- 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
