Semantic tracking with PGAI

semantic_tracking.sql

CREATE EXTENSION IF NOT EXISTS ai CASCADE;
DROP TABLE IF EXISTS vecs CASCADE;
DROP TABLE IF EXISTS interests CASCADE;
DROP TABLE IF EXISTS notifications CASCADE;

-- Create or replace the function to embed content
CREATE OR REPLACE FUNCTION embed(content TEXT) RETURNS VECTOR AS $$
DECLARE
    vectorized_content VECTOR;
BEGIN
    vectorized_content := openai_embed(
        'text-embedding-ada-002',
        content
    )::VECTOR;
    RETURN vectorized_content;
END;
$$ IMMUTABLE LANGUAGE plpgsql;

CREATE TABLE vecs (
  content TEXT PRIMARY KEY,
  embeds VECTOR GENERATED ALWAYS AS (embed(content)) STORED
);

CREATE TABLE interests (
  content TEXT PRIMARY KEY,
  embeds VECTOR GENERATED ALWAYS AS (embed(content)) STORED
);


CREATE TABLE notifications (
  id SERIAL PRIMARY KEY,
  created_at TIMESTAMP DEFAULT clock_timestamp(),
  content TEXT REFERENCES vecs(content),
  summary JSONB
);

-- fetch all interests in vectors and calculate the similarity
-- insert notifications in the table in case find cosine similarity < 0.2
CREATE OR REPLACE FUNCTION check_interests_similarity() RETURNS TRIGGER AS $$
DECLARE
  interest RECORD;
  similarity_percent NUMERIC;
  distance FLOAT;
  summary jsonb := '{}'::jsonb;
BEGIN
  for interest in select * from interests
  loop
    distance := NEW.embeds <=> interest.embeds;
    if (distance < 0.2) THEN
      similarity_percent := ((1.0 - distance) * 100)::numeric(10,2);
      summary := summary || jsonb_build_object(
        interest.content, similarity_percent
      );
    END IF;

  end loop;

  -- check if summary has any elements and persist it in the notifications table
 IF (summary != '{}'::jsonb) THEN
      insert into notifications (content, summary) values
      (NEW.content, summary);
  end if;
  RETURN NEW;
END;
$$ LANGUAGE plpgsql;


CREATE TRIGGER check_interests_similarity_trigger
AFTER INSERT OR UPDATE ON vecs
FOR EACH ROW EXECUTE FUNCTION check_interests_similarity();


INSERT INTO interests (content) VALUES
  ('Satisfied customer'),
  ('Unsatisfied customer'),
  ('Product return');

insert into vecs (content) VALUES
('The bike looks good but I want to return it and get my money back'),
('Works perfectly for my running days'),
('This shoes are terrible, pay me back now!'),
('Shoes dont fit so I want to return it'),
('The bike is very good for my commuting days');

table notifications;

I had a lot of trouble understanding the data model at first. If you named the vecs table customer_feedback and the interests table either category or sentiment it would help a great deal to convey the intention.

It's unfortunate that the openai_embed function isn't immutable. it will be in the next version and then wrapping it in the immutable embed function won't be necessary. I guess we need to review all our examples on the next release.

This is really cool.

Thanks @jgpruitt ! I already renamed it! Thanks for all suggestions!

Latest version: https://gist.github.com/jonatas/6b4070b687578856c3cb98fb61d68171#comments