dspy-rag-pipeline_skill

Overview

This skill builds retrieval-augmented generation (RAG) pipelines in the DSPy framework using ColBERTv2 retrievers and configurable language models. It provides modular components to configure retrieval, define generation signatures, assemble RAG modules, and run production or multi-hop retrieval workflows. Use it to produce grounded, factual answers from your document corpus and to optimize retrieval + generation jointly.

How this skill works

The skill wires a ColBERTv2 retrieval model into DSPy as a dspy.Retrieve component and pairs it with a generation signature (dspy.Signature) or chain-of-thought generator. A RAG module retrieves k passages for a query, supplies them as context to the LM, and returns a Prediction with both context and answer. Optionally it supports iterative multi-hop retrieval where refined queries are generated between hops to collect additional evidence.

When to use it

• You have user queries that require external knowledge beyond the model’s training data
• You maintain a searchable document corpus or hosted ColBERTv2 index
• You need grounded, verifiable answers backed by retrieved passages
• You want to jointly optimize retrieval and prompt/generation behavior
• You need multi-hop retrieval for complex, compositional questions

Best practices

• Tune k (number of passages) to balance recall and noise—start small and validate grounding
• Design clear signature field descriptions so the LM understands expected outputs
• Validate grounding: check that answers are supported by retrieved passages before trusting them
• Consider multi-hop retrieval for complex queries that need iterative evidence gathering
• Monitor latency and index hosting requirements; ColBERTv2 needs a hosted index and adds retrieval latency

Example use cases

• Build a production RAG service that answers factual user queries with citations from your document corpus
• Create a development pipeline that evaluates baseline RAG performance and then optimizes prompts and demos via bootstrapping
• Implement multi-hop RAG for complex question answering that requires chaining facts from multiple passages
• Run automated evaluation on dev sets with a custom grounding metric to measure correctness and source support
• Compile and save an optimized RAG module for deployment after evaluator-driven improvements