dspy

Original：🇺🇸 English
Not Translated
8 scriptsChecked / no sensitive code detected

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.

3installs
Sourcezpankz/mcp-skillset
Added on

NPX Install

npx skill4agent add zpankz/mcp-skillset dspy

SKILL.md Content

DSPy: Declarative Language Model Programming

Stanford NLP's framework for programming—not prompting—language models.

Quick Start

python
import dspy

# 1. Configure
dspy.settings.configure(lm=dspy.OpenAI(model='gpt-4o-mini'))

# 2. Define Module
qa = dspy.ChainOfThought("question -> answer")

# 3. Run
response = qa(question="What is the capital of France?")
print(response.answer)

Learning Path (DAG)

The DSPy framework follows a natural progression from core concepts through optimization to advanced applications. Use this directed acyclic graph to understand dependencies and navigate the skill components.

Foundation Layer (Start Here)

  1. Configuring Language Models
    • Prerequisites: None
    • Next: Signatures, Modules, Datasets
  2. Designing Signatures
    • Prerequisites: LM Configuration
    • Next: Modules, Optimization
  3. Building Modules
    • Prerequisites: Signatures
    • Next: Optimization, Applications
  4. Creating Datasets
    • Prerequisites: None
    • Next: Optimization

Optimization Layer

  1. Few-Shot Learning
    • Prerequisites: Modules, Datasets
    • Techniques: LabeledFewShot, BootstrapFewShot, KNNFewShot
    • Next: Applications
  2. Instruction Optimization
    • Prerequisites: Modules, Datasets
    • Techniques: COPRO, MIPROv2, GEPA
    • Next: Applications
  3. Finetuning Models
    • Prerequisites: Modules, Datasets
    • Techniques: BootstrapFinetune
    • Next: Applications
  4. Ensemble Strategies
    • Prerequisites: Multiple trained modules
    • Next: Applications

Application Layer

  1. Building RAG Pipelines
    • Prerequisites: Modules, Optimization (recommended)
  2. Evaluating Programs
    • Prerequisites: Modules, Datasets
  3. Integrating Haystack
    • Prerequisites: Modules, Haystack knowledge

Advanced Features (Cross-Cutting)

  1. Assertions & Validation
    • Prerequisites: Modules
  2. Typed Outputs
    • Prerequisites: Signatures
  3. Multi-Chain Comparison
    • Prerequisites: ChainOfThought module

Reference Documentation

  • Modules Reference - Complete module catalog
  • Optimizers Reference - All optimization techniques
  • Examples Reference - Real-world implementations

Common Workflows

Workflow 1: Basic QA System

  1. Configure LM → Design Signature → Build Module
  2. Path: 
    configuring-language-models.md
    → 
    designing-signatures.md
    → 
    building-modules.md

Workflow 2: Optimized RAG System

  1. Configure LM → Build RAG Module → Optimize with Few-Shot → Evaluate
  2. Path: 
    configuring-language-models.md
    → 
    building-rag-pipelines.md
    → 
    few-shot-learning.md
    → 
    evaluating-programs.md

Workflow 3: Production Agent

  1. Configure LM → Design Signature → Build ReAct Module → Add Assertions → Optimize Instructions → Evaluate
  2. Path: 
    configuring-language-models.md
    → 
    designing-signatures.md
    → 
    building-modules.md
    → 
    assertions-validation.md
    → 
    instruction-optimization.md
    → 
    evaluating-programs.md

Installation

bash
pip install dspy
# Or with specific providers
pip install dspy[anthropic]  # Claude
pip install dspy[openai]     # GPT
pip install dspy[all]        # All providers

Additional Resources

  • Official Docs: dspy.ai
  • GitHub: github.com/stanfordnlp/dspy