Working with Embeddings

Introduction

Embeddings are numeric vector representations of text (or other data) that capture meaning, so that similar items have similar vectors. In Spring AI, an embedding model turns text into these vectors, which power semantic search and retrieval-augmented generation. Embeddings are the bridge between raw content and AI-powered retrieval.

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.

In Practice

Spring AI’s EmbeddingClient converts text into vectors that are stored in a vector database. At query time, the user’s text is embedded and compared by similarity to find the most relevant content, which is then provided to the language model as context.

Key Points

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

References

• Spring AI Embeddings — Spring AI embeddings guide