---
title: Vectorizer quick start | Tiger Data Docs
description: This page shows you how to create an Ollama-based vectorizer in a self-hosted Postgres instance. We also show how simple it is to do semantic search on the automatically embedded data!
---

# Vectorizer quick start

This page shows you how to create an Ollama-based vectorizer in a self-hosted PostgreSQL instance. We also show how simple it is to do semantic search on the automatically embedded data! If you prefer working with the OpenAI API instead of self-hosting models, you can jump over to the [openai quick start](/reference/pgai/vectorizer/quick-start-openai/index.md).

## Setup a local development environment

To set up a development environment, use a docker compose file that includes a:

- PostgreSQL deployment image with the TimescaleDB and pgai extensions installed
- pgai vectorizer worker image
- ollama image to host embedding and large language models

On your local machine:

1. **Create the Docker configuration for a local developer environment**

   Create the following `compose.yaml` in a new directory:

   ```
   name: pgai
   services:
    db:
      image: timescale/timescaledb-ha:pg17
      environment:
        POSTGRES_PASSWORD: postgres
      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
        OLLAMA_HOST: http://ollama:11434
      command: [ "--poll-interval", "5s" ]
    ollama:
      image: ollama/ollama
   volumes:
    data:
   ```

2. **Start the database**

   Terminal window

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

3. **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
   ```

4. **Start the vectorizer worker and ollama**

   Terminal window

   ```
    docker compose up -d
   ```

## Create and run a vectorizer

Now we can create and run a vectorizer. A vectorizer is a pgai concept, it processes data in a table and automatically creates embeddings for it.

1. **Connect 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_ollama('nomic-embed-text', 768),
        destination => ai.destination_table('blog_contents_embeddings')
   );
   ```

6. **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
   ```

7. **See the embeddings in action**

   Run the following search query to retrieve the embeddings:

   ```
   SELECT
       chunk,
       embedding <=>  ai.ollama_embed('nomic-embed-text', 'good food', host => 'http://ollama:11434') as distance
   FROM blog_contents_embeddings
   ORDER BY distance
   LIMIT 10;
   ```

The results look like:

| chunk                                                                       | distance           |
| --------------------------------------------------------------------------- | ------------------ |
| Maintaining a healthy diet can be challenging for busy professionals…       | 0.5030059372474176 |
| PostgreSQL is a powerful, open source object-relational database system…    | 0.5868937074856113 |
| PostgreSQLBlogging can be a great way to share your thoughts and expertise… | 0.5928412342761966 |
| As we look towards the future, artificial intelligence continues to evolve… | 0.6161160890734267 |
| Cloud computing has revolutionized the way businesses operate…              | 0.6664001441252841 |

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).