feat: content-type aware chunking and unified embedding (#444)

* feat: content-type aware chunking and unified embedding

- Add chunking.py with HTML, Markdown, and plain text detection
- Add embedding.py with mean pooling for large content
- Create dedicated commands: embed_note, embed_insight, embed_source
- Use fire-and-forget pattern for embedding via submit_command()
- Refactor rebuild_embeddings_command to delegate to individual commands
- Remove legacy commands and needs_embedding() methods
- Reduce chunk size to 1500 chars for Ollama compatibility
- Update CLAUDE.md documentation for new architecture

Fixes #350, #142

* fix: address code review issues

- Note.save() now returns command_id for tracking embedding jobs
- Add length check after generate_embeddings() to fail fast on mismatch
- Add numpy as explicit dependency (was transitive)
- Remove hardcoded chunk sizes from docstrings

* docs: address code review comments

- Rename "SYNC PATH" to "DOMAIN MODEL PATH" in embedding router
- Add test_chunking.py and test_embedding.py to Testing Strategy
- Clarify auto-embedding behavior for each domain model

* fix: clean thinking tags from prompt graph output

Adds clean_thinking_content() to prompt.py to handle extended thinking
models that return <think>...</think> tags. This fixes empty titles
when saving notes from chat.

* chore: remove local docker-compose from git

* fix(frontend): handle null parent_id in search results

Add defensive check for null parent_id in search results to prevent
"Cannot read properties of null (reading 'split')" error. This can
happen with orphaned records in the database.

* fix: cascade delete embeddings and insights when source is deleted

When deleting a Source, now also deletes associated:
- source_embedding records
- source_insight records

This prevents orphaned records that cause null parent_id errors
in vector search results.

* fix: add cleanup for orphan embedding/insight records in migration 10

Deletes source_embedding and source_insight records where the
linked source no longer exists (source.id = NONE).

* chore: bump esperanto to 2.16

Increases ctx_num for Ollama models to accommodate larger notebook
context windows. See: https://github.com/lfnovo/esperanto/pull/69

open_notebook/domain/CLAUDE.md

@@ -33,7 +33,7 @@ Two base classes support different persistence patterns: **ObjectModel** (mutabl
   - `add_insight()`: Generate and store insights with embeddings
 
 - **Note**: Standalone or linked notes
-  - `needs_embedding()`: Always True (searchable)
+  - `save()`: Submits `embed_note` command after save (fire-and-forget)
   - `add_to_notebook()`: Link to notebook
 
 - **SourceInsight, SourceEmbedding**: Derived content models
@@ -55,7 +55,7 @@ Two base classes support different persistence patterns: **ObjectModel** (mutabl
 
 - **Async/await**: All DB operations async; always use await
 - **Polymorphic get()**: `ObjectModel.get(id)` determines subclass from ID prefix (table:id format)
-- **Auto-embedding**: `save()` generates embeddings if `needs_embedding()` returns True
+- **Fire-and-forget embedding**: Models submit embed_* commands after save via `submit_command()` (non-blocking)
 - **Nullable fields**: Declare via `nullable_fields` ClassVar to allow None in database
 - **Timestamps**: `created` and `updated` auto-managed as ISO strings
 - **Fire-and-forget jobs**: `source.vectorize()` returns command_id without waiting
@@ -75,14 +75,17 @@ Two base classes support different persistence patterns: **ObjectModel** (mutabl
 - **RecordModel singleton**: __new__ returns existing instance; call `clear_instance()` in tests
 - **Source.command field**: Stored as RecordID; auto-parsed from strings via field_validator
 - **Text truncation**: `Note.get_context(short)` hardcodes 100-char limit
-- **Embedding async**: Only Note and SourceInsight embed on save; Source too large (uses async job)
+- **Auto-embedding behavior**:
+  - `Note.save()` → auto-submits `embed_note` command
+  - `Source.save()` → does NOT auto-submit (must call `vectorize()` explicitly)
+  - `Source.add_insight()` → auto-submits `embed_insight` command
 - **Relationship strings**: Must match SurrealDB schema (reference, artifact, refers_to)
 
 ## How to Add New Model
 
 1. Inherit from ObjectModel with table_name ClassVar
 2. Define Pydantic fields with validators
-3. Override `needs_embedding()` if searchable
+3. Override `save()` to submit embedding command if searchable (use `submit_command("embed_*", id)`)
 4. Add custom methods for domain logic (get_X, add_to_Y)
 5. Implement `_prepare_save_data()` if custom serialization needed