RLAMA is a powerful AI-driven question-answering tool for your documents, seamlessly integrating with your local Ollama models. It enables you to create, manage, and interact with Retrieval-Augmented Generation (RAG) systems tailored to your documentation needs.
- Vision & Roadmap
- Installation
- Available Commands
- rag - Create a RAG system
- crawl-rag - Create a RAG system from a website
- wizard - Create a RAG system with interactive setup
- watch - Set up directory watching for a RAG system
- watch-off - Disable directory watching for a RAG system
- check-watched - Check a RAG's watched directory for new files
- web-watch - Set up website monitoring for a RAG system
- web-watch-off - Disable website monitoring for a RAG system
- check-web-watched - Check a RAG's monitored website for updates
- run - Use a RAG system
- api - Start API server
- list - List RAG systems
- delete - Delete a RAG system
- list-docs - List documents in a RAG
- list-chunks - Inspect document chunks
- view-chunk - View chunk details
- add-docs - Add documents to RAG
- crawl-add-docs - Add website content to RAG
- update-model - Change LLM model
- update - Update RLAMA
- version - Display version
- hf-browse - Browse GGUF models on Hugging Face
- run-hf - Run a Hugging Face GGUF model
- Uninstallation
- Supported Document Formats
- Troubleshooting
- Using OpenAI Models
RLAMA aims to become the definitive tool for creating local RAG systems that work seamlessly for everyone—from individual developers to large enterprises. Here's our strategic roadmap:
- ✅ Basic RAG System Creation: CLI tool for creating and managing RAG systems
- ✅ Document Processing: Support for multiple document formats (.txt, .md, .pdf, etc.)
- ✅ Document Chunking: Advanced semantic chunking with multiple strategies (fixed, semantic, hierarchical, hybrid)
- ✅ Vector Storage: Local storage of document embeddings
- ✅ Context Retrieval: Basic semantic search with configurable context size
- ✅ Ollama Integration: Seamless connection to Ollama models
- ✅ Cross-Platform Support: Works on Linux, macOS, and Windows
- ✅ Easy Installation: One-line installation script
- ✅ API Server: HTTP endpoints for integrating RAG capabilities in other applications
- ✅ Web Crawling: Create RAGs directly from websites
- ✅ Guided RAG Setup Wizard: Interactive interface for easy RAG creation
- ✅ Hugging Face Integration: Access to 45,000+ GGUF models from Hugging Face Hub
- Prompt Compression: Smart context summarization for limited context windows
- ✅ Adaptive Chunking: Dynamic content segmentation based on semantic boundaries and document structure
- ✅ Minimal Context Retrieval: Intelligent filtering to eliminate redundant content
- Parameter Optimization: Fine-tuned settings for different model sizes
- Multi-Model Embedding Support: Integration with various embedding models
- Hybrid Retrieval Techniques: Combining sparse and dense retrievers for better accuracy
- Embedding Evaluation Tools: Built-in metrics to measure retrieval quality
- Automated Embedding Cache: Smart caching to reduce computation for similar queries
- Lightweight Web Interface: Simple browser-based UI for the existing CLI backend
- Knowledge Graph Visualization: Interactive exploration of document connections
- Domain-Specific Templates: Pre-configured settings for different domains
- Multi-User Access Control: Role-based permissions for team environments
- Integration with Enterprise Systems: Connectors for SharePoint, Confluence, Google Workspace
- Knowledge Quality Monitoring: Detection of outdated or contradictory information
- System Integration API: Webhooks and APIs for embedding RLAMA in existing workflows
- AI Agent Creation Framework: Simplified system for building custom AI agents with RAG capabilities
- Multi-Step Retrieval: Using the LLM to refine search queries for complex questions
- Cross-Modal Retrieval: Support for image content understanding and retrieval
- Feedback-Based Optimization: Learning from user interactions to improve retrieval
- Knowledge Graphs & Symbolic Reasoning: Combining vector search with structured knowledge
RLAMA's core philosophy remains unchanged: to provide a simple, powerful, local RAG solution that respects privacy, minimizes resource requirements, and works seamlessly across platforms.
- Ollama installed and running
curl -fsSL https://raw.githubusercontent.com/dontizi/rlama/main/install.sh | shRLAMA is built with:
- Core Language: Go (chosen for performance, cross-platform compatibility, and single binary distribution)
- CLI Framework: Cobra (for command-line interface structure)
- LLM Integration: Ollama API (for embeddings and completions)
- Storage: Local filesystem-based storage (JSON files for simplicity and portability)
- Vector Search: Custom implementation of cosine similarity for embedding retrieval
RLAMA follows a clean architecture pattern with clear separation of concerns:
rlama/
├── cmd/ # CLI commands (using Cobra)
│ ├── root.go # Base command
│ ├── rag.go # Create RAG systems
│ ├── run.go # Query RAG systems
│ └── ...
├── internal/
│ ├── client/ # External API clients
│ │ └── ollama_client.go # Ollama API integration
│ ├── domain/ # Core domain models
│ │ ├── rag.go # RAG system entity
│ │ └── document.go # Document entity
│ ├── repository/ # Data persistence
│ │ └── rag_repository.go # Handles saving/loading RAGs
│ └── service/ # Business logic
│ ├── rag_service.go # RAG operations
│ ├── document_loader.go # Document processing
│ └── embedding_service.go # Vector embeddings
└── pkg/ # Shared utilities
└── vector/ # Vector operations
- Document Processing: Documents are loaded from the file system, parsed based on their type, and converted to plain text.
- Embedding Generation: Document text is sent to Ollama to generate vector embeddings.
- Storage: The RAG system (documents + embeddings) is stored in the user's home directory (~/.rlama).
- Query Process: When a user asks a question, it's converted to an embedding, compared against stored document embeddings, and relevant content is retrieved.
- Response Generation: Retrieved content and the question are sent to Ollama to generate a contextually-informed response.
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Documents │────>│ Document │────>│ Embedding │
│ (Input) │ │ Processing │ │ Generation │
└─────────────┘ └─────────────┘ └─────────────┘
│
▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Query │────>│ Vector │<────│ Vector Store│
│ Response │ │ Search │ │ (RAG System)│
└─────────────┘ └─────────────┘ └─────────────┘
▲ │
│ ▼
┌─────────────┐ ┌─────────────┐
│ Ollama │<────│ Context │
│ LLM │ │ Building │
└─────────────┘ └─────────────┘
RLAMA is designed to be lightweight and portable, focusing on providing RAG capabilities with minimal dependencies. The entire system runs locally, with the only external dependency being Ollama for LLM capabilities.
You can get help on all commands by using:
rlama --helpThese flags can be used with any command:
--host string Ollama host (default: localhost)
--port string Ollama port (default: 11434)
--num-thread int Number of threads for Ollama to use (default: 0, use Ollama default)Performance Optimization:
- Use
--num-thread 16(or your CPU core count) to potentially improve processing speed - Ollama often uses half the available cores by default
- Setting this to your full core count can significantly speed up text generation and embeddings
Usage Examples:
# Use 16 threads for better performance
rlama --num-thread 16 run my-docs
# Create a RAG with optimized thread usage
rlama --num-thread 16 rag llama3 documentation ./docs
# Run with custom host and thread settings
rlama --host 192.168.1.100 --port 11434 --num-thread 16 run my-ragRLAMA stores data in ~/.rlama by default. To use a different location:
-
Command-line flag (highest priority):
# Use with any command rlama --data-dir /path/to/custom/directory run my-rag -
Environment variable:
# Set the environment variable export RLAMA_DATA_DIR=/path/to/custom/directory rlama run my-rag
The precedence order is: command-line flag > environment variable > default location.
Creates a new RAG system by indexing all documents in the specified folder.
rlama rag [model] [rag-name] [folder-path]Parameters:
model: Name of the Ollama model to use (e.g., llama3, mistral, gemma) or a Hugging Face model using the formathf.co/username/repository[:quantization].rag-name: Unique name to identify your RAG system.folder-path: Path to the folder containing your documents.
Example:
# Using a standard Ollama model
rlama rag llama3 documentation ./docs
# Using a Hugging Face model
rlama rag hf.co/bartowski/Llama-3.2-1B-Instruct-GGUF my-rag ./docs
# Using a Hugging Face model with specific quantization
rlama rag hf.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF:Q5_K_M my-rag ./docsCreates a new RAG system by crawling a website and indexing its content.
rlama crawl-rag [model] [rag-name] [website-url]Parameters:
model: Name of the Ollama model to use (e.g., llama3, mistral, gemma).rag-name: Unique name to identify your RAG system.website-url: URL of the website to crawl and index.
Options:
--max-depth: Maximum crawl depth (default: 2)--concurrency: Number of concurrent crawlers (default: 5)--exclude-path: Paths to exclude from crawling (comma-separated)--chunk-size: Character count per chunk (default: 1000)--chunk-overlap: Overlap between chunks in characters (default: 200)--chunking-strategy: Chunking strategy to use (options: "fixed", "semantic", "hybrid", "hierarchical", default: "hybrid")
RLAMA offers multiple advanced chunking strategies to optimize document retrieval:
- Fixed: Traditional chunking with fixed size and overlap, respecting sentence boundaries when possible.
- Semantic: Intelligently splits documents based on semantic boundaries like headings, paragraphs, and natural topic shifts.
- Hybrid: Automatically selects the best strategy based on document type and content (markdown, HTML, code, or plain text).
- Hierarchical: For very long documents, creates a two-level chunking structure with major sections and sub-chunks.
The system automatically adapts to different document types:
- Markdown documents: Split by headers and sections
- HTML documents: Split by semantic HTML elements
- Code documents: Split by functions, classes, and logical blocks
- Plain text: Split by paragraphs with contextual overlap
Example:
# Create a new RAG from a documentation website
rlama crawl-rag llama3 docs-rag https://docs.example.com
# Customize crawling behavior
rlama crawl-rag llama3 blog-rag https://blog.example.com --max-depth=3 --exclude-path=/archive,/tags
# Create a RAG with semantic chunking
rlama rag llama3 documentation ./docs --chunking-strategy=semantic
# Use hierarchical chunking for large documents
rlama rag llama3 book-rag ./books --chunking-strategy=hierarchicalProvides an interactive step-by-step wizard for F438 creating a new RAG system.
rlama wizardThe wizard guides you through:
- Naming your RAG
- Choosing an Ollama model
- Selecting document sources (local folder or website)
- Configuring chunking parameters
- Setting up file filtering
Example:
rlama wizard
# Follow the prompts to create your customized RAGConfigure a RAG system to automatically watch a directory for new files and add them to the RAG.
rlama watch [rag-name] [directory-path] [interval]Parameters:
rag-name: Name of the RAG system to watch.directory-path: Path to the directory to watch for new files.interval: Time in minutes to check for new files (use 0 to check only when the RAG is used).
Example:
# Set up directory watching to check every 60 minutes
rlama watch my-docs ./watched-folder 60
# Set up directory watching to only check when the RAG is used
rlama watch my-docs ./watched-folder 0
# Customize what files to watch
rlama watch my-docs ./watched-folder 30 --exclude-dir=node_modules,tmp --process-ext=.md,.txtDisable automatic directory watching for a RAG system.
rlama watch-off [rag-name]Parameters:
rag-name: Name of the RAG system to disable watching.
Example:
rlama watch-off my-docsManually check a RAG's watched directory for new files and add them to the RAG.
rlama check-watched [rag-name]Parameters:
rag-name: Name of the RAG system to check.
Example:
rlama check-watched my-docsConfigure a RAG system to automatically monitor a website for updates and add new content to the RAG.
rlama web-watch [rag-name] [website-url] [interval]Parameters:
rag-name: Name of the RAG system to monitor.website-url: URL of the website to monitor.interval: Time in minutes between checks (use 0 to check only when the RAG is used).
Example:
# Set up website monitoring to check every 60 minutes
rlama web-watch my-docs https://example.com 60
# Set up website monitoring to only check when the RAG is used
rlama web-watch my-docs https://example.com 0
# Customize what content to monitor
rlama web-watch my-docs https://example.com 30 --exclude-path=/archive,/tagsDisable automatic website monitoring for a RAG system.
rlama web-watch-off [rag-name]Parameters:
rag-name: Name of the RAG system to disable monitoring.
Example:
rlama web-watch-off my-docsManually check a RAG's monitored website for new updates and add them to the RAG.
rlama check-web-watched [rag-name]Parameters:
rag-name: Name of the RAG system to check.
Example:
rlama check-web-watched my-docsStarts an interactive session to interact with an existing RAG system.
rlama run [rag-name]Parameters:
rag-name: Name of the RAG system to use.--context-size: (Optional) Number of context chunks to retrieve (default: 20)
Example:
rlama run documentation
> How do I install the project?
> What are the main features?
> exitContext Size Tips:
- Smaller values (5-15) for faster responses with key information
- Medium values (20-40) for balanced performance
- Larger values (50+) for complex questions needing broad context
- Consider your model's context window limits
rlama run documentation --context-size=50 # Use 50 context chunksStarts an HTTP API server that exposes RLAMA's functionality through RESTful endpoints.
rlama api [--port PORT]Parameters:
--port: (Optional) Port number to run the API server on (default: 11249)
Example:
rlama api --port 8080Available Endpoints:
-
Query a RAG system -
POST /ragcurl -X POST http://localhost:11249/rag \ -H "Content-Type: application/json" \ -d '{ "rag_name": "documentation", "prompt": "How do I install the project?", "context_size": 20 }'
Request fields:
rag_name(required): Name of the RAG system to queryprompt(required): Question or prompt to send to the RAGcontext_size(optional): Number of chunks to include in contextmodel(optional): Override the model used by the RAG
-
Check server health -
GET /healthcurl http://localhost:11249/health
Integration Example:
// Node.js example
const response = await fetch('http://localhost:11249/rag', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
rag_name: 'my-docs',
prompt: 'Summarize the key features'
})
});
const data = await response.json();
console.log(data.response);Displays a list of all available RAG systems.
rlama listPermanently deletes a RAG system and all its indexed documents.
rlama delete [rag-name] [--force/-f]Parameters:
rag-name: Name of the RAG system to delete.--forceor-f: (Optional) Delete without asking for confirmation.
Example:
rlama delete old-projectOr to delete without confirmation:
rlama delete old-project --forceDisplays all documents in a RAG system with metadata.
rlama list-docs [rag-name]Parameters:
rag-name: Name of the RAG system
Example:
rlama list-docs documentationList and filter document chunks in a RAG system with various options:
# Basic chunk listing
rlama list-chunks [rag-name]
# With content preview (shows first 100 characters)
rlama list-chunks [rag-name] --show-content
# Filter by document name/ID substring
rlama list-chunks [rag-name] --document=readme
# Combine options
rlama list-chunks [rag-name] --document=api --show-contentOptions:
--show-content: Display chunk content preview--document: Filter by document name/ID substring
Output columns:
- Chunk ID (use with view-chunk command)
- Document Source
- Chunk Position (e.g., "2/5" for second of five chunks)
- Content Preview (if enabled)
- Created Date
Display detailed information about a specific chunk.
rlama view-chunk [rag-name] [chunk-id]Parameters:
rag-name: Name of the RAG systemchunk-id: Chunk identifier from list-chunks
Example:
rlama view-chunk documentation doc123_chunk_0Add new documents to an existing RAG system.
rlama add-docs [rag-name] [folder-path] [flags]Parameters:
rag-name: Name of the RAG systemfolder-path: Path to documents folder
Example:
rlama add-docs documentation ./new-docs --exclude-ext=.tmpAdd content from a website to an existing RAG system.
rlama crawl-add-docs [rag-name] [website-url]Parameters:
rag-name: Name of the RAG systemwebsite-url: URL of the website to crawl and add to the RAG
Options:
--max-depth: Maximum crawl depth (default: 2)--concurrency: Number of concurrent crawlers (default: 5)--exclude-path: Paths to exclude from crawling (comma-separated)--chunk-size: Character count per chunk (default: 1000)--chunk-overlap: Overlap between chunks in characters (default: 200)
Example:
# Add blog content to an existing RAG
rlama crawl-add-docs my-docs https://blog.example.com
# Customize crawling behavior
rlama crawl-add-docs knowledge-base https://docs.example.com --max-depth=1 --exclude-path=/apiUpdate the LLM model used by a RAG system.
rlama update-model [rag-name] [new-model]Parameters:
rag-name: Name of the RAG systemnew-model: New Ollama model name
Example:
rlama update-model documentation deepseek-r1:7b-instructChecks if a new version of RLAMA is available and installs it.
rlama update [--force/-f]Options:
--forceor-f: (Optional) Update without asking for confirmation.
Displays the current version of RLAMA.
rlama --versionor
rlama -vSearch and browse GGUF models available on Hugging Face.
rlama hf-browse [search-term] [flags]Parameters:
search-term: (Optional) Term to search for (e.g., "llama3", "mistral")
Flags:
--open: Open the search results in your default web browser--quant: Specify quantization type to suggest (e.g., Q4_K_M, Q5_K_M)--limit: Limit number of results (default: 10)
Examples:
# Search for GGUF models and show command-line help
rlama hf-browse "llama 3"
# Open browser with search results
rlama hf-browse mistral --open
# Search with specific quantization suggestion
rlama hf-browse phi --quant Q4_K_MRun a Hugging Face GGUF model directly using Ollama. This is useful for testing models before creating a RAG system with them.
rlama run-hf [huggingface-model] [flags]Parameters:
huggingface-model: Hugging Face model path in the formatusername/repository
Flags:
--quant: Quantization to use (e.g., Q4_K_M, Q5_K_M)
Examples:
# Try a model in chat mode
rlama run-hf bartowski/Llama-3.2-1B-Instruct-GGUF
# Specify quantization
rlama run-hf mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF --quant Q5_K_MTo uninstall RLAMA:
If you installed via go install:
rlama uninstallRLAMA stores its data in ~/.rlama. To remove it:
rm -rf ~/.rlamaRLAMA supports many file formats:
- Text:
.txt,.md,.html,.json,.csv,.yaml,.yml,.xml,.org - Code:
.go,.py,.js,.java,.c,.cpp,.cxx,.h,.rb,.php,.rs,.swift,.kt,.ts,.tsx,.f,.F,.F90,.el,.svelte - Documents:
.pdf,.docx,.doc,.rtf,.odt,.pptx,.ppt,.xlsx,.xls,.epub
Installing dependencies via install_deps.sh is recommended to improve support for certain formats.
If you encounter connection errors to Ollama:
- Check that Ollama is running.
- By default, Ollama must be accessible at
http://localhost:11434or the host and port specified by the OLLAMA_HOST environment variable. - If your Ollama instance is running on a different host or port, use the
--hostand--portflags:rlama --host 192.168.1.100 --port 8000 list rlama --host my-ollama-server --port 11434 run my-rag
- Check Ollama logs for potential errors.
If you encounter problems with certain formats:
- Install dependencies via
./scripts/install_deps.sh. - Verify that your system has the required tools (
pdftotext,tesseract, etc.).
If the answers are not relevant:
- Check that the documents are properly indexed with
rlama list. - Make sure the content of the documents is properly extracted.
- Try rephrasing your question more precisely.
- Consider adjusting chunking parameters during RAG creation
For any other issues, please open an issue on the GitHub repository providing:
- The exact command used.
- The complete output of the command.
- Your operating system and architecture.
- The RLAMA version (
rlama --version).
RLAMA provides multiple ways to connect to your Ollama instance:
-
Command-line flags (highest priority):
rlama --host 192.168.1.100 --port 8080 run my-rag
-
Environment variable:
# Format: "host:port" or just "host" export OLLAMA_HOST=remote-server:8080 rlama run my-rag
-
Default values (used if no other method is specified):
- Host:
localhost - Port:
11434
- Host:
The precedence order is: command-line flags > environment variable > default values.
# Quick answers with minimal context
rlama run my-docs --context-size=10
# Deep analysis with maximum context
rlama run my-docs --context-size=50
# Balance between speed and depth
rlama run my-docs --context-size=30rlama rag llama3 my-project ./code \
--exclude-dir=node_modules,dist \
--process-ext=.go,.ts \
--exclude-ext=.spec.ts# List chunks with content preview
rlama list-chunks my-project --show-content
# Filter chunks from specific document
rlama list-chunks my-project --document=architectureGet full command help:
rlama --helpCommand-specific help:
rlama rag --help
rlama list-chunks --help
rlama update-model --helpAll commands support the global --host and --port flags for custom Ollama connections.
The precedence order is: command-line flags > environment variable > default values.
RLAMA now supports using GGUF models directly from Hugging Face through Ollama's native integration:
# Search for GGUF models on Hugging Face
rlama hf-browse "llama 3"
# Open browser with search results
rlama hf-browse mistral --openBefore creating a RAG, you can test a Hugging Face model directly:
# Try a model in chat mode
rlama run-hf bartowski/Llama-3.2-1B-Instruct-GGUF
# Specify quantization
rlama run-hf mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF --quant Q5_K_MUse Hugging Face models when creating RAG systems:
# Create a RAG with a Hugging Face model
rlama rag hf.co/bartowski/Llama-3.2-1B-Instruct-GGUF my-rag ./docs
# Use specific quantization
rlama rag hf.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF:Q5_K_M my-rag ./docsRLAMA supports using OpenAI models with two approaches:
Set your default OpenAI API key in the web interface or via environment variable. This key will be automatically used for all RLAMA commands without needing to specify a profile.
Via Web Interface:
- Navigate to Settings → Default API Keys
- Enter your OpenAI API key (starts with
sk-) - Click Save Default API Keys
Via Environment Variable:
export OPENAI_API_KEY="your-api-key"Usage with default keys:
# These commands will automatically use your default OpenAI API key
rlama rag o3-mini my-rag ./documents
rlama rag gpt-4o another-rag ./docs
rlama update-model my-rag gpt-4o
rlama run my-ragCreate named profiles for different OpenAI accounts or organizations. Use these when you need to switch between different API keys.
Create profiles:
# Create profiles for different accounts
rlama profile add work-account openai "sk-work-api-key"
rlama profile add personal-account openai "sk-personal-api-key"Usage with named profiles:
# Specify profile with --profile flag
rlama rag o3-mini work-rag ./documents --profile work-account
rlama rag gpt-4o personal-rag ./docs --profile personal-account
rlama update-model my-rag gpt-4o --profile work-account| Model | Input Price | Output Price | Context | Description |
|---|---|---|---|---|
| o3-mini ⭐ | $1.10/1M | $4.40/1M | 200K | Latest reasoning model, 93% cheaper than o1 |
| o1-pro | $150.00/1M | $600.00/1M | 200K | Most powerful reasoning model (Enterprise) |
| o1 | $15.00/1M | $60.00/1M | 200K | Advanced reasoning model |
| Model | Input Price | Output Price | Context | Description |
|---|---|---|---|---|
| GPT-4.5 🆕 | $75.00/1M | $150.00/1M | 128K | Natural conversation, emotional intelligence |
| GPT-4.1 🆕 | $30.00/1M | $60.00/1M | 1M | Latest GPT-4 with 1M context window |
| GPT-4.1-nano 🆕 | $5.00/1M | $15.00/1M | 128K | Lightweight version of GPT-4.1 |
| GPT-4o 🔥 | $5.00/1M | $15.00/1M | 128K | Multimodal with images and audio support |
| GPT-4o mini 💰 | $0.15/1M | $0.60/1M | 128K | Efficient version of GPT-4o |
| Model | Input Price | Output Price | Context | Description |
|---|---|---|---|---|
| GPT-3.5 Turbo | $0.50/1M | $1.50/1M | 16K | Fast and economical model |
Legend: ⭐ = Recommended, 🆕 = New (2025), 🔥 = Popular, 💰 = Budget-friendly
Cost Optimization Tips:
- Use context caching for 50% reduction on repeated content
- Choose appropriate context window sizes
- Test multiple models for your specific use case
- Consider o3-mini for reasoning tasks at reduced cost
Note: Only inference uses OpenAI API. Document embeddings still use Ollama for processing.
For most users, setting up default API keys is the simplest approach:
Via Web Interface:
- Open RLAMA web interface
- Go to Settings → Default API Keys
- Enter your OpenAI API key
- Save the configuration
Commands will automatically use your default key:
# No --profile needed - uses default key automatically
rlama rag o3-mini my-rag ./documents
rlama update-model my-rag gpt-4o
rlama run my-ragFor users managing multiple OpenAI accounts or organizations:
Via CLI:
# Create profiles for different environments
rlama profile add work-openai openai "sk-work-key..."
rlama profile add personal-openai openai "sk-personal-key..."Via Web Interface:
- Navigate to Settings → Named Profiles
- Click "New Profile"
- Fill in the profile details:
- Name: Unique identifier (e.g.,
work-account,personal-account) - Provider: OpenAI (automatically selected)
- API Key: Your OpenAI API key (starts with
sk-) - Description: Optional description for the profile
- Name: Unique identifier (e.g.,
# List all profiles
rlama profile list
# Delete a profile
rlama profile delete old-profile# Specify profile with --profile flag
rlama rag gpt-4o work-rag ./documents --profile work-openai
rlama rag o3-mini personal-rag ./documents --profile personal-openai
# Update models with specific profiles
rlama update-model work-rag gpt-4o --profile work-openai
rlama update-model personal-rag o3-mini --profile personal-openaiThe RLAMA web interface provides:
- Real-time validation of API key format
- Secure storage with masked key display
- Integration examples showing exact CLI commands
- Model pricing table with latest 2025 rates
- Usage guidance for both default keys and named profiles
Default API Keys:
- ✅ Simple setup - configure once, use everywhere
- ✅ No need to remember profile names
- ✅ Automatic usage in all commands
- ✅ Perfect for single OpenAI account users
Named Profiles:
- ✅ Multiple API keys management
- ✅ Project-specific configurations
- ✅ Environment separation (dev/staging/prod)
- ✅ Organization account switching
- ✅ Audit trail with usage tracking
# 1. Set default API key in web interface (one-time setup)
# 2. Use RLAMA commands directly - no profiles needed
rlama rag o3-mini my-docs ./docs
rlama run my-docs # Uses default key automatically# 1. Create profiles for different environments
rlama profile add dev-openai openai "sk-dev-key..."
rlama profile add prod-openai openai "sk-prod-key..."
# 2. Create RAGs with specific profiles
rlama rag o3-mini dev-docs ./dev-docs --profile dev-openai
rlama rag gpt-4o prod-docs ./prod-docs --profile prod-openai
# 3. Use RAGs with their associated profiles
rlama run dev-docs # Must specify profile or use default
rlama run prod-docs # Profile is remembered per RAGThis dual approach ensures RLAMA works seamlessly for both simple single-account usage and complex multi-account enterprise scenarios.