Include model parameter in LLM provider calls across automation, bot, and keyword modules to ensure correct model selection based on configuration. This improves flexibility and consistency in LLM usage.
Removed the legacy TABLES_SERVER environment variable check and related database connection logic. Simplified the bootstrap process to always generate new credentials and write them to .env file. Also updated drive monitor log message to use "Drive" instead of "S3" for consistency. #464
Refactored the compact_prompt_for_bots function to use structured JSON messages instead of plain text formatting. Removed unused execute_compact_prompt method and related code from automation service as the functionality is now handled elsewhere. The changes include:
- Using serde_json to structure messages for LLM
- Improved error handling and fallback mechanism
- Cleaned up obsolete compact prompt execution code
- Renamed `execute_compact_prompt` to `compact_prompt_for_bots` and simplified logic
- Removed redundant comments and empty lines in test files
- Consolidated prompt compaction threshold handling
- Cleaned up UI logging implementation by removing unnecessary whitespace
- Improved code organization in ui_tree module
The changes focus on code quality improvements, removing clutter, and making the prompt compaction logic more straightforward. Test files were cleaned up to be more concise.
- Removed unused `id` and `app_state` fields from `ChatPanel`; updated constructor to accept but ignore the state, reducing memory footprint.
- Switched database access in `ChatPanel` from a raw `Mutex` lock to a connection pool (`app_state.conn.get()`), improving concurrency and error handling.
- Reordered and cleaned up imports in `status_panel.rs` and formatted struct fields for readability.
- Updated VS Code launch configuration to pass `--noui` argument, enabling headless mode for debugging.
- Bumped several crate versions in `Cargo.lock` (e.g., `bitflags` to 2.10.0, `syn` to 2.0.108, `cookie` to 0.16.2) and added the new `ashpd` dependency, aligning the project with latest library releases.
Uncommented bootstrap and package_manager directories in add-req.sh to include them in build process. Refactored bootstrap module for cleaner initialization and improved component handling logic.
Update the LLM server command construction to include a new `--reasoning-format deepseek` argument, enabling explicit selection of the DeepSeek reasoning format. Replace the short `-ngl` flag with the more descriptive `--n-gpu-layers` to improve readability and consistency with other CLI options. This change enhances configurability for models requiring specific reasoning formats and clarifies GPU layer configuration.
Add `info!` statements that output the exact command used to launch the LLM server on both Windows and Unix platforms. This enhances observability and aids debugging by showing the constructed command line before the process is spawned.
- Added retrieval of `llm-server-reasoning-format` configuration in `src/llm/local.rs`.
- When the config value is non‑empty, the server start command now includes `--reasoning-format <value>`.
- Updated argument construction to conditionally append the new flag.
- Cleaned up `src/automation/mod.rs` by removing an unused `std::sync::Arc` import, simplifying the module and eliminating a dead dependency.
Removed the conversation history loading logic in `BotOrchestrator` and replaced it with a placeholder string, commenting out related prompt construction and tracing. This change streamlines prompt generation while debugging and prevents unnecessary history processing.
In the local LLM server setup, eliminated the `llm-server-ctx-size` configuration and its corresponding command‑line argument, as the context size parameter is no longer required. This simplifies server initialization and avoids passing an unused flag.
Adjusted the command strings used to start the LLM and embedding servers on both Windows and Unix.
- Replaced the previous log redirection `../../../../logs/llm/stdout.log` with simpler local files (`llm-stdout.log` and `stdout.log`).
- Updated both normal and embedding server launch commands to use the new paths.
This change simplifies log management, ensures logs are correctly written regardless of the working directory, and resolves issues where the previous relative path could be invalid or inaccessible.
- Deduplicate consecutive messages with same role in conversation history
- Add n_predict configuration option for LLM server
- Prevent duplicate message storage in session manager
- Update announcement schedule timing from 37 to 55 minutes
- Add default n_predict value in default bot config
- Refactor cron matching to use individual variables for each time component with additional debug logging
- Replace SETEX with atomic SET NX EX for job locking in Redis
- Add better error handling and logging for job execution tracking
- Skip execution if Redis is unavailable or job is already held
- Add verbose flag to LLM server startup command for better logging
- Added `trace!` logging in `bot_memory.rs` to record retrieved memory values for easier debugging.
- Refactored `BotOrchestrator` in `bot/mod.rs`:
- Removed duplicate session save block and consolidated message persistence.
- Replaced low‑level LLM streaming with a structured `UserMessage` and `stream_response` workflow, improving error handling and readability.
- Updated configuration loading in `config/mod.rs`:
- Imported `get_default_bot` and enhanced `get_config` to fall back to the default bot configuration when the primary query fails.
- Established a fresh DB connection for the fallback path to avoid borrowing issues.
- Updated `BootstrapManager` to use `AppConfig::from_env().expect(...)` and `AppConfig::from_database(...).expect(...)` ensuring failures are explicit rather than silently ignored.
- Refactored error propagation in bootstrap flow to use `?` where appropriate, improving reliability of configuration loading.
- Added import of `llm_models` in `bot` module and introduced `ConfigManager` usage to fetch the LLM model identifier at runtime.
- Integrated dynamic LLM model handler selection via `llm_models::get_handler(&model)`.
- Replaced static environment variable retrieval for embedding configuration with runtime
