AI Image Search with Amazon Bedrock and Supabase Vector in Python
Supabase Supabase
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 Published On Mar 26, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models. In this video 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 Python client.

- Read the blogpost: https://supabase.com/blog/semantic-im...
- Find the code: https://github.com/supabase/supabase/...

Presented by Thor Schaeff (@thorwebdev https://go.thor.bio/x)

CHAPTERS:
00:00 Intro to Amazon Bedrock
00:23 Amazon Titan use cases
00:58 Set up Amazon Bedrock and vecs in Python
01:29 Create a Postgres database with pgvector
02:04 Install the Python dependencies with Poetry
03:03 Get the Postgres connection string from Supabase
04:13 Set up boto3 Amazon Bedrock Python client
05:22 Generate image embeddings and store them to Supabase
06:42 Turn image files into base64 string in Python
07:17 Create JSON body for Amazon Bedrock Titan Model API
07:42 Invoke Amazon Titan Multimodal model
08:29 Run the embedding seed migration script
08:58 Upsert embeddings into pgvector with the vets client
09:06 Create an index for pgvector
09:12 Inspect the pgvector embeddings in the Supabase Dashboard
09:39 Semantic image search in Python
10:52 Demo of Semantic Image Search

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ā–¶ Ā Ā Ā ā€¢Ā BuildĀ AIĀ AppsĀ FREEĀ Ā -Ā AĀ FreeĀ LocalĀ AI...Ā Ā 
ā–¶ Ā Ā Ā ā€¢Ā SupabaseĀ Vector:Ā TheĀ PostgresĀ VectorĀ ...Ā Ā 

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ABOUT SUPABASE:
Supabase is the open source Firebase alternative. Supabase provides a full Postgres database for every project with pgvector, backups, realtime, and more. Add and manage email and password, passwordless, OAuth, and mobile logins to your project through a suite of identity providers and APIs.

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