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Declarative AI Infrastructure: Define Pipelines, Not Plumbing

Replace thousands of lines of orchestration code with declarative computed columns. Pixeltable handles execution, dependencies, caching, and incremental updates automatically.

Quick Start View on GitHub Documentation

The Challenge

Traditional AI infrastructure requires imperative orchestration: loading data, managing state, scheduling jobs, handling retries, tracking dependencies. This code is brittle, hard to test, and grows faster than the actual application logic.

The Solution

Pixeltable's declarative model lets you define what to compute, not how. Computed columns express your pipeline logic. The engine handles execution order, dependency tracking, incremental updates, caching, and error recovery.

Implementation Guide

Step-by-step walkthrough with code examples

Step 1 of 2

Declarative Columns

Express your entire pipeline as computed columns — no orchestrator needed.

python

1import pixeltable as pxt
2from pixeltable.functions import openai
3
4# Define what you want, not how to get it
5images = pxt.create_table('app.images', {
6    'image': pxt.Image,
7    'title': pxt.String,
8})
9
10# AI inference as a computed column
11images.add_computed_column(
12    description=openai.chat_completions(
13        model='gpt-4o-mini',
14        messages=[{
15            'role': 'user',
16            'content': [images.image, 'Describe this image.']
17        }]
18    ).choices[0].message.content
19)
20
21# Dependent columns run in correct order automatically
22images.add_computed_column(
23    tags=openai.chat_completions(
24        model='gpt-4o-mini',
25        messages=[{
26            'role': 'user',
27            'content': images.description.apply(
28                lambda d: f"Extract 5 tags from: {d}"
29            )
30        }]
31    ).choices[0].message.content
32)

Pixeltable tracks dependencies between columns and executes them in the right order. No DAG configuration files.

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Key Benefits

90% reduction in infrastructure code

Automatic dependency tracking and execution ordering

Incremental updates — only recompute what changed

Built-in caching, error handling, and retry logic

Full data lineage and versioning

Real Applications

ML model training data pipelines

Automated data enrichment workflows

AI application backends

Research experimentation platforms

Prerequisites

Basic Python programming

Understanding of AI workflows

Python 3.9+

API keys for AI providers

Performance

Code Reduction

Less infrastructure code

90%

Dev Speed

From concept to production

10x faster

Learn More

Declarative AI Pipelines: Open Standard

Why we built Pixeltable open source

Dependency Graph Magic

How computed columns maintain data consistency

Build a complete Retrieval-Augmented Generation pipeline with Pixeltable. Ingest documents, chunk text, generate embeddings, index for retrieval, and generate LLM answers — no vector database or orchestrator required.

Computer Vision Pipeline: Object Detection, Classification, and Search

Build optimized computer vision workflows with Pixeltable. Run YOLOX, CLIP, and custom models as computed columns with automatic batching, caching, and incremental processing.

AI Agents & MCP: Give Your Agents Persistent Multimodal Memory

Build AI agents with durable memory and tool-calling capabilities using Pixeltable and Model Context Protocol (MCP). Store conversations, images, and documents as queryable tables that agents can read from and write to.

Ready to Get Started?

Install Pixeltable and start building in minutes. One pip install, no infrastructure to manage.

View on GitHub Quick Start Guide Starter Kit