diff --git a/apps/docs/components/Navigation/NavigationMenu/NavigationMenu.constants.ts b/apps/docs/components/Navigation/NavigationMenu/NavigationMenu.constants.ts
index c36bc455b6f..ff66fca757a 100644
--- a/apps/docs/components/Navigation/NavigationMenu/NavigationMenu.constants.ts
+++ b/apps/docs/components/Navigation/NavigationMenu/NavigationMenu.constants.ts
@@ -1495,6 +1495,10 @@ export const ai = {
           name: 'Image search with OpenAI CLIP',
           url: '/guides/ai/examples/image-search-openai-clip',
         },
+        {
+          name: 'Semantic search with Amazon Titan',
+          url: '/guides/ai/examples/semantic-image-search-amazon-titan',
+        },
         {
           name: 'Building ChatGPT Plugins',
           url: '/guides/ai/examples/building-chatgpt-plugins',
@@ -1525,6 +1529,10 @@ export const ai = {
           name: 'Roboflow',
           url: '/guides/ai/integrations/roboflow',
         },
+        {
+          name: 'Amazon Bedrock',
+          url: '/guides/ai/integrations/amazon-bedrock',
+        },
       ],
     },
   ],
diff --git a/apps/docs/content/guides/ai.mdx b/apps/docs/content/guides/ai.mdx
index ba7bcada9b6..8ce2de8c1a9 100644
--- a/apps/docs/content/guides/ai.mdx
+++ b/apps/docs/content/guides/ai.mdx
@@ -99,6 +99,12 @@ export const integrations = [
       'OpenAI is an AI research and deployment company. Supabase provides a simple way to use OpenAI in your applications.',
     href: '/guides/ai/examples/building-chatgpt-plugins',
   },
+  {
+    name: 'Amazon Bedrock',
+    description:
+      'A fully managed service that offers a choice of high-performing foundation models from leading AI companies.',
+    href: '/guides/ai/amazon-bedrock',
+  },
   {
     name: 'Hugging Face',
     description:
diff --git a/apps/docs/content/guides/ai/examples/semantic-image-search-amazon-titan.mdx b/apps/docs/content/guides/ai/examples/semantic-image-search-amazon-titan.mdx
new file mode 100644
index 00000000000..c2dd616366e
--- /dev/null
+++ b/apps/docs/content/guides/ai/examples/semantic-image-search-amazon-titan.mdx
@@ -0,0 +1,234 @@
+---
+id: 'semantic-image-search-python-amazon-titan'
+title: 'Semantic Image Search with Amazon Titan'
+description: 'Implement semantic image search with Amazon Titan and Supabase Vector in Python.'
+subtitle: 'Implement semantic image search with Amazon Titan and Supabase Vector in Python.'
+tocVideo: 'A3uND5sgiO0'
+---
+
+[Amazon Bedrock](https://aws.amazon.com/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](https://aws.amazon.com/bedrock/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](/docs/guides/ai/vecs-python-client).
+
+You can find the full application code as a Python Poetry project on [GitHub](https://github.com/supabase/supabase/tree/master/examples/ai/aws_bedrock_image_search).
+
+## Create a new Python project with Poetry
+
+[Poetry](https://python-poetry.org/) provides packaging and dependency management for Python. If you haven't already, install poetry via pip:
+
+```shell
+pip install poetry
+```
+
+Then initialize a new project:
+
+```shell
+poetry new aws_bedrock_image_search
+```
+
+## Spin up a Postgres Database with pgvector
+
+If you haven't already, head over to [database.new](https://database.new) and create a new project. Every Supabase project comes with a full Postgres database and the [pgvector extension](/docs/guides/database/extensions/pgvector) 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](https://supabase.com/dashboard/project/_/settings/database). Select "Use connection pooling" with `Mode: Session` for a direct connection to your Postgres database. It will look something like this:
+
+```txt
+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`](https://github.com/supabase/vecs#vecs): Supabase Vector Python Client.
+- [`boto3`](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html): AWS SDK for Python.
+- [`matplotlib`](https://matplotlib.org/): for displaying our image result.
+
+```shell
+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:
+
+```python
+import sys
+import boto3
+import vecs
+import json
+import base64
+from matplotlib import pyplot as plt
+from matplotlib import image as mpimg
+from typing import Optional
+
+DB_CONNECTION = "postgresql://postgres.[PROJECT-REF]:[YOUR-PASSWORD]@aws-0-[REGION].pooler.supabase.com:5432/postgres"
+```
+
+Next, get the [credentials to your AWS account](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html) and instantiate the `boto3` client:
+
+```python
+bedrock_client = boto3.client(
+    'bedrock-runtime',
+    region_name='us-west-2',
+    # Credentials from your AWS account
+    aws_access_key_id='<replace_your_own_credentials>',
+    aws_secret_access_key='<replace_your_own_credentials>',
+    aws_session_token='<replace_your_own_credentials>',
+)
+```
+
+## 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](https://github.com/supabase/supabase/tree/master/examples/ai/aws_bedrock_image_search/images) or you can find license free images on [unsplash](https://unsplash.com).
+
+To send images to the Amazon Bedrock API we need to need to encode them as `base64` strings. Create the following helper methods:
+
+```python
+def readFileAsBase64(file_path):
+    """Encode image as base64 string."""
+    try:
+        with open(file_path, "rb") as image_file:
+            input_image = base64.b64encode(image_file.read()).decode("utf8")
+        return input_image
+    except:
+        print("bad file name")
+        sys.exit(0)
+
+
+def construct_bedrock_image_body(base64_string):
+    """Construct the request body.
+
+    https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-mm.html
+    """
+    return json.dumps(
+        {
+            "inputImage": base64_string,
+            "embeddingConfig": {"outputEmbeddingLength": 1024},
+        }
+    )
+
+
+def get_embedding_from_titan_multimodal(body):
+    """Invoke the Amazon Titan Model via API request."""
+    response = bedrock_client.invoke_model(
+        body=body,
+        modelId="amazon.titan-embed-image-v1",
+        accept="application/json",
+        contentType="application/json",
+    )
+
+    response_body = json.loads(response.get("body").read())
+    print(response_body)
+    return response_body["embedding"]
+
+
+def encode_image(file_path):
+    """Generate embedding for the image at file_path."""
+    base64_string = readFileAsBase64(file_path)
+    body = construct_bedrock_image_body(base64_string)
+    emb = get_embedding_from_titan_multimodal(body)
+    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:
+
+```python
+def seed():
+    # create vector store client
+    vx = vecs.create_client(DB_CONNECTION)
+
+    # get or create a collection of vectors with 1024 dimensions
+    images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
+
+    # Generate image embeddings with Amazon Titan Model
+    img_emb1 = encode_image('./images/one.jpg')
+    img_emb2 = encode_image('./images/two.jpg')
+    img_emb3 = encode_image('./images/three.jpg')
+    img_emb4 = encode_image('./images/four.jpg')
+
+    # add records to the *images* collection
+    images.upsert(
+        records=[
+            (
+                "one.jpg",       # the vector's identifier
+                img_emb1,        # the vector. list or np.array
+                {"type": "jpg"}  # associated  metadata
+            ), (
+                "two.jpg",
+                img_emb2,
+                {"type": "jpg"}
+            ), (
+                "three.jpg",
+                img_emb3,
+                {"type": "jpg"}
+            ), (
+                "four.jpg",
+                img_emb4,
+                {"type": "jpg"}
+            )
+        ]
+    )
+    print("Inserted images")
+
+    # index the collection for fast search performance
+    images.create_index()
+    print("Created index")
+```
+
+Add this method as a script in your `pyproject.toml` file:
+
+```toml
+[tool.poetry.scripts]
+seed = "image_search.main:seed"
+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](https://supabase.com/dashboard/project/_/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:
+
+```python
+def search(query_term: Optional[str] = None):
+    if query_term is None:
+        query_term = sys.argv[1]
+
+    # create vector store client
+    vx = vecs.create_client(DB_CONNECTION)
+    images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
+
+    # Encode text query
+    text_emb = get_embedding_from_titan_multimodal(json.dumps(
+        {
+            "inputText": query_term,
+            "embeddingConfig": {"outputEmbeddingLength": 1024},
+        }
+    ))
+
+    # query the collection filtering metadata for "type" = "jpg"
+    results = images.query(
+        data=text_emb,                      # required
+        limit=1,                            # number of records to return
+        filters={"type": {"$eq": "jpg"}},   # metadata filters
+    )
+    result = results[0]
+    print(result)
+    plt.title(result)
+    image = mpimg.imread('./images/' + result)
+    plt.imshow(image)
+    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.
diff --git a/apps/docs/content/guides/ai/integrations/amazon-bedrock.mdx b/apps/docs/content/guides/ai/integrations/amazon-bedrock.mdx
new file mode 100644
index 00000000000..d18f9a6bc83
--- /dev/null
+++ b/apps/docs/content/guides/ai/integrations/amazon-bedrock.mdx
@@ -0,0 +1,139 @@
+---
+id: 'ai-amazon-bedrock'
+title: 'Amazon Bedrock'
+description: 'Learn how to integrate Supabase with Amazon Bedrock, a fully managed service of high-performing foundation models.'
+sidebar_label: 'Amazon Bedrock'
+tocVideo: 'A3uND5sgiO0'
+---
+
+[Amazon Bedrock](https://aws.amazon.com/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.
+
+This guide will walk you through an example using Amazon Bedrock SDK with `vecs`. We will create embeddings using the Amazon Titan Embeddings G1 – Text v1.2 (amazon.titan-embed-text-v1) model, insert these embeddings into a PostgreSQL database using vecs, and then query the collection to find the most similar sentences to a given query sentence.
+
+## Create an Environment
+
+First, you need to set up your environment. You will need Python 3.7+ with the `vecs` and `boto3` libraries installed.
+
+You can install the necessary Python libraries using pip:
+
+```sh
+pip install vecs boto3
+```
+
+You'll also need:
+
+- [Credentials to your AWS account](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)
+- [A Postgres Database with the pgvector extension](hosting.md)
+
+## Create Embeddings
+
+Next, we will use Amazon’s Titan Embedding G1 - Text v1.2 model to create embeddings for a set of sentences.
+
+```python
+import boto3
+import vecs
+import json
+
+client = boto3.client(
+    'bedrock-runtime',
+    region_name='us-east-1',
+	# Credentials from your AWS account
+    aws_access_key_id='<replace_your_own_credentials>',
+    aws_secret_access_key='<replace_your_own_credentials>',
+    aws_session_token='<replace_your_own_credentials>',
+)
+
+dataset = [
+    "The cat sat on the mat.",
+    "The quick brown fox jumps over the lazy dog.",
+    "Friends, Romans, countrymen, lend me your ears",
+    "To be or not to be, that is the question.",
+]
+
+embeddings = []
+
+for sentence in dataset:
+    # invoke the embeddings model for each sentence
+    response = client.invoke_model(
+        body= json.dumps({"inputText": sentence}),
+        modelId= "amazon.titan-embed-text-v1",
+        accept = "application/json",
+        contentType = "application/json"
+    )
+    # collect the embedding from the response
+    response_body = json.loads(response["body"].read())
+    # add the embedding to the embedding list
+    embeddings.append((sentence, response_body.get("embedding"), {}))
+
+```
+
+### Store the Embeddings with vecs
+
+Now that we have our embeddings, we can insert them into a PostgreSQL database using vecs.
+
+```python
+import vecs
+
+DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"
+
+# create vector store client
+vx = vecs.Client(DB_CONNECTION)
+
+# create a collection named 'sentences' with 1536 dimensional vectors
+# to match the default dimension of the Titan Embeddings G1 - Text model
+sentences = vx.get_or_create_collection(name="sentences", dimension=1536)
+
+# upsert the embeddings into the 'sentences' collection
+sentences.upsert(records=embeddings)
+
+# create an index for the 'sentences' collection
+sentences.create_index()
+```
+
+### Querying for Most Similar Sentences
+
+Now, we query the `sentences` collection to find the most similar sentences to a sample query sentence. First need to create an embedding for the query sentence. Next, we query the collection we created earlier to find the most similar sentences.
+
+```python
+query_sentence = "A quick animal jumps over a lazy one."
+
+# create vector store client
+vx = vecs.Client(DB_CONNECTION)
+
+# create an embedding for the query sentence
+response = client.invoke_model(
+        body= json.dumps({"inputText": query_sentence}),
+        modelId= "amazon.titan-embed-text-v1",
+        accept = "application/json",
+        contentType = "application/json"
+    )
+
+response_body = json.loads(response["body"].read())
+
+query_embedding = response_body.get("embedding")
+
+# query the 'sentences' collection for the most similar sentences
+results = sentences.query(
+    data=query_embedding,
+    limit=3,
+    include_value = True
+)
+
+# print the results
+for result in results:
+    print(result)
+```
+
+This returns the most similar 3 records and their distance to the query vector.
+
+```
+('The quick brown fox jumps over the lazy dog.', 0.27600620558852)
+('The cat sat on the mat.', 0.609986272479202)
+('To be or not to be, that is the question.', 0.744849503688346)
+```
+
+## Resources
+
+- [Amazon Bedrock](https://aws.amazon.com/bedrock)
+- [Amazon Titan](https://aws.amazon.com/bedrock/titan)
+- [Semantic Image Search with Amazon Titan](/docs/guides/ai/examples/semantic-image-search-amazon-titan)
diff --git a/apps/www/_blog/2024-03-26-semantic-image-search-amazon-bedrock.mdx b/apps/www/_blog/2024-03-26-semantic-image-search-amazon-bedrock.mdx
new file mode 100644
index 00000000000..6e6b9677b31
--- /dev/null
+++ b/apps/www/_blog/2024-03-26-semantic-image-search-amazon-bedrock.mdx
@@ -0,0 +1,247 @@
+---
+title: 'Implementing semantic image search with Amazon Titan and Supabase Vector'
+description: 'Implementing semantic image search with Amazon Titan and Supabase Vector in Python.'
+author: thor_schaeff
+image: getting-started/amazon-bedrock/amazon-bedrock-supabase-vecs.png
+thumb: getting-started/amazon-bedrock/amazon-bedrock-supabase-vecs.png
+categories:
+  - engineering
+tags:
+  - postgres
+  - developers
+  - ai
+date: '2024-03-26'
+toc_depth: 3
+---
+
+[Amazon Bedrock](https://aws.amazon.com/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](https://aws.amazon.com/bedrock/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 post 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](/docs/guides/ai/vecs-python-client).
+
+You can find the full application code as a Python Poetry project on [GitHub](https://github.com/supabase/supabase/tree/master/examples/ai/aws_bedrock_image_search).
+
+## Create a new Python project with Poetry
+
+[Poetry](https://python-poetry.org/) provides packaging and dependency management for Python. If you haven't already, install poetry via pip:
+
+```shell
+pip install poetry
+```
+
+Then initialize a new project:
+
+```shell
+poetry new aws_bedrock_image_search
+```
+
+## Spin up a Postgres Database with pgvector
+
+If you haven't already, head over to [database.new](https://database.new) and create a new project. Every Supabase project comes with a full Postgres database and the [pgvector extension](/docs/guides/database/extensions/pgvector) 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](https://supabase.com/dashboard/project/_/settings/database). Select "Use connection pooling" with `Mode: Session` for a direct connection to your Postgres database. It will look something like this:
+
+```txt
+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`](https://github.com/supabase/vecs#vecs): Supabase Vector Python Client.
+- [`boto3`](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html): AWS SDK for Python.
+- [`matplotlib`](https://matplotlib.org/): for displaying our image result.
+
+```shell
+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:
+
+```python
+import sys
+import boto3
+import vecs
+import json
+import base64
+from matplotlib import pyplot as plt
+from matplotlib import image as mpimg
+from typing import Optional
+
+DB_CONNECTION = "postgresql://postgres.[PROJECT-REF]:[YOUR-PASSWORD]@aws-0-[REGION].pooler.supabase.com:5432/postgres"
+```
+
+Next, get the [credentials to your AWS account](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html) and instantiate the `boto3` client:
+
+```python
+bedrock_client = boto3.client(
+    'bedrock-runtime',
+    region_name='us-west-2',
+    # Credentials from your AWS account
+    aws_access_key_id='<replace_your_own_credentials>',
+    aws_secret_access_key='<replace_your_own_credentials>',
+    aws_session_token='<replace_your_own_credentials>',
+)
+```
+
+## 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](https://github.com/supabase/supabase/tree/master/examples/ai/aws_bedrock_image_search/images) or you can find license free images on [unsplash](https://unsplash.com).
+
+To send images to the Amazon Bedrock API we need to need to encode them as `base64` strings. Create the following helper methods:
+
+```python
+def readFileAsBase64(file_path):
+    """Encode image as base64 string."""
+    try:
+        with open(file_path, "rb") as image_file:
+            input_image = base64.b64encode(image_file.read()).decode("utf8")
+        return input_image
+    except:
+        print("bad file name")
+        sys.exit(0)
+
+
+def construct_bedrock_image_body(base64_string):
+    """Construct the request body.
+
+    https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-mm.html
+    """
+    return json.dumps(
+        {
+            "inputImage": base64_string,
+            "embeddingConfig": {"outputEmbeddingLength": 1024},
+        }
+    )
+
+
+def get_embedding_from_titan_multimodal(body):
+    """Invoke the Amazon Titan Model via API request."""
+    response = bedrock_client.invoke_model(
+        body=body,
+        modelId="amazon.titan-embed-image-v1",
+        accept="application/json",
+        contentType="application/json",
+    )
+
+    response_body = json.loads(response.get("body").read())
+    print(response_body)
+    return response_body["embedding"]
+
+
+def encode_image(file_path):
+    """Generate embedding for the image at file_path."""
+    base64_string = readFileAsBase64(file_path)
+    body = construct_bedrock_image_body(base64_string)
+    emb = get_embedding_from_titan_multimodal(body)
+    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:
+
+```python
+def seed():
+    # create vector store client
+    vx = vecs.create_client(DB_CONNECTION)
+
+    # get or create a collection of vectors with 1024 dimensions
+    images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
+
+    # Generate image embeddings with Amazon Titan Model
+    img_emb1 = encode_image('./images/one.jpg')
+    img_emb2 = encode_image('./images/two.jpg')
+    img_emb3 = encode_image('./images/three.jpg')
+    img_emb4 = encode_image('./images/four.jpg')
+
+    # add records to the *images* collection
+    images.upsert(
+        records=[
+            (
+                "one.jpg",       # the vector's identifier
+                img_emb1,        # the vector. list or np.array
+                {"type": "jpg"}  # associated  metadata
+            ), (
+                "two.jpg",
+                img_emb2,
+                {"type": "jpg"}
+            ), (
+                "three.jpg",
+                img_emb3,
+                {"type": "jpg"}
+            ), (
+                "four.jpg",
+                img_emb4,
+                {"type": "jpg"}
+            )
+        ]
+    )
+    print("Inserted images")
+
+    # index the collection for fast search performance
+    images.create_index()
+    print("Created index")
+```
+
+Add this method as a script in your `pyproject.toml` file:
+
+```toml
+[tool.poetry.scripts]
+seed = "image_search.main:seed"
+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](https://supabase.com/dashboard/project/_/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:
+
+```python
+def search(query_term: Optional[str] = None):
+    if query_term is None:
+        query_term = sys.argv[1]
+
+    # create vector store client
+    vx = vecs.create_client(DB_CONNECTION)
+    images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
+
+    # Encode text query
+    text_emb = get_embedding_from_titan_multimodal(json.dumps(
+        {
+            "inputText": query_term,
+            "embeddingConfig": {"outputEmbeddingLength": 1024},
+        }
+    ))
+
+    # query the collection filtering metadata for "type" = "jpg"
+    results = images.query(
+        data=text_emb,                      # required
+        limit=1,                            # number of records to return
+        filters={"type": {"$eq": "jpg"}},   # metadata filters
+    )
+    result = results[0]
+    print(result)
+    plt.title(result)
+    image = mpimg.imread('./images/' + result)
+    plt.imshow(image)
+    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.
+
+## More Supabase
+
+- [Getting started with Amazon Bedrock and vecs](/docs/guides/ai/integrations/amazon-bedrock)
+- [Matryoshka embeddings: faster OpenAI vector search using Adaptive Retrieval](/blog/matryoshka-embeddings)
diff --git a/apps/www/public/images/blog/getting-started/amazon-bedrock/amazon-bedrock-supabase-vecs.png b/apps/www/public/images/blog/getting-started/amazon-bedrock/amazon-bedrock-supabase-vecs.png
new file mode 100644
index 00000000000..a4d4dcb1d1e
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diff --git a/examples/ai/aws_bedrock_image_search/.gitignore b/examples/ai/aws_bedrock_image_search/.gitignore
new file mode 100644
index 00000000000..c791ff59a37
--- /dev/null
+++ b/examples/ai/aws_bedrock_image_search/.gitignore
@@ -0,0 +1,3 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
\ No newline at end of file
diff --git a/examples/ai/aws_bedrock_image_search/README.md b/examples/ai/aws_bedrock_image_search/README.md
new file mode 100644
index 00000000000..705e2d7625b
--- /dev/null
+++ b/examples/ai/aws_bedrock_image_search/README.md
@@ -0,0 +1,41 @@
+# Image Search with Amazon Bedrock and Supabase Vector
+
+In this example we're implementing image search using the [Amazon Titan Multimodal Embeddings G1](https://aws.amazon.com/bedrock/titan), a set of pre-trained high-performing image, multimodal, and text model, accessible via a fully managed API.
+
+We're implementing two methods in the [`/image_search/main.py` file](/image_search/main.py):
+
+1. The `seed` method generates embeddings for the images in the `images` folder and upserts them into a collection in Supabase Vector.
+2. The `search` method generates an embedding from the search query and performs a vector similarity search query.
+
+## Setup
+
+- Install poetry: `pip install poetry`
+- Activate the virtual environment: `poetry shell`
+  - (to leave the venv just run `exit`)
+- Install app dependencies: `poetry install`
+
+## Run locally
+
+### Generate the embeddings and seed the collection
+
+- `supabase start`
+- `poetry run seed`
+- Check the embeddings stored in the local Supabase Dashboard: http://localhost:54323/project/default/editor > schema: vecs
+
+### Perform a search
+
+- `poetry run search "bike in front of red brick wall"`
+
+## Run on hosted Supabase project
+
+- Set `DB_CONNECTION` with the connection string from your hosted Supabase Dashboard: https://supabase.com/dashboard/project/_/settings/database > Connection string > URI
+
+## Attributions
+
+### Models
+
+[Amazon Titan Multimodal Embeddings G1](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-mm.html)
+
+### Images
+
+Images from https://unsplash.com/license via https://picsum.photos/
diff --git a/examples/ai/aws_bedrock_image_search/image_search/main.py b/examples/ai/aws_bedrock_image_search/image_search/main.py
new file mode 100644
index 00000000000..dd83817c9be
--- /dev/null
+++ b/examples/ai/aws_bedrock_image_search/image_search/main.py
@@ -0,0 +1,137 @@
+import sys
+import boto3
+import vecs
+import json
+import base64
+from matplotlib import pyplot as plt
+from matplotlib import image as mpimg
+from typing import Optional
+
+DB_CONNECTION = "postgresql://postgres:postgres@localhost:54322/postgres"
+
+bedrock_client = boto3.client(
+    'bedrock-runtime',
+    region_name='us-west-2',
+    # Credentials from your AWS account
+    aws_access_key_id='<replace_your_own_credentials>',
+    aws_secret_access_key='<replace_your_own_credentials>',
+    aws_session_token='<replace_your_own_credentials>',
+)
+
+
+def readFileAsBase64(file_path):
+    """Encode image as base64 string."""
+    try:
+        with open(file_path, "rb") as image_file:
+            input_image = base64.b64encode(image_file.read()).decode("utf8")
+        return input_image
+    except:
+        print("bad file name")
+        sys.exit(0)
+
+
+def construct_bedrock_image_body(base64_string):
+    """Construct the request body.
+
+    https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-mm.html
+    """
+    return json.dumps(
+        {
+            "inputImage": base64_string,
+            "embeddingConfig": {"outputEmbeddingLength": 1024},
+        }
+    )
+
+
+def get_embedding_from_titan_multimodal(body):
+    """Invoke the Amazon Titan Model via API request."""
+    response = bedrock_client.invoke_model(
+        body=body,
+        modelId="amazon.titan-embed-image-v1",
+        accept="application/json",
+        contentType="application/json",
+    )
+
+    response_body = json.loads(response.get("body").read())
+    print(response_body)
+    return response_body["embedding"]
+
+
+def encode_image(file_path):
+    """Generate embedding for the image at file_path."""
+    base64_string = readFileAsBase64(file_path)
+    body = construct_bedrock_image_body(base64_string)
+    emb = get_embedding_from_titan_multimodal(body)
+    return emb
+
+
+def seed():
+    # create vector store client
+    vx = vecs.create_client(DB_CONNECTION)
+
+    # get or create a collection of vectors with 1024 dimensions
+    images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
+
+    # Generate image embeddings with Amazon Titan Model
+    img_emb1 = encode_image('./images/one.jpg')
+    img_emb2 = encode_image('./images/two.jpg')
+    img_emb3 = encode_image('./images/three.jpg')
+    img_emb4 = encode_image('./images/four.jpg')
+
+    # add records to the *images* collection
+    images.upsert(
+        records=[
+            (
+                "one.jpg",       # the vector's identifier
+                img_emb1,        # the vector. list or np.array
+                {"type": "jpg"}  # associated  metadata
+            ), (
+                "two.jpg",
+                img_emb2,
+                {"type": "jpg"}
+            ), (
+                "three.jpg",
+                img_emb3,
+                {"type": "jpg"}
+            ), (
+                "four.jpg",
+                img_emb4,
+                {"type": "jpg"}
+            )
+        ]
+    )
+    print("Inserted images")
+
+    # index the collection for fast search performance
+    images.create_index()
+    print("Created index")
+
+
+def search(query_term: Optional[str] = None):
+    if query_term is None:
+        query_term = sys.argv[1]
+    
+    # create vector store client
+    vx = vecs.create_client(DB_CONNECTION)
+    images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
+
+    # Encode text query
+    text_emb = get_embedding_from_titan_multimodal(json.dumps(
+        {
+            "inputText": query_term,
+            "embeddingConfig": {"outputEmbeddingLength": 1024},
+        }
+    ))
+
+    # query the collection filtering metadata for "type" = "jpg"
+    results = images.query(
+        data=text_emb,                      # required
+        limit=1,                            # number of records to return
+        filters={"type": {"$eq": "jpg"}},   # metadata filters
+    )
+    result = results[0]
+    print(result)
+    plt.title(result)
+    image = mpimg.imread('./images/' + result)
+    plt.imshow(image)
+    plt.show()
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new file mode 100644
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+
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+description = "Module for decorators, wrappers and monkey patching."
+category = "main"
+optional = false
+python-versions = ">=3.6"
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+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.11"
+content-hash = "52193cbe33f6ea90a7284b67ba45e95408eb14efd6f84d50abeafc11fe5889fa"
diff --git a/examples/ai/aws_bedrock_image_search/pyproject.toml b/examples/ai/aws_bedrock_image_search/pyproject.toml
new file mode 100644
index 00000000000..da8c9af710b
--- /dev/null
+++ b/examples/ai/aws_bedrock_image_search/pyproject.toml
@@ -0,0 +1,21 @@
+[tool.poetry]
+name = "image-search"
+version = "0.1.0"
+description = "Image Search with Supabase Vector"
+authors = ["thorwebdev <thor@supabase.io>"]
+readme = "README.md"
+packages = [{include = "image_search"}]
+
+[tool.poetry.dependencies]
+python = "^3.11"
+matplotlib = "^3.7.1"
+vecs = "^0.4.3"
+boto3 = "^1.34.59"
+
+[tool.poetry.scripts]
+seed = "image_search.main:seed"
+search = "image_search.main:search"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/examples/ai/aws_bedrock_image_search/supabase/.gitignore b/examples/ai/aws_bedrock_image_search/supabase/.gitignore
new file mode 100644
index 00000000000..a3ad88055b7
--- /dev/null
+++ b/examples/ai/aws_bedrock_image_search/supabase/.gitignore
@@ -0,0 +1,4 @@
+# Supabase
+.branches
+.temp
+.env
diff --git a/examples/ai/aws_bedrock_image_search/supabase/config.toml b/examples/ai/aws_bedrock_image_search/supabase/config.toml
new file mode 100644
index 00000000000..2af8d3e1813
--- /dev/null
+++ b/examples/ai/aws_bedrock_image_search/supabase/config.toml
@@ -0,0 +1,161 @@
+# A string used to distinguish different Supabase projects on the same host. Defaults to the
+# working directory name when running `supabase init`.
+project_id = "aws_bedrock_image_search"
+
+[api]
+enabled = true
+# Port to use for the API URL.
+port = 54321
+# Schemas to expose in your API. Tables, views and stored procedures in this schema will get API
+# endpoints. public and storage are always included.
+schemas = ["public", "storage", "graphql_public"]
+# Extra schemas to add to the search_path of every request. public is always included.
+extra_search_path = ["public", "extensions"]
+# The maximum number of rows returns from a view, table, or stored procedure. Limits payload size
+# for accidental or malicious requests.
+max_rows = 1000
+
+[db]
+# Port to use for the local database URL.
+port = 54322
+# Port used by db diff command to initialize the shadow database.
+shadow_port = 54320
+# The database major version to use. This has to be the same as your remote database's. Run `SHOW
+# server_version;` on the remote database to check.
+major_version = 15
+
+[db.pooler]
+enabled = false
+# Port to use for the local connection pooler.
+port = 54329
+# Specifies when a server connection can be reused by other clients.
+# Configure one of the supported pooler modes: `transaction`, `session`.
+pool_mode = "transaction"
+# How many server connections to allow per user/database pair.
+default_pool_size = 20
+# Maximum number of client connections allowed.
+max_client_conn = 100
+
+[realtime]
+enabled = true
+# Bind realtime via either IPv4 or IPv6. (default: IPv6)
+# ip_version = "IPv6"
+# The maximum length in bytes of HTTP request headers. (default: 4096)
+# max_header_length = 4096
+
+[studio]
+enabled = true
+# Port to use for Supabase Studio.
+port = 54323
+# External URL of the API server that frontend connects to.
+api_url = "http://127.0.0.1"
+# OpenAI API Key to use for Supabase AI in the Supabase Studio.
+openai_api_key = "env(OPENAI_API_KEY)"
+
+# Email testing server. Emails sent with the local dev setup are not actually sent - rather, they
+# are monitored, and you can view the emails that would have been sent from the web interface.
+[inbucket]
+enabled = true
+# Port to use for the email testing server web interface.
+port = 54324
+# Uncomment to expose additional ports for testing user applications that send emails.
+# smtp_port = 54325
+# pop3_port = 54326
+
+[storage]
+enabled = true
+# The maximum file size allowed (e.g. "5MB", "500KB").
+file_size_limit = "50MiB"
+
+[auth]
+enabled = true
+# The base URL of your website. Used as an allow-list for redirects and for constructing URLs used
+# in emails.
+site_url = "http://127.0.0.1:3000"
+# A list of *exact* URLs that auth providers are permitted to redirect to post authentication.
+additional_redirect_urls = ["https://127.0.0.1:3000"]
+# How long tokens are valid for, in seconds. Defaults to 3600 (1 hour), maximum 604,800 (1 week).
+jwt_expiry = 3600
+# If disabled, the refresh token will never expire.
+enable_refresh_token_rotation = true
+# Allows refresh tokens to be reused after expiry, up to the specified interval in seconds.
+# Requires enable_refresh_token_rotation = true.
+refresh_token_reuse_interval = 10
+# Allow/disallow new user signups to your project.
+enable_signup = true
+# Allow/disallow testing manual linking of accounts
+enable_manual_linking = false
+
+[auth.email]
+# Allow/disallow new user signups via email to your project.
+enable_signup = true
+# If enabled, a user will be required to confirm any email change on both the old, and new email
+# addresses. If disabled, only the new email is required to confirm.
+double_confirm_changes = true
+# If enabled, users need to confirm their email address before signing in.
+enable_confirmations = false
+
+# Uncomment to customize email template
+# [auth.email.template.invite]
+# subject = "You have been invited"
+# content_path = "./supabase/templates/invite.html"
+
+[auth.sms]
+# Allow/disallow new user signups via SMS to your project.
+enable_signup = true
+# If enabled, users need to confirm their phone number before signing in.
+enable_confirmations = false
+# Template for sending OTP to users
+template = "Your code is {{ .Code }} ."
+
+# Use pre-defined map of phone number to OTP for testing.
+[auth.sms.test_otp]
+# 4152127777 = "123456"
+
+# This hook runs before a token is issued and allows you to add additional claims based on the authentication method used.
+[auth.hook.custom_access_token]
+# enabled = true
+# uri = "pg-functions://<database>/<schema>/<hook_name>"
+
+
+# Configure one of the supported SMS providers: `twilio`, `twilio_verify`, `messagebird`, `textlocal`, `vonage`.
+[auth.sms.twilio]
+enabled = false
+account_sid = ""
+message_service_sid = ""
+# DO NOT commit your Twilio auth token to git. Use environment variable substitution instead:
+auth_token = "env(SUPABASE_AUTH_SMS_TWILIO_AUTH_TOKEN)"
+
+# Use an external OAuth provider. The full list of providers are: `apple`, `azure`, `bitbucket`,
+# `discord`, `facebook`, `github`, `gitlab`, `google`, `keycloak`, `linkedin_oidc`, `notion`, `twitch`,
+# `twitter`, `slack`, `spotify`, `workos`, `zoom`.
+[auth.external.apple]
+enabled = false
+client_id = ""
+# DO NOT commit your OAuth provider secret to git. Use environment variable substitution instead:
+secret = "env(SUPABASE_AUTH_EXTERNAL_APPLE_SECRET)"
+# Overrides the default auth redirectUrl.
+redirect_uri = ""
+# Overrides the default auth provider URL. Used to support self-hosted gitlab, single-tenant Azure,
+# or any other third-party OIDC providers.
+url = ""
+
+[analytics]
+enabled = false
+port = 54327
+vector_port = 54328
+# Configure one of the supported backends: `postgres`, `bigquery`.
+backend = "postgres"
+
+# Experimental features may be deprecated any time
+[experimental]
+# Configures Postgres storage engine to use OrioleDB (S3)
+orioledb_version = ""
+# Configures S3 bucket URL, eg. <bucket_name>.s3-<region>.amazonaws.com
+s3_host = "env(S3_HOST)"
+# Configures S3 bucket region, eg. us-east-1
+s3_region = "env(S3_REGION)"
+# Configures AWS_ACCESS_KEY_ID for S3 bucket
+s3_access_key = "env(S3_ACCESS_KEY)"
+# Configures AWS_SECRET_ACCESS_KEY for S3 bucket
+s3_secret_key = "env(S3_SECRET_KEY)"
diff --git a/examples/ai/aws_bedrock_image_search/supabase/seed.sql b/examples/ai/aws_bedrock_image_search/supabase/seed.sql
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/examples/ai/aws_bedrock_image_search/tests/__init__.py b/examples/ai/aws_bedrock_image_search/tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d