moai/.planning/phases/04-single-model-qa/04-02-PLAN.md

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

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.

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

Task 1: Integrate AI client into bot lifecycle src/moai/bot/main.py 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). python -c "import moai.bot.main; print('main imports ok')" main.py imports and initializes AI client alongside database in post_init

Task 2: Create /ask handler for single model queries src/moai/bot/handlers/discussion.py, src/moai/bot/handlers/__init__.py Create discussion.py with /ask handler:

1. Create src/moai/bot/handlers/discussion.py:

   """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 python -c "from moai.bot.handlers.discussion import ask_command; print('handler ok')" /ask handler registered, validates model name, sends typing indicator, calls AI client

Task 3: Update help text and status src/moai/bot/handlers/commands.py, src/moai/bot/handlers/status.py 1. Update HELP_TEXT in commands.py to include: ``` *Questions* /ask - 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" python -c "from moai.bot.handlers.commands import HELP_TEXT; print('/ask' in HELP_TEXT)" Help shows /ask command, status shows AI router status

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

<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>

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

Note: This completes Phase 4. Summary should note M3 milestone complete.