Generate text embedding

This code sample demonstrates how to embed text with a pre-trained, foundational model.

Explore further

For detailed documentation that includes this code sample, see the following:

• Text embeddings API

Code sample

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from __future__ import annotations

from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel


def embed_text() -> list[list[float]]:
    """Embeds texts with a pre-trained, foundational model.

    Returns:
        A list of lists containing the embedding vectors for each input text
    """

    # A list of texts to be embedded.
    texts = ["banana muffins? ", "banana bread? banana muffins?"]
    # The dimensionality of the output embeddings.
    dimensionality = 3072
    # The task type for embedding. Check the available tasks in the model's documentation.
    task = "RETRIEVAL_DOCUMENT"

    model = TextEmbeddingModel.from_pretrained("gemini-embedding-001")
    kwargs = dict(output_dimensionality=dimensionality) if dimensionality else {}

    embeddings = []
    # gemini-embedding-001 takes one input at a time
    for text in texts:
        text_input = TextEmbeddingInput(text, task)
        embedding = model.get_embeddings([text_input], **kwargs)
        print(embedding)
        # Example response:
        # [[0.006135190837085247, -0.01462465338408947, 0.004978656303137541, ...]]
        embeddings.append(embedding[0].values)

    return embeddings

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