Agent Skills: Rag System Builder

Rag System Builder

Overview

This skill creates complete RAG (Retrieval-Augmented Generation) systems that combine semantic search with LLM-powered Q&A. Users can ask natural language questions and receive accurate answers grounded in your document collection.

Quick Start

from sentence_transformers import SentenceTransformer
import anthropic

# Setup
model = SentenceTransformer('all-MiniLM-L6-v2')
client = anthropic.Anthropic()

# Retrieve context (simplified)
query = "What are the safety requirements?"
query_embedding = model.encode(query, normalize_embeddings=True)
# ... search for similar chunks ...

# Generate answer
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": f"Context: {context}\n\nQuestion: {query}"}]
)
print(response.content[0].text)

When to Use

• Building AI assistants for technical documentation
• Creating Q&A systems for standards libraries
• Developing chatbots with domain expertise
• Enabling natural language queries over knowledge bases
• Adding AI-powered search to existing document systems

Prerequisites

• Knowledge base with extracted text (see knowledge-base-builder)
• Vector embeddings for semantic search (see semantic-search-setup)
• API key: ANTHROPIC_API_KEY or OPENAI_API_KEY

Related Skills

• knowledge-base-builder - Build the document database first
• semantic-search-setup - Generate vector embeddings
• pdf-text-extractor - Extract text from PDFs
• document-rag-pipeline - Complete end-to-end pipeline

Version History

• 1.2.0 (2026-01-02): Added Quick Start, Execution Checklist, Error Handling, Metrics sections; updated frontmatter with version, category, related_skills
• 1.1.0 (2025-12-30): Added hybrid search (BM25+vector), reranking, streaming responses
• 1.0.0 (2025-10-15): Initial release with basic RAG implementation

Sub-Skills

• Best Practices

Sub-Skills

• Execution Checklist
• Error Handling
• Metrics
• Dependencies

Sub-Skills

• Architecture
• Step 1: Vector Embeddings Table (+4)
• System Prompt Template (+1)
• 1. Cache Embeddings (+2)
• Example Usage
• Advanced: Hybrid Search (BM25 + Vector)
• Advanced: Reranking
• Streaming Responses