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Technical Blueprint for a Privacy-First AI Assistant MVP

Y Consulting
27 min readMay 31, 2025

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This blueprint includes:

  • Architecture and flow diagram
  • Recommended open-source models (including 2 for cross-referencing)
  • Tools for memory, RAG, voice input/output, and chat UI
  • Best practices for privacy and safety
  • Scalable setup plan (from local to cloud)

I’ll get back to you shortly with a complete build plan.

Introduction

Building a personal AI assistant that runs locally on a MacBook Air M2 requires careful design to balance performance, privacy, and broad expertise. The goal is an MVP (Minimum Viable Product) that acts as a “personal expert in everything.” It should handle diverse questions with reasoning, maintain conversational context, and support a chat interface as well as voice input/output. All of this must be achieved with privacy and safety as top priorities — meaning user data stays local (unless migrated by choice) and the assistant avoids harmful or inaccurate responses. We will design an architecture that uses two open-source language models working in tandem for cross-checking answers, Retrieval-Augmented Generation (RAG) to inject relevant knowledge from documents, persistent memory for long-term context, and modular components that can scale from the M2 laptop to the cloud.

Architecture Overview

At a high level, the assistant comprises several components: a Conversational UI (with text chat and optional speech I/O), a Speech-to-Text (STT) engine for voice input, a Text-to-Speech (TTS) engine for voice output, a Conversation Manager to orchestrate dialogue and tool usage, a Vector Database for document retrieval and long-term memory, and two Language Model (LLM) instances (Model A and Model B) that collaborate (one generating answers, the other verifying/refining them). This modular design ensures each piece can be maintained or scaled independently.

Architecture of the MVP AI assistant.

The user interacts via a chat UI (text input or spoken queries). The assistant transcribes voice input locally (using an STT model like Whisper) and feeds the text query into the conversation manager. The manager retrieves relevant documents from a vector database (for grounding via RAG) and sends the query + context to LLM Model A (e.g. Dolphin 3.0) for an initial answer. The draft answer is then passed to LLM Model B (e.g. a DeepSeek R1 distilled model) for verification and improvement. The verified answer returns to the user through the chat UI and is optionally spoken aloud via TTS. The system also logs key information to a persistent memory store (for long-term learning and context in future interactions). This design keeps all data local on the Mac, preserving privacy by default, and can be extended or scaled as needed.

Open-Source Language Models for Dual LLM Supervision

Model Selection: We choose two complementary open-source LLMs to serve as the “brain” of the assistant. The first model (Model A) will be a versatile instruction-following model that generates answers, and the second model (Model B) will act as a “supervisor” or checker to improve accuracy and safety of those answers. For Model A, a strong candidate is Dolphin 3.0 — an instruction-tuned model built on Meta’s Llama 3 architecture. Dolphin 3.0 is explicitly designed as a comprehensive local AI assistant, capable of coding, math, and general Q&A tasks (Dolphin3.0-Llama3.2–3B). It supports customizable system prompts and multi-platform deployment (Ollama, HF Transformers, etc.) for easy integration (Dolphin3.0-Llama3.2–3B). Importantly, Dolphin3.0 emphasizes user control: running it locally gives you full control over your data and the model’s behavior, unlike cloud-based models (Dolphin3.0-Llama3.2–3B). This makes it ideal for a privacy-first assistant.

For Model B, we recommend DeepSeek R1 (or a variant thereof) as a supervisory model. DeepSeek R1 is a state-of-the-art open large model known for advanced reasoning and accuracy (DeepSeek — Wikipedia) (DeepSeek R1: All you need to know ). While the full DeepSeek-R1 is extremely large (a 671B-parameter Mixture-of-Experts model), the project provides distilled smaller models (down to ~1.5B and 70B parameters) that are much more feasible to run on limited hardware (DeepSeek R1: All you need to know ). These distilled versions, based on bases like Qwen or Llama, retain strong performance — often outperforming other open models of similar size (DeepSeek R1: All you need to know ). For the MacBook Air M2, a reasonable approach is to use Dolphin 3.0’s ~8B model as Model A and a distilled DeepSeek variant (~7B–13B) as Model B (to cross-check answers). Both models are open-source (DeepSeek R1 is MIT-licensed (DeepSeek R1: All you need to know )), and quantized formats (e.g. 4-bit int quantization) are available to reduce memory usage. This dual-model setup means Model A will generate a candidate answer, and Model B (trained for robust reasoning) will review or refine it — reducing hallucinations or errors by having a “second pair of eyes” on every response.

Why Dolphin 3.0 + DeepSeek? Dolphin 3.0 provides a reliable general-purpose assistant personality with fine-grained control and alignment (it even allows enforcing ethical guidelines via system prompts (Dolphin3.0-Llama3.2–3B)). DeepSeek’s model brings top-tier reasoning and factual accuracy thanks to its unique training (reinforcement learning that fostered self-verification and advanced chain-of-thought skills (DeepSeek R1: All you need to know )). Using them together, we get the best of both: Dolphin ensures user instructions and style are followed, while DeepSeek verifies and corrects factuality or logic. Both can run locally with appropriate optimizations, and each is maintained by active open-source communities for long-term support.

Chat UI and Voice Interface

To make the assistant accessible, we’ll implement a Conversational User Interface that supports text and voice. On the MacBook Air, a simple approach is to use a local web app framework or GUI toolkit for the chat UI:

  • Web UI Option: Use Gradio or Streamlit (Python libraries) to spin up a local web interface with a chatbox. These frameworks make it easy to display conversation history and add components like buttons (e.g. for “Record Voice”).
  • Desktop UI Option: Alternatively, use an Electron or Tauri app with a web front-end, or even a simple SwiftUI app on macOS. However, starting with a web-based UI is faster for MVP.

For Voice Input, integrate an open-source Speech-to-Text engine. Whisper from OpenAI is a great choice — it’s state-of-the-art and open-source, and can run locally via the openai-whisper Python package or optimized ports like whisper.cpp. A medium-sized Whisper model can transcribe speech in real-time on the M2. The voice input flow will be: the user presses a “record” button (or a wake word), the app records audio (e.g. via microphone), then the Whisper model transcribes it to text. This text is sent to the conversation manager as if the user had typed it. All audio processing stays on-device, preserving privacy.

For Voice Output, use a Text-to-Speech engine to speak the assistant’s responses. macOS provides built-in TTS voices that can be accessed via the say command or APIs, which is a quick solution. Alternatively, open-source neural TTS projects like Coqui TTS or Mozilla TTS offer more natural voices; you can bundle a pretrained English voice model for offline use. The conversation manager will send the final answer text to the TTS module to generate audio. The audio is then played through the Mac’s speakers (or headphones). With this addition, the assistant offers a full voice assistant experience similar to Siri or Alexa, but with local processing.

In summary, the UI will display all interactions (including transcribed queries and the assistant’s text answers), and also handle routing audio: user speech -> STT -> text query, and text answer -> TTS -> spoken reply. Using open-source STT/TTS ensures no cloud services are needed for voice, aligning with the privacy requirement.

Retrieval-Augmented Generation (RAG) for Knowledge

No single model (especially one running offline) can know everything or have the latest information from personal documents. We incorporate Retrieval-Augmented Generation (RAG) to give the assistant grounded knowledge. RAG works by injecting relevant external data into the model’s prompt based on the user’s query (Decoding the AI Virtual Assistant Design Architecture: An In-Depth Look into Design Components | by Senol Isci | Medium). In practice, this means we maintain a vector database of text embeddings for documents (e.g. user’s files, notes, or other reference materials) and possibly a snapshot of certain external knowledge. When the user asks a question, the system will: (1) embed the user’s query into the same vector space, (2) search the vector DB for the closest matches (i.e. relevant document snippets), and (3) retrieve those snippets to include alongside the user’s query for the LLM to see. By providing relevant context, the LLM’s answer will be grounded in real data, which reduces hallucination and improves accuracy (Building a Private AI Assistant with Local LLMs — A Practical Guide | by Jose Liendro | White Prompt Blog).

Tooling for RAG: We can use an open-source vector database like ChromaDB or FAISS to store and query embeddings. ChromaDB is a simple choice (pip-installable and can persist data to disk). For generating embeddings, we’ll use either a smaller language model or a dedicated embedding model from HuggingFace (e.g. all-MiniLM-L6-v2 or similar) to convert documents and queries into high-dimensional vectors. This embedding model runs locally on the M2 as well. We will build a document ingestion pipeline: feed in the user’s documents (PDFs, text, etc.), automatically split them into chunks (to fit into model context windows), and compute embeddings for each chunk (Building a Private AI Assistant with Local LLMs — A Practical Guide | by Jose Liendro | White Prompt Blog). These are stored in the vector DB with references to the source documents.

At query time, before calling the LLM, the conversation manager will perform a similarity search in the vector DB for the current query. Top relevant chunks (for example, the top 3–5 passages) are retrieved. These passages are then inserted into the prompt given to Model A — typically formatted as: “Relevant information:\n[…passage1…]\n[…passage2…]\nUser’s question: …”. The LLM will use this information to craft its answer, thereby citing or at least using correct facts from the documents. This method ensures the assistant can answer questions about personal or external knowledge it was never explicitly trained on, by leveraging the documents at runtime. For example, if the user asks about details in their financial report, the system will fetch the relevant report section and the LLM will incorporate those details into its answer. RAG is a proven strategy to keep LLMs relevant and factual (Building LLM Applications With Vector Databases — Neptune.ai) and is essential for our “expert in everything” goal.

We should note that the vector DB can store not just static documents but also latest information (if we periodically crawl a knowledge source or allow an optional internet lookup mode). However, since we prioritize privacy, the MVP will focus on user-provided data only. (An internet retrieval tool could be added later, toggled only when the user permits, directing queries to an API or search engine and then feeding results into the prompt.)

Persistent Long-Term Memory

Beyond static documents, the assistant needs to remember conversational context and user preferences over the long term — even across sessions. Large Language Models have a limited context window (few thousand tokens), so the assistant must implement a strategy for long-term memory (LTM). Long-term memory allows the AI to “maintain context over extended periods and continuously learn about the user’s preferences” (Towards Ethical Personal AI Applications: Practical Considerations for AI Assistants with Long-Term Memory), enabling more personalized and context-aware interactions.

Memory Storage: We will create a persistent memory store (could be a simple database or file) where the assistant records important information it learns or discussions it has. One approach is to leverage the same vector database for this purpose by storing embeddings of past conversation snippets (in a separate collection from the documents). For example, after each conversation or at intervals, the assistant can summarize key facts or outcomes (e.g. “User’s favorite color is blue”, “Last advice given about project X was to do Y”) and store those as vectors. When a new query comes in, we not only retrieve from the document knowledge base, but also from the memory DB to see if any past context is relevant to the current question. This way, even if the conversation history isn’t explicitly in the prompt, the assistant can recall pertinent details (via retrieval) as needed.

Another complementary method is to save full conversation transcripts to disk (for transparency and later analysis), and maintain a running summary of the conversation that gets refined over time. For instance, a summary of previous interactions can be prepended to the prompt (rotating out less important details as needed). Tools like LangChain provide “ConversationBufferMemory” with summarization — but we can implement a simple version ourselves: whenever the conversation context gets too large, summarize older messages and store that summary as context.

Using persistent memory will make the assistant feel more personal and consistent. It can greet the user by name, remember past queries, avoid repeating the same questions, and incrementally build a profile of the user’s interests (all stored locally). This should be done with user consent and transparency. The memory DB can be as straightforward as a JSON or SQLite database for MVP. As interactions grow, we might integrate more advanced memory techniques (knowledge graphs of user facts, etc.), but RAG with an embedding store is a solid starting point.

Privacy and Safety Considerations

Privacy: From the outset, this architecture is designed so that all data processing happens locally on the MacBook Air M2. The user’s voice, text inputs, documents, and the models’ outputs never have to leave the device. This guarantees a high degree of data privacy — the user isn’t sending their queries or files to any third-party cloud (which is a key difference from using cloud LLM APIs). Dolphin 3.0 and DeepSeek R1 are open-source models running on local hardware, so there is no remote call that could leak content. Dolphin3.0 in particular was created to give users “full control over their data and the model’s ethical guidelines” when used as a local alternative to cloud AI (Dolphin3.0-Llama3.2–3B). We will enforce this by not including any telemetry or analytics in the MVP. If we implement optional cloud connectivity (for migration or web search), it will be opt-in and made clear to the user when data is leaving the device.

To further enhance privacy, the persistent memory and document stores can be encrypted on disk (for example, using filesystem encryption or an encrypted SQLite DB). This protects the data in case the laptop is compromised. On the Mac, FileVault can handle full-disk encryption, which covers these files. When migrating to cloud, we’ll discuss secure deployment to maintain privacy there as well (e.g., single-tenant servers, encryption in transit, etc.).

Safety: Ensuring the assistant does not produce harmful or unsafe outputs is crucial. Open-source models may not have the extensive guardrails that commercial systems do, but we can leverage a few strategies:

  • Model Alignment: Choose models that have been fine-tuned for helpfulness and harmlessness. Dolphin 3.0 was built with alignment in mind (it allows system-level control of behavior), and DeepSeek R1’s training included reinforcement learning to encourage harmless responses (DeepSeek R1: All you need to know ). We will start by using their default alignment. For instance, Dolphin3.0 might refuse or safely complete certain requests by design (user reports indicate it has some built-in guardrails).
  • System and Role Prompts: We will craft a system prompt that clearly instructs the assistant about what it should not do (e.g., no advice on violence, self-harm, etc., and no leaking sensitive info). Because we have two models interacting, we can have the second model (Model B) also check the content. For example, Model B could be prompted: “Review the assistant’s answer for any harmful or sensitive content and revise if necessary to ensure it’s safe and polite.” This acts as an additional content filter.
  • Open-Source Moderation Tools: We might integrate a lightweight moderation layer. Projects like OpenAI’s content filter aren’t open, but there are open-source classifiers for toxicity or hate speech (e.g., from the 🤗 Hugging Face hub). As an MVP, a simpler route is to maintain a list of banned keywords or use regex checks for obviously problematic outputs and then either block or warn if they appear. Over time, this can be improved with a fine-tuned safety model.

Finally, we will include the user in the loop for safety: the assistant can ask for confirmation if a request is potentially sensitive (“Do you really want me to proceed with that?”), and the user can provide feedback or ratings on answers. All these measures aim to create a trustworthy assistant. Because it’s local, the user has ultimate control — if something seems off, they can inspect logs or even the code to see why, an advantage of open-source transparency.

Step-by-Step Development Plan

Step 1: Environment Setup (Local Dev on Mac M2)
Begin by setting up the development environment on the MacBook Air M2. Install Python 3 (if not already installed) and required libraries. Key tools and libraries include:

  • PyTorch (with MPS support) — Apple’s Metal Performance Shaders backend allows PyTorch to use the M2 GPU. Install the latest PyTorch which supports Apple silicon.
  • Transformers and Hugging Face Hub — for loading the LLM models (if using the Transformers Python API). pip install transformers accelerate will be useful.
  • llama.cpp / Ollama (optional) — These are efficient inference engines for LLMs. Ollama, for instance, simplifies running models like Dolphin 3.0 on Mac (one-line install and model download (cognitivecomputations/Dolphin3.0-Llama3.1–8B · Hugging Face)】. You can use Ollama CLI to fetch a GGUF quantized model of Dolphin3.0 (8B) which is optimized for Apple silicon. Similarly, get a quantized DeepSeek distilled model if available. If using pure Python, alternatively use transformers with a 4-bit quantization configuration (possible via bitsandbytes or ggml model files).
  • Whisper — Install OpenAI’s Whisper (pip install whisper-openai) or download whisper.cpp and compile it for Mac (to use a faster C++ inference). The tiny or base models are fast; the small or medium model gives better accuracy with still reasonable speed.
  • TTS library — If using system TTS, none needed (you can call macOS say via subprocess or use AVFoundation in Python via pyobjc). For an open-source TTS, install a library like pip install TTS (Coqui TTS) and download a pretrained voice model.
  • Vector DB — Install ChromaDB (pip install chromadb) or FAISS (pip install faiss-cpu). Chroma is easier for Python use and can persist to a local file. Also install SentenceTransformers or similar for embedding model (pip install sentence-transformers).

Verify the environment by writing a small test to load a model and generate a sentence, and ensure that Metal/MPS is being used (you can check that torch.device('mps') works). Keep in mind that large models might not fit in memory – this is where quantization or using smaller variants is important. For MVP, start with the smaller models (e.g. Dolphin 3B or 8B, DeepSeek 1.5B variant, etc.) so that they comfortably run on 16GB RAM. Note: As a reference, running a 70B model on a Mac M1 with Metal was found to be very slow or impossible due to memor (Building a Private AI Assistant with Local LLMs — A Practical Guide | by Jose Liendro | White Prompt Blog)】, so sticking to <10B models initially is wise for responsiveness.

Step 2: Basic Chat Application (Text Mode)
Implement a basic chat loop with one LLM to ensure the core functionality works. Start with Model A (Dolphin 3.0 8B, for example). Load the model either via the Transformers API (in inference mode, using half precision or 4-bit if available) or through an API like Ollama. Create a simple loop or script where the user can input text and the model outputs a response. At this stage:

  • Define a system prompt that sets the assistant’s persona (e.g. “You are an AI assistant running locally. You are helpful, polite, and knowledgeable in all domains. Answer concisely.”). Many open models will accept a system prompt in a chat format.
  • Implement conversation context: you can start by simply concatenating the last N exchanges (user and assistant messages) up to a token limit. Since we will later use retrieval for long context, keeping only recent messages in the prompt is okay for now.
  • Ensure the model’s output is streamed or printed out. If using a web UI (like Gradio), set up a chatbot interface where user messages and responses appear. For MVP, even a console-based interaction is fine.

Test this with a variety of questions to see that the model responds. This will also give a baseline of its behavior (check how accurate or coherent the answers are, and how it handles not knowing something). If Dolphin3.0 (or chosen model) has any specific chat format (like requiring roles <s>[INST] ...), make sure to format prompts accordingly per its documentation.

Step 3: Incorporate Retrieval (RAG) Mechanism
Next, integrate the retrieval pipeline to ground the assistant’s knowledge in external data:

  1. Prepare the Vector Database: Choose a few documents or knowledge sources relevant to what you might ask. For instance, create a “Personal Docs” folder with some text or markdown files (or use a sample knowledge base). Use a Python script to split these documents into chunks (e.g. 500 tokens each, or based on sentences/paragraphs). For each chunk, generate an embedding using a pre-trained model (e.g. sentence_transformers model like all-MiniLM). Store these embeddings in the vector DB along with the chunk text and metadata (document name, etc.). With ChromaDB, you can do this in a few lines (add texts with embeddings to a collection).
  2. Query-time Retrieval: Modify the chat pipeline so that when the user asks a question, before calling the LLM you perform a similarity search in the vector DB. Take the user’s query, embed it with the same model, and retrieve (say) the top 3 matching chunks. Then compose the prompt to Model A that includes these chunks. For example:
SYSTEM: {system_prompt including persona and an instruction to use provided info} 
USER QUESTION: "...user's question..." 
CONTEXT: "...text of chunk1...\n...text of chunk2...\n..." 
ASSISTANT:

(The exact formatting may vary; some designs put the context in an earlier system message or just prepend it. The key is the model sees the context).

  1. Generate with Context: Call the LLM with this prompt and get the answer. The model should ideally incorporate the context snippets. Test this by asking something that’s answered in one of the documents — the assistant’s answer should reflect the content from the documents. You may need to experiment with prompt wording (e.g. instructing the model “Use the following information to answer…”).

This step validates the RAG setup. If the model still hallucinates or ignores the context, try increasing the number of retrieved chunks or making the system prompt more explicit (“If the answer is in the provided context, use it directly. If not, say you don’t know.”). According to experiences, providing up to ~5 relevant documents can improve accuracy, but too many can confuse the mode (Building a Private AI Assistant with Local LLMs — A Practical Guide | by Jose Liendro | White Prompt Blog)】, so tune this number. Once working, you have a functional knowledge-grounded assistant.

Step 4: Dual-LLM Answer Verification Pipeline
With Model A (the answer generator) and retrieval in place, now integrate Model B for supervision. Load the second model (e.g. a DeepSeek distilled 7B model). This could be done in the same process (if memory allows both) or by calling an external service (but since we want local, ideally load both models in memory). You might use separate pipelines, e.g., one transformers model for Dolphin and another for DeepSeek. Ensure you allocate them to MPS or CPU appropriately (you might run them sequentially to avoid memory contention – generate with A, then free some cache and run B, etc., if needed).

Implement the logic as follows:

  • After getting the initial answer from Model A (let’s call it answer_draft), formulate a prompt for Model B that includes the original question, possibly the retrieved context again, and the draft answer. For example, you could prompt B with:
    System: “You are a fact-checker AI. Verify the assistant’s answer against the reference and correct any mistakes.”
    User: original question + “\nReference:\n” + retrieved docs text + “\nAssistant’s answer:\n” + answer_draft + “\n\nIs the answer correct and well-reasoned? If not, provide a better answer.”
    Assistant: (Model B’s response)
    This turns Model B into a reviewer that will ideally identify inaccuracies or add details from the reference that Model A missed. DeepSeek’s reasoning strength can shine here, as it was designed for chain-of-thought and self-correctio (DeepSeek R1: All you need to know )】.
  • Parse Model B’s output as the final answer. In many cases Model B might output a full corrected answer. If it instead gives a verdict or partial response, you might refine the prompt or logic (you can explicitly ask it to “provide the final improved answer to the user”).
  • Optional: You could even have a few iterations (like a back-and-forth where A revises after B’s critique), but that can increase latency. For MVP, one round of check should suffice.

Test this pipeline. Ask some questions where you suspect Model A might make an error or hallucinate, and see if Model B catches it. For example, a tricky factual question or a math word problem. If Model B is much smaller, there is a risk it could sometimes degrade the answer — monitor this. If results are consistently good, great. If Model B sometimes underperforms, consider switching roles (maybe the stronger model should be the final arbiter). In practice, DeepSeek’s distilled models at 70B would be ideal for verification, but those can’t run on M2 — so you might try both models on a simple prompt you know the answer to, to gauge which is more reliable. Adjust the design accordingly (even possibility: have B just give a “Yes, correct” or “No, because…” and then decide to use A’s or not — but merging answers is more complex logic). For now, the straightforward approach is taking B’s answer as final output to the user.

Step 5: Add Voice Input Integration
Enable voice query capability. For this, wire up the Speech-to-Text module:

  • Use the Whisper model (perhaps Whisper tiny or base for faster performance on CPU, or the coreml optimized version for M2). You can use the Python API: load the Whisper model and call whisper.transcribe(audio) on recorded audio.
  • In the UI, add a button or command to start recording audio from the microphone. On macOS, you might use the sounddevice Python library or pyaudio to capture microphone input. Record a few seconds (or continuously until user stops). Save to a WAV file or buffer.
  • Pass this audio to Whisper and get the transcribed text. Display the transcribed text in the chat UI (so the user can see what was understood).
  • Then feed this text into the same pipeline from Step 4 (i.e., as if the user typed it). The rest — retrieval, LLM A, LLM B, etc. — stays unchanged. The user should then see the answer appear (and can also hear it in the next step).

Test the voice input end-to-end: Speak a question into the mic (e.g. “What’s the capital of France?” or something from your documents), let it transcribe and answer. Tune Whisper model size if needed; the M2 can likely handle the small model for decent accuracy. Ensure that background noise is handled or maybe push to use the English-only model if all queries are English (which is faster). This feature adds convenience and sets the stage for a full voice assistant experience.

Step 6: Add Voice Output Integration
Now implement the Text-to-Speech so the assistant can talk. If using macOS built-in TTS, it’s straightforward: after obtaining the final answer text, call the say command via os.system(f'say {answer_text}') or use the AVSpeechSynthesizer through a Python bridge. This will speak the text in a default voice. If using an open-source TTS, you would load the TTS model (which could be large – if too heavy, consider using system TTS for MVP) and generate audio from the text. The generated audio can be played using an audio library (like simpleaudio or PyGame).

Integrate this so that after the answer is ready (and displayed), the assistant speaks it. Make sure to do this after all model processing is done to avoid overlapping (or you can even start streaming TTS as the text comes in, but that’s advanced). The result should be that a user can talk to the assistant and hear it respond, entirely offline.

Step 7: Implement Persistent Memory Storage
Extend the system with long-term memory. Decide on a storage format — e.g., a SQLite database or even a JSON file. A simple solution: maintain a dictionary of key–value facts about the user or past context (e.g., "user_name": "Alice", "prefers_detailed_answers": True, "known_facts": ["Alice has two cats", ...]). Each time something noteworthy comes up, update this store. However, a more flexible approach is to use the vector DB:

  • Create a separate collection in the vector database for “memory”. After each conversation turn (or each user query), take the user’s question and assistant’s answer, concatenate them into a blob of text, and generate an embedding. Store that with metadata like timestamp. This way you accumulate a searchable log of all interactions.
  • Additionally, allow user to explicitly tell the assistant to remember something: e.g. “Assistant, remember that my birthday is June 10.” The system can catch commands like “remember” and directly store that fact (as a piece of text) into the memory DB.
  • When a new question comes, besides document retrieval, also query this memory DB for relevant context. For instance, if the user asks “Do you remember what advice you gave me about my car?”, the memory vector search would surface the prior conversation about the car, and the assistant can use that to answer.

Implement saving the vector DB to disk (ChromaDB does this by default if you specify a persist directory). Also, save the raw conversation logs in a text file for transparency. Test by simulating a multi-turn dialogue: ask something in one turn, then later ask a related follow-up that requires memory. Check if the assistant can recall details. You may need to tweak how aggressively to retrieve from memory (maybe always include the top 1 memory result in the context if above a similarity threshold).

At this step, you have a rudimentary long-term memory. The assistant is effectively learning from each conversation (since it stores and can recall that information later), which achieves the goal of continuous learning without retraining the model each time.

Step 8: Safety Filter and Final Polishing
Before deploying the MVP, implement some safety checks and fine-tune the overall experience:

  • Safety Checks: Introduce a function to scan the user’s query and the assistant’s answer for red flags. This could be as simple as keyword matching (for very harmful requests) or using the second LLM itself. For instance, we might repurpose Model B in some cases: before generating an answer, ask Model B (in a yes/no mode) “Is this user query appropriate to answer? Does it violate any policies?” Given DeepSeek’s reasoning, it might catch tricky cases. Alternatively, maintain a small list of disallowed content and have a hard filter. If a violation is found, either refuse (“I’m sorry, I cannot assist with that request”) or sanitize the answer.
  • Test Edge Cases: Try prompts that could cause the model to produce unwanted content (e.g., requests for personal data, hateful content, etc.) and verify the assistant’s response is safe. Adjust system prompt or filtering rules as needed.
  • Performance Tuning: Evaluate the response time on the Mac. If answers are too slow, consider optimizations: e.g., use smaller models (maybe 3B Dolphin if 8B is too slow), or quantize to 4-bit if not already. Streaming generation is important for usability — ensure the UI can display partial output from Model A as it’s generated (both Gradio and a custom UI can handle token streaming). This makes it feel responsive.
  • UI Enhancements: Improve the chat interface with features like: a clear conversation reset button, a toggle to enable/disable the second model checking (for faster but unverified answers), and an option to upload new documents to the knowledge base on the fly. Also indicate in the UI when information was retrieved from docs or memory (transparency).
  • Accuracy Testing: Ask a variety of knowledge questions (some covered by docs, some not) to see how often the assistant is correct. If it’s hallucinating, you may tighten the system prompt or increase reliance on Model B’s corrections. If it’s too cautious, you can adjust temperature or prompts for creativity vs factual tasks.

By the end of this step, the MVP should be fully functional: you can chat or speak to it and get answers, it remembers context, uses your data for grounded answers, and respects safety constraints. All of this runs on the MacBook Air M2 with no external dependencies.

Step 9: Documentation and Links
Prepare a list of all open-source components used, along with links and versions, for reference. This includes:

  • Dolphin 3.0 model (HuggingFace link for the specific variant used, e.g. “cognitivecomputations/Dolphin3.0-Llama3.1–8B” for 8B model)
  • DeepSeek R1 distilled model (link or source, e.g. a HuggingFace or official release for the 1.5B or 7B version)
  • Whisper model (link to GitHub or HF model “openai/whisper-small”)
  • TTS voice model if used (link to Coqui model zoo or similar)
  • Vector DB (ChromaDB GitHub)
  • Any framework like LangChain or Gradio if used
  • This documentation helps future maintenance and also attribution per licenses.

Make sure all these tools are properly credited and their licenses allow this use (all mentioned ones are permissive). The user of the MVP can then easily find and update individual components.

Migration Strategy to Cloud Scalability

While the MVP runs locally for a single user, we plan for an easy path to scale up or deploy to the cloud when needed (for more power or multi-device access). Here’s the strategy for migration:

  • Modular Services: We can separate the components into microservices or modules that could run on different machines. For example, the LLM inference could be one service, the vector database another, and the front-end UI another. Containerize each component using Docker. This way, on the Mac everything runs locally, but in the cloud, each container can be deployed to a suitable instance. We’ll ensure the code is written such that endpoints for each service are configurable (e.g., the UI doesn’t assume the model is in-process; it can call a REST API for the model server).
  • Scaling the Models: On cloud infrastructure (with powerful GPUs), we can switch to larger model variants for better performance and accuracy. For instance, we could deploy DeepSeek R1 70B on an NVIDIA GPU server to dramatically improve reasoning (DeepSeek’s 70B distilled model is “competitive on many tasks (DeepSeek R1: All you need to know )】 and would offer a quality jump). Similarly, Dolphin 3.0 has larger merges (there’s a 24B version based on Mistral, and possibly a 70B if one merges Llama2 70B). These larger models wouldn’t run on a Mac, but on cloud VMs with 48GB+ GPUs they can. The architecture doesn’t change — we just point the Model A and Model B inference calls to remote endpoints serving these bigger models. The open-source nature means we could host them ourselves or use a service like Hugging Face Inference Endpoint or Fireworks.ai (which hosts DeepSeek R1 models in the clou (DeepSeek R1: All you need to know )】) if we wanted managed inference.
  • Data Migration: The vector database (Chroma) can be run in a server mode or replaced with a more scalable solution like Weaviate or Milvus managed in the cloud. We will export the local embeddings data and import to the cloud vector DB. The persistent memory entries and documents should be synced in a secure way. Possibly, use a cloud storage (own S3 bucket or database) to keep this data so that the cloud instance and local can share knowledge updates. If privacy is a concern, even the cloud could be a private server under the user’s control (e.g., a personal NAS or rented server where only they have access).
  • Privacy in Cloud: If migrating to a cloud server, we maintain privacy by not using shared/public services. Ideally, the user deploys the Docker containers on a private cloud instance (like an EC2 VM or a Digital Ocean droplet). All communications between the UI (which the user could run in a browser) and the cloud server should be encrypted (HTTPS/WSS). We’ll implement authentication if needed to prevent others from accessing the assistant. Essentially, treat the cloud like an extension of the user’s private environment. Data at rest on the cloud can be encrypted and the server locked down. In scenarios where even that is not acceptable, the user might stick to local only — but at least the option is there for heavier workloads.
  • Architecture Diagram Updates: In a cloud scenario, the diagram stays similar, except the “LLM Model A/B” and “Retriever DB” blocks would reside on a server (with perhaps scalable replicas for load), and the UI could be a thin client. The design with a load balancer or API gateway can be introduced to handle requests if scaling to multiple users. The blog reference in our research showed an architecture with a load balancer, backend workers for embeddings, and GPU instances for the mode (Building a Private AI Assistant with Local LLMs — A Practical Guide | by Jose Liendro | White Prompt Blog)】 — we can take inspiration from that if scaling up.
  • Testing on Cloud: Once deployed, test the assistant via the internet connection, ensuring latency is still reasonable (with a powerful GPU, the model inference might actually be faster than on the Mac, offsetting network latency). Also, verify that all safety measures still hold in the new environment.

The migration strategy is intentionally incremental — one could even do a hybrid: keep using the local Mac for some parts (like the UI or STT/TTS) and call the cloud for the heavy LLM inference. This could be useful if, for example, the user is on the go with a weaker device and wants to leverage a home server’s GPU. The key is our modular design and use of open standards ensures flexibility: we can swap model implementations (local vs remote) without changing the core logic.

Conclusion

By following this blueprint, we will have a fully functional AI assistant MVP that runs on a MacBook Air M2, capable of rich conversational assistance with voice and memory, all while keeping the user’s data private. We carefully chose open-source components — from the LLMs (Dolphin 3.0 and DeepSeek R1) to the tools (Whisper, vector DB, etc.) — to align with privacy and transparency goals. Each design decision, from using dual models for self-supervision to employing RAG for factual grounding, was made to enhance accuracy and trustworthiness of the assistant. The development plan outlined concrete steps to build and integrate each component, ensuring the process is actionable. And with the migration strategy in place, the solution is future-proof: when more horsepower is needed, the same system can scale up to cloud GPU servers with minimal changes, possibly unlocking even more powerful models and capabilities.

This guide provides a clear roadmap to go from idea to implementation of your personal “expert in everything” AI — one that is private-by-design, safety-conscious, and extensible. By starting small (local MVP) and keeping the architecture modular, you set the stage for continuous improvement, be it fine-tuning models to your domain or scaling out to serve more complex tasks. With the blueprint and references provided, you can confidently commence building your AI assistant, knowing it is grounded in proven approaches and open technologies that put you in control.

References and Resources

AI
Ai Agent
Artificial Intelligence

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Y Consulting
Y Consulting

Written by Y Consulting

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We write about tech, startups, AI, and related events about these topics in South-East Asia. If you would like your project to be featured, message us directly.

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