Build the assistant as a local pipeline: capture speech, transcribe it, retrieve relevant passages from a local document index, generate an answer with a local language model, then speak it through local text-to-speech. It is offline only when every runtime component and required asset stays on your hardware; downloading models, cloud speech services, remote updates, or telemetry can introduce network traffic.
How the assistant works
A voice assistant with retrieval-augmented generation (RAG) combines speech processing with document search. The speech components turn a spoken question into text and the generated answer back into audio. RAG supplies the language model with relevant passages from your own files instead of asking it to rely only on what it learned during training.
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- Capture audio from a microphone.
- Optionally detect a wake word and identify when speech starts and ends.
- Transcribe speech to text with local speech-to-text (STT).
- Normalize the question if needed, then create a local embedding—a vector representation of its meaning.
- Search a local index for relevant document passages.
- Give the question and retrieved passages to a local language model (LLM) to generate an answer.
- Convert the answer to speech with local text-to-speech (TTS) and play it through a speaker.
Keep the stages modular. That makes it easier to replace a model or identify whether delay or errors come from transcription, retrieval, generation, or speech output. Home Assistant describes a voice pipeline built from wake-word detection, STT, intent handling, and TTS; for a custom document-answering assistant, retrieval and local LLM generation fit between transcription and speech synthesis. See Home Assistant’s Assist pipeline documentation and its guide to a fully local voice assistant.
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What “offline” actually requires
A component being called “local” does not by itself prove that the complete system operates offline. Models and packages may need to be downloaded first, and software can have update checks or telemetry. A cloud speech endpoint or a remote model API makes the corresponding part of the interaction network-dependent.
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Download the models and software you intend to use while connected, then test the complete runtime with outbound network access blocked. Check that speech recognition, embedding generation, retrieval, LLM inference, and speech playback still work. Also review logs and firewall activity: a successful answer alone does not establish that nothing attempted to contact a remote service.
- Keep STT, embeddings, the vector index, the LLM, and TTS on local hardware.
- Store required model files and application assets locally before disconnecting.
- Check whether the software sends telemetry or performs network checks, and disable or block those requests where possible.
- Keep updates and new model downloads as explicit maintenance tasks rather than assuming they happen offline.
Home Assistant’s local-assistant guide describes a microphone, local STT, local processing, and local TTS. That is a product description of its setup, not proof that every custom combination of software and hardware has been independently verified to remain offline.
Build it in stages
1. Prove local model inference
Choose a local model runtime and download the answer model and embedding model while network access is available. Test a prompt and response on the intended host. Then block network access and repeat the test. This catches missing model files and remote dependencies before you add document processing or audio.
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Ollama’s April 8, 2024 article on embedding models explains how vectors can represent semantic meaning and gives mxbai-embed-large, nomic-embed-text, and all-minilm as examples. They are examples in that dated article, not a definitive current ranking or a guarantee that a particular model fits your hardware.
2. Ingest documents and build a local index
Extract readable text from the files you want the assistant to answer from. Keep provenance with every passage, such as its file name, page or section, and ingestion timestamp. Divide text into coherent chunks, embed each chunk locally, and store the vectors alongside the original text and metadata in a local database.
There is no universal parser, chunk size, overlap, or vector database established by the cited documentation. The right choices depend on the material: a manual with numbered steps, a collection of policies, and scanned pages may need different extraction and chunking approaches. Start with a small representative set and inspect what the parser actually extracted before indexing a large collection.
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3. Test retrieval without voice
Ask representative questions directly against the index and inspect the passages returned. A relevant-sounding answer is not enough if the search selected the wrong section. Include exact names, codes, dates, and section references in your test set. If semantic similarity misses exact terms, add lexical search or metadata filters rather than expecting vector search alone to solve every query.
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Ollama’s article establishes the basic approach—create embeddings and compare vectors to find semantically similar text—but does not promise that semantic search alone will retrieve the right passage for every kind of document or question.
4. Generate grounded answers
Pass the user’s question and a limited set of retrieved passages to the local LLM. In its instructions, tell the model to answer from those passages, say when the supplied context does not establish an answer, and retain source labels so the interface or spoken reply can identify the document. These instructions can improve the design but cannot guarantee that a model will never invent or misstate a fact.
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Treat retrieved text as untrusted input, especially if the documents may contain instructions. Keep a clear distinction between system instructions and source passages. Make the initial assistant answer questions only; do not add actions or external tools until you have tested retrieval and grounding thoroughly.
5. Add speech after the text flow works
Connect local STT to the tested retrieval-and-generation path, then connect local TTS to its response. Home Assistant documents Speech-to-Phrase or Whisper for STT and Piper for TTS in its local voice assistant guide. The options suit different jobs:
| Component | Trade-off | Published performance example |
|---|---|---|
| Speech-to-Phrase | Fast, but recognizes a subset of supported Assist commands rather than arbitrary open-ended speech. | Home Assistant reports under one second on Home Assistant Green or Raspberry Pi 4. This is a vendor-published device example, not a guarantee for other setups. |
| Whisper | Supports open-ended transcription, but can demand more compute and take longer on modest hardware. | Home Assistant reports around 8 seconds per voice command on Raspberry Pi 4 and under one second on an Intel NUC. These are vendor-published examples, not independent benchmarks or predictions for arbitrary configurations. |
| Piper TTS | A local neural text-to-speech system; Home Assistant describes it as optimized for Raspberry Pi 4. | Home Assistant reports that a Raspberry Pi can generate 1.6 seconds of speech per second with a medium-quality model. The cited setup details are unspecified, so treat this as indicative rather than promised performance. |
The timings above are not directly comparable end-to-end benchmarks: they describe different components and devices. Measure your own complete pipeline with the models and workload you plan to use.
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6. Add wake-word detection and a satellite last
A satellite is the microphone-and-speaker endpoint that handles audio capture and playback, and may also run wake-word detection. Home Assistant documents a Linux computer with a USB microphone or speakerphone as one approach, and names the M5Stack ATOM Echo Development Kit as another option. Choose microphone placement and hardware for your room, speaking distance, background noise, and echo rather than assuming one device suits every space.
Home Assistant’s documentation says openWakeWord is English-only. Its wake-word pages explain the Home Assistant approach to wake words and how to enable a wake word. In Home Assistant’s documented microphone-satellite arrangement, audio is streamed to the host for wake-word checking. Account for that audio path when deciding where processing should happen and what your privacy requirements are.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose hardware by measuring the whole workload
Decide on the host after selecting the STT, embedding, and LLM models you intend to run. Measure the result on representative questions: hardware that handles a constrained recognizer and TTS may not run a larger local LLM responsively. The cited examples do not establish that a Raspberry Pi can comfortably run every current local RAG stack.
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- Microphone and speaker: Use equipment suited to the room and distance. A USB microphone or speakerphone is one documented category, not a universal product recommendation. Test for room noise, echo, and speaker feedback.
- Model and index maintenance: Record which embedding model created the index. Changing embedding models generally requires rebuilding that index so stored document vectors and query vectors remain compatible.
- Satellite placement: If the satellite streams audio to a host for wake-word checking, include that connection in your privacy and network design.
Evaluate accuracy, latency, and privacy separately
Track latency at each stage instead of timing only the full spoken answer. Useful checkpoints include end-of-speech detection, transcription, embedding, retrieval, time to the first generated token, completion of the answer, and TTS playback. This helps identify whether a slow response comes from speech processing, search, model generation, or audio output.
Use two different evaluation sets. For speech recognition, include the accents, languages, room noise, and speaking styles expected in actual use. For RAG, use representative questions with known answers as well as questions whose answers are absent from the index. For each RAG test, record whether the correct passage was retrieved, whether the answer stayed within the available evidence, and whether the source label was correct.
- Test exact identifiers and dates as well as paraphrased questions.
- Inspect the retrieved passages, not just the final answer.
- Include out-of-scope questions to see whether the assistant acknowledges missing evidence.
- Repeat tests with the network blocked and check for unexpected outbound requests.
Common design mistakes to avoid
- Calling the system offline because the LLM is local: STT, TTS, embeddings, model downloads, telemetry, and updates also affect whether the system depends on a network.
- Adding voice before validating retrieval: Audio makes failures harder to diagnose. Confirm that the index returns useful passages before introducing transcription and playback.
- Assuming an LLM searches files by itself: RAG requires text extraction, chunking, embeddings, an index, retrieval, and then answer generation. A language model alone does not search your document collection.
- Expecting semantic search to handle every exact match: Test identifiers, codes, and dates; add lexical or metadata-based retrieval where those matter.
- Trusting a prompt as a complete safeguard: Instructions to answer only from retrieved context help define intended behavior but do not eliminate hallucinations or risks from hostile text in documents.
- Choosing hardware from a single published timing: Component-level examples do not predict end-to-end latency for your models, documents, and audio setup.
Make offline operation a test, not an assumption
A practical build is complete only when it answers representative questions from the intended documents, speaks intelligibly at acceptable speed, identifies its sources, and continues working with network access blocked. Keep the pipeline modular, preserve document provenance, and use measured results from your own setup to decide whether to change a model, retrieval method, or host.
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