Working with Embeddings

Introduction

Spring AI provides abstractions for working with embeddings and vector representations.

Definition

Embeddings are numerical representations of text that capture semantic meaning for AI applications.

Types

Text Embeddings

Converting text to vector representations

Embedding Models

Different embedding models and providers

Embedding Storage

Storing and retrieving embeddings

Embedding Search

Semantic search using embeddings

Use Cases

• Semantic search applications
• Document similarity matching
• Recommendation systems
• Content clustering
• Knowledge base search

Implementation

Spring AI’s embedding abstractions support multiple providers and storage backends.

Key Points

• Provider-agnostic embeddings
• Efficient vector operations
• Scalable storage options
• Semantic search capabilities

References

• Spring AI Embeddings — Spring AI embeddings guide