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https://github.com/patterns-ai-core/langchainrb_rails
https://github.com/patterns-ai-core/langchainrb_rails
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/patterns-ai-core/langchainrb_rails
- Owner: patterns-ai-core
- License: mit
- Created: 2023-06-23T13:14:16.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-29T23:03:55.000Z (7 months ago)
- Last Synced: 2024-04-30T04:42:52.232Z (7 months ago)
- Language: Ruby
- Size: 122 KB
- Stars: 123
- Watchers: 9
- Forks: 17
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.YML
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-rails-bootstrapped-br - Langchainrb Rails - Integração do Langchain com Ruby on Rails para incorporar modelos de linguagem avançados em suas aplicações. (🤖 **AI**)
- awesome-rails-bootstrapped-br - Langchainrb Rails - Integração do Langchain com Ruby on Rails para incorporar modelos de linguagem avançados em suas aplicações. (🤖 **AI**)
README
💎🔗 Langchain.rb for Rails
---
The fastest way to sprinkle AI ✨ on top of your Rails app. Add OpenAI-powered question-and-answering in minutes.
Available for paid consulting engagements! [Email me](mailto:[email protected]).
[](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)
[](https://twitter.com/rushing_andrei)
## Dependencies
* 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
```
If bundler is not being used to manage dependencies, install the gem by executing:
```bash
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
### 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
```
### Qdrant Generator - adds vectorsearch to your ActiveRecord model
```bash
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:
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