How is the RAG pipeline optimized?

The skill integrates with DSPy optimizers like BootstrapFewShot to automatically refine prompts and select the best few-shot examples based on defined validation metrics.

DSPy is a framework for programmatically defining and optimizing LLM pipelines, replacing manual prompt engineering with signatures and automated optimizers.

Does it support grounding validation?

Yes, it includes best practices and code examples for validating that generated answers are directly supported by the retrieved context.

How does this skill handle retrieval?

It utilizes the ColBERTv2 model to retrieve relevant context passages from a document index, which are then used to ground the LLM's responses.

Can I use this for multi-hop questions?

Yes, the skill includes specialized patterns for multi-hop RAG, allowing the system to perform iterative searches to answer complex queries requiring multiple pieces of information.

DSPy RAG Pipeline Builder

Name: DSPy RAG Pipeline Builder
Author: OmidZamani

byOmidZamani

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データサイエンスとML

Builds and optimizes retrieval-augmented generation pipelines using the DSPy framework and ColBERTv2 for grounded, factual AI responses.

This skill enables developers to construct sophisticated RAG systems within the DSPy framework, moving beyond manual prompt engineering to a programmatic, optimizable approach. It facilitates the integration of ColBERTv2 retrievers, the design of custom signatures for context-aware generation, and the implementation of multi-hop retrieval for complex queries. By providing standardized patterns for retrieval, generation, and validation, it allows for the systematic optimization of LLM pipelines to ensure factual accuracy and grounded responses based on specific document corpora.

主な機能

01Programmatic RAG pipeline construction using DSPy modules

02Seamless integration with ColBERTv2 for high-quality retrieval

03Built-in validation and grounding metrics for response quality

04Multi-hop retrieval support for complex, iterative information gathering

05Automated optimization patterns for retrieval and generation

0631 GitHub stars

ユースケース

01Building a question-answering system over a private document corpus

02Implementing multi-step research agents that gather information from multiple sources

03Optimizing existing RAG pipelines to improve factual accuracy and reduce hallucinations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add omidzamani/dspy-skills dspy-rag-pipeline

For use in Claude.ai and ChatGPT

Download Skill