version: "1.0.0"

RAG Retrieval Skill

Query your local document knowledge base using semantic search and get AI-powered answers.

Overview

This skill enables RAG (Retrieval-Augmented Generation) queries against your locally indexed documents. It uses semantic search to find relevant documents and generates answers using Claude Haiku.

Usage

/skill rag-retrieval "How to configure the API?"

Features

• Semantic Search: Uses vector similarity to find relevant documents
• Hybrid Retrieval: Combines vector search with keyword matching for better accuracy
• Context-Aware Answers: Uses claude-haiku-4-5-20251001 to generate responses
• Citation Support: Shows sources for generated answers
• Performance Monitoring: Tracks query latency and accuracy

Arguments

• query (required): Your question or search query
• --top-k (optional): Number of documents to retrieve (default: 5)
• --threshold (optional): Minimum similarity score (default: 0.7)
• --mode (optional): Search mode - "hybrid", "vector", or "keyword" (default: "hybrid")

Examples

Basic Query

/skill rag-retrieval "What is the authentication process?"

Retrieve More Context

/skill rag-retrieval "How to handle errors?" --top-k 10

Vector-Only Search

/skill rag-retrieval "API rate limits" --mode vector

Configuration

The skill uses the following configuration from config/default.yaml:

• retrieval.top_k: Default number of documents to retrieve
• retrieval.hybrid_ratio: Balance between vector and keyword search (0.7 = 70% vector)
• claude.model: LLM model for response generation
• claude.max_tokens: Maximum response length

Performance

Typical latencies:

• Vector search: <100ms
• End-to-end response: <5 seconds
• Indexing: ~0.5s per 100 documents

Requirements

• Indexed documents in data/vectors/
• Valid Anthropic API key in environment
• At least 2GB RAM for vector operations

Troubleshooting

No Results Found

• Ensure documents are indexed: python scripts/index.py --input data/documents
• Lower the similarity threshold: --threshold 0.5
• Try keyword mode if vector search fails

Slow Responses

• Reduce top_k value for faster retrieval
• Check if vector index is optimized for your document count
• Monitor memory usage with python -m src.monitoring.tcp_server

API Errors

• Verify ANTHROPIC_API_KEY environment variable
• Check API rate limits and quota
• Review logs in logs/rag_cli.log