Added topic modeling example [skip ci]

Author: ankane

README.md

@@ -25,6 +25,7 @@ Or check out some examples:
 
 - [Embeddings](examples/openai_embeddings.rb) with OpenAI
 - [Binary embeddings](examples/cohere_embeddings.rb) with Cohere
+- [Topic modeling](examples/topic_modeling.rb) with tomoto.rb
 - [User-based recommendations](examples/disco_user_recs.rb) with Disco
 - [Item-based recommendations](examples/disco_item_recs.rb) with Disco
 - [Bulk loading](examples/bulk_loading.rb) with `COPY`

examples/Gemfile

@@ -6,3 +6,4 @@ gem "disco"
 gem "numo-narray"
 gem "pg"
 gem "sequel"
+gem "tomoto"

examples/topic_modeling.rb

@@ -0,0 +1,38 @@
+require "pg"
+require "pgvector"
+require "tomoto"
+
+conn = PG.connect(dbname: "pgvector_example")
+conn.exec("CREATE EXTENSION IF NOT EXISTS vector")
+
+conn.exec("DROP TABLE IF EXISTS documents")
+conn.exec("CREATE TABLE documents (id bigserial PRIMARY KEY, content text, embedding vector(20))")
+
+def generate_embeddings(input)
+  model = Tomoto::LDA.new(k: 20)
+  stop_words = Set.new(["the", "is"])
+  input.each do |text|
+    model.add_doc(text.downcase.split.reject { |w| stop_words.include?(w) })
+  end
+  model.train(100) # iterations
+  input.map.with_index do |_, i|
+    model.docs[i].topics.values
+  end
+end
+
+input = [
+  "The dog is barking",
+  "The cat is purring",
+  "The bear is growling"
+]
+embeddings = generate_embeddings(input)
+
+input.zip(embeddings) do |content, embedding|
+  conn.exec_params("INSERT INTO documents (content, embedding) VALUES ($1, $2)", [content, embedding])
+end
+
+document_id = 1
+result = conn.exec_params("SELECT content FROM documents WHERE id != $1 ORDER BY embedding <=> (SELECT embedding FROM documents WHERE id = $1) LIMIT 5", [document_id])
+result.each do |row|
+  puts row["content"]
+end