---
phase: 04-single-model-qa
plan: 02
type: execute
---

<objective>
Implement /ask command for single model Q&A and integrate AI client into bot lifecycle.

Purpose: Complete M3 milestone - users can ask questions to individual AI models through Telegram.
Output: Working /ask command that sends questions to AI models and returns responses.
</objective>

<execution_context>
~/.claude/get-shit-done/workflows/execute-phase.md
~/.claude/get-shit-done/templates/summary.md
</execution_context>

<context>
@.planning/PROJECT.md
@.planning/ROADMAP.md
@.planning/STATE.md
@.planning/phases/04-single-model-qa/04-01-SUMMARY.md (will exist after 04-01)

# Key files:
@src/moai/bot/main.py
@src/moai/bot/config.py
@src/moai/bot/handlers/__init__.py
@src/moai/bot/handlers/projects.py
@src/moai/core/ai_client.py (will exist after 04-01)

**Tech available:**
- python-telegram-bot, sqlalchemy, httpx, aiosqlite, openai (added in 04-01)
- AI client abstraction (04-01)

**Established patterns:**
- Handler registration in handlers/__init__.py
- get_selected_project() helper in projects.py
- Module-level init pattern (database.py, ai_client.py)
- Async command handlers

**Constraining decisions:**
- Thin handlers delegating to core (CLAUDE.md)
- Service layer for business logic
</context>

<tasks>

<task type="auto">
  <name>Task 1: Integrate AI client into bot lifecycle</name>
  <files>src/moai/bot/main.py</files>
  <action>
Modify main.py to initialize AI client during bot startup:

1. Import init_ai_client from moai.core.ai_client
2. In post_init callback (or create one if needed), after database init:
   - Call init_ai_client(config)
   - Log "AI client initialized with {config.AI_ROUTER}"

Follow existing pattern - database is initialized in post_init, AI client goes right after.
Keep error handling minimal - if AI_API_KEY is missing, let it fail at first use rather than at startup (user may just want to test bot commands first).
  </action>
  <verify>python -c "import moai.bot.main; print('main imports ok')"</verify>
  <done>main.py imports and initializes AI client alongside database in post_init</done>
</task>

<task type="auto">
  <name>Task 2: Create /ask handler for single model queries</name>
  <files>src/moai/bot/handlers/discussion.py, src/moai/bot/handlers/__init__.py</files>
  <action>
Create discussion.py with /ask handler:

1. Create src/moai/bot/handlers/discussion.py:
   ```python
   """Discussion handlers for MoAI bot."""
   from telegram import Update
   from telegram.ext import ContextTypes

   from moai.bot.handlers.projects import get_selected_project
   from moai.core.ai_client import get_ai_client, MODEL_MAP

   async def ask_command(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
       """Handle /ask <model> <question> command.

       Examples:
           /ask claude What is Python?
           /ask gpt Explain async/await
       """
       args = context.args or []

       if len(args) < 2:
           available = ", ".join(MODEL_MAP.keys())
           await update.message.reply_text(
               f"Usage: /ask <model> <question>\n"
               f"Available models: {available}\n\n"
               f"Example: /ask claude What is Python?"
           )
           return

       model_name = args[0].lower()
       question = " ".join(args[1:])

       # Validate model
       if model_name not in MODEL_MAP:
           available = ", ".join(MODEL_MAP.keys())
           await update.message.reply_text(
               f"Unknown model: {model_name}\n"
               f"Available: {available}"
           )
           return

       # Get project context if available (optional for /ask)
       project = await get_selected_project(context)
       project_context = f"Project: {project.name}\n" if project else ""

       # Send "typing" indicator while waiting for AI
       await update.message.chat.send_action("typing")

       try:
           client = get_ai_client()
           response = await client.complete(
               model=model_name,
               messages=[{"role": "user", "content": question}],
               system_prompt=f"{project_context}You are a helpful AI assistant."
           )

           # Format response with model name
           await update.message.reply_text(
               f"*{model_name.title()}:*\n\n{response}",
               parse_mode="Markdown"
           )
       except Exception as e:
           await update.message.reply_text(f"Error: {e}")
   ```

2. Update handlers/__init__.py:
   - Import ask_command from discussion
   - Add CommandHandler("ask", ask_command) to register_handlers
  </action>
  <verify>python -c "from moai.bot.handlers.discussion import ask_command; print('handler ok')"</verify>
  <done>/ask handler registered, validates model name, sends typing indicator, calls AI client</done>
</task>

<task type="auto">
  <name>Task 3: Update help text and status</name>
  <files>src/moai/bot/handlers/commands.py, src/moai/bot/handlers/status.py</files>
  <action>
1. Update HELP_TEXT in commands.py to include:
   ```
   *Questions*
   /ask <model> <question> - Ask a single model
   ```
   Add after the Project Management section.

2. Update status.py to show AI client status:
   - Import get_ai_client (wrapped in try/except)
   - In status_command, add a line showing AI router configured
   - Example: "AI Router: requesty ✓" or "AI Router: not configured"
  </action>
  <verify>python -c "from moai.bot.handlers.commands import HELP_TEXT; print('/ask' in HELP_TEXT)"</verify>
  <done>Help shows /ask command, status shows AI router status</done>
</task>

</tasks>

<verification>
Before declaring plan complete:
- [ ] `ruff check src` passes
- [ ] Bot starts without errors (with valid AI_API_KEY in env)
- [ ] /help shows /ask command
- [ ] /status shows AI router status
- [ ] /ask without args shows usage
- [ ] /ask with invalid model shows available models
</verification>

<success_criteria>

- AI client initialized in bot lifecycle
- /ask command works with model validation
- Help text updated with /ask
- Status shows AI configuration
- M3 milestone: Single model Q&A working
  </success_criteria>

<output>
After completion, create `.planning/phases/04-single-model-qa/04-02-SUMMARY.md`

Note: This completes Phase 4. Summary should note M3 milestone complete.
</output>