Updated logic in heartFC_chat.py and relationship_manager.py to prioritize 'chat_info_platform', then 'user_platform', and finally a default when determining platform information. Added a fallback to 'unknown' in get_person_id if platform is None, improving robustness when platform data is missing.
Co-Authored-By: tt-P607 <68868379+tt-P607@users.noreply.github.com>
Added 'aiohttp_gemini' to allowed client types in APIProvider. Updated TaskConfig defaults: max_tokens to 800, temperature to 0.7, and concurrency_count to 1 for improved default behavior.
Introduces methods to dynamically determine talk frequency and expression learning settings based on current time and chat stream ID. Adds helper functions for parsing stream config strings, handling both global and stream-specific overrides for chat and expression configurations.
Refactored configuration classes to inherit from a new ValidatedConfigBase using Pydantic for robust validation and error reporting. Updated api_ada_configs.py, config.py, config_base.py, and official_configs.py to replace dataclasses with Pydantic models, add field validation, and improve error messages. This change enhances configuration reliability and developer feedback for misconfigurations. Also includes minor code cleanups and removal of unused variables in other modules.
Cleaned up various files by removing unused or redundant import statements to improve code readability and reduce potential confusion. No functional changes were made.
Replaces the L2 cache layer's SQLite implementation with an async SQLAlchemy-based database model (CacheEntries). Updates cache_manager.py to use db_query and db_save for cache operations, adds semantic cache handling with ChromaDB, and introduces async cache clearing and expiration cleaning methods. Adds the CacheEntries model and integrates it into the database API.
Extended the list of dangerous keywords in the _partially_shield_content method to cover more attack vectors, including system commands, privilege escalation, information leakage, and social engineering. This enhances the robustness of the message shielding mechanism against prompt injection and related attacks.
Enhanced the default_patterns list in PromptInjectionDetector with more comprehensive and specific regular expressions to improve detection of prompt injection attempts, including new patterns for admin commands, system prompts, privilege escalation, and sensitive information leakage. This update aims to strengthen the robustness of the anti-prompt-injection mechanism.
Introduced a ProcessResult enum to standardize anti-injector message processing outcomes. Updated anti_injector.py to return ProcessResult values instead of booleans, and refactored bot.py to handle these results with improved logging and clearer control flow. This change improves code clarity and maintainability for anti-prompt injection logic.
Introduces a comprehensive anti-prompt injection system for LLMs, including rule-based and LLM-based detection, user ban/whitelist management, message shielding, and statistics tracking. Adds new modules under src/chat/antipromptinjector, integrates anti-injection checks into the message receive flow, updates configuration and database models, and provides test scripts. Also updates templates and logger aliases to support the new system.
