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+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "473081cc",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 1\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16ee4216",
+   "metadata": {},
+   "source": [
+    "# Structured Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "670078c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_data(n: int) -> List[dict]:\n",
+    "    \"\"\"Get n datapoints.\"\"\"\n",
+    "    return [{\"name\": \"foo\", \"value\": \"bar\"}] * n\n",
+    "\n",
+    "\n",
+    "tools = [get_data]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e04164b",
+   "metadata": {},
+   "source": [
+    "We will use a prompt from the hub - you can inspect the prompt more at [https://smith.langchain.com/hub/hwchase17/openai-functions-agent](https://smith.langchain.com/hub/hwchase17/openai-functions-agent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d8c5d907",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import hub\n",
+    "from langchain.agents import AgentExecutor, create_openai_functions_agent\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "# Get the prompt to use - you can modify this!\n",
+    "# If you want to see the prompt in full, you can at: https://smith.langchain.com/hub/hwchase17/openai-functions-agent\n",
+    "prompt = hub.pull(\"hwchase17/openai-functions-agent\")\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
+    "\n",
+    "agent = create_openai_functions_agent(llm, tools, prompt)\n",
+    "agent_executor = AgentExecutor(agent=agent, tools=tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cba9a9eb",
+   "metadata": {},
+   "source": [
+    "## Stream intermediate steps\n",
+    "\n",
+    "Let's look at how to stream intermediate steps. We can do this easily by just using the `.stream` method on the AgentExecutor\n",
+    "\n",
+    "We can then parse the results to get actions (tool inputs) and observtions (tool outputs)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b6bd9bf2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calling Tool ```get_data``` with input ```{'n': 3}```\n",
+      "Got result: ```[{'name': 'foo', 'value': 'bar'}, {'name': 'foo', 'value': 'bar'}, {'name': 'foo', 'value': 'bar'}]```\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in agent_executor.stream({\"input\": \"get me three datapoints\"}):\n",
+    "    # Agent Action\n",
+    "    if \"actions\" in chunk:\n",
+    "        for action in chunk[\"actions\"]:\n",
+    "            print(\n",
+    "                f\"Calling Tool ```{action.tool}``` with input ```{action.tool_input}```\"\n",
+    "            )\n",
+    "    # Observation\n",
+    "    elif \"steps\" in chunk:\n",
+    "        for step in chunk[\"steps\"]:\n",
+    "            print(f\"Got result: ```{step.observation}```\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af9e32fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}