rag-search

name	rag-search
description	Rechercher dans une base de connaissances indexée. Utiliser quand l'utilisateur veut trouver des documents, effectuer une recherche sémantique, ou interroger un index de documents.
provider	qmd
available-providers	["qmd","pinecone","weaviate"]

rag-search Skill

Search the qmd index for relevant documents. This skill uses qmd under the hood.

Prerequisites

• qmd installed: bun install -g @tobilu/qmd
• Collection set up: Use /rag-index first

Verify setup:

qmd status

Workflow

1. Verify Knowledge Base

qmd status

Should show your collection(s) with document counts.

2. Run Search

qmd query "<query>" --json

Examples:

qmd query "authentication flow" --json
qmd query "API design patterns" --json
qmd query "deployment process" --json

3. Present Results

Parse the JSON output and present:

• Document path
• Relevance score
• Relevant excerpt

Arguments

Argument	Type	Default	Description
query	string	required	Search query
mode	string	query	Search mode: query, vsearch, search
limit	int	5	Number of results
collection	string	all	Restrict to specific collection

Search Modes

Mode	Description
query	Semantic search (default)
vsearch	Vector search with scores
search	Hybrid search

Examples

# Basic search
qmd query "authentication" --json

# Limit results
qmd query "API design" --limit 10 --json

# Search specific collection
qmd query "deployment" --collection api-docs --json

# Vector search with scores
qmd vsearch "configuration" --json

Output Format

JSON output structure:

{
  "results": [
    {
      "path": "docs/guide.md",
      "score": 0.89,
      "content": "..."
    }
  ]
}

Integration with Agents

When using this skill:

1. Run the search query
2. Parse JSON results
3. Present top results with scores
4. Optionally read full documents for deeper context

Troubleshooting

If no results:

1. Check collection exists: qmd status
2. Verify embeddings generated: qmd embed
3. Try broader query terms

Provider-Specific Notes

qmd (current)

• Storage: Local SQLite with sqlite-vec extension
• Embeddings: Local model (no API key required)
• Best for: Small to medium corpora, offline usage

pinecone (planned)

• Storage: Pinecone cloud
• Embeddings: OpenAI or custom embeddings
• Best for: Large-scale production deployments

weaviate (planned)

• Storage: Weaviate instance (self-hosted or cloud)
• Embeddings: Configurable
• Best for: Enterprise deployments with hybrid search