How does the metric integration work?

You can use standard Haystack evaluators, like the SASEvaluator for semantic similarity, as the scoring function (metric) that guides DSPy's optimization algorithms.

Which Haystack components are compatible with this skill?

Any component that follows the Haystack 2.x API, particularly retrievers, generators, and prompt builders, can be integrated using the patterns provided in this skill.

Do I need to rewrite my entire Haystack pipeline to use this?

No, you can wrap your existing Haystack components (like retrievers) inside a DSPy module specifically for the optimization phase, then export the resulting optimized prompt back to your original pipeline.

What is the main benefit of using DSPy with Haystack?

It replaces manual prompt engineering with a programmatic, data-driven approach, allowing you to automatically find the best few-shot examples and instructions for your Haystack pipeline based on quantitative metrics.

DSPy Haystack Integration

Name: DSPy Haystack Integration
Author: OmidZamani

byOmidZamani

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数据科学与机器学习

Optimizes Haystack RAG pipelines by leveraging DSPy's data-driven prompt tuning and programmatic optimization capabilities.

关于

This skill bridges the gap between the Haystack orchestration framework and DSPy’s optimization engine, allowing developers to automatically tune prompts for RAG pipelines instead of relying on manual trial and error. It provides a structured workflow to wrap Haystack retrievers in DSPy modules, define custom metrics for evaluation, and extract optimized few-shot examples to re-integrate into production Haystack pipelines. This is ideal for teams looking to move from static, generic prompts to high-performance, data-validated LLM applications without abandoning their existing infrastructure.

主要功能

Wraps Haystack retrievers into DSPy modules for programmatic optimization
Automates prompt tuning using DSPy's BootstrapFewShot optimizers
Provides standardized patterns for RAG-specific prompt extraction
Integrates Haystack evaluators like SASEvaluator as DSPy metrics
11 GitHub stars
Extracts optimized few-shot examples for production Haystack templates

使用场景

Scaling LLM applications by automating prompt engineering across diverse datasets
Improving RAG accuracy by using data-driven few-shot example selection
Converting a manual Haystack pipeline into a self-optimizing RAG system

关于

主要功能

Wraps Haystack retrievers into DSPy modules for programmatic optimization
Automates prompt tuning using DSPy's BootstrapFewShot optimizers
Provides standardized patterns for RAG-specific prompt extraction
Integrates Haystack evaluators like SASEvaluator as DSPy metrics
11 GitHub stars
Extracts optimized few-shot examples for production Haystack templates

使用场景

Scaling LLM applications by automating prompt engineering across diverse datasets
Improving RAG accuracy by using data-driven few-shot example selection
Converting a manual Haystack pipeline into a self-optimizing RAG system