What is dspy-rag-pipeline?

A DSPy pipeline that searches a document collection for relevant passages and uses them to produce an answer. RAG, or retrieval-augmented generation, means finding supporting information before writing the response.

Use it for question answering over documents, multi-step retrieval, and pipelines that return both the retrieved passages and the generated answer.

It helps answers stay grounded in a document corpus instead of relying only on the language model's general knowledge.

dspy-rag-pipeline — Skill for Claude Code by OmidZamani/dspy-skills

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add OmidZamani/dspy-skills --skill dspy-rag-pipeline

Clone the repo

git clone --depth 1 https://github.com/OmidZamani/dspy-skills

Made for: Claude Code.

Or install dspy-skills, the plugin that ships this one along with the rest of its 24 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-rag-pipeline/github.svg)](https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-rag-pipeline)

Your own site

<a href="https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-rag-pipeline"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-rag-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-rag-pipeline"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-rag-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,819 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00037	$0.01819
Opus 5.5	$0.00015	$0.00728
Sonnet 5.5	$0.00007	$0.00364
Haiku 4.5	$0.00004	$0.00182

Measured 1mo ago against content hash 56e3b3f7c056, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dspy-rag-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (example.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/dspy-rag-pipeline/SKILL.md · 253 lines

How it starts

The opening of the file, as written. The whole thing — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DSPy RAG Pipeline

Goal

Build retrieval-augmented generation pipelines with ColBERTv2 that can be systematically optimized.

When to Use

Questions require external knowledge
You have a document corpus to search
Need grounded, factual responses
Want to optimize retrieval + generation jointly

Optimize this pipeline: dspy-miprov2-optimizer, dspy-bootstrap-fewshot
Build local semantic retrieval: dspy-embedding-retrieval
Evaluate results: dspy-evaluation-suite
Design signatures: dspy-signature-designer

Inputs

Input	Type	Description
`question`	`str`	User query
`k`	`int`	Number of passages to retrieve
`rm`	`dspy.Retrieve`	Retrieval model (ColBERTv2)

Outputs

Output	Type	Description
`context`	`list[str]`	Retrieved passages
`answer`	`str`	Generated response

Workflow

Phase 1: Configure Retrieval

import dspy

# Configure LM and retriever
colbert = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
dspy.configure(
    lm=dspy.LM("openai/gpt-4o-mini"),
    rm=colbert
)

Phase 2: Define Signature

class GenerateAnswer(dspy.Signature):
    """Answer questions with short factoid answers."""
    context: list[str] = dspy.InputField(desc="May contain relevant facts")
    question: str = dspy.InputField()
    answer: str = dspy.OutputField(desc="Often between 1 and 5 words")

Phase 3: Build RAG Module

class RAG(dspy.Module):
    def __init__(self, num_passages=3):
        super().__init__()
        self.retrieve = dspy.Retrieve(k=num_passages)
        self.generate = dspy.ChainOfThought(GenerateAnswer)
    
    def forward(self, question):
        context = self.retrieve(question).passages
        pred = self.generate(context=context, question=question)
        return dspy.Prediction(context=context, answer=pred.answer)

Read the full file on GitHub · 253 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

example.py 489 B runs code

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 253 lines · 37 tokens per session scan A 56e3b3f7c056

Subscribe to this mod's changes

dspy-rag-pipeline is a skill published in the GitHub repository OmidZamani/dspy-skills (124 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 1,819 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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dspy-rag-pipeline

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

DSPy RAG Pipeline

Goal

When to Use

Related Skills

Inputs

Outputs

Workflow

Phase 1: Configure Retrieval

Phase 2: Define Signature

Phase 3: Build RAG Module

What ships with it

What this file has done since we first saw it

Other skills, from other repositories

molecular-rag

llamaindex

dspy

dspy

dspy

dspy