feat(graphrag-vectors): add filtering, timestamps, and CRUD operations (#2236)

* feat(graphrag-vectors): add filtering, timestamps, and CRUD operations

Implement the vector store enhancements from the graphrag-vectors-design spec:

New modules:
- filtering.py: Pydantic-based filter expression system with F builder,
  operator overloads, JSON serialization, client-side evaluate(), and
  per-backend compilation (SQL for LanceDB/CosmosDB, OData for Azure AI Search)
- timestamp.py: ISO 8601 timestamp explosion into filterable component fields

Enhanced VectorStoreDocument:
- data: dict for user-defined metadata fields
- create_date / update_date: automatic ISO 8601 timestamps

Enhanced VectorStore base class:
- fields config for typed metadata columns
- insert / count / remove / update CRUD methods
- select, filters, include_vectors params on search methods
- Automatic timestamp explosion on insert/update
- User-defined date field explosion

Backend implementations (LanceDB, Azure AI Search, CosmosDB):
- Full filter compilation to native query languages
- Typed schema creation with user-defined fields
- All new CRUD operations

Breaking changes:
- search_by_id raises IndexError when document not found
- Updated indexer_adapters.py caller to handle the new exception

Tests:
- 54 unit tests for filtering and timestamp modules
- 28 LanceDB integration tests covering CRUD, filters, timestamps, select,
  include_vectors, and user-defined date field explosion

* fix: resolve CI build failures (formatting, lint, pyright, test mocks)

- Fix ruff formatting and lint errors across all changed files
- Refactor filtering.py: move operator overloads from monkey-patching to
  direct class methods for pyright visibility
- Use validation_alias/serialization_alias with populate_by_name for
  Pydantic AND/OR/NOT models (pyright + runtime compatible)
- Use Operator enum members instead of string literals in FieldRef
- Add missing abstract methods (insert, count, remove, update) to test
  mock VectorStore classes
- Update mock method signatures to match base class (select, filters,
  include_vectors params)
- Add docstrings to FieldRef magic methods (ruff D105)
- Fix noqa:S608 placement in cosmosdb.py

* feat: add top-level vector_size to VectorStoreConfig

Add a vector_size field (default 3072) to VectorStoreConfig so users
can set it once instead of on every individual index schema. The value
is propagated to new IndexSchema entries during validation.

* chore: add semversioner patch entry

* chore: add ismatch and ftype to spellcheck dictionary

* Add example notebooks for LanceDB, Azure AI Search, and CosmosDB vector stores

- Three notebooks demonstrating: document loading, similarity search, metadata
  filtering with F builder, timestamp filtering, document update/removal
- Sample data files (text_units.parquet, embeddings.text_unit_text.parquet)
- Add CPY001, SLF001, DTZ005 to notebook lint ignores in pyproject.toml

* refactor: extract model/tokenizer creation from generate_text_embeddings into callers