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Implement unified textual content and picture search with a CLIP mannequin utilizing Amazon SageMaker and Amazon OpenSearch Service

The rise of textual content and semantic search engines has made ecommerce and retail companies search simpler for its customers. Engines like google powered by unified textual content and picture can present further flexibility in search options. You should utilize each textual content and pictures as queries. For instance, you might have a folder of a whole bunch of household photos in your laptop computer. You need to shortly discover a image that was taken if you and your greatest pal have been in entrance of your outdated home’s swimming pool. You should utilize conversational language like “two individuals stand in entrance of a swimming pool” as a question to go looking in a unified textual content and picture search engine. You don’t must have the suitable key phrases in picture titles to carry out the question.

Amazon OpenSearch Service now helps the cosine similarity metric for k-NN indexes. Cosine similarity measures the cosine of the angle between two vectors, the place a smaller cosine angle denotes a better similarity between the vectors. With cosine similarity, you may measure the orientation between two vectors, which makes it a sensible choice for some particular semantic search purposes.

Contrastive Language-Picture Pre-Coaching (CLIP) is a neural community skilled on a wide range of picture and textual content pairs. The CLIP neural community is ready to venture each pictures and textual content into the identical latent area, which implies that they are often in contrast utilizing a similarity measure, equivalent to cosine similarity. You should utilize CLIP to encode your merchandise’ pictures or description into embeddings, after which retailer them into an OpenSearch Service k-NN index. Then your prospects can question the index to retrieve merchandise that they’re serious about.

You should utilize CLIP with Amazon SageMaker to carry out encoding. Amazon SageMaker Serverless Inference is a purpose-built inference service that makes it simple to deploy and scale machine studying (ML) fashions. With SageMaker, you may deploy serverless for dev and check, after which transfer to real-time inference if you go to manufacturing. SageMaker serverless helps you save value by cutting down infrastructure to 0 throughout idle occasions. That is excellent for constructing a POC, the place you’ll have lengthy idle occasions between growth cycles. You can even use Amazon SageMaker batch remodel to get inferences from giant datasets.

On this publish, we reveal the way to construct a search utility utilizing CLIP with SageMaker and OpenSearch Service. The code is open supply, and it’s hosted on GitHub.

Resolution overview

OpenSearch Service gives text-matching and embedding k-NN search. We use embedding k-NN search on this resolution. You should utilize each picture and textual content as a question to go looking objects from the stock. Implementing this unified picture and textual content search utility consists of two phases:

  • k-NN reference index – On this part, you move a set of corpus paperwork or product pictures by means of a CLIP mannequin to encode them into embeddings. Textual content and picture embeddings are numerical representations of the corpus or pictures, respectively. You save these embeddings right into a k-NN index in OpenSearch Service. The idea underpinning k-NN is that related information factors exist in shut proximity within the embedding area. For instance, the textual content “a purple flower,” the textual content “rose,” and a picture of purple rose are related, so these textual content and picture embeddings are shut to one another within the embedding area.
  • k-NN index question – That is the inference part of the appliance. On this part, you submit a textual content search question or picture search question by means of the deep studying mannequin (CLIP) to encode as embeddings. Then, you employ these embeddings to question the reference k-NN index saved in OpenSearch Service. The k-NN index returns related embeddings from the embedding area. For instance, should you move the textual content of “a purple flower,” it will return the embeddings of a purple rose picture as an identical merchandise.

The next determine illustrates the answer structure.

The workflow steps are as follows:

  1. Create a SageMaker mannequin from a pretrained CLIP mannequin for batch and real-time inference.
  2. Generate embeddings of product pictures utilizing a SageMaker batch remodel job.
  3. Use SageMaker Serverless Inference to encode question picture and textual content into embeddings in actual time.
  4. Use Amazon Easy Storage Service (Amazon S3) to retailer the uncooked textual content (product description) and pictures (product pictures) and picture embedding generated by the SageMaker batch remodel jobs.
  5. Use OpenSearch Service because the search engine to retailer embeddings and discover related embeddings.
  6. Use a question operate to orchestrate encoding the question and carry out a k-NN search.

We use Amazon SageMaker Studio notebooks (not proven within the diagram) because the built-in growth atmosphere (IDE) to develop the answer.

Arrange resolution assets

To arrange the answer, full the next steps:

  1. Create a SageMaker area and a consumer profile. For directions, check with Step 5 of Onboard to Amazon SageMaker Area Utilizing Fast setup.
  2. Create an OpenSearch Service area. For directions, see Creating and managing Amazon OpenSearch Service domains.

You can even use an AWS CloudFormation template by following the GitHub directions to create a site.

You’ll be able to join Studio to Amazon S3 from Amazon Digital Non-public Cloud (Amazon VPC) utilizing an interface endpoint in your VPC, as an alternative of connecting over the web. Through the use of an interface VPC endpoint (interface endpoint), the communication between your VPC and Studio is performed completely and securely throughout the AWS community. Your Studio pocket book can hook up with OpenSearch Service over a personal VPC to make sure safe communication.

OpenSearch Service domains supply encryption of knowledge at relaxation, which is a safety function that helps stop unauthorized entry to your information. Node-to-node encryption gives a further layer of safety on high of the default options of OpenSearch Service. Amazon S3 mechanically applies server-side encryption (SSE-S3) for every new object until you specify a unique encryption possibility.

Within the OpenSearch Service area, you may connect identity-based insurance policies outline who can entry a service, which actions they will carry out, and if relevant, the assets on which they will carry out these actions.

Encode pictures and textual content pairs into embeddings

This part discusses the way to encode pictures and textual content into embeddings. This contains getting ready information, making a SageMaker mannequin, and performing batch remodel utilizing the mannequin.

Knowledge overview and preparation

You should utilize a SageMaker Studio pocket book with a Python 3 (Knowledge Science) kernel to run the pattern code.

For this publish, we use the Amazon Berkeley Objects Dataset. The dataset is a group of 147,702 product listings with multilingual metadata and 398,212 distinctive catalogue pictures. We solely use the merchandise pictures and merchandise names in US English. For demo functions, we use roughly 1,600 merchandise. For extra particulars about this dataset, check with the README. The dataset is hosted in a public S3 bucket. There are 16 recordsdata that embody product description and metadata of Amazon merchandise within the format of listings/metadata/listings_<i>.json.gz. We use the primary metadata file on this demo.

You utilize pandas to load the metadata, then choose merchandise which have US English titles from the info body. Pandas is an open-source information evaluation and manipulation instrument constructed on high of the Python programming language. You utilize an attribute referred to as main_image_id to determine a picture. See the next code:

meta = pd.read_json("s3://amazon-berkeley-objects/listings/metadata/listings_0.json.gz", strains=True)
def func_(x):
    us_texts = [item["value"] for merchandise in x if merchandise["language_tag"] == "en_US"]
    return us_texts[0] if us_texts else None
meta = meta.assign(item_name_in_en_us=meta.item_name.apply(func_))
meta = meta[~meta.item_name_in_en_us.isna()][["item_id", "item_name_in_en_us", "main_image_id"]]
print(f"#merchandise with US English title: {len(meta)}")

There are 1,639 merchandise within the information body. Subsequent, hyperlink the merchandise names with the corresponding merchandise pictures. pictures/metadata/pictures.csv.gz accommodates picture metadata. This file is a gzip-compressed CSV file with the next columns: image_id, peak, width, and path. You’ll be able to learn the metadata file after which merge it with merchandise metadata. See the next code:

image_meta = pd.read_csv("s3://amazon-berkeley-objects/pictures/metadata/pictures.csv.gz")
dataset = meta.merge(image_meta, left_on="main_image_id", right_on="image_id")

data sample

You should utilize the SageMaker Studio pocket book Python 3 kernel built-in PIL library to view a pattern picture from the dataset:

from sagemaker.s3 import S3Downloader as s3down
from pathlib import Path
from PIL import Picture
def get_image_from_item_id(item_id = "B0896LJNLH", return_image=True):
    s3_data_root = "s3://amazon-berkeley-objects/pictures/small/"
    item_idx = dataset.question(f"item_id == '{item_id}'").index[0]
    s3_path = dataset.iloc[item_idx].path
    local_data_root = f'./information/pictures'
    local_file_name = Path(s3_path).identify
    s3down.obtain(f'{s3_data_root}{s3_path}', local_data_root)
    local_image_path = f"{local_data_root}/{local_file_name}"
    if return_image:
        img =
        return img, dataset.iloc[item_idx].item_name_in_en_us
        return local_image_path, dataset.iloc[item_idx].item_name_in_en_us
picture, item_name = get_image_from_item_id()

glass cup and title

Mannequin preparation

Subsequent, create a SageMaker mannequin from a pretrained CLIP mannequin. Step one is to obtain the pre-trained mannequin weighting file, put it right into a mannequin.tar.gz file, and add it to an S3 bucket. The trail of the pretrained mannequin will be discovered within the CLIP repo. We use a pretrained ResNet-50 (RN50) mannequin on this demo. See the next code:

rm -rf $BUILD_ROOT
cd $BUILD_ROOT && tar -czvf mannequin.tar.gz .
aws s3 cp $BUILD_ROOT/mannequin.tar.gz  $S3_PATH

You then want to supply an inference entry level script for the CLIP mannequin. CLIP is carried out utilizing PyTorch, so you employ the SageMaker PyTorch framework. PyTorch is an open-source ML framework that accelerates the trail from analysis prototyping to manufacturing deployment. For details about deploying a PyTorch mannequin with SageMaker, check with Deploy PyTorch Fashions. The inference code accepts two atmosphere variables: MODEL_NAME and ENCODE_TYPE. This helps us swap between completely different CLIP mannequin simply. We use ENCODE_TYPE to specify if we need to encode a picture or a bit of textual content. Right here, you implement the model_fn, input_fn, predict_fn, and output_fn capabilities to override the default PyTorch inference handler. See the next code:

!mkdir -p code
%%writefile code/
import io
import torch
import clip
from PIL import Picture
import json
import logging
import sys
import os
import torch
import torch.nn as nn
import torch.nn.useful as F
from torchvision.transforms import ToTensor
logger = logging.getLogger(__name__)
MODEL_NAME = os.environ.get("MODEL_NAME", "")
# ENCODE_TYPE could possibly be IMAGE or TEXT
ENCODE_TYPE = os.environ.get("ENCODE_TYPE", "TEXT")
system = torch.system("cuda" if torch.cuda.is_available() else "cpu")
# defining mannequin and loading weights to it.
def model_fn(model_dir):
    mannequin, preprocess = clip.load( part of(model_dir, MODEL_NAME), system=system)
    return {"model_obj": mannequin, "preprocess_fn": preprocess}
def load_from_bytearray(request_body):
    return picture
# information loading
def input_fn(request_body, request_content_type):
    assert request_content_type in (
    ), f"{request_content_type} is an unknown sort."
    if request_content_type == "utility/json":
        information = json.hundreds(request_body)["inputs"]
    elif request_content_type == "utility/x-image":
        image_as_bytes = io.BytesIO(request_body)
        information =
    return information
# inference
def predict_fn(input_object, mannequin):
    model_obj = mannequin["model_obj"]
    # for picture preprocessing
    preprocess_fn = mannequin["preprocess_fn"]
    assert ENCODE_TYPE in ("TEXT", "IMAGE"), f"{ENCODE_TYPE} is an unknown encode sort."
    # preprocessing
    if ENCODE_TYPE == "TEXT":
        input_ = clip.tokenize(input_object).to(system)
    elif ENCODE_TYPE == "IMAGE":
        input_ = preprocess_fn(input_object).unsqueeze(0).to(system)
    # inference
    with torch.no_grad():
        if ENCODE_TYPE == "TEXT":
            prediction = model_obj.encode_text(input_)
        elif ENCODE_TYPE == "IMAGE":
            prediction = model_obj.encode_image(input_)
    return prediction
# Serialize the prediction end result into the specified response content material sort
def output_fn(predictions, content_type):
    assert content_type == "utility/json"
    res = predictions.cpu().numpy().tolist()
return json.dumps(res)

The answer requires further Python packages throughout mannequin inference, so you may present a necessities.txt file to permit SageMaker to put in further packages when internet hosting fashions:

%%writefile code/necessities.txt

You utilize the PyTorchModel class to create an object to comprise the data of the mannequin artifacts’ Amazon S3 location and the inference entry level particulars. You should utilize the item to create batch remodel jobs or deploy the mannequin to an endpoint for on-line inference. See the next code:

from sagemaker.pytorch import PyTorchModel
from sagemaker import get_execution_role, Session
function = get_execution_role()
shared_params = dict(
clip_image_model = PyTorchModel(
    env={'MODEL_NAME': '', "ENCODE_TYPE": "IMAGE"},
clip_text_model = PyTorchModel(
    env={'MODEL_NAME': '', "ENCODE_TYPE": "TEXT"},

Batch remodel to encode merchandise pictures into embeddings

Subsequent, we use the CLIP mannequin to encode merchandise pictures into embeddings, and use SageMaker batch remodel to run batch inference.

Earlier than creating the job, use the next code snippet to repeat merchandise pictures from the Amazon Berkeley Objects Dataset public S3 bucket to your individual bucket. The operation takes lower than 10 minutes.

from multiprocessing.pool import ThreadPool
import boto3
from tqdm import tqdm
from urllib.parse import urlparse
s3_sample_image_root = "s3://<your-bucket>/<your-prefix-for-sample-images>"
s3_data_root = "s3://amazon-berkeley-objects/pictures/small/"
consumer = boto3.consumer('s3')
def upload_(args):
    consumer.copy_object(CopySource=args["source"], Bucket=args["target_bucket"], Key=args["target_key"])
arugments = []
for idx, file in dataset.iterrows():
    argument = {}
    argument["source"] = (s3_data_root + file.path)[5:]
    argument["target_bucket"] = urlparse(s3_sample_image_root).netloc
    argument["target_key"] = urlparse(s3_sample_image_root).path[1:] + file.path
with ThreadPool(4) as p:
    r = checklist(tqdm(p.imap(upload_, arugments), complete=len(dataset)))

Subsequent, you carry out inference on the merchandise pictures in a batch method. The SageMaker batch remodel job makes use of the CLIP mannequin to encode all the pictures saved within the enter Amazon S3 location and uploads output embeddings to an output S3 folder. The job takes round 10 minutes.

batch_input = s3_sample_image_root + "/"
output_path = f"s3://<your-bucket>/inference/output"
clip_image_transformer = clip_image_model.transformer(

Load embeddings from Amazon S3 to a variable, so you may ingest the info into OpenSearch Service later:

embedding_root_path = "./information/embedding"
s3down.obtain(output_path, embedding_root_path)
embeddings = []
for idx, file in dataset.iterrows():
    embedding_file = f"{embedding_root_path}/{file.path}.out"

Create an ML-powered unified search engine

This part discusses the way to create a search engine that that makes use of k-NN search with embeddings. This contains configuring an OpenSearch Service cluster, ingesting merchandise embedding, and performing free textual content and picture search queries.

Arrange the OpenSearch Service area utilizing k-NN settings

Earlier, you created an OpenSearch cluster. Now you’re going to create an index to retailer the catalog information and embeddings. You’ll be able to configure the index settings to allow the k-NN performance utilizing the next configuration:

index_settings = {
  "settings": {
    "index.knn": True,
    "index.knn.space_type": "cosinesimil"
  "mappings": {
    "properties": {
      "embeddings": {
        "sort": "knn_vector",
        "dimension": 1024 #Ensure that is the dimensions of the embeddings you generated, for RN50, it's 1024

This instance makes use of the Python Elasticsearch consumer to speak with the OpenSearch cluster and create an index to host your information. You’ll be able to run %pip set up elasticsearch within the pocket book to put in the library. See the next code:

import boto3
import json
from requests_aws4auth import AWS4Auth
from elasticsearch import Elasticsearch, RequestsHttpConnection
def get_es_client(host = "<your-opensearch-service-domain-url>",
    port = 443,
    area = "<your-region>",
    index_name = "clip-index"):
    credentials = boto3.Session().get_credentials()
    awsauth = AWS4Auth(credentials.access_key,
    headers = {"Content material-Kind": "utility/json"}
    es = Elasticsearch(hosts=[{'host': host, 'port': port}],
                       timeout=60 # for connection timeout errors
    return es
es = get_es_client()
es.indices.create(index=index_name, physique=json.dumps(index_settings))

Ingest picture embedding information into OpenSearch Service

You now loop by means of your dataset and ingest objects information into the cluster. The information ingestion for this apply ought to end inside 60 seconds. It additionally runs a easy question to confirm if the info has been ingested into the index efficiently. See the next code:

# ingest_data_into_es
for idx, file in tqdm(dataset.iterrows(), complete=len(dataset)):
    physique = file[['item_name_in_en_us']].to_dict()
    physique['embeddings'] = embeddings[idx]
    es.index(index=index_name, id=file.item_id, doc_type="_doc", physique=physique)
# Examine that information is certainly in ES
res =
    index=index_name, physique={
        "question": {
                "match_all": {}
assert len(res["hits"]["hits"]) > 0

Carry out a real-time question

Now that you’ve a working OpenSearch Service index that accommodates embeddings of merchandise pictures as our stock, let’s take a look at how one can generate embedding for queries. It’s essential create two SageMaker endpoints to deal with textual content and picture embeddings, respectively.

You additionally create two capabilities to make use of the endpoints to encode pictures and texts. For the encode_text operate, you add that is earlier than an merchandise identify to translate an merchandise identify to a sentence for merchandise description. memory_size_in_mb is about as 6 GB to serve the underline Transformer and ResNet fashions. See the next code:

text_predictor = clip_text_model.deploy(
image_predictor = clip_image_model.deploy(
def encode_image(file_name="./information/pictures/0e9420c6.jpg"):    
    with open(file_name, "rb") as f:
        payload = f.learn()
        payload = bytearray(payload)
    res = image_predictor.predict(payload)
    return res[0]
def encode_name(item_name):
    res = text_predictor.predict({"inputs": [f"this is a {item_name}"]})
    return res[0]

You’ll be able to firstly plot the image that might be used.

item_image_path, item_name = get_image_from_item_id(item_id = "B0896LJNLH", return_image=False)
feature_vector = encode_image(file_name=item_image_path)

glass cup

Let’s take a look at the outcomes of a easy question. After retrieving outcomes from OpenSearch Service, you get the checklist of merchandise names and pictures from dataset:

def search_products(embedding, ok = 3):
    physique = {
        "dimension": ok,
        "_source": {
            "exclude": ["embeddings"],
        "question": {
            "knn": {
                "embeddings": {
                    "vector": embedding,
                    "ok": ok,
    res =, physique=physique)
    pictures = []
    for hit in res["hits"]["hits"]:
        id_ = hit["_id"]
        picture, item_name = get_image_from_item_id(id_)
        picture.name_and_score = f'{hit["_score"]}:{item_name}'
    return pictures
def display_images(
    pictures: [PilImage], 
    columns=2, width=20, peak=8, max_images=15, 
    label_wrap_length=50, label_font_size=8):
    if not pictures:
        print("No pictures to show.")
    if len(pictures) > max_images:
        print(f"Exhibiting {max_images} pictures of {len(pictures)}:")
    peak = max(peak, int(len(pictures)/columns) * peak)
    plt.determine(figsize=(width, peak))
    for i, picture in enumerate(pictures):
        plt.subplot(int(len(pictures) / columns + 1), columns, i + 1)
        if hasattr(picture, 'name_and_score'):
            plt.title(picture.name_and_score, fontsize=label_font_size); 
pictures = search_products(feature_vector)


The primary merchandise has a rating of 1.0, as a result of the 2 pictures are the identical. Different objects are various kinds of glasses within the OpenSearch Service index.

You should utilize textual content to question the index as nicely:

feature_vector = encode_name("drinkware glass")
pictures = search_products(feature_vector)


You’re now capable of get three photos of water glasses from the index. Yow will discover the pictures and textual content throughout the identical latent area with the CLIP encoder. One other instance of that is to seek for the phrase “pizza” within the index:

feature_vector = encode_name("pizza")
pictures = search_products(feature_vector)

pizza results

Clear up

With a pay-per-use mannequin, Serverless Inference is a cheap possibility for an rare or unpredictable visitors sample. If in case you have a strict service-level settlement (SLA), or can’t tolerate chilly begins, real-time endpoints are a better option. Utilizing multi-model or multi-container endpoints present scalable and cost-effective options for deploying giant numbers of fashions. For extra data, check with Amazon SageMaker Pricing.

We advise deleting the serverless endpoints when they’re not wanted. After ending this train, you may take away the assets with the next steps (you may delete these assets from the AWS Administration Console, or utilizing the AWS SDK or SageMaker SDK):

  1. Delete the endpoint you created.
  2. Optionally, delete the registered fashions.
  3. Optionally, delete the SageMaker execution function.
  4. Optionally, empty and delete the S3 bucket.


On this publish, we demonstrated the way to create a k-NN search utility utilizing SageMaker and OpenSearch Service k-NN index options. We used a pre-trained CLIP mannequin from its OpenAI implementation.

The OpenSearch Service ingestion implementation of the publish is just used for prototyping. If you wish to ingest information from Amazon S3 into OpenSearch Service at scale, you may launch an Amazon SageMaker Processing job with the suitable occasion sort and occasion rely. For one more scalable embedding ingestion resolution, check with Novartis AG makes use of Amazon OpenSearch Service Okay-Nearest Neighbor (KNN) and Amazon SageMaker to energy search and advice (Half 3/4).

CLIP gives zero-shot capabilities, which makes it doable to undertake a pre-trained mannequin immediately with out utilizing switch studying to fine-tune a mannequin. This simplifies the appliance of the CLIP mannequin. If in case you have pairs of product pictures and descriptive textual content, you may fine-tune the mannequin with your individual information utilizing switch studying to additional enhance the mannequin efficiency. For extra data, see Studying Transferable Visible Fashions From Pure Language Supervision and the CLIP GitHub repository.

Concerning the Authors

Kevin Du is a Senior Knowledge Lab Architect at AWS, devoted to helping prospects in expediting the event of their Machine Studying (ML) merchandise and MLOps platforms. With greater than a decade of expertise constructing ML-enabled merchandise for each startups and enterprises, his focus is on serving to prospects streamline the productionalization of their ML options. In his free time, Kevin enjoys cooking and watching basketball.

Ananya Roy is a Senior Knowledge Lab architect specialised in AI and machine studying based mostly out of Sydney Australia . She has been working with numerous vary of consumers to supply architectural steerage and assist them to ship efficient AI/ML resolution through information lab engagement. Previous to AWS , she was working as senior information scientist and handled large-scale ML fashions throughout completely different industries like Telco, banks and fintech’s. Her expertise in AI/ML has allowed her to ship efficient options for advanced enterprise issues, and he or she is enthusiastic about leveraging cutting-edge applied sciences to assist groups obtain their objectives.



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