RLAMA : A powerful local document question-answering tool that connects to your local Ollama model to create and manage RAG systems.

RLAMA

Knowledge Management Efficiency Tools #Document Management #Knowledge Retrieval #Privacy Security #Local Processing #Developer-friendly Standard Picks Paid

Overview :

RLAMA is a local document question-answering tool that connects to a local Ollama model to provide users with document indexing, querying, and interactive functions. It supports multiple document formats, ensuring that data is processed entirely locally to protect privacy and security. This tool is primarily aimed at developers and technical users to improve the efficiency of document management and knowledge retrieval, particularly for handling sensitive documents and private knowledge bases. The current product is an open-source free version, with potential for future feature expansion.

Target Users :

RLAMA is primarily designed for developers and technical users, especially those who need to handle sensitive documents, build private knowledge bases, or require efficient document management and query capabilities. It is also suitable for researchers, internal enterprise knowledge management system developers, and users with strict requirements for local data security.

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Use Cases

Enterprise internal document management system: Use RLAMA to create a private RAG system for quick retrieval of technical documents and project manuals.

Researchers querying literature: Index and query research papers using RLAMA to improve research efficiency.

Personal knowledge base management: Import personal notes, tutorials, and other documents into RLAMA for interactive querying at any time.

Features

Supports multiple document formats (such as PDF, DOCX, TXT, etc.) to meet the needs of different users.

Processes all data locally to ensure privacy and security, with no risk of data leakage.

Creates interactive RAG sessions for users to easily query the document knowledge base.

Simple and easy-to-use command-line tool allowing users to quickly create, manage, and delete RAG systems via commands.

Supports document indexing and intelligent retrieval to improve document query efficiency.

Developer-friendly, developed in Go language, easy to extend and integrate.

Provides API interfaces for developers to perform secondary development and integration.

How to Use

1. Install RLAMA: Download and install the installation package for macOS, Linux, or Windows from the official website.

2. Create a RAG system: Use the command `rlama rag [model] [rag-name] [folder-path]` to specify the model, system name, and document folder path to create a RAG system.

3. Index documents: Place the documents you need to query into the specified folder, and RLAMA will automatically index them and generate embedding vectors.

4. Start an interactive session: Start an interactive session to query the document knowledge base using the command `rlama run [rag-name]`.

5. Manage RAG systems: Use `rlama list` to list all RAG systems, or use `rlama delete [rag-name]` to delete unnecessary systems.