EmbeddingsSpaceComponent

Introduction

The EmbeddingsSpaceComponent represents an embeddings space, facilitating the management and operations within it. Think of embeddings as a numeric representation of the meaning behind the text. Similar to how coordinates help pinpoint locations on Earth, in the embeddings space, semantically similar concepts cluster closer together. Before diving in, make sure to follow the Getting Started Guide to install the library in your project. It'd also be advisable to familiarize yourself with the core abstractions.

Overview

Leveraging the power of embeddings models, this component allows you to represent pieces of text in an embeddings space. Once you generate an embedding, it's stored using an embeddings database, along with its metadata for efficient retrieval.

Warning: The current version supports the OpenAI embeddings model and Pinecone for storage. Please provide the necessary credentials for these services. Note: these services may involve costs, always review their pricing details before use.

The EmbeddingsSpaceComponent interface defines the following methods:

Constructor

To instantiate an EmbeddingsSpaceComponent, provide both an EmbeddingsGenerationModel and an EmbeddingsStore.

EmbeddingsGenerationModel openAIModel = new OpenAIEmbeddingsGenerationModel("YOUR_OPENAI_API_KEY");
EmbeddingsStore pineconeStore = new PineconeEmbeddingsStore("YOUR_PINECONE_URL", "YOUR_PINECONE_API_KEY", "YOUR_PINECONE_SPACE");
EmbeddingsSpaceComponent embeddingsSpace = new EmbeddingsSpaceComponent(openAIModel, pineconeStore);

save

Generates and persists an embedding for a given text.

• Parameters:
  • text: Text to be embedded.
• Returns: The generated embedding.

String sampleText = "Hello, eLLMental!";
Embedding embedding = embeddingsSpace.save(sampleText);

mostSimilarEmbeddings

Fetches embeddings semantically closest to a reference text or embedding.

With a reference text:

• Parameters:
  • referenceText: The text for comparison.
  • limit: Maximum number of similar embeddings to return.

// First we add a few embeddings to the space
embeddingsSpace.save("Hello, eLLMental!");
embeddingsSpace.save("Hello, world!");

List<Embedding> closestNeighbors = embeddingsSpace.mostSimilarEmbeddings("Greetings!", 3);
// closestNeighbors will contain the embeddings for "Hello, eLLMental!" and "Hello, world!"

With a reference embedding:

• Parameters:
  • referenceEmbedding: The embedding for comparison.
  • limit: Maximum number of similar embeddings to return.

// First we add a few embeddings to the space
embeddingsSpace.add("Hello, eLLMental!");
embeddingsSpace.add("Hello, world!");
        
Embedding embedding = embeddingsSpace.generate("Hello everyone!");

List<Embedding> closestNeighbors = embeddingsSpace.mostSimilarEmbeddings(embedding, 3);
// closestNeighbors will contain the embeddings for "Hello, eLLMental!" and "Hello, world!"

get

Retrieves an embedding from the embeddings store using its ID.

• Parameters:
  • id: The ID of the embedding to retrieve.
• Returns: The desired embedding or null if not found.

Embedding embedding = embeddingsSpace.get("embedding-id");

delete

Deletes an embedding from the embeddings store using its ID.

• Parameters:
  • id: The ID of the embedding to delete.

embeddingsSpace.delete("embedding-id");