Update README.md

Author: andreibondarev

README.md

@@ -21,90 +21,87 @@ The fastest way to sprinkle AI ✨ on top of your Rails app. Add OpenAI-powered
 ## Installation
 
 Install the gem and add to the application's Gemfile by executing:
-
-    bundle add langchainrb_rails
+```bash
+bundle add langchainrb_rails
+```
 
 If bundler is not being used to manage dependencies, install the gem by executing:
+```bash
+gem install langchainrb_rails
+```
 
-    gem install langchainrb_rails
-
-## Configuration w/PgVector (requires Postgres 11+)
-
-1. Generate changes to support vectorsearch in your chosen ActiveRecord model
+## Configuration w/ [Pgvector](https://github.com/pgvector/pgvector) (requires Postgres 11+)
 
+1. Run the Rails generator to add vectorsearch to your ActiveRecord model
 ```bash
 rails generate langchainrb_rails:pgvector --model=Product --llm=openai
 ```
 
-This adds required dependencies to your Gemfile, creates the initializer file `config/initializers/langchainrb_rails.rb`, database migrations to support vectorsearch, and adds the necessary code to the ActiveRecord model to enable vectorsearch.
-
-2. Bundle && Migrate
+This adds required dependencies to your Gemfile, creates the `config/initializers/langchainrb_rails.rb` initializer file, database migrations, and adds the necessary code to the ActiveRecord model to enable vectorsearch.
 
+2. Bundle and migrate
 ```bash
 bundle install && rails db:migrate
 ```
 
-3. Set the env var `OPENAI_API_KEY` to your OpenAI API key.
-
-4. Generate embeddings for your model
+3. Set the env var `OPENAI_API_KEY` to your OpenAI API key: https://platform.openai.com/account/api-keys
+```ruby
+ENV["OPENAI_API_KEY"]= 
+```
 
+5. Generate embeddings for your model
 ```ruby
-[YOUR MODEL].embed!
+Product.embed!
 ```
 
 This can take a while depending on the number of database records.
 
 ## Usage
 
 ### Question and Answering
-
 ```ruby
 Product.ask("list the brands of shoes that are in stock")
 ```
 
 Returns a `String` with a natural language answer. The answer is assembled using the following steps:
 
-1. Turn the `question` into an embedding using the selected LLM.
-2. Find records that most closely match the question using Postgres vector similarity search (#similarity_search).
-3. Create a prompt using the question and insert the records (via `#as_vector`) into the prompt as context.
-4. Generate a completion using the prompt and the selected LLM.
+1. An embedding is generated for the passed in `question` using the selected LLM.
+2. We calculate a [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to find records that most closely match your question's embedding.
+3. A prompt is created using the question and the above records (their `#as_vector` representation )are added as context.
+4. This prompt is passed to the LLM to generate an answer
 
 ### Similarity Search
-
 ```ruby
 Product.similarity_search("t-shirt")
 ```
 
-Returns ActiveRecord records that most closely match the `query` using Postgres vector similarity search.
+Returns ActiveRecord relation that most closely matches the `query` using vector search.
 
 ## Customization
 
-## Changing the vector representation of a record
-
-By default, embeddings are generated by calling the following within your model:
+### Changing the vector representation of a record
 
+By default, embeddings are generated by calling the following method on your model instance:
 ```ruby
 to_json(except: :embedding)
 ```
 
 You can override this by defining an `#as_vector` method in your model:
-
 ```ruby
 def as_vector
-  res = to_json(except: :embedding, :owner_id, :user_id, :category_id)
-  res.merge({ "owner" => owner.name, "user" => user.name, "category" => category.name })
+  { name: name, description: description, category: category.name, ... }.to_json
 end
 ```
 
 Re-generate embeddings after modifying this method:
 
 ```ruby
-[YOUR MODEL].embed!
+Product.embed!
 ```
 
 ## Rails Generators
 
-### PgVector Generator
+### Pgvector Generator
 
 ```bash
 rails generate langchainrb_rails:pgvector --model=Product --llm=openai
@@ -124,7 +121,3 @@ Pinecone Generator does the following:
 2. Adds necessary code to the ActiveRecord model to enable vectorsearch
 3. Adds `pinecone` gem to the Gemfile
 
-### Chroma Generator - adds vectorsearch to your ActiveRecord model
-```bash
-rails generate langchainrb_rails:chroma --model=Product --llm=openai
-```