add tool calling

vibe_bot/main.py

@@ -33,6 +33,7 @@ from vibe_bot.config import (
     VOICES_LIST,
 )
 from vibe_bot.database import CustomBotManager, get_database
+from vibe_bot.tools import get_channel_members, get_channel_members_impl
 
 if TYPE_CHECKING:
     from discord.ext.commands import Bot
@@ -495,9 +496,35 @@ async def _speak_with_bot(
         if context:
             prompts = context + prompts
 
-        bot_response = llama_wrapper.chat_completion_with_history(
+        # Build tool definitions from LangChain tools
+        speak_tools: list[dict[str, object]] = [
+            {
+                "type": "function",
+                "function": {
+                    "name": get_channel_members.name,
+                    "description": get_channel_members.description,
+                    "parameters": get_channel_members.args_schema.model_json_schema(),  # type: ignore
+                },
+            },
+        ]
+
+        def speak_tool_executor(tool_name: str, tool_args: dict[str, str]) -> str:
+            """Execute a tool by name with the given arguments."""
+            if tool_name == "get_channel_members":
+                return get_channel_members_impl(ctx.channel)
+            return f"Unknown tool: {tool_name}"
+
+        async def speak_tool_call_notifier(tool_name: str, tool_args: dict[str, str]) -> None:
+            """Send a notification message when a tool is called."""
+            if tool_name == "get_channel_members":
+                await ctx.send(f"**{bot_name}** is looking at the channel members...")
+
+        bot_response = await llama_wrapper.chat_completion_with_tools(
             system_prompt=system_prompt_edit,
             prompts=prompts,
+            tools=speak_tools,
+            tool_executor=speak_tool_executor,
+            tool_call_notifier=speak_tool_call_notifier,
             openai_url=CHAT_ENDPOINT,
             openai_api_key=CHAT_ENDPOINT_KEY,
             model=CHAT_MODEL,
@@ -869,10 +896,36 @@ async def handle_chat(
 
     system_prompt_edit = f"{system_prompt}\nKeep your responses under 2-3 sentences.\n\nUser Information:\n{get_user_info(ctx.author)}"
 
+    # Build tool definitions from LangChain tools
+    tools: list[dict[str, object]] = [
+        {
+            "type": "function",
+            "function": {
+                "name": get_channel_members.name,
+                "description": get_channel_members.description,
+                "parameters": get_channel_members.args_schema.model_json_schema(),  # type: ignore
+            },
+        },
+    ]
+
+    def tool_executor(tool_name: str, tool_args: dict[str, str]) -> str:
+        """Execute a tool by name with the given arguments."""
+        if tool_name == "get_channel_members":
+            return get_channel_members_impl(ctx.channel)
+        return f"Unknown tool: {tool_name}"
+
+    async def tool_call_notifier(tool_name: str, tool_args: dict[str, str]) -> None:
+        """Send a notification message when a tool is called."""
+        if tool_name == "get_channel_members":
+            await ctx.send(f"{bot_name} is looking at the channel members...")
+
     try:
-        bot_response = llama_wrapper.chat_completion_with_history(
+        bot_response = await llama_wrapper.chat_completion_with_tools(
             system_prompt=system_prompt_edit,
             prompts=prompts,
+            tools=tools,
+            tool_executor=tool_executor,
+            tool_call_notifier=tool_call_notifier,
             openai_url=CHAT_ENDPOINT,
             openai_api_key=CHAT_ENDPOINT_KEY,
             model=CHAT_MODEL,