There are 2 ways to use this API as below
**NOTE: **You need to upload the text as a string - currently you can’t upload entire documents (like PDF or other formats). An option around this is to first extract the text using a library (e.g. PyMUPDF for PDF to text) and then use the URL as part of your pipeline:
url = “https://chatgpt-powered-question-answering-over-documents.p.rapidapi.com/qa877”
payload = {
“text”: text,
“query”: “Summarize in 3 sentences”
}
headers = {
“content-type”: “application/json”,
“X-RapidAPI-Key”: “BLAH”,
“X-RapidAPI-Host”: “chatgpt-powered-question-answering-over-documents.p.rapidapi.com”
}
response = requests.request(“POST”, url, json=payload, headers=headers)
print(response.text)
{
“answer”: “BERT is a deep bidirectional transformer pre-trained for language understanding. It is trained on a 3.3 billion word corpus with a batch size of 256 sequences for 1,000,000 steps. The pre-training tasks include Masked LM and Next Sentence Prediction, which are illustrated with examples.”,
“id”: “1875011f-1a4e-40af-9b5c-77e6ba1729fd”,
“message”: “Successful”,
“status”: true
}
import requests
url = “https://chatgpt-powered-question-answering-over-documents.p.rapidapi.com/qa877”
payload = {
“id”: “1875011f-1a4e-40af-9b5c-77e6ba1729fd”,
“query”: “What is BERT trained on?”
}
headers = {
“content-type”: “application/json”,
“X-RapidAPI-Key”: “BLAH”,
“X-RapidAPI-Host”: “chatgpt-powered-question-answering-over-documents.p.rapidapi.com”
}
response = requests.request(“POST”, url, json=payload, headers=headers)
print(response.text)
{
“answer”: “BERT is trained on unlabeled data over different pre-training tasks.”,
“id”: “1875011f-1a4e-40af-9b5c-77e6ba1729fd”,
“message”: “Successful”,
“status”: true
}
And that’s it! Feel free to let me know if you have any questions.