DSPy Implementation Speedrun

Project Overview

This project is a "speedrun" exploration into various implementations and use cases of the DSPy framework. The primary goal is to rapidly prototype and document different ways DSPy can be leveraged for building and optimizing language model-based applications.

The focus is on practical examples, quick iterations, and learning through doing.

Purpose

• To serve as a personal collection of DSPy implementation patterns.
• To quickly test and understand different DSPy features and modules.
• To experiment with integrating DSPy with various language models and tools.
• To document common challenges and solutions encountered during rapid DSPy development.

Implementations Explored (Examples - Add your specific modules here)

This project aims to cover a range of DSPy functionalities. As the speedrun progresses, examples might include:

• Basic Signatures and Predictors: Demonstrating core DSPy concepts.
• Few-shot Learning: Using dspy.Predict with examples.
• Optimizers: Exploring teleprompters like BootstrapFewShot or MIPRO.
• Modules: Building custom DSPy modules.
• Retrieval Augmented Generation (RAG): Simple RAG pipelines with dspy.Retrieve.
• Multi-hop Reasoning: Implementing ChainOfThought or more complex reasoning patterns.
• Tool Use / Agents: Integrating external tools or building simple agentic behaviors.
• Specific Model Integrations: Showcasing DSPy with different LLMs (e.g., Gemini, OpenAI models, local models).
• Image Generation & Multimodal (if applicable): As seen with the ImagePredict and ImageGenerator examples.

Getting Started

Prerequisites

• Python 3.9+
• uv (for package management, as per makefile)
• API keys for relevant services (e.g., Gemini, Fal AI) set up as environment variables or in a .env file (see app/core/config.py).

Installation & Setup

1. Clone the repository (if applicable):

   git clone <your-repo-url>
   cd dspy-speedrun

2. Install dependencies: The makefile uses uv for environment and package management.

   make install

   This will sync dependencies from pyproject.toml.

Running the Application

The main entry point for examples can typically be run using:

make run

This command executes app.main as a module. Refer to the makefile for other available commands like linting, formatting, and cleaning.

Contributing

As this is a personal speedrun project, direct contributions might not be the focus. However, feel free to fork, learn, and adapt any code for your own explorations!

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