The Tool Desk
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The title describes a project subject, not a verified parts list or test report. The architecture below is based on documented Home Assistant and Ollama capabilities; it does not claim a particular computer, microphone, model, document set or measured result was used for the friend.
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What the assistant actually does
A natural-language request travels through several independent jobs. Keeping those jobs separate makes the design easier to replace, measure and secure.
- Capture: a microphone on a voice endpoint records speech. A wake word or push-to-talk action decides when to listen.
- Speech-to-text: a local engine converts audio into text.
- Conversation or intent handling: Home Assistant interprets the text either as a home-control intent or as a question for a conversational agent.
- Retrieval (for document questions): the question is converted to an embedding, similar document passages are found, and those passages are supplied as context.
- Generation: a local LLM writes an answer from the question and retrieved context.
- Speech output: a local TTS engine turns the answer into audio for the speaker.
Home Assistant documents voice as modular components—wake-word detection, STT, intent recognition and TTS—and its developer overview separates conversation processing from intent execution (Home Assistant voice architecture). The Wyoming integration can connect services such as Whisper, Piper, Speech-to-Phrase and openWakeWord, including a compute-heavy service running on another computer on the same local network (Wyoming Protocol).
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A compact data-flow view is:
Microphone/endpoint → wake word or push-to-talk → local STT → conversation or intent layer → (document question) query embedding → vector/semantic retrieval → selected passages → local LLM → local TTS → speaker.
Define “offline” precisely
“Offline” is not a property of a product name; it is a property of the configured end-to-end path. Audio, transcription, embeddings, vector search, LLM inference and speech synthesis all need local implementations if none of that content is to leave the home.
Home Assistant’s documented local-voice setup describes the intended flow this way: “Your spoken commands never leave your home: a microphone hears you, a local speech-to-text engine turns your voice into text, Home Assistant figures out what you want, and a local text-to-speech engine speaks the answer back.” (Home Assistant local voice guide) That statement applies to the documented local Assist configuration, not automatically to every installation.
Audit the actual deployment for:
- Cloud STT, TTS or LLM fallbacks enabled in a pipeline or integration.
- External retrieval APIs or document URLs used at question time.
- Remote access, telemetry and update checks that transmit content or metadata.
- A voice endpoint that forwards audio to a service outside the trusted network.
- A local Home Assistant instance connected to an external conversational agent.
A system can keep voice processing local while still using the internet for unrelated updates, or it can quietly send prompts to a cloud model. Describe those cases separately rather than calling the whole system offline.
Choose speech recognition for the real vocabulary
Home Assistant presents two useful local STT directions. Speech-to-Phrase is a close-ended recognizer: it transcribes phrases it knows and is optimized for supported Assist commands. Whisper is open-ended and can handle broader language, but normally demands more compute.
| Option | Best fit | Trade-off | Documented example timing |
|---|---|---|---|
| Speech-to-Phrase | Fast, predictable home-control phrases | Limited coverage outside its supported command vocabulary | Home Assistant reports under one second on Home Assistant Green or Raspberry Pi 4 |
| Whisper | Open-ended questions and varied wording | Higher CPU/GPU and memory demand; latency depends strongly on hardware and model | Home Assistant gives around eight seconds on Raspberry Pi 4 and under one second on an Intel NUC |
Those figures are illustrative examples on an undated Home Assistant documentation page, accessed in 2026—not controlled benchmarks, hardware-wide guarantees or measurements of the titled project (source and conditions).
Select STT by testing the intended language, microphone, room noise, vocabulary, acceptable response time and available compute. Home Assistant notes that local language support requires all three pieces: a local STT model, Home Assistant sentence support and a local TTS model (language-support guidance). An open-ended document assistant generally needs a recognizer that can transcribe questions and names not present in a fixed home-control grammar.
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Build the document side as a separate RAG pipeline
Retrieval-augmented generation (RAG) does not put a friend’s files into an LLM’s permanent memory. It creates a searchable representation and supplies selected evidence only when a question is asked.
1. Index the approved files
Parse each chosen PDF, text file, web export or other supported format. Preserve the original filename, section heading, page or URL and modification date as metadata. Split the extracted text into sections that retain enough context to make a passage meaningful; very small fragments lose context, while very large fragments dilute retrieval precision.
2. Create and store embeddings
An embedding model converts each passage into a vector. Store the vector beside its text and metadata in a local vector or semantic-search store. Ollama documents embeddings for semantic search and RAG and maintains a list of embedding models in its capability documentation (Ollama embeddings documentation).
3. Retrieve at question time
Embed the transcribed question with the same embedding approach, search for nearby vectors and select a small set of passages. Keep source metadata attached so the answer can name the file and section. If the top results are unrelated or below a chosen relevance threshold, the assistant should say that the answer was not found in the indexed documents instead of guessing.
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4. Generate a grounded response
Send the question and retrieved passages to the local LLM with instructions to stay within that evidence. A useful response can quote or link the source passage, distinguish a direct statement from an inference and ask a clarifying question when several documents conflict.
RAG quality depends on extraction quality, current files, chunk boundaries, embedding-model fit, retrieval relevance and the generator’s discipline. These are evaluation dimensions for the actual build, not performance results established for this project.
Keep conversation and device control bounded
Document answers and home automation are different risk classes. A model that can summarize a handbook does not need permission to unlock a door, change an alarm state or alter system configuration.
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Home Assistant’s built-in LLM Assist API exposes the intent and entities made available to its conversation agent, while explicitly excluding administrative tasks (Home Assistant LLM API). Expose only the entities required for the use case, separate read-only answers from actions and require confirmation for consequential operations.
- Test ambiguous names, missing entities and contradictory instructions.
- Return a clear failure when an intent is unsupported or a device is unavailable.
- Log which intent was executed without storing more voice content than necessary.
- Keep document retrieval from granting new device-control permissions.
Choose an endpoint and local compute plan
A physical satellite is optional. An existing phone, computer or microphone-and-speaker setup can provide an endpoint, while a dedicated device can improve placement, wake-word consistency and muting.
Home Assistant’s Voice Preview Edition documentation describes an endpoint with dual microphones, speaker output and a physical switch that cuts power to the microphones. It supports local-processing configurations, but the documentation does not establish that this device was used for the friend’s project (Voice Preview Edition documentation).
Place the microphone away from the speaker, fans and reflective surfaces. Decide where compute runs:
- On the endpoint: simplest network path, but limited by the device’s CPU, RAM and storage.
- On a local server: supports larger Whisper or LLM models and can serve several endpoints, but adds network and power dependencies.
- Split across local machines: Wyoming services can run elsewhere on the home network; document the address, firewall rules and failure behavior.
Do not select a specific computer, microphone or speaker without requirements and measurements from the intended room and language.
Implement in stages
- Make push-to-talk local voice work first. Confirm local STT and TTS, then inspect logs or network monitoring to verify that audio and text do not leave the network.
- Add wake-word detection. Measure false activations, missed activations and behavior when the endpoint is muted.
- Connect the conversation layer. Keep ordinary home intents separate from document questions and expose only required entities.
- Build the document index offline. Parse a small, known set of files, retain metadata and record the index version whenever files change.
- Add retrieval before generation. Display the selected passages and sources during development so irrelevant matches are visible.
- Attach the local LLM and TTS. Require evidence-backed answers and provide a not-found response for weak retrieval.
- Test failure paths. Unplug the network, stop each service, use an unsupported language and ask questions whose answers are absent or contradictory.
What to measure before calling it successful
There is no dated independent benchmark or project-specific test result establishing performance for the titled build. A credible evaluation should record:
- STT word and name errors in the target room, language and microphone position.
- Time from end of speech to transcript, retrieved passages and spoken answer.
- Wake-word false positives and misses over representative household activity.
- Whether the correct document and section appear among retrieved results.
- Grounded-answer rate, citation or source-display quality and appropriate not-found behavior.
- Recovery when a model, endpoint, network link or document index is unavailable.
- Every outbound connection observed during normal use, updates and error handling.
These measurements reveal whether the bottleneck is acoustics, STT, retrieval, LLM generation or TTS instead of masking all delays as “the assistant.”
What is established—and what still needs confirmation
The cited platform documentation establishes a modular Home Assistant voice pipeline, local STT/TTS options, Wyoming’s local-network service pattern, an LLM API with bounded Assist capabilities and Ollama’s embedding support. It does not identify the friend’s host operating system, endpoint hardware, wake word, STT/TTS models, LLM runtime, document formats, chunking strategy, vector database, network-isolation method or measured accuracy and latency. Those details must be obtained before presenting the work as a reproducible case study.
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