README.md in langchainrb_rails-0.1.2 vs README.md in langchainrb_rails-0.1.3
- old
+ new
@@ -1,35 +1,116 @@
💎🔗 Langchain.rb for Rails
---
-⚡ Building applications with LLMs through composability ⚡
+The fastest way to sprinkle AI ✨ on top of your Rails app. Add OpenAI-powered question-and-answering in minutes.
-👨💻👩💻 CURRENTLY SEEKING PEOPLE TO FORM THE CORE GROUP OF MAINTAINERS WITH
-
[](https://badge.fury.io/rb/langchainrb_rails)
[](http://rubydoc.info/gems/langchainrb_rails)
[](https://github.com/andreibondarev/langchainrb_rails/blob/main/LICENSE.txt)
[](https://discord.gg/WDARp7J2n8)
+## Dependencies
-Langchain.rb is a library that's an abstraction layer on top many emergent AI, ML and other DS tools. The goal is to abstract complexity and difficult concepts to make building AI/ML-supercharged applications approachable for traditional software engineers.
+* Ruby 3.0+
+* Postgres 11+
+## Table of Contents
+
+- [Installation](#installation)
+- [Generators](#rails-generators)
+
## Installation
Install the gem and add to the application's Gemfile by executing:
+```bash
+bundle add langchainrb_rails
+```
- 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](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 `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: https://platform.openai.com/account/api-keys
+```ruby
+ENV["OPENAI_API_KEY"]=
+```
+
+5. Generate embeddings for your model
+```ruby
+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. 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 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 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
+ { name: name, description: description, category: category.name, ... }.to_json
+end
+```
+
+Re-generate embeddings after modifying this method:
+
+```ruby
+product.embed!
+```
+
## Rails Generators
-### Pinecone Generator - adds vectorsearch to your ActiveRecord model
+### Pgvector Generator
+```bash
+rails generate langchainrb_rails:pgvector --model=Product --llm=openai
```
+
+### Pinecone Generator - adds vectorsearch to your ActiveRecord model
+```bash
rails generate langchainrb_rails:pinecone --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.
@@ -37,5 +118,6 @@
Pinecone Generator does the following:
1. Creates the `config/initializers/langchainrb_rails.rb` initializer file
2. Adds necessary code to the ActiveRecord model to enable vectorsearch
3. Adds `pinecone` gem to the Gemfile
+