Question Answering | Endpoints | Openai Api Tutorial

Introduction

Question answering with the OpenAI API allows you to get precise answers to questions based on a given context. This tutorial covers how to use the question answering endpoint effectively, its parameters, and examples in JavaScript and Python.

Endpoint Overview

The question answering endpoint leverages advanced AI models to provide accurate answers to questions based on the provided context.

Using the Question Answering Endpoint

API Request

To get an answer, send a POST request to the endpoint URL with your API key, the context, and the question.

POST /v1/answers HTTP/1.1
Host: api.openai.com
Content-Type: application/json
Authorization: Bearer YOUR_API_KEY

{
    "model": "text-davinci-003",
    "question": "What is the capital of France?",
    "context": "France, officially the French Republic, is a country whose territory consists of metropolitan France in Western Europe and several overseas regions and territories. The capital of France is Paris."
}

In this example, a question about the capital of France is answered using the "text-davinci-003" model.

API Response

The API responds with the answer based on the provided context and question.

HTTP/1.1 200 OK
Content-Type: application/json

{
    "answers": [
        "Paris"
    ]
}

The response includes the answer(s) to the question based on the given context.

Parameters

Here are some common parameters you can use with the question answering endpoint:

question: The question you want answered.
context: The context or body of text based on which the question will be answered.
model: The model to use for answering (e.g., "text-davinci-003").

Examples in JavaScript

Here's how you can use the question answering endpoint in JavaScript:

const axios = require('axios');

const apiKey = 'YOUR_API_KEY';
const endpoint = 'https://api.openai.com/v1/answers';

async function getAnswer(question, context) {
    try {
        const response = await axios.post(endpoint, {
            model: 'text-davinci-003',
            question: question,
            context: context
        }, {
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${apiKey}`
            }
        });

        console.log(response.data.answers);
    } catch (error) {
        console.error('Error:', error);
    }
}

getAnswer('What is the capital of France?', 'France, officially the French Republic, is a country whose territory consists of metropolitan France in Western Europe and several overseas regions and territories. The capital of France is Paris.');

Examples in Python

Here's how you can use the question answering endpoint in Python:

import requests
import json

api_key = 'YOUR_API_KEY'
endpoint = 'https://api.openai.com/v1/answers'

def get_answer(question, context):
    headers = {
        'Content-Type': 'application/json',
        'Authorization': f'Bearer {api_key}'
    }
    data = {
        'model': 'text-davinci-003',
        'question': question,
        'context': context
    }

    response = requests.post(endpoint, headers=headers, data=json.dumps(data))
    if response.status_code == 200:
        answers = response.json()['answers']
        for answer in answers:
            print(answer)
    else:
        print(f"Error: {response.status_code}, {response.text}")

get_answer('What is the capital of France?', 'France, officially the French Republic, is a country whose territory consists of metropolitan France in Western Europe and several overseas regions and territories. The capital of France is Paris.');

Conclusion

The question answering endpoint in the OpenAI API provides a powerful tool to get precise answers to questions based on a provided context. By understanding its usage, parameters, and seeing examples in JavaScript and Python, you can integrate question answering seamlessly into various applications for improved information retrieval and user interaction.

Question Answering using OpenAI API