Retrieval-Augmented Generation (RAG) improves large language models (LLMs) by retrieving relevant document snippets, thereby enhancing response accuracy. The rlama tool facilitates a fully local, offline RAG setup, preserving data privacy and eliminating cloud dependencies. While supporting a variety of model sizes, rlama particularly optimizes for smaller models. It simplifies the multi-component traditional RAG process into a single command-line interface (CLI) tool, allowing users to ingest documents, generate embeddings, and manage a hybrid vector store for efficient querying and retrieval of contextual information.
In RAG, a knowledge store is queried to retrieve pertinent documents added to the LLM prompt, helping ground the model's output with factual data.
Rlama streamlines the entire traditional RAG process into a single CLI tool, handling document ingestion, embedding generation, and context retrieval.
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