RLAMA

RLAMA RLAMA Blog Documentation RLAMA-Pro loading... RLAMA is a powerful open-source document question-answering tool that connects to your local Ollama models. It enables you to create, manage, and interact with Retrieval-Augmented Generation (RAG) systems tailored to your documents. It emphasizes local processing, wide format support, and flexible integration to fit into various workflows.

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What is RLAMA?

RLAMA is a complete RAG solution designed for local, private document processing. It supports Connecting to local models (Ollama, llama3, OpenAI, Hugging Face as options), offline-first operation, and semantic document chunking to provide accurate question-answering over your own data. It offers a visual and CLI-based workflow to create and manage RAG systems, agents, and crews that can perform specialized tasks.

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Core Capabilities

• Local storage and processing with no data sent externally
• Advanced semantic chunking for optimal context retrieval
• Multiple document formats (.txt, .md, .pdf, etc.)
• Web crawling to create RAGs from websites
• Directory watching for automatic RAG updates
• Hugging Face integration with 45,000+ GGUF models
• Flexible integration options (HTTP API server, cross-platform support, model compatibilities)
• OpenAI model support alongside Ollama AI Agents & Crews
• Agent-based workflows with roles such as researcher, writer, coder
• RAG search, code execution, and web search tools within agents

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How RLAMA Works

• Create and configure RAG systems from your documents with simple commands
• Support for PDFs, Markdown, Text files, and more with intelligent parsing
• Offline-first processing ensures privacy and security
• Smart chunking to retrieve the right context from large documents
• Interactive query sessions to ask questions in natural language
• Automatic updates to RAGs when source documents change

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How to Use RLAMA

1. Install RLAMA (free and open source).
2. Create a RAG from a folder of documents: rlama rag [model] [rag-name] [folder-path]
3. Start an interactive session: rlama run [rag-name]
4. Manage RAGs with commands like list, delete, watch, api, update
5. Use the Visual Builder (RLAMA Unlimited) for a no-code experience to build RAGs quickly

Example commands:

• Create a RAG: rlama rag llama3.documentation ./docs
• Run a RAG: rlama run documentation
• List RAGs: rlama list
• Watch for changes: rlama watch documentation ./docs
• Start API: rlama api --port 8080

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Supported File Formats

• Text: .txt, .md, .html, .json, .csv, .yaml, .yml, .xml, .org
• Code: .go, .py, .js, .java, .c, .cpp, .h, .rb, .php, .rs, .swift, .kt, .ts, etc.
• Documents: .pdf, .docx, .doc, .rtf, .odt, .pptx, .ppt, .xlsx, .xls, .epub

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Features Overview

• Local, private processing with 100% offline support
• Simple setup and configuration of RAG systems
• Support for PDFs, Markdown, Text, and more with intelligent parsing
• Interactive query sessions for natural language questions
• Automatic document watching to keep RAGs up to date
• Visual RAG Builder for quick creation with no coding
• Drag-and-drop document uploads and straightforward configuration
• Supports multiple embedding models and sources (Local Folder, Website)
• Cross-platform: macOS, Linux, Windows
• API server for integration into external apps
• Flexible agent ecosystem with researchers, writers, coders, etc.

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Why Choose RLAMA?

• 100% local processing for privacy and data security
• Open-source with a vibrant ecosystem and integrations
• Rapid setup to create powerful document-based Q&A systems
• Suitable for personal projects, research, and enterprise-like workflows

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Safety & Privacy Considerations

• All processing can be kept local; data never leaves your environment
• Ensure appropriate handling of sensitive information when integrating with external sources

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Quick Start Summary

• RLAMA is a free, open-source CLI tool with a visual builder option (RLAMA Unlimited)
• Create RAGs from your documents, run interactive sessions, and expose an API if needed
• Supports a wide range of document formats and models

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Additional Resources

• Documentation and examples in the repository
• Community and support channels for RLAMA

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Core Features

• Simple setup: Create and configure RAG systems with minimal commands
• Multiple document formats: PDFs, Markdown, TXT, etc., with intelligent parsing
• Offline-first: 100% local processing without external data transmission
• Intelligent chunking: Optimized context retrieval
• Interactive sessions: Natural language querying
• Document watching: Automatic updates when documents change
• Visual RAG Builder: Create RAGs in minutes with no coding
• Easy drag-and-drop document uploads
• Model flexibility: Ollama, OpenAI, Hugging Face integration
• Cross-platform support: macOS, Linux, Windows