Get a Knowledge Structure

Overview

This is a description of one of the six use cases for the AI Search Engine—retrieving a small structured knowledge base on the requested subject based on information from the internet.

An output example:

{
  'title': 'Nikola Tesla', 
  'type': 'Engineer and futurist', 
  'description': None, 
  'born': 'July 10, 1856, Smiljan, Croatia', 
  'died': 'January 7, 1943 (age 86 years), The New Yorker A Wyndham Hotel, New York, NY'
}

The output will be the requested information retrieved from the internet—or empty brackets {} if nothing was found or if the entered query does not match the selected search type (for example, querying something like "I love Antarctica" instead of some topic).

How to make a call

Check how this call is made in the example below.

Note that queries can include advanced search syntax:

• Search for an exact match: Enter a word or phrase using \" before and after it. For example, \"tallest building\".

• Search for a specific site: Enter site: in front of a site or domain. For example, site:youtube.com cat videos.

• Exclude words from your search: Enter - in front of a word that you want to leave out. For example, jaguar speed -car.

API Schema

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Example

First, the standard chat completion endpoint with your query is called. It returns an ID, which must then be passed as the sole input parameter followup_id to the specific second endpoint:

import requests
from openai import OpenAI

# Insert your LAPLAS API Key instead of <YOUR_API_KEY>:
API_KEY = '<YOUR_API_KEY>'
API_URL = 'https://api.apilaplas.com'

# Call the standart chat completion endpoint to get an ID
def complete_chat():
    client = OpenAI(
        base_url=API_URL,
        api_key=API_KEY,
    )    

    response = client.chat.completions.create(
        model="bagoodex/bagoodex-search-v1",
        messages=[
            {
                "role": "user",
                "content": "Who is Nicola Tesla",
            },
        ],
    )
    
    # Extract the ID from the response
    gen_id = response.id  
    print(f"Generated ID: {gen_id}")
    
    # Call the second endpoint with the generated ID
    get_knowledge(gen_id)

def get_knowledge(gen_id):
    params = {'followup_id': gen_id}
    headers = {'Authorization': f'Bearer {API_KEY}'}
    
    response = requests.get(f'{API_URL}/v1/bagoodex/knowledge', headers=headers, params=params)
    print(response.json())

# Run the function
complete_chat()

Model Response:

{
  'title': 'Nikola Tesla', 
  'type': 'Engineer and futurist', 
  'description': None, 
  'born': 'July 10, 1856, Smiljan, Croatia', 
  'died': 'January 7, 1943 (age 86 years), The New Yorker A Wyndham Hotel, New York, NY'
}