{"id":4117,"date":"2023-11-04T23:14:04","date_gmt":"2023-11-04T23:14:04","guid":{"rendered":"http:\/\/localhost:10003\/how-to-build-a-question-answering-system-with-openai-gpt-3-and-python\/"},"modified":"2023-11-05T05:47:59","modified_gmt":"2023-11-05T05:47:59","slug":"how-to-build-a-question-answering-system-with-openai-gpt-3-and-python","status":"publish","type":"post","link":"http:\/\/localhost:10003\/how-to-build-a-question-answering-system-with-openai-gpt-3-and-python\/","title":{"rendered":"How to Build a Question Answering System with OpenAI GPT-3 and Python"},"content":{"rendered":"

In this tutorial, we will learn how to build a question answering system using OpenAI GPT-3 and Python. OpenAI GPT-3 is a powerful language model that can generate human-like text and answer questions based on the given context.<\/p>\n

To build the question answering system, we will use the OpenAI Python library, which provides easy access to the GPT-3 API. We will write a Python script that takes a context and a question as input and outputs the answer using GPT-3.<\/p>\n

Here are the steps we will follow in this tutorial:
\n1. Set up OpenAI GPT-3
\n2. Understand the GPT-3 API
\n3. Create the Python script
\n4. Test the question answering system<\/p>\n

Let’s get started!<\/p>\n

Step 1: Set up OpenAI GPT-3<\/h2>\n

Before we can use GPT-3, we need to set up an OpenAI account and obtain an API key. Follow these steps to set up GPT-3:<\/p>\n

    \n
  1. Go to the OpenAI website (https:\/\/openai.com\/) and sign up for an account.<\/li>\n
  2. Once you have an account, go to the OpenAI API page (https:\/\/platform.openai.com\/signup) and join the waitlist for GPT-3 access.<\/li>\n
  3. Once you have access, create an API key on the API settings page.<\/li>\n<\/ol>\n

    Now that we have an API key, we can move on to the next step.<\/p>\n

    Step 2: Understand the GPT-3 API<\/h2>\n

    The GPT-3 API allows us to interact with the GPT-3 language model. It provides various endpoints for different types of tasks, including question answering. To understand how the API works, let’s look at an example API call:<\/p>\n

    import openai\n\nopenai.ChatCompletion.create(\n  model=\"gpt-3.5-turbo\",\n  messages=[\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n        {\"role\": \"user\", \"content\": \"Who won the world series in 2020?\"},\n        {\"role\": \"assistant\", \"content\": \"The Los Angeles Dodgers won the World Series in 2020.\"},\n        {\"role\": \"user\", \"content\": \"Where was it played?\"}\n    ]\n)\n<\/code><\/pre>\n
    In this example, the model<\/code> parameter specifies the version of GPT-3 we want to use. The messages<\/code> parameter is a list of role\/content pairs that represent the conversation. Each conversation consists of alternating user and assistant messages.<\/p>\n
    The API will return a response that contains the assistant’s reply to the last user message. We can extract the answer from the response and use it in our question answering system.<\/p>\n
    Step 3: Create the Python script<\/h2>\n
    Now that we understand the basics of the GPT-3 API, let’s create a Python script that interacts with the API and builds our question answering system. Follow the steps below to create the script:<\/p>\n
      \n
    1. Install the OpenAI Python library by running the following command in your command-line interface:<\/li>\n<\/ol>\n
      pip install openai\n<\/code><\/pre>\n
        \n
      1. Import the openai<\/code> module and set up your API key:<\/li>\n<\/ol>\n
        import openai\n\nopenai.api_key = \"YOUR_API_KEY\"\n<\/code><\/pre>\n
          \n
        1. Define a function that takes a context and a question as input and returns the answer using GPT-3:<\/li>\n<\/ol>\n
          def answer_question(context, question):\n    conversation = [\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n        {\"role\": \"user\", \"content\": context},\n        {\"role\": \"user\", \"content\": question},\n        {\"role\": \"assistant\", \"content\": \"\"}\n    ]\n\n    response = openai.ChatCompletion.create(\n        model=\"gpt-3.5-turbo\",\n        messages=conversation\n    )\n\n    answer = response['choices'][0]['message']['content']\n    return answer\n<\/code><\/pre>\n
          In this function, we create a conversation with the given context and question. We pass the conversation to the GPT-3 API and extract the assistant’s reply as the answer.<\/p>\n
            \n
          1. Test the question answering system by calling the answer_question<\/code> function with a context and a question:<\/li>\n<\/ol>\n
            context = \"The Los Angeles Dodgers won the World Series in 2020.\"\nquestion = \"Who won the World Series in 2020?\"\n\nanswer = answer_question(context, question)\nprint(answer)\n<\/code><\/pre>\n
            When you run the script, it should output the answer to the question based on the given context.<\/p>\n
            Step 4: Test the question answering system<\/h2>\n
            Now that our question answering system is ready, let’s test it with different contexts and questions. Here are a few examples:<\/p>\n
            context = \"The capital of France is Paris.\"\nquestion = \"What is the capital of France?\"\nanswer = answer_question(context, question)\nprint(answer)\n<\/code><\/pre>\n
            This should output “Paris” as the answer.<\/p>\n
            context = \"Python is a programming language.\"\nquestion = \"What is Python?\"\nanswer = answer_question(context, question)\nprint(answer)\n<\/code><\/pre>\n
            This should output “Python is a programming language.” as the answer.<\/p>\n
            You can test the question answering system with various contexts and questions to see how it performs.<\/p>\n
            Conclusion<\/h2>\n
            In this tutorial, we have learned how to build a question answering system using OpenAI GPT-3 and Python. We set up GPT-3, understood the GPT-3 API, created a Python script, and tested the question answering system. OpenAI GPT-3 provides a powerful way to generate human-like text and answer questions based on the given context. You can further enhance the question answering system by expanding the conversation and incorporating more user messages.<\/p>\n
            Remember to keep your API key secure and avoid exposing it publicly. OpenAI offers detailed documentation on the GPT-3 API, which you can refer to for more advanced usage and options. Have fun experimenting with the GPT-3 language model and building innovative applications!<\/p>\n","protected":false},"excerpt":{"rendered":"
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