🦙 Using Unity Catalog AI with LlamaIndex

Integrate Unity Catalog AI with LlamaIndex to directly use UC functions as tools in LlamaIndex-based agent applications. This guide covers installation, client setup, and examples to get started.

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Installation

Install the Unity Catalog AI LlamaIndex integration from PyPI:

pip install unitycatalog-llamaindex

Prerequisites

• Python version: Python 3.10 or higher is required.

Note: Depending on what you’re doing with LlamaIndex, you may need to install additional packages from PyPI.

Unity Catalog

Ensure that you have a functional UC server set up and that you are able to access the catalog and schema where defined functions are stored.

Databricks Unity Catalog

To interact with Databricks Unity Catalog, install the optional package dependency when installing the integration package:

pip install unitycatalog-llamaindex[databricks]

Tutorial

Client Setup

Create an instance of the Functions Client

from unitycatalog.client import ApiClient, Configuration
from unitycatalog.ai.core.client import UnitycatalogFunctionClient

config = Configuration()
# This is the default address when starting a UnityCatalog server locally. Update this to the uri
# of your running UnityCatalog server.
config.host = "http://localhost:8080/api/2.1/unity-catalog"

# Create the UnityCatalog client
api_client = ApiClient(configuration=config)

# Use the UnityCatalog client to create an instance of the AI function client
client = UnitycatalogFunctionClient(api_client=api_client)

Client Setup - Databricks

Create an instance of the Unity Catalog Functions client

from unitycatalog.ai.core.databricks import DatabricksFunctionClient

client = DatabricksFunctionClient()

Creating a UC function

Create a Python function within Unity Catalog

CATALOG = "your_catalog"
SCHEMA = "your_schema"

func_name = f"{CATALOG}.{SCHEMA}.code_function"

def code_function(code: str) -> str:
    """
    Executes Python code.

    Args:
        code (str): The python code to execute.
    Returns:
        str: The result of the execution of the Python code.
    """
    import sys
    from io import StringIO
    stdout = StringIO()
    sys.stdout = stdout
    exec(code)
    return stdout.getvalue()

client.create_python_function(
    func=code_function,
    catalog=CATALOG,
    schema=SCHEMA
)

Creating a toolkit instance

Here we create an instance of our UC function as a toolkit, then verify that the tool is behaving properly by executing the function.

from unitycatalog.ai.llama_index.toolkit import UCFunctionToolkit

# Create a UCFunctionToolkit that includes the UC function
toolkit = UCFunctionToolkit(function_names=[func_name])

# Fetch the tools stored in the toolkit
tools = toolkit.tools
python_exec_tool = tools[0]

# Execute the tool directly
result = python_exec_tool.call(code="print(1 + 1)")
print(result)  # Outputs: {"format": "SCALAR", "value": "2\n"}

Using the tool in a LlamaIndex ReActAgent

With our interface to our UC function defined as a LlamaIndex tool collection, we can directly use it within a LlamaIndex agent application. Below, we are going to create a simple ReActAgent and verify that our agent properly calls our UC function.

from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent

llm = OpenAI()

agent = ReActAgent.from_tools(tools, llm=llm, verbose=True)

agent.chat("Please call a python execution tool to evaluate the result of 42 + 97.")