AgentSkillsCN

rag-retrieval

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SKILL.md

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

code
/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

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

Retrieve More Context

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

Vector-Only Search

code
/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