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Documentation Index

Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt

Use this file to discover all available pages before exploring further.

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System requirements

Before installing, ensure your system meets these requirements:
  • Python 3.10 or higher
  • Linux, MacOS, or Windows

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Installation

It is recommended that you install Pixeltable in a virtual environment.
1

Create virtual environment

python -m venv .venv
2

Activate environment

source .venv/bin/activate
3

Install Pixeltable

pip install pixeltable

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Getting help

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Build an image analysis app

This guide will help you spin up a functioning AI workload in 5 minutes.
1

Install Required Packages

Pixeltable requires only a minimal set of Python packages by default. To use AI models, you’ll need to install additional dependencies.
pip install torch transformers openai
2

Create a Table

import pixeltable as pxt

# Create a namespace and table
pxt.create_dir('quickstart', if_exists='replace_force')
t = pxt.create_table('quickstart/images', {'image': pxt.Image})
Tables are persistent: your data survives restarts and can be queried anytime.
3

Add AI Object Detection

from pixeltable.functions import huggingface

# Add DETR object detection as a computed column
t.add_computed_column(
    detections=huggingface.detr_for_object_detection(
        t.image,
        model_id='facebook/detr-resnet-50'
    )
)

# Extract labels from detections
t.add_computed_column(labels=t.detections.label_text)
Computed columns run automatically whenever new data is inserted.
4

Insert Data

# Insert a few images
t.insert([
  {'image': 'https://raw.githubusercontent.com/pixeltable/pixeltable/release/docs/resources/images/000000000001.jpg'},
  {'image': 'https://raw.githubusercontent.com/pixeltable/pixeltable/release/docs/resources/images/000000000025.jpg'}
])
You can insert images from URLs and/or local paths in any combination.
5

Query Results

# Query results
t.select(t.image, t.labels).collect()
Expected output:
imagelabels
[Image][car, parking meter, truck, car, car, truck]
[Image][giraffe, giraffe]
6

(Optional) Add LLM Vision

You’ll need an OpenAI API key to use this step. If you don’t have one, you can safely skip this step.
import os
from pixeltable.functions import openai

# Set your API key
os.environ['OPENAI_API_KEY'] = 'your-key-here'

messages = [
    {
        'role': 'user',
        'content': [
            {'type': 'text', 'text': 'Describe this image in one sentence.'},
            {'type': 'image_url', 'image_url': t.image},
        ],
    }
]
t.add_computed_column(
    response=openai.chat_completions(messages, model='gpt-4o-mini')
)

t.select(t.image, t.labels, t.response.choices[0].message.content).collect()

# See the full text of the response in row 0
t.select(t.response.choices[0].message.content).collect()[0]
Pixeltable orchestrates LLM calls for optimized throughput, handling rate limiting, retries, and caching automatically.
7

Insert More Data

t.insert([
  {'image': 'https://raw.githubusercontent.com/pixeltable/pixeltable/release/docs/resources/images/000000000034.jpg'},
  {'image': 'https://raw.githubusercontent.com/pixeltable/pixeltable/release/docs/resources/images/000000000057.jpg'}
])

t.select(t.image, t.labels).collect()
When new data is inserted into tables, Pixeltable incrementally runs all computed columns against the new data, ensuring the table is up to date. If you completed the optional LLM Vision step, the descriptions will also be generated automatically for these new images.
Pixeltable automatically:
  1. Created a persistent multimodal table
  2. Downloaded and cached the DETR model
  3. Ran inference on your image
  4. Stored all results (including computed columns) for instant retrieval
  5. Will incrementally process any new images you insert

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Next Steps

10-Minute Tour

A deeper walkthrough with video, embeddings, and similarity search.

Deployment Overview

Three production patterns: Full Backend (FastAPI + React), Batch Processing (export to your DB), and Declarative Serving (API from TOML).

HTTP Serving

Expose tables and queries as HTTP endpoints with pxt serve or FastAPIRouter.

Create a Project

uvx pixeltable-new myapp — scaffold a full project (serving, backend, or batch) in one command.
Last modified on May 17, 2026