LLM Answer Generation

Using Statista’s Research AI API to create or complement a chatbot

We’re working on this feature in beta. If you’re interested, reach out to: [e-mail] for a beta key.

This guide provides a simple workflow example of the Research AI API.

Using your API key, submit a question to the POST endpoint described in the API Reference.

import requests
import time

url_post = "https://api.statista.ai/research-ai/v1/research_ai"

payload = {"question": "What are the economic impacts of climate change in Europe?"}
headers = {
    "x-api-key": "<your_research_ai_api_key>",
    "Content-Type": "application/json"
}

response = requests.request("POST", url_post, json=payload, headers=headers)

In the response we should receive a research_token that can be used to poll the research_ai_status endpoint.

research_token = response.text['research_token']
url_get = "https://api.statista.ai/research-ai/v1/research_ai_status"

querystring = {"research_token": 'research_token'}

poll_attempts = 0
while poll_attempts < 10:
    response = requests.request("GET", url_get, headers=headers, params=querystring)
    print(response.status_code)
    poll_attempts += 1
    if response.status_code == 200:
        print('Research AI has a generated answer ready for us!')
        break
    time.sleep(0.5)

On a successful poll, you’ll be able to fetch the answer from the payload:

print(response.text['response']['answer'])

In addition you’ll be able to extract key facts and sources that the answer is based on:

for key_fact in response.text['response']['key_facts']:
    print(key_fact)