AI Smart Assistance

CloudBeaver offers the ability to construct SQL queries using natural language through AI smart completion feature. This capability is achieved through integrations with both OpenAI's GPT-3 language model and Azure OpenAI.

Note: CloudBeaver is not affiliated with OpenAI. Integration is achieved through the public API.

• To utilize this feature, register with OpenAI and obtain a secret key.

With the AI smart completion feature, you can type queries in natural language and CloudBeaver will convert them into SQL statements. This tool simplifies writing complex queries by interpreting your input and automatically generating the correct SQL code.

To activate the AI features in CloudBeaver, configure the API token:

1. Navigate to Administration page -> Server Configuration tab

2. Ensure the AI Service option is activated.

3. Navigate to AI Settings tab -> Choose an engine

4. In the API token field, input your AI secret key.

5. Save the changes.

For instructions on utilizing the AI features, visit the .

We prioritize data safety and user privacy. In this section, we outline how data is managed and the measures taken to protect user privacy when using the AI features.

To enable the AI features capabilities, table and column names from the current database schema are transmitted to OpenAI. This step is crucial for accurately translating user requests into SQL queries. Key considerations regarding data privacy are as follows:

• No Table Data: Only metadata like table and column names are shared with OpenAI. Actual table data is not transmitted.
• Log Transparency: The entire request can be logged for your review. To enable this, navigate to AI Settings tab and check the Write GPT queries to debug log option.

• Azure OpenAI privacy: If you use Azure OpenAI, be aware that it operates under its own privacy policy. It's recommended to review their terms before using.

To utilize the AI-enhanced functionalities within CloudBeaver, certain configurations and setup processes are required. This section offers a comprehensive guide on initial setup and customization options to tailor the AI integration according to specific preferences.

1. Sign up on the OpenAI platform.

2. Navigate to the API Keys section and generate a new secret key.

3. Insert this key into CloudBeaver's Engine Settings.

1. Sign up on the Azure platform.

2. Navigate to the Azure Portal and create a new AI service under the AI + Machine Learning section.

3. Generate and copy the credentials for the newly created service.

4. Insert these credentials into ClouBeaver's Engine Settings.

For specific requirements or troubleshooting, you might want to adjust some of the following settings:

• Navigate to Administration page -> AI Settings -> Engine settings to access these settings.

There is also an option to switch the Engine from OpenAI to Azure OpenAI. Azure provides a set of distinct settings:

To interact with databases using the AI Smart completion feature:

1. Launch the SQL Editor.

2. Click on the AI smart completion icon located in the left toolbar of the SQL Editor.

3. Input your natural language request in the AI smart completion window.

4. Click Translate to obtain the SQL query.

Prompts history allows you to review previous prompts in the scope of the session for the chosen tab.

To hide the AI smart completion icon in the SQL Editor:

• Navigate to Administration page -> Server Configuration tab -> Services section.

• Deselect AI option.

When using AI to generate SQL queries, it's essential to provide clear and specific input. Here's how to optimize your questions:

• Language: While AI supports multiple languages, it's recommended to use English for best results.
• Database knowledge: Familiarity with your database structure enhances the accuracy of generated queries.
• Explicit details: If you know certain tables or columns that should be part of the query, include them in your request for better accuracy.

For instance, if you're using the CloudBeaver sample SQLite database, you might phrase your request as:

Example 1: "List all customers from Italy"

Resulting SQL:

SELECT *
FROM customers
WHERE country = 'Italy';

Example 2: "montre les clients de France"

SELECT *
FROM customer
WHERE country = 'France';

Example 3: "show customers who purchased blues tracks, use joins"

SELECT c.FirstName, c.LastName, t.Name, g.Name
FROM Customer c
         JOIN Invoice i ON c.CustomerId = i.CustomerId
         JOIN InvoiceLine il ON i.InvoiceId = il.InvoiceId
         JOIN Track t ON il.TrackId = t.TrackId
         JOIN Genre g ON t.GenreId = g.GenreId
WHERE g.Name = 'Blues'
ORDER BY c.LastName, c.FirstName;

Example 4: "get names of customers who purchased blues tracks, use joins"

SELECT DISTINCT c.FirstName, c.LastName
FROM Customer c
         JOIN Invoice i ON c.CustomerId = i.CustomerId
         JOIN InvoiceLine il ON i.InvoiceId = il.InvoiceId
         JOIN Track t ON il.TrackId = t.TrackId
         JOIN Genre g ON t.GenreId = g.GenreId
WHERE g.Name = 'Blues';