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feat: add amazon bedrock vecs example. (#21963)
* feat: add amazon bedrock vecs example. * feat: add Amazon Bedrock guides. * feat: add blogpost. * chore: add video. * Apply suggestions from code review Co-authored-by: Oliver Rice <github@oliverrice.com> Co-authored-by: Greg Richardson <greg.nmr@gmail.com> * chore: update example. * chore: updates from review. --------- Co-authored-by: Oliver Rice <github@oliverrice.com> Co-authored-by: Greg Richardson <greg.nmr@gmail.com>
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@@ -1495,6 +1495,10 @@ export const ai = {
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name: 'Image search with OpenAI CLIP',
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url: '/guides/ai/examples/image-search-openai-clip',
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},
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{
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name: 'Semantic search with Amazon Titan',
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url: '/guides/ai/examples/semantic-image-search-amazon-titan',
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},
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{
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name: 'Building ChatGPT Plugins',
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url: '/guides/ai/examples/building-chatgpt-plugins',
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@@ -1525,6 +1529,10 @@ export const ai = {
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name: 'Roboflow',
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url: '/guides/ai/integrations/roboflow',
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},
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{
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name: 'Amazon Bedrock',
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url: '/guides/ai/integrations/amazon-bedrock',
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},
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],
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},
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],
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@@ -99,6 +99,12 @@ export const integrations = [
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'OpenAI is an AI research and deployment company. Supabase provides a simple way to use OpenAI in your applications.',
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href: '/guides/ai/examples/building-chatgpt-plugins',
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},
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{
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name: 'Amazon Bedrock',
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description:
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'A fully managed service that offers a choice of high-performing foundation models from leading AI companies.',
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href: '/guides/ai/amazon-bedrock',
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},
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{
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name: 'Hugging Face',
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description:
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@@ -0,0 +1,234 @@
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---
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id: 'semantic-image-search-python-amazon-titan'
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title: 'Semantic Image Search with Amazon Titan'
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description: 'Implement semantic image search with Amazon Titan and Supabase Vector in Python.'
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subtitle: 'Implement semantic image search with Amazon Titan and Supabase Vector in Python.'
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tocVideo: 'A3uND5sgiO0'
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---
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[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.
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[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.
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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).
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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).
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## Create a new Python project with Poetry
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[Poetry](https://python-poetry.org/) provides packaging and dependency management for Python. If you haven't already, install poetry via pip:
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```shell
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pip install poetry
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```
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Then initialize a new project:
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```shell
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poetry new aws_bedrock_image_search
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```
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## Spin up a Postgres Database with pgvector
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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.
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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.
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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:
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```txt
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postgresql://postgres.[PROJECT-REF]:[YOUR-PASSWORD]@aws-0-[REGION].pooler.supabase.com:5432/postgres
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```
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## Install the dependencies
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We will need to add the following dependencies to our project:
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- [`vecs`](https://github.com/supabase/vecs#vecs): Supabase Vector Python Client.
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- [`boto3`](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html): AWS SDK for Python.
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- [`matplotlib`](https://matplotlib.org/): for displaying our image result.
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```shell
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poetry add vecs boto3 matplotlib
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```
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## Import the necessary dependencies
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At the top of your main python script, import the dependencies and store your `DB URL` from above in a variable:
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```python
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import sys
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import boto3
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import vecs
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import json
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import base64
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from matplotlib import pyplot as plt
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from matplotlib import image as mpimg
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from typing import Optional
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DB_CONNECTION = "postgresql://postgres.[PROJECT-REF]:[YOUR-PASSWORD]@aws-0-[REGION].pooler.supabase.com:5432/postgres"
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```
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Next, get the [credentials to your AWS account](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html) and instantiate the `boto3` client:
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```python
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bedrock_client = boto3.client(
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'bedrock-runtime',
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region_name='us-west-2',
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# Credentials from your AWS account
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aws_access_key_id='<replace_your_own_credentials>',
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aws_secret_access_key='<replace_your_own_credentials>',
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aws_session_token='<replace_your_own_credentials>',
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)
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```
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## Create embeddings for your images
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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).
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To send images to the Amazon Bedrock API we need to need to encode them as `base64` strings. Create the following helper methods:
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```python
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def readFileAsBase64(file_path):
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"""Encode image as base64 string."""
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try:
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with open(file_path, "rb") as image_file:
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input_image = base64.b64encode(image_file.read()).decode("utf8")
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return input_image
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except:
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print("bad file name")
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sys.exit(0)
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def construct_bedrock_image_body(base64_string):
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"""Construct the request body.
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https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-mm.html
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"""
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return json.dumps(
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{
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"inputImage": base64_string,
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"embeddingConfig": {"outputEmbeddingLength": 1024},
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}
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)
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def get_embedding_from_titan_multimodal(body):
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"""Invoke the Amazon Titan Model via API request."""
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response = bedrock_client.invoke_model(
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body=body,
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modelId="amazon.titan-embed-image-v1",
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accept="application/json",
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contentType="application/json",
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)
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response_body = json.loads(response.get("body").read())
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print(response_body)
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return response_body["embedding"]
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def encode_image(file_path):
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"""Generate embedding for the image at file_path."""
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base64_string = readFileAsBase64(file_path)
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body = construct_bedrock_image_body(base64_string)
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emb = get_embedding_from_titan_multimodal(body)
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return emb
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```
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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:
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```python
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def seed():
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# create vector store client
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vx = vecs.create_client(DB_CONNECTION)
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# get or create a collection of vectors with 1024 dimensions
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images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
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# Generate image embeddings with Amazon Titan Model
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img_emb1 = encode_image('./images/one.jpg')
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img_emb2 = encode_image('./images/two.jpg')
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img_emb3 = encode_image('./images/three.jpg')
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img_emb4 = encode_image('./images/four.jpg')
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# add records to the *images* collection
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images.upsert(
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records=[
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(
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"one.jpg", # the vector's identifier
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img_emb1, # the vector. list or np.array
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{"type": "jpg"} # associated metadata
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), (
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"two.jpg",
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img_emb2,
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{"type": "jpg"}
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), (
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"three.jpg",
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img_emb3,
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{"type": "jpg"}
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), (
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"four.jpg",
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img_emb4,
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{"type": "jpg"}
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)
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]
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)
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print("Inserted images")
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# index the collection for fast search performance
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images.create_index()
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print("Created index")
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```
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Add this method as a script in your `pyproject.toml` file:
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```toml
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[tool.poetry.scripts]
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seed = "image_search.main:seed"
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search = "image_search.main:search"
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```
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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.
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## Perform an image search from a text query
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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:
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```python
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def search(query_term: Optional[str] = None):
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if query_term is None:
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query_term = sys.argv[1]
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# create vector store client
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vx = vecs.create_client(DB_CONNECTION)
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images = vx.get_or_create_collection(name="image_vectors", dimension=1024)
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# Encode text query
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text_emb = get_embedding_from_titan_multimodal(json.dumps(
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{
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"inputText": query_term,
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"embeddingConfig": {"outputEmbeddingLength": 1024},
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}
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))
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# query the collection filtering metadata for "type" = "jpg"
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results = images.query(
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data=text_emb, # required
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limit=1, # number of records to return
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filters={"type": {"$eq": "jpg"}}, # metadata filters
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)
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result = results[0]
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print(result)
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plt.title(result)
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image = mpimg.imread('./images/' + result)
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plt.imshow(image)
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plt.show()
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```
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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.
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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".
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## Conclusion
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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.
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@@ -0,0 +1,139 @@
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---
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id: 'ai-amazon-bedrock'
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title: 'Amazon Bedrock'
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description: 'Learn how to integrate Supabase with Amazon Bedrock, a fully managed service of high-performing foundation models.'
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sidebar_label: 'Amazon Bedrock'
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tocVideo: 'A3uND5sgiO0'
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---
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[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.
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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.
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## Create an Environment
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First, you need to set up your environment. You will need Python 3.7+ with the `vecs` and `boto3` libraries installed.
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You can install the necessary Python libraries using pip:
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```sh
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pip install vecs boto3
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```
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You'll also need:
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- [Credentials to your AWS account](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)
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- [A Postgres Database with the pgvector extension](hosting.md)
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## Create Embeddings
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Next, we will use Amazon’s Titan Embedding G1 - Text v1.2 model to create embeddings for a set of sentences.
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```python
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import boto3
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import vecs
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import json
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client = boto3.client(
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'bedrock-runtime',
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region_name='us-east-1',
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# Credentials from your AWS account
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aws_access_key_id='<replace_your_own_credentials>',
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aws_secret_access_key='<replace_your_own_credentials>',
|
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aws_session_token='<replace_your_own_credentials>',
|
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)
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dataset = [
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"The cat sat on the mat.",
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"The quick brown fox jumps over the lazy dog.",
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"Friends, Romans, countrymen, lend me your ears",
|
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"To be or not to be, that is the question.",
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]
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embeddings = []
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for sentence in dataset:
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# invoke the embeddings model for each sentence
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response = client.invoke_model(
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body= json.dumps({"inputText": sentence}),
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modelId= "amazon.titan-embed-text-v1",
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accept = "application/json",
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contentType = "application/json"
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||||
)
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# collect the embedding from the response
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response_body = json.loads(response["body"].read())
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# add the embedding to the embedding list
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embeddings.append((sentence, response_body.get("embedding"), {}))
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|
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```
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### Store the Embeddings with vecs
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||||
|
||||
Now that we have our embeddings, we can insert them into a PostgreSQL database using vecs.
|
||||
|
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```python
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import vecs
|
||||
|
||||
DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"
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||||
|
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# create vector store client
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vx = vecs.Client(DB_CONNECTION)
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# create a collection named 'sentences' with 1536 dimensional vectors
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# to match the default dimension of the Titan Embeddings G1 - Text model
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sentences = vx.get_or_create_collection(name="sentences", dimension=1536)
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# upsert the embeddings into the 'sentences' collection
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sentences.upsert(records=embeddings)
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|
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# create an index for the 'sentences' collection
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sentences.create_index()
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```
|
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|
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### Querying for Most Similar Sentences
|
||||
|
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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
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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)
|
||||
@@ -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)
|
||||
BIN
Binary file not shown.
|
After Width: | Height: | Size: 48 KiB |
@@ -0,0 +1,3 @@
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
@@ -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/
|
||||
@@ -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()
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 16 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 16 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 16 KiB |
+925
@@ -0,0 +1,925 @@
|
||||
# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "boto3"
|
||||
version = "1.34.59"
|
||||
description = "The AWS SDK for Python"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">= 3.8"
|
||||
files = [
|
||||
{file = "boto3-1.34.59-py3-none-any.whl", hash = "sha256:004e67b078be58d34469406f93cc8b95bc43becef4bbe44523a0b8e51f84c668"},
|
||||
{file = "boto3-1.34.59.tar.gz", hash = "sha256:162edf182e53c198137a28432a626dba103f787a8f5000ed4758b73ccd203fa0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
botocore = ">=1.34.59,<1.35.0"
|
||||
jmespath = ">=0.7.1,<2.0.0"
|
||||
s3transfer = ">=0.10.0,<0.11.0"
|
||||
|
||||
[package.extras]
|
||||
crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
|
||||
|
||||
[[package]]
|
||||
name = "botocore"
|
||||
version = "1.34.59"
|
||||
description = "Low-level, data-driven core of boto 3."
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">= 3.8"
|
||||
files = [
|
||||
{file = "botocore-1.34.59-py3-none-any.whl", hash = "sha256:4bc112dafb1679ab571117593f7656604726a3da0e5ae5bad00ea772fa40e75c"},
|
||||
{file = "botocore-1.34.59.tar.gz", hash = "sha256:24edb4d21d7c97dea0c6c4a80d36b3809b1443a30b0bd5e317d6c319dfac823f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
jmespath = ">=0.7.1,<2.0.0"
|
||||
python-dateutil = ">=2.1,<3.0.0"
|
||||
urllib3 = {version = ">=1.25.4,<2.1", markers = "python_version >= \"3.10\""}
|
||||
|
||||
[package.extras]
|
||||
crt = ["awscrt (==0.19.19)"]
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.0.7"
|
||||
description = "Python library for calculating contours of 2D quadrilateral grids"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "contourpy-1.0.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:95c3acddf921944f241b6773b767f1cbce71d03307270e2d769fd584d5d1092d"},
|
||||
{file = "contourpy-1.0.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:fc1464c97579da9f3ab16763c32e5c5d5bb5fa1ec7ce509a4ca6108b61b84fab"},
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||||
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||||
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||||
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||||
[[package]]
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
|
||||
[package.extras]
|
||||
crt = ["botocore[crt] (>=1.33.2,<2.0a.0)"]
|
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{file = "wrapt-1.16.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a452f9ca3e3267cd4d0fcf2edd0d035b1934ac2bd7e0e57ac91ad6b95c0c6389"},
|
||||
{file = "wrapt-1.16.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:43aa59eadec7890d9958748db829df269f0368521ba6dc68cc172d5d03ed8060"},
|
||||
{file = "wrapt-1.16.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:72554a23c78a8e7aa02abbd699d129eead8b147a23c56e08d08dfc29cfdddca1"},
|
||||
{file = "wrapt-1.16.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:d2efee35b4b0a347e0d99d28e884dfd82797852d62fcd7ebdeee26f3ceb72cf3"},
|
||||
{file = "wrapt-1.16.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:6dcfcffe73710be01d90cae08c3e548d90932d37b39ef83969ae135d36ef3956"},
|
||||
{file = "wrapt-1.16.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:eb6e651000a19c96f452c85132811d25e9264d836951022d6e81df2fff38337d"},
|
||||
{file = "wrapt-1.16.0-cp311-cp311-win32.whl", hash = "sha256:66027d667efe95cc4fa945af59f92c5a02c6f5bb6012bff9e60542c74c75c362"},
|
||||
{file = "wrapt-1.16.0-cp311-cp311-win_amd64.whl", hash = "sha256:aefbc4cb0a54f91af643660a0a150ce2c090d3652cf4052a5397fb2de549cd89"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:5eb404d89131ec9b4f748fa5cfb5346802e5ee8836f57d516576e61f304f3b7b"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9090c9e676d5236a6948330e83cb89969f433b1943a558968f659ead07cb3b36"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:94265b00870aa407bd0cbcfd536f17ecde43b94fb8d228560a1e9d3041462d73"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f2058f813d4f2b5e3a9eb2eb3faf8f1d99b81c3e51aeda4b168406443e8ba809"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:98b5e1f498a8ca1858a1cdbffb023bfd954da4e3fa2c0cb5853d40014557248b"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:14d7dc606219cdd7405133c713f2c218d4252f2a469003f8c46bb92d5d095d81"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:49aac49dc4782cb04f58986e81ea0b4768e4ff197b57324dcbd7699c5dfb40b9"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:418abb18146475c310d7a6dc71143d6f7adec5b004ac9ce08dc7a34e2babdc5c"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-win32.whl", hash = "sha256:685f568fa5e627e93f3b52fda002c7ed2fa1800b50ce51f6ed1d572d8ab3e7fc"},
|
||||
{file = "wrapt-1.16.0-cp312-cp312-win_amd64.whl", hash = "sha256:dcdba5c86e368442528f7060039eda390cc4091bfd1dca41e8046af7c910dda8"},
|
||||
{file = "wrapt-1.16.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:d462f28826f4657968ae51d2181a074dfe03c200d6131690b7d65d55b0f360f8"},
|
||||
{file = "wrapt-1.16.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a33a747400b94b6d6b8a165e4480264a64a78c8a4c734b62136062e9a248dd39"},
|
||||
{file = "wrapt-1.16.0-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b3646eefa23daeba62643a58aac816945cadc0afaf21800a1421eeba5f6cfb9c"},
|
||||
{file = "wrapt-1.16.0-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ebf019be5c09d400cf7b024aa52b1f3aeebeff51550d007e92c3c1c4afc2a40"},
|
||||
{file = "wrapt-1.16.0-cp36-cp36m-musllinux_1_1_aarch64.whl", hash = "sha256:0d2691979e93d06a95a26257adb7bfd0c93818e89b1406f5a28f36e0d8c1e1fc"},
|
||||
{file = "wrapt-1.16.0-cp36-cp36m-musllinux_1_1_i686.whl", hash = "sha256:1acd723ee2a8826f3d53910255643e33673e1d11db84ce5880675954183ec47e"},
|
||||
{file = "wrapt-1.16.0-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:bc57efac2da352a51cc4658878a68d2b1b67dbe9d33c36cb826ca449d80a8465"},
|
||||
{file = "wrapt-1.16.0-cp36-cp36m-win32.whl", hash = "sha256:da4813f751142436b075ed7aa012a8778aa43a99f7b36afe9b742d3ed8bdc95e"},
|
||||
{file = "wrapt-1.16.0-cp36-cp36m-win_amd64.whl", hash = "sha256:6f6eac2360f2d543cc875a0e5efd413b6cbd483cb3ad7ebf888884a6e0d2e966"},
|
||||
{file = "wrapt-1.16.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:a0ea261ce52b5952bf669684a251a66df239ec6d441ccb59ec7afa882265d593"},
|
||||
{file = "wrapt-1.16.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7bd2d7ff69a2cac767fbf7a2b206add2e9a210e57947dd7ce03e25d03d2de292"},
|
||||
{file = "wrapt-1.16.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9159485323798c8dc530a224bd3ffcf76659319ccc7bbd52e01e73bd0241a0c5"},
|
||||
{file = "wrapt-1.16.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a86373cf37cd7764f2201b76496aba58a52e76dedfaa698ef9e9688bfd9e41cf"},
|
||||
{file = "wrapt-1.16.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:73870c364c11f03ed072dda68ff7aea6d2a3a5c3fe250d917a429c7432e15228"},
|
||||
{file = "wrapt-1.16.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:b935ae30c6e7400022b50f8d359c03ed233d45b725cfdd299462f41ee5ffba6f"},
|
||||
{file = "wrapt-1.16.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:db98ad84a55eb09b3c32a96c576476777e87c520a34e2519d3e59c44710c002c"},
|
||||
{file = "wrapt-1.16.0-cp37-cp37m-win32.whl", hash = "sha256:9153ed35fc5e4fa3b2fe97bddaa7cbec0ed22412b85bcdaf54aeba92ea37428c"},
|
||||
{file = "wrapt-1.16.0-cp37-cp37m-win_amd64.whl", hash = "sha256:66dfbaa7cfa3eb707bbfcd46dab2bc6207b005cbc9caa2199bcbc81d95071a00"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1dd50a2696ff89f57bd8847647a1c363b687d3d796dc30d4dd4a9d1689a706f0"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:44a2754372e32ab315734c6c73b24351d06e77ffff6ae27d2ecf14cf3d229202"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8e9723528b9f787dc59168369e42ae1c3b0d3fadb2f1a71de14531d321ee05b0"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dbed418ba5c3dce92619656802cc5355cb679e58d0d89b50f116e4a9d5a9603e"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:941988b89b4fd6b41c3f0bfb20e92bd23746579736b7343283297c4c8cbae68f"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:6a42cd0cfa8ffc1915aef79cb4284f6383d8a3e9dcca70c445dcfdd639d51267"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1ca9b6085e4f866bd584fb135a041bfc32cab916e69f714a7d1d397f8c4891ca"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:d5e49454f19ef621089e204f862388d29e6e8d8b162efce05208913dde5b9ad6"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-win32.whl", hash = "sha256:c31f72b1b6624c9d863fc095da460802f43a7c6868c5dda140f51da24fd47d7b"},
|
||||
{file = "wrapt-1.16.0-cp38-cp38-win_amd64.whl", hash = "sha256:490b0ee15c1a55be9c1bd8609b8cecd60e325f0575fc98f50058eae366e01f41"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9b201ae332c3637a42f02d1045e1d0cccfdc41f1f2f801dafbaa7e9b4797bfc2"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:2076fad65c6736184e77d7d4729b63a6d1ae0b70da4868adeec40989858eb3fb"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c5cd603b575ebceca7da5a3a251e69561bec509e0b46e4993e1cac402b7247b8"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b47cfad9e9bbbed2339081f4e346c93ecd7ab504299403320bf85f7f85c7d46c"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f8212564d49c50eb4565e502814f694e240c55551a5f1bc841d4fcaabb0a9b8a"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:5f15814a33e42b04e3de432e573aa557f9f0f56458745c2074952f564c50e664"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:db2e408d983b0e61e238cf579c09ef7020560441906ca990fe8412153e3b291f"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:edfad1d29c73f9b863ebe7082ae9321374ccb10879eeabc84ba3b69f2579d537"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-win32.whl", hash = "sha256:ed867c42c268f876097248e05b6117a65bcd1e63b779e916fe2e33cd6fd0d3c3"},
|
||||
{file = "wrapt-1.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:eb1b046be06b0fce7249f1d025cd359b4b80fc1c3e24ad9eca33e0dcdb2e4a35"},
|
||||
{file = "wrapt-1.16.0-py3-none-any.whl", hash = "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1"},
|
||||
{file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
|
||||
]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "52193cbe33f6ea90a7284b67ba45e95408eb14efd6f84d50abeafc11fe5889fa"
|
||||
@@ -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"
|
||||
@@ -0,0 +1,4 @@
|
||||
# Supabase
|
||||
.branches
|
||||
.temp
|
||||
.env
|
||||
@@ -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)"
|
||||
Whitespace-only changes.
Whitespace-only changes.
Reference in new issue
Block a user