Hey everyone! Today, we're diving deep into the world of Supabase and Postgres, and how you can leverage them to create a powerful vector database. If you're new to this, don't worry! We'll break it down step by step, making it super easy to understand. Let's get started!

What is a Vector Database?

Before we jump into Supabase and Postgres, let's quickly cover what a vector database actually is. In essence, a vector database is a type of database that stores data as vectors, which are numerical representations of data points. Think of it like converting images, text, or audio into a series of numbers that a computer can easily understand and compare. The magic happens when you want to find similar items. Instead of relying on exact matches, vector databases use the concept of similarity search. They find vectors that are close to each other in a high-dimensional space, meaning that the corresponding data points are similar in meaning or content. This is incredibly useful for applications like:

• Recommendation Systems: Suggesting products, movies, or articles that a user might like based on their past behavior.
• Semantic Search: Finding documents or web pages that are relevant to a query, even if they don't contain the exact keywords.
• Image Recognition: Identifying objects or scenes in images by comparing their visual features.
• Fraud Detection: Spotting unusual patterns in financial transactions that might indicate fraudulent activity.

Vector databases really shine when dealing with unstructured data. Traditional databases struggle with comparing and searching through large volumes of text, images, or audio. Vector databases, on the other hand, are designed to handle this type of data efficiently. They use specialized indexing techniques and distance metrics to speed up the similarity search process. For example, techniques like approximate nearest neighbor (ANN) search allow you to quickly find the most similar vectors, even in datasets with billions of entries. Some popular vector database solutions include Pinecone, Milvus, and Weaviate. However, what if I told you that you can build your own vector database using tools you might already be familiar with, like Postgres and Supabase? Keep reading to find out how!

Why Supabase and Postgres?

So, why should you consider using Supabase and Postgres for your vector database needs? Well, there are several compelling reasons. First off, Postgres is a rock-solid, open-source relational database that has been around for decades. It's known for its reliability, scalability, and extensive feature set. You might already be using Postgres in your existing projects, which means you can avoid the complexity of introducing a new database system. Supabase, on the other hand, is like a supercharged version of Postgres. It provides a suite of tools and services that make it incredibly easy to build and deploy real-time applications. Think of it as Firebase, but built on top of Postgres. With Supabase, you get features like:

• Authentication: Easily manage user accounts and authentication with built-in support for social logins and password recovery.
• Realtime Subscriptions: Subscribe to database changes and receive updates in real-time, perfect for building collaborative applications.
• Auto-generated APIs: Automatically generate RESTful APIs for your database tables, making it easy to access your data from your frontend.
• Storage: Store and serve files directly from Supabase, with support for access control and image transformations.
• Edge Functions: Deploy serverless functions that run close to your users, reducing latency and improving performance.

But the real magic happens when you combine Postgres with the pgvector extension. This extension adds support for storing and querying vectors directly within your Postgres database. It provides specialized data types and indexing techniques that are optimized for vector similarity search. This means you can leverage the power of Postgres and Supabase to build a fully-fledged vector database without having to rely on external services. Plus, you get the added benefits of using a familiar database system, such as ACID transactions, data integrity, and a rich ecosystem of tools and libraries. This approach also gives you more control over your data and infrastructure. You're not locked into a specific vendor or platform, and you can easily migrate your data if needed. Overall, Supabase and Postgres provide a flexible, powerful, and cost-effective solution for building vector databases. They're a great choice for developers who want to leverage the power of vector search without the complexity of managing specialized database systems.

Setting up Postgres with the pgvector Extension

Okay, let's get our hands dirty and set up Postgres with the pgvector extension. This is where the fun begins! First, you'll need to have Postgres installed on your system. If you don't already have it, you can download it from the official Postgres website or use a package manager like apt or brew. Once you have Postgres installed, you'll need to install the pgvector extension. The easiest way to do this is to use the CREATE EXTENSION command in a Postgres client like psql. Here's how:

1. Connect to your Postgres database:

   psql -U your_username -d your_database
   

   Replace your_username and your_database with your actual Postgres username and database name.

2. Create the pgvector extension:

   CREATE EXTENSION vector;
   

   This will install the pgvector extension in your database. You only need to do this once per database.

3. Verify the installation:

   \dx
   

   This will list all the installed extensions in your database. You should see vector in the list.

That's it! You've successfully installed the pgvector extension. Now you can start creating tables with vector columns and performing similarity searches.

Using Supabase

If you're using Supabase, the process is even easier. Supabase comes with the pgvector extension pre-installed, so you don't need to do anything extra. Just create a new Supabase project, connect to your database, and you're good to go! To connect to your Supabase database, you can use the Supabase client libraries or a standard Postgres client like psql. Supabase provides a connection string in the Supabase dashboard that you can use to connect to your database. Once you're connected, you can start creating tables with vector columns and performing similarity searches, just like you would with a regular Postgres database. Supabase also provides a user-friendly interface for managing your database, including a SQL editor, a table editor, and a schema visualizer. This makes it incredibly easy to work with your vector database and build your applications.

Creating Tables with Vector Columns

Now that we have the pgvector extension installed, let's create a table with a vector column. This is where we'll store our vector embeddings. Let's say we want to build a product recommendation system. We can create a table called products with the following columns:

• id: The unique identifier for the product.
• name: The name of the product.
• description: A short description of the product.
• embedding: A vector embedding of the product's description.

Here's the SQL code to create the table:

CREATE TABLE products (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name TEXT,
    description TEXT,
    embedding VECTOR(1536)
);

Let's break down this code:

• CREATE TABLE products: This creates a new table called products.
• id UUID PRIMARY KEY DEFAULT gen_random_uuid(): This creates a column called id that is a UUID (Universally Unique Identifier) and is the primary key for the table. The DEFAULT gen_random_uuid() part automatically generates a unique ID for each new row.
• name TEXT: This creates a column called name that stores the name of the product as text.
• description TEXT: This creates a column called description that stores the description of the product as text.
• embedding VECTOR(1536): This creates a column called embedding that stores the vector embedding of the product's description. The VECTOR(1536) part specifies that the vector has 1536 dimensions. This is the size of the vectors generated by OpenAI's text-embedding-ada-002 embedding model.

Important: The VECTOR(1536) part is crucial. It tells Postgres that this column will store vectors with 1536 dimensions. You'll need to adjust this number based on the size of your vector embeddings. If you're using a different embedding model, make sure to use the correct dimension size.

Inserting Data and Generating Embeddings

Now that we have our products table, let's insert some data and generate embeddings for the product descriptions. This is where we'll use an embedding model to convert our text data into vectors. There are many different embedding models available, such as OpenAI's text-embedding-ada-002, Sentence Transformers, and GloVe. For this example, we'll use OpenAI's text-embedding-ada-002 model, which is a popular choice for its high quality and ease of use. To use the OpenAI API, you'll need to have an OpenAI API key. You can get one by signing up for an OpenAI account and creating a new API key. Once you have your API key, you can use it to generate embeddings for your product descriptions. Here's how you can do it in Python:

import openai
import os

openai.api_key = os.environ.get("OPENAI_API_KEY")

def get_embedding(text, model="text-embedding-ada-002"):
   text = text.replace("\n", " ")
   return openai.Embedding.create(input = [text], model=model)['data'][0]['embedding']

product_descriptions = [
    "A comfortable and stylish t-shirt made from 100% cotton.",
    "A durable and waterproof backpack for hiking and camping.",
    "A sleek and modern laptop with a powerful processor and long battery life."
]

embeddings = [get_embedding(description) for description in product_descriptions]

print(embeddings)

This code does the following:

1. Imports the necessary libraries: openai for interacting with the OpenAI API and os for accessing environment variables.
2. Sets the OpenAI API key: This retrieves your OpenAI API key from an environment variable called OPENAI_API_KEY.
3. Defines a function called get_embedding: This function takes a text string as input and returns the vector embedding of the text using the OpenAI text-embedding-ada-002 model.
4. Defines a list of product descriptions: This is a list of text descriptions for our products.
5. Generates embeddings for the product descriptions: This iterates over the product_descriptions list and calls the get_embedding function for each description to generate its vector embedding. The resulting embeddings are stored in a list called embeddings.
6. Prints the embeddings: This prints the generated embeddings to the console.

Now that we have our embeddings, we can insert them into our products table. Here's the SQL code to do that:

INSERT INTO products (name, description, embedding) VALUES
('T-Shirt', 'A comfortable and stylish t-shirt made from 100% cotton.', '[embedding_1]'),
('Backpack', 'A durable and waterproof backpack for hiking and camping.', '[embedding_2]'),
('Laptop', 'A sleek and modern laptop with a powerful processor and long battery life.', '[embedding_3]');

Replace [embedding_1], [embedding_2], and [embedding_3] with the actual vector embeddings that you generated using the Python code above. You'll need to format the embeddings as a comma-separated list of numbers enclosed in square brackets. For example:

[0.123, 0.456, 0.789, ...]

Important: Make sure that the number of dimensions in your embeddings matches the VECTOR dimension specified when you created the table (1536 in this case). If the dimensions don't match, you'll get an error.

Performing Similarity Searches

Now for the grand finale: performing similarity searches! This is where we'll use the pgvector extension to find products that are similar to a given query. Let's say we want to find products that are similar to the query "a good shirt". First, we need to generate an embedding for the query using the same embedding model that we used for the product descriptions. Here's how you can do it in Python:

query = "a good shirt"
query_embedding = get_embedding(query)
print(query_embedding)

This code generates an embedding for the query "a good shirt" and prints it to the console. Now, we can use this embedding to perform a similarity search in our products table. Here's the SQL code to do that:

SELECT id, name, description, 1 - (embedding <=> '[query_embedding]') AS similarity
FROM products
ORDER BY similarity DESC
LIMIT 5;

Let's break down this code:

• SELECT id, name, description, 1 - (embedding <=> '[query_embedding]') AS similarity: This selects the id, name, and description columns from the products table, as well as a calculated column called similarity. The 1 - (embedding <=> '[query_embedding]') part calculates the cosine distance between the product embedding and the query embedding. The <=> operator is a special operator provided by the pgvector extension that calculates the cosine distance between two vectors. We subtract the cosine distance from 1 to get a similarity score between 0 and 1, where 1 means the vectors are identical and 0 means they are completely dissimilar.
• FROM products: This specifies that we're querying the products table.
• ORDER BY similarity DESC: This orders the results by the similarity column in descending order, so the most similar products are returned first.
• LIMIT 5: This limits the results to the top 5 most similar products.

Replace [query_embedding] with the actual vector embedding that you generated for the query "a good shirt". You'll need to format the embedding as a comma-separated list of numbers enclosed in square brackets, just like we did when inserting the product embeddings. When you run this query, you'll get a list of the top 5 most similar products to the query "a good shirt", along with their similarity scores. The higher the similarity score, the more similar the product is to the query. You can use these results to build a product recommendation system that suggests products to users based on their search queries. Congratulations! You've successfully built a vector database using Supabase and Postgres!

Indexing for Speed

One thing to keep in mind is that similarity searches can be slow, especially on large datasets. To speed up the searches, you can create an index on the embedding column. The pgvector extension provides several indexing options, such as IVF (Inverted File Index) and HNSW (Hierarchical Navigable Small World). The best indexing option depends on your specific use case and dataset. For most cases, HNSW is a good choice. Here's how you can create an HNSW index on the embedding column:

CREATE INDEX ON products
USING hnsw (embedding vector_cosine_ops);

This will create an HNSW index on the embedding column using the cosine distance operator (vector_cosine_ops). This index will significantly speed up similarity searches, especially on large datasets. Important: Creating an index can take a while, especially on large tables. Be patient and let the index build completely before running similarity searches.

Conclusion

Alright guys, that's it! You've learned how to build a vector database using Supabase and Postgres with the pgvector extension. You've learned how to create tables with vector columns, insert data and generate embeddings, perform similarity searches, and create indexes for speed. With this knowledge, you can build a wide variety of applications that leverage the power of vector search, such as recommendation systems, semantic search engines, and image recognition systems. So go forth and build amazing things! Remember to experiment with different embedding models, indexing options, and similarity metrics to find the best solution for your specific use case. And don't be afraid to ask for help if you get stuck. The Supabase and Postgres communities are full of friendly and helpful people who are always willing to lend a hand. Happy coding!