Add tools_pydantic_ai_tools_integration.ipynb tutorial notebook

notebook/tools_pydantic_ai_tools_integration.ipynb

@@ -24,7 +24,16 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "## Imports\n",
+    "\n",
+    "Import necessary modules and tools.\n",
+    "- `BaseModel`: Used to define data structures for tool inputs and outputs.\n",
+    "- `RunContext`: Provides context during the execution of tools.\n",
+    "- `PydanticAITool`: Represents a tool in the PydanticAI framework.\n",
+    "- `AssistantAgent` and `UserProxyAgent`: Agents that facilitate communication in the AG2 framework.\n",
+    "- `PydanticAIInteroperability`: A bridge for integrating PydanticAI tools with the AG2 framework."
+   ]
   },
   {
    "cell_type": "code",
@@ -73,6 +82,22 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tool Integration\n",
+    "\n",
+    "Integrate the PydanticAI tool with AG2.\n",
+    "\n",
+    "- Define a `Player` model using `BaseModel` to structure the input data.\n",
+    "- Use `RunContext` to securely inject dependencies (like the `Player` instance) into the tool function without exposing them to the LLM.\n",
+    "- Implement `get_player` to define the tool's functionality, accessing `ctx.deps` for injected data.\n",
+    "- Convert the tool to an AG2-compatible format with `PydanticAIInteroperability` and register it for execution and LLM communication.\n",
+    "- Convert the PydanticAI tool into an AG2-compatible format using `convert_tool`.\n",
+    "- Register the tool for both execution and communication with the LLM by associating it with the `user_proxy` and `chatbot`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -96,13 +121,25 @@
     "pydantic_ai_interop = PydanticAIInteroperability()\n",
     "pydantic_ai_tool = PydanticAITool(get_player, takes_ctx=True)\n",
     "\n",
+    "# player will be injected as a dependency\n",
     "player = Player(name=\"Luka\", age=25)\n",
     "ag2_tool = pydantic_ai_interop.convert_tool(tool=pydantic_ai_tool, deps=player)\n",
     "\n",
     "ag2_tool.register_for_execution(user_proxy)\n",
     "ag2_tool.register_for_llm(chatbot)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initiate a conversation between the `UserProxyAgent` and the `AssistantAgent`.\n",
+    "\n",
+    "- Use the `initiate_chat` method to send a message from the `user_proxy` to the `chatbot`.\n",
+    "- In this example, the user requests the chatbot to retrieve player information, providing \"goal keeper\" as additional context.\n",
+    "- The `Player` instance is securely injected into the tool using `RunContext`, ensuring the chatbot can retrieve and use this data during the interaction."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,