Intermediate20 minmediaresearchenterprise

Multimodal AI Apps: Process Any Data Type in One System

Build applications that work with images, videos, audio, and documents simultaneously. Pixeltable treats all modalities as first-class column types with automatic cross-modal operations.

Quick Start View on GitHub Documentation

The Challenge

Multimodal AI requires integrating separate systems for each data type: image processing libraries, video frameworks, audio tools, document parsers. Each has different APIs, storage formats, and execution models. Cross-modal operations (e.g., searching images by text) require even more integration work.

The Solution

Pixeltable provides a unified table interface for all data types. Images, videos, audio, and documents are native column types. Cross-modal operations like text-to-image search work out of the box with embedding indexes.

Implementation Guide

Step-by-step walkthrough with code examples

Step 1 of 2

Unified Table

One table handles all data types — no separate systems.

python

1import pixeltable as pxt
2from pixeltable.functions import openai
3
4# Single table, multiple modalities
5content = pxt.create_table('app.content', {
6    'image': pxt.Image,
7    'video': pxt.Video,
8    'audio': pxt.Audio,
9    'document': pxt.Document,
10    'title': pxt.String,
11    'metadata': pxt.Json,
12})
13
14# AI processing across modalities
15content.add_computed_column(
16    image_description=openai.chat_completions(
17        model='gpt-4o-mini',
18        messages=[{
19            'role': 'user',
20            'content': [content.image, 'Describe this image.']
21        }]
22    ).choices[0].message.content
23)
24
25content.add_computed_column(
26    transcript=openai.transcriptions(
27        audio=content.audio, model='whisper-1'
28    )
29)

No separate systems for each data type. All modalities live in one table with shared metadata and queries.

Use arrow keys to navigate

Key Benefits

Unified interface for all data types — no integration overhead

80% less code vs managing separate systems

Cross-modal operations work natively

One query language for structured + semantic search

Automatic format handling for all media types

Real Applications

Content management and digital asset management

E-commerce product catalogs with mixed media

Research platforms processing mixed data types

Media production asset search and management

Prerequisites

Python programming experience

Basic understanding of AI models

Python 3.9+

API keys for AI models

Storage for media files

Performance

Integration Time

vs building separate pipelines per modality

5x faster

Learn More

Building Multimodal Apps

Complete guide to multimodal development

Pixelsearch: Multimodal Search Engine

Build your own multimodal search engine

Build an end-to-end video analysis system with Pixeltable. Ingest video, extract frames, run multimodal AI models, generate embeddings, and enable semantic search — all as computed columns on a table.

Production RAG: From Documents to Answers in One System

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.

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