This repository is an "Awesome List" curating resources for DSPy, a Python library designed to compile declarative language model calls into self-improving pipelines. It targets developers and researchers working with large language models (LLMs) who want to move beyond traditional prompt engineering towards more robust, programmable, and optimizable LLM applications. The benefit is a structured approach to building complex LLM workflows that can automatically refine themselves.

How It Works

DSPy operates on a declarative programming paradigm, allowing users to define what they want the LLM to achieve rather than how. It compiles these high-level specifications into optimized prompts and execution logic. Key components include signatures (defining input/output), modules (reusable LLM components), and optimizers (which automatically tune prompts and module parameters for better performance). This approach abstracts away the complexities of prompt engineering, enabling iterative development and automated optimization.

Quick Start & Requirements

• Install DSPy via pip: pip install dspy
• DSPy requires Python 3.8+ and is compatible with various LLM providers (OpenAI, Cohere, Anthropic, etc.) and tools like LangChain and LlamaIndex.
• Official Documentation: https://dspy-docs.vercel.app/

Highlighted Details

• Extensive collection of projects, papers, articles, and videos showcasing DSPy's application in areas like RAG, multi-label classification, AI agents, and synthetic data generation.
• Includes resources on integrating DSPy with other popular tools such as LangChain, LlamaIndex, FastAPI, and Weaviate.
• Features discussions on advanced topics like DSPy Assertions for computational constraints and DSPy's role in red teaming LLMs.
• Demonstrates DSPy's capability for automated prompt engineering and pipeline optimization.

Maintenance & Community

• The list is actively curated, with numerous community contributions and linked projects.
• Resources point to various community channels and developer discussions.

Licensing & Compatibility

• DSPy itself is typically licensed under the MIT License, promoting broad compatibility and commercial use.
• The curated resources cover a range of projects, each with its own licensing; users should verify individual project licenses.

Limitations & Caveats

This list is a curated collection and not the DSPy library itself. While it provides an overview of DSPy's ecosystem, users must refer to individual project READMEs for specific setup, dependencies, and limitations. Some linked projects may be experimental or in early development stages.