> ## Documentation Index > Fetch the complete documentation index at: https://docs.graphify.com/llms.txt > Use this file to discover all available pages before exploring further. # Run Graphify locally > Choose a code-only workflow or add local semantic inference. The open-source engine stores its graph on your filesystem. It does not require a Graphify account. ## Code-only workflow ```bash theme={null} uv tool install graphifyy graphify extract . --code-only graphify cluster-only . --no-label graphify export html graphify query "entry points" ``` `--code-only` is an `extract` flag. It skips semantic document and media extraction. It is not a flag you need to pass to the assistant skill. ## Include documents with a local model Install the Ollama extra and run an Ollama model suitable for your content. Select an installed model in `OLLAMA_MODEL`, then: ```bash theme={null} uv tool install "graphifyy[ollama]" graphify extract ./docs --backend ollama ``` Graphify defaults to the local Ollama endpoint. If you override the endpoint to a remote host, that changes where the content is sent. Large documents may need a smaller `--token-budget` or lower `--max-concurrency`. ## Understand the boundaries * Local structural parsing does not send code to a model. * A cloud coding assistant still controls where its own prompts and context go. * A headless semantic backend receives the documents or media you ask it to process. * PR commands contact GitHub, and model-assisted CLI triage can call your configured provider. * Query logging is off by default in the current implementation; [configuration](/reference/configuration) describes the opt-in controls. The generated graph can contain source text, labels, paths, and document concepts. Keep it in a location appropriate for that source material.