rlama

Manage local RAG systems for semantic search over documents using Ollama.

41|3|Updated Jan 2, 2026

Search Tags:#semantic search #rag #knowledge base #document management #ollama #local llm

Ready to run

npx skills add https://github.com/tdimino/claude-code-minoan --skill rlama

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rlama
Source: https://github.com/tdimino/claude-code-minoan/tree/main/skills/integration-automation/rlama
Command: npx skills add https://github.com/tdimino/claude-code-minoan --skill rlama

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a fully local, offline solution for managing Retrieval-Augmented Generation (RAG) systems, enabling you to build and query knowledge bases without sending data to the cloud.

Core Features & Use Cases

Local RAG Management: Create, query, add/remove documents, and delete RAG systems entirely on your machine.
Diverse Document Support: Indexes text, documents (PDF, DOCX), code, and more.
Use Case: Build a private, searchable knowledge base from your research papers, personal notes, and project documentation that you can query using natural language, all while ensuring your data never leaves your local environment.

Quick Start

Use the rlama skill to query your local RAG system named 'my-docs' with the question 'what is the main idea?'.

Frequently Asked Questions about rlama

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

FAQPage Schema

How do I build a local RAG knowledge base for semantic search without cloud access?▼

To build a local RAG knowledge base for semantic search without cloud access, you can create a system entirely offline using Ollama for local LLM inference and embedding. This allows you to index diverse file types like PDFs, markdown, and code, and query them locally.

Can I query PDF and code files using natural language completely offline?▼

Yes, you can query PDF and code files using natural language completely offline. The system manages Retrieval-Augmented generation natively on your machine, supporting diverse document formats while ensuring your data never leaves the local environment.

Do I need Ollama installed to run a local RAG system for document management?▼

Yes, you need Ollama installed to run this local RAG system for document management. The system operates entirely offline and relies specifically on Ollama to handle local LLM inference and text embedding for your knowledge base.

What is the best way to manage the document lifecycle of a local knowledge base?▼

The best way to manage the document lifecycle of a local knowledge base is through a dedicated RAG management interface. This approach supports adding and removing documents, querying with natural language, and deleting entire RAG systems securely on your machine.

What are the limitations of using a fully offline RAG system for knowledge base search?▼

A limitation of using a fully offline RAG system for knowledge base search is the absolute dependency on local hardware resources. Because all LLM inference and embedding operations occur entirely offline via Ollama, performance is constrained by your machine's computing capacity.