Integrate suffixed encrypted storage across extraction, review, training, and inference. Add shared-password web authentication with two-hour sessions, private request logging, and an isolated profile-building pipeline.
Fix encrypted dataset discovery at training entrypoints and duplicate CSV imports. Add synthetic storage, authentication, and log checks; update documentation and frontend assets.
- Added default values for batch size, max tokens, and timeout in distill_profile.py.
- Refactored required configuration functions to support section-specific error messages.
- Introduced positive and optional positive integer validation functions for configuration.
- Updated resolve_llm_args to utilize new configuration validation functions.
- Modified organize_memories.py to load agent distill configuration and handle missing model gracefully.
- Improved stage 2 output file management in make_dataset_stage2.py, ensuring proper validation against manifest files.
- Added new web assets including HTML, CSS, and JS files for profile review interface.
- Set up default static directory for serving web assets in profile_review.py.
- Implemented `organize_memories.py` to manage and organize extracted profile/event memories by dimension.
- Introduced new schemas and rules for attribute hierarchy and preference attributes in `memory_organization.py`.
- Created `profile_hierarchy.py` for generating a hierarchical structure of attributes for better browsing.
- Enhanced classification and summarization processes to support new dimensions and attributes.
- Introduced `distill_event.py` for processing chat records and extracting events related to user interactions.
- Implemented functions for handling event payloads, rendering event chats, and managing state during processing.
- Enhanced `chat_distill.py` with a detailed prompt for extracting surface and inferred events from chat records, focusing on relevant information and event types.
- Updated logging and progress tracking for improved user feedback during event processing.
- Introduced `agent_distill_args` in `settings.template.jsonc` to specify LLM backend for agent distillation.
- Added `AgentDistillArgs` class in `config_models.py` to support new configuration parameters.
- Enhanced `CodexExecClient` in `llm_client.py` to manage request directories and improve command execution handling.
- Created `distill_state.py` for managing state extraction and processing logic related to agent distillation.
- Updated `distill.py` to enhance state extraction functions, introducing required configuration validation for model parameters.
- Refactored prompt definitions in `chat_distill.py`, replacing the previous state extraction prompt with a more structured template and adding a section for current state extraction.
- Improved memory filtering logic to better handle current state payloads and ensure relevant information is retained for future conversations.
- Updated subproject commit to reflect changes in the repository state.
- Introduced `__init__.py` for agent data construction utilities.
- Added `distill.py` containing functions for processing chat data, managing state, and saving JSON files.
- Created `chat_distill.py` with a detailed prompt for extracting memory states from chat records, focusing on various types of information such as stable facts, preferences, experiences, goals, and current states.
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