dspy

仓库: davila7/claude-code-templates
安装量: 200
排名: #4309

安装

npx skills add https://github.com/davila7/claude-code-templates --skill dspy

DSPy: Declarative Language Model Programming When to Use This Skill

Use DSPy when you need to:

Build complex AI systems with multiple components and workflows Program LMs declaratively instead of manual prompt engineering Optimize prompts automatically using data-driven methods Create modular AI pipelines that are maintainable and portable Improve model outputs systematically with optimizers Build RAG systems, agents, or classifiers with better reliability

GitHub Stars: 22,000+ | Created By: Stanford NLP

Installation

Stable release

pip install dspy

Latest development version

pip install git+https://github.com/stanfordnlp/dspy.git

With specific LM providers

pip install dspy[openai] # OpenAI pip install dspy[anthropic] # Anthropic Claude pip install dspy[all] # All providers

Quick Start Basic Example: Question Answering import dspy

Configure your language model

lm = dspy.Claude(model="claude-sonnet-4-5-20250929") dspy.settings.configure(lm=lm)

Define a signature (input → output)

class QA(dspy.Signature): """Answer questions with short factual answers.""" question = dspy.InputField() answer = dspy.OutputField(desc="often between 1 and 5 words")

Create a module

qa = dspy.Predict(QA)

Use it

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

Chain of Thought Reasoning import dspy

lm = dspy.Claude(model="claude-sonnet-4-5-20250929") dspy.settings.configure(lm=lm)

Use ChainOfThought for better reasoning

class MathProblem(dspy.Signature): """Solve math word problems.""" problem = dspy.InputField() answer = dspy.OutputField(desc="numerical answer")

ChainOfThought generates reasoning steps automatically

cot = dspy.ChainOfThought(MathProblem)

response = cot(problem="If John has 5 apples and gives 2 to Mary, how many does he have?") print(response.rationale) # Shows reasoning steps print(response.answer) # "3"

Core Concepts 1. Signatures

Signatures define the structure of your AI task (inputs → outputs):

Inline signature (simple)

qa = dspy.Predict("question -> answer")

Class signature (detailed)

class Summarize(dspy.Signature): """Summarize text into key points.""" text = dspy.InputField() summary = dspy.OutputField(desc="bullet points, 3-5 items")

summarizer = dspy.ChainOfThought(Summarize)

When to use each:

Inline: Quick prototyping, simple tasks Class: Complex tasks, type hints, better documentation 2. Modules

Modules are reusable components that transform inputs to outputs:

dspy.Predict

Basic prediction module:

predictor = dspy.Predict("context, question -> answer") result = predictor(context="Paris is the capital of France", question="What is the capital?")

dspy.ChainOfThought

Generates reasoning steps before answering:

cot = dspy.ChainOfThought("question -> answer") result = cot(question="Why is the sky blue?") print(result.rationale) # Reasoning steps print(result.answer) # Final answer

dspy.ReAct

Agent-like reasoning with tools:

from dspy.predict import ReAct

class SearchQA(dspy.Signature): """Answer questions using search.""" question = dspy.InputField() answer = dspy.OutputField()

def search_tool(query: str) -> str: """Search Wikipedia.""" # Your search implementation return results

react = ReAct(SearchQA, tools=[search_tool]) result = react(question="When was Python created?")

dspy.ProgramOfThought

Generates and executes code for reasoning:

pot = dspy.ProgramOfThought("question -> answer") result = pot(question="What is 15% of 240?")

Generates: answer = 240 * 0.15

  1. Optimizers

Optimizers improve your modules automatically using training data:

BootstrapFewShot

Learns from examples:

from dspy.teleprompt import BootstrapFewShot

Training data

trainset = [ dspy.Example(question="What is 2+2?", answer="4").with_inputs("question"), dspy.Example(question="What is 3+5?", answer="8").with_inputs("question"), ]

Define metric

def validate_answer(example, pred, trace=None): return example.answer == pred.answer

Optimize

optimizer = BootstrapFewShot(metric=validate_answer, max_bootstrapped_demos=3) optimized_qa = optimizer.compile(qa, trainset=trainset)

Now optimized_qa performs better!

MIPRO (Most Important Prompt Optimization)

Iteratively improves prompts:

from dspy.teleprompt import MIPRO

optimizer = MIPRO( metric=validate_answer, num_candidates=10, init_temperature=1.0 )

optimized_cot = optimizer.compile( cot, trainset=trainset, num_trials=100 )

BootstrapFinetune

Creates datasets for model fine-tuning:

from dspy.teleprompt import BootstrapFinetune

optimizer = BootstrapFinetune(metric=validate_answer) optimized_module = optimizer.compile(qa, trainset=trainset)

Exports training data for fine-tuning

  1. Building Complex Systems Multi-Stage Pipeline import dspy

class MultiHopQA(dspy.Module): def init(self): super().init() self.retrieve = dspy.Retrieve(k=3) self.generate_query = dspy.ChainOfThought("question -> search_query") self.generate_answer = dspy.ChainOfThought("context, question -> answer")

def forward(self, question):
    # Stage 1: Generate search query
    search_query = self.generate_query(question=question).search_query

    # Stage 2: Retrieve context
    passages = self.retrieve(search_query).passages
    context = "\n".join(passages)

    # Stage 3: Generate answer
    answer = self.generate_answer(context=context, question=question).answer
    return dspy.Prediction(answer=answer, context=context)

Use the pipeline

qa_system = MultiHopQA() result = qa_system(question="Who wrote the book that inspired the movie Blade Runner?")

RAG System with Optimization import dspy from dspy.retrieve.chromadb_rm import ChromadbRM

Configure retriever

retriever = ChromadbRM( collection_name="documents", persist_directory="./chroma_db" )

class RAG(dspy.Module): def init(self, num_passages=3): super().init() self.retrieve = dspy.Retrieve(k=num_passages) self.generate = dspy.ChainOfThought("context, question -> answer")

def forward(self, question):
    context = self.retrieve(question).passages
    return self.generate(context=context, question=question)

Create and optimize

rag = RAG()

Optimize with training data

from dspy.teleprompt import BootstrapFewShot

optimizer = BootstrapFewShot(metric=validate_answer) optimized_rag = optimizer.compile(rag, trainset=trainset)

LM Provider Configuration Anthropic Claude import dspy

lm = dspy.Claude( model="claude-sonnet-4-5-20250929", api_key="your-api-key", # Or set ANTHROPIC_API_KEY env var max_tokens=1000, temperature=0.7 ) dspy.settings.configure(lm=lm)

OpenAI lm = dspy.OpenAI( model="gpt-4", api_key="your-api-key", max_tokens=1000 ) dspy.settings.configure(lm=lm)

Local Models (Ollama) lm = dspy.OllamaLocal( model="llama3.1", base_url="http://localhost:11434" ) dspy.settings.configure(lm=lm)

Multiple Models

Different models for different tasks

cheap_lm = dspy.OpenAI(model="gpt-3.5-turbo") strong_lm = dspy.Claude(model="claude-sonnet-4-5-20250929")

Use cheap model for retrieval, strong model for reasoning

with dspy.settings.context(lm=cheap_lm): context = retriever(question)

with dspy.settings.context(lm=strong_lm): answer = generator(context=context, question=question)

Common Patterns Pattern 1: Structured Output from pydantic import BaseModel, Field

class PersonInfo(BaseModel): name: str = Field(description="Full name") age: int = Field(description="Age in years") occupation: str = Field(description="Current job")

class ExtractPerson(dspy.Signature): """Extract person information from text.""" text = dspy.InputField() person: PersonInfo = dspy.OutputField()

extractor = dspy.TypedPredictor(ExtractPerson) result = extractor(text="John Doe is a 35-year-old software engineer.") print(result.person.name) # "John Doe" print(result.person.age) # 35

Pattern 2: Assertion-Driven Optimization import dspy from dspy.primitives.assertions import assert_transform_module, backtrack_handler

class MathQA(dspy.Module): def init(self): super().init() self.solve = dspy.ChainOfThought("problem -> solution: float")

def forward(self, problem):
    solution = self.solve(problem=problem).solution

    # Assert solution is numeric
    dspy.Assert(
        isinstance(float(solution), float),
        "Solution must be a number",
        backtrack=backtrack_handler
    )

    return dspy.Prediction(solution=solution)

Pattern 3: Self-Consistency import dspy from collections import Counter

class ConsistentQA(dspy.Module): def init(self, num_samples=5): super().init() self.qa = dspy.ChainOfThought("question -> answer") self.num_samples = num_samples

def forward(self, question):
    # Generate multiple answers
    answers = []
    for _ in range(self.num_samples):
        result = self.qa(question=question)
        answers.append(result.answer)

    # Return most common answer
    most_common = Counter(answers).most_common(1)[0][0]
    return dspy.Prediction(answer=most_common)

Pattern 4: Retrieval with Reranking class RerankedRAG(dspy.Module): def init(self): super().init() self.retrieve = dspy.Retrieve(k=10) self.rerank = dspy.Predict("question, passage -> relevance_score: float") self.answer = dspy.ChainOfThought("context, question -> answer")

def forward(self, question):
    # Retrieve candidates
    passages = self.retrieve(question).passages

    # Rerank passages
    scored = []
    for passage in passages:
        score = float(self.rerank(question=question, passage=passage).relevance_score)
        scored.append((score, passage))

    # Take top 3
    top_passages = [p for _, p in sorted(scored, reverse=True)[:3]]
    context = "\n\n".join(top_passages)

    # Generate answer
    return self.answer(context=context, question=question)

Evaluation and Metrics Custom Metrics def exact_match(example, pred, trace=None): """Exact match metric.""" return example.answer.lower() == pred.answer.lower()

def f1_score(example, pred, trace=None): """F1 score for text overlap.""" pred_tokens = set(pred.answer.lower().split()) gold_tokens = set(example.answer.lower().split())

if not pred_tokens:
    return 0.0

precision = len(pred_tokens & gold_tokens) / len(pred_tokens)
recall = len(pred_tokens & gold_tokens) / len(gold_tokens)

if precision + recall == 0:
    return 0.0

return 2 * (precision * recall) / (precision + recall)

Evaluation from dspy.evaluate import Evaluate

Create evaluator

evaluator = Evaluate( devset=testset, metric=exact_match, num_threads=4, display_progress=True )

Evaluate model

score = evaluator(qa_system) print(f"Accuracy: {score}")

Compare optimized vs unoptimized

score_before = evaluator(qa) score_after = evaluator(optimized_qa) print(f"Improvement: {score_after - score_before:.2%}")

Best Practices 1. Start Simple, Iterate

Start with Predict

qa = dspy.Predict("question -> answer")

Add reasoning if needed

qa = dspy.ChainOfThought("question -> answer")

Add optimization when you have data

optimized_qa = optimizer.compile(qa, trainset=data)

  1. Use Descriptive Signatures

❌ Bad: Vague

class Task(dspy.Signature): input = dspy.InputField() output = dspy.OutputField()

✅ Good: Descriptive

class SummarizeArticle(dspy.Signature): """Summarize news articles into 3-5 key points.""" article = dspy.InputField(desc="full article text") summary = dspy.OutputField(desc="bullet points, 3-5 items")

  1. Optimize with Representative Data

Create diverse training examples

trainset = [ dspy.Example(question="factual", answer="...).with_inputs("question"), dspy.Example(question="reasoning", answer="...").with_inputs("question"), dspy.Example(question="calculation", answer="...").with_inputs("question"), ]

Use validation set for metric

def metric(example, pred, trace=None): return example.answer in pred.answer

  1. Save and Load Optimized Models

Save

optimized_qa.save("models/qa_v1.json")

Load

loaded_qa = dspy.ChainOfThought("question -> answer") loaded_qa.load("models/qa_v1.json")

  1. Monitor and Debug

Enable tracing

dspy.settings.configure(lm=lm, trace=[])

Run prediction

result = qa(question="...")

Inspect trace

for call in dspy.settings.trace: print(f"Prompt: {call['prompt']}") print(f"Response: {call['response']}")

Comparison to Other Approaches Feature Manual Prompting LangChain DSPy Prompt Engineering Manual Manual Automatic Optimization Trial & error None Data-driven Modularity Low Medium High Type Safety No Limited Yes (Signatures) Portability Low Medium High Learning Curve Low Medium Medium-High

When to choose DSPy:

You have training data or can generate it You need systematic prompt improvement You're building complex multi-stage systems You want to optimize across different LMs

When to choose alternatives:

Quick prototypes (manual prompting) Simple chains with existing tools (LangChain) Custom optimization logic needed Resources Documentation: https://dspy.ai GitHub: https://github.com/stanfordnlp/dspy (22k+ stars) Discord: https://discord.gg/XCGy2WDCQB Twitter: @DSPyOSS Paper: "DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines" See Also references/modules.md - Detailed module guide (Predict, ChainOfThought, ReAct, ProgramOfThought) references/optimizers.md - Optimization algorithms (BootstrapFewShot, MIPRO, BootstrapFinetune) references/examples.md - Real-world examples (RAG, agents, classifiers)

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