> ## Documentation Index
> Fetch the complete documentation index at: https://pearsdb.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Question Answering with MindsDB and OpenAI using SQL

## Introduction

In this blog post, we present how to create OpenAI models within MindsDB. In this example, we ask a question to a model and get an answer. The input data is taken from our sample MySQL database.

## Prerequisites

To follow along, you can sign up for an account at [cloud.mindsdb.com](https://cloud.mindsdb.com/register/nlp). Alternatively, head to [MindsDB documentation](https://docs.mindsdb.com/) and follow the instructions to manually set up a local instance of MindsDB via [Docker](/setup/self-hosted/docker) or [pip](/setup/self-hosted/pip/source).

## Tutorial

In this tutorial, we create a predictive model to answer questions in a specified domain.

We use a table from our MySQL public demo database, so let's start by connecting MindsDB to it:

```sql theme={null}
CREATE DATABASE mysql_demo_db
WITH ENGINE = 'mysql',
PARAMETERS = {
    "user": "user",
    "password": "MindsDBUser123!",
    "host": "db-demo-data.cwoyhfn6bzs0.us-east-1.rds.amazonaws.com",
    "port": "3306",
    "database": "public"
};
```

Now that we've connected our database to MindsDB, let's query the data to be used in the example:

```sql theme={null}
SELECT *
FROM mysql_demo_db.questions
LIMIT 3;
```

Here is the output:

```sql theme={null}
+------------------+--------------------------------------------------------+-------------+
| article_title    | question                                               | true_answer |
+------------------+--------------------------------------------------------+-------------+
| Alessandro_Volta | Was Volta an Italian physicist?                        | yes         |
| Alessandro_Volta | Is Volta buried in the city of Pittsburgh?             | no          |
| Alessandro_Volta | Did Volta have a passion for the study of electricity? | yes         |
+------------------+--------------------------------------------------------+-------------+
```

Let's create a model table to answer all questions from the input dataset:

<Tip>
  Before creating an OpenAI model, please create an engine, providing your OpenAI API key:

  ```sql theme={null}
  CREATE ML_ENGINE openai_engine
  FROM openai
  USING
  	api_key = 'your-openai-api-key';
  ```
</Tip>

```sql theme={null}
CREATE MODEL question_answering_model
PREDICT answer
USING
    engine = 'openai_engine',
    prompt_template = 'answer the question of text:{{question}} about text:{{article_title}}';
```

In practice, the `CREATE MODEL` statement triggers MindsDB to generate an AI table called `question_answering_model` that uses the OpenAI integration to predict a column named `answer`. The model lives inside the default `mindsdb` project. In MindsDB, projects are a natural way to keep artifacts, such as models or views, separate according to what predictive task they solve. You can learn more about MindsDB projects [here](/sql/project).

The `USING` clause specifies the parameters that this handler requires.

* The `engine` parameter defines that we use the `openai` engine.
* The `prompt_template` parameter conveys the structure of a message that is to be completed with additional text generated by the model.

<Note>
  Follow [this instruction](/integrations/ai-engines/openai#setup) to set up the OpenAI integration in MindsDB.
</Note>

Once the `CREATE MODEL` statement has started execution, we can check the status of the creation process with the following query:

```sql theme={null}
DESCRIBE question_answering_model;
```

It may take a while to register as complete depending on the internet connection. Once the creation is complete, the behavior is the same as with any other AI table – you can query it either by specifying synthetic data in the actual query:

```sql theme={null}
SELECT article_title, question, answer
FROM question_answering_model
WHERE question = 'Was Abraham Lincoln the sixteenth President of the United States?'
AND article_title = 'Abraham_Lincoln';
```

Here is the output data:

```sql theme={null}
+------------------+-------------------------------------------------------------------+------------------------------------------------------------------------+
| article_title    | question                                                          | answer                                                                 |
+------------------+-------------------------------------------------------------------+------------------------------------------------------------------------+
| Abraham_Lincoln  | Was Abraham Lincoln the sixteenth President of the United States? | Yes, Abraham Lincoln was the sixteenth President of the United States. |
+------------------+-------------------------------------------------------------------+------------------------------------------------------------------------+
```

Or by joining with another table for batch predictions:

```sql theme={null}
SELECT input.article_title, input.question, output.answer
FROM mysql_demo_db.questions AS input
JOIN question_answering_model AS output
LIMIT 3;
```

Here is the output data:

```sql theme={null}
+------------------+--------------------------------------------------------+--------------------------------------------------------+
| article_title    | question                                               | answer                                                 |
+------------------+--------------------------------------------------------+--------------------------------------------------------+
| Alessandro_Volta | Was Volta an Italian physicist?                        | Yes, Volta was an Italian physicist.                   |
| Alessandro_Volta | Is Volta buried in the city of Pittsburgh?             | No, Volta is not buried in the city of Pittsburgh.     |
| Alessandro_Volta | Did Volta have a passion for the study of electricity? | Yes, Volta had a passion for the study of electricity. |
+------------------+--------------------------------------------------------+--------------------------------------------------------+
```

The `questions` table is used to make batch predictions. Upon joining the `question_answering_model` model with the `questions` table, the model uses all values from the `article_title` and `question` columns.

## Leverage the NLP Capabilities with MindsDB

By integrating databases and OpenAI using MindsDB, developers can easily extract insights from text data with just a few SQL commands. These powerful natural language processing (NLP) models are capable of answering questions with or without context and completing general prompts.

Furthermore, these models are powered by large pre-trained language models from OpenAI, so there is no need for manual development work. Ultimately, this provides developers with an easy way to incorporate powerful NLP capabilities into their applications while saving time and resources compared to traditional ML development pipelines and methods. All in all, MindsDB makes it possible for developers to harness the power of OpenAI efficiently!

MindsDB is now the fastest-growing open-source applied machine-learning platform in the world. Its community continues to contribute to more than 70 data-source and ML-framework integrations. Stay tuned for the upcoming features - including more control over the interface parameters and fine-tuning models directly from MindsDB!

Experiment with OpenAI models within MindsDB and unlock the ML capability over your data in minutes. Remember to [sign-up for a free demo account](https://cloud.mindsdb.com/register/nlp) and follow the tutorials, perhaps this time using your data.

Finally, if MindsDB's vision to democratize ML sounds exciting, head to our [community Slack](https://mindsdb.com/joincommunity), where you can get help and find people to chat about using other available data sources, ML frameworks, or writing a handler to bring your own!

Follow our introduction to MindsDB's OpenAI integration [here](https://mindsdb.com/blog/extract-insights-from-text-inside-databases-using-openai-gpt3-and-mindsdb-integration). Also, we've got a variety of tutorials that use MySQL and MongoDB:

* [Sentiment Analysis in MySQL](/nlp/sentiment-analysis-inside-mysql-with-openai)
* [Text Summarization in MySQL](/nlp/text-summarization-inside-mysql-with-openai)
* [Sentiment Analysis in MongoDB](/nlp/sentiment-analysis-inside-mongodb-with-openai)
* [Question Answering in MongoDB](/nlp/question-answering-inside-mongodb-with-openai)
* [Text Summarization in MongoDB](/nlp/text-summarization-inside-mongodb-with-openai)

## What's Next?

Have fun while trying it out yourself!

* Bookmark [MindsDB repository on GitHub](https://github.com/mindsdb/mindsdb).
* Sign up for a free [MindsDB account](https://cloud.mindsdb.com/register/nlp).
* Engage with the MindsDB community on [Slack](https://mindsdb.com/joincommunity) or [GitHub](https://github.com/mindsdb/mindsdb/discussions) to ask questions and share your ideas and thoughts.

If this tutorial was helpful, please give us a GitHub star [here](https://github.com/mindsdb/mindsdb).