Add Spellcheck and import hogwarts (#419)

examples/chat_agent_executor_with_function_calling/high-level-tools.ipynb

@@ -72,7 +72,7 @@
    "id": "d63dbfc7-a5c1-4a03-991c-f0789ba52c52",
    "metadata": {},
    "source": [
-    "We can now invoke this executor. The input to this must be a dictionary with a single `messsages` key that contains a list of messages."
+    "We can now invoke this executor. The input to this must be a dictionary with a single `messages` key that contains a list of messages."
    ]
   },
   {

examples/chat_agent_executor_with_function_calling/respond-in-format.ipynb

@@ -14,6 +14,146 @@
     "Any modifications of that example are called below with **MODIFICATION**, so if you are looking for the differences you can just search for that."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1977bac1",
+   "metadata": {},
+   "source": [
+    "## Set up the State\n",
+    "\n",
+    "The main type of graph in `langgraph` is the [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph).\n",
+    "This graph is parameterized by a `State` object that it passes around to each node.\n",
+    "Each node then returns operations the graph uses to `update` that state.\n",
+    "These operations can either SET specific attributes on the state (e.g. overwrite the existing values) or ADD to the existing attribute.\n",
+    "Whether to set or add is denoted by annotating the `State` object you use to construct the graph.\n",
+    "\n",
+    "For this example, the state we will track will just be a list of messages.\n",
+    "We want each node to just add messages to that list.\n",
+    "Therefore, we will use a `TypedDict` with one key (`messages`) and annotate it so that the `messages` attribute is \"append-only\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de1db3c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing_extensions import TypedDict\n",
+    "from typing import Annotated\n",
+    "from langgraph.graph.message import add_messages\n",
+    "\n",
+    "# Add messages essentially does this with more\n",
+    "# robust handling\n",
+    "# def add_messages(left: list, right: list):\n",
+    "#     return left + right\n",
+    "\n",
+    "\n",
+    "class State(TypedDict):\n",
+    "    messages: Annotated[list, add_messages]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8a08594",
+   "metadata": {},
+   "source": [
+    "## Set up the tools\n",
+    "\n",
+    "We will first define the tools we want to use.\n",
+    "For this simple example, we will use create a placeholder search engine.\n",
+    "It is really easy to create your own tools - see documentation [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools) on how to do that.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23a2ca43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def search(query: str):\n",
+    "    \"\"\"Call to surf the web.\"\"\"\n",
+    "    # This is a placeholder, but don't tell the LLM that...\n",
+    "    return [\"The answer to your question lies within.\"]\n",
+    "\n",
+    "\n",
+    "tools = [search]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44c73446",
+   "metadata": {},
+   "source": [
+    "We can now wrap these tools in a simple [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode).\n",
+    "This is  a simple class that takes in a list of messages containing an [AIMessages with tool_calls](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.tool_calls), runs the tools, and returns the output as [ToolMessage](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolMessage.html#langchain_core.messages.tool.ToolMessage)s.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "979512e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import ToolNode\n",
+    "\n",
+    "tool_node = ToolNode(tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b07b9229",
+   "metadata": {},
+   "source": [
+    "## Set up the model\n",
+    "\n",
+    "Now we need to load the chat model we want to use.\n",
+    "This should satisfy two criteria:\n",
+    "\n",
+    "1. It should work with messages, since our state is primarily a list of messages (chat history).\n",
+    "2. It should work with tool calling, since we are using a prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)\n",
+    "\n",
+    "**Note:** these model requirements are not requirements for using LangGraph - they are just requirements for this particular example.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c8132c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "model = ChatAnthropic(model=\"claude-3-haiku-20240307\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "979f0310",
+   "metadata": {},
+   "source": [
+    "\n",
+    "After we've done this, we should make sure the model knows that it has these tools available to call.\n",
+    "We can do this by converting the LangChain tools into the format for function calling, and then bind them to the model class.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "055d84bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = model.bind_tools(tools)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7cbd446a-808f-4394-be92-d45ab818953c",