RAG Without the Cloud

Retrieval-Augmented Generation (RAG) has become the standard approach for making AI systems knowledgeable about specific domains. Instead of relying solely on what a model learned during training, RAG retrieves relevant context from a knowledge base and uses it to generate more accurate, grounded responses.

It's powerful. It's also, in most implementations, a privacy nightmare.

How RAG Typically Works

The standard RAG pipeline looks like this:

Your documents are uploaded to a cloud service
An embedding model (often OpenAI's) converts them to vectors
Vectors are stored in a hosted database (Pinecone, Weaviate, etc.)
Queries hit a cloud API, retrieve context, and generate responses

At every step, your data passes through third-party infrastructure. Your notes, emails, and meeting transcripts become training data for someone else's models, attack surfaces for potential breaches, and leverage for vendor lock-in.

Our Approach

Nexus Note implements the full RAG pipeline locally:

Local embedding — Models run on your Mac using Apple's MLX framework
Local vector storage — SQLite with vector extensions, no external database
Local inference — Reasoning models run on-device
Local orchestration — The entire pipeline executes without network calls

The Technical Challenges

Building RAG locally isn't trivial. Cloud providers have invested billions in infrastructure optimized for this workload. Going local means solving several hard problems:

Model efficiency: We can't run GPT-4-class models on a laptop. Instead, we use smaller, specialized models fine-tuned for specific tasks. A 7B parameter model running on Apple Silicon can match larger models for many retrieval and reasoning tasks.

Embedding quality: Local embedding models have improved dramatically. Models like BGE and E5 provide excellent semantic search quality while running efficiently on consumer hardware.

Index performance: Vector search at scale requires careful engineering. We use approximate nearest neighbor algorithms that balance accuracy with speed, keeping queries fast even with large knowledge bases.

The Tradeoffs

Local RAG isn't free. You need capable hardware (M1 Mac or better). Initial indexing takes time. Some advanced capabilities require more compute than a laptop can provide.

But for personal knowledge management—your notes, your decisions, your context—the tradeoffs are worth it. You get:

Complete privacy by default
No API costs or rate limits
Offline capability
Full data portability

RAG without the cloud isn't just possible—for personal AI, it's preferable.