{
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"
    }
   },
   "cell_type": "markdown",
   "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
   "metadata": {},
   "source": [
    "# How to wait for user input\n",
    "\n",
    "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding. \n",
    "\n",
    "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to stop graph execution at a specific step. At this breakpoint, we can wait for human input. Once we have input from the user, we can add it to the graph state and proceed.\n",
    "\n",
    "![Screenshot 2024-07-08 at 5.26.26 PM.png](attachment:02ae42da-d1a4-4849-984a-6ab0bbf759bd.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cbd446a-808f-4394-be92-d45ab818953c",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First we need to install the packages required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "%pip install --quiet -U langgraph langchain_anthropic langchain_openai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d",
   "metadata": {},
   "source": [
    "Next, we need to set API keys for Anthropic and / or OpenAI (the LLM(s) we will use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ANTHROPIC_API_KEY:  ········\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "\n",
    "def _set_env(var: str):\n",
    "    if not os.environ.get(var):\n",
    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
    "\n",
    "\n",
    "_set_env(\"OPENAI_API_KEY\")\n",
    "_set_env(\"ANTHROPIC_API_KEY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c",
   "metadata": {},
   "source": [
    "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
    "_set_env(\"LANGCHAIN_API_KEY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cf1fad-5ab6-49c5-b0c8-15a1b6e8cf21",
   "metadata": {},
   "source": [
    "## Simple Usage\n",
    "\n",
    "Let's look at very basic usage of this. One intuitive approach is simply to create a node, `human_feedback`, that will get user feedback. This allows us to place our feedback gathering at a specific, chosen point in our graph.\n",
    " \n",
    "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` our `human_feedback` node.\n",
    "\n",
    "2) We set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer) to save the state of the graph up until this node.\n",
    "\n",
    "3) We use `.update_state` to update the state of the graph with the human response we get.\n",
    "\n",
    "* We [use the `as_node` parameter](https://langchain-ai.github.io/langgraph/concepts/low_level/#update-state) to apply this state update as the specified node, `human_feedback`.\n",
    "* The graph will then resume execution as if the `human_feedback` node just acted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "58eae42d-be32-48da-8d0a-ab64471657d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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YDrJK6UdnKznlh0zmHv8AIHxxxD973gKaxeg7uZe1+ohDDQ6j+CoHl/jfzTP6g5vnjaNjts4uaS1MxJfjdlz4e/ETxFKmm73Ozw0x75hkM9IC1uQ5I6oJ33rx83K8f+bnvcPO0s/ZeERYnLLlc8/ObqScn2hERaGgREQBERAEREAREQBERAEREAUXqr5sZj0Ob6hUoovVXzYzHoc31CgMf8B36KfDz0OX7RKt0WF+A79FPh56HL9olW6IAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAovVXzYzHoc31CpRReqvmxmPQ5vqFAY/wCA79FPh56HL9olW6LC/Ad+inw89Dl+0SrdEAREQBERAEREAREQBERAEREARcc9iKrGZJpWRMHa6RwaP3lR/SnCj/u9D2lnvWyjKWpAlEUX0qwvfFD2lnvTpVhe+KHtLPets3PdZmzJRFF9KsL3xQ9pZ706VYXvih7Sz3pm57rFmSi8seGL4YF3wcMpQwL9Cuz2OzuNkfDlTlPgzWy8zmSRBniX7loMbidx+MA8m69J9KsL3xQ9pZ71hPhn8NsJx14HZbHU8hj5tQ4sHJYotsML3SsB5oh19fjGczQOzmLCexM3PdYszz/4C3hgZPJQ6F4NYzQHw11WORljNnLFjYoA98kkxi8Qdtg7YNL/AJTuUbjdfoUvDn8mtwnxnDrQOQ1xnbFWlqDUBMFaGzK1kkFJjvK07FpkeOYg9oZGR2r2f0qwvfFD2lnvTNz3WLMlEUX0qwvfFD2lnvTpVhe+KHtLPembnusWZKIovpVhe+KHtLPenSrC98UPaWe9M3PdYsyURRfSrC98UPaWe9d2rerXmF1axFYaO0xPDgP3LDhKOlowc6Ii0AREQBVDV2rp6lsYnEhhyBaHz2ZBzR1GHs6vypHfkt7AAXO6uVr7XYnZVryzSHaONpe4/mA3KyHTT5LeKjyM+xt5I/DZ3Dfrc8Agdfka3laPzNCljaMXUfZq8S7haKqz+bUj4/TVG3N4/IxnMWyNjZyO0zz179QI5Wj8zQB+Zc3R/Fj/ALbT9Qz3Kk8X+Kg4W3NGyWH1ocXlcsaN6exG97o4/g80g8WGHcvL42NA2dvvsBuQpHG8ZdHZfCVMtVzIkpWcnHhmE1pmyNuSPDGQvjLA+NxLm/zwAAQTsOtRutUlrkzuJwj8q0WLL0fxfdtP1DfcnR/F920/UN9yg9Q8U9LaUs5eDLZZlKTE1YLt7xkMhbBDNI6ON5cGkEFzXDqJ223Ow61Xn+Elw8jlswuzdgWa7BLJW/BVzx3iiCfHCPxXM6LYfjACwdXX1ha5ypvMy5QWtovvR/F920/UN9ydH8X3bT9Q33Krak416L0pjcTkMhmf/Z5WA2qU1SrNaE0IDSZP+Ex2zQHt3cdgNwuXNcZNHYCtg7FvNMdHnK77OM+CQS2XXY2Bhd4psTXFx2lYeUDcgkgENOzOT3mMqG1Fk6P4vu2n6hvuTo/i+7afqG+5dfSersRrjBw5fB3W38fM5zGyta5hDmuLXNc1wDmuBBBa4AjbsVQ47cW2cH9GNyUcBs5K3Yip0o3155YRI+RreaTxTXENaHF23UXbcreshM5PeYcoqOU9Rd+j+L7tp+ob7k6P4vu2n6hvuVKdxfw2kNPYKTV2agdlslW+FMjxmLtB0kZ6xJ8G2kmjaA5oJf2HcHbsEjleMejcNp/FZqfOQzY/Lb/AHUo5LUlrYbu8XHE1z3cv5Wzfk+XZM5PeYyo7SydH8X3bT9Q33J0fxfdtP1Dfcq1Z4zaLqaNi1VJn6/4Dmm+DR2GNe575ty3xIiDTIZNwRycvMNj1dSyq34Uj+bK5Gp8Bfp2pq3H4MWZqlhkoqzQNkmc5hIcJWvLwByjs2LSUzk95msqkI62b10fxfdtP1DfcnR/F920/UN9yrWG4zaN1Bp7L5ujmmSUMQSMh4yCWKaqdt9nwvYJG7js+T1+TdcukuLektb0cpbxOYY+HFgOvC3DJUfVaWlwdIyZrHNaQCQ4jYgHr6imcnvM2yovtLB0fxfdtP1DfcuGTSmJfKJo6MVSy3cts0x4iZpPlEjNnD9/kWXaz8JTAt4c6rzWjb0eVyuIxr8hCy5Rsx15mhzW87XOawSM3cOtjvKOtaXjtZYjKakv6frW/H5fHwRT3II4nlsDZN+QOftyBxAJDN+bbr2261lVakdKk+JjKhLQWzTOq7mMuQY3MTuu153iKrkHNAe157I5tthuTsGvAG5IaRzbF19WU5GhFlKFipOCYpmFjtjsRv5QfIR2g+Qq76DzU2oNIYu7ZcHW3ReLsOaNgZWEskIHkHM1ymf8AyQy+1aH6eTONi6KptSjqZPoiKE551slUGQx1qqTsJ4nR7+bcEf8A9WS6Vkc/TeND2uZLHA2GRjhsWvYOV4P6HNIWxrOtVYGXTmRs5WpA6bFW3mW5HEN31pSADKG+WN23ytutrvlbEOcWTRWXB01r1r7e9ljoYOqqc2pdplXFzD3snrDhZNUpWLcNPUTp7MkMTntgj+B2Gh7yBs1vM5o3PVuQPKsy1rpDPdJ9f5Wrgshbq09Z6fzkcNeu4vuQQQVvHugHUJHDlduG77lpHb1L0rWsw3IGT15WTwyDmZJG4Oa4ecEdRXIqulOzOxKmpe+6x5P4oVc3xBt8VMjjdKahjq3dPYmrRFvGSxSW3R3JHv5Iy3m3Ad1tIDgBuRsQTsbcNdd4SlnJuoznGO0jHV+GGF3iTL8MkcY+fbbm5SDy777HdaaiwYVOzvf3p+55L0XS1XhND8M8PncbrWnpuHTrhLS03BNDbdkRLsIrLmcskTBHsW7ljSSeY9SmODeks7ipuBkN/BZKi/CY/O1L/wAIrPAqvL4hGHv25dnhp5Xb7OA6iV6cRDVUUrafej7GZ8DMRew8Gu23aVikLGrslZrixE6PxkT3tLZGbgbtd1kOHUetPCCxF7NaLxcGPpWL87NQYmZ0daJ0jmxsuxOe8hoJDWtBJPYACSrXqnh3pbXEteTUOncZnJK4LYXZCoyYxg7bhvMDtvsP3L7pXh7pjQz7LtO6exmDdZDRMcfVZCZA3fl5uUDfbc7b+cob5LycjsMX4maXsYjjfd1JlsbrHJ6fymIr1K9nR1q2yWrPDJIXRSx1nteWOEgcHEEA79m5Kgs5wzr6S1BorP0tPa2Zo44e1Vmo4a9b/CuOsz2G2S+YQymWQPJeHAOcA4NJ7AvUqJc1dFO55qt6Rh0fY4f62wOkdUWsNUy+QvZXFXRLcyolsQiBlsxSPe9xHiweXfmDZN9t99oPKVs5lMxntSQaR1GKrtf4jLR1ZMZIyzJVhpxiSVsZ7QCwjt332adndS9YolzDorb7tY80Z86zz2W4h6/0hgczgHz4jH4mrHao+Kv2zHZc+zYjrv6+dkMhbHzjdxb1BVfI8Os1rGzxJpafxmrWQZzSleCld1YZxJcnhsPdJE58x5ouYSNaGP5O15aOUEr1+iXDop62efOKOs7fE3gbrLAYrQmq8ZkDhH8tW3h3xNEgLGiCLb8a7rO3iwW7NPWOoGxcHNLZbhXq3O6VuC9msZlAM5X1FNBu6Sw4NZZhsPaA0P5g17N+1ji0fzFsKEgAknYBDdU/mym9J8ke2JjnvIaxoJJPYArLwuqPqaDxRka5j7DZLnK4bFvjpHSgEeQjn7FVMViunsniIgJMAHbW7PXy2R5YYj2OB7HuHUBu0fKJ5dUAAGw6grdnTp5D1t38LX+5y8bVUmoR7D6iIoTlhERAVfJ8N8Dk7MlkVpaNmQ7vlx9iSuXnfclwYQHHfykErofFRQ73zXtv3K7op1XqL/sSKrOOhSZSPiood75r237k+Kih3vmvbfuV3RZz9Tb5G2eqbzKR8VFDvfNe2/cnxUUO9817b9yu6Jn6m3yGeqbzKR8VFDvfNe2/culneGdShhMhaizGZEsFeSRhNzcbhpI8n5loii9VfNjMehzfUKZ+pt8hnqm8zDfBkxFjipwJ0jqrPZrKyZfJ13yWHQWfFsJEr2jZu3V1NC0/4qKHe+a9t+5ULwHfop8PPQ5ftEq3RM/U2+Qz1TeZSPiood75r237k+Kih3vmvbfuV3RM/U2+Qz1TeZSPiood75r237k+Kih3vmvbfuV3RM/U2+Qz1TeZSPiood75r237lzwcKsAHh1uO1ldjv4vIW5Jov2xk8h/a0q4IsZ+r2SMOrUehyZ/McbYmNYxoYxoAa1o2AHmC/pEUBEEREAREQBERAEREAREQBReqvmxmPQ5vqFSii9VfNjMehzfUKAx/wHfop8PPQ5ftEq3RYX4Dv0U+Hnocv2iVbogCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCi9VfNjMehzfUKlF4G/lTOCk2YweD4n0GOkdimNxWTbuTy13SF0MnmAEkj2nykys8yA9C+A79FPh56HL9olW6L8ov5NbgWeIPFmXWuSrF+E0ryywF7fky3nfigN+3kG8nV1hwj86/V1AEREAREQBERAEREAREQBERAEREARFEaszw01gLV8RiaZoDIIS7l8bM4hsbN/Ju4gb+QblbRi5NRWtmUm3ZHFqPWFDTTo4ZRNavTDeKlVZzyvHnPYGN/vPLW+TffqVUk15qey4ugxGMpR/kixbfLJ+0NYAP0An9Kj6VR8BlnsSus3rDvGWLD+17vMPM0dgb2ALsrd1IQ0Rjfvf2+526eCgl8+lnJ0z1d/VsJ/FMoXWcuc19pLL6czFDCWMZlKslSwzml35HtIJB8jhvuD5CAfIpRsjXlwa4OLTyuAO+x8x/ev6WM+91cCbolHYUHgPobJ+D/w5paRwcWJswQyPnnuT+MEtmZ5+U93KNt9g1o/M1o6+1aF0z1d/VsJ/FMuNEz73VwHRKOw5o9b6qiO8mOw9lo/IZYliJ/aWO/yVh09ryrmbbKNqvNism4bsr2Ni2XYbnxcg+S/YAnbqcACSNutZ/BrTT1nUEuChzuMlzkQ3kxjLkZssG2+5iB5h1fmUleow5Gs6CdpcwkODmktcxwO7XNcOtrgQCHDYggEEELOdjLRONu9e7e9ZFPB0pL5NDNVRVjQeoZ8zQsVLzw/J4+TxM0gAHjmkbxy7DqHM3tAAHMHbDbZWdayi4OzOJKLg3FhERaGoREQBERAEREAREQBUTio882loj+Kky3y/MeWrYc0fxNB/wq9qt8QMLPmdOvNRhlv05GXK8YOxkew7lm/99pcz/EpqLSnp7brirEtKSjUi3tKqsSk1lqbG8dJsZqPUFjTmCs24ocFTGLiko5WMwgujNrYuZY5+f5Bc3qaOUO3Wz07cV+tHYhdzRvG4PlHnB8xHYR5CqLl+DWPz+sq2eyWez12CteiycGFmuNNCKzG0CORrOTmHLtzcvPy83XsqrTTsz0k03ZxMh4cZjLcKeHnF7WFjPXc/Fic1muTF2YK7IpbDJuqZzo42vBcR1gHlAJ2aNgpzRGpuLDc9i5cnUzV7C3K8z8jNlaONrQ0z4lz431zXsPe5vOGt5ZA47O35twtCp8E8JTy+pLAvZObEahdPJkNPTTtdj5ZJmhssgZyc4c7bc7P23JIC4tJ8Ho9DRSinqfUuXrxUpKdPG5TINlrV2EDZrQGNLiOUAF5cQNwD1lCFU5Ky2d5l+kte68xOheFmtcrqw6gr6muUMfkMTPjq8LG/CjyNlhfExrg9ri0kEkOHNsG9QXpVYJwg8HqbT+jdE2tSXszkc1gKbJq2nchkY3Y+ndEZaHNEbDuW7kNcXPDdzyhX4Z/iXuN9FacA8u2p5v8A6KwbU24r5r+ZQeO2Fq4nW/D3JSYOljcDFqOtbuahpRs+GNtuc5kcT2gNPipHOYHyczjsdi3yre1m+Q4F4vNaniy2Vz+o8tThyAykGDuZDnoRWA7mY4M5Q4hrutrC8tGw6tlpBIaCSdgOskoSQi0232nNoh7mcQcixn8yXGROk287ZXhu/wDE9aQqPw0x75hkM9IC1uQ5I6oJ33rx83K8f+bnvcPO0s/ZeFcraJKOxJcvTUefxMlKrJoIiKArBERAEREAREQBERAEREBTNRaGnfclyGClgrWZXc9inO0iCw7yu3b1xvPlcA4HytJ6xWJH5uo4staYybHD8uuYp43foLX7/vaD+Za0imzif4438/fiW6eKqU1ZajIvwhf/ALOZr2T71x2MvbqwSTS6fzMcUbS97zU6mgDcntWwqL1V82Mx6HN9QplUtzmTdOqbEZBpXXdfXGn6WcwOLyuUxFxpfXtwVd2SAEtJHX5wR+xS34Qv/wBnM17J96iPAd+inw89Dl+0SrdEyqW5zHTqmxGSR2crOeWHTOYe/wAgfHHEP3veAprF6Du5l7X6iEMNDqP4KgeX+N/NM/qDm+eNo2O2zi5pLVoKJnIx0wjZ7e0jni6s1bUERFCUgiIgCIiAIiIAiIgCIiAIiIAiIgCi9VfNjMehzfUKlFF6q+bGY9Dm+oUBj/gO/RT4eehy/aJVuiwvwHfop8PPQ5ftEq3RAEREAREQBERAEREAREQBERAEREAREQBERAFF6q+bGY9Dm+oVKLyx4YvhgXfBwylDAv0K7PY7O42R8OVOU+DNbLzOZJEGeJfuWgxuJ3H4wDyboC5+A79FPh56HL9olW6L89fAW8MDJ5KHQvBrGaA+GuqxyMsZs5YsbFAHvkkmMXiDtsHbBpf8p3KNxuv0KQBERAEREAREQBERAEREAREQBERAF0bmcx2Pm8VayFWtLtzck0zWO28+xK7yyjUOOqXuI+aNmrDYLaVPlMsYdt1zdm62vGMJVJ6oq/NL1K2IrrD0nVavb72ND6VYXvih7Sz3p0qwvfFD2lnvWd9HsX3bT9Qz3J0exfdtP1DPcqXTaG6+RxOu4fTfH+jROlWF74oe0s96wnwz+G2E468DstjqeQx82ocWDksUW2GF7pWA80Q6+vxjOZoHZzFhPYrl0exfdtP1DPcnR7F920/UM9ydNobr5DruH03x/o86fya3CfGcOtA5DXGdsVaWoNQEwVobMrWSQUmO8rTsWmR45iD2hkZHavZ/SrC98UPaWe9Z30exfdtP1DPcnR7F920/UM9ydNobr5DruH03x/o0TpVhe+KHtLPenSrC98UPaWe9Z30exfdtP1DPcnR7F920/UM9ydNobr5DruH03x/o0eDUeJszMihylKWV52axlhhc4+YAFSKxjJYihUv4CWClXgkGWqAPjia0j/ijygLZ1bjKFSmqkL6b6+47GExKxdPOJW02CIiFwIiIAiIgCIiAIiIAswy/9I2c9Dp/5zLT1mGX/pGznodP/OZa1fy9XwX/AKicv4n+Un/rzRzoiLzJ4MgtY64wegcU3I56+2jWfK2CP5D5JJZDvsyONgL3uIBPK0E7AnyKv/HroQabbnnahhjxZujHOkkhlY+OyQXCKSMtD43EDsc0do843pvhFaUv5DUmgdSR0c7lMLhZ7keSq6aszQ32Nnia1k0fiXNe7kLSHNady2Q9R61Wruh6VnG6azGm9Oashnt63xdnIO1EbU9ySKDdosPEz3vZG1p23dy7ADcAbKeMItJsvwo0nCLk3d+FvD1Nlw3FvSWdwmYy9fMMho4ckZF16GSo+ps3m/4kczWvaC0ggkdfk3VT0Xx6x/EPi50c0/NHbwjcA7Jvnmpz17AmFhkbQBKG7xljtwQ3rPY7yKgcVdA6hz2peLE+PwlrIV3u03fjqGMtjyrKskkk8DHOHK93K1oIB7eUHtCtejs3b1v4QsWoYtNahw2Jj0nJTNjNYuSoDMbkb/FjmH87l3P59iRuBus5EUm/fYbZmmoOS06NurQn68jb0RFXOcReb/5rA/ren/qha4sjzf8AzWB/W9P/AFQtcXosL+Wj4v0Pa/B/yz8X6BERTHbCIiAIiIAiIgCIiALMMv8A0jZz0On/AJzLT1Vs5w7oZ3MS5N9zIVLMsbIn/BLHi2ua3m5dxt2/KKy4qdOdNu116p+hUxdB4ijKlF2bt5pmb6n4W6O1rkGX9QaXxGausjELbF+lHM8MBJDQ5wJ23cTt+cqI/wDT7wy/sBpv/wDVw/7VqXxVUe+M37b9yfFVR74zftv3KgsDb9XkzgL4TiErKouZUtLaI09oetNX09hKGEgneJJYsfXZC17tttyGgbnZTakviqo98Zv237k+Kqj3xm/bfuWOgJ66i4M0fwatJ3c1zI1R2f09i9VYmbF5nH1srjp+XxtS5E2WJ/K4Obu1wIOxAP6QFY/iqo98Zv237k+Kqj3xm/bfuWOr19RcGF8FrJ3U1zMuHg/8Mx2aA04P0YyH/au3h+C2gdP5OvkcZozBY+/Xdzw2a2PiZJG7ztcG7grRviqo98Zv237k+Kqj3xm/bfuW3Qf5eTJOqsS9Dq+ZWc3/AM1gf1vT/wBULXFTK/CzGw3almTIZW0aszLEcc9rmZztO7SRt19auavQgqVKNNO9m3xsdnA4aWFpZuTvpuEREOgEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREB//9k=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from typing import TypedDict\n",
    "from langgraph.graph import StateGraph, START, END\n",
    "from langgraph.checkpoint.memory import MemorySaver\n",
    "from IPython.display import Image, display\n",
    "\n",
    "class State(TypedDict):\n",
    "    input: str\n",
    "    user_feedback: str\n",
    "\n",
    "def step_1(state):\n",
    "    print(\"---Step 1---\")\n",
    "    pass\n",
    "\n",
    "def human_feedback(state):\n",
    "    print(\"---human_feedback---\")\n",
    "    pass\n",
    "\n",
    "def step_3(state):\n",
    "    print(\"---Step 3---\")\n",
    "    pass\n",
    "\n",
    "builder = StateGraph(State)\n",
    "builder.add_node(\"step_1\", step_1)\n",
    "builder.add_node(\"human_feedback\", step_2)\n",
    "builder.add_node(\"step_3\", step_3)\n",
    "builder.add_edge(START, \"step_1\")\n",
    "builder.add_edge(\"step_1\", \"human_feedback\")\n",
    "builder.add_edge(\"human_feedback\", \"step_3\")\n",
    "builder.add_edge(\"step_3\", END)\n",
    "\n",
    "# Set up memory\n",
    "memory = MemorySaver()\n",
    "\n",
    "# Add \n",
    "graph = builder.compile(checkpointer=memory, interrupt_before=[\"human_feedback\"])\n",
    "\n",
    "# View\n",
    "display(Image(graph.get_graph().draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce0fe2bc-86fc-465f-956c-729805d50404",
   "metadata": {},
   "source": [
    "Run until our breakpoint at `step_2` - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "eb8e7d47-e7c9-4217-b72c-08394a2c4d3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input': 'hello world'}\n",
      "---Step 1---\n"
     ]
    }
   ],
   "source": [
    "# Input\n",
    "initial_input = {\"input\": \"hello world\"}\n",
    "\n",
    "# Thread\n",
    "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
    "\n",
    "# Run the graph until the first interruption\n",
    "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a7d545-ab19-4800-985b-62837d060809",
   "metadata": {},
   "source": [
    "Now, we can just manually update our graph state with with the user input - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2165a1bc-1c5b-411f-9e9c-a2b9627e5d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Tell me how you want to update the state:  go to step 3!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--State after update--\n",
      "StateSnapshot(values={'input': 'hello world', 'user_feedback': 'go to step 3!'}, next=('step_3',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef3e216-b8a2-6db4-8002-966ecca671d0'}}, metadata={'source': 'update', 'step': 2, 'writes': {'human_feedback': {'user_feedback': 'go to step 3!'}}}, created_at='2024-07-09T18:31:13.083519+00:00', parent_config=None)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('step_3',)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get user input\n",
    "user_input = input(\"Tell me how you want to update the state: \")\n",
    "\n",
    "# We now update the state as if we are the human_feedback node\n",
    "graph.update_state(thread, {\"user_feedback\": user_input}, as_node=\"human_feedback\")\n",
    "    \n",
    "# We can check the state\n",
    "print(\"--State after update--\")\n",
    "print(graph.get_state(thread))\n",
    "\n",
    "# We can check the next node, showing that it is node 3 (which follows human_feedback)\n",
    "graph.get_state(thread).next"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccc4a84a-02f2-4b79-a5a5-22173645526d",
   "metadata": {},
   "source": [
    "We can proceed after our breakpoint - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3cca588f-e8d8-416b-aba7-0f3ae5e51598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---Step 3---\n"
     ]
    }
   ],
   "source": [
    "# Continue the graph execution\n",
    "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75a1060-47aa-4cc6-8c41-e6ba2e9d7923",
   "metadata": {},
   "source": [
    "We can see our feedback was added to state - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2b83e5ca-8497-43ca-bff7-7203e654c4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input': 'hello world', 'user_feedback': 'go to step 3!'}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.get_state(thread).values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36f89e5",
   "metadata": {},
   "source": [
    "## Agent\n",
    "\n",
    "In the context of agents, waiting for user feedback is useful to ask clarifying questions.\n",
    " \n",
    "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n",
    "\n",
    "We will use OpenAI and / or Anthropic's models and a fake tool (just for demo purposes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f5319e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the state\n",
    "from langgraph.graph import MessagesState, START\n",
    "\n",
    "# Set up the tool\n",
    "# We will have one real tool - a search tool\n",
    "# We'll also have one \"fake\" tool - a \"ask_human\" tool\n",
    "# Here we define any ACTUAL tools\n",
    "from langchain_core.tools import tool\n",
    "from langgraph.prebuilt import ToolNode\n",
    "\n",
    "\n",
    "@tool\n",
    "def search(query: str):\n",
    "    \"\"\"Call to surf the web.\"\"\"\n",
    "    # This is a placeholder for the actual implementation\n",
    "    # Don't let the LLM know this though 😊\n",
    "    return [\n",
    "        f\"I looked up: {query}. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n",
    "    ]\n",
    "\n",
    "\n",
    "tools = [search]\n",
    "tool_node = ToolNode(tools)\n",
    "\n",
    "# Set up the model\n",
    "from langchain_anthropic import ChatAnthropic\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
    "model = ChatOpenAI(model=\"gpt-4o\")\n",
    "\n",
    "# We are going \"bind\" all tools to the model\n",
    "# We have the ACTUAL tools from above, but we also need a mock tool to ask a human\n",
    "# Since `bind_tools` takes in tools but also just tool definitions,\n",
    "# We can define a tool definition for `ask_human`\n",
    "\n",
    "from langchain_core.pydantic_v1 import BaseModel\n",
    "\n",
    "\n",
    "class AskHuman(BaseModel):\n",
    "    \"\"\"Ask the human a question\"\"\"\n",
    "\n",
    "    question: str\n",
    "\n",
    "\n",
    "model = model.bind_tools(tools + [AskHuman])\n",
    "\n",
    "# Define nodes and conditional edges\n",
    "\n",
    "from langchain_core.messages import ToolMessage\n",
    "\n",
    "from langgraph.prebuilt import ToolInvocation\n",
    "\n",
    "\n",
    "# Define the function that determines whether to continue or not\n",
    "def should_continue(state):\n",
    "    messages = state[\"messages\"]\n",
    "    last_message = messages[-1]\n",
    "    # If there is no function call, then we finish\n",
    "    if not last_message.tool_calls:\n",
    "        return \"end\"\n",
    "    # If tool call is asking Human, we return that node\n",
    "    # You could also add logic here to let some system know that there's something that requires Human input\n",
    "    # For example, send a slack message, etc\n",
    "    elif last_message.tool_calls[0][\"name\"] == \"AskHuman\":\n",
    "        return \"ask_human\"\n",
    "    # Otherwise if there is, we continue\n",
    "    else:\n",
    "        return \"continue\"\n",
    "\n",
    "\n",
    "# Define the function that calls the model\n",
    "def call_model(state):\n",
    "    messages = state[\"messages\"]\n",
    "    response = model.invoke(messages)\n",
    "    # We return a list, because this will get added to the existing list\n",
    "    return {\"messages\": [response]}\n",
    "\n",
    "\n",
    "# We define a fake node to ask the human\n",
    "def ask_human(state):\n",
    "    pass\n",
    "\n",
    "\n",
    "# Build the graph\n",
    "\n",
    "from langgraph.graph import END, StateGraph\n",
    "\n",
    "# Define a new graph\n",
    "workflow = StateGraph(MessagesState)\n",
    "\n",
    "# Define the three nodes we will cycle between\n",
    "workflow.add_node(\"agent\", call_model)\n",
    "workflow.add_node(\"action\", tool_node)\n",
    "workflow.add_node(\"ask_human\", ask_human)\n",
    "\n",
    "# Set the entrypoint as `agent`\n",
    "# This means that this node is the first one called\n",
    "workflow.add_edge(START, \"agent\")\n",
    "\n",
    "# We now add a conditional edge\n",
    "workflow.add_conditional_edges(\n",
    "    # First, we define the start node. We use `agent`.\n",
    "    # This means these are the edges taken after the `agent` node is called.\n",
    "    \"agent\",\n",
    "    # Next, we pass in the function that will determine which node is called next.\n",
    "    should_continue,\n",
    "    # Finally we pass in a mapping.\n",
    "    # The keys are strings, and the values are other nodes.\n",
    "    # END is a special node marking that the graph should finish.\n",
    "    # What will happen is we will call `should_continue`, and then the output of that\n",
    "    # will be matched against the keys in this mapping.\n",
    "    # Based on which one it matches, that node will then be called.\n",
    "    {\n",
    "        # If `tools`, then we call the tool node.\n",
    "        \"continue\": \"action\",\n",
    "        # We may ask the human\n",
    "        \"ask_human\": \"ask_human\",\n",
    "        # Otherwise we finish.\n",
    "        \"end\": END,\n",
    "    },\n",
    ")\n",
    "\n",
    "# We now add a normal edge from `tools` to `agent`.\n",
    "# This means that after `tools` is called, `agent` node is called next.\n",
    "workflow.add_edge(\"action\", \"agent\")\n",
    "\n",
    "# After we get back the human response, we go back to the agent\n",
    "workflow.add_edge(\"ask_human\", \"agent\")\n",
    "\n",
    "# Set up memory\n",
    "from langgraph.checkpoint.memory import MemorySaver\n",
    "\n",
    "memory = MemorySaver()\n",
    "\n",
    "# Finally, we compile it!\n",
    "# This compiles it into a LangChain Runnable,\n",
    "# meaning you can use it as you would any other runnable\n",
    "# We add a breakpoint BEFORE the `ask_human` node so it never executes\n",
    "app = workflow.compile(checkpointer=memory, interrupt_before=[\"ask_human\"])\n",
    "\n",
    "display(Image(app.get_graph().draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159",
   "metadata": {},
   "source": [
    "## Interacting with the Agent\n",
    "\n",
    "We can now interact with the agent. Let's ask it to ask the user where they are, then tell them the weather. \n",
    "\n",
    "This should make it use the `ask_human` tool first, then use the normal tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cfd140f0-a5a6-4697-8115-322242f197b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "Use the search tool to ask the user where they are, then look up the weather there\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  AskHuman (call_LDo62KBPQKZWxPI5IHxPBF0w)\n",
      " Call ID: call_LDo62KBPQKZWxPI5IHxPBF0w\n",
      "  Args:\n",
      "    question: Can you tell me where you are located?\n"
     ]
    }
   ],
   "source": [
    "from langchain_core.messages import HumanMessage\n",
    "\n",
    "config = {\"configurable\": {\"thread_id\": \"2\"}}\n",
    "input_message = HumanMessage(\n",
    "    content=\"Use the search tool to ask the user where they are, then look up the weather there\"\n",
    ")\n",
    "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n",
    "    event[\"messages\"][-1].pretty_print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc168c90-a374-4280-a9a6-8bc232dbb006",
   "metadata": {},
   "source": [
    "We now want to update this thread with a response from the user. We then can kick off another run. \n",
    "\n",
    "Because we are treating this as a tool call, we will need to update the state as if it is a response from a tool call. In order to do this, we will need to check the state to get the ID of the tool call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "63598092-d565-4170-9773-e092d345f8c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('agent',)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tool_call_id = app.get_state(config).values[\"messages\"][-1].tool_calls[0][\"id\"]\n",
    "\n",
    "# We now create the tool call with the id and the response we want\n",
    "tool_message = [\n",
    "    {\"tool_call_id\": tool_call_id, \"type\": \"tool\", \"content\": \"san francisco\"}\n",
    "]\n",
    "\n",
    "# # This is equivalent to the below, either one works\n",
    "# from langchain_core.messages import ToolMessage\n",
    "# tool_message = [ToolMessage(tool_call_id=tool_call_id, content=\"san francisco\")]\n",
    "\n",
    "# We now update the state\n",
    "# Notice that we are also specifying `as_node=\"ask_human\"`\n",
    "# This will apply this update as this node,\n",
    "# which will make it so that afterwards it continues as normal\n",
    "app.update_state(config, {\"messages\": tool_message}, as_node=\"ask_human\")\n",
    "\n",
    "# We can check the state\n",
    "# We can see that the state currently has the `agent` node next\n",
    "# This is based on how we define our graph,\n",
    "# where after the `ask_human` node goes (which we just triggered)\n",
    "# there is an edge to the `agent` node\n",
    "app.get_state(config).next"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a30c9fb-2a40-45cc-87ba-406c11c9f0cf",
   "metadata": {},
   "source": [
    "We can now tell the agent to continue. We can just pass in `None` as the input to the graph, since no additional input is needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a9f599b5-1a55-406b-a76b-f52b3ca06975",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  search (call_LJlkCFfHvAS2taKHTaMmORE5)\n",
      " Call ID: call_LJlkCFfHvAS2taKHTaMmORE5\n",
      "  Args:\n",
      "    query: current weather in San Francisco\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: search\n",
      "\n",
      "[\"I looked up: current weather in San Francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "The current weather in San Francisco is sunny. Enjoy the good weather! 🌞\n"
     ]
    }
   ],
   "source": [
    "for event in app.stream(None, config, stream_mode=\"values\"):\n",
    "    event[\"messages\"][-1].pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6f972d1-3d99-4fc1-8b33-92b71e74835d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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