Embeddings are a way to represent text in a numerical format as vectors. They are used in a variety of applications, including search and retrieval, clustering, labeling data and anomaly detection.

Notebooks make it easy and fast to compute embeddings for a dataset on a GPU. If you want to follow along, you can checkout this notebook and run it in your own Oxen.ai account. When running this example, try an A10 GPU with 4GB of memory and 4 CPU cores. This will allow us to compute over 1,000 embeddings per second 🔥

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Setting Up The Interface

Marimo allows you to define UI elements that can be used to define the input repository, dataset, model name and number of rows to compute embeddings for. First lets setup a simple form that allows us to kick off the embedding computation.

Use the following code in your first cell to setup the UI.

import marimo as mo

model_name_ui = mo.ui.text(value="BAAI/bge-large-en-v1.5", full_width=True)
oxen_repo_name = mo.ui.text(value="ox/Simple-Wikipedia-50k", full_width=True)
oxen_dataset_name = mo.ui.text(value="train_0_50000.parquet", full_width=True)
num_rows = mo.ui.number(value=10000)

run_form = mo.md(
    """
    Model Name
    {model_name}
    Repo Name
    {oxen_repo_name}
    File Name
    {oxen_dataset_name}
    Num Rows
    {num_rows}
    """
).batch(
    oxen_repo_name=oxen_repo_name,
    oxen_dataset_name=oxen_dataset_name,
    model_name=model_name_ui,
    num_rows=num_rows
).form(
    submit_button_label="Compute",
    bordered=False,
    show_clear_button=True,
    clear_button_label="Reset"
)

run_form

To wait for the button to be clicked, use the mo.stop function and check if the run_form.value is None.

# If the button is not pressed, stop execution
mo.stop(
    run_form.value is None
)

Then download the data using the values from the form and the Remote Repo class.

from oxen import RemoteRepo
import pandas as pd

repo = RemoteRepo(oxen_repo_name.value)
repo.download(oxen_dataset_name.value, revision="main")
df = pd.read_parquet(oxen_dataset_name.value)

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Compute Embeddings

This example will use the sentence_transformers library to compute the embeddings with the default model as BAAI/bge-large-en-v1.5. Find more information about the model here.

from sentence_transformers import SentenceTransformer

model_name = model_name_ui.value
print(f"Loading: {model_name}")
model = SentenceTransformer(model_name, device="cuda")
print(f"Model Loaded: {model_name}")

Now we can compute the embeddings for the dataset. We will compute them in batches to take full advantage of the GPU. In this example, we are just computing the embeddings for the title column, but you can compute the embeddings for any text column in the dataset. mo.status.progress_bar is used to show a progress bar in the UI as we compute the embeddings.

You should see the model computing over 1,000 embeddings per second 🔥

# How many embeddings to compute at once
batch_size = 128

# Determine how many rows you want to process
rows_to_process = num_rows.value
# Copy the data frame
result_df = df.iloc[:rows_to_process].copy()
computed_embeddings = []

# Process the dataframe in batches
with mo.status.progress_bar(total=len(result_df)) as bar:
    for i in range(0, len(result_df), batch_size):
        if i % 10 == 0:
            print(f"Computed {i} embeddings")

        # Get the current batch
        batch = result_df.iloc[i:i+batch_size]

        # Extract texts from the batch
        texts = batch['title'].tolist()

        # Compute embeddings for the batch
        batch_embeddings = model.encode(texts, normalize_embeddings=True)

        # Add the batch embeddings to the overall list
        computed_embeddings.extend(batch_embeddings)
        bar.update(increment=batch_size)

# Add embeddings to the data frame
result_df['embedding'] = computed_embeddings

The embeddings will now be in the result_df data frame in a new column called embedding.

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Save the Embeddings

Once you have computed the embeddings, save them to your Oxen.ai repository to share with your team. Oxen.ai will version the embeddings and allow you to track changes so that you can try out different models and configurations without worrying about losing your previous work.

def save_embeddings(df, username="YOUR_USERNAME", repo_name="YOUR_REPO_NAME", filename="embeddings.parquet", branch="main"):
    # Connect to the remote repo
    repo = RemoteRepo(f"{username}/{repo_name}")
    # Checkout the branch
    repo.create_checkout_branch(branch)
    # Save data to disk
    df.to_parquet(filename, index=False)

    # Check if the file exists or has changed on the remote
    if not repo.file_exists(filename) or repo.file_has_changes(filename):
        # Stage/upload data to remote repository
        repo.add(filename)
        # Commit data with a message
        repo.commit(f"Adding {filename}")
    else:
        print("File has no changes")
save_embeddings(result_df, username="YOUR_USERNAME", repo_name="YOUR_REPO_NAME", filename="embeddings.parquet", branch="embeddings")

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Search Nearest Neighbors

To check how well the embeddings encode the text, let’s build a little search tool. We will use cosine_similarity from sklearn to build a simple nearest neighbor search.

from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
def embedding_similarity(df, query_embedding, text_column='text', embedding_column='embedding', top_k=5):
    # Make sure query_embedding is a 2D array for sklearn's cosine_similarity
    if len(query_embedding.shape) == 1:
        query_embedding = query_embedding.reshape(1, -1)

    # Stack all embeddings from the DataFrame into a 2D array
    embeddings_matrix = np.vstack(df[embedding_column].values)

    # Calculate cosine similarity between query and all embeddings
    similarities = cosine_similarity(query_embedding, embeddings_matrix).flatten()

    # Create a results DataFrame with similarities
    results_df = df.copy()
    results_df['similarity_score'] = similarities

    # Sort by similarity score (descending) and get top_k results
    results_df = results_df.sort_values('similarity_score', ascending=False).head(top_k)

    # Keep only text and similarity score for cleaner output
    return results_df[[text_column, 'similarity_score']]

Now we can use the embedding_similarity function to search for the nearest neighbors of a query.

search_term_embedding = model.encode(search_term_ui.value, normalize_embeddings=True)
embedding_similarity(result_df, search_term_embedding, text_column='title')

Build a text input so that we can enter any term we want and see similar titles.

search_term_ui = mo.ui.text(value="Denver Broncos", full_width=True)
mo.md(f"""
Enter any term to see it's neighbors
{search_term_ui}
""")