Skip to content

Latest commit

 

History

History
137 lines (107 loc) · 5.62 KB

vectorizer-quick-start.md

File metadata and controls

137 lines (107 loc) · 5.62 KB
Raw

Vectorizer quick start

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!
If you prefer working with the openai-api instead of self hosting models, you can jump over to the openai quick start.

Setup a local development environment

We use a docker-compose file to set up a development environment, it includes a:

  • Postgres 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 docker-compose.yml in a new directory:

    name: pgai 
services:
 db:
   image: timescale/timescaledb-ha:pg17
   environment:
     POSTGRES_PASSWORD: postgres
   ports:
     - "5432:5432"
   volumes:
     - ./data:/var/lib/postgresql/data
 vectorizer-worker:
   image: timescale/pgai-vectorizer-worker:v0.2.1
   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

  2. Start the services

     docker compose up -d

Download your embedding model

Before we start we need to tell ollama to download an embedding model so we can use it with pgai. For this example we will use the "nomic-embed-text" model. To download it into the container simply run:

docker compose exec ollama ollama pull nomic-embed-text

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. 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_ollama('nomic-embed-text', 768),
     chunking => ai.chunking_recursive_character_text_splitter('contents')
);

  6. 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

  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;

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 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.