The fastest way to sprinkle AI โจ on top of your Rails app. Add OpenAI-powered question-and-answering in minutes.
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- Ruby 3.0+
- Postgres 11+
Install the gem and add to the application's Gemfile by executing:
bundle add langchainrb_rails
If bundler is not being used to manage dependencies, install the gem by executing:
gem install langchainrb_rails
Configuration w/ Pgvector (requires Postgres 11+)
- Run the Rails generator to add vectorsearch to your ActiveRecord model
rails generate langchainrb_rails:pgvector --model=Product --llm=openai
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.
- Bundle and migrate
bundle install && rails db:migrate
- Set the env var
OPENAI_API_KEY
to your OpenAI API key: https://platform.openai.com/account/api-keys
ENV["OPENAI_API_KEY"]=
- Generate embeddings for your model
Product.embed!
This can take a while depending on the number of database records.
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:
- An embedding is generated for the passed in
question
using the selected LLM. - We calculate a cosine similarity to find records that most closely match your question's embedding.
- A prompt is created using the question and the above records (their
#as_vector
representation )are added as context. - This prompt is passed to the LLM to generate an answer
Product.similarity_search("t-shirt")
Returns ActiveRecord relation that most closely matches the query
using vector search.
By default, embeddings are generated by calling the following method on your model instance:
to_json(except: :embedding)
You can override this by defining an #as_vector
method in your model:
def as_vector
{ name: name, description: description, category: category.name, ... }.to_json
end
Re-generate embeddings after modifying this method:
Product.embed!
rails generate langchainrb_rails:pgvector --model=Product --llm=openai
rails generate langchainrb_rails:pinecone --model=Product --llm=openai
rails generate langchainrb_rails:qdrant --model=Product --llm=openai
Available --llm
options: cohere
, google_palm
, hugging_face
, llama_cpp
, ollama
, openai
, and replicate
. The selected LLM will be used to generate embeddings and completions.
The --model
option is used to specify which ActiveRecord model vectorsearch capabilities will be added to.
Pinecone Generator does the following:
- Creates the
config/initializers/langchainrb_rails.rb
initializer file - Adds necessary code to the ActiveRecord model to enable vectorsearch
- Adds
pinecone
gem to the Gemfile