Agent skill

rag-search

Search RAG database for relevant content. Use for semantic queries over processed documents, code, or papers.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/rag-search

SKILL.md

RAG Search

This skill helps you search processed document databases using semantic similarity and retrieval-time optimizations.

Quick Search

bash
# Basic vector search
uv run processor search ./lancedb "how does the caching work"

# Hybrid search (vector + keyword)
uv run processor search ./lancedb "ConfigParser yaml loading" --hybrid

# Search code
uv run processor search ./lancedb "authentication middleware" --table code_chunks

Available Tables

Table Content
text_chunks Documents, papers, markdown (default)
code_chunks Source code
image_chunks Figures from papers
chunks Unified table (if created with --table-mode unified)

MCP Server

Start the RAG MCP server for programmatic access:

bash
uv run rag-mcp

Generate a config template:

bash
uv run rag-mcp --config_generate

Configure in Claude Desktop (claude_desktop_config.json):

json
{
  "mcpServers": {
    "rag": {
      "command": "uv",
      "args": ["run", "rag-mcp"],
      "cwd": "/path/to/processor"
    }
  }
}

Available MCP Tools

  • search - Vector/hybrid search with optimizations
  • search_images - Search image chunks
  • list_tables - List available tables
  • generate_config - Create config template

Retrieval Optimizations

Enable these for better results at the cost of latency:

Optimization Flag Latency Best For
Hybrid Search hybrid=True +10-30ms Keyword-heavy queries
HyDE use_hyde=True +200-500ms Knowledge questions
Reranking rerank=True +50-200ms High precision needs
Parent Expansion expand_parents=True +5-20ms Broader context

Recommended Combinations

Fast search (default): No optimizations - pure vector similarity

Better recall:

python
search(query="...", hybrid=True)

Knowledge questions:

python
search(query="what is...", use_hyde=True, rerank=True)

Code search:

python
search(query="...", table="code_chunks", hybrid=True)

Maximum precision:

python
search(query="...", hybrid=True, use_hyde=True, rerank=True)

Configuration

Edit rag_config.yaml to set defaults:

yaml
# Embedding profiles (must match processor config)
text_profile: "low"
code_profile: "low"
ollama_host: "http://localhost:11434"

# Default search behavior
default_limit: 5
default_hybrid: false

# HyDE settings (uses Claude SDK by default, falls back to Ollama)
hyde:
  enabled: false
  backend: "claude_sdk"  # claude_sdk (default) or ollama
  claude_model: "haiku"  # haiku, sonnet, opus
  ollama_model: "llama3.2:latest"  # fallback

# Reranking settings
reranker:
  enabled: false
  model: "BAAI/bge-reranker-v2-m3"
  top_k: 20
  top_n: 5

Understanding Results

Results include:

  • content: Matched chunk text
  • source_file: Original file path
  • score: Similarity (0-1, higher is better)
  • metadata: Additional fields (section, language, etc.)

Troubleshooting

No results found

  1. Check database exists: uv run processor stats ./lancedb
  2. Verify table has data: --table text_chunks
  3. Try broader query terms

Poor quality results

  1. Enable hybrid search: --hybrid
  2. Check embedding profiles match processor config
  3. Consider reranking: rerank=True

Slow searches

  1. Disable HyDE if not needed
  2. Reduce rerank_top_k
  3. Check Ollama server performance

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