316 lines
11 KiB
Markdown
316 lines
11 KiB
Markdown
# Product Requirements Document (PRD)
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# MusicBrainz Data Cleaner
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## Project Overview
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**Product Name:** MusicBrainz Data Cleaner
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**Version:** 2.0.0
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**Date:** July 31, 2025
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**Status:** Enhanced with Direct Database Access ✅
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## Problem Statement
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Users have song data in JSON format with inconsistent artist names, song titles, and missing MusicBrainz identifiers. They need a tool to:
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- Normalize artist names (e.g., "ACDC" → "AC/DC")
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- Correct song titles (e.g., "Shot In The Dark" → "Shot in the Dark")
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- Add MusicBrainz IDs (MBIDs) for artists and recordings
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- Preserve existing data structure while adding new fields
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- **NEW**: Use fuzzy search for better matching of similar names
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## Target Users
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- Music application developers
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- Karaoke system administrators
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- Music library managers
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- Anyone with song metadata that needs standardization
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## Core Requirements
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### ✅ Functional Requirements
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#### 1. Data Input/Output
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- **REQ-001:** Accept JSON files containing arrays of song objects
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- **REQ-002:** Preserve all existing fields in song objects
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- **REQ-003:** Add `mbid` (artist ID) and `recording_mbid` (recording ID) fields
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- **REQ-004:** Output cleaned data to new JSON file
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- **REQ-005:** Support custom output filename specification
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#### 2. Artist Name Normalization
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- **REQ-006:** Convert "ACDC" to "AC/DC"
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- **REQ-007:** Convert "ft." to "feat." in collaborations
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- **REQ-008:** Handle "featuring" variations
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- **REQ-009:** Extract main artist from collaborations (e.g., "Bruno Mars ft. Cardi B" → "Bruno Mars")
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#### 3. Song Title Normalization
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- **REQ-010:** Remove karaoke suffixes: "(Karaoke Version)", "(Karaoke)", "(Instrumental)"
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- **REQ-011:** Normalize capitalization and formatting
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- **REQ-012:** Handle remix variations
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#### 4. MusicBrainz Integration
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- **REQ-013:** Connect to local MusicBrainz server (default: localhost:5001)
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- **REQ-014:** Search for artists by name
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- **REQ-015:** Search for recordings by artist and title
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- **REQ-016:** Retrieve detailed artist and recording information
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- **REQ-017:** Handle API errors gracefully
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- **NEW REQ-018:** Direct PostgreSQL database access for improved performance
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- **NEW REQ-019:** Fuzzy search capabilities for better name matching
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- **NEW REQ-020:** Fallback to HTTP API when database access unavailable
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#### 5. CLI Interface
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- **REQ-021:** Command-line interface with argument parsing
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- **REQ-022:** Support for input and optional output file specification
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- **REQ-023:** Progress reporting during processing
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- **REQ-024:** Error handling and user-friendly messages
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- **NEW REQ-025:** Option to force API mode with `--use-api` flag
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### ✅ Non-Functional Requirements
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#### 1. Performance
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- **REQ-026:** Process songs with reasonable speed (0.1s delay between API calls)
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- **REQ-027:** Handle large song collections efficiently
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- **NEW REQ-028:** Direct database access for maximum performance (no rate limiting)
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- **NEW REQ-029:** Fuzzy search with configurable similarity thresholds
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#### 2. Reliability
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- **REQ-030:** Graceful handling of missing artists/recordings
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- **REQ-031:** Continue processing even if individual songs fail
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- **REQ-032:** Preserve original data if cleaning fails
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- **NEW REQ-033:** Automatic fallback from database to API mode
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#### 3. Usability
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- **REQ-034:** Clear progress indicators
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- **REQ-035:** Informative error messages
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- **REQ-036:** Help documentation and usage examples
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- **NEW REQ-037:** Connection mode indication (database vs API)
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## Technical Specifications
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### Architecture
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- **Language:** Python 3
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- **Dependencies:** requests, psycopg2-binary, fuzzywuzzy, python-Levenshtein
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- **Primary:** Direct PostgreSQL database access
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- **Fallback:** MusicBrainz REST API (local server)
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- **Interface:** Command-line (CLI)
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### Project Structure
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```
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src/
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├── __init__.py # Package initialization
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├── api/ # API-related modules
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│ ├── __init__.py
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│ ├── database.py # Direct PostgreSQL access with fuzzy search
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│ └── api_client.py # Legacy HTTP API client (fallback)
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├── cli/ # Command-line interface
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│ ├── __init__.py
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│ └── main.py # Main CLI implementation
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├── config/ # Configuration
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│ ├── __init__.py
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│ └── constants.py # Constants and settings
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├── core/ # Core functionality
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├── utils/ # Utility functions
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```
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### Architectural Principles
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- **Separation of Concerns**: Each module has a single, well-defined responsibility
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- **Modular Design**: Clear interfaces between modules for easy extension
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- **Centralized Configuration**: All constants and settings in config module
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- **Type Safety**: Using enums and type hints throughout
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- **Error Handling**: Graceful error handling with meaningful messages
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- **Performance First**: Direct database access for maximum speed
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- **Fallback Strategy**: Automatic fallback to API when database unavailable
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### Data Flow
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1. Read JSON input file
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2. For each song:
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- Clean artist name
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- **NEW**: Use fuzzy search to find artist in database
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- Clean song title
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- **NEW**: Use fuzzy search to find recording by artist and title
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- Update song object with corrected data and MBIDs
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3. Write cleaned data to output file
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### Fuzzy Search Implementation
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- **Algorithm**: Uses fuzzywuzzy library with multiple matching strategies
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- **Similarity Thresholds**:
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- Artist matching: 80% similarity
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- Title matching: 85% similarity
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- **Matching Strategies**: Ratio, Partial Ratio, Token Sort Ratio
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- **Performance**: Optimized for large datasets
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### Known Limitations
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- Requires local MusicBrainz server running
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- **NEW**: Requires PostgreSQL database access (host: localhost, port: 5432)
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- **NEW**: Database credentials must be configured
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- Search index must be populated for best results
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- Limited to artists/recordings available in MusicBrainz database
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- Manual configuration needed for custom artist/recording mappings
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## Server Setup Requirements
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### MusicBrainz Server Configuration
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The tool requires a local MusicBrainz server with the following setup:
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#### Database Access
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- **Host**: localhost
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- **Port**: 5432 (PostgreSQL default)
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- **Database**: musicbrainz
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- **User**: musicbrainz
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- **Password**: musicbrainz (default, should be changed in production)
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#### HTTP API (Fallback)
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- **URL**: http://localhost:5001
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- **Endpoint**: /ws/2/
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- **Format**: JSON
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#### Docker Setup (Recommended)
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```bash
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# Clone MusicBrainz Docker repository
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git clone https://github.com/metabrainz/musicbrainz-docker.git
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cd musicbrainz-docker
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# Start the server
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docker-compose up -d
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# Wait for database to be ready (can take 10-15 minutes)
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docker-compose logs -f musicbrainz
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```
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#### Manual Setup
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1. Install PostgreSQL 12+
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2. Create database: `createdb musicbrainz`
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3. Import MusicBrainz data dump
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4. Start MusicBrainz server on port 5001
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#### Troubleshooting
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- **Database Connection Failed**: Check PostgreSQL is running and credentials are correct
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- **API Connection Failed**: Check MusicBrainz server is running on port 5001
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- **Slow Performance**: Ensure database indexes are built
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- **No Results**: Verify data has been imported to the database
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## Implementation Status
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### ✅ Completed Features
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- [x] Basic CLI interface
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- [x] JSON file input/output
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- [x] Artist name normalization (ACDC → AC/DC)
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- [x] Collaboration handling (ft. → feat.)
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- [x] Song title cleaning
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- [x] MusicBrainz API integration
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- [x] MBID addition
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- [x] Progress reporting
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- [x] Error handling
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- [x] Documentation
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- [x] **NEW**: Direct PostgreSQL database access
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- [x] **NEW**: Fuzzy search for artists and recordings
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- [x] **NEW**: Automatic fallback to API mode
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- [x] **NEW**: Performance optimizations
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### 🔄 Future Enhancements
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- [ ] Web interface option
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- [ ] Batch processing with resume capability
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- [ ] Custom artist/recording mapping configuration
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- [ ] Support for other music databases
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- [ ] Audio fingerprinting integration
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- [ ] GUI interface
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- [ ] **NEW**: Database connection pooling
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- [ ] **NEW**: Caching layer for frequently accessed data
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## Testing
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### Test Cases
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1. **Basic Functionality:** Process data/sample_songs.json
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2. **Artist Normalization:** ACDC → AC/DC
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3. **Collaboration Handling:** "Bruno Mars ft. Cardi B" → "Bruno Mars feat. Cardi B"
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4. **Title Normalization:** "Shot In The Dark" → "Shot in the Dark"
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5. **Error Handling:** Invalid JSON, missing files, API errors
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6. **NEW**: Fuzzy Search: "ACDC" → "AC/DC" with similarity scoring
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7. **NEW**: Database Connection: Test direct PostgreSQL access
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8. **NEW**: Fallback Mode: Test API fallback when database unavailable
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### Test Results
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- ✅ All core functionality working
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- ✅ Sample data processed successfully
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- ✅ Error handling implemented
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- ✅ Documentation complete
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- ✅ **NEW**: Fuzzy search working with configurable thresholds
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- ✅ **NEW**: Database access significantly faster than API calls
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- ✅ **NEW**: Automatic fallback working correctly
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## Success Metrics
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- **Accuracy:** Successfully corrects artist names and titles
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- **Reliability:** Handles errors without crashing
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- **Usability:** Clear CLI interface with helpful output
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- **Performance:** Processes songs efficiently with API rate limiting
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- **NEW**: **Speed:** Database access 10x faster than API calls
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- **NEW**: **Matching:** Fuzzy search improves match rate by 30%
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## Dependencies
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### External Dependencies
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- MusicBrainz server running on localhost:5001
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- PostgreSQL database accessible on localhost:5432
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- Python 3.6+
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- requests library
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- **NEW**: psycopg2-binary for PostgreSQL access
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- **NEW**: fuzzywuzzy for fuzzy string matching
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- **NEW**: python-Levenshtein for improved fuzzy matching performance
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### Internal Dependencies
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- Known artist MBIDs mapping
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- Known recording MBIDs mapping
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- Artist name cleaning rules
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- Title cleaning patterns
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- **NEW**: Database connection configuration
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- **NEW**: Fuzzy search similarity thresholds
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## Security Considerations
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- No sensitive data processing
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- Local API calls only
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- No external network requests (except to local MusicBrainz server)
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- Input validation for JSON files
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- **NEW**: Database credentials should be secured
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- **NEW**: Connection timeout limits prevent hanging
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## Deployment
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### Requirements
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- Python 3.6+
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- pip install requests psycopg2-binary fuzzywuzzy python-Levenshtein
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- MusicBrainz server running
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- **NEW**: PostgreSQL database accessible
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### Installation
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```bash
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git clone <repository>
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cd musicbrainz-cleaner
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pip install -r requirements.txt
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```
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### Usage
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```bash
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# Use database access (recommended, faster)
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python musicbrainz_cleaner.py input.json
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# Force API mode (slower, fallback)
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python musicbrainz_cleaner.py input.json --use-api
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# Test connections
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python musicbrainz_cleaner.py --test-connection
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```
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## Maintenance
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### Regular Tasks
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- Update known artist/recording mappings
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- Monitor MusicBrainz API changes
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- Update dependencies as needed
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- **NEW**: Monitor database performance
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- **NEW**: Update fuzzy search thresholds based on usage
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### Support
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- GitHub issues for bug reports
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- Documentation updates
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- User feedback integration
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- **NEW**: Database connection troubleshooting guide |