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An offline voice assistant that answers questions about personal documents needs more than a local language model: it needs a speech pipeline, a document index, retrieval that supplies useful passages, and a configuration that keeps every relevant processing step on local devices. This is a practical design blueprint, not a verified account of a particular friend’s hardware, software, or test results.
What an offline RAG voice assistant does
The assistant handles two kinds of work: it converts spoken questions into text and speaks back an answer; for questions about personal files, it also finds relevant passages and gives them to a language model as context. RAG, or retrieval-augmented generation, means the model uses retrieved material at answer time. It does not mean the model has memorized the documents.
A useful high-level flow is:
Microphone or endpoint → wake word or push-to-talk → speech-to-text → conversation layer → document retrieval when needed → local language model → text-to-speech → speaker.
Home Assistant documents voice assistants as modular pipelines, with separate components for wake-word detection, speech recognition, conversation or intent handling, and speech synthesis. Its developer overview explains the component roles, while the pipeline documentation describes audio input and pipeline events: Voice in Home Assistant and Assist pipelines.
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Home Assistant is one documented way to organize these pieces, not evidence that a specific build used it. The same architecture can be implemented with other local components; the important requirement is to trace what processes each stage and where it runs.
How document retrieval fits into the voice pipeline
Document Q&A has an indexing path that happens before a person asks a question, and a retrieval path that runs when a question arrives.
Prepare the documents
- Choose and extract sources. Select the files the assistant is allowed to use. Extract readable text from each format and check that tables, headings, and other important context survive extraction.
- Split text into useful passages. Divide the extracted text into sections small enough to retrieve, but large enough to keep definitions and surrounding context together. Store source details such as file name, section, and page when available.
- Create embeddings and index them. An embedding model converts each passage into a numeric representation that can support semantic search. Store the vectors with the passage text and its source metadata in a searchable index.
Ollama documents embeddings for semantic search and RAG, and lists embedding models in its documentation: Ollama: Embeddings. That establishes an available capability, not which embedding model, database, parser, or indexing setup a particular project should use.
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Answer a question
- Transcribe the spoken question into text.
- Embed the question and search the index for passages that appear relevant.
- Give the selected passages, their source details, and the question to the language model.
- Ask the model to answer from those passages, identify the sources, and say when the material does not provide an answer.
- Turn the response into speech and play it through the chosen endpoint.
Retrieval is a separate step from generation: a fluent answer is not proof that the right material was found. Check whether the documents extract cleanly, passage boundaries retain context, retrieved results actually support the answer, and the response distinguishes evidence from uncertainty. For an answer that cannot be checked against a source, a useful fallback is to say the answer was not found in the indexed documents.
Choose speech recognition for the actual use case
The key choice is between a recognizer optimized for a limited set of known commands and one intended to transcribe open-ended speech. Home Assistant describes Speech-to-Phrase as a close-ended model that is fast on modest hardware but covers a limited set of Assist commands; Whisper is the more open-ended option and can require more processing time.
| Option | Best fit | Documented timing example | Trade-off |
|---|---|---|---|
| Speech-to-Phrase | Known, supported home-control phrases | Home Assistant gives an illustrative figure of under one second on Home Assistant Green or Raspberry Pi 4. | Fast on modest hardware, but limited to the commands and language support it covers. |
| Whisper | More open-ended transcription | Home Assistant gives illustrative figures of around eight seconds on Raspberry Pi 4 and under one second on Intel NUC. | Broader transcription use, with speed dependent on hardware. |
These are examples on Home Assistant’s undated local voice documentation page, accessed in 2026—not controlled benchmarks or results from a friend’s build. Treat them as indications that hardware can change latency, not as promises for another setup. See Home Assistant’s local voice assistant guide.
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Before choosing a model, test the languages, names, vocabulary, microphone, and room where it will actually be used. Home Assistant notes that a language needs support from local STT, Home Assistant sentence handling, and local TTS; a recognizer alone does not make the whole voice interaction available in that language. Its Voice Preview Edition documentation also distinguishes focused local processing for common home-control phrases from the greater compute needs of full local speech processing.
Decide what “offline” means for this system
“Local” is an end-to-end property, not a label attached to one model. To describe the system as offline, establish where each of these steps runs and whether it makes network requests:
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- Audio capture and wake-word detection
- Speech recognition and transcription
- Question embedding and document search
- Language-model inference
- Speech synthesis
- Any optional search, integrations, telemetry, or remote access
Home Assistant’s guide to a fully local voice assistant says its documented local STT and TTS setup sends no data to external servers for processing. Its cloud voice guide describes a different option. That distinction applies to the configured Home Assistant path; a local microphone or endpoint does not keep data local if a later component sends audio or text to a cloud speech service, external LLM, retrieval API, or online document source. See the local voice guide and Home Assistant voice control.
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Wyoming can connect Home Assistant with voice services such as Whisper, Piper, Speech-to-Phrase, and openWakeWord. A compute-heavy service can run on a separate computer on the local network, so the endpoint does not have to host every model. That is still a networked setup, even when the services remain inside the home network: Wyoming Protocol.
For a strong privacy claim, document the configured path and verify that optional cloud calls and fallbacks are disabled or understood. A system that works without an internet connection is a stronger offline claim than one whose main inference is local but whose setup, updates, integrations, or remote access still depend on the internet.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Pick an endpoint and local compute without assuming a parts list
The microphone and speaker can be an existing phone or computer, a separate mic-and-speaker setup, or a dedicated voice satellite. The right choice depends on room acoustics, wake-word reliability, microphone placement, speaker quality, and whether the endpoint needs a physical mute control. No particular endpoint or host computer is established for this project.
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Home Assistant’s Voice Preview Edition documentation describes a possible endpoint with dual microphones, speaker output, and a physical switch that cuts power to the microphones. It supports local-processing configurations, but its availability as an option does not show that it was used in a particular build: Voice Preview Edition.
Separate the endpoint decision from the compute decision. A small endpoint can capture and play audio while a local-network computer runs heavier speech or language models. This can make the room hardware simpler, but it introduces dependence on that host, its availability, and the local network.
Keep document answers and device control within clear limits
Answering from files and acting on a home are different permissions. A document assistant can be limited to retrieval and spoken answers; if it can also control devices, give it only the entities and actions needed for its intended use.
Home Assistant’s built-in LLM Assist API exposes intent and exposed-entity capabilities in line with what its built-in conversation agent can access, and does not perform administrative tasks. This is a platform boundary, not a substitute for choosing which entities to expose or testing the exact requests the assistant can perform: Home Assistant API for Large Language Models.
- Test ambiguous commands and requests the assistant cannot safely interpret.
- Require confirmation where an action has meaningful consequences.
- Check behavior when speech recognition fails, retrieval returns weak results, or a required service is unavailable.
- Keep document-answer permissions separate from device-control permissions where possible.
A practical evaluation checklist
Before relying on the assistant, evaluate the whole path rather than judging only how natural the spoken answer sounds.
- Speech: Does it understand the intended language, names, and common phrases from the chosen microphone and room?
- Latency: How long does the actual hardware take to transcribe, retrieve, generate, and speak a response?
- Retrieval: For representative questions, does the index return the right source passages? Do source names or page details help a person verify the answer?
- Grounding: Does the model stay within the retrieved evidence, and can it say when the documents do not answer the question?
- Privacy: Which components make network requests, and what happens when the internet connection is unavailable?
- Safety: Which actions can the assistant invoke, what requires confirmation, and how does it fail when a request is unclear?
These checks establish whether a particular configuration meets its needs. Platform documentation describes available components and example behavior, but it cannot establish the accuracy, latency, retrieval quality, or privacy properties of a build that has not been measured.
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