{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
   "metadata": {},
   "source": [
    "# Human-in-the-loop\n",
    "\n",
    "In this example we will build a chat executor that has a human in the loop. We will use the human to approve specific actions.\n",
    "\n",
    "This examples builds off the base chat executor. It is highly recommended you learn about that executor before going through this notebook. You can find documentation for that example [here](./base.ipynb).\n",
    "\n",
    "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": "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": [
    "!pip install --quiet -U langchain langchain_openai tavily-python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d",
   "metadata": {},
   "source": [
    "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import getpass\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
    "os.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily 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": null,
   "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
    "os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ac643b-cb06-4724-a80c-2862ba4773f1",
   "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 a built-in search tool via Tavily.\n",
    "However, 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": 1,
   "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "\n",
    "tools = [TavilySearchResults(max_results=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58",
   "metadata": {},
   "source": [
    "We can now wrap these tools in a simple ToolExecutor.\n",
    "This is a real simple class that takes in a ToolInvocation and calls that tool, returning the output.\n",
    "A ToolInvocation is any class with `tool` and `tool_input` attribute.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langgraph.prebuilt import ToolExecutor\n",
    "\n",
    "tool_executor = ToolExecutor(tools)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5497ed70-fce3-47f1-9cad-46f912bad6a5",
   "metadata": {},
   "source": [
    "## Set up the model\n",
    "\n",
    "Now we need to load the chat model we want to use.\n",
    "Importantly, this should satisfy two criteria:\n",
    "\n",
    "1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n",
    "2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n",
    "\n",
    "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "# We will set streaming=True so that we can stream tokens\n",
    "# See the streaming section for more information on this.\n",
    "model = ChatOpenAI(temperature=0, streaming=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77995c0-bae2-4cee-a036-8688a90f05b9",
   "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 OpenAI function calling, and then bind them to the model class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.tools.render import format_tool_to_openai_function\n",
    "\n",
    "functions = [format_tool_to_openai_function(t) for t in tools]\n",
    "model = model.bind_functions(functions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8b9211-93d0-4ad5-aa7a-9c09099c53ff",
   "metadata": {},
   "source": [
    "## Define the agent state\n",
    "\n",
    "The main type of graph in `langgraph` is the `StatefulGraph`.\n",
    "This graph is parameterized by a state object that it passes around to each node.\n",
    "Each node then returns operations 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 construct the graph with.\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 always added to.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea793afa-2eab-4901-910d-6eed90cd6564",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import TypedDict, Annotated, Sequence\n",
    "import operator\n",
    "from langchain_core.messages import BaseMessage\n",
    "\n",
    "\n",
    "class AgentState(TypedDict):\n",
    "    messages: Annotated[Sequence[BaseMessage], operator.add]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4",
   "metadata": {},
   "source": [
    "## Define the nodes\n",
    "\n",
    "We now need to define a few different nodes in our graph.\n",
    "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/docs/expression_language/).\n",
    "There are two main nodes we need for this:\n",
    "\n",
    "1. The agent: responsible for deciding what (if any) actions to take.\n",
    "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n",
    "\n",
    "We will also need to define some edges.\n",
    "Some of these edges may be conditional.\n",
    "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n",
    "The path that is taken is not known until that node is run (the LLM decides).\n",
    "\n",
    "1. Conditional Edge: after the agent is called, we should either:\n",
    "   a. If the agent said to take an action, then the function to invoke tools should be called\n",
    "   b. If the agent said that it was finished, then it should finish\n",
    "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n",
    "\n",
    "Let's define the nodes, as well as a function to decide how what conditional edge to take.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b547109f-f9e8-4e77-a7e7-ed2bae7a72ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langgraph.prebuilt import ToolInvocation\n",
    "import json\n",
    "from langchain_core.messages import FunctionMessage\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 \"function_call\" not in last_message.additional_kwargs:\n",
    "        return \"end\"\n",
    "    # Otherwise if there is, we continue\n",
    "    else:\n",
    "        return \"continue\"\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]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac402f66-4442-4a1f-9f9b-4a5d97532ceb",
   "metadata": {},
   "source": [
    "**MODIFICATION**\n",
    "\n",
    "We modify the function that is calling the tool to first ask for user approval to continue. Note that this is a simple example and we could modify it to change the tool input, use some other channel besides input, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "73fd6432-42e8-472a-89ca-bb5ddbbcc35a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function to execute tools\n",
    "def call_tool(state):\n",
    "    messages = state['messages']\n",
    "    # Based on the continue condition\n",
    "    # we know the last message involves a function call\n",
    "    last_message = messages[-1]\n",
    "    # We construct an ToolInvocation from the function_call\n",
    "    action = ToolInvocation(\n",
    "        tool=last_message.additional_kwargs[\"function_call\"][\"name\"],\n",
    "        tool_input=json.loads(last_message.additional_kwargs[\"function_call\"][\"arguments\"]),\n",
    "    )\n",
    "    response = input(prompt=f\"[y/n] continue with: {action}?\")\n",
    "    if response == \"n\":\n",
    "        raise ValueError\n",
    "    # We call the tool_executor and get back a response\n",
    "    response = tool_executor.invoke(action)\n",
    "    # We use the response to create a FunctionMessage\n",
    "    function_message = FunctionMessage(content=str(response), name=action.tool)\n",
    "    # We return a list, because this will get added to the existing list\n",
    "    return {\"messages\": [function_message]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b",
   "metadata": {},
   "source": [
    "## Define the graph\n",
    "\n",
    "We can now put it all together and define the graph!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "813ae66c-3b58-4283-a02a-36da72a2ab90",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langgraph.graph import StateGraph, END\n",
    "# Define a new graph\n",
    "workflow = StateGraph(AgentState)\n",
    "\n",
    "# Define the two nodes we will cycle between\n",
    "workflow.add_node(\"agent\", call_model)\n",
    "workflow.add_node(\"action\", call_tool)\n",
    "\n",
    "# Set the entrypoint as `agent`\n",
    "# This means that this node is the first one called\n",
    "workflow.set_entry_point(\"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",
    "        # 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",
    "# 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",
    "app = workflow.compile()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547c3931-3dae-4281-ad4e-4b51305594d4",
   "metadata": {},
   "source": [
    "## Use it!\n",
    "\n",
    "We can now use it!\n",
    "This now exposes the [same interface](https://python.langchain.com/docs/expression_language/) as all other LangChain runnables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f544977e-31f7-41f0-88c4-ec9c27b8cecb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output from node 'agent':\n",
      "---\n",
      "{'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\\n  \"query\": \"weather in San Francisco\"\\n}', 'name': 'tavily_search_results_json'}})]}\n",
      "\n",
      "---\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "[y/n] continue with: tool='tavily_search_results_json' tool_input={'query': 'weather in San Francisco'}? n\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain_core\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmessages\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HumanMessage\n\u001b[1;32m      3\u001b[0m inputs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: [HumanMessage(content\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhat is the weather in sf\u001b[39m\u001b[38;5;124m\"\u001b[39m)]}\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mapp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# stream() yields dictionaries with output keyed by node name\u001b[39;49;00m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mOutput from node \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mkey\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m:\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/workplace/permchain/langgraph/pregel/__init__.py:528\u001b[0m, in \u001b[0;36mPregel.transform\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m    519\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m    520\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    521\u001b[0m     \u001b[38;5;28minput\u001b[39m: Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    527\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]]:\n\u001b[0;32m--> 528\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform_stream_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    529\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    530\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    531\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    532\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    533\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    534\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    535\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    536\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\n",
      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:1226\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m   1224\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1225\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1226\u001b[0m         chunk: Output \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;28mnext\u001b[39m, iterator)  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m   1227\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m   1228\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n",
      "File \u001b[0;32m~/workplace/permchain/langgraph/pregel/__init__.py:313\u001b[0m, in \u001b[0;36mPregel._transform\u001b[0;34m(self, input, run_manager, config, input_keys, output_keys)\u001b[0m\n\u001b[1;32m    303\u001b[0m done, inflight \u001b[38;5;241m=\u001b[39m concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mwait(\n\u001b[1;32m    304\u001b[0m     [\n\u001b[1;32m    305\u001b[0m         executor\u001b[38;5;241m.\u001b[39msubmit(proc\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    309\u001b[0m     timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstep_timeout,\n\u001b[1;32m    310\u001b[0m )\n\u001b[1;32m    312\u001b[0m \u001b[38;5;66;03m# interrupt on failure or timeout\u001b[39;00m\n\u001b[0;32m--> 313\u001b[0m \u001b[43m_interrupt_or_proceed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdone\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minflight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    315\u001b[0m \u001b[38;5;66;03m# apply writes to channels\u001b[39;00m\n\u001b[1;32m    316\u001b[0m _apply_writes(checkpoint, channels, pending_writes, config, step \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/workplace/permchain/langgraph/pregel/__init__.py:611\u001b[0m, in \u001b[0;36m_interrupt_or_proceed\u001b[0;34m(done, inflight, step)\u001b[0m\n\u001b[1;32m    609\u001b[0m             inflight\u001b[38;5;241m.\u001b[39mpop()\u001b[38;5;241m.\u001b[39mcancel()\n\u001b[1;32m    610\u001b[0m         \u001b[38;5;66;03m# raise the exception\u001b[39;00m\n\u001b[0;32m--> 611\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m    612\u001b[0m         \u001b[38;5;66;03m# TODO this is where retry of an entire step would happen\u001b[39;00m\n\u001b[1;32m    614\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inflight:\n\u001b[1;32m    615\u001b[0m     \u001b[38;5;66;03m# if we got here means we timed out\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.1/lib/python3.11/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:3596\u001b[0m, in \u001b[0;36mRunnableBindingBase.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   3590\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m   3591\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   3592\u001b[0m     \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m   3593\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   3594\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m   3595\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[0;32m-> 3596\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbound\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3597\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3598\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3599\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3600\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:1774\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m   1772\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1773\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 1774\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1775\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1776\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m   1777\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1778\u001b[0m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1779\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1780\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1781\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   1782\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:3074\u001b[0m, in \u001b[0;36mRunnableLambda.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   3072\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Invoke this runnable synchronously.\"\"\"\u001b[39;00m\n\u001b[1;32m   3073\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 3074\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3075\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3076\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3077\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3078\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3079\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3080\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   3081\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   3082\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot invoke a coroutine function synchronously.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3083\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `ainvoke` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3084\u001b[0m     )\n",
      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:975\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m    971\u001b[0m     context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m    972\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(var_child_runnable_config\u001b[38;5;241m.\u001b[39mset, child_config)\n\u001b[1;32m    973\u001b[0m     output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m    974\u001b[0m         Output,\n\u001b[0;32m--> 975\u001b[0m         \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    976\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    977\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m    978\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m    979\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    980\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    981\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    982\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    983\u001b[0m     )\n\u001b[1;32m    984\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    985\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/config.py:326\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    325\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:2950\u001b[0m, in \u001b[0;36mRunnableLambda._invoke\u001b[0;34m(self, input, run_manager, config, **kwargs)\u001b[0m\n\u001b[1;32m   2948\u001b[0m                 output \u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m   2949\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2950\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2951\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m   2952\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2953\u001b[0m \u001b[38;5;66;03m# If the output is a runnable, invoke it\u001b[39;00m\n\u001b[1;32m   2954\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, Runnable):\n",
      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/config.py:326\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    325\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[7], line 14\u001b[0m, in \u001b[0;36mcall_tool\u001b[0;34m(state)\u001b[0m\n\u001b[1;32m     12\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(prompt\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[y/n] continue with: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maction\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m?\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# We call the tool_executor and get back a response\u001b[39;00m\n\u001b[1;32m     16\u001b[0m response \u001b[38;5;241m=\u001b[39m tool_executor\u001b[38;5;241m.\u001b[39minvoke(action)\n",
      "\u001b[0;31mValueError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from langchain_core.messages import HumanMessage\n",
    "\n",
    "inputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\n",
    "for output in app.stream(inputs):\n",
    "    # stream() yields dictionaries with output keyed by node name\n",
    "    for key, value in output.items():\n",
    "        print(f\"Output from node '{key}':\")\n",
    "        print(\"---\")\n",
    "        print(value)\n",
    "    print(\"\\n---\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08ae8246-11d5-40e1-8567-361e5bef8917",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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