[FEATURE]: Document offline‑first AI architecture and modular retrieval design for SmartNotes

[sharma-sugurthi]

Feature and its Use Cases

before we pick specific LLMs or vector DBs, SmartNotes needs a clearly documented AI architecture that fits its constraints: offline‑first, privacy‑preserving, modular, and desktop‑only (no SaaS backend).

this issue proposes adding documentation that defines how AI components (chunking, embeddings, retrieval, knowledge graph, etc.) should be structured and how they interact with the rest of the app.

motivation

• Local‑first desktop app: Align contributors on the fact that SmartNotes is a desktop, local‑first application, not a cloud service.
• Pluggable AI components: Ensure future AI work (chunking, semantic/hybrid search, knowledge graph, RAG, etc.) is modular and replaceable, not hard‑wired to one stack.
• Avoid server‑style anti‑patterns: Prevent tightly‑coupled, server‑like architectures that are hard to run offline or on low‑resource machines.

proposed Scope

add an architecture document (for example docs/ai-architecture.md or an “AI Architecture” section in the main README) that covers:

1. aI pipeline overview

• Notes → markdown parsing → chunking → embedding → indexing → retrieval (keyword + semantic + hybrid) → UI (search bar, related notes, assistant)

2. core principles

• Offline‑by‑default: All features must work without network access.
• Privacy‑first: No notes sent to third‑party services by default; any optional cloud integration must be opt‑in and clearly separated.
• Modularity: Well‑defined interfaces for:
  • chunkers
  • embedding engines
  • vector stores / indexes
  • keyword search engine
  • hybrid retrieval combiner (e.g. Reciprocal Rank Fusion)
• Desktop‑only by design: Avoid background servers or heavy daemons; everything should run inside the Electron/Node process or as very light helpers.

3. module boundaries & contracts (high level)

document expected inputs/outputs and responsibilities for:

• chunk(note) → Chunk[]
• embed(chunks) → Embedding[]
• index(chunks, embeddings) → void
• search(query) → RankedResults (with keyword / semantic / hybrid as strategies)
• Knowledge‑graph layer (wiki‑links, backlinks, related notes).

and also describe how these modules talk to the vault/storage layer without tightly coupling to SQLite / filesystem internals.

4. scalability & performance considerations

• design assumptions for large vaults (e.g. 10k+ notes).
• batching, incremental indexing, caching, and avoiding full rescans.
• keeping everything responsive in a desktop UI (no long‑blocking operations in the main thread).

Additional Context

this issue is based on the maintainer’s guidance that SmartNotes should:

• run fully offline by default.
• be privacy‑first with no mandatory cloud dependency.
• remain modular so models and AI components can be swapped later.
• avoid heavy server‑based systems and SaaS‑style backends.

the goal of this documentation is to capture those principles in a concrete, contributor‑friendly form and to guide future features like chunking strategies, hybrid retrieval, editor UX for AI, knowledge‑graph exploration, and performance design for large note collections.

Code of Conduct

• I have joined the Discord server and will post updates there
• I have searched existing issues to avoid duplicates