KrisHD_AI

KrisHD_AI is an Express + SQLite app for account-based chat, persona management, roleplay chat sessions, and a simple persona market backed by a remote model API.

Setup

Install dependencies with npm install.
Copy .env.example to .env and set at least SESSION_SECRET.
Start the app with npm run dev for local development or npm start for a normal server run.

Scripts

npm start: run the production server entrypoint.
npm run dev: run the server with Node watch mode.
npm run lint: syntax-check the backend and browser modules.
npm test: run API tests with Node's built-in test runner.

Environment Variables

PORT: HTTP port for the Express server.
DB_PATH: SQLite database path. Use :memory: for isolated tests.
SESSION_SECRET: session signing secret. Required outside local throwaway environments.
COOKIE_SECURE: set to true when serving over HTTPS.
SESSION_MAX_AGE_MS: cookie lifetime.
CHAT_LIMIT: per-account maximum chat sessions.
CHAT_HISTORY_LIMIT: number of recent messages included in model prompts.
MODEL_API_BASE_URL: base URL of the remote model backend. The server expects /chat and /tags.
MODEL_REQUEST_TIMEOUT_MS: timeout for chat generation requests.
MODEL_LIST_TIMEOUT_MS: timeout for model list requests.
MODEL_UNLOAD_AFTER_MS: delay before an idle model unload is attempted.
MODELS_CACHE_TTL_MS: model list cache TTL.
AUTH_RATE_LIMIT_*: rate-limit window and thresholds for registration/login.
CHAT_RATE_LIMIT_*: rate-limit window and thresholds for chat and retry endpoints.
DROP_LEGACY_CHATS: when true, migrate any legacy chats table into chat_sessions / chat_messages and remove the old table.

Model Backend Expectations

The server is configured around an Ollama-compatible API surface:

POST /chat: accepts { model, messages, stream: true } and returns newline-delimited JSON stream chunks with message.content.
GET /tags: returns the available model list.

The refactor adds explicit upstream timeout handling, cached model listing, and GET /health for degraded-state visibility.

Schema Notes

Primary runtime tables are:

users
chat_sessions
chat_messages
personas
user_settings
persona_market

Legacy chats migration is handled during startup. New code only reads the session/message schema.

Persona And Market Flow

Assistant and user personas are stored separately through persona_type.
Roleplay chats can bind an assistant persona, an optional user persona, and scenario metadata to a chat session.
Publishing copies a local persona into persona_market.
Collecting clones a market persona back into the current user's personas table with source metadata.

Frontend Structure

The main browser entrypoint is still public/script.js, but shared API/bootstrap concerns are now split into public/app/ modules:

api.js: JSON request helpers
constants.js: theme and onboarding constants
dom.js: cached DOM references

This keeps the next round of UI extraction lower-risk without changing the app's behavior surface.

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