Welcome to Pixeltable
AI Data infrastructure providing a declarative, incremental approach for multimodal workloads.
Pixeltable is an open-source Python library that provides a declarative interface for building AI applications. Think of it as a unified data infrastructure that lets you focus on innovation rather than plumbing.
Open Source AI Data Infrastructure
Express complex operations through simple table operations and computed columns:
- Data transformations
- Model inference
- Custom logic
- Multimodal data handling
π― 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
π‘ 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)
π 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
- 10-Minute Tutorial: Get started with Pixeltable
- Core Concepts: Learn the fundamental building blocks
- Example Gallery: Explore real-world applications
π Popular Tutorials
Computer Vision
Natural Language Processing
- Document Indexing and RAG
- Embedding and Vector Indexes
- Incremental Prompt Engineering and Model Comparison
Multimodal Applications
π Next Steps
- Installation Guide: https://docs.pixeltable.com/docs/installation
- API Reference: https://pixeltable.github.io/pixeltable/api/pixeltable/
- GitHub Repository: https://github.com/pixeltable/pixeltable
- Community & Support: Contributions and Discussions
Updated about 22 hours ago