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!

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)

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!

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.

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.

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

• 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