RLAMA
RLAMA (Retrieval-Augmented Local Assistant Model Agent) is an open-source document question-answering tool that connects to local Ollama models to create, manage and interact with RAG systems for processing and querying various document formats while keeping all data private and local.
https://rlama.dev/?ref=aipure
Product Information
Updated:Mar 11, 2025
What is RLAMA
RLAMA is a powerful AI-driven document assistant designed specifically for developers and technical users who want to efficiently query and retrieve information from their document collections. Built with Go, it provides a streamlined solution for document question-answering without relying on cloud services. The tool requires Go 1.21+ and Ollama to be installed locally, making it a completely self-contained system that processes everything on your own machine.
Key Features of RLAMA
RLAMA (Retrieval-Augmented Local Assistant Model Agent) is an open-source document question-answering tool that enables users to create and manage RAG systems locally using Ollama models. It processes various document formats, generates embeddings, and provides an interactive querying interface while maintaining complete privacy by keeping all data processing on the local machine.
Local Document Processing: Processes and indexes documents entirely locally using Ollama models, ensuring data privacy and security
Multi-Format Support: Handles numerous file formats including text, code, PDFs, DOCX, and other document types for comprehensive document analysis
Interactive RAG Sessions: Provides an interactive interface for querying document knowledge bases using natural language
Simple Management Interface: Offers straightforward commands for creating, listing, and deleting RAG systems
Use Cases of RLAMA
Technical Documentation Management: Developers can index and query large codebases and technical documentation for quick information retrieval
Research Analysis: Researchers can process and query multiple research papers and documents to find relevant information and connections
Personal Knowledge Base: Individuals can create a searchable knowledge base from their personal documents and notes
Local Business Document Processing: Small businesses can organize and query their internal documents while maintaining data privacy
Pros
Complete privacy with local processing
Open-source and free to use
Easy to set up and use with minimal dependencies
Supports wide range of document formats
Cons
Requires Go 1.21+ and Ollama to be installed
Limited to local computing resources
May have performance limitations with very large document sets
How to Use RLAMA
Install Prerequisites: Ensure you have Go 1.21+ and Ollama installed and running on your system. Also verify required tools like pdftotext and tesseract are installed.
Install RLAMA: Install RLAMA using Go. The exact installation command is not provided in the sources but it likely uses 'go install'.
Create a RAG System: Use command 'rlama rag [model] [rag-name] [folder-path]' to create a new RAG system. For example: 'rlama rag llama3 documentation ./docs' - this will process and index all documents in the specified folder.
Verify RAG System Creation: Use 'rlama list' to check that your RAG system was created successfully and documents were properly indexed.
Start Interactive Session: Use 'rlama run [rag-name]' to start an interactive session with your RAG system. For example: 'rlama run documentation'
Query Documents: In the interactive session, ask questions in natural language about your documents. RLAMA will retrieve relevant passages and generate answers using the Ollama model.
Manage RAG Systems: Use 'rlama delete [rag-name] --force' to remove unwanted RAG systems, and 'rlama update' to keep RLAMA up to date with the latest version.
Troubleshooting: If issues occur, verify document content extraction, try rephrasing questions more precisely, or open an issue on GitHub with exact commands used.
RLAMA FAQs
RLAMA (Retrieval-Augmented Local Assistant Model Agent) is an open-source AI-driven question-answering tool that connects to local Ollama models for document processing and information retrieval. It allows users to create, manage, and interact with RAG systems for document needs.
RLAMA Video
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