docs: describe offline-first AI architecture#19
docs: describe offline-first AI architecture#19sharma-sugurthi wants to merge 4 commits intoAOSSIE-Org:mainfrom
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WalkthroughAdds an "AI Architecture" section to the README and a new Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes Suggested labels
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Actionable comments posted: 3
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@docs/ai-architecture.md`:
- Around line 21-30: The diagram currently shows both KW (Keyword search) and
Sem (Semantic search) pointing to a single Index which contradicts the doc text;
update the diagram so KW points to a separate KeywordIndex (or Keyword backend)
node and Sem points to a VectorIndex/VectorStore node (replace or split the
existing Index node), and adjust the arrows so Fuse (Hybrid ranking) still
consumes KW and Sem while KG stays connected to UI_Related—ensure you rename the
nodes (e.g., Index -> KeywordIndex and VectorIndex) so the visual matches the
textual statement that keyword search is independent of the vector store.
- Around line 132-146: The index contract currently only exposes index(chunks,
embeddings) → void which lacks explicit upsert/delete semantics and prevents
deterministic eviction of stale/re-chunked notes; update the contract to add
explicit operations such as upsert(chunks, embeddings) → void (for inserting or
replacing chunk entries) and deleteByIds(ids) or deleteByNoteIds(noteIds) → void
(for removing stale chunks), and ensure searchByEmbedding(queryEmbedding,
options) → RankedResults remains unchanged; implement these new methods in the
index storage implementation so incremental updates can call upsert for changed
chunks and deleteByIds for removed/re-chunked notes to avoid full rebuilds.
- Around line 120-121: The doc currently presents a remote LLM as if it can
serve as a drop-in Embedding implementation; update the wording to use an
embedding-service example (e.g., a local embedding model via a JS/TS transformer
library or a hosted embedding API) instead of a generic remote LLM, explicitly
state that the default implementation should be a local embedding model and that
any remote LLM-based generation/assistant services are strictly optional and
belong to the separate assistant/generation layer (refer to the "Embedding"
contract and the phrase "Default implementation" to make this distinction
clear).
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README.mddocs/ai-architecture.md
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fixed all the 3 issues.. |
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@docs/ai-architecture.md`:
- Line 182: Change the lowercase "markdown" to the proper noun "Markdown" in the
sentence that reads "Parse wiki‑style links (e.g. `[[Note]]`, `[[Note|Alias]]`)
from markdown." so it becomes "from Markdown." Update the text in the
docs/ai-architecture.md content for the sentence containing the wiki-style link
example.
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docs/ai-architecture.md
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Actionable comments posted: 2
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@docs/ai-architecture.md`:
- Line 1: Change the document title heading from "## AI Architecture for
SmartNotes" to a top-level H1 by replacing the leading "##" with "#" so the file
begins with "# AI Architecture for SmartNotes" (update the existing heading
string to the H1 form to resolve MD041).
- Around line 30-45: Update the architecture diagram and explanatory text to add
the assistant/generation path: introduce an explicit "Assistant" consumer node
(e.g., UI_Assistant or Assistant_Gen) and show arrows from both the retrieval
pipeline (Fuse --> UI_Search and hybrid retrieval/semantic search outputs) and
the knowledge-graph layer (KG --> UI_Related) into that Assistant node; in the
prose, state that retrieval results and KG context feed an optional local
assistant which performs prompt construction, grounding and generation using
local models (no network), and clarify that the assistant can consume
keyword/semantic/hybrid retrieval outputs and related-note context for answer
generation and suggested links. Ensure references to UI_Search, UI_Related,
Fuse, KG, and the new Assistant node are included so implementers know where
retrieval output should be routed.
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docs/ai-architecture.md
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Fixes #17
Description
This PR introduces the foundational documentation for SmartNotes' AI architecture. It establishes the technical guidelines and core principles needed to build AI features that are strictly offline-first, privacy-preserving, and modular.
Key Changes
docs/ai-architecture.mdto detail the high-level AI pipeline (chunking, embedding, indexing, and retrieval) using a Mermaid flowchart.Summary by CodeRabbit