Embedding/Vector Index
Learn how to create, populate and query embedding indexes in Pixeltable
Learn more about embedding/vector indexes with this in-depth guide.
What are Embedding Indexes?
Embedding indexes in Pixeltable enable semantic search and similarity-based retrieval across different data modalities. They store vector representations of your content, allowing you to find related items based on meaning rather than exact matching.
Unlike traditional indexes that search by keywords, embedding indexes capture the semantic essence of your data, making it possible to:
- Find content with similar meaning even when using different words
- Match content across different modalities (text-to-image, image-to-text)
- Rank results based on semantic relevance
- Build powerful retrieval systems for RAG applications
Pixeltable makes working with embeddings simple by:
- Managing the lifecycle of embedding computations
- Automatically updating indexes when data changes
- Providing a unified interface for different embedding models
- Supporting multiple index types on the same column
Overview
Embedding indexes in Pixeltable are:
- Declarative: Define the index structure and embedding functions once
- Maintainable: Pixeltable automatically keeps indexes up-to-date on changes
- Flexible: Support multiple index types on the same column
- Multimodal: Handle text, images, audio, and documents
In this guide, we’ll create a semantic search system for images and text. Make sure you have the required dependencies installed:
Phase 1: Setup
The setup phase defines your schema and creates embedding indexes.
Supported Index Options
Similarity Metrics
Index Configuration
Phase 2: Insert
The insert phase populates your indexes with data. Pixeltable automatically computes embeddings and maintains index consistency.
Large batch insertions are more efficient than multiple single insertions as they reduce the number of embedding computations.
Phase 3: Query
The query phase allows you to search your indexed content using the similarity()
function.
Advanced Query Patterns
Management Operations
Drop Index
Update Index
Best Practices
- Cache embedding models in production UDFs
- Use batching for better performance
- Consider index size vs. search speed tradeoffs
- Monitor embedding computation time
Additional Resources
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