What are User-Defined Functions?

User-Defined Functions (UDFs) in Pixeltable allow you to extend the platform with custom Python code. They bridge the gap between Pixeltable’s built-in operations and your specific data processing needs, enabling you to create reusable components for transformations, analysis, and AI workflows. Pixeltable UDFs offer several key advantages:

Reusability: Define a function once and use it across multiple tables and operations
Type Safety: Strong typing ensures data compatibility throughout your workflows
Performance: Batch processing and caching capabilities optimize execution
Integration: Seamlessly combine custom code with Pixeltable’s query system
Flexibility: Process any data type including text, images, videos, and embeddings

UDFs can be as simple as a basic transformation or as complex as a multi-stage ML workflow. Pixeltable offers three types of custom functions to handle different scenarios:

import pixeltable as pxt

# Basic UDF for text transformation
@pxt.udf
def clean_text(text: str) -> str:
    """Clean and normalize text data."""
    return text.lower().strip()

# Use in a computed column
documents = pxt.get_table('my_documents')
documents.add_computed_column(
    clean_content=clean_text(documents.content)
)

User-Defined Functions in Pixeltable

Learn more about UDFs and UDAs with our in-depth guide.

This guide covers five types of custom functions in Pixeltable:

Basic User-Defined Functions (UDFs)
Tables as UDFs
User-Defined Aggregates (UDAs)
Retrieval UDFs
MCP UDFs

1. Basic User-Defined Functions (UDFs)

Overview

UDFs allow you to:

Write custom Python functions for data processing
Integrate them into computed columns and queries
Optimize performance through batching
Create reusable components for your data workflow

All UDFs require type hints for parameters and return values. This enables Pixeltable to validate and optimize your data workflow before execution.

Creating Basic UDFs

@pxt.udf
def add_tax(price: float, rate: float) -> float:
    return price * (1 + rate)

# Use in computed column
table.add_computed_column(
    price_with_tax=add_tax(table.price)
)

from typing import List

@pxt.udf
def process_tags(tags: List[str]) -> str:
    return ", ".join(sorted(tags))

# Use in computed column
table.add_computed_column(
    formatted_tags=process_tags(table.tags)
)

UDF Types

Local UDFs
Module UDFs
Batched UDFs

# Defined directly in your code
@pxt.udf
def extract_year(date_str: str) -> int:
    return int(date_str.split('-')[0])

# Used immediately
table.add_computed_column(
    year=extract_year(table.date)
)

Local UDFs are serialized with their columns. Changes to the UDF only affect new columns.

# In my_functions.py
@pxt.udf
def clean_text(text: str) -> str:
    return text.strip().lower()

# In your application
from my_functions import clean_text
table.add_computed_column(
    clean_content=clean_text(table.content)
)

Module UDFs are referenced by path. Changes to the UDF affect all uses after reload.

from pixeltable.func import Batch

@pxt.udf(batch_size=32)
def process_batch(items: Batch[str]) -> Batch[str]:
    results = []
    for item in items:
        results.append(item.upper())
    return results

# Used like a regular UDF
table.add_computed_column(
    processed=process_batch(table.text)
)

Batched UDFs process multiple rows at once for better performance.

Supported Types

Basic Types

Native Python types supported in UDFs:

@pxt.udf
def process_data(
    text: str,           # String data
    count: int,          # Integer numbers
    score: float,        # Floating point
    active: bool,        # Boolean
    items: list[str],    # Generic lists
    meta: dict[str,any]  # Dictionaries
) -> str:
    return "Processed"

Complex Types

Pixeltable-specific types:

@pxt.udf
def process_media(
    img: PIL.Image.Image,    # Images
    embeddings: pxt.Array,   # Numerical arrays
    config: pxt.Json,        # JSON data
    doc: pxt.Document        # Documents
) -> str:
    return "Processed"

Performance Optimization

Batching

@pxt.udf(batch_size=16)
def embed_texts(
    texts: Batch[str]
) -> Batch[pxt.Array]:
    # Process multiple texts at once
    return model.encode(texts)

Caching

@pxt.udf
def expensive_operation(text: str) -> str:
    # Cache model instance
    if not hasattr(expensive_operation, 'model'):
        expensive_operation.model = load_model()
    return expensive_operation.model(text)

Async Support

from typing import Literal, Union, Any
import json

@pxt.udf
async def chat_completions(
    messages: list,
    *,
    model: str,
    model_kwargs: dict | None = None,
) -> dict:

    # Setup API request with proper context management
    result = await openai_client.chat_completions(
        messages=messages,
        model=model,
        model_kwargs=model_kwargs
    )

    # Process response
    return json.loads(result.text)

# Example usage in a computed column
table.add_computed_column(
    response=chat_completions(
        [
            {'role': 'system', 'content': 'You are a helpful assistant.'},
            {'role': 'user', 'content': table.prompt}
        ],
        model='gpt-4o-mini'
    )
)

Async UDFs are specifically designed for handling external API calls, such as LLM calls, database queries, or web service interactions. They should not be used for general computation or data processing. They keep your Pixeltable workflows responsive by allowing background execution of time-consuming operations.

Best Practices for Basic UDFs

Type Safety

Always provide complete type hints
Use specific types over generic ones
Validate input ranges where appropriate

@pxt.udf
def validate_score(score: float) -> float:
    if not 0 <= score <= 100:
        raise ValueError("Score must be between 0 and 100")
    return score

Performance

Use batching for GPU operations
Cache expensive resources
Process data in chunks when possible

@pxt.udf(batch_size=32)
def process_chunk(items: Batch[str]) -> Batch[str]:
    if not hasattr(process_chunk, 'model'):
        process_chunk.model = load_expensive_model()
    return process_chunk.model.process_batch(items)

Organization

Keep related UDFs in modules
Use clear, descriptive names
Document complex operations

@pxt.udf
def normalize_text(
    text: str,
    lowercase: bool = True,
    remove_punctuation: bool = True
) -> str:
    """Normalize text by optionally lowercasing and removing punctuation."""
    if lowercase:
        text = text.lower()
    if remove_punctuation:
        text = text.translate(str.maketrans("", "", string.punctuation))
    return text

Table UDFs

Define clear input and output columns for your table UDFs
Implement cleanup routines for tables that grow large
Balance between too many small tables and monolithic tables
Use clear naming conventions for tables and their UDFs
Document the purpose and expected inputs for each table UDF

2. Tables as UDFs

Overview

Tables as UDFs allow you to:

Convert entire tables into reusable functions
Create modular and complex data processing workflows
Encapsulate multi-step operations
Share workflows between different tables and applications

Tables as UDFs are particularly powerful for building AI agents and complex automation workflows that require multiple processing steps.

Creating Table UDFs

Basic Workflow
Complete Example

Step 1: Create a Specialized Table

# Create a table with your workflow
finance_agent = pxt.create_table('directory/financial_analyst',
                                {'prompt': pxt.String})
# Add computed columns for processing
finance_agent.add_computed_column(/* ... */)

Step 2: Convert to UDF

# Convert table to UDF by specifying return column
finance_agent_udf = pxt.udf(finance_agent,
                           return_value=finance_agent.answer)

Step 3: Use the Table UDF

# Use like any other UDF
result_table.add_computed_column(
    result=finance_agent_udf(result_table.prompt)
)

Setup and Tools

import time

import yfinance as yf

import pixeltable as pxt
from pixeltable.functions.openai import chat_completions, invoke_tools

DIRECTORY = 'agent'
OPENAI_MODEL = 'gpt-4o-mini'

# Create Fresh Directory
pxt.drop_dir(DIRECTORY, force=True)
pxt.create_dir(DIRECTORY, if_exists='ignore')


# yfinance tool for getting stock information
@pxt.udf
def stock_info(ticker: str) -> dict | None:
    """Get stock info for a given ticker symbol."""
    stock = yf.Ticker(ticker)
    return stock.info


# Helper UDF to create a prompt with tool outputs
@pxt.udf
def create_prompt(question: str, tool_outputs: list[dict]) -> str:
    return f"""
    QUESTION:

    {question}

    RESULTS:

    {tool_outputs}
    """

Step 1: Create Agent Table

# Create Financial Analyst Agent Table
finance_agent = pxt.create_table(
    f'{DIRECTORY}/financial_analyst',
    {'prompt': pxt.String},
    if_exists='ignore'
)

# Prepare initial messages for LLM
messages = [{'role': 'user', 'content': finance_agent.prompt}]

# Define available tools
tools = pxt.tools(stock_info)

# Get initial response with tool calls
finance_agent.add_computed_column(
    initial_response=chat_completions(
        model=OPENAI_MODEL,
        messages=messages,
        tools=tools,
        tool_choice=tools.choice(required=True)
    )
)

# Execute the requested tools
finance_agent.add_computed_column(
    tool_output=invoke_tools(tools, finance_agent.initial_response)
)

# Create prompt with tool results
finance_agent.add_computed_column(
    stock_response_prompt=create_prompt(
        finance_agent.prompt,
        finance_agent.tool_output
    )
)

# Generate final response using tool results
final_messages = [
    {'role': 'system', 'content': "Answer the user's question based on the results."},
    {'role': 'user', 'content': finance_agent.stock_response_prompt},
]

finance_agent.add_computed_column(
    final_response=chat_completions(
        model=OPENAI_MODEL,
        messages=final_messages
    )
)

# Extract answer text
finance_agent.add_computed_column(
    answer=finance_agent.final_response.choices[0].message.content
)

Step 2: Convert to UDF

# Convert the finance_agent table to a UDF
finance_agent_udf = pxt.udf(
    finance_agent,
    return_value=finance_agent.answer
)

Step 3: Create Consumer Table

# Create a Portfolio Manager table that uses the finance agent
portfolio_manager = pxt.create_table(
    f'{DIRECTORY}/portfolio_manager',
    {'prompt': pxt.String},
    if_exists='ignore'
)

# Add the finance agent UDF as a computed column
portfolio_manager.add_computed_column(
    result=finance_agent_udf(portfolio_manager.prompt)
)

Step 4: Test the Workflow

# Get the portfolio manager table
portfolio_manager = pxt.get_table(f'{DIRECTORY}/portfolio_manager')

# Insert a test query
portfolio_manager.insert([
    {'prompt': 'What is the price of NVDIA?'}
])

# View results
result = portfolio_manager.select(portfolio_manager.result).collect()
print(result)

Flow Diagram

Agent Table UDF Flow

Key Benefits of Table UDFs

Modularity

Break complex workflows into reusable components that can be tested and maintained separately.

Encapsulation

Hide implementation details and expose only the necessary inputs and outputs through clean interfaces.

Composition

Combine multiple specialized agents to build more powerful workflows through function composition.

Advanced Techniques

Chaining Multiple Table UDFs

You can create a workflow of table UDFs to handle complex multi-stage processing:

# Create a chain of specialized agents
research_agent = pxt.udf(research_table, return_value=research_table.findings)
analysis_agent = pxt.udf(analysis_table, return_value=analysis_table.insights)
report_agent = pxt.udf(report_table, return_value=report_table.document)

# Use them in sequence
workflow.add_computed_column(research=research_agent(workflow.query))
workflow.add_computed_column(analysis=analysis_agent(workflow.research))
workflow.add_computed_column(report=report_agent(workflow.analysis))

Parallel Processing with Table UDFs

Execute multiple table UDFs in parallel and combine their results:

# Define specialized agents for different tasks
stock_agent = pxt.udf(stock_table, return_value=stock_table.analysis)
news_agent = pxt.udf(news_table, return_value=news_table.summary)
sentiment_agent = pxt.udf(sentiment_table, return_value=sentiment_table.score)

# Process in parallel
portfolio.add_computed_column(stock_data=stock_agent(portfolio.ticker))
portfolio.add_computed_column(news_data=news_agent(portfolio.ticker))
portfolio.add_computed_column(sentiment=sentiment_agent(portfolio.ticker))

# Combine results
portfolio.add_computed_column(report=combine_insights(
    portfolio.stock_data,
    portfolio.news_data,
    portfolio.sentiment
))

3. User-Defined Aggregates (UDAs)

Overview

UDAs enable you to:

Create custom aggregation functions
Process multiple rows into a single result
Use them in group_by operations
Build reusable aggregation logic

Creating UDAs

@pxt.uda
class sum_of_squares(pxt.Aggregator):
    def __init__(self):
        self.cur_sum = 0

    def update(self, val: int) -> None:
        self.cur_sum += val * val

    def value(self) -> int:
        return self.cur_sum

UDA Components

Initialization (__init__)
- Sets up initial state
- Defines parameters
- Called once at start
Update Method (update)
- Processes each input row
- Updates internal state
- Must handle all value types
Value Method (value)
- Returns final result
- Called after all updates
- Performs final calculations

Using UDAs

# Basic usage
table.select(sum_of_squares(table.value)).collect()

# With grouping
table.group_by(table.category).select(
    table.category,
    sum_of_squares(table.value)
).collect()

Best Practices for UDAs

Manage state carefully
Handle edge cases and errors
Optimize for performance
Use appropriate type hints
Document expected behavior

4. Retrieval UDFs

Overview

Retrieval UDFs allow you to:

Convert a Pixeltable table into a lookup function
Use tables as tools for LLM agents
Build RAG (Retrieval Augmented Generation) pipelines
Create parameterized queries that can be called like functions

Creating Retrieval UDFs

A retrieval UDF queries a table based on input parameters and returns matching rows:

import pixeltable as pxt

# Create a knowledge base table
kb = pxt.create_table('app/knowledge_base', {
    'topic': pxt.String,
    'category': pxt.String,
    'content': pxt.String
})

kb.insert([
    {'topic': 'python', 'category': 'programming', 'content': 'Python is a high-level language...'},
    {'topic': 'rust', 'category': 'programming', 'content': 'Rust is a systems language...'},
    {'topic': 'photosynthesis', 'category': 'biology', 'content': 'Plants convert sunlight...'}
])

# Create a retrieval UDF from the table
lookup_kb = pxt.retrieval_udf(
    kb,
    name='search_knowledge_base',
    description='Search the knowledge base by topic and category',
    parameters=['topic', 'category'],  # Columns to use as search parameters
    limit=5  # Max results to return
)

# Use it as a function
results = lookup_kb(topic='python', category='programming')
# Returns: [{'topic': 'python', 'category': 'programming', 'content': 'Python is...'}]

Using Retrieval UDFs as LLM Tools

Retrieval UDFs integrate seamlessly with Pixeltable’s tool-calling system:

from pixeltable.functions.openai import chat_completions, invoke_tools

# Create tools from the retrieval UDF
tools = pxt.tools(lookup_kb)

# Use in an agent workflow
agent = pxt.create_table('app/agent', {'question': pxt.String})

agent.add_computed_column(
    initial_response=chat_completions(
        model='gpt-4o-mini',
        messages=[{'role': 'user', 'content': agent.question}],
        tools=tools,
        tool_choice=tools.choice(required=True)
    )
)

agent.add_computed_column(
    retrieved_data=invoke_tools(tools, agent.initial_response)
)

Parameters

Parameter	Description
`table`	The Pixeltable table to query
`name`	Name for the UDF (defaults to table name)
`description`	Description for LLM tool usage
`parameters`	Columns to use as query parameters (defaults to all non-computed columns)
`limit`	Maximum number of rows to return

Retrieval UDFs are perfect for building knowledge bases that LLM agents can query. Combine them with embedding indexes for semantic search capabilities.

5. MCP UDFs

Overview

MCP UDFs allow you to:

Connect to a running MCP server.
Use tools exposed by the MCP server as Pixeltable UDFs.
Integrate external services and custom logic into your Pixeltable workflows.

Example

Here is a simple example of an MCP server running in a separate Python script:

from mcp.server.fastmcp import FastMCP

mcp = FastMCP('PixeltableDemo', stateless_http=True)

@mcp.tool()
def pixelmultiple(a: int, b: int) -> int:
    """Computes the Pixelmultiple of two integers."""
    return (a + 22) * b

if __name__ == '__main__':
    mcp.run(transport='streamable-http')

import pixeltable as pxt

# Connect to the MCP server
# This returns a list of UDFs, one for each tool.
udfs = pxt.mcp_udfs('http://localhost:8000/mcp')
pixelmultiple_udf = udfs[0]

# Use the UDF in a table
t = pxt.create_table('test_mcp', {'a': pxt.Int, 'b': pxt.Int})
t.add_computed_column(
    pixelmultiple=pixelmultiple_udf(a=t.a, b=t.b)
)
t.insert([{'a': 3, 'b': 4}])
# The 'pixelmultiple' column will contain (3 + 22) * 4 = 100
t.collect()

Additional Resources

SDK Reference

Complete API reference

​What are User-Defined Functions?

​User-Defined Functions in Pixeltable

​1. Basic User-Defined Functions (UDFs)

​Overview

​Creating Basic UDFs

​UDF Types

​Supported Types

​Performance Optimization

Batching

Caching

Async Support

​Best Practices for Basic UDFs

​2. Tables as UDFs

​Overview

​Creating Table UDFs

​Step 1: Create a Specialized Table

​Step 2: Convert to UDF

​Step 3: Use the Table UDF

​Flow Diagram

Agent Table UDF Flow

​Key Benefits of Table UDFs

Modularity

Encapsulation

Composition

​Advanced Techniques

​3. User-Defined Aggregates (UDAs)

​Overview

​Creating UDAs

​UDA Components

​Using UDAs

​Best Practices for UDAs

​4. Retrieval UDFs

​Overview

​Creating Retrieval UDFs

​Using Retrieval UDFs as LLM Tools

​Parameters

​5. MCP UDFs

​Overview

​Example

​Additional Resources

SDK Reference

What are User-Defined Functions?

User-Defined Functions in Pixeltable

1. Basic User-Defined Functions (UDFs)

Overview

Creating Basic UDFs

UDF Types

Supported Types

Performance Optimization

Best Practices for Basic UDFs

2. Tables as UDFs

Overview

Creating Table UDFs

Step 1: Create a Specialized Table

Step 2: Convert to UDF

Step 3: Use the Table UDF

Flow Diagram

Key Benefits of Table UDFs

Advanced Techniques

3. User-Defined Aggregates (UDAs)

Overview

Creating UDAs

UDA Components

Using UDAs

Best Practices for UDAs

4. Retrieval UDFs

Overview

Creating Retrieval UDFs

Using Retrieval UDFs as LLM Tools

Parameters

5. MCP UDFs

Overview

Example

Additional Resources