dspy

Program and optimize language models with a declarative framework.

Updated Oct 23, 2024

Search Tags:#optimization #RAG #AI #programming #language models

Ready to run

npx skills add https://github.com/lenadlm/docker --skill dspy-lenadlm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy
Source: https://github.com/lenadlm/docker/tree/main/hermes-skills/skills/mlops/research/dspy
Command: npx skills add https://github.com/lenadlm/docker --skill dspy-lenadlm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dspy, openai, anthropic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the process of programming and optimizing language models for various AI applications, such as question answering, reasoning, and classification, by providing a declarative framework for constructing modular AI pipelines.

Core Features & Use Cases

Declarative Programming: Program language models declaratively without manual prompt engineering.
Automatic Optimization: Optimize prompts and models using data-driven methods and optimizers.
Modular AI: Build maintainable and portable AI pipelines with reusable components.
Use Case: Develop a custom RAG (Retrieval-Augmented Generation) system for information retrieval and answer generation tasks.

Quick Start

Use the dspy skill to create a QA module for answering questions about the given text.

Frequently Asked Questions about dspy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize language models without manual prompt engineering?▼

You can optimize language models without manual prompt engineering by using a declarative framework to construct modular AI pipelines. This approach leverages data-driven methods and optimizers to automatically refine prompts and models for tasks like question answering and reasoning.

What is declarative programming for AI pipelines?▼

Declarative programming for AI pipelines is a method that allows developers to define the desired logic for language models without writing manual prompts. It enables the construction of maintainable, portable systems with reusable components for various AI applications.

How do I build a custom RAG system for information retrieval?▼

You can build a custom RAG system for information retrieval by programming language models using a declarative framework. This allows you to construct modular AI pipelines that integrate retrieval and answer generation tasks efficiently.

Do I need specific libraries to program language models declaratively?▼

Yes, you need the dspy, openai, and anthropic libraries to implement and optimize language models using this declarative framework. These dependencies are required to construct modular AI pipelines and execute tasks like classification and reasoning.

Can I use this framework for both reasoning and classification tasks?▼

Yes, you can use this declarative framework to build modular AI pipelines for both reasoning and classification tasks. It supports various AI applications by allowing developers to program and optimize language models for specific use cases.

What is the best way to automate prompt optimization for AI applications?▼

The best way to automate prompt optimization is by using data-driven methods and optimizers within a declarative framework. This approach automatically refines language models for AI applications, eliminating the need for manual prompt adjustments.