mirror of
https://github.com/crowetic/nc-talk-ai.git
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Pascal Kienast
43ba436a61
- Rewrite the four longest guides (RAG background jobs, webhook debug, bot setup, onboarding) at roughly half the length: drop historical bug-fix notes, duplicated command blocks, ASCII mega-diagrams and filler sections, keep everything operationally useful. - Remove provider-specific recommendations; examples are now neutral OpenAI-compatible endpoints. - Fix stale content: dead doc links in docs/README.md, old repo issue URLs, catalogue tools (now provided via the tool-provider extension point, not built-in), supported NC versions, clone URL in quick start. - Slim DEVELOPMENT.md down to build/test/migration essentials. docs/ shrinks from 2850 to 1278 lines with no loss of setup, debugging or architecture coverage.
132 lines
4.6 KiB
Markdown
132 lines
4.6 KiB
Markdown
# Chat Room Onboarding
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When a bot is @mentioned in a Talk room for the first time, it enters **onboarding mode**: it introduces itself, asks how the user wants to interact, optionally asks custom questions, and stores the answers for future conversations.
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## Flow
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1. User mentions the bot for the first time (`@mybot hello`).
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2. The bot asks whether it should respond **A:** only when @mentioned, or **B:** to every message in the chat. The user replies `A` or `B` — no @mention needed during onboarding.
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3. If the bot has custom onboarding questions, it asks them next (branching supported). Otherwise onboarding completes immediately.
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4. The bot confirms and is ready; answers are stored and injected into its system prompt from then on.
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## Response Modes
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- **`mention`** — bot only responds when @mentioned. Default; recommended for shared rooms.
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- **`always`** — bot responds to every message. Useful for dedicated bot rooms or 1:1 chats.
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## Custom Onboarding Questions
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Bot creators can define up to **5 questions** per bot, with **multiple-choice branching** and **free-text** answers. Answers are stored permanently and appended to the LLM's system prompt.
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Questions are stored as a JSON tree:
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```json
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{
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"start": "q1",
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"questions": [
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{
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"id": "q1",
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"text": "What is your main use case?",
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"answers": [
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{ "id": "a", "text": "Learning", "next": "q2a" },
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{ "id": "b", "text": "Teaching", "next": null }
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]
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},
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{
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"id": "q2a",
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"text": "What subject interests you most?",
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"answers": [
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{ "id": "a", "text": "Mathematics", "next": null },
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{ "id": "b", "text": "Science", "next": null }
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]
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}
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]
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}
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```
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- `start` — ID of the first question
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- `next: "qX"` — continue with that question; `next: null` — end of the branch, onboarding completes
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- `type: "free_text"` on an answer — the user replies with arbitrary text, stored verbatim:
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```json
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{
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"id": "q1",
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"text": "What is your current field of study?",
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"answers": [ { "id": "text", "type": "free_text", "next": null } ]
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}
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```
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## How Answers Reach the LLM
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Answers are stored per room in `educai_chat_rooms.onboarding_answers` as Q&A pairs:
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```json
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[
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{ "question_id": "q1", "question_text": "What is your main use case?",
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"answer_id": "a", "answer_text": "Learning" }
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]
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```
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On every message, `BotService::processMessage()` appends the onboarding context to the system prompt:
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```
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[Original System Prompt]
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## User Onboarding Context
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The user has provided the following information during onboarding:
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- **What is your main use case?** → Learning
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Use this context to personalize your responses.
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```
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System-prompt injection keeps the context persistent (unlike conversation history), token-efficient, and separate from the conversation flow.
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## Reset Command
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```
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((RESET)) → resets all bots in the room
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((RESET)) @mybot → resets only that bot
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```
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Reset deletes the room state and the bot's conversation history for that room; the next mention triggers fresh onboarding. This is also the easiest way to test question flows.
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## Message Routing
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For each incoming message, `TalkHandler` checks in order:
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1. `((RESET))` command → handle reset.
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2. Bot @mentioned → load room state: no state → start onboarding; onboarding in progress → treat message as an answer; completed → process normally.
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3. No mention → if a bot in this room is mid-onboarding, treat the message as its answer; else if an `always`-mode bot is active, process the message; otherwise ignore.
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## Database Schema
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`educai_chat_rooms`:
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| Column | Description |
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| `bot_id` | FK to `educai_bots` |
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| `room_token` | Talk room identifier |
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| `response_mode` | `mention` or `always` |
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| `onboarding_status` | `mode_selection`, `questions`, or `completed` |
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| `current_question_id` | question being asked (nullable) |
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| `onboarding_answers` | JSON array of Q&A pairs |
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| `activated_by` | user who first activated the bot |
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`educai_bots.onboarding_questions` holds the JSON question tree (nullable).
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## Key Files
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| File | Purpose |
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| `lib/Service/OnboardingService.php` | Onboarding flow logic |
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| `lib/Webhook/TalkHandler.php` | Message routing and onboarding detection |
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| `lib/Service/BotService.php` | Context injection into the system prompt |
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| `lib/Db/ChatRoom.php` / `ChatRoomMapper.php` | Room state persistence |
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| `src/components/BotForm.vue` | UI for configuring questions |
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## Tips
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- Keep questions short — users answer in a chat interface.
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- Limit branching to 2–3 levels.
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- Reference the onboarding answers in the system prompt so the bot actually uses them.
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