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Vectorizer quick start

This page shows you how to create a vectorizer in a self-hosted Postgres instance, then use the pgai vectorizer worker to create embeddings from data in your database. To finish off we show how simple it is to do a semantic search query on the embedded data in one query!

Setup a local developer environment

The local developer environment is a docker configuration you use to develop and test pgai, vectorizers and vectorizer worker locally. It includes a:

  • Postgres deployment image with the TimescaleDB and pgai extensions installed
  • pgai vectorizer worker image

On your local machine:

  1. Create the Docker configuration for a local developer environment

    Add the following docker configuration to <timescale-folder>/docker-compose.yml:

    name: pgai
services:
  db:
    image: timescale/timescaledb-ha:pg16
    environment:
      POSTGRES_PASSWORD: postgres
      OPENAI_API_KEY: <your-api-key>
    ports:
      - "5432:5432"
    volumes:
      - ./data:/var/lib/postgresql/data
  vectorizer-worker:
    image: timescale/pgai-vectorizer-worker:0.1.0
    environment:
      PGAI_VECTORIZER_WORKER_DB_URL: postgres://postgres:postgres@db:5432/postgres
      OPENAI_API_KEY: <your-api-key>

  2. Tune the developer image for your AI provider

    Replace the instances of OPENAI_API_KEY with a key from your AI provider.

  3. Start the database

     docker compose up -d db

Create and run a vectorizer

To create and run a vectorizer, then query the auto-generated embeddings created by the vectorizer:

  1. Connection to the database in your local developer environment

    • Docker: docker exec -it pgai-db-1 psql -U postgres
    • psql: psql postgres://postgres:postgres@localhost:5432/postgres

  2. Enable pgai on your database

    CREATE EXTENSION IF NOT EXISTS ai CASCADE;

  3. Create the blog table with the following schema

    CREATE TABLE blog (
    id SERIAL PRIMARY KEY,
    title TEXT,
    authors TEXT,
    contents TEXT,
    metadata JSONB
);

  4. Insert some data into blog

    INSERT INTO blog (title, authors, contents, metadata)
VALUES
('Getting Started with PostgreSQL', 'John Doe', 'PostgreSQL is a powerful, open source object-relational database system...', '{"tags": ["database", "postgresql", "beginner"], "read_time": 5, "published_date": "2024-03-15"}'),

('10 Tips for Effective Blogging', 'Jane Smith, Mike Johnson', 'Blogging can be a great way to share your thoughts and expertise...', '{"tags": ["blogging", "writing", "tips"], "read_time": 8, "published_date": "2024-03-20"}'),

('The Future of Artificial Intelligence', 'Dr. Alan Turing', 'As we look towards the future, artificial intelligence continues to evolve...', '{"tags": ["AI", "technology", "future"], "read_time": 12, "published_date": "2024-04-01"}'),

('Healthy Eating Habits for Busy Professionals', 'Samantha Lee', 'Maintaining a healthy diet can be challenging for busy professionals...', '{"tags": ["health", "nutrition", "lifestyle"], "read_time": 6, "published_date": "2024-04-05"}'),

('Introduction to Cloud Computing', 'Chris Anderson', 'Cloud computing has revolutionized the way businesses operate...', '{"tags": ["cloud", "technology", "business"], "read_time": 10, "published_date": "2024-04-10"}');

  5. Create a vectorizer for blog

    SELECT ai.create_vectorizer(
   'blog'::regclass,
   destination => 'blog_contents_embeddings',
   embedding => ai.embedding_openai('text-embedding-3-small', 768),
   chunking => ai.chunking_recursive_character_text_splitter('contents')
);

  6. Run the vectorizer worker

    When you install pgai on Timescale Cloud, vectorizers are run automatically using TimescaleDB scheduling. For self-hosted, you run a pgai vectorizer worker so the vectorizer can process the data in blog.

    In a new terminal, start the vectorizer worker:

    docker compose up -d vectorizer-worker

  7. Check the vectorizer worker logs

    docker compose logs -f vectorizer-worker

    You see the vectorizer worker pick up the table and process it.

     vectorizer-worker-1  | 2024-10-23 12:56:36 [info     ] running vectorizer             vectorizer_id=1

  8. See the embeddings in action

    Run the following search query to retrieve the embeddings:

    SELECT
    chunk,
    embedding <=>  ai.openai_embed('text-embedding-3-small', 'good food', dimensions=>768) as distance
FROM blog_contents_embeddings
ORDER BY distance;

The results look like:

chunk distance
Maintaining a healthy diet can be challenging for busy professionals... 0.6720892190933228
Blogging can be a great way to share your thoughts and expertise... 0.7744888961315155
PostgreSQL is a powerful, open source object-relational database system... 0.815629243850708
Cloud computing has revolutionized the way businesses operate... 0.8913049921393394
As we look towards the future, artificial intelligence continues to evolve... 0.9215681301612775

That's it, you're done. You now have a table in Postgres that pgai vectorizer automatically creates and syncs embeddings for. You can use this vectorizer for semantic search, RAG or any other AI app you can think of! If you have any questions, reach out to us on Discord.