dspy-categorical

What problem does it solve?

This Skill simplifies the creation and optimization of complex LLM applications by providing a declarative framework based on category theory, enabling users to build modular, type-safe, and optimizable AI pipelines.

Core Features & Use Cases

• Declarative Programming: Define LLM interactions using typed signatures and compositional modules.
• Prompt Optimization: Automatically tune prompts using advanced optimizers like BootstrapFewShot and MIPROv2.
• Modular Design: Compose simple modules (Predict, ChainOfThought, ReAct) into complex reasoning chains or parallel workflows.
• Use Case: Develop a multi-step AI assistant that first retrieves relevant documents, then synthesizes an answer with step-by-step reasoning, and finally formats the output according to specific constraints, all while optimizing the underlying prompts for better performance.

Quick Start

Use the dspy-categorical skill to define a ChainOfThought module for question answering and optimize it using BootstrapFewShot with provided training examples.