feat: add comprehensive documentation for AI Workspace, including architecture, memory model, profiles, services, and security guidelines

docs/local-rag-index.md

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+# Local RAG Index
+
+The local RAG index is the first retrieval layer over canonical Markdown memory.
+
+## Purpose
+
+It helps AI clients quickly find relevant snippets without loading the whole project knowledge vault into context.
+
+```text
+project-knowledge/ Markdown
+        ↓
+scripts/aiw/indexer.py
+        ↓
+.aiw/indexes/<profile>/project-knowledge.jsonl
+        ↓
+MCP tool: memory_hybrid_search
+```
+
+## Current Implementation
+
+The current indexer is dependency-free and lexical. It is intentionally simple so it can run on a new machine without a vector database.
+
+Build:
+
+```bash
+python3 scripts/aiw/indexer.py build --profile fidelity
+```
+
+Status:
+
+```bash
+python3 scripts/aiw/indexer.py status --profile fidelity
+```
+
+Search:
+
+```bash
+python3 scripts/aiw/indexer.py search "dismissal lifecycle" --profile fidelity
+```
+
+## What It Stores
+
+- source path;
+- heading;
+- text chunk;
+- mtime;
+- content hash;
+- chunk id.
+
+## What It Does Not Do
+
+- It does not replace Markdown.
+- It does not write project facts.
+- It does not index templates as real notes.
+- It does not send data to a cloud service.
+
+## Future Options
+
+Future phases may add:
+
+- better full-text ranking;
+- semantic embeddings;
+- Qdrant or Chroma as optional local vector stores;
+- hybrid lexical + semantic search;
+- index status in the menu bar app.
+
+Keep this as a derived layer. The project knowledge vault remains canonical.