---
title: Vectorizer quick start with OpenAI | Tiger Data Docs
description: This page shows you how to create a vectorizer in a self-hosted Postgres instance, then use
---

# Vectorizer quick start with OpenAI

This page shows you how to create a vectorizer in a self-hosted PostgreSQL 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 semantic search 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:

- PostgreSQL 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**

   Copy the following configuration into a file named `compose.yaml`:

   ```
   name: pgai
   services:
     db:
       image: timescale/timescaledb-ha:pg16
       environment:
         POSTGRES_PASSWORD: postgres
         OPENAI_API_KEY: <your-api-key>
       ports:
         - "5432:5432"
       volumes:
         - data:/home/postgres/pgdata/data
     vectorizer-worker:
       image: timescale/pgai-vectorizer-worker:latest
       environment:
         PGAI_VECTORIZER_WORKER_DB_URL: postgres://postgres:postgres@db:5432/postgres
         OPENAI_API_KEY: <your-api-key>
   volumes:
     data:
   ```

2. **Tune the developer image for your AI provider**

   Replace `OPENAI_API_KEY` with a key from your AI provider.

3. **Start the database**

   Terminal window

   ```
    docker compose up -d db
   ```

4. **Install pgai in your database**

   Terminal window

   ```
   docker compose run --rm --entrypoint "python -m pgai install -d postgres://postgres:postgres@db:5432/postgres" vectorizer-worker
   ```

## 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 compose exec -it db psql`
   - 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,
      loading => ai.loading_column('contents'),
      embedding => ai.embedding_openai('text-embedding-3-small', 768),
      destination => ai.destination_table('blog_contents_embeddings')
   );
   ```

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:

   Terminal window

   ```
   docker compose up -d vectorizer-worker
   ```

7. **Check the vectorizer worker logs**

   Terminal window

   ```
   docker compose logs -f vectorizer-worker
   ```

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

   Terminal window

   ```
    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 PostgreSQL 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](https://discord.gg/KRdHVXAmkp).