Home RAG: Your Personal Sovereign AI Assistant

You are on the couch on a Tuesday evening. Your phone is in the kitchen, your laptop is closed on the desk, and the filing cabinet is in the hallway where it always sits, untouched for months. You have a dentist appointment Thursday but you cannot remember if it is 9:00 AM or 10:00 AM. The appointment card is somewhere in a stack of mail on the counter. The confirmation email is in an inbox with 4,000 unread messages. You could search. You could dig. Instead, you speak into the room:

No phone was picked up. No app was opened. No email was searched. No filing cabinet was opened. The answer came from a small box sitting on a shelf in your living room -- a computer running a local language model, a vector database of your personal documents, a speech-to-text engine, and a text-to-speech voice. The microphone heard you. Whisper transcribed your speech. The RAG system retrieved your appointment card, the email confirmation, the kids' school documents, and the soccer schedule. The local LLM composed the answer. A neural voice spoke it back. The entire round trip -- from your mouth to the answer -- happened in under three seconds, and not a single byte of data left your home.

This is not a thought experiment. Every component of this system exists today, in 2026, as open-source software that runs on commodity hardware you can buy for $150. The local LLM is Ollama running a 7B or 8B parameter model. The vector database is Qdrant or LanceDB. The embedding model is all-MiniLM, which runs on a CPU. The speech-to-text is Whisper, which runs locally. The text-to-speech is a local neural TTS model. The web interface is a simple page running on your home network. None of this requires an internet connection for basic operation. None of this requires a subscription. None of this sends your data anywhere.

The gap between "the components exist" and "every home has one" is not a technology gap. It is an integration gap, an awareness gap, and a convenience gap. The technology is ready. The packaging is not. This page describes the vision, the architecture, the hardware, the security model, and the use cases for a home RAG system -- because the future this describes is one you can build today, not one you have to wait for.

Every family generates a stream of documents that never stops. Medical records arrive after every doctor visit -- lab results, medication lists, referral letters, discharge summaries. Insurance documents pile up -- policy renewals, explanation-of-benefits statements, claim correspondence. School papers come home weekly -- permission slips, report cards, IEP updates, lunch menus, homework, study guides. Tax documents accumulate all year -- W-2s, 1099s, receipts, charitable donation letters, mortgage interest statements. Then there are the operational documents of running a household -- appliance manuals, warranty cards, repair receipts, home maintenance schedules, HVAC filter sizes, paint color codes for each room.

Where are these documents right now? They are scattered. The medical records are in a portal you forgot the password to. The insurance papers are in a filing cabinet in the garage. The school papers are in a backpack, in a folder, on the kitchen counter, or already lost. The tax documents are in a shoebox. The appliance manuals are in a drawer -- except for the one you actually need, which you threw away the day after you bought the appliance. The warranty information is on a card you mailed in, or did not, and you cannot remember which. The photos are on three different phones and two cloud accounts. The voice memos you recorded of the contractor explaining the roof repair are on your phone, unfiled, unsearchable, and will never be listened to again.

The existing solutions are all inadequate. Cloud storage (Google Drive, Dropbox, iCloud) puts your documents on someone else's server, indexed by their software, searchable only through their interface, and subject to their terms of service. A filing cabinet is physically local but not searchable -- you cannot ask it a question. A phone full of scanned PDFs is searchable by filename only, and you will never name your files well enough to find them by natural language. A notebook full of notes is not searchable at all unless you flip through every page.

A home RAG system solves this by bringing all of those documents into one searchable, private, local knowledge base. You scan the medical records into the system. You import the insurance PDFs. You forward the school emails. You photograph the permission slips. You upload the tax documents. You import the photos with auto-captioning. You record the voice memos and they are auto-transcribed. Every document is chunked, embedded, and stored in a local vector database. When you ask a question -- by voice, from across the room -- the system searches every document at once, finds the relevant passages, and answers in natural language. You never search for a document again. You just ask.

The Privacy Imperative

The reason this has to be sovereign -- local, on your hardware -- and not cloud-based is simple: these documents are the most private data in your life. Your medical records. Your child's school records. Your tax returns. Your insurance claims. Your family photos. Your voice. Handing this to a cloud AI company means handing them the raw material of your family's life and trusting them not to misuse it, not to train on it, not to leak it, not to hand it over on a subpoena, not to get breached. The track record of cloud companies on every one of these fronts is poor. Your medical records do not belong on a server you do not control. Your child's IEP does not belong in a training dataset. Your tax returns do not belong in a data breach. Home RAG keeps them home.

A home RAG system is a small computer running in your home, connected to your local network, running a stack of open-source software that together provide document storage, search, voice interaction, and AI-powered question answering. It is not a server rack. It is not a data center. It is a box the size of a book, or smaller, that sits on a shelf, plugs into a wall outlet, and runs quietly 24/7. You interact with it by voice -- through a microphone in the room -- or through a web page on your phone, tablet, or laptop.

The system has seven core components. Each one is open source, runs locally, and has been battle-tested in production environments. Together, they form a complete home AI assistant.

• Small Computer A mini PC, a Raspberry Pi 5 with an NVMe SSD, or a repurposed laptop. This is the hardware host. It runs Linux (typically Ubuntu Server or Debian), sits on your home network, and needs no monitor or keyboard after initial setup. It runs quietly, uses little power (10-30 watts for a mini PC, 5-12 watts for a Raspberry Pi), and operates 24/7 like a router.
• Local LLM Ollama running a 7B or 8B parameter model -- Llama 3.1 8B, Qwen 2.5 7B, Mistral 7B, or Phi-3.5. These models run in 4-bit or 5-bit quantization, requiring 4-8 GB of RAM. They generate answers at 10-30 tokens per second on CPU (mini PC) or 40-80 tokens per second on GPU. This is the "brain" -- it reads retrieved documents and generates natural-language answers.
• Vector Database Qdrant (recommended for features) or LanceDB (recommended for simplicity). Both are lightweight, run locally as a single binary or embedded library, and store the embeddings of your document chunks. A home collection of 10,000-50,000 documents produces roughly 500,000 to 5 million chunks -- well within the capacity of either database on a single machine.
• Embedding Model all-MiniLM-L6-v2 (384 dimensions, 80 MB) or BGE-small-en (384 dimensions, 130 MB). These models run on CPU, generate embeddings in 5-20 milliseconds per chunk, and require no GPU. The embedding model converts document chunks and user queries into vectors for similarity search. It is the indexer of the system.
• Voice Interface Whisper (speech-to-text) for input and a local neural TTS model (Piper, Coqui, or VITS) for output. Whisper-base or Whisper-small runs on CPU at real-time or near-real-time speed. The TTS model generates natural-sounding speech in under 500 milliseconds. A microphone in the room captures your voice. A speaker outputs the answer. No cloud, no phone required, no "Hey Google" sending your voice to a server.
• Web Interface A simple web page served from the home server, accessible from any device on your home network. You use it to upload documents, browse the document store, ask text questions, configure access levels for family members, and review system logs. It is the admin panel -- not the primary interface. The primary interface is your voice.
• No Internet Required For basic operation -- voice queries, document search, question answering -- no internet connection is needed. The internet is used only for initial software installation, model downloads, and optional updates. Once the system is set up, you can disconnect your router from the outside world and the home AI still works. It answers from local documents using local models. The internet is a dependency for setup, not for operation.

The Physical Setup

Physically, the system is simple. A small computer (mini PC or Raspberry Pi) sits on a shelf or in a closet, connected to your home router via Ethernet or Wi-Fi. A USB microphone -- either a standalone unit or one built into a small speaker/mic device placed in the room -- captures voice input. A small speaker outputs the voice responses. The computer has an SSD for document storage and the vector database. Total footprint: smaller than a hardcover book. Total power draw: less than a desk lamp. Total noise: none, because there are no fans or they run at inaudible speeds. You do not think about it. It is infrastructure, like a router, and it runs 24/7.

If you want voice in multiple rooms, you add microphone/speaker units in each room, connected back to the central server over your home network. The brain stays in one place. The ears and mouth go wherever you need them. This is the same model as a Sonos system or a multi-room speaker setup -- one controller, many endpoints -- except the controller is an LLM and the endpoints are microphones and speakers.

The voice interface is the defining feature of a home RAG system. It is what makes the system usable from across the room, without a screen, without a phone, without touching anything. The round trip -- from you speaking to the system answering -- involves six steps, each running locally:

Compare this to a cloud voice assistant -- Alexa, Google Assistant, Siri. When you speak to one of those, your audio is recorded, compressed, and transmitted over the internet to a cloud server. The transcription happens on their server. The query is sent to their LLM (or their intent classifier). The retrieval happens against their data (not yours). The response is generated, sent back over the internet, and played on your speaker. At every stage, your voice and your query are on someone else's server. They are logged. They may be reviewed by humans for "quality improvement." They are stored indefinitely. A home RAG system eliminates every one of those network hops. The microphone is yours. The speaker is yours. The server is yours. The data is yours.

A home RAG system is only as useful as the documents it can search. The ingestion pipeline is how your family's documents -- currently scattered across paper, phones, email, and filing cabinets -- get into the local vector database. The goal is to make ingestion as frictionless as possible, because the system that requires you to manually name, tag, and organize every document is a system you will stop using within a month.

Paper Documents: Phone Camera + OCR

The majority of household documents still arrive on paper -- medical visit summaries, insurance letters, school permission slips, warranty cards, tax documents, receipts. The ingestion pipeline for paper is: photograph the document with your phone, upload the image to the home server (via the web interface or a dedicated phone app), and the system runs OCR (Tesseract or PaddleOCR, both open source and local) to extract the text. The text is chunked, embedded, and stored in the vector database. The original image is also stored for reference. You can photograph a stack of mail in 30 seconds and have it all searchable within a minute.

PDFs: Direct Import

Digital PDFs -- insurance policies, tax returns, appliance manuals, school newsletters -- are imported directly. The system extracts text from the PDF (using PyMuPDF or pdfplumber), chunks it, embeds it, and stores it. Scanned PDFs (image-only) are routed through the OCR pipeline. The system handles both transparently. You drag a PDF onto the web interface, or email it to a local ingestion address, or drop it in a watched folder, and it is in the system within seconds.

Email: Import & Parse

Email is one of the largest sources of household documents -- school communications, insurance correspondence, appointment confirmations, travel itineraries, receipts. The system can import email via IMAP from your existing account, parse the body and attachments, and ingest each email as a searchable document. You can filter by sender, date range, or folder. The imported emails are stored locally; the originals remain in your email account. This gives you natural-language search over your entire email history without uploading any of it to a cloud AI service.

Photos: Import with Auto-Captioning

Photos are ingested with automatic captioning. A local vision-language model (such as LLaVA or a small BLIP model) generates a text description of each photo -- "a family at the beach, sunny day, two children building a sandcastle." This caption, along with any EXIF metadata (date, location), is chunked, embedded, and stored. The photo itself is stored on the local SSD. When you ask "show me pictures from the beach trip," the system searches the captions, finds the relevant photos, and displays them on the nearest screen or describes them in voice. Your photos become searchable by content without ever being uploaded to a cloud service that might use them for training.

Voice Memos: Auto-Transcription with Whisper

Voice memos -- the recordings you make when you want to remember something but do not want to type -- are ingested with automatic transcription. You record a memo on your phone (or speak directly to the home system), the audio is sent to the local Whisper model, which transcribes it to text. The transcript is chunked, embedded, and stored. The original audio is also stored. You can search voice memos by content: "what did the contractor say about the roof?" retrieves the transcript of the memo you recorded when the contractor was explaining the flashing repair. Voice memos become searchable documents instead of forgotten recordings.

Automatic Chunking & Embedding

Behind the scenes, every ingested document goes through the same pipeline: text extraction, chunking (splitting the document into 256-512 token chunks with overlap), embedding (each chunk is converted to a 384-dimensional vector using all-MiniLM), and storage (the chunk, its embedding, and metadata -- source, date, access level -- are stored in the vector database). This pipeline runs automatically. You do not see it. You do not configure it. You drop a document in, and a few seconds later it is searchable. For a deeper dive into chunking strategies, see our Chunking Strategies page. For embedding model selection, see Embedding Models for Sovereign RAG.

The home RAG system is not a search engine. It is a conversational assistant. The difference is critical. A search engine returns a list of documents. A conversational assistant understands your question, retrieves the relevant information, composes a direct answer in natural language, and remembers the conversation so that follow-up questions work without you restating context.

Consider this sequence of interactions:

Notice what happened in that conversation. The first question established the topic: appointments. The second question -- "what about the kids?" -- did not need to specify "appointments" because the system retained the context. The third question introduced a new action (setting a reminder), which the system understood referenced "Emma's slip" from the previous answer. The fourth question -- "what did her last report card say?" -- used "her" to refer to Emma, established two turns earlier, and the system retrieved Emma's report card from the document store.

This is the power of conversational RAG over simple search. A search engine cannot resolve "what about the kids?" -- it has no context. A search engine cannot set a reminder or understand "her" refers to Emma. A conversational assistant can, because it maintains conversation history, uses the LLM to resolve references, and retrieves new documents as the conversation evolves. The LLM is the orchestrator. The RAG pipeline is the memory. The voice interface is the interaction.

The conversation buffer is configurable. For a home system, retaining the last 5-10 turns is typically sufficient and uses negligible memory. The buffer resets after a period of silence (configurable, typically 5-10 minutes) or when a new wake word session begins. This mirrors how human conversation works -- you do not expect someone to remember what you said an hour ago unless you bring it up again.

A home RAG system is not a single shared brain that everyone accesses equally. Different family members need access to different documents. A parent needs access to the family tax returns, insurance policies, and financial documents. A teenager needs access to their school materials but not the family tax returns. A young child needs access to their homework help but not medical records. The system implements role-based access control (RBAC) at the document level, enforced locally.

The Three Access Levels

How Role-Based Access Works in Practice

Each document in the system is tagged with an access level at ingestion. Family documents are tagged "family." Personal documents are tagged with the owner's identity ("mom," "dad," "emma," "liam"). Children's sensitive documents are tagged "kids-restricted" and accessible only to parent accounts. When a family member asks a question, the system filters the vector search to only retrieve chunks that the speaker is authorized to access. The LLM never sees documents the speaker cannot access, so it cannot accidentally reveal them.

Speaker identification can be explicit (you log in on the web interface, or you say your name) or implicit (voice biometrics). Voice biometrics -- identifying who is speaking by their voiceprint -- is a local capability that does not require cloud processing. A small voice embedding model (such as ECAPA-TDNN or a lightweight variant) can distinguish between family members with high accuracy after a brief enrollment phase (each family member speaks 10-20 phrases). This lets the system know who is asking and apply the correct access level automatically.

The Parent / Admin Role

Parents have an admin role that goes beyond document access. Admins can: add and remove family member accounts, set access levels on documents, review query logs (who asked what), configure the wake word, update models, manage backups, and set up remote access (if desired, via VPN). The admin role is authenticated with a password or biometric, not just voice, because voice biometrics alone are not sufficient for admin-level actions. A child mimicking a parent's voice might fool the speaker identification, but they cannot bypass the admin authentication.

The vision and architecture above describe what the system is. The use cases below describe what it does -- the concrete, daily interactions that make a home RAG system worth having. These are not speculative. Each one maps directly to documents a family already has and questions they already ask (and currently fail to answer efficiently).

The security model of a home RAG system is built on one principle: everything stays on the home network. No component of the system -- the microphone, the transcription, the retrieval, the LLM, the documents, the vector database, the voice output -- communicates with any server outside your home during normal operation. The system is air-gapped from the internet by design, not by configuration.

Network Isolation

The home server sits on your local network, behind your router's firewall. It listens for requests only from local IP addresses. By default, it does not expose any port to the internet. If you want to access the system remotely -- from work, from your phone on cellular -- you do so through a VPN (WireGuard or Tailscale), which authenticates you and encrypts the connection end-to-end. There is no web-facing login page. There is no public API. The system is invisible to the internet.

Encryption at Rest

All documents, embeddings, and the vector database are stored on an encrypted SSD. LUKS (Linux Unified Key Setup) provides full-disk encryption. The encryption key is stored in a keyfile on a USB drive that you keep in a safe, or it is unlocked at boot via a passphrase. If the SSD is removed from the system, the data is unreadable without the key. This protects against physical theft of the hardware -- a burglar who takes the server gets an encrypted disk they cannot read.

Parent / Admin Access vs. Child / Limited Access

The role-based access described in the Privacy Levels section is the core of the security model. Parents have admin access -- they can see all documents, configure the system, review logs, and manage backups. Children have limited access -- they can query documents tagged for their access level, and they cannot access parent-level documents, financial records, or restricted children's documents. The access control is enforced at the retrieval layer (the vector database filters results by access level before passing them to the LLM), which means the LLM literally never sees documents the speaker is not authorized to access.

Audit Logging

Every query is logged: who asked, when, what was asked, which documents were retrieved, and what answer was given. Logs are stored locally, encrypted, and accessible only to admin accounts. This gives parents visibility into how the system is being used -- what the kids are asking, what information is being retrieved -- without compromising the privacy of the queries themselves (the logs are on the home server, not sent anywhere). If a child asks something inappropriate, the system can be configured to notify parents via the admin interface.

Backup & Recovery

Backups are encrypted and stored on a second local drive (an external USB SSD kept in a fireproof safe) or on a network-attached storage (NAS) device on the same local network. Backups are never sent to the cloud unless you explicitly configure an encrypted offsite backup to a storage provider you trust, using client-side encryption (the backup is encrypted on your server before upload, and the cloud provider only stores ciphertext). The default is local-only backup. Recovery is a matter of restoring the encrypted backup to a replacement SSD.

One of the most important properties of a home RAG system is that it does not require expensive hardware. A system that costs $5,000 to build is a system that few families will have. A system that costs $150 to build is a system that anyone can have. The hardware falls into three tiers, each of which is a complete, functional home RAG system -- the difference is speed, model size, and how many family members can use it simultaneously.

Budget Tier: Raspberry Pi 5 + SSD (~$150)

The Raspberry Pi 5 (8 GB model) with a 1 TB NVMe SSD connected via a USB 3.0 enclosure is the entry point. It runs Linux, Ollama, a small model (Phi-3.5 mini at 3.8B parameters, or Qwen 2.5 3B), Qdrant or LanceDB, all-MiniLM for embeddings, and Whisper-tiny for speech-to-text. This is a functional home RAG system for $150. The trade-offs are speed (3-8 tokens per second on the LLM, meaning a typical answer takes 5-15 seconds to generate) and model quality (smaller models are less capable than 8B models). Voice works, but the response time is long enough that you notice the wait. For a family that wants to try home RAG without a significant investment, this is the starting point. Many families will find it sufficient and never upgrade.

Standard Tier: Mini PC with 16GB RAM (~$400) -- Recommended

A mini PC with an Intel N100 or AMD Ryzen 5 processor, 16 GB of RAM, and a 1 TB NVMe SSD is the recommended configuration for most families. This hardware runs Llama 3.1 8B or Qwen 2.5 7B in 4-bit quantization at 10-20 tokens per second -- fast enough for natural voice interaction with a 3-6 second total round trip. It handles a document collection of up to 50,000 documents (enough for a family's entire lifetime of paperwork). It runs Whisper-small for near-real-time transcription. It can serve a family of 4-6 people with simultaneous queries. It does not have a GPU, so photo auto-captioning with LLaVA is slow (but still functional, just batch-processed overnight). This is the best balance of cost, capability, and performance. If you are building one home RAG system, build this one.

Performance Tier: Mini PC with GPU (~$800+)

A mini PC or small form-factor workstation with a dedicated GPU (NVIDIA RTX 4060 with 8 GB VRAM, or an AMD equivalent) and 32 GB of RAM is the performance configuration. The GPU runs the LLM at 40-80 tokens per second -- fast enough that the voice response feels instantaneous. Whisper runs on the GPU for sub-second transcription. Photo auto-captioning with LLaVA runs in real-time, so new photos are captioned the moment they are ingested. This tier can run larger models (14B class, or 8B models at higher precision for better quality) and handles 100,000+ documents without breaking a sweat. If you want the home RAG system to feel as fast and capable as a high-end cloud assistant -- but private -- this is the hardware. The cost is higher, but still less than a single year of most family's cloud service subscriptions across photos, documents, backup, and AI tools.

Repurposed Laptop: The Free Option

A laptop you already own, with 8 GB or more of RAM, running Linux, can serve as a home RAG system. It sits closed on a shelf, plugged into power, connected to the network via Wi-Fi, and runs the same software stack as a mini PC. Performance will depend on the laptop's CPU (an Intel i5/i7 or AMD Ryzen 5/7 from the last 5 years will perform comparably to a modern mini PC). The advantage is cost: $0 if you already have the laptop. The disadvantage is power consumption (laptops draw 30-60 watts, more than a mini PC's 10-30 watts) and thermal management (a closed laptop running 24/7 may run hot). For a family testing the concept before investing in dedicated hardware, this is the path of least resistance.

A natural question: "Why not just ask ChatGPT?" The answer is fundamental and it is worth being explicit about it, because the difference is not incremental -- it is categorical.

When you ask ChatGPT "when is my dentist appointment?" it will tell you that it does not know, and suggest you check your calendar. When you ask a home RAG system, it retrieves your appointment card and the confirmation email and tells you the date, time, dentist name, and the pre-appointment instructions. When you ask ChatGPT "what did my blood test show?" it will tell you it cannot access your medical records. When you ask a home RAG system, it retrieves your lab results and summarizes them. The difference is not about model intelligence -- it is about knowledge access. ChatGPT is smart but contextless. Home RAG is smart and has your context.

You could, of course, upload your documents to ChatGPT. You could paste your lab results into the chat. You could upload your insurance PDF. But every time you do, you are sending your most private documents to a server owned by a corporation that has changed its data retention policy multiple times, that has been breached, that trains on customer data in some tiers, and that can hand your data to law enforcement or third parties under terms you agreed to without reading. The moment you paste your lab results into a cloud AI chat, those results are on someone else's server, under someone else's control, governed by someone else's policy. You cannot unsend them.

The honest trade-off is in the last two rows. ChatGPT, as a general-purpose model, is smarter than a 7B local model. If you ask "explain quantum entanglement" or "write a Python script to scrape a website," ChatGPT will give a better answer. Home RAG is not trying to replace ChatGPT for general knowledge. It is trying to do something ChatGPT fundamentally cannot: answer questions about your life, from your documents, privately, without sending anything to a corporation. For that specific -- and arguably more important -- use case, home RAG is not an alternative to ChatGPT. It is the only option that works.

The ideal setup for many families will be both: a home RAG system for personal documents and private voice queries, and ChatGPT (or another cloud AI) for general knowledge and tasks that do not involve personal data. They serve different purposes. But the home RAG system is the one that knows your life, and it is the one that no corporation can take away, change the pricing on, or shut down.

The trajectory is clear. Local LLMs are getting better every month -- the 7B models of 2026 are better than the 70B models of 2023. Embedding models are improving. Vector databases are getting faster and lighter. Whisper is already production-quality. Neural TTS is already natural-sounding. Hardware is getting cheaper -- a $400 mini PC in 2026 outperforms a $2,000 workstation from 2022. The component curve is all moving in the right direction, and every improvement makes home RAG more capable without making it more expensive.

As this becomes more common, every home will have a personal AI assistant. Not a cloud assistant that lives on a server in another state and knows nothing about you. A local assistant that lives on a box in your house, that has read your documents, that knows your schedule, that remembers your conversations, that answers your voice -- and that never sends a single byte of your life to a corporation. This is not a far-future vision. It is a near-future reality, and for the technically inclined, it is a present reality. The components exist. The integration is the work. The work is happening.

The Generational Shift

For our parents, the filing cabinet was the document management system. For us, it became a mix of filing cabinets, email inboxes, cloud drives, and phone apps -- scattered, partially searchable, never integrated. For our children, the home RAG system will be the document management system: one place, voice-accessible, naturally searchable, completely private. The shift from scattered-and-cloud to centralized-and-sovereign is a generational shift in how families interact with their own information. It will happen the way every generational technology shift happens -- slowly, then suddenly.

The Sovereignty Principle

The deeper principle is sovereignty. Your documents are yours. Your voice is yours. Your questions are yours. Your answers are yours. When you hand these to a corporation, you lose custody, you lose control, and you lose the ability to verify what happens to them. A home RAG system restores the custody, the control, and the verifiability. The box is in your house. The disk is encrypted with your key. The access policy is set by you. The logs are on your hardware. The model runs on your CPU. Nothing about your family's information passes through anyone else's hands. In a world where every other service wants to ingest your data, process it, monetize it, and keep it forever, a home RAG system is the place where your data stays yours.

This is the vision that the rest of the RAG library on this site is building toward. Every technical page -- chunking strategies, embedding models, vector databases, retrieval methods, multimodal RAG, graph RAG -- exists to make this vision real and robust. The philosophy comes first because the technology without the philosophy is just another way to hand your data to someone else. Home RAG is the philosophy made concrete: a box in your house that knows your life and answers your questions, and never tells anyone else about it.