DSRs

A high-performance DSPy rewrite in Rust for building LM-powered applications

Documentation • API Reference • Examples • Issues • Discord

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🚀 Overview

DSRs (DSPy Rust) is a ground-up rewrite of the DSPy framework in Rust, designed for building robust, high-performance applications powered by Language Models. Unlike a simple port, DSRs leverages Rust's type system, memory safety, and concurrency features to provide a more efficient and reliable foundation for LM applications.

📦 Installation

Add DSRs to your Cargo.toml:

[dependencies]
# Option 1: Use the shorter alias (recommended)
dsrs = { package = "dspy-rs", version = "0.5.0" }

# Option 2: Use the full name
dspy-rs = "0.5.0"

Or use cargo:

# Option 1: Add with alias (recommended)
cargo add dsrs --package dspy-rs

# Option 2: Add with full name
cargo add dspy-rs

🔧 Quick Start

Here's a simple example to get you started:

use anyhow::Result;
use dspy_rs::*;

#[Signature]
struct SentimentAnalyzer {
    /// Predict the sentiment of the given text 'Positive', 'Negative', or 'Neutral'.

    #[input]
    pub text: String,

    #[output]
    pub sentiment: String,
}

#[tokio::main]
async fn main() -> Result<()> {
    let lm = LM::builder()
        .api_key(std::env::var("OPENAI_API_KEY")?.into())
        .config(
            LMConfig::builder()
                .model("gpt-4.1-nano".to_string())
                .temperature(0.5)
                .build(),
        )
        .build();

    configure(lm, ChatAdapter);

    // Create a predictor
    let predictor = Predict::new(SentimentAnalyzer::new());

    // Prepare input
    let example = example! {
        "text": "input" => "Acme is a great company with excellent customer service.",
    };

    // Execute prediction
    let result = predictor.forward(example).await?;

    println!("Answer: {}", result.get("sentiment", None));

    Ok(())
}

Result:

Answer: "Positive"

🏗️ Architecture

DSRs follows a modular architecture with clear separation of concerns:

dsrs/
├── core/           # Core abstractions (LM, Module, Signature)
├── adapter/        # LM provider adapters (OpenAI, etc.)
├── data/           # Data structures (Example, Prediction)
├── predictors/     # Built-in predictors (Predict, Chain, etc.)
├── evaluate/       # Evaluation framework and metrics
└── macros/         # Derive macros for signatures

Core Components

1. Signatures - Define Input/Output Specifications

#[Signature(cot)]  // Enable chain-of-thought reasoning
struct TranslationSignature {
    /// Translate the text accurately while preserving meaning
    
    #[input]
    pub text: String,
    
    #[input]
    pub target_language: String,
    
    #[output]
    pub translation: String,
}

2. Modules - Composable Pipeline Components

#[derive(Builder)]
pub struct CustomModule {
    predictor: Predict,
}

impl Module for CustomModule {
    async fn forward(&self, inputs: Example) -> Result<Prediction> {
        // Your custom logic here
        self.predictor.forward(inputs).await
    }
}

3. Predictors - Pre-built LM Interaction Patterns

// Get prediction
let predict = Predict::new(MySignature::new());

4. Language Models - Configurable LM Backends

// Configure with OpenAI
let lm = LM::builder()
    .api_key(secret_key)
    .model("gpt-4")
    .temperature(0.7)
    .max_tokens(1000)
    .build();

5. Evaluation - Evaluating your Modules

impl Evaluator for MyModule {
    async fn metric(&self, example: &Example, prediction: &Prediction) -> f32 {
        // Define your custom metric logic
        let expected = example.get("answer", None);
        let predicted = prediction.get("answer", None);
        
        // Example: Exact match metric
        if expected.to_lowercase() == predicted.to_lowercase() {
            1.0
        } else {
            0.0
        }
    }
}

// Evaluate your module
let test_examples = load_test_data();
let module = MyModule::new();

// Automatically runs predictions and computes average metric
let score = module.evaluate(test_examples).await;
println!("Average score: {}", score);

6. Optimization - Optimize your Modules

DSRs provides two powerful optimizers:

COPRO (Collaborative Prompt Optimization)

#[derive(Optimizable)]
pub struct MyModule {
    #[parameter]
    predictor: Predict,
}

// Create and configure the optimizer
let optimizer = COPRO::builder()
    .breadth(10)  // Number of candidates per iteration
    .depth(3)     // Number of refinement iterations
    .build();

// Prepare training data
let train_examples = load_training_data();

// Compile optimizes the module in-place
let mut module = MyModule::new();
optimizer.compile(&mut module, train_examples).await?;

MIPROv2 (Multi-prompt Instruction Proposal Optimizer v2) - Advanced optimizer using LLMs

// MIPROv2 uses a 3-stage process:
// 1. Generate execution traces
// 2. LLM generates candidate prompts with best practices
// 3. Evaluate and select the best prompt

let optimizer = MIPROv2::builder()
    .num_candidates(10)    // Number of candidate prompts to generate
    .num_trials(20)        // Number of evaluation trials
    .minibatch_size(25)    // Examples per evaluation
    .temperature(1.0)      // Temperature for prompt generation
    .build();

optimizer.compile(&mut module, train_examples).await?;

See examples/08-optimize-mipro.rs for a complete example (requires parquet feature).

Component Freezing:

// The Optimizable derive macro automatically implements the trait and marks Module Optimizable
#[derive(Builder, Optimizable)]
pub struct ComplexPipeline {
    #[parameter]  // Mark optimizable components
    analyzer: Predict,
    
    // Non-parameter fields won't be optimized
    summarizer: Predict,
    
    // Non-parameter fields won't be optimized
    config: Config,
}

📚 Examples

Example 1: Multi-Step Reasoning Pipeline

use dsrs::prelude::*;

#[Signature]
struct AnalyzeSignature {
    #[input]
    pub text: String,
    
    #[output]
    pub sentiment: String,
    
    #[output]
    pub key_points: String,
}

#[Signature]
struct SummarizeSignature {
    #[input]
    pub key_points: String,
    
    #[output]
    pub summary: String,
}

#[derive(Builder)]
pub struct AnalysisPipeline {
    analyzer: Predict,
    summarizer: Predict,
}

impl Module for AnalysisPipeline {
    async fn forward(&self, inputs: Example) -> Result<Prediction> {
        // Step 1: Analyze the text
        let analysis = self.analyzer.forward(inputs).await?;
        
        // Step 2: Summarize key points
        let summary_input = example! {
            "key_points": "input" => analysis.get("key_points", None),
        };
        let summary = self.summarizer.forward(summary_input).await?;
        
        // Combine results
        Ok(prediction! {
            "sentiment" => analysis.get("sentiment", None),
            "key_points" => analysis.get("key_points", None),
            "summary" => summary.get("summary", None),
        })
    }
}

🧪 Testing

Run the test suite:

# All tests
cargo test

# Specific test
cargo test test_predictors

# With output
cargo test -- --nocapture

# Run examples
cargo run --example 01-simple

🛠️ Other Features

Chain of Thought (CoT) Reasoning

#[Signature(cot)]  // Enable CoT with attribute
struct ComplexReasoningSignature {
    #[input(desc="Question")
    pub problem: String,
    
    #[output]
    pub solution: String,
}

Optimizer Comparison

Feature	COPRO	MIPROv2
Approach	Iterative refinement	LLM-guided generation
Complexity	Simple	Advanced
Best For	Quick optimization	Best results
Training Data	Uses scores	Uses traces & descriptions
Prompting Tips	No	Yes (15+ best practices)
Program Understanding	Basic	LLM-generated descriptions
Few-shot Examples	No	Yes (auto-selected)

When to use COPRO:

• Fast iteration needed
• Simple tasks
• Limited compute budget

When to use MIPROv2:

• Best possible results needed
• Complex reasoning tasks
• Have good training data (15+ examples recommended)

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📈 Project Status

⚠️ Beta Release - DSRs is in active development. The API is stabilizing but may have breaking changes.

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/krypticmouse/dsrs.git
cd dsrs

# Build the project
cargo build

# Run tests
cargo test

# Run with examples
cargo run --example 01-simple

# Check formatting
cargo fmt -- --check

# Run clippy
cargo clippy -- -D warnings

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• Inspired by the original DSPy framework
• Built with the amazing Rust ecosystem
• Special thanks to the DSPy community for the discussion and ideas
• MIPROv2 implementation

🔗 Resources

• Documentation
• API Reference
• Examples
• GitHub Issues
• Discord Community
• Original DSPy Paper

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Built with 🦀 by the DSPy x Rust community
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