Knowledge collections

Knowledge Collections in Waterflai are organized sets of data that your AI applications can use as a knowledge base. This page will guide you through the process of creating and managing collections.

Creating a New Collection

To create a new Knowledge Collection in your Waterflai workspace:

1. Navigate to the Knowledge page in your Waterflai dashboard.

2. Click the "Add Collection" button in the Collections section.

3. You'll be taken through a three-step process:

   Step 1: Source Selection

   • Choose the connector (data source) for your collection.

   Step 2: Collection Configuration

   • Configure the collection settings specific to the chosen connector.

   • Select an embeddings model for your collection.

   Step 3: Finalize

   • Provide a name and description for your collection.

4. Click "Save" to create the new collection.

Managing Existing Collections

Once you've created collections, you can manage them from the Collections table:

Viewing Collections

• The Collections table displays all your knowledge collections.

• Each row shows the collection's name, a truncated description, and the associated connector type.

Editing a Collection

To edit a collection's settings:

1. Click on the collection name in the table.

2. You'll be taken to the collection edition page where you can modify all settings.

3. Navigate through the steps using the "Next" and "Back" buttons.

4. Click "Save" when you're done to apply your changes.

Deleting a Collection

To remove a collection:

1. Click the three-dot menu at the end of the collection's row.

2. Select "Delete" from the dropdown menu.

3. Confirm the deletion in the confirmation dialog.

Note: Deleting a collection will permanently remove its data. Ensure you no longer need the collection before deletion.

Collection Preview

When creating or editing a collection, you'll see a preview panel on the right side of the screen. This preview feature is a powerful tool designed to help you configure your collection accurately.

Embeddings Models

When creating or editing a collection, you'll sometimes need to select an embeddings model (specially when using vector store). This model is used to convert your data into vector representations, enabling efficient similarity search and retrieval.

Best Practices

1. Naming: Use clear, descriptive names for your collections to easily identify their purpose or contents.

2. Description: Provide detailed descriptions for your collections, including information about the data source and intended use.

3. Regular Review: Periodically review your collections to ensure they contain up-to-date and relevant information.

4. Testing: After creating or modifying a collection, test it in a sample project to ensure it performs as expected.

By following these instructions, you'll be able to effectively create and manage Knowledge Collections in Waterflai, providing your AI applications with rich, organized data sources to enhance their capabilities.