DSPy.rb is a Ruby framework that allows you to program LLMs through signatures and modules rather than manual prompt engineering, making AI features more predictable and maintainable.

How does prompt optimization work in this skill?

The skill facilitates the use of MIPROv2 and other optimization techniques to automatically improve LLM performance based on provided training examples and metrics.

Which LLM providers are supported?

It supports a wide range of providers including Anthropic Claude, OpenAI, Google Gemini, OpenRouter, and local models via Ollama.

Can I use DSPy.rb with Ruby on Rails?

Yes, DSPy.rb is designed to work seamlessly with Rails, providing patterns for initializers and organizing LLM logic within the app/llm directory.

Does it support multimodal inputs?

Yes, it includes a unified DSPy::Image interface for processing images alongside text across different supported providers.

DSPy.rb LLM Framework

Name: DSPy.rb LLM Framework
Author: 8b-is

by8b-is

데이터 과학 및 ML

Builds type-safe, composable, and optimized LLM applications using the programmatic DSPy paradigm in Ruby.

소개

DSPy.rb enables Ruby developers to program LLMs rather than manually engineering prompts, bringing structure and predictability to AI-powered features. By defining requirements through type-safe signatures and composable modules, developers can build complex multi-step workflows, deploy agents with tool-use capabilities, and integrate with multiple providers like Anthropic, OpenAI, and Ollama. This skill provides the architectural patterns and templates needed to move LLM logic from fragile strings into version-controlled, testable, and optimizable Ruby code.

주요 기능

Multi-provider configuration for OpenAI, Anthropic, Gemini, and Ollama
0 GitHub stars
Composable modules for building complex, multi-step AI pipelines
Support for Predict, ChainOfThought, ReAct, and CodeAct predictors
Automatic prompt optimization using MIPROv2 techniques
Type-safe signatures for LLM operations with runtime validation

사용 사례

Creating reliable data extraction and classification pipelines in Rails
Optimizing AI prompts programmatically through testing and evaluation
Building autonomous tool-using agents for complex task execution

소개

주요 기능

Multi-provider configuration for OpenAI, Anthropic, Gemini, and Ollama
0 GitHub stars
Composable modules for building complex, multi-step AI pipelines
Support for Predict, ChainOfThought, ReAct, and CodeAct predictors
Automatic prompt optimization using MIPROv2 techniques
Type-safe signatures for LLM operations with runtime validation

사용 사례

Creating reliable data extraction and classification pipelines in Rails
Optimizing AI prompts programmatically through testing and evaluation
Building autonomous tool-using agents for complex task execution