Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching personal documents, or performing document Q&A. Runs 100% locally with Ollama - no cloud, no data leaving your machine.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching personal documents, or performing document Q&A. Runs 100% locally with Ollama - no cloud, no data leaving your machine.
allowed-tools
Bash(rlama:*), Read
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:
# Verify Ollama is running
ollama list
# If not running, start it
brew services start ollama # macOS# or: ollama serve
Quick Reference
Query a RAG (Most Common)
Query an existing RAG system with a natural language question:
# Non-interactive query (returns answer and exits)
rlama run <rag-name> --query "your question here"# With more context chunks for complex questions
rlama run <rag-name> --query --context-size 30
rlama run <rag-name> --query --show-context
rlama run <rag-name> --query -m deepseek-r1:8b
"explain the authentication flow"
# Show which documents contributed to the answer
"what are the API endpoints?"
# Use a different model for answering
"summarize the architecture"
Script wrapper for cleaner output:
python3 ~/.claude/skills/rlama/scripts/rlama_query.py <rag-name> "your query"
python3 ~/.claude/skills/rlama/scripts/rlama_query.py my-docs "what is the main idea?" --show-sources
Retrieve-Only Mode (Claude Synthesizes)
Get raw chunks without local LLM generation. Claude reads the chunks directly and synthesizes a stronger answer than local models can produce.
When to use retrieve vs standard query:
Scenario
Use
Quick lookup, local model sufficient
rlama_query.py (standard)
Complex synthesis, nuanced reasoning
rlama_retrieve.py (retrieve-only)
Claude needs raw evidence to cite
rlama_retrieve.py (retrieve-only)
Offline/no Ollama for generation
rlama_retrieve.py (retrieve-only)
# Retrieve top 10 chunks (human-readable)
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query"# Retrieve as JSON for programmatic use
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" --json
# More chunks for broad queries
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" -k 20
# 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/scripts/rlama_retrieve.py --list
External LLM Synthesis (optional—retrieve chunks AND synthesize via OpenRouter, TogetherAI, Ollama, or any OpenAI-compatible endpoint):
# Synthesize via OpenRouter (auto-detected from model with /)
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" --synthesize --synth-model anthropic/claude-sonnet-4
# Synthesize via TogetherAI
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" --synthesize --provider togetherai
# Synthesize via local Ollama (fully offline, uses research-grade system prompt)
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" --synthesize --provider ollama
# Synthesize via custom endpoint
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" --synthesize --endpoint https://my-api.com/v1/chat/completions
Environment variables for synthesis:
Variable
Provider
OPENROUTER_API_KEY
OpenRouter (default, auto-detected first)
TOGETHER_API_KEY
TogetherAI
SYNTH_API_KEY
Custom endpoint (via --endpoint)
(none needed)
Ollama (local, no auth)
Provider auto-detection: model names with / → OpenRouter, otherwise → TogetherAI. Falls back to whichever API key is set.
Small local models (7B) use a tuned prompt optimized for Qwen (structured output, anti-hedge, domain-keyword aware). Cloud providers use a strict research-grade prompt with mandatory citations.
First run builds an embedding cache (~30s for 3K chunks, ~10min for 25K chunks). Subsequent queries are <1s. Large RAGs use incremental checkpointing—if Ollama crashes mid-build, re-run to resume from the last checkpoint. Individual chunks are truncated to 5K chars to stay within nomic-embed-text's context window.
Benchmarking:
# Retrieval quality only
python3 ~/.claude/skills/rlama/scripts/rlama_bench.py <rag-name> --retrieval-only
# Full synthesis benchmark (8 test cases)
python3 ~/.claude/skills/rlama/scripts/rlama_bench.py <rag-name> --provider ollama --verbose
# Single test case
python3 ~/.claude/skills/rlama/scripts/rlama_bench.py <rag-name> --provider ollama --case 0
# JSON output for analysis
python3 ~/.claude/skills/rlama/scripts/rlama_bench.py <rag-name> --provider ollama --json
# List all RAGs
rlama list
# List documents in a specific RAG
rlama list-docs <rag-name>
# Inspect chunks (debugging)
rlama list-chunks <rag-name> --document=filename.pdf
rlama remove-doc <rag-name> <document-id>
# Document ID is typically the filename
rlama remove-doc my-notes old-note.md
rlama remove-doc research outdated-paper.pdf
# Force remove without confirmation
rlama remove-doc my-notes old-note.md --force
Delete a RAG
rlama delete <rag-name>
# Or manually remove the data directoryrm -rf ~/.rlama/<rag-name>
Advanced Features
Web Crawling
Create a RAG from website content:
# Crawl a website and create RAG
rlama crawl-rag llama3.2 docs-rag https://docs.example.com
# Add web content to existing RAG
rlama crawl-add-docs my-rag https://blog.example.com
Run RLAMA as an API server for programmatic access:
# Start API server
rlama api --port 11249
# Query via API
curl -X POST http://localhost:11249/rag \
-H "Content-Type: application/json" \
-d '{
"rag_name": "my-docs",
"prompt": "What are the key points?",
"context_size": 20
}'
Model Management
# Update the model used by a RAG
rlama update-model <rag-name> <new-model>
# Example: Switch to a more powerful model
rlama update-model my-rag deepseek-r1:8b
# Use Hugging Face models
rlama rag hf.co/username/repo my-rag ./docs
rlama rag hf.co/username/repo:Q4_K_M my-rag ./docs
# Use OpenAI models (requires OPENAI_API_KEY)export OPENAI_API_KEY="your-key"
rlama rag gpt-4-turbo my-openai-rag ./docs
Configuration
Data Directory
By default, RLAMA stores data in ~/.rlama/. Change this with --data-dir:
# Use custom data directory
rlama --data-dir=/path/to/custom list
rlama --data-dir=/projects/rag-data rag llama3.2 project-rag ./docs
# Or set via environment (add to ~/.zshrc)export RLAMA_DATA_DIR="/path/to/custom"
Ollama Configuration
# Custom Ollama host
rlama --host=192.168.1.100 --port=11434 run my-rag
# Or via environmentexport OLLAMA_HOST="http://192.168.1.100:11434"
Default Model
The skill uses qwen2.5:7b by default (changed from llama3.2 in Jan 2026). For legacy mode:
# Use the old llama3.2 default
python3 ~/.claude/skills/rlama/scripts/rlama_manage.py create my-rag ./docs --legacy
# Per-command model override
rlama rag deepseek-r1:8b my-rag ./docs
# For queries
rlama run my-rag --query "question" -m deepseek-r1:8b
# Create from multiple folders
rlama rag llama3.2 personal-kb ~/Documents
rlama add-docs personal-kb ~/Notes
rlama add-docs personal-kb ~/Downloads/papers
# Query
rlama run personal-kb --query "what did I write about project management?"
Code Documentation
# Index project docs
rlama rag llama3.2 project-docs ./docs ./README.md
# Query architecture
rlama run project-docs --query "how does authentication work?" --context-size 25
Research Papers
# Create research RAG
rlama rag llama3.2 papers ~/Papers --exclude-ext=.bib
# Add specific paper
rlama add-docs papers ./new-paper.pdf
# Query with high context
rlama run papers --query "what methods are used for evaluation?" --context-size 30
Interactive Wizard
For guided RAG creation:
rlama wizard
Resilient Indexing (Skip Problem Files)
For folders with mixed content where some files may exceed embedding context limits (e.g., large PDFs), use the resilient script that processes files individually and skips failures:
# Create RAG, skipping files that fail
python3 ~/.claude/skills/rlama/scripts/rlama_resilient.py create my-rag ~/Documents
# Add to existing RAG, skipping failures
python3 ~/.claude/skills/rlama/scripts/rlama_resilient.py add my-rag ~/MoreDocs
# With docs-only filter
python3 ~/.claude/skills/rlama/scripts/rlama_resilient.py create research ~/Papers --docs-only
# With legacy model
python3 ~/.claude/skills/rlama/scripts/rlama_resilient.py create my-rag ~/Docs --legacy
The script reports which files were added and which were skipped due to errors.
Progress Monitoring
Monitor long-running RLAMA operations in real-time using the logging system.
Tail the Log File
# Watch all operations in real-timetail -f ~/.rlama/logs/rlama.log
# Filter by RAG nametail -f ~/.rlama/logs/rlama.log | grep my-rag
# Pretty-print with jqtail -f ~/.rlama/logs/rlama.log | jq -r '"\(.ts) [\(.cat)] \(.msg)"'# Show only progress updatestail -f ~/.rlama/logs/rlama.log | jq -r 'select(.data.i) | "\(.ts) [\(.cat)] \(.data.i)/\(.data.total) \(.data.file // .data.status)"'
Check Operation Status
# Show active operations
python3 ~/.claude/skills/rlama/scripts/rlama_status.py
# Show recent completed operations
python3 ~/.claude/skills/rlama/scripts/rlama_status.py --recent
# Show both active and recent
python3 ~/.claude/skills/rlama/scripts/rlama_status.py --all
# Follow mode (formatted tail -f)
python3 ~/.claude/skills/rlama/scripts/rlama_status.py --follow
# JSON output
python3 ~/.claude/skills/rlama/scripts/rlama_status.py --json
Log File Format
Logs are written in JSON Lines format to ~/.rlama/logs/rlama.log:
