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Tools

Tools on the Abacus.AI platform are reusable components that enable integration with external systems and execution of custom code. They provide a structured way to extend platform capabilities and automate workflows.

Types of Tools​

The Abacus.AI platform supports several types of tools:

  • PYTHON FUNCTION: Custom Python code tools for executing arbitrary logic
  • CONNECTOR: Integration tools that allow you to connect to external platforms (e.g., Jira, Confluence)

Creating Tools​

To create a new tool on the Abacus.AI platform:

  1. Navigate to Custom Code in the left navigation bar

Note: This is available outside of projects, so navigate to https://abacus.ai/app or access directly from this link

  1. Select Tools from the dropdown menu
  2. Click Create Tool in the top right corner

Tool Creation Process​

  1. Basic Configuration:

    • Provide a name for the tool
    • Select the tool type (Python Function or Connector)

  2. Tool-Specific Configuration:

    Python Function Tools can be created using two approaches:

    • Write custom Python code directly
    • Provide detailed instructions describing the tool's purpose, functionality, and invocation criteria

    Connector Tools require:

    • Selection of an existing platform connector (Jira or Confluence)
    • Detailed instructions describing the tool's purpose, functionality, and invocation criteria

Implementation Details​

When creating a custom tool, Abacus automatically generates a notebook with sample code to guide you through the process.

Standard Response Format: By default, tools return JSON output that the LLM can process after invocation.

Document Response Format: Tools can also return documents using the following notation (note: this differs from AI Workflows):

return {
'file_blob': abacusai.Blob(
    contents=bytes_data, #bytes of file
    mime_type=(
        'application/vnd.openxmlformats-officedocument.'
        'presentationml.presentation'
    ),
    filename=f'my_document.pptx',
    size=len(bytes_data)
    )
}

For detailed examples of tool configurations and prompting best practices, see the Tool Examples page.

Adding tools to Chatbots​

After you have create a tool, you can add it to any custom Chatbot using the model training options:

The tool will now be used by the LLM based on the prompting you have provided.

Finally, please note we offer many built-in user connectors that you can utilize for custom Chatbots. Once you have configured your Chatbot with one of these, it will be able to utilize the individual's access credentials to query data on the fly from multiple systems such as:

  • Google Drive
  • One Drive
  • Sharepoint
  • Salesforce
  • Gmail
  • Outlook
  • And others..