Embedding Generation

Overview

This notebook demonstrates how to generate embeddings from text using Semantica's embedding modules. You'll learn to use EmbeddingGenerator and TextEmbedder to create vector representations of text.

Learning Objectives

Use EmbeddingGenerator to generate embeddings
Use TextEmbedder for text embedding generation
Generate embeddings for multiple texts
Understand embedding dimensions

Step 1: Generate Embeddings

Generate embeddings using EmbeddingGenerator.

In [ ]:

from semantica.embeddings import EmbeddingGenerator

generator = EmbeddingGenerator()

texts = [
    "Apple Inc. is a technology company.",
    "Microsoft Corporation develops software.",
    "Amazon provides cloud services."
]

embeddings = generator.generate(texts)

print(f"Generated embeddings for {len(texts)} texts")
print(f"Embedding dimension: {len(embeddings[0]) if embeddings else 0}")
print(f"First embedding shape: {len(embeddings[0]) if embeddings else 'N/A'}")

Step 2: Text Embedding

Use TextEmbedder for text-specific embeddings.

In [ ]:

from semantica.embeddings import TextEmbedder

text_embedder = TextEmbedder()

text = "Semantic knowledge graphs enable intelligent data processing."

embedding = text_embedder.embed_text(text)

print(f"Generated embedding for text")
print(f"Embedding dimension: {len(embedding)}")
print(f"First 5 values: {embedding[:5]}")

Summary

You've learned how to generate embeddings:

EmbeddingGenerator: Generate embeddings for multiple texts
TextEmbedder: Generate text-specific embeddings

Next: Learn how to store and search vectors in the Vector_Store notebook.

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