AI & Vectors

Semantic Image Search with Amazon Titan

Implement semantic image search with Amazon Titan and Supabase Vector in Python.


Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon. Each model is accessible through a common API which implements a broad set of features to help build generative AI applications with security, privacy, and responsible AI in mind.

Amazon Titan is a family of foundation models (FMs) for text and image generation, summarization, classification, open-ended Q&A, information extraction, and text or image search.

In this guide we'll look at how we can get started with Amazon Bedrock and Supabase Vector in Python using the Amazon Titan multimodal model and the vecs client.

You can find the full application code as a Python Poetry project on GitHub.

Create a new Python project with Poetry

Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:


_10
pip install poetry

Then initialize a new project:


_10
poetry new aws_bedrock_image_search

Spin up a Postgres Database with pgvector

If you haven't already, head over to database.new and create a new project. Every Supabase project comes with a full Postgres database and the pgvector extension preconfigured.

When creating your project, make sure to note down your database password as you will need it to construct the DB_URL in the next step.

You can find the database connection string in your Supabase Dashboard database settings. Select "Use connection pooling" with Mode: Session for a direct connection to your Postgres database. It will look something like this:


_10
postgresql://postgres.[PROJECT-REF]:[YOUR-PASSWORD]@aws-0-[REGION].pooler.supabase.com:5432/postgres

Install the dependencies

We will need to add the following dependencies to our project:

  • vecs: Supabase Vector Python Client.
  • boto3: AWS SDK for Python.
  • matplotlib: for displaying our image result.

_10
poetry add vecs boto3 matplotlib

Import the necessary dependencies

At the top of your main python script, import the dependencies and store your DB URL from above in a variable:


_10
import sys
_10
import boto3
_10
import vecs
_10
import json
_10
import base64
_10
from matplotlib import pyplot as plt
_10
from matplotlib import image as mpimg
_10
from typing import Optional
_10
_10
DB_CONNECTION = "postgresql://postgres.[PROJECT-REF]:[YOUR-PASSWORD]@aws-0-[REGION].pooler.supabase.com:5432/postgres"

Next, get the credentials to your AWS account and instantiate the boto3 client:


_10
bedrock_client = boto3.client(
_10
'bedrock-runtime',
_10
region_name='us-west-2',
_10
# Credentials from your AWS account
_10
aws_access_key_id='<replace_your_own_credentials>',
_10
aws_secret_access_key='<replace_your_own_credentials>',
_10
aws_session_token='<replace_your_own_credentials>',
_10
)

Create embeddings for your images

In the root of your project, create a new folder called images and add some images. You can use the images from the example project on GitHub or you can find license free images on unsplash.

To send images to the Amazon Bedrock API we need to need to encode them as base64 strings. Create the following helper methods:


_44
def readFileAsBase64(file_path):
_44
"""Encode image as base64 string."""
_44
try:
_44
with open(file_path, "rb") as image_file:
_44
input_image = base64.b64encode(image_file.read()).decode("utf8")
_44
return input_image
_44
except:
_44
print("bad file name")
_44
sys.exit(0)
_44
_44
_44
def construct_bedrock_image_body(base64_string):
_44
"""Construct the request body.
_44
_44
https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-mm.html
_44
"""
_44
return json.dumps(
_44
{
_44
"inputImage": base64_string,
_44
"embeddingConfig": {"outputEmbeddingLength": 1024},
_44
}
_44
)
_44
_44
_44
def get_embedding_from_titan_multimodal(body):
_44
"""Invoke the Amazon Titan Model via API request."""
_44
response = bedrock_client.invoke_model(
_44
body=body,
_44
modelId="amazon.titan-embed-image-v1",
_44
accept="application/json",
_44
contentType="application/json",
_44
)
_44
_44
response_body = json.loads(response.get("body").read())
_44
print(response_body)
_44
return response_body["embedding"]
_44
_44
_44
def encode_image(file_path):
_44
"""Generate embedding for the image at file_path."""
_44
base64_string = readFileAsBase64(file_path)
_44
body = construct_bedrock_image_body(base64_string)
_44
emb = get_embedding_from_titan_multimodal(body)
_44
return emb

Next, create a seed method, which will create a new Supabase Vector Collection, generate embeddings for your images, and upsert the embeddings into your database:


_40
def seed():
_40
# create vector store client
_40
vx = vecs.create_client(DB_CONNECTION)
_40
_40
# get or create a collection of vectors with 1024 dimensions
_40
images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
_40
_40
# Generate image embeddings with Amazon Titan Model
_40
img_emb1 = encode_image('./images/one.jpg')
_40
img_emb2 = encode_image('./images/two.jpg')
_40
img_emb3 = encode_image('./images/three.jpg')
_40
img_emb4 = encode_image('./images/four.jpg')
_40
_40
# add records to the *images* collection
_40
images.upsert(
_40
records=[
_40
(
_40
"one.jpg", # the vector's identifier
_40
img_emb1, # the vector. list or np.array
_40
{"type": "jpg"} # associated metadata
_40
), (
_40
"two.jpg",
_40
img_emb2,
_40
{"type": "jpg"}
_40
), (
_40
"three.jpg",
_40
img_emb3,
_40
{"type": "jpg"}
_40
), (
_40
"four.jpg",
_40
img_emb4,
_40
{"type": "jpg"}
_40
)
_40
]
_40
)
_40
print("Inserted images")
_40
_40
# index the collection for fast search performance
_40
images.create_index()
_40
print("Created index")

Add this method as a script in your pyproject.toml file:


_10
[tool.poetry.scripts]
_10
seed = "image_search.main:seed"
_10
search = "image_search.main:search"

After activating the virtual environtment with poetry shell you can now run your seed script via poetry run seed. You can inspect the generated embeddings in your Supabase Dashboard by visiting the Table Editor, selecting the vecs schema, and the image_vectors table.

Perform an image search from a text query

With Supabase Vector we can easily query our embeddings. We can use either an image as the search input or alternatively we can generate an embedding from a string input and use that as the query input:


_28
def search(query_term: Optional[str] = None):
_28
if query_term is None:
_28
query_term = sys.argv[1]
_28
_28
# create vector store client
_28
vx = vecs.create_client(DB_CONNECTION)
_28
images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
_28
_28
# Encode text query
_28
text_emb = get_embedding_from_titan_multimodal(json.dumps(
_28
{
_28
"inputText": query_term,
_28
"embeddingConfig": {"outputEmbeddingLength": 1024},
_28
}
_28
))
_28
_28
# query the collection filtering metadata for "type" = "jpg"
_28
results = images.query(
_28
data=text_emb, # required
_28
limit=1, # number of records to return
_28
filters={"type": {"$eq": "jpg"}}, # metadata filters
_28
)
_28
result = results[0]
_28
print(result)
_28
plt.title(result)
_28
image = mpimg.imread('./images/' + result)
_28
plt.imshow(image)
_28
plt.show()

By limiting the query to one result, we can show the most relevant image to the user. Finally we use matplotlib to show the image result to the user.

That's it, go ahead and test it out by running poetry run search and you will be presented with an image of a "bike in front of a red brick wall".

Conclusion

With just a couple of lines of Python you are able to implement image search as well as reverse image search using the Amazon Titan multimodal model and Supabase Vector.