instructor-rb

Structured extraction in Ruby, powered by llms, designed for simplicity, transparency, and control.

---

Instructor-rb is a Ruby library that makes it a breeze to work with structured outputs from large language models (LLMs). Built on top of EasyTalk, it provides a simple, transparent, and user-friendly API to manage validation, retries, and streaming responses. Get ready to supercharge your LLM workflows!

Getting Started

1. Install Instructor-rb at the command prompt if you haven't yet:

   $ gem install instructor-rb

2. In your Ruby project, require the gem:

   require 'instructor'

3. At the beginning of your script, initialize and patch the client:

   For the OpenAI client:

   client = Instructor.from_openai(OpenAI::Client)

   For the Anthropic client:

   client = Instructor.from_anthropic(Anthropic::Client)

Usage

export your API key:

export OPENAI_API_KEY=sk-...

or for Anthropic:

export ANTHROPIC_API_KEY=sk-...

Then use Instructor by defining your schema in Ruby using the define_schema block and EasyTalk's schema definition syntax. Here's an example in:

require 'instructor'

class UserDetail
  include EasyTalk::Model

  define_schema do
    property :name, String
    property :age, Integer
  end
end

client = Instructor.from_openai(OpenAI::Client).new

user = client.chat(
  parameters: {
    model: 'gpt-3.5-turbo',
    messages: [{ role: 'user', content: 'Extract Jason is 25 years old' }]
  },
  response_model: UserDetail
)

user.name
# => "Jason"
user.age
# => 25

ℹ️ Tip: Support in other languages

Check out ports to other languages below:

- [Python](https://www.github.com/jxnl/instructor)
- [TS/JS](https://github.com/instructor-ai/instructor-js/)
- [Ruby](https://github.com/instructor-ai/instructor-rb)
- [Elixir](https://github.com/thmsmlr/instructor_ex/)

If you want to port Instructor to another language, please reach out to us on [Twitter](https://twitter.com/jxnlco) we'd love to help you get started!

Why use Instructor?

1. OpenAI Integration — Integrates seamlessly with OpenAI's API, facilitating efficient data management and manipulation.

2. Customizable — It offers significant flexibility. Users can tailor validation processes and define unique error messages.

3. Tested and Trusted — Its reliability is proven by extensive real-world application.

Installing Instructor is a breeze.

Contributing

If you want to help out, checkout some of the issues marked as good-first-issue or help-wanted. Found here. They could be anything from code improvements, a guest blog post, or a new cook book.

Checkout the contribution guide for details on how to set things up, testing, changesets and guidelines.

License

This project is licensed under the terms of the MIT License.

TODO

• Add patch
  • Mode.FUNCTIONS
  • Mode.TOOLS
  • Mode.MD_JSON
  • Mode.JSON
• Add response_model
• Support async
• Support stream=True, Partial[T] and iterable[T]
• Support Streaming
• Optional/Maybe types
• Add Tutorials, include in docs
  • Text Classification
  • Search Queries
  • Query Decomposition
  • Citations
  • Knowledge Graph
  • Self Critique
  • Image Extracting Tables
  • Moderation
  • Entity Resolution
  • Action Item and Dependency Mapping
• Logging for Distillation / Finetuning
• Add llm_validator