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Welcome to Pixeltable

AI Data infrastructure providing a declarative, incremental approach for multimodal workloads.

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Most AI teams face a difficult choice today. Either spend months building complex infrastructure for production-ready AI applications, or use high-level frameworks that hide critical functionality and limit their ability to innovate. Pixeltable eliminates this tradeoff. Pixeltable enables developers to build production-grade AI applications while maintaining full control over their data and processing pipeline. Teams no longer have to choose between ease of development and control over their application logic.

Transformations, model inference, and custom logic are embedded as computed columns

Transformations, model inference, and custom logic are embedded as computed columns

Open Source AI Data Infrastructure

Express complex operations through simple table operations and computed columns:

  • Data transformations
  • Model inference
  • Custom logic
  • Multimodal data handling

🎯 The Problem

Building AI applications today requires juggling multiple tools and writing complex infrastructure code to:

  • Process and store different types of data (text, images, video, audio)
  • Track changes and maintain data lineage
  • Scale processing efficiently
  • Move from development to production

💡 The Solution

Pixeltable unifies all these operations under a simple, declarative interface. Pixeltable features built-in versioning, lineage tracking, and incremental updates, enabling users to store, transform, index, and iterate on data for their ML workflows. It combines data storage, versioning, indexing, and orchestration under a unified table interface, enabling data scientists and ML engineers to focus on modeling and experimentation rather than data plumbing.

import pixeltable as pxt

# Create a video table
videos = pxt.create_table('videos', {'video': pxt.VideoType()})

# Automatic frame extraction
frames = pxt.create_view(
    'frames', 
    videos, 
    iterator=FrameIterator.create(video=videos.video)
)

# Add AI processing - only runs on new data
frames['detections'] = yolox(frames.frame)

🚀 Quick Start

pip install pixeltable

Core Use Cases

1. LLM Development & RAG

Industry Challenge

Organizations implementing LLM applications face significant hurdles in managing document processing, tracking model decisions, and maintaining efficient RAG systems. Traditional approaches lead to:

  • Costly reprocessing of entire document bases for minor changes
  • Lack of transparency in model decision-making
  • Complex management of chunking strategies and embeddings
  • Difficulty comparing performance across different approaches

Pixeltable Solution

# Declarative document processing with automatic versioning
docs = pxt.create_table('knowledge_base', {'document': pxt.DocumentType()})

# Flexible chunking strategies with computed views
chunks = pxt.create_view(
    'chunks',
    docs,
    iterator=DocumentSplitter.create(
        document=docs.document,
        separators='token_limit',
        limit=300
    )
)

# Automatic embedding generation and indexing
chunks.add_embedding_index('text', string_embed=e5_embed)

Business Impact

  • Cost Reduction: 70%+ reduction in processing costs through incremental updates
  • Quality Improvement: Complete lineage tracking ensures answer accuracy
  • Development Speed: Rapid experimentation with different strategies
  • Operational Efficiency: Built-in versioning eliminates manual tracking

2. Computer Vision Workflows

Industry Challenge

Computer vision teams struggle with:

  • Managing large-scale video and image datasets
  • Tracking model versions and annotations
  • Maintaining consistency between development and production
  • Efficiently processing incremental updates

Pixeltable Solution

# Unified video processing pipeline
videos = pxt.create_table('videos', {'video': pxt.VideoType()})

# Automatic frame extraction and management
frames = pxt.create_view(
    'frames',
    videos,
    iterator=FrameIterator.create(video=videos.video)
)

# Integrated object detection and annotation
frames['detections'] = yolox(frames.frame)
frames['annotations'] = draw_boxes(frames.frame, frames.detections.boxes)

Business Impact

  • Resource Optimization: Lazy evaluation reduces storage and compute costs
  • Quality Assurance: Automatic lineage tracking for all model outputs
  • Development Efficiency: Seamless integration with annotation tools
  • Deployment Confidence: Production parity with development environment

3. Multimodal AI Applications

Industry Challenge

Organizations building multimodal AI applications face:

  • Complex integration of different data types
  • Difficult relationship management between modalities
  • Lack of unified search capabilities
  • Complex pipeline maintenance

Pixeltable Solution

# Unified multimodal data management
content = pxt.create_table('content', {
    'video': pxt.VideoType(),
    'audio': pxt.AudioType(),
    'transcript': pxt.StringType()
})

# Automated cross-modal processing
content['audio'] = extract_audio(content.video)
content['transcript'] = openai.transcriptions(content.audio)

Business Impact

  • Simplified Architecture: Single interface for all data types
  • Enhanced Search: Unified search across modalities
  • Reduced Complexity: Automated pipeline management
  • Faster Development: Built-in transformations between modalities

First Steps

📚 Popular Tutorials

Computer Vision

Natural Language Processing

Multimodal Applications

🔍 Next Steps

Updated about 14 hours ago