Web Search

Enable AI models to search the web in real-time, providing up-to-date information with citations and source attribution.

Prerequisites

pip install apertis

Get your API Key from Apertis

Basic Web Search

from apertis import Apertis

def main():
    client = Apertis()

    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[
            {"role": "user", "content": "What are the latest developments in AI?"}
        ],
        web_search=True
    )

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

if __name__ == "__main__":
    main()

Web Search with Citations

from apertis import Apertis

def main():
    client = Apertis()

    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[
            {"role": "user", "content": "What is the current stock price of Apple?"}
        ],
        web_search=True
    )

    message = response.choices[0].message

    print("Response:", message.content)

    # Access citations if available
    if hasattr(message, 'citations') and message.citations:
        print("\nSources:")
        for citation in message.citations:
            print(f"  - {citation.title}: {citation.url}")

if __name__ == "__main__":
    main()

Factual Questions

from apertis import Apertis

def main():
    client = Apertis()

    questions = [
        "Who won the latest Super Bowl?",
        "What is the current population of Tokyo?",
        "When is the next solar eclipse?",
    ]

    for question in questions:
        response = client.chat.completions.create(
            model="gpt-4.1",
            messages=[
                {"role": "user", "content": question}
            ],
            web_search=True
        )
        print(f"Q: {question}")
        print(f"A: {response.choices[0].message.content}\n")

if __name__ == "__main__":
    main()

Research Assistant

from apertis import Apertis

def main():
    client = Apertis()

    topic = "quantum computing applications in drug discovery"

    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[
            {
                "role": "system",
                "content": "You are a research assistant. Provide comprehensive, well-cited answers."
            },
            {
                "role": "user",
                "content": f"""Research the following topic and provide:
1. Current state of the field
2. Key players and organizations
3. Recent breakthroughs
4. Future outlook

Topic: {topic}"""
            }
        ],
        web_search=True
    )

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

if __name__ == "__main__":
    main()

News Summary

from apertis import Apertis

def main():
    client = Apertis()

    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[
            {
                "role": "user",
                "content": """Summarize today's top technology news. Include:
- Company announcements
- Product launches
- Industry trends
Provide sources for each item."""
            }
        ],
        web_search=True
    )

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

if __name__ == "__main__":
    main()

Streaming with Web Search

from apertis import Apertis

def main():
    client = Apertis()

    stream = client.chat.completions.create(
        model="gpt-4.1",
        messages=[
            {"role": "user", "content": "What are the current trends in renewable energy?"}
        ],
        web_search=True,
        stream=True
    )

    for chunk in stream:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="", flush=True)

    print()

if __name__ == "__main__":
    main()

Competitive Analysis

from apertis import Apertis

def main():
    client = Apertis()

    company = "OpenAI"

    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[
            {
                "role": "user",
                "content": f"""Provide a competitive analysis for {company}:
1. Recent product announcements
2. Market positioning
3. Key competitors
4. Strategic moves
5. Industry perception

Use only current, verifiable information."""
            }
        ],
        web_search=True
    )

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

if __name__ == "__main__":
    main()

Multi-Turn with Web Search

from apertis import Apertis

def main():
    client = Apertis()

    messages = [
        {"role": "user", "content": "What is the current status of the James Webb Space Telescope?"}
    ]

    # First query
    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=messages,
        web_search=True
    )

    print("Assistant:", response.choices[0].message.content)

    # Follow-up
    messages.append({"role": "assistant", "content": response.choices[0].message.content})
    messages.append({"role": "user", "content": "What are its most recent discoveries?"})

    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=messages,
        web_search=True
    )

    print("\nAssistant:", response.choices[0].message.content)

if __name__ == "__main__":
    main()

Supported Models

Web search is available on:

Provider	Models
OpenAI	gpt-4.1, gpt-4.1-mini
Anthropic	claude-sonnet-4.5, claude-opus-4-5-20251101
Google	gemini-3-pro-preview, gemini-2.5-flash

View all models →

API Reference

Parameter	Type	Description
web_search	bool	Enable web search (set to True)

Citation Object

Field	Type	Description
title	str	Source page title
url	str	Source URL
snippet	str	Relevant excerpt (if available)

Best Practices

1. Be specific - More specific queries yield better search results
2. Request citations - Ask the model to cite sources in its response
3. Use for current events - Web search is ideal for recent/real-time information
4. Verify important facts - Cross-reference critical information
5. Consider rate limits - Web search may have additional rate limiting