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- Create BackupManager class in backup.py - Implement create_backup() for safe file copying - Implement restore_backup() with original path recovery - Add cleanup_old_backups() for removing old backups - Include backup manifest with JSON persistence - Add get_backup_info(), list_backups(), get_statistics() - Add verify_backups() for integrity checking - Update __init__.py to export BackupManager Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Create CLI module structure - Implement dedupe.py with rich UI components - Add configuration management (DedupeConfig) - Implement interactive duplicate group display - Add selection strategies (manual, oldest, newest, largest, smallest) - Include dry-run mode support - Add user confirmation prompts - Implement formatted output with rich tables and panels - Add comprehensive command-line arguments - Include helper functions for formatting (size, datetime)
- Replace mock data with real DuplicateDetector integration - Add progress tracking with tqdm support - Integrate BackupManager for safe mode - Implement actual file deletion with error handling - Add file removal logic with backup creation - Convert FileMetadata objects to display format - Include logging for operations
- Create test_dedupe_cli.py with comprehensive tests - Test dry-run mode with SHA256 and MD5 - Test size filters for large files only - Test non-recursive mode - Include test file creation with known duplicates - Add test summary and reporting
- Resolve backup_path when storing in manifest - Ensures consistent path keys for manifest lookups - Fixes restore_backup() on systems with symlinked temp dirs - All functional tests now pass Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add --batch flag for automatic strategy application - Update DedupeConfig to include batch parameter - Modify get_user_selection to support batch mode - Display batch mode status in configuration panel - Skip per-group confirmation in batch mode - Improve configuration display formatting
- Create detailed user guide for dedupe CLI - Document all command-line options - Include usage examples for common scenarios - Add troubleshooting section - Include best practices and safety guidelines - Add performance tips and integration examples
- Created ComparisonViewer class for interactive duplicate review - Terminal-based image preview with ASCII art generation - Metadata display: dimensions, resolution, format, file size, modification date - Interactive selection interface (keep/delete/skip/auto) - Side-by-side comparison layout using Rich library - Batch review operations for multiple duplicate groups - User decision recording with DuplicateReview dataclass - Automatic best-quality selection based on resolution, size, and format - Cross-platform support using Pillow - Quality scoring algorithm for image comparison - Review summary with space savings calculation Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Implement ImageDeduplicator with support for pHash, dHash, aHash - Add Hamming distance calculation for similarity comparison - Implement find_duplicates for directory scanning - Add cluster_by_similarity for image grouping - Support batch processing with progress callbacks - Add corrupt image handling and validation - Create image_utils module with helper functions - Support JPEG, PNG, GIF, BMP, TIFF, WebP formats - Add ImageMetadata class for image information - Implement quality comparison utilities Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Created comprehensive demo script showing all viewer features - Demonstrates single comparison, batch review, metadata display - Shows interactive selection and quality scoring algorithm - Includes detailed documentation of scoring weights and format preferences - Ready-to-run example for testing the UI Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Created detailed README with usage examples - Documented all features: visual comparison, metadata display, interactive selection - Explained quality scoring algorithm with examples - Added integration guide with deduplication service - Included performance metrics and best practices - Added troubleshooting section for common issues - Documented keyboard shortcuts and error handling Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Test suite with real PIL-generated images - Verify all hash methods (pHash, dHash, aHash) - Test Hamming distance calculations - Validate duplicate detection and clustering - Test image validation and metadata extraction - All tests passing successfully Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Export ImageDeduplicator class - Export ImageMetadata and utility functions - Update module docstring - Organize imports alphabetically Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Create comprehensive README for image deduplication - Document all API methods and parameters - Add usage patterns and examples - Include performance considerations - Document supported formats and limitations - Add troubleshooting guide - Create example script with multiple use cases - Document hash methods and thresholds Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- JSON-based preference storage with schema v1.0 - Atomic file writes using temporary files - Schema validation and migration framework - Backup/restore functionality - Error recovery with fallback to defaults - Thread-safe operations with RLock - Conflict resolution with recency/frequency weighting - Import/export functionality - Statistics tracking
Implemented core preference tracking engine with: - PreferenceTracker class for managing user corrections - Support for file moves, renames, and category overrides - Thread-safe operations using RLock - Preference metadata with confidence and frequency tracking - In-memory preference management - Real-time preference updates - Correction history tracking - Statistics and export/import functionality - Convenience functions for common operations Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- DirectoryPrefs: Hierarchical preference management with inheritance - Per-directory preference scoping - Parent directory inheritance with path walking - Override capabilities to stop inheritance - Deep merge for nested preference dictionaries - Clean API with metadata management - ConflictResolver: Deterministic conflict resolution - Multi-factor weighting (recency, frequency, confidence) - Exponential decay for recency weighting - Normalized frequency weights with diminishing returns - Confidence scoring with defaults - Tie-breaking using most recent preference - Ambiguity scoring for user input decisions - Deterministic resolution for reproducibility Both classes include comprehensive docstrings, type hints, and examples. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add exports for Stream C classes to intelligence module. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Schema validation tests - Load/save roundtrip tests - Error recovery and backup tests - Preference CRUD operation tests - Conflict resolution tests - Import/export tests - Statistics tests - Thread safety tests - Performance benchmarks (<10ms lookup, <100ms save) - Clear preferences tests Coverage: All core functionality including edge cases
Enhanced get_preference() method to: - Match folder mapping preferences by file extension - Ignore source directory for folder mapping lookups - Use extension-based matching for better preference retrieval - Added comprehensive test script with thread-safety tests All tests pass successfully, including: - Basic tracking operations - Preference confidence updates - Export/import functionality - Thread-safe concurrent operations Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- test_directory_prefs.py: 26 test cases covering: - Basic set/get operations - Single and multi-level inheritance - Parent override functionality - Deep merge of nested dictionaries - Path normalization - Metadata filtering - Edge cases and complex scenarios - test_conflict_resolver.py: 35 test cases covering: - Weight initialization and normalization - Recency-based conflict resolution - Frequency-based conflict resolution - Confidence scoring - Combined factor resolution - Tie-breaking with recency - Ambiguity detection - User input requirements - Deterministic resolution - Real-world scenarios Tests ensure comprehensive coverage of all functionality. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Added detailed README with: - Complete usage examples - API documentation - Preference and correction type descriptions - Thread safety guarantees - Confidence scoring algorithm - Performance characteristics - Integration guidelines Stream A (Core Preference Tracking) complete. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Fix timezone-aware/naive datetime mismatch in ConflictResolver - Make datetime.utcnow() timezone-naive for compatibility - Update _parse_timestamp to return naive datetime - Fix test_needs_user_input_custom_threshold to use appropriate test data - All 50 tests now pass (31 ConflictResolver + 19 DirectoryPrefs) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Usage examples with code snippets - JSON schema v1.0 specification - Conflict resolution algorithm description - Error recovery mechanisms - Performance benchmarks - Storage location details
Document all deliverables, test results, and technical details. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add PreferenceStore to __init__.py exports - Export DirectoryPreference dataclass - Export SchemaVersion enum - Integration test passes successfully
Stream A: Pattern detection and analysis algorithms - PatternAnalyzer class for structure analysis - Directory structure analysis with depth control - File naming pattern detection (9 common patterns) - Content-based clustering algorithms - Location pattern recognition - Statistical analysis of file distributions Features: - Detects naming patterns (prefix, suffix, date, version, case styles) - Analyzes location-based organization - Creates content clusters by type and location - Infers categories from names and file types - Configurable minimum pattern count and max depth Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Stream B: Recommendation generation and confidence scoring - SuggestionEngine class with AI integration - Multi-factor confidence scoring system (7 factors) - Suggestion types: move, rename, tag, restructure, delete, merge - ConfidenceScorer with weighted scoring model - Batch suggestion generation and ranking - Detailed explanation generator with reasoning Features: - Integration points for AI models (Gemini 2.0, Claude) - Pattern-based move suggestions - Rename suggestions matching conventions - Restructure suggestions for clusters - Configurable confidence thresholds - User history integration - Comprehensive metadata tracking Data Models: - Suggestion with confidence levels - SuggestionBatch for grouped recommendations - ConfidenceFactors with 7-factor weighted scoring Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
| def fetch_one(self, query: str, params: Optional[Tuple] = None) -> Optional[sqlite3.Row]: | ||
| """ | ||
| Execute query and fetch one result. | ||
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| Args: | ||
| query: SQL query string | ||
| params: Query parameters tuple | ||
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| Returns: | ||
| Single row result or None | ||
| """ | ||
| cursor = self.execute_query(query, params) | ||
| return cursor.fetchone() | ||
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| def fetch_all(self, query: str, params: Optional[Tuple] = None) -> List[sqlite3.Row]: | ||
| """ | ||
| Execute query and fetch all results. | ||
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| Args: | ||
| query: SQL query string | ||
| params: Query parameters tuple | ||
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| Returns: | ||
| List of row results | ||
| """ | ||
| cursor = self.execute_query(query, params) | ||
| return cursor.fetchall() |
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🧩 Analysis chain
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Acquire the lock around entire execute+fetch operations in fetch_one() and fetch_all().
The methods call execute_query() which acquires and releases the lock before returning the cursor. With check_same_thread=False, concurrent threads can interleave fetch operations on the same connection, corrupting its state. WAL mode doesn't prevent cursor interleaving. Wrap the entire execute+fetch sequence under the lock, or refactor to avoid returning cursors across lock boundaries.
Suggested fix
def fetch_one(self, query: str, params: Optional[Tuple] = None) -> Optional[sqlite3.Row]:
- cursor = self.execute_query(query, params)
- return cursor.fetchone()
+ with self._lock:
+ conn = self.get_connection()
+ cursor = conn.execute(query) if params is None else conn.execute(query, params)
+ return cursor.fetchone()
def fetch_all(self, query: str, params: Optional[Tuple] = None) -> List[sqlite3.Row]:
- cursor = self.execute_query(query, params)
- return cursor.fetchall()
+ with self._lock:
+ conn = self.get_connection()
+ cursor = conn.execute(query) if params is None else conn.execute(query, params)
+ return cursor.fetchall()🤖 Prompt for AI Agents
In `@file_organizer_v2/src/file_organizer/history/database.py` around lines 212 -
238, fetch_one and fetch_all call execute_query which acquires/releases the DB
lock and returns a cursor, then fetch is done outside the lock causing possible
concurrent cursor interleaving; change these methods to acquire the same lock
for the entire execute+fetch sequence (i.e., lock before calling execute_query
and keep it held until after cursor.fetchone()/fetchall()), or refactor by
adding an internal helper (e.g., _execute_and_fetch) that runs execute and fetch
while holding the lock, and ensure execute_query no longer returns a cursor
across the lock boundary.
| import uuid | ||
| transaction_id = str(uuid.uuid4()) | ||
| started_at = datetime.utcnow() | ||
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| metadata_json = json.dumps(metadata or {}) | ||
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| query = """ | ||
| INSERT INTO transactions (transaction_id, started_at, status, metadata) | ||
| VALUES (?, ?, ?, ?) | ||
| """ | ||
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| params = ( | ||
| transaction_id, | ||
| started_at.isoformat() + 'Z', | ||
| TransactionStatus.IN_PROGRESS.value, | ||
| metadata_json | ||
| ) | ||
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| self.db.execute_query(query, params) | ||
| self.db.get_connection().commit() |
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Two start_transaction() and commit_transaction() calls need atomic protection with DatabaseManager.transaction().
execute_query() releases its lock immediately after executing, leaving commit() unprotected on the shared connection (check_same_thread=False). This creates a race condition where concurrent threads can interleave executes and commits. Use db.transaction() to keep insert and commit atomic:
with self.db.transaction() as conn:
conn.execute(query, params)Applies to both start_transaction() (line 159–160) and commit_transaction() (line 190–191).
🤖 Prompt for AI Agents
In `@file_organizer_v2/src/file_organizer/history/tracker.py` around lines 141 -
160, start_transaction() and commit_transaction() perform an INSERT and a
subsequent commit using self.db.execute_query and
self.db.get_connection().commit which releases the DB lock between operations,
causing a race on the shared connection; wrap both the insert and the commit in
the DatabaseManager.transaction() context so the execute and commit run
atomically (use with self.db.transaction() as conn: then conn.execute(query,
params) for start_transaction() and similarly perform the update/commit inside
the same transaction context in commit_transaction()), replacing direct calls to
self.db.execute_query and self.db.get_connection().commit.
| def commit_transaction(self, transaction_id: str) -> bool: | ||
| """ | ||
| Commit a transaction, marking it as completed. | ||
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| Args: | ||
| transaction_id: Transaction ID to commit | ||
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| Returns: | ||
| True if successful, False otherwise | ||
| """ | ||
| completed_at = datetime.utcnow() | ||
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| query = """ | ||
| UPDATE transactions | ||
| SET status = ?, completed_at = ? | ||
| WHERE transaction_id = ? | ||
| """ | ||
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| params = ( | ||
| TransactionStatus.COMPLETED.value, | ||
| completed_at.isoformat() + 'Z', | ||
| transaction_id | ||
| ) | ||
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| try: | ||
| self.db.execute_query(query, params) | ||
| self.db.get_connection().commit() | ||
| logger.info(f"Committed transaction {transaction_id}") | ||
| return True | ||
| except Exception as e: | ||
| logger.error(f"Failed to commit transaction {transaction_id}: {e}") | ||
| return False | ||
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Commit transaction updates without holding the database lock.
The commit_transaction() and start_transaction() methods split the UPDATE and COMMIT into separate database calls, releasing the lock between them. This creates a race condition where concurrent threads could interfere with the transaction state update. Use db.transaction() context manager to serialize the operation atomically, matching the pattern already used in log_operation() and rollback_transaction().
Both methods need this fix:
💡 Suggested fixes
In commit_transaction() (line 190-191):
try:
- self.db.execute_query(query, params)
- self.db.get_connection().commit()
+ with self.db.transaction() as conn:
+ conn.execute(query, params)
logger.info(f"Committed transaction {transaction_id}")
return TrueIn start_transaction() (line 159-160):
- self.db.execute_query(query, params)
- self.db.get_connection().commit()
+ with self.db.transaction() as conn:
+ conn.execute(query, params)🤖 Prompt for AI Agents
In `@file_organizer_v2/src/file_organizer/history/tracker.py` around lines 165 -
197, The UPDATE+COMMIT in commit_transaction() (and likewise in
start_transaction()) must be executed inside the same DB transaction to avoid
the race: wrap the call to self.db.execute_query(...) and the commit in the
db.transaction() context manager used by log_operation() and
rollback_transaction(), remove the separate self.db.get_connection().commit()
call, and perform the UPDATE of transactions.status/completed_at (in
commit_transaction) and the status/start_time UPDATE (in start_transaction)
within that context so the lock is held atomically until commit.
📋 Deferred CodeRabbit Issues - Created as Individual TicketsFollowing the PR #67 code review, 9 issues have been created for deferred items that require more complex architectural changes or further analysis: Performance Optimizations (2 issues)
Code Quality (1 issue)
Logic & Consistency (3 issues)
Complex Edge Cases (3 issues)
Implementation PriorityImmediate (High Priority):
Next Sprint (Medium-High Priority): Future (Medium Priority): Low Priority (Technical Debt): All issues are labeled with |
📋 New CodeRabbit Issues - Created as GitHub TicketsFollowing CodeRabbit's latest review (2026-01-21), 6 additional issues have been created: 🔴 High Priority (2 issues)Issue #77: Remove misleading .doc support or implement real legacy .doc extraction
Issue #78: Add validation for chunk_size parameter in FileHasher
🟡 Medium/Low Priority (4 issues)Issue #79: Replace deprecated IOError with OSError in text extractor
Issue #80: Replace print() with structured logging in FileHasher
Issue #81: Consolidate duplicate SUPPORTED_FORMATS constant
Issue #82: Rename 'format' parameter to avoid shadowing Python built-in
🎯 Previous Issues Still TrackedIssues #68-#76 from the previous deferred items remain open and tracked. 📊 Total Issue Count
All issues are labeled with |
|
Approved |
✅ All Issues Linked to Phase 4 Intelligence EpicAll 15 technical debt issues from CodeRabbit reviews have been successfully linked to the Phase 4 Intelligence epic using CCPM (Claude Code Project Management) for future tracking and implementation. 📊 Epic TrackingLabel: 🔗 Technical Debt Issues Linked🔴 High Priority (3)
🟡 Medium Priority (7)
🟢 Low Priority (5)
🎯 Benefits of Epic Linkage
📈 Next StepsThese issues are now part of the Phase 4 Intelligence backlog and can be:
All issues documented in |
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Pull request overview
Copilot reviewed 55 out of 125 changed files in this pull request and generated 13 comments.
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| removed_backups = [] | ||
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| # Find and remove old backups | ||
| for backup_key, _metadata in list(manifest.items()): |
There was a problem hiding this comment.
Variable name _metadata in iteration is unused but metadata is referenced. Should be metadata instead of _metadata.
| for backup_key, _metadata in list(manifest.items()): | |
| for backup_key, metadata in list(manifest.items()): |
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| # Co-occurrence patterns | ||
| for tag1, cooccur_tags in self.tag_cooccurrence.items(): | ||
| for tag2, _count in cooccur_tags.most_common(5): |
There was a problem hiding this comment.
Variable name _count in iteration is unused but count is referenced. Should be count instead of _count.
| for tag2, _count in cooccur_tags.most_common(5): | |
| for tag2, count in cooccur_tags.most_common(5): |
| manifest = self._load_manifest() | ||
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| backups = [] | ||
| for backup_key, _metadata in manifest.items(): |
There was a problem hiding this comment.
Variable name _metadata in iteration is unused but metadata is referenced. Should be metadata instead of _metadata.
| for backup_key, _metadata in manifest.items(): | |
| for backup_key, metadata in manifest.items(): |
| total_size = 0 | ||
| existing_backups = 0 | ||
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| for backup_key, _metadata in manifest.items(): |
There was a problem hiding this comment.
Variable name _metadata in iteration is unused but metadata is referenced on line 269. Should be metadata instead of _metadata.
| manifest = self._load_manifest() | ||
| issues = [] | ||
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| for backup_key, _metadata in manifest.items(): |
There was a problem hiding this comment.
Variable name _metadata in iteration is unused but metadata is referenced on line 269. Should be metadata instead of _metadata.
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| # Suggest based on directory | ||
| if directory and directory in self.directory_tags: | ||
| for tag, _count in self.directory_tags[directory].most_common(15): |
There was a problem hiding this comment.
Variable name _count in iteration is unused. Consider using count if needed.
| if existing_tags: | ||
| for existing_tag in existing_tags: | ||
| if existing_tag in self.tag_cooccurrence: | ||
| for tag, _count in self.tag_cooccurrence[existing_tag].most_common(5): |
There was a problem hiding this comment.
Variable name _count in iteration is unused. Consider using count if needed.
| """ | ||
| if not self.is_fitted: | ||
| raise RuntimeError( | ||
| "Vectorizer not fitted. Call fit_transform() from e first." |
There was a problem hiding this comment.
Corrected error message 'from e first' to 'first'.
| "Vectorizer not fitted. Call fit_transform() from e first." | |
| "Vectorizer not fitted. Call fit_transform() first." |
| """ | ||
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| from pathlib import Path | ||
| from typing import , Optional, Callable |
There was a problem hiding this comment.
Empty type in import statement. The import is missing a type name before the comma.
| from typing import , Optional, Callable | |
| from typing import Optional, Callable |
| quality_metrics: QualityMetrics | ||
| time_savings: TimeSavings | ||
| trends: dict[str, TrendData] = field(default_factory=dict) | ||
| generated_at: datetime = field(default_factory=datetime.utcnow) |
There was a problem hiding this comment.
Using datetime.utcnow as default_factory will set the same timestamp for all instances created in the same session. Should use lambda: datetime.utcnow() instead.
📊 Final Summary
Epic Completion Status
13/13 Issues Completed (100%)
📈 Implementation Metrics
Code Delivered:
Time Efficiency:
Worktree: /Users/rahul/Projects/epic-phase-4-intelligence
Branch: epic/phase-4-intelligence
Status: Clean, all changes committed and pushed
🚀 Features Delivered
Deduplication (Issues #46, #47, #48)
Intelligence System (Issues #49, #50, #51)
Smart Features (Issues #52, #54)
Operations (Issues #53, #55)
Analytics (Issue #56)
📚 Documentation
8 comprehensive guides created:
🔍 What's Next
Immediate Actions:
Post-Merge:
🎯 Key Achievements
Epic Status: ✅ COMPLETE
PR Status: 🟢 READY FOR REVIEW
Branch: epic/phase-4-intelligence → main
Summary by CodeRabbit
New Features
Documentation
Examples
Tests
✏️ Tip: You can customize this high-level summary in your review settings.