Rlama

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 (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.

# 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/scripts/rlama_retrieve.py --list

First run per collection builds an embedding cache (~60s for 4K chunks). Subsequent queries are <1s.

Local LLM Query (Fallback Only)

Use rlama run only when Claude is not in the loop (e.g., standalone CLI usage, cron jobs, scripts):

# Local model generates the answer (weaker than Claude synthesis)
rlama run <rag-name> --query "your question here"

# With more context chunks
rlama run <rag-name> --query "explain the authentication flow" --context-size 30

# Show source documents
rlama run <rag-name> --query "what are the API endpoints?" --show-context

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

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:

Provider auto-detection: model names with / → OpenRouter, otherwise → TogetherAI. Falls back to whichever API key is set.

Quality tiers:

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. Reasoning mode (--reasoning) uses qwen3.5:9b with the strict prompt and 4096 max tokens—best local option for complex cross-document synthesis.

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 the embedding model'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

Scores: retrieval precision, topic coverage, grounding, directness (anti-hedge), composite (0-100).

Create a RAG

Index documents from a folder into a new RAG system:

# Basic creation (uses llama3.2 by default)
rlama rag llama3.2 <rag-name> <folder-path>

# Examples
rlama rag llama3.2 my-notes ~/Notes
rlama rag llama3.2 project-docs ./docs
rlama rag llama3.2 research-papers ~/Papers

# With exclusions
rlama rag llama3.2 codebase ./src --exclude-dir=node_modules,dist,.git --exclude-ext=.log,.tmp

# Only specific file types
rlama rag llama3.2 markdown-docs ./docs --process-ext=.md,.txt

# Custom chunking strategy
rlama rag llama3.2 my-rag ./docs --chunking=semantic --chunk-size=1500 --chunk-overlap=300

Chunking strategies:

• hybrid (default) - Combines semantic and fixed chunking
• semantic - Respects document structure (paragraphs, sections)
• fixed - Fixed character count chunks
• hierarchical - Preserves document hierarchy

List RAG Systems

# 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

Manage Documents

Add documents to existing RAG:

rlama add-docs <rag-name> <folder-or-file>

# Examples
rlama add-docs my-notes ~/Notes/new-notes
rlama add-docs research ./papers/new-paper.pdf

Remove a document:

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 directory
rm -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

Directory Watching

Automatically update RAG when files change:

# Enable watching
rlama watch <rag-name> <folder-path>

# Check for new files manually
rlama check-watched <rag-name>

# Disable watching
rlama watch-off <rag-name>

Website Watching

Monitor websites for content updates:

rlama web-watch <rag-name> https://docs.example.com
rlama check-web-watched <rag-name>
rlama web-watch-off <rag-name>

Reranking

Improve result relevance with reranking:

# Add reranker to existing RAG
rlama add-reranker <rag-name>

# Configure reranker weight (0-1, default 0.7)
rlama update-reranker <rag-name> --reranker-weight=0.8

# Disable reranking
rlama rag llama3.2 my-rag ./docs --disable-reranker

API Server

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 environment
export 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

Recommended models:

Reasoning mode (--reasoning flag) uses qwen3.5:9b for local Ollama synthesis with the strict research-grade prompt (normally reserved for cloud providers). This gives research-quality cited answers without leaving the machine. Override the model via RLAMA_REASONING_MODEL env var.

# Reasoning mode — complex cross-document synthesis (think OFF, fast)
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag> "complex query" --synthesize --reasoning

# Reasoning mode with thinking (chain-of-thought, slower but deeper)
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag> "complex query" --synthesize --reasoning --think

# Equivalent explicit invocation
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag> "query" --synthesize --provider ollama --synth-model qwen3.5:9b

Thinking mode produces internal chain-of-thought reasoning before the answer. The thinking text is included in JSON output (synthesis.thinking field) but not printed in plain text mode. Use for ambiguous cross-document analysis where you want to see the model's working.

Supported File Types

RLAMA indexes these formats:

• Text: .txt, .md, .markdown
• Documents: .pdf, .docx, .doc
• Code: .py, .js, .ts, .go, .rs, .java, .rb, .cpp, .c, .h
• Data: .json, .yaml, .yml, .csv
• Web: .html, .htm
• Org-mode: .org

Example Workflows

Personal Knowledge Base

# 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-time
tail -f ~/.rlama/logs/rlama.log

# Filter by RAG name
tail -f ~/.rlama/logs/rlama.log | grep my-rag

# Pretty-print with jq
tail -f ~/.rlama/logs/rlama.log | jq -r '"\(.ts) [\(.cat)] \(.msg)"'

# Show only progress updates
tail -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:

{"ts": "2026-02-03T12:34:56.789", "level": "info", "cat": "INGEST", "msg": "Progress 45/100", "data": {"op_id": "ingest_abc123", "i": 45, "total": 100, "file": "doc.pdf", "eta_sec": 85}}

Operations State

Active and recent operations are tracked in ~/.rlama/logs/operations.json:

{
  "active": {
    "ingest_abc123": {
      "type": "ingest",
      "rag_name": "my-docs",
      "started": "2026-02-03T12:30:00",
      "processed": 45,
      "total": 100,
      "eta_sec": 85
    }
  },
  "recent": [...]
}

Troubleshooting

"Ollama not found"

# Check Ollama status
ollama --version
ollama list

# Start Ollama
brew services start ollama  # macOS
ollama serve                # Manual start

"Model not found"

# Pull the required model
ollama pull llama3.2
ollama pull nomic-embed-text  # Embedding model

Slow Indexing

• Use smaller embedding models
• Exclude large binary files: --exclude-ext=.bin,.zip,.tar
• Exclude build directories: --exclude-dir=node_modules,dist,build

Poor Query Results

1. Increase context size: --context-size=30
2. Use a better model: -m deepseek-r1:8b
3. Re-index with semantic chunking: --chunking=semantic
4. Enable reranking: rlama add-reranker <rag-name>

Index Corruption

# Delete and recreate
rm -rf ~/.rlama/<rag-name>
rlama rag llama3.2 <rag-name> <folder-path>

CLI Reference

Full command reference available at:

rlama --help
rlama <command> --help

Or see references/rlama-commands.md for complete documentation.