1. Start the docker compose file.

   docker compose up -d
   

2. Download the Ollama models. We'll use the all-minilm model for embeddings and the tinyllama model for reasoning.

   docker compose exec ollama ollama pull all-minilm
   docker compose exec ollama ollama pull tinyllama
   

Create a table, run a vectorizer, and perform semantic search

1. Connect to the database in your local developer environment The easiest way connect to the database is with the following command: docker compose exec -it db psql.

   Alternatively, you can connect to the database with the following connection string: postgres://postgres:postgres@localhost:5432/postgres.

2. Enable pgai on your database

   CREATE EXTENSION IF NOT EXISTS ai CASCADE;

3. Create a table with the data you want to embed from a huggingface dataset

   We'll create a table named wiki from a few rows of the english-language wikimedia/wikipedia dataset.

   First, we'll create the table:

   CREATE TABLE wiki (
       id      TEXT PRIMARY KEY,
       url     TEXT,
       title   TEXT,
       text    TEXT
   );

   Then, we'll load the data from the huggingface dataset:

   SELECT ai.load_dataset('wikimedia/wikipedia', '20231101.en', table_name=>'wiki', batch_size=>5, max_batches=>1, if_table_exists=>'append');

   Related documentation: load dataset from huggingface.

4. Create a vectorizer for wiki

   To enable semantic search on the wiki table, we need to create vector embeddings for the text column. We use a vectorizer to automatically create these embeddings and keep them in sync with the data in the wiki table.

   SELECT ai.create_vectorizer(
        'wiki'::regclass,
        destination => 'wiki_embeddings',
        embedding => ai.embedding_ollama('all-minilm', 384),
        chunking => ai.chunking_recursive_character_text_splitter('text')
   );

   Related documentation: vectorizer usage guide and vectorizer API reference.

5. Check the progress of the vectorizer embedding creation

   select * from ai.vectorizer_status;

   Click to see the output

   id	source_table	target_table	view	pending_items
   1	public.wiki	public.wiki_embeddings_store	public.wiki_embeddings	10000

   All the embeddings have been created when the pending_items column is 0. This may take a few minutes as the model is running locally and not on a GPU.

6. Search the embeddings

   We'll search the embeddings for the concept of "properties of light" even though these words are not in the text of the articles. This is possible because vector embeddings capture the semantic meaning of the text.

   Semantic search is a powerful feature in its own right, but it is also a key component of Retrieval Augmented Generation (RAG).

   SELECT title, chunk
   FROM wiki_embeddings 
   ORDER BY embedding <=> ai.ollama_embed('all-minilm', 'properties of light')
   LIMIT 1;

   Click to see the output

   title	chunk
   Albedo	Water reflects light very differently from typical terrestrial materials. The reflectivity of a water surface is calculated using the Fresnel equations....

   This query selects from the wiki_embeddings view, which is created by the vectorizer and joins the embeddings with the original data in the wiki table to give us the ability to search using the embeddings but still be able to access (or filter on) all the data in the original table (e.g. the title column).

   Note the ai.ollama_embed function is used to call the all-minilm model. This is part of pgai's model calling capabilities.