- 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
Add `context_length` and `context_max_length` fields to the `BotResponse` struct across the codebase.
Initialize these fields with default values (0) for existing response constructions and populate them using `ConfigManager` to retrieve the configured maximum context size for each bot.
Import `ConfigManager` in `bot/mod.rs` to access configuration.
These changes enable tracking of the current and maximum context sizes in bot responses, supporting future features that rely on context management.
Changed async Redis operations to synchronous in add_suggestion_keyword function. Removed unnecessary async/await and tokio::spawn since the operations are now blocking. This simplifies the code while maintaining the same functionality of storing suggestions and context state in Redis. Error handling remains robust with proper early returns.
Updated references from `redis_client`, `s3_client`, and `custom_conn` to unified names `cache`, `drive`, and `conn` for consistency across modules. Adjusted `add_suggestion_keyword` to use clearer parameter naming and enhanced custom syntax registration for better readability and maintainability.
Updated `ConfigManager::get_config` to use Diesel query builder instead of raw SQL for improved safety and maintainability. Adjusted parameter naming and integrated schema references. Also refactored `ensure_llama_servers_running` to fetch configuration from the database using `AppState` and `ConfigManager`. Removed unused imports in bootstrap module.
- Split AIConfig into separate LLMConfig and embedding config structs
- Update create_site.rs to use config.llm instead of config.ai
- Improve config loading comments in bootstrap manager
- Add new LLM-related environment variables with defaults
- Maintain backward compatibility with existing config loading
- Clean up unused AIConfig struct and related code
The change better organizes the AI-related configuration by separating LLM and embedding configurations, making the code more maintainable and flexible for future AI service integrations.
- Replace direct database connection establishment with shared `establish_pg_connection` utility
- Add "llm" to required components list in bootstrap manager
- Lower default RUST_LOG level from debug to info in VSCode config
- Clean up imports and connection error messages
- Remove hardcoded database URL strings in favor of centralized connection handling
The changes improve code maintainability by centralizing database connection logic and adding support for the new LLM component in the bootstrap process.
- Added new ADD_SUGGESTION keyword handler to support sending suggestions in responses
- Removed unused env import in hear_talk module
- Simplified bot_id assignment to use static string
- Added suggestions field to BotResponse struct
- Improved SET_CONTEXT keyword to take both name and value parameters
- Fixed whitespace in auth handler
- Enhanced error handling for suggestion sending
The changes improve the suggestion system functionality while cleaning up unused code and standardizing response handling.
- Added AWS SDK S3 dependencies including aws-config, aws-sdk-s3, and related crates
- Removed opendal dependency and replaced with AWS SDK S3 client
- Implemented new get_file_content helper function using AWS SDK
- Updated MinIOHandler to use AWS SDK client instead of opendal Operator
- Modified file change detection to work with AWS SDK's S3 client
The change was made to standardize on AWS's official SDK for S3 operations, which provides better maintenance and feature support compared to the opendal crate. This also aligns with AWS best practices for interacting with S3 services.
- Added `data_download_list` field to `ComponentConfig` struct in `component.rs`.
- Implemented processing of `data_download_list` in the `PackageManager` to download files asynchronously in `facade.rs`.
- Updated `installer.rs` to initialize `data_download_list` for various components.
- Refactored `download_file` function in `utils.rs` to return `anyhow::Error` for better error handling.
- Replace simple message count with token-based calculation
- Add token estimation function (4 chars ≈ 1 token)
- Set MAX_TOKENS to 5000 and MIN_DISPLAY_PERCENTAGE to 20
- Update context usage display to show token count percentage
- Track tokens for both user and assistant messages
- Handle server-provided context usage as ratio of MAX_TOKENS
- Rename script_name to param in automation flow and DB schema
- Add BotMemory model and bot_memories table
- Remove script_name field from automation
- Enable sqlite support via rusqlite and related crates (optional)
- Update prompts and queries to use param instead of script_name
- Remove deprecated annoucements GBai templates and align add-req.sh
- Refactor main to initialize automation service and simplify startup
- Extract LLM generation into `execute_llm_generation` and simplify
keyword handling.
- Prepend system prompt and session context to LLM prompts in
`BotOrchestrator`.
- Parse incoming WebSocket messages as JSON and use the `content` field.
- Add async `get_session_context` and stop injecting Redis context into
conversation history.
- Change default LLM URL to `http://48.217.66.81:8080` throughout the
project.
- Use the existing DB pool instead of creating a separate custom
connection.
- Update `start.bas` to call LLM and set a new context string.
- Refactor web client message handling: separate event processing,
improve streaming logic, reset streaming state on thinking end, and
remove unused test functions.
- Migrate core services to store Arc<AppState> and use locks
- Centralize state in AppState with Arc-wrapped managers
- Update handlers to pass Arc<AppState> via web::Data
- Add Default for AppState and initialize components in main
- Update debug.json program path from gbserver to botserver
