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rag-pipeline-builder

Generate RAG pipelines with document ingestion, chunking, and retrieval architectures.

8|1|Updated Mar 1, 2026
Search Tags:#rag#ml pipeline#vector search#document ingestion#cost estimation#retrieval-augmented-generation
One-click install
npx skills add https://github.com/mrlynn/claude-skills --skill rag-pipeline-builder-mrlynn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag-pipeline-builder
Source: https://github.com/mrlynn/claude-skills/tree/main/rag-pipeline-builder
Command: npx skills add https://github.com/mrlynn/claude-skills --skill rag-pipeline-builder-mrlynn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires voyageai, mongodb, nodejs, python, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the creation of end-to-end retrieval-augmented generation systems, reducing development time and complexity.

Core Features & Use Cases

  • Pipeline Automation: Generates comprehensive RAG system architectures with proven patterns.
  • Cost Estimation & Optimization: Provides detailed estimates for embedding, storage, and query costs for planning resources.
  • Use Case: A developer wants to quickly set up a semantic search over company documentation with minimal configuration and cost insights.

Quick Start

Describe your document collection, run cost estimations, and generate a pipeline aligned with best practices.

Frequently Asked Questions about rag-pipeline-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a production-ready RAG pipeline from scratch?▼

To build a retrieval-augmented generation pipeline from scratch, you need to configure document ingestion, chunking strategies, and embedding models. This Skill automates generating these architectures using proven patterns to reduce development time.

What is the best way to estimate costs for a RAG pipeline?▼

Estimating RAG pipeline costs involves calculating embedding generation, vector storage, and query expenses. This Skill provides detailed cost estimation and optimization features to help you plan resources before deploying your document search solution.

Does MongoDB Atlas Vector Search work with Python for document ingestion?▼

Yes, MongoDB Atlas Vector Search integrates with Python tools to handle document ingestion and retrieval. You can use this Skill to configure the pipeline, aligning MongoDB and Python dependencies with best practices for scalable architectures.

How do I set up semantic search over company documentation with minimal configuration?▼

Setting up semantic search over company documentation requires mapping chunking strategies to embedding configurations. This Skill allows developers to quickly describe their document collection and generate a ready-to-deploy pipeline with minimal setup.

Do I need Node.js and Python to configure a retrieval-augmented generation system?▼

Yes, building this retrieval-augmented generation system requires both Node.js and Python environments, alongside Voyage AI and MongoDB dependencies. These tools facilitate the end-to-end pipeline automation, from document ingestion to vector search retrieval.

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