### Install and Run DSPy Examples

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

Installs DSPy, pulls a language model, and runs basic and persona examples.

```bash
make install
ollama pull llama3
python examples/basic/hello_world.py
python examples/personas/support_sam.py
```

--------------------------------

### Clone and Setup DSPy Project

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/README.md

Clone the DSPy 0-to-1 guide repository and set up the development environment using make commands.

```bash
git clone <repository-url>
cd dspy-0to1-guide
make dev-setup
```

--------------------------------

### Clone Repository and Set Up Development Environment

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Use this option to clone the DSPy repository and set up a local development environment, including activating a virtual environment and running a quick start example.

```bash
git clone <repository-url>
cd dspy-0to1-guide

make dev-setup

source .venv/bin/activate

make quick-start
```

--------------------------------

### Run First DSPy Example

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/README.md

Execute the initial quick-start example after setting up the DSPy environment.

```bash
make quick-start
```

--------------------------------

### Run DSPy Repository Examples

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Execute pre-built examples to test DSPy's capabilities. Use these commands to run basic, advanced, or all available examples.

```bash
# Basic examples
make run-basic

# Advanced examples  
make run-advanced

# All examples
make run-all-examples
```

--------------------------------

### Install Ollama on Linux

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Install Ollama on Linux by downloading and executing the official installation script.

```bash
curl -fsSL https://ollama.ai/install.sh | sh
```

--------------------------------

### DSPy Project Repository Structure

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/README.md

Overview of the directory structure for the DSPy 0-to-1 guide project, including documentation, examples, datasets, and source code.

```bash
├── docs/                    # Comprehensive documentation
├── examples/
│   ├── basic/              # Hello world, basic patterns
│   ├── personas/           # Real-world use cases
│   ├── advanced/           # GEPA, Pydantic, async
│   └── infrastructure/     # Monitoring, deployment
├── datasets/               # Sample data and metrics
├── scripts/                # Automation and utilities
├── tests/                  # Comprehensive test suite
└── src/                    # Reusable components
```

--------------------------------

### Copy Example Environment File

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Copy the example environment file to create a new .env file for your configuration.

```bash
cp .env.example .env
```

--------------------------------

### Available DSPy Project Commands

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/README.md

List of make commands available for managing the DSPy project, including installation, testing, running examples, evaluation, and Docker builds.

```bash
make help              # Show all available commands
make install           # Install dependencies
make test              # Run tests
make run-examples      # Run all examples
make evaluate          # Run evaluation pipeline
make docker-build      # Build Docker image
```

--------------------------------

### Basic DSPy Installation

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Install the core DSPy package using pip.

```bash
pip install dspy-ai
```

--------------------------------

### Install and Run Ollama

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

Install Ollama for local LLM execution and pull a model. This is an optional step for privacy or cost reasons.

```bash
brew install ollama  # MacOS example
```

```bash
ollama pull llama3
```

```bash
ollama serve
```

--------------------------------

### DSPy Development Installation

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Install DSPy with specific dependencies suitable for development, including transformers, sentence-transformers, and faiss-cpu.

```bash
pip install dspy-ai transformers sentence-transformers faiss-cpu
```

--------------------------------

### Check DSPy Installation and Version

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Imports the DSPy library to confirm its installation and prints the installed version and file location, useful for troubleshooting module not found errors.

```python
# Check DSPy installation
import dspy
print(f"DSPy version: {dspy.__version__}")
print(f"DSPy location: {dspy.__file__}")
```

--------------------------------

### Install Ollama on macOS

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Install Ollama on macOS using Homebrew.

```bash
brew install ollama
```

--------------------------------

### DSPy Evaluation Setup

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

Initialize DSPy's Evaluate class with a training dataset and a chosen metric, such as answer_exact_match.

```python
from dspy import Evaluate, metrics

evaluate = Evaluate(
    trainset=train_examples,  # Assuming train_examples is defined as above
    metric=metrics.answer_exact_match,  # use exact match metric
)
```

--------------------------------

### DSPy Installation with Additional Dependencies

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Install DSPy along with a comprehensive set of additional dependencies for extended functionality.

```bash
pip install dspy-ai[all]
```

--------------------------------

### Build a DSPy Agent with a Calculator Tool

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

Create an agent that can reason and act by interacting with external tools. This example demonstrates an agent with a calculator tool using the ReAct module.

```python
import dspy

# Configure the model
dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))

# Define a calculator tool
def calculator(expression: str) -> float:
    return eval(expression)

# Create the agent module
class CalculatorAgent(dspy.Module):
    def __init__(self):
        super().__init__()
        # Register the calculator tool with the ReAct module
        self.react = dspy.ReAct("question -> answer", tools=[calculator])

    def forward(self, question: str):
        return self.react(question=question)

agent = CalculatorAgent()
print(agent("What is 2 + 2 * 5?"))  # The model can call calculator() to compute the answer
```

--------------------------------

### DSPy Evaluation Dataset Preparation

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

Prepare a dataset for DSPy evaluation using dspy.Example objects. Each example should contain inputs and expected outputs, with inputs explicitly defined.

```python
from dspy import Example

# Define some QA examples
train_examples = [
    Example(question="What is the capital of France?", answer="Paris").with_inputs("question"),
    Example(question="Who wrote 1984?", answer="George Orwell").with_inputs("question"),
]
```

--------------------------------

### DSPy Core Concepts Example

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Demonstrates the integration of signatures, modules, optimizers, and evaluation in DSPy. This example defines a sentiment analysis task, creates a module to perform it, prepares training data, optimizes the module using BootstrapFewShot, and evaluates both the baseline and optimized modules.

```python
import dspy
from dspy import Example, Evaluate, metrics
from dspy.teleprompt import BootstrapFewShot

# 1. Define signature (what we want to accomplish)
class SentimentAnalysis(dspy.Signature):
    """Analyze sentiment in customer reviews."""
    review: str = dspy.InputField(desc="Customer review text")
    sentiment: str = dspy.OutputField(desc="positive, negative, or neutral")
    confidence: float = dspy.OutputField(desc="Confidence score 0-1")

# 2. Create module (how to accomplish it)
class SentimentAnalyzer(dspy.Module):
    def __init__(self):
        super().__init__()
        self.analyze = dspy.ChainOfThought(SentimentAnalysis)
    
    def forward(self, review: str):
        return self.analyze(review=review)

# 3. Prepare data for optimization
train_data = [
    Example(review="This product is amazing!", sentiment="positive", confidence=0.9),
    Example(review="Terrible quality, waste of money", sentiment="negative", confidence=0.95),
    # ... more examples
]

# 4. Optimize the module
analyzer = SentimentAnalyzer()
optimizer = BootstrapFewShot(metric=metrics.answer_exact_match)
optimized_analyzer = optimizer.compile(analyzer, trainset=train_data)

# 5. Evaluate performance
evaluator = Evaluate(testset=test_data, metric=metrics.answer_exact_match)
baseline_score = evaluator(analyzer)
optimized_score = evaluator(optimized_analyzer)

print(f"Baseline: {baseline_score:.2%}")
print(f"Optimized: {optimized_score:.2%}")
```

--------------------------------

### Pull and Run Ollama Model

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Pull the Llama3 model from Ollama and start the Ollama server.

```bash
ollama pull llama3
ollama serve
```

--------------------------------

### BootstrapFewShot Optimizer Configuration

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Configure the BootstrapFewShot optimizer to automatically generate few-shot examples for improving a DSPy module. Specify metrics and the number of examples to generate and use.

```python
from dspy.teleprompt import BootstrapFewShot
from dspy import metrics, Example

# Prepare training data
train_examples = [
    Example(
        question="What is the capital of France?",
        answer="Paris"
    ).with_inputs("question"),
    Example(
        question="Who wrote Romeo and Juliet?",
        answer="William Shakespeare"
    ).with_inputs("question"),
    # ... more examples
]

# Create and configure optimizer
teleprompter = BootstrapFewShot(
    metric=metrics.answer_exact_match,
    max_bootstrapped_demos=5,  # Number of examples to generate
    max_labeled_demos=3        # Number of labeled examples to use
)

# Original module
qa_module = SimpleQA()

# Optimized version
optimized_qa = teleprompter.compile(qa_module, trainset=train_examples)
```

--------------------------------

### Install Specific DSPy Dependencies

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Installs specific common dependencies for DSPy, such as those for OpenAI, Transformers, and Sentence Transformers, which can help resolve import errors.

```bash
# Or install specific dependencies
pip install openai transformers sentence-transformers
```

--------------------------------

### Example of Prompt Brittleness

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/01_motivation.md

Demonstrates how a minor change in prompt wording can significantly alter LLM output quality. Version A works well, while Version B performs poorly due to the removal of a single word.

```python
# Version A (works well)
prompt = "Analyze the following contract carefully and extract key terms:"

# Version B (performs poorly)
prompt = "Analyze the following contract and extract key terms:"
# Removing "carefully" significantly impacts quality
```

--------------------------------

### Implement a ReAct Module with Tools

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

The ReAct module combines reasoning with the ability to act using tools. This example shows a CalculatorAgent that can use a 'calculator' tool to evaluate expressions during its reasoning process.

```python
def calculator(expression: str) -> float:
    """Safe calculator function."""
    return eval(expression)

class CalculatorAgent(dspy.Module):
    def __init__(self):
        super().__init__()
        self.react = dspy.ReAct("question -> answer", tools=[calculator])
    
    def forward(self, question: str):
        # Can call tools during reasoning
        return self.react(question=question)
```

--------------------------------

### Python Script to Test DSPy Installation

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

This script verifies basic DSPy functionality and checks environment configurations like API keys and Python version. It's designed to be saved as a Python file and executed.

```python
import dspy
import os

def test_basic_functionality():
    """Test basic DSPy functionality."""
    try:
        # Configure with a simple model
        dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))
        
        # Create a simple module
        predictor = dspy.Predict("input -> output")
        result = predictor(input="Hello DSPy!")
        
        print("✅ DSPy is working correctly!")
        print(f"Test result: {result.output}")
        return True
        
    except Exception as e:
        print(f"❌ Error: {e}")
        return False

def test_environment():
    """Check environment configuration."""
    issues = []
    
    # Check API keys
    if not os.getenv('OPENAI_API_KEY'):
        issues.append("OPENAI_API_KEY not found in environment")
    
    # Check Python version
    import sys
    if sys.version_info < (3, 9):
        issues.append(f"Python {sys.version} is too old. Requires 3.9+")
    
    if issues:
        print("⚠️  Environment issues:")
        for issue in issues:
            print(f"  - {issue}")
    else:
        print("✅ Environment looks good!")
    
    return len(issues) == 0

if __name__ == "__main__":
    print("Testing DSPy installation...")
    env_ok = test_environment()
    
    if env_ok:
        func_ok = test_basic_functionality()
        if func_ok:
            print("\n🎉 DSPy is ready to use!")
        else:
            print("\n🔧 Check your configuration and try again.")
    else:
        print("\n🔧 Fix environment issues and try again.")
```

--------------------------------

### Signature with Constrained Output and Validation

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Advanced signatures can include constraints and validation for outputs. This example shows a 'category' output constrained to specific words and a 'confidence' score with a numerical range.

```python
class ClassificationTask(dspy.Signature):
    """Classify text into predefined categories."""
    text: str = dspy.InputField()
    
    # Constrained output with specific format
    category: str = dspy.OutputField(
        desc="One of: positive, negative, neutral",
        format="single_word"
    )
    confidence: float = dspy.OutputField(
        desc="Confidence score between 0 and 1",
        constraints="0 <= confidence <= 1"
    )
```

--------------------------------

### Check Ollama Service Status

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Verifies if the Ollama service is running by making a request to its API. If it's not running, you can start it using `ollama serve`.

```bash
# Check if Ollama is running
curl http://localhost:11434/api/tags

# Start Ollama service
ollama serve

# Pull required model
ollama pull llama3
```

--------------------------------

### Define Signature Output Fields with Multiple Outputs

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Output fields specify expected outputs with descriptions. This example demonstrates defining multiple outputs for complex tasks, including structured data extraction like risk scores and factors.

```python
class RiskAssessment(dspy.Signature):
    """Assess risk levels in business documents."""
    document: str = dspy.InputField()
    
    # Multiple outputs
    risk_score: int = dspy.OutputField(desc="Risk score from 1-10")
    risk_factors: str = dspy.OutputField(desc="Key risk factors identified")
    mitigation: str = dspy.OutputField(desc="Recommended mitigation strategies")
```

--------------------------------

### Minimal DSPy 'Hello World' Program

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

A basic DSPy program demonstrating a custom module with Chain-of-Thought reasoning for a math question. Configure the language model before use.

```python
import dspy

# Configure the language model (OpenAI's gpt‑4o‑mini for this example)
dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))

class MathQA(dspy.Module):
    def __init__(self):
        super().__init__()
        # Define the module using Chain‑of‑Thought reasoning
        self.solve = dspy.ChainOfThought("question -> answer: float")

    def forward(self, question: str):
        return self.solve(question=question)

# Instantiate and invoke the module
qa = MathQA()
result = qa("What is 3 * 7 + 2?")
print(result)
```

--------------------------------

### COPRO Optimizer Configuration

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Set up the COPRO (Coordinate Ascent Prompt Optimization) optimizer for advanced prompt tuning. Configure parameters like breadth and depth for optimization iterations.

```python
from dspy.teleprompt import COPRO

# Advanced prompt optimization
copro = COPRO(
    metric=metrics.answer_exact_match,
    breadth=10,  # Number of prompt candidates to try
    depth=3      # Optimization iterations
)

optimized_module = copro.compile(module, trainset=examples)
```

--------------------------------

### Traditional Prompt Engineering for Support Ticket Analysis

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/01_motivation.md

Illustrates a manual approach to analyzing support tickets using a hardcoded prompt. This method requires manual prompt tuning and lacks systematic evaluation, making it fragile and hard to maintain.

```python
# ❌ Fragile, hard to maintain
def analyze_support_ticket(ticket):
    prompt = f"""
    You are a customer support expert. Analyze this ticket and classify it. 
    
    Ticket: {ticket}
    
    Classify as: technical, billing, or general
    Urgency: high, medium, low
    Response: professional response
    """
    
    # Manual optimization required
    # Model-specific prompt tuning
    # No systematic evaluation
    return call_llm(prompt)
```

--------------------------------

### Sequential Module Composition

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Demonstrates how to compose DSPy modules sequentially, where the output of one module serves as the input for the next.

```python
class Pipeline(dspy.Module):
    def __init__(self):
        super().__init__()
        self.step1 = dspy.ChainOfThought("input -> intermediate")
        self.step2 = dspy.Predict("intermediate -> output")
    
    def forward(self, input_data):
        intermediate = self.step1(input=input_data)
        output = self.step2(intermediate=intermediate.intermediate)
        return output
```

--------------------------------

### DSPy Automatic Prompt Optimization with BootstrapFewShot

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/01_motivation.md

Shows how to use DSPy's BootstrapFewShot teleprompter to automatically optimize a module. This process involves generating, testing, and selecting the best prompt variations based on a training dataset and a specified metric.

```python
from dspy.teleprompt import BootstrapFewShot
from dspy import metrics

# Automatic optimization
teleprompter = BootstrapFewShot(metric=metrics.answer_exact_match)
optimized_analyzer = teleprompter.compile(
    ContractAnalyzer(), 
    trainset=contract_examples
)

# DSPy automatically:
# 1. Generates prompt variations
# 2. Tests them on your data
# 3. Selects the best performing version
# 4. Creates few-shot examples
```

--------------------------------

### Experiment with DSPy Signatures

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Illustrates two ways to define signatures in DSPy: a simple string-based format and a more detailed class-based format with input/output field descriptions.

```python
# String-based signature
simple_sig = dspy.Predict("question -> answer")

# Detailed class-based signature
class DetailedQA(dspy.Signature):
    """Provide detailed answers to questions with confidence scores."""
    question: str = dspy.InputField(desc="User's question")
    context: str = dspy.InputField(desc="Additional context", default="")
    answer: str = dspy.OutputField(desc="Detailed answer")
    confidence: float = dspy.OutputField(desc="Confidence from 0-1")

detailed_qa = dspy.Predict(DetailedQA)
```

--------------------------------

### Experiment with Different DSPy Models

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Demonstrates how to configure DSPy to use various language models within the same code. This highlights DSPy's model-agnostic nature.

```python
# Try different models with the same code
models_to_test = [
    'openai/gpt-4o-mini',
    'openai/gpt-4',
    'anthropic/claude-3-sonnet',
    'ollama_chat/llama3'  # if you have Ollama running
]

for model in models_to_test:
    print(f"\nTesting with {model}:")
    dspy.configure(lm=dspy.LM(model))
    qa = SimpleQA() # Assuming SimpleQA is defined elsewhere
    result = qa("What is machine learning?")
    print(f"Answer: {result.answer[:100]}...")
```

--------------------------------

### Reinstall DSPy with All Dependencies

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Use this command to uninstall DSPy and then reinstall it with all optional dependencies, which can resolve import errors.

```bash
# Reinstall with dependencies
pip uninstall dspy-ai
pip install dspy-ai[all]
```

--------------------------------

### Configure Environment Variables for API Keys

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Set environment variables for API keys for various language model providers like OpenAI, Anthropic, and Cohere, as well as for local Ollama models.

```bash
# Required for OpenAI models
OPENAI_API_KEY=your_openai_api_key_here

# Optional: Other providers
ANTHROPIC_API_KEY=your_anthropic_api_key_here
COHERE_API_KEY=your_cohere_api_key_here

# Optional: Local models
OLLAMA_HOST=http://localhost:11434
OLLAMA_MODEL=llama3
```

--------------------------------

### Create a Retrieval-Augmented Generation Module

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

Implements a basic Retrieval-Augmented Generation (RAG) pipeline. It first retrieves relevant context and then uses a ChainOfThought module to generate an answer.

```python
import dspy

dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))

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

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

--------------------------------

### DSPy Declarative Approach for Support Ticket Analysis

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/01_motivation.md

Demonstrates the DSPy approach using signatures and modules for analyzing support tickets. This method enables automatic optimization and evaluation, leading to more robust and self-improving AI applications.

```python
# ✅ Robust, self-improving
class SupportTicketAnalysis(dspy.Signature):
    """Analyze and classify customer support tickets"""
    ticket: str = dspy.InputField()
    category: str = dspy.OutputField(desc="technical, billing, or general")
    urgency: str = dspy.OutputField(desc="high, medium, or low")
    response: str = dspy.OutputField(desc="Professional customer response")

class SupportAnalyzer(dspy.Module):
    def __init__(self):
        super().__init__()
        self.analyze = dspy.ChainOfThought(SupportTicketAnalysis)
    
    def forward(self, ticket: str):
        return self.analyze(ticket=ticket)

# Automatic optimization and evaluation
analyzer = SupportAnalyzer()
optimized = optimize_with_data(analyzer, support_examples)
```

--------------------------------

### BetterTogether Optimizer Configuration

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Initialize the BetterTogether optimizer for jointly optimizing multiple modules within a pipeline. Requires a custom metric for evaluation.

```python
from dspy.teleprompt import BetterTogether

# Jointly optimize multiple modules
pipeline = ComplexPipeline()  # Module with multiple sub-modules
optimizer = BetterTogether(metric=custom_metric)
optimized_pipeline = optimizer.compile(pipeline, trainset=examples)
```

--------------------------------

### Configure DSPy with Local Ollama Model

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Configure DSPy to use a local Llama3 model served via Ollama.

```python
import dspy

# Configure DSPy to use local Ollama
dspy.configure(lm=dspy.LM('ollama_chat/llama3'))
```

--------------------------------

### Configure DSPy with Cohere Model

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Configure DSPy to use Cohere's Command-R-plus model.

```python
import dspy

# Cohere
dspy.configure(lm=dspy.LM('cohere/command-r-plus'))
```

--------------------------------

### Configure DSPy with OpenAI GPT Models

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Configure DSPy to use OpenAI's GPT-4o-mini and GPT-4 models.

```python
import dspy

# OpenAI GPT models
dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))
dspy.configure(lm=dspy.LM('openai/gpt-4'))
```

--------------------------------

### Check OpenAI API Key Configuration

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Verifies if the OPENAI_API_KEY environment variable is set. It also shows how to set the API key directly in code, though this is not recommended for production environments.

```python
# Check if API key is loaded
import os
print("OpenAI API Key:", os.getenv('OPENAI_API_KEY', 'Not found'))

# Set API key in code (not recommended for production)
os.environ['OPENAI_API_KEY'] = 'your-api-key-here'
```

--------------------------------

### Define Basic and Class-based Signatures

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Signatures define the input/output behavior of a task. Use string-based signatures for simple tasks or class-based signatures for detailed input/output field definitions with descriptions.

```python
import dspy

# String-based signature (simple)
qa_signature = "question -> answer"

# Class-based signature (detailed)
class QuestionAnswering(dspy.Signature):
    """Answer questions accurately based on given context."""
    question: str = dspy.InputField(desc="User's question")
    context: str = dspy.InputField(desc="Relevant background information")
    answer: str = dspy.OutputField(desc="Accurate, concise answer")
```

--------------------------------

### Configure DSPy with Anthropic Claude Model

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Configure DSPy to use Anthropic's Claude-3-Sonnet model.

```python
import dspy

# Anthropic Claude
dspy.configure(lm=dspy.LM('anthropic/claude-3-sonnet'))
```

--------------------------------

### Parallel Module Composition

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Illustrates parallel composition where multiple modules process input concurrently, and their results are combined.

```python
class ParallelProcessor(dspy.Module):
    def __init__(self):
        super().__init__()
        self.analyzer1 = dspy.Predict("text -> sentiment")
        self.analyzer2 = dspy.Predict("text -> topics")
        self.combiner = dspy.ChainOfThought("sentiment, topics -> summary")
    
    def forward(self, text):
        # Parallel processing
        sentiment = self.analyzer1(text=text)
        topics = self.analyzer2(text=text)
        
        # Combine results
        summary = self.combiner(
            sentiment=sentiment.sentiment,
            topics=topics.topics
        )
        return summary
```

--------------------------------

### DSPy Retrieval-Augmented Generation (RAG) Pipeline

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

A DSPy pipeline demonstrating RAG by retrieving context from Wikipedia using ColBERTv2 and then generating an answer with Chain-of-Thought. Ensure your model is configured.

```python
import dspy

# Configure your model (e.g., local LLM or cloud API)
dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))

# Define a function to call an external retrieval service
# Here we use DSPy's built‑in ColBERTv2 retriever; you could also use your own search API
def search_wikipedia(query: str) -> list[str]:
    results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=3)
    return [x['text'] for x in results]

class RAGPipeline(dspy.Module):
    def __init__(self):
        super().__init__()
        self.retrieve = dspy.Retrieve(k=3)  # retrieval via ColBERT
        self.generate = dspy.ChainOfThought("question, context -> answer")

    def forward(self, question: str):
        # Step 1: fetch relevant context
        context = self.retrieve(question)
        # Step 2: ask the model to answer using the context
        return self.generate(question=question, context=context)

# Usage
rag = RAGPipeline()
question = "Who invented the telephone?"
answer = rag(question)
print(answer)
```

--------------------------------

### DSPy Configuration for Production Deployment

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/01_motivation.md

Configures DSPy for production use by setting the language model, enabling automatic response caching, and activating built-in observability. This ensures efficient and monitored LLM interactions.

```python
import dspy

# Configure with caching and monitoring
dspy.configure(
    lm=dspy.LM('openai/gpt-4'),
    cache=True,  # Automatic response caching
    monitor=True  # Built-in observability
)
```

--------------------------------

### DSPy Summarization Module

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

A DSPy module for text summarization using Chain-of-Thought. Configure the language model before instantiation.

```python
import dspy

# Configure the model
dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))

class Summarizer(dspy.Module):
    def __init__(self):
        super().__init__()
        self.summarize = dspy.ChainOfThought("document -> summary")

    def forward(self, document: str):
        return self.summarize(document=document)

# Example
doc = "DSPy is a framework for programming language models..."
summary = Summarizer()(doc)
print(summary)
```

--------------------------------

### Define Signature Input Fields

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Input fields specify the data a module expects. Include descriptions for better prompting and use type hints for validation. Supports various data types like strings and integers.

```python
class DocumentAnalysis(dspy.Signature):
    """Analyze documents for key insights."""
    document: str = dspy.InputField(desc="Raw document text")
    focus_area: str = dspy.InputField(desc="Specific area to analyze")
    max_length: int = dspy.InputField(desc="Maximum response length")
```

--------------------------------

### DSPy Basic Question Answering Module

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Defines and uses a simple DSPy module for question answering using ChainOfThought. It's configured to use the 'openai/gpt-4o-mini' model.

```python
import dspy

# Configure the language model
dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))

class SimpleQA(dspy.Module):
    """Simple question-answering module using ChainOfThought."""
    
    def __init__(self):
        super().__init__()
        self.generate_answer = dspy.ChainOfThought("question -> answer")
    
    def forward(self, question: str):
        return self.generate_answer(question=question)

# Create and use the module
qa = SimpleQA()
response = qa("What is the capital of France?")

print("Question:", "What is the capital of France?")
print("Answer:", response.answer)
if hasattr(response, 'rationale'):
    print("Reasoning:", response.rationale)
```

--------------------------------

### Implement a ChainOfThought Module

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

The ChainOfThought module enables step-by-step reasoning. When called, it automatically includes intermediate reasoning steps in its process to arrive at the final answer.

```python
class ReasoningQA(dspy.Module):
    def __init__(self):
        super().__init__()
        self.cot = dspy.ChainOfThought("question -> answer")
    
    def forward(self, question: str):
        # Automatically includes reasoning steps
        return self.cot(question=question)
```

--------------------------------

### DSPy Math Question Answering Module

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/03_installation.md

Defines and uses a DSPy module for mathematical reasoning with step-by-step solutions, configured for 'openai/gpt-4o-mini'.

```python
import dspy

dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))

class MathQA(dspy.Module):
    """Mathematical reasoning with step-by-step solution."""
    
    def __init__(self):
        super().__init__()
        self.solve = dspy.ChainOfThought("question -> answer: float")
    
    def forward(self, question: str):
        result = self.solve(question=question)
        return result

# Test with a math problem
math_qa = MathQA()
problem = "What is 15% of 240, plus 30?"

result = math_qa(problem)
print(f"Problem: {problem}")
print(f"Answer: {result.answer}")
print(f"Reasoning: {result.rationale}")
```

--------------------------------

### Implement a Basic Predict Module

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

The Predict module is the simplest DSPy module, performing basic prompting. It takes a signature and uses it to predict outputs based on given inputs.

```python
class SimpleQA(dspy.Module):
    def __init__(self):
        super().__init__()
        self.predictor = dspy.Predict("question -> answer")
    
    def forward(self, question: str):
        return self.predictor(question=question)
```

--------------------------------

### Tightly Coupled Prompt Logic in Python

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/01_motivation.md

Illustrates how prompt logic embedded directly within Python functions leads to poor reusability, difficult testing, and maintenance challenges. This function is hard to adapt for different models or use cases.

```python
# Tightly coupled, hard to reuse
def analyze_contract(text):
    prompt = f"""
    You are an expert legal analyst. Please analyze this contract very carefully.
    Look for the following specific terms and conditions:
    1. Payment terms and deadlines
    2. Termination clauses and conditions
    3. Liability limitations and responsibilities
    
    Contract text: {text}
    
    Format your response as JSON with the following structure:
    {{"payment_terms": "...", "termination": "...", "liability": "..."}}
    """
    return openai.complete(prompt)
```

--------------------------------

### Evaluate a DSPy Module

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

Quantify the performance of a DSPy module using a dataset and a metric. Ensure you use a separate test set for final validation.

```python
result = evaluate(rag)  # rag is the RAGPipeline defined earlier
print("Accuracy:", result)
```

--------------------------------

### DSPy TypedPredictor with Pydantic Integration

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/01_motivation.md

Demonstrates using DSPy's TypedPredictor to enforce structured output validation with Pydantic models. This ensures that the LLM's output conforms to a predefined schema, enhancing reliability.

```python
# Pydantic integration for validation
from pydantic import BaseModel

class StructuredOutput(BaseModel):
    confidence: float
    analysis: str
    risk_level: str

class ValidatedAnalyzer(dspy.Module):
    def __init__(self):
        super().__init__()
        self.analyze = dspy.TypedPredictor(
            ContractAnalysis,
            output_type=StructuredOutput
        )
```

--------------------------------

### Implement a ProgramOfThought Module for Code Generation

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

The ProgramOfThought module is designed for tasks requiring code generation and execution. It can generate and run code to solve problems, such as mathematical equations.

```python
class MathSolver(dspy.Module):
    def __init__(self):
        super().__init__()
        self.pot = dspy.ProgramOfThought("math_problem -> solution")
    
    def forward(self, math_problem: str):
        # Generates and executes code to solve problems
        return self.pot(math_problem=math_problem)
```

--------------------------------

### DSPy Declarative Signature for Contract Analysis

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/01_motivation.md

Defines a contract analysis task using DSPy's declarative programming paradigm. This signature clearly specifies inputs and expected outputs, promoting type safety and readability.

```python
import dspy

# Declarative: What should happen
class ContractAnalysis(dspy.Signature):
    """Extract key legal terms from contracts"""
    contract_text: str = dspy.InputField(desc="Raw contract text")
    payment_terms: str = dspy.OutputField(desc="Payment terms and deadlines")
    termination: str = dspy.OutputField(desc="Termination conditions")
    liability: str = dspy.OutputField(desc="Liability limitations")
```

--------------------------------

### DSPy Composable Module for Contract Analysis

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/01_motivation.md

Implements a reusable contract analysis module using DSPy's ChainOfThought primitive. This module leverages the defined signature and can be easily composed with other DSPy components.

```python
# Composable module
class ContractAnalyzer(dspy.Module):
    def __init__(self):
        super().__init__()
        self.analyze = dspy.ChainOfThought(ContractAnalysis)
    
    def forward(self, contract_text: str):
        return self.analyze(contract_text=contract_text)
```

--------------------------------

### Define a Custom AdvancedRAG Module

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Create a custom DSPy module that composes multiple sub-modules for a multi-step retrieval-augmented generation pipeline. This includes query rewriting, retrieval, reranking, and generation.

```python
class AdvancedRAG(dspy.Module):
    """Multi-step retrieval-augmented generation."""
    
    def __init__(self, k=5):
        super().__init__()
        self.k = k
        
        # Compose multiple modules
        self.query_rewriter = dspy.ChainOfThought("question -> rewritten_query")
        self.retriever = dspy.Retrieve(k=self.k)
        self.reranker = dspy.Predict("query, contexts -> ranked_contexts")
        self.generator = dspy.ChainOfThought("query, context -> answer")
    
    def forward(self, question: str):
        # Multi-step pipeline
        rewritten = self.query_rewriter(question=question)
        contexts = self.retriever(rewritten.rewritten_query)
        ranked = self.reranker(query=question, contexts=contexts)
        answer = self.generator(query=question, context=ranked.ranked_contexts)
        
        return dspy.Prediction(
            answer=answer.answer,
            reasoning=answer.reasoning,
            contexts=contexts,
            rewritten_query=rewritten.rewritten_query
        )
```

--------------------------------

### Define a Custom Semantic Similarity Metric

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/docs/02_core_concepts.md

Implement a custom metric for DSPy optimization using sentence embeddings and cosine similarity. This metric can be used to evaluate the semantic closeness of predictions to ground truth answers.

```python
def semantic_similarity_metric(prediction, ground_truth):
    """Custom metric using sentence embeddings."""
    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer('all-MiniLM-L6-v2')
    
    pred_embedding = model.encode([prediction.answer])
    true_embedding = model.encode([ground_truth.answer])
    
    # Cosine similarity
    similarity = np.dot(pred_embedding[0], true_embedding[0]) / (
        np.linalg.norm(pred_embedding[0]) * np.linalg.norm(true_embedding[0])
    )
    
    return max(0, similarity)  # Ensure non-negative score

# Use custom metric in optimization
optimizer = BootstrapFewShot(metric=semantic_similarity_metric)
```

--------------------------------

### Define a Question Answering Signature

Source: https://github.com/evalops/dspy-0to1-guide/blob/main/README.md

Defines the input and output fields for a question answering task. This signature acts as a contract for modules implementing this task.

```python
import dspy

class QA(dspy.Signature):
    """Question answering task."""
    context: str = dspy.InputField(desc="Background information")
    question: str = dspy.InputField()
    answer: str = dspy.OutputField(desc="Accurate answer")
```

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