch|shop", 
  # To be search
  "tbs": "custom to be search criteria",
  # allow async request
  "async": "true|false",
  # output format
  "output": "json|html"
}

# define the search search
search = GoogleSearch(params)
# override an existing parameter
search.params_dict["location"] = "Portland"
# search format return as raw html
html_results = search.get_html()
# parse results
#  as python Dictionary
dict_results = search.get_dict()
#  as JSON using json package
json_results = search.get_json()
#  as dynamic Python object
object_result = search.get_object()

In addition to Google, it supports multiple mainstream search engines on the market. Here are examples of switching to other search engines such as Bing, Yandex, and Yahoo.

Bing

from serpapi import BingSearch
search = BingSearch({"q": "Coffee", "location": "Austin,Texas"})
data = search.get_dict()

Yandex

from serpapi import YandexSearch
search = YandexSearch({"text": "Coffee"})
data = search.get_dict()

Yahoo

from serpapi import YahooSearch
search = YahooSearch({"p": "Coffee"})
data = search.get_dict()

The only difference lies in the definition; the parameters used are the same as the params above.

This tutorial mainly helps you understand SerpAPI and its basic usage. More details on integrating with local LLM models will be covered later.