Manage local RAG systems for semantic search over documents using Ollama.
npx skills add https://github.com/tdimino/claude-code-minoan --skill rlama
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
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.
Use the rlama skill to query your local RAG system named 'my-docs' with the question 'what is the main idea?'.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
rawvegRun and interact with local AI models via the Ollama API.
sylin-orgIntegrate chat endpoints, embeddings, and RAG workflows with vector search.
adeboldPerform vector-based semantic search against an AgentDB index.
cloudlinessHost a scalable managed vector database for similarity search.
