# Local RAG A fully local RAG (Retrieval-Augmented Generation) system. Give it any text file, ask it questions. Everything stays local. * **Encoderfile** handles embedding locally. * **Llamafile** runs the LLM locally. * **NumPy** handles similarity search in memory. This is a good fit for offline environments, sensitive documents, or anywhere you need a simple, self-contained question-answering system without cloud dependencies. Check out the full code and instructions in [GitHub](https://github.com/mozilla-ai/encoderfile/tree/main/examples/local-rag). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.mozilla.ai/encoderfile/cookbooks/local-rag.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.