Updated example to use load_async [skip ci]

Author: ankane

README.md

@@ -501,15 +501,15 @@ Perform keyword search
 
 ```ruby
 query = "growling bear"
-keyword_results = Document.keyword_search(query).first(20)
+keyword_results = Document.keyword_search(query).limit(20).load_async
 ```
 
 And semantic search (the query prefix is specific to the [embedding model](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1#mxbai-embed-large-v1))
 
 ```ruby
 query_prefix = "Represent this sentence for searching relevant passages: "
 query_embedding = embed.(query_prefix + query)
-semantic_results = Document.nearest_neighbors(:embedding, query_embedding, distance: "cosine").first(20)
+semantic_results = Document.nearest_neighbors(:embedding, query_embedding, distance: "cosine").limit(20).load_async
 ```
 
 And rerank the results

examples/hybrid/example.rb

@@ -2,6 +2,7 @@
 require "informers"
 require "neighbor"
 
+ActiveRecord.async_query_executor = :global_thread_pool
 ActiveRecord::Base.establish_connection adapter: "postgresql", database: "neighbor_test"
 ActiveRecord::Schema.verbose = false
 ActiveRecord::Schema.define do
@@ -39,12 +40,12 @@ class Document < ActiveRecord::Base
 Document.insert_all!(documents)
 
 query = "growling bear"
-keyword_results = Document.keyword_search(query).first(20)
+keyword_results = Document.keyword_search(query).limit(20).load_async
 
 # the query prefix is specific to the embedding model (https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)
 query_prefix = "Represent this sentence for searching relevant passages: "
 query_embedding = embed.(query_prefix + query)
-semantic_results = Document.nearest_neighbors(:embedding, query_embedding, distance: "cosine").first(20)
+semantic_results = Document.nearest_neighbors(:embedding, query_embedding, distance: "cosine").limit(20).load_async
 
 results = (keyword_results + semantic_results).uniq(&:id)
 p rerank.(query, results.map(&:content), top_k: 5).map { |v| results[v[:doc_id]] }.map(&:content)