rlamalisted

# RLAMA - Local RAG System **RLAMA** (Retrieval-Augmented Language Model Adapter) provides fully local, offline RAG for semantic search over your documents. ## When to Use This Skill - Building knowledge bases from local documents - Searching personal notes, research papers, or code documentation - Document-based Q&A without sending data to the cloud - Indexing project documentation for quick semantic lookup - Creating searchable archives of PDFs, markdown, or code files ## Prerequisites RLAMA requires Ollama running locally: ```bash # Verify Ollama is running ollama list # If not running, start it brew services start ollama # macOS # or: ollama serve ``` ## Quick Reference ### Query a RAG (Default: Retrieve-Only) **Always use retrieve-only mode by default.** Claude synthesizes far better answers than local 7B models. The raw chunks give Claude direct evidence to reason over and cite. ```bash # DEFAULT: Retrieve top 10 chunks — Claude reads and synthesizes python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" # More chunks for broad queries python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" -k 20 # JSON output for programmatic use python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" --json # Force rebuild embedding cache python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" --rebuild-cache # List RAGs with cache status python3 ~/.claude/skills/rlama/