DSPy - A framework for programming language models with declarative self-improving Python

DSPy A framework for programming language models with declarative self-improving Python

What is DSPy?

DSPy (Declarative Self-improving Python) is a sophisticated framework designed to transform how developers interact with language models. Rather than relying on traditional prompting methods, it enables developers to write compositional Python code that can be automatically optimized for high-quality outputs.

The framework provides a comprehensive ecosystem of tools including modules for describing AI behavior, optimizers for tuning prompts and weights, and a rich set of components for building complex AI systems. It supports multiple language model providers and offers features like automatic caching, structured outputs, and sophisticated evaluation metrics for measuring system performance.

Features

Modular Programming: Write AI behavior as code instead of prompts
Automated Optimization: Built-in algorithms for tuning prompts and weights
Multiple LM Support: Compatible with various language model providers
Structured Signatures: Define input/output behavior with typed signatures
Composable Modules: Build complex AI systems from simple components
Evaluation Tools: Built-in metrics and evaluation frameworks
Automatic Caching: Efficient handling of LM calls

Use Cases

Building classification systems
Implementing RAG pipelines
Creating AI agents and chatbots
Developing multi-stage AI workflows
Information extraction systems
Mathematical reasoning applications

FAQs

What language models does DSPy support?

DSPy supports multiple LM providers including OpenAI, Anthropic, Databricks, local LMs through Ollama, and dozens of other providers through LiteLLM integration.
How does DSPy optimization work?

DSPy optimizers work by synthesizing good few-shot examples, proposing better natural-language instructions for prompts, and building datasets for modules to finetune LM weights in the system.
What is the cost of running DSPy optimizations?

A typical simple optimization run costs around $2 USD and takes about 20 minutes, though costs can vary based on LM choice, dataset size, and configuration.