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Local Deep Research v0.4.0 Release Notes
We're excited to announce Local Deep Research v0.4.0, bringing significant improvements to search capabilities, model integrations, and overall system performance.
Major Enhancements
LLM Improvements
- Custom OpenAI Endpoint Support: Added support for custom OpenAI-compatible endpoints
- Dynamic Model Fetching: Improved model discovery for both OpenAI and Anthropic using their official packages
- Increased Context Window: Enhanced default context window size and maximum limits
Search Enhancements
- Journal Quality Assessment: Added capability to estimate journal reputation and quality for academic sources
- Enhanced SearXNG Integration: Fixed API key handling and prioritized SearXNG in auto search
- Elasticsearch Improvements: Added English translations to Chinese content in Elasticsearch files
User Experience
- Search Engine Visibility: Added display of selected search engine during research
- Better API Key Management: Improved handling of search engine API keys from database settings
- Custom Context Windows: Added user-configurable context window size for LLMs
System Improvements
- Logging System Upgrade: Migrated to
logurufor improved logging capabilities - Memory Optimization: Fixed high memory usage when journal quality filtering is enabled
- Resumable Benchmarks: Added support for resuming interrupted benchmark runs
Bug Fixes
- Fixed broken SearXNG API key setting
- Memory usage optimizations for journal quality filtering
- Cleanup of OpenAI endpoint model loading features
- Various fixes for evaluation scripts
- Improved settings manager reliability
Development Improvements
- Added test coverage for settings manager
- Cleaner code organization for LLM integration
- Enhanced API key handling from database settings
New Contributors
- @JayLiu7319 contributed support for Custom OpenAI Endpoint models
Full Changelog
For complete details of all changes, see the full changelog.
Installation
Download the Windows Installer or install via pip:
pip install local-deep-research
Requires Ollama or other LLM provider. See the README for complete setup instructions.