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docs/changelog.md
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All notable changes to the AskVeracity fact-checking and misinformation detection system will be documented in this file.
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## [0.4.2] - 2025-04-28
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### Added
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### Planned Features
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- Enhanced visualization of evidence relevance
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- Support for user feedback on verification results
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- Streamlined fact-checking using only relevant sources
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- Source weighting for improved result relevance
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All notable changes to the AskVeracity fact-checking and misinformation detection system will be documented in this file.
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## [0.4.3] - 2025-06-14
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### Added
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- Integrated detailed performance tracking for evidence retrieval:
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- Logged source-wise success/failure counts for `RSS`, `Wikidata`, and `OpenAlex`
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- Captured confidence scores and processing durations in `agent.py`
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- Added a **Confidence Note** message in the app UI to clarify that the displayed confidence percentage reflects overall verdict certainty, whereas individual evidence may vary in confidence based on source reliability
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### Changed
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- Refactored multiple `try-except` blocks in `evidence_retrieval.py` to ensure logging of both successful and failed retrieval attempts
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- Enhanced fallback mechanism for RSS feeds:
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- AI category now always uses both `AI-specific` and `Technology` RSS feeds
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- Updated sampling logic to avoid negative values
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- Improved robustness in logging:
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- Used `.get()` with defaults when accessing dictionary keys in logs to avoid `KeyError`
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- Cleaned up imports across all modules to remove redundancy and optimize performance
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- Moved module-level imports from function body to the top across several files (e.g., `agent.py`, `app.py`, `models.py`)
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- Refactored confidence logging in `truth_classifier()` and `process_claim()` for centralized tracking
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### Removed
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- Unused or redundant imports: `langdetect`, `spacy`, `re`, `json`, `ssl`, `sys`, `Timer`, etc.
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- `PerformanceTracker` initialization in files where it is no longer required (`classification.py`, `evaluate_performance.py`)
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## [0.4.2] - 2025-04-28
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### Added
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### Planned Features
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- Enhanced visualization of evidence relevance
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- Display agent reasoning process for greater transparency
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- Support for user feedback on verification results
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- Streamlined fact-checking using only relevant sources
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- Source weighting for improved result relevance
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