rag-implementation

Build Retrieval-Augmented Generation systems with vector databases and semantic search.

3|1|Updated Dec 30, 2025

Search Tags:#embeddings #rag #retrieval #reranking #vector-search

Ready to run

npx skills add https://github.com/48Nauts-Operator/opencode-baseline --skill rag-implementation-48nauts-operator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-implementation
Source: https://github.com/48Nauts-Operator/opencode-baseline/tree/main/.opencode/skill/llm-dev/rag-implementation
Command: npx skills add https://github.com/48Nauts-Operator/opencode-baseline --skill rag-implementation-48nauts-operator

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.

Core Features & Use Cases

Vector stores, embeddings, and retrieval strategies
Reranking and cross-encoder options
End-to-end example pipelines for QA and knowledge bases

Quick Start

Set up a RetrievalQA pipeline with a vector store and a text retriever.