RLAMA (Retrieval-Augmented Local Assistant Model Agent) is an open-source AI solution that integrates with local AI models to create, manage, and interact with Retrieval-Augmented Generation (RAG) systems. It allows users to build powerful document question-answering systems with multiple document formats, advanced semantic chunking, and local storage and processing.
Open-source tool for building document question-answering systems with local AI models.
- Query project documentation, manuals, and specifications
- Create secure RAG systems for sensitive documents with full privacy
- Query research papers, textbooks, and study materials for faster learning
- RLAMA can be installed and used via the command line. Users can create RAG systems by indexing folders of documents
- query documents in an interactive session
- and manage RAG systems with commands like `rlama rag`
- `rlama run`
- `rlama list`
- and `rlama delete`. RLAMA Unlimited offers a visual interface for building RAG systems without coding.
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