Added Cohere example [skip ci]

Author: ankane

README.md

@@ -24,6 +24,7 @@ And follow the instructions for your database library:
 Or check out some examples:
 
 - [Embeddings](examples/openai_embeddings.rb) with OpenAI
+- [Binary embeddings](examples/cohere_embeddings.rb) with Cohere
 - [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/cohere_embeddings.rb

@@ -0,0 +1,44 @@
+require "json"
+require "net/http"
+require "pg"
+
+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 bit(1024))")
+
+# https://docs.cohere.com/reference/embed
+def fetch_embeddings(texts, input_type)
+  url = "https://api.cohere.com/v1/embed"
+  headers = {
+    "Authorization" => "Bearer #{ENV.fetch("CO_API_KEY")}",
+    "Content-Type" => "application/json"
+  }
+  data = {
+    texts: texts,
+    model: "embed-english-v3.0",
+    input_type: input_type,
+    embedding_types: ["ubinary"]
+  }
+
+  response = Net::HTTP.post(URI(url), data.to_json, headers).tap(&:value)
+  JSON.parse(response.body)["embeddings"]["ubinary"].map { |e| e.map { |v| v.chr.unpack1("B*") }.join }
+end
+
+input = [
+  "The dog is barking",
+  "The cat is purring",
+  "The bear is growling"
+]
+embeddings = fetch_embeddings(input, "search_document")
+input.zip(embeddings) do |content, embedding|
+  conn.exec_params("INSERT INTO documents (content, embedding) VALUES ($1, $2)", [content, embedding])
+end
+
+query = "forest"
+query_embedding = fetch_embeddings([query], "search_query")[0]
+result = conn.exec_params("SELECT content FROM documents ORDER BY embedding <~> $1 LIMIT 5", [query_embedding])
+result.each do |row|
+  puts row["content"]
+end