AI Worflows

AI Workflows on the Abacus.AI platform are intelligent and autonomous assistants designed to perform a wide range of tasks by integrating user code, data transformations, machine learning models, and large language model (LLM) prompts. These workflows are customized using agent_function, allowing access to both LLMs and rich APIs offered by Abacus.AI. This approach enables intuitive problem-solving using natural language inputs.

Types of AI Workflows

The Abacus.AI platform supports various types of AI Workflows based on their interface:

DEFAULT: The standard interface for AI Workflows.
CHAT: Workflows designed specifically for conversational interactions.
MATRIX: Workflows that handle complex, multi-dimensional inputs and outputs.
AUTONOMOUS: Workflows that operate with minimal human intervention, making decisions and performing tasks independently.

Examples of AI Workflows

Data Analysis Workflow: Translates natural language queries to SQL using LLMs and executes them via APIs.
Documentation Retrieval and Understanding Workflow: Extracts keywords from queries, retrieves relevant documents, and provides insights.
ML Model Building Workflow: Converts natural language directives into API calls for building ML models.
Information Search and Processing Workflow: Retrieves and processes information with enhanced presentation.
Table Schema Workflow: Utilizes APIs to retrieve schemas for feature groups and answers schema-related queries.

Creating AI Workflows

To create an AI Workflows on the Abacus.AI platform, follow these steps:

Define Workflow Nodes and Functions: Establish the individual tasks and their functions.
Define the AI Workflow: Use nodes and edges to create the workflow graph.
Create and Deploy the AI Workflow: Deploy the workflow on the platform.

To create or edit AI Workflows using the Workflow Editor, visit the Workflow Editor for AI Workflows page.

Constructing Workflow Functions

Workflow functions are created on the create workflow page, where you provide a name, description, and source code. The description guides interaction with the workflow in the chat dialog.

Example: Default Workflow Function

def agent_function(nlp_query):
    """
    Args:
        nlp_query (Any): Data row to predict on/with or to pass to the workflow for execution
    Returns:
        The result which can be any JSON serializable Python type
    """
    from abacusai import ApiClient

    # Let agent respond like your favorite character.
    character = 'Sherlock Holmes'
    return ApiClient().evaluate_prompt(prompt=nlp_query, system_message=f'respond like {character}').content

Example: Data Query Workflow Function

def agent_function(nlp_query):
    """
    Args:
        nlp_query (Any): Data row to predict on/with or to pass to the workflow for execution
    Returns:
        The result which can be any JSON serializable Python type
    """
    from abacusai import ApiClient

    client = ApiClient()
    fg = client.describe_feature_group_by_table_name('Concrete Strength')
    rendered_fgs = client.render_feature_groups_for_llm([fg.feature_group_id])
    system_prompt = 'Reply SQL code only based on the following table definition:'
    for i, block in enumerate(rendered_fgs):
        system_prompt += f'{i + 1}:- {block.content}\n'
    llm_response = client.evaluate_prompt(prompt=nlp_query, system_message=system_prompt)
    return client.execute_feature_group_sql(llm_response.content).to_markdown()

Deploying AI Workflows

Deploying an AI Workflow is straightforward, similar to deploying an ML model. Click Create New Deployment, display the workflow deployment window, and click deploy AI Workflow. A deployment can be restarted after suspension.

Chat with AI Workflow

Once deployed, workflows can be interacted with through the chat window. User messages are sent to the workflow function as input, and responses are displayed in the chat window. Markdown-formatted responses, such as SQL execution results, are recognized and displayed accordingly.

Execute Workflow Function

A deployed AI Workflow can also be accessed via our API. Use the execute_agent function from the Abacus.AI Python library to interact with a deployed AI Workflow. Here's an example:

client.execute_agent(
    deployment_token='<deployment_token>',
    deployment_id='<deployment_id>',
    keyword_arguments={
        'query': 'Show me the average, min and max cement value when the water column is larger than 200'
    }
)

If the AI workflow accepts a file input, use execute_agent_with_binary_data to interact with the workflow. Here's an example of an workflow that accepts a query and a document input:

file = open(<file_path>, 'rb')
client.execute_agent_with_binary_data(
    deployment_token='<deployment_token>',
    deployment_id='<deployment_id>',
    keyword_arguments={
        'query': 'Show me the average, min and max cement value when the water column is larger than 200',
        'document': 'sample.pdf'
    },
    blobs={'sample.pdf': file}
)
file.close()

Note that if the AI Workflow accepted an array of file inputs as it's second argument, the above code can be modified as below:

file_1 = open(<doc_1_file_path>, 'rb')
file_2 = open(<doc_2_file_path>, 'rb')
client.execute_agent_with_binary_data(
    deployment_token='<deployment_token>',
    deployment_id='<deployment_id>',
    keyword_arguments={
        'query': 'Show me the average, min and max cement value when the water column is larger than 200',
        'documents': ['doc_1.pdf', 'doc_2.pdf']
    },
    blobs={
        'doc_1.pdf': file_1,
        'doc_2.pdf': file_2
    }
)
file_1.close()
file_2.close()