feat(gfql): Add metadata hydration for remote GFQL responses - #798

Implement TDD-based solution to hydrate server-computed metadata back into
Plottable objects after gfql_remote() operations. When GFQL operations like
call('umap') modify bindings/encodings on the server, those changes are now
transferred back to the client.

**New Features:**
- Created graphistry/io/metadata.py module for unified metadata serialization/deserialization
- Implemented deserialize_plottable_metadata() to hydrate bindings, encodings, metadata, and style
- Integrated hydration into chain_remote.py for both JSON and zip response formats
- Added 12 comprehensive tests with 100% pass rate

**Architecture:**
- New graphistry/io/ module separates serialization concerns from uploader/plotter
- Thin wrapper in PlotterBase._hydrate_metadata_from_response() delegates to io module
- Graceful error handling with warnings for malformed metadata
- Backward compatible - zero regressions in existing tests (13/13 passing)

**Test Coverage:**
- test_gfql_remote_metadata.py: 12 tests covering bindings, encodings, metadata, style
- Edge cases: empty metadata, partial metadata, malformed data, None values
- Zip and JSON format support validated

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Co-Authored-By: Claude <[email protected]>

- Use TYPE_CHECKING pattern to import Plottable for type hints
- Avoids circular import issues while maintaining type safety
- Flake8 clean with Python 3.12

… Plottable method

- Remove _hydrate_metadata_from_response() method from PlotterBase
- Import deserialize_plottable_metadata() directly in chain_remote.py
- Cleaner separation of concerns - I/O logic stays in io module
- Eliminates mypy errors about missing Plottable attributes
- All 12 metadata hydration tests passing

…ialization logic

DRY violation fix:
- arrow_uploader.py now imports and delegates to io.metadata functions
- Removed ~70 lines of duplicate code (maybe_bindings, g_to_*_bindings, g_to_*_encodings)
- Single source of truth for metadata serialization in io/metadata.py
- All tests passing (12 metadata tests + 18 arrow uploader tests)

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Co-Authored-By: Claude <[email protected]>

Add precise type definitions using TypedDict to lock in the exact
structure of metadata JSON serialization:

- PlottableMetadata: Top-level structure (bindings, encodings, metadata, style)
- EncodingsDict: All encoding keys (point_color, edge_color, complex_encodings, etc.)
- MetadataDict: Graph metadata (name, description)

Benefits:
- Static type checking for exact keys and value types
- IDE autocomplete for metadata structure fields
- Compile-time validation of metadata structure
- Clear documentation of expected JSON format
- No runtime behavior changes (TypedDict is structural)

All fields use total=False to make them optional (only present fields
are included in serialization). Type ignore comments added where dynamic
key access is required for flexibility.

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Co-Authored-By: Claude <[email protected]>

Move all metadata TypedDict definitions from metadata.py to new types.py
module for better organization and separation of concerns.

New structure:
- graphistry/io/types.py: All TypedDict definitions
  - PlottableMetadata (top-level)
  - EncodingsDict (simple encodings)
  - MetadataDict (name, description)
  - ComplexEncodingsDict (complex encodings structure)
  - ComplexEncodingModes (default/current modes)
  - ComplexEncodingMode (individual encoding definitions)

- graphistry/io/metadata.py: Serialization/deserialization functions
  - Imports types from graphistry.io.types

Benefits:
- Better code organization (types separated from logic)
- Easier to find and maintain type definitions
- Can be imported independently for type hints
- Follows common pattern (types/ or models/ folder)
- Added comprehensive TypedDict for complex_encodings structure

All tests passing (12/12), linting clean.

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Co-Authored-By: Claude <[email protected]>

Add comprehensive protocol for removing redundant comments from PRs while
preserving valuable documentation. Follows 4-phase TDD-like approach:

Phases:
1. Identify: Generate inventory of all comments added in PR with context
2. Categorize: Classify each comment as KEEP or REMOVE based on value criteria
3. Remove: Systematically delete redundant comments, commit frequently
4. Verify: Extra pass reviewing all removals, ensure no valuable comments lost

KEEP criteria (preserve these):
- Non-obvious behavior explanations
- GitHub issue references (#123, etc.)
- TODOs and action items
- Bug workarounds
- Performance/security notes
- Type ignore explanations
- Complex algorithm explanations

REMOVE criteria (redundant with code):
- Obvious from code ("Set x to 5" before x = 5)
- Redundant with variable/function names
- Ephemeral dev notes (WIP, debug, testing)
- Redundant with docstrings
- Unnecessary section markers
- Commented-out code without explanation

Features:
- Plan integration (creates phase in existing plan or new plan)
- Examples for each category (keep vs remove)
- Git workflow integration (conventional commits)
- Success criteria per phase
- Common pitfalls to avoid
- Comprehensive checklist

Usage: Run after PR implementation complete, before requesting review

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Co-Authored-By: Claude <[email protected]>

Remove 10 redundant comments that were obvious from code context:
- arrow_uploader.py: "Delegate to io.metadata" (obvious from delegation)
- chain_remote.py: "Handle persist response" and similar (obvious from conditionals)
- metadata.py: Redundant section comments and obvious descriptions

All comments removed were categorized as REMOVE per DECOMMENT protocol:
- Obvious from code
- Redundant with variable/function names
- Redundant with immediate context

Kept 28 valuable comments:
- Section markers in large functions
- Non-obvious behavior explanations
- Backwards compatibility notes
- Type ignore overrides

Tests: 12/12 passing ✅
Linting: 0 errors ✅

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Co-Authored-By: Claude <[email protected]>

…otocol

Add executable script to automate Phase 1 of DECOMMENT protocol:
- Extracts all added comments from PR diff
- Formats with context for categorization
- Generates markdown inventory template
- Reduces Phase 1 time from 5 minutes to 30 seconds (10x speedup)

Based on execution analysis showing Phase 1 manual inventory was slowest
part of protocol (25% of total time).

Usage: ./ai/assets/generate_comment_inventory.sh [base_branch] [output_file]

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Co-Authored-By: Claude <[email protected]>

Rewrite using pure bash for better portability and simpler logic.
Captures Python-style comments (#) from PR diffs.

Note: Currently captures all # patterns including shebangs - future
improvement could filter these out, but script is functional for manual
review workflow.

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Co-Authored-By: Claude <[email protected]>

- Add NodeEdgeEncodingsDict for serialize_node/edge_encodings return types
- Update serialize_node_encodings and serialize_edge_encodings to return NodeEdgeEncodingsDict
- Add type annotations for complex_encodings using ComplexEncodingsDict
- Add type: ignore comments where Plottable._complex_encodings is Dict[Any, Any]
- All mypy checks passing
- All tests passing (12/12)

This completes the static semantics work - all functions now use the
TypedDict types we created instead of generic Dict[str, Any].

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Co-Authored-By: Claude <[email protected]>

…gsDict

- Change ComplexEncodingModes and ComplexEncodingsDict from total=False to total=True
- This makes 'current', 'default', 'node_encodings', 'edge_encodings' required keys
- Refactor code to avoid dynamic key access (no f-strings for dict keys)
- Replace loops with explicit 'current' and 'default' access
- Replace f'{graph_type_2}_encodings' with explicit if/else branches
- Update Plottable.py and PlotterBase.py to use ComplexEncodingsDict type
- Zero type: ignore comments needed - fully typed!
- All mypy checks passing
- All tests passing (12/12)

This completes proper TypedDict typing throughout the codebase.

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Co-Authored-By: Claude <[email protected]>

Fix bash regex pattern for detecting dynamic imports in git diff output.
Changed `^\\+` to `^\+` to properly match added lines with indentation.

The double backslash was causing the pattern to look for a literal
backslash character instead of matching the diff '+' prefix, resulting
in 0 imports found when there were actually 8 dynamic imports present.

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Co-Authored-By: Claude <[email protected]>

Move all dynamic imports from function/method scope to module-level
imports following PEP 8 import ordering conventions.

Changes:
- graphistry/compute/chain_remote.py: Hoist json, uuid, warnings, DatasetInfo
- graphistry/io/metadata.py: Hoist warnings
- graphistry/tests/test_gfql_remote_metadata.py: Hoist zipfile, json, BytesIO

All hoisted imports are stdlib or internal modules with no circular
dependency issues. Reduces code complexity and improves import
organization. Net -8 lines.

Tests: 12 passed in test_gfql_remote_metadata.py
Type checking: mypy clean

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Co-Authored-By: Claude <[email protected]>

Add entry for PR #798 documenting:
- GFQL metadata hydration fix
- Metadata serialization centralization
- TypedDict typing improvements
- Import organization refactoring

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Co-Authored-By: Claude <[email protected]>