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Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS

Authored by: Merve Noyan

Embeddings are semantically meaningful compressions of information. They can be used to do similarity search, zero-shot classification or simply train a new model. Use cases for similarity search include searching for similar products in e-commerce, content search in social media and more. This notebook walks you through using 🤗transformers, 🤗datasets and FAISS to create and index embeddings from a feature extraction model to later use them for similarity search. Let’s install necessary libraries.

    !pip install -q datasets faiss-gpu transformers sentencepiece

For this tutorial, we will use CLIP model to extract the features. CLIP is a revolutionary model that introduced joint training of a text encoder and an image encoder to connect two modalities.

    import torch
    from PIL import Image
    from transformers import AutoImageProcessor, AutoModel, AutoTokenizer
    import faiss
    import numpy as np

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    model = AutoModel.from_pretrained("openai/clip-vit-base-patch16").to(device)
    processor = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch16")
    tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch16")

Load the dataset. To keep this notebook light, we will use a small captioning dataset, jmhessel/newyorker_caption_contest.

    from datasets import load_dataset

    ds = load_dataset("jmhessel/newyorker_caption_contest", "explanation")

See an example.

    >>> ds["train"][0]["image"]

    ds["train"][0]["image_description"]

We don’t have to write any function to embed examples or create an index. 🤗 datasets library’s FAISS integration abstracts these processes. We can simply use `map` method of the dataset to create a new column with the embeddings for each example like below. Let’s create one for text features on the prompt column.

    dataset = ds["train"]
    ds_with_embeddings = dataset.map(
        lambda example: {
            "embeddings": model.get_text_features(
                **tokenizer([example["image_description"]], truncation=True, return_tensors="pt").to("cuda")
            )[0]
            .detach()
            .cpu()
            .numpy()
        }
    )

We can do the same and get the image embeddings.

    ds_with_embeddings = ds_with_embeddings.map(
        lambda example: {
            "image_embeddings": model.get_image_features(**processor([example["image"]], return_tensors="pt").to("cuda"))[
                0
            ]
            .detach()
            .cpu()
            .numpy()
        }
    )

Now, we create an index for each column.

    # create FAISS index for text embeddings
    ds_with_embeddings.add_faiss_index(column="embeddings")

    # create FAISS index for image embeddings
    ds_with_embeddings.add_faiss_index(column="image_embeddings")

##
Querying the data with text prompts

We can now query the dataset with text or image to get similar items from it.

    prmt = "a snowy day"
    prmt_embedding = (
        model.get_text_features(**tokenizer([prmt], return_tensors="pt", truncation=True).to("cuda"))[0]
        .detach()
        .cpu()
        .numpy()
    )
    scores, retrieved_examples = ds_with_embeddings.get_nearest_examples("embeddings", prmt_embedding, k=1)

    >>> def downscale_images(image):
    ...     width = 200
    ...     ratio = width / float(image.size[0])
    ...     height = int((float(image.size[1]) * float(ratio)))
    ...     img = image.resize((width, height), Image.Resampling.LANCZOS)
    ...     return img

    >>> images = [downscale_images(image) for image in retrieved_examples["image"]]
    >>> # see the closest text and image
    >>> print(retrieved_examples["image_description"])
    >>> display(images[0])

    ['A man is in the snow. A boy with a huge snow shovel is there too. They are outside a house.']

##
Querying the data with image prompts

Image similarity inference is similar, where you just call `get_image_features`.

    >>> import requests

    >>> # image of a beaver
    >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/beaver.png"
    >>> image = Image.open(requests.get(url, stream=True).raw)
    >>> display(downscale_images(image))

Search for the similar image.

    img_embedding = (
        model.get_image_features(**processor([image], return_tensors="pt", truncation=True).to("cuda"))[0]
        .detach()
        .cpu()
        .numpy()
    )
    scores, retrieved_examples = ds_with_embeddings.get_nearest_examples("image_embeddings", img_embedding, k=1)

Display the most similar image to the beaver image.

    >>> images = [downscale_images(image) for image in retrieved_examples["image"]]
    >>> # see the closest text and image
    >>> print(retrieved_examples["image_description"])
    >>> display(images[0])

    ['Salmon swim upstream but they see a grizzly bear and are in shock. The bear has a smug look on his face when he sees the salmon.']

##
Saving, pushing and loading the embeddings

We can save the dataset with embeddings with `save_faiss_index`.

    ds_with_embeddings.save_faiss_index("embeddings", "embeddings/embeddings.faiss")

    ds_with_embeddings.save_faiss_index("image_embeddings", "embeddings/image_embeddings.faiss")

It’s a good practice to store the embeddings in a dataset repository, so we will create one and push our embeddings there to pull later. We will login to Hugging Face Hub, create a dataset repository there and push our indexes there and load using `snapshot_download`.

    from huggingface_hub import HfApi, notebook_login, snapshot_download

    notebook_login()

    from huggingface_hub import HfApi

    api = HfApi()
    api.create_repo("merve/faiss_embeddings", repo_type="dataset")
    api.upload_folder(
        folder_path="./embeddings",
        repo_id="merve/faiss_embeddings",
        repo_type="dataset",
    )

    snapshot_download(repo_id="merve/faiss_embeddings", repo_type="dataset", local_dir="downloaded_embeddings")

We can load the embeddings to the dataset with no embeddings using `load_faiss_index`.

    ds = ds["train"]
    ds.load_faiss_index("embeddings", "./downloaded_embeddings/embeddings.faiss")
    # infer again
    prmt = "people under the rain"

    prmt_embedding = (
        model.get_text_features(**tokenizer([prmt], return_tensors="pt", truncation=True).to("cuda"))[0]
        .detach()
        .cpu()
        .numpy()
    )

    scores, retrieved_examples = ds.get_nearest_examples("embeddings", prmt_embedding, k=1)

    >>> display(retrieved_examples["image"][0])

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