02_langraph_components.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d4aaae8-428a-4753-bd92-9a3cc69ab24a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: openai in /home/abram/anaconda3/lib/python3.12/site-packages (1.61.0)\n",
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      "Requirement already satisfied: pygraphviz in /home/abram/anaconda3/lib/python3.12/site-packages (1.14)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "# Install general packages\n",
    "!{sys.executable} -m pip install openai langchain langgraph tavily-python langchain-openai langchain-community propcache\n",
    "\n",
    "# Install pygraphviz with specific build options\n",
    "!{sys.executable} -m pip install pygraphviz --config-settings=--include-dirs=/usr/include/graphviz --config-settings=--library-dirs=/usr/lib/graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0f696c3-5e06-4c28-b482-51d075843371",
   "metadata": {},
   "outputs": [],
   "source": [
    " from langgraph.graph import StateGraph, END\n",
    "from typing import TypedDict, Annotated\n",
    "import operator\n",
    "from dotenv import load_dotenv\n",
    "from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage, ToolMessage\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ebb6f88-f1af-4785-a4ee-d24f1ab46f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00691cb4-e6c4-4a88-9b81-ca0bb89c9e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'langchain_community.tools.tavily_search.tool.TavilySearchResults'>\n",
      "tavily_search_results_json\n"
     ]
    }
   ],
   "source": [
    "tool = TavilySearchResults(max_results=4) #increased number of results\n",
    "print(type(tool))\n",
    "print(tool.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "079f3e98-4062-4a2c-ac4f-d52eb77ee0f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AgentState(TypedDict):\n",
    "    messages: Annotated[list[AnyMessage], operator.add]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06f3759f-8a61-4ecd-a289-0c55c6b9ed5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent:\n",
    "\n",
    "    def __init__(self, model, tools, system=\"\"):\n",
    "        self.system = system\n",
    "        graph = StateGraph(AgentState)\n",
    "        graph.add_node(\"llm\", self.call_openai)\n",
    "        graph.add_node(\"action\", self.take_action)\n",
    "        graph.add_conditional_edges(\n",
    "            \"llm\",\n",
    "            self.exists_action,\n",
    "            {True: \"action\", False: END}\n",
    "        )\n",
    "        graph.add_edge(\"action\", \"llm\")\n",
    "        graph.set_entry_point(\"llm\")\n",
    "        self.graph = graph.compile()\n",
    "        self.tools = {t.name: t for t in tools}\n",
    "        self.model = model.bind_tools(tools)\n",
    "\n",
    "    def exists_action(self, state: AgentState):\n",
    "        result = state['messages'][-1]\n",
    "        return len(result.tool_calls) > 0\n",
    "\n",
    "    def call_openai(self, state: AgentState):\n",
    "        messages = state['messages']\n",
    "        if self.system:\n",
    "            messages = [SystemMessage(content=self.system)] + messages\n",
    "        message = self.model.invoke(messages)\n",
    "        return {'messages': [message]}\n",
    "\n",
    "    def take_action(self, state: AgentState):\n",
    "        tool_calls = state['messages'][-1].tool_calls\n",
    "        results = []\n",
    "        for t in tool_calls:\n",
    "            print(f\"Calling: {t}\")\n",
    "            if not t['name'] in self.tools:      # check for bad tool name from LLM\n",
    "                print(\"\\n ....bad tool name....\")\n",
    "                result = \"bad tool name, retry\"  # instruct LLM to retry if bad\n",
    "            else:\n",
    "                result = self.tools[t['name']].invoke(t['args'])\n",
    "            results.append(ToolMessage(tool_call_id=t['id'], name=t['name'], content=str(result)))\n",
    "        print(\"Back to the model!\")\n",
    "        return {'messages': results}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f128be43-dd49-4ecf-aaeb-14f143502e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"\"\"You are a smart research assistant. Use the search engine to look up information. \\\n",
    "You are allowed to make multiple calls (either together or in sequence). \\\n",
    "Only look up information when you are sure of what you want. \\\n",
    "If you need to look up some information before asking a follow up question, you are allowed to do that!\n",
    "\"\"\"\n",
    "\n",
    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")  #reduce inference cost\n",
    "abot = Agent(model, [tool], system=prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5dc4d729-968f-4f20-8ed4-2838fd9fa0ea",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Install pygraphviz to draw graphs: `pip install pygraphviz`.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/langchain_core/runnables/graph_png.py:136\u001b[0m, in \u001b[0;36mPngDrawer.draw\u001b[0;34m(self, graph, output_path)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpygraphviz\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpgv\u001b[39;00m  \u001b[38;5;66;03m# type: ignore[import]\u001b[39;00m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/pygraphviz/__init__.py:24\u001b[0m\n\u001b[1;32m     22\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1.14\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01magraph\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m AGraph, Node, Edge, Attribute, ItemAttribute, DotError\n\u001b[1;32m     26\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAGraph\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEdge\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttribute\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mItemAttribute\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDotError\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/pygraphviz/agraph.py:17\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpathlib\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m graphviz \u001b[38;5;28;01mas\u001b[39;00m gv\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mcontextlib\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/pygraphviz/graphviz.py:10\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m __package__ \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m:\n\u001b[0;32m---> 10\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _graphviz\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mImportError\u001b[0m: libcdt.so.5: cannot open shared object file: No such file or directory",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[0;32m----> 3\u001b[0m Image(\u001b[43mabot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_graph\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_png\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Error:\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# ImportError: Install pygraphviz to draw graphs: `pip install pygraphviz`.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/langchain_core/runnables/graph.py:556\u001b[0m, in \u001b[0;36mGraph.draw_png\u001b[0;34m(self, output_file_path, fontname, labels)\u001b[0m\n\u001b[1;32m    543\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mlangchain_core\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrunnables\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph_png\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m PngDrawer\n\u001b[1;32m    545\u001b[0m default_node_labels \u001b[38;5;241m=\u001b[39m {node\u001b[38;5;241m.\u001b[39mid: node\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnodes\u001b[38;5;241m.\u001b[39mvalues()}\n\u001b[1;32m    547\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mPngDrawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    548\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfontname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    549\u001b[0m \u001b[43m    \u001b[49m\u001b[43mLabelsDict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    550\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m    551\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdefault_node_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    552\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[43mlabels\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnodes\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    553\u001b[0m \u001b[43m        \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    554\u001b[0m \u001b[43m        \u001b[49m\u001b[43medges\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43medges\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    555\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m--> 556\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_file_path\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/langchain_core/runnables/graph_png.py:139\u001b[0m, in \u001b[0;36mPngDrawer.draw\u001b[0;34m(self, graph, output_path)\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    138\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInstall pygraphviz to draw graphs: `pip install pygraphviz`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m    141\u001b[0m \u001b[38;5;66;03m# Create a directed graph\u001b[39;00m\n\u001b[1;32m    142\u001b[0m viz \u001b[38;5;241m=\u001b[39m pgv\u001b[38;5;241m.\u001b[39mAGraph(directed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, nodesep\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.9\u001b[39m, ranksep\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m)\n",
      "\u001b[0;31mImportError\u001b[0m: Install pygraphviz to draw graphs: `pip install pygraphviz`."
     ]
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "# Image(abot.graph.get_graph().draw_png())\n",
    "\n",
    "# Error:\n",
    "# ImportError: Install pygraphviz to draw graphs: `pip install pygraphviz`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4cfb52de-5b49-419d-98eb-6cff738109e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling: {'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_XXCSGeWziMahoBuzVExoBCXI', 'type': 'tool_call'}\n",
      "Back to the model!\n"
     ]
    }
   ],
   "source": [
    "messages = [HumanMessage(content=\"What is the weather in sf?\")]\n",
    "result = abot.graph.invoke({\"messages\": messages})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "638ae6a5-dc59-48df-9b60-36fe46998206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'messages': [HumanMessage(content='What is the weather in sf?', additional_kwargs={}, response_metadata={}),\n",
       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_XXCSGeWziMahoBuzVExoBCXI', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 153, 'total_tokens': 175, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a57c7947-3297-4ae9-ae08-1fa178e8507c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_XXCSGeWziMahoBuzVExoBCXI', 'type': 'tool_call'}], usage_metadata={'input_tokens': 153, 'output_tokens': 22, 'total_tokens': 175, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}),\n",
       "  ToolMessage(content='[{\\'url\\': \\'https://www.weatherapi.com/\\', \\'content\\': \"{\\'location\\': {\\'name\\': \\'San Francisco\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 37.775, \\'lon\\': -122.4183, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1738375518, \\'localtime\\': \\'2025-01-31 18:05\\'}, \\'current\\': {\\'last_updated_epoch\\': 1738375200, \\'last_updated\\': \\'2025-01-31 18:00\\', \\'temp_c\\': 13.3, \\'temp_f\\': 55.9, \\'is_day\\': 0, \\'condition\\': {\\'text\\': \\'Light rain\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/night/296.png\\', \\'code\\': 1183}, \\'wind_mph\\': 11.4, \\'wind_kph\\': 18.4, \\'wind_degree\\': 154, \\'wind_dir\\': \\'SSE\\', \\'pressure_mb\\': 1023.0, \\'pressure_in\\': 30.2, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 80, \\'cloud\\': 100, \\'feelslike_c\\': 11.7, \\'feelslike_f\\': 53.0, \\'windchill_c\\': 8.6, \\'windchill_f\\': 47.5, \\'heatindex_c\\': 10.7, \\'heatindex_f\\': 51.2, \\'dewpoint_c\\': 10.2, \\'dewpoint_f\\': 50.4, \\'vis_km\\': 4.8, \\'vis_miles\\': 2.0, \\'uv\\': 0.0, \\'gust_mph\\': 17.5, \\'gust_kph\\': 28.2}}\"}, {\\'url\\': \\'https://world-weather.info/forecast/usa/san_francisco/january-2025/\\', \\'content\\': \"Weather in San Francisco in January 2025 (California) - Detailed Weather Forecast for a Month Weather World Weather in San Francisco Weather in San Francisco in January 2025 San Francisco Weather Forecast for January 2025, is based on previous years\\' statistical data. +55°+46° +54°+45° +57°+48° +55°+48° +55°+48° +55°+46° +57°+50° +59°+50° +57°+50° +59°+50° +55°+48° +57°+48° +57°+48° +57°+50° +55°+46° +55°+48° +57°+48° +57°+48° +57°+48° +55°+48° +57°+48° +55°+48° +55°+48° +55°+46° +57°+48° +57°+48° +59°+48° +59°+48° +59°+48° +59°+48° +61°+50° Extended weather forecast in San Francisco HourlyWeek10-Day14-Day30-DayYear Weather in Washington, D.C.+30° Sacramento+45° Pleasanton+50° Redwood City+54° San Leandro+54° San Mateo+54° San Rafael+50° San Ramon+48° South San Francisco+55° Vallejo+50° Palo Alto+52° Pacifica+59° Berkeley+48° Castro Valley+54° Concord+48° Daly City+55° Leona Heights+54° Centennial+54° world\\'s temperature today day day Temperature units\"}, {\\'url\\': \\'https://www.easeweather.com/north-america/united-states/california/city-and-county-of-san-francisco/san-francisco/january\\', \\'content\\': \"January 2025 Weather - San Francisco Weather in San Francisco for January 2025 Your guide to San Francisco weather in January - trends and predictions In general, the average temperature in San Francisco at the beginning of January is 13\\\\xa0°F. San Francisco experiences heavy rainfall in January, with over 20 rainy days and an average total precipitation 110.4\\\\xa0mm. New Year\\'s Day weather in San Francisco 🎉 - See the complete forecast for Monday, January 1. San Francisco in January average weather Temperatures trend during January in San Francisco San Francisco in January - FAQ Explore the daily rainfall trends and prepare for San Franciscos January weather\\\\xa0💧 Get accurate weather forecasts for San Francisco, located at latitude 37.775 and longitude -122.419.\"}, {\\'url\\': \\'https://www.weathertab.com/en/d/o/01/united-states/california/san-francisco/\\', \\'content\\': \\'Calendar Forecast Daily Forecast Detailed Forecast San Francisco, CA Daily Weather Forecast for January 2025 Our San Francisco, California Daily Weather Forecast for January 2025, developed from a specialized dynamic long-range model, provides precise daily temperature and rainfall predictions. Click or Tap on any day for a detailed forecast. Waning Crescent Waning Crescent Waning Crescent Precipitation Forecast Rain Frequency 6 to 8 days Temperature Forecast Temperature Forecast Normal Weather Forecast Legend WeatherTAB helps you plan activities on days with the least risk of rain. Low risk of rain/snow. Medium risk of rain/snow. High risk of rain/snow. Forecast risk of rain/snow. Click or Tap on any day for a detailed forecast. Sporting Event Forecasts Weather forecasts that start where the others stop\\'}]', name='tavily_search_results_json', tool_call_id='call_XXCSGeWziMahoBuzVExoBCXI'),\n",
       "  AIMessage(content='The current weather in San Francisco is as follows:\\n- Temperature: 13.3°C (55.9°F)\\n- Condition: Light rain\\n- Wind Speed: 18.4 kph (11.4 mph) from SSE\\n- Humidity: 80%\\n- Cloud Cover: 100%\\n- Feels like: 11.7°C (53.0°F)\\n\\nIf you need more detailed information or forecasts, please let me know!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 95, 'prompt_tokens': 1382, 'total_tokens': 1477, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0c89f313-f5bd-4769-9728-2f7220852566-0', usage_metadata={'input_tokens': 1382, 'output_tokens': 95, 'total_tokens': 1477, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c6cbf9f7-55af-4a4a-a4aa-5607c2542c63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The current weather in San Francisco is as follows:\\n- Temperature: 13.3°C (55.9°F)\\n- Condition: Light rain\\n- Wind Speed: 18.4 kph (11.4 mph) from SSE\\n- Humidity: 80%\\n- Cloud Cover: 100%\\n- Feels like: 11.7°C (53.0°F)\\n\\nIf you need more detailed information or forecasts, please let me know!'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['messages'][-1].content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2d013daf-690b-4d3d-9cee-431651b5bae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling: {'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_dbpQIxySBKdPvKiOMfWYzMcI', 'type': 'tool_call'}\n",
      "Calling: {'name': 'tavily_search_results_json', 'args': {'query': 'weather in Los Angeles'}, 'id': 'call_0iPYCxq3uE1nUL2BAkjPI7zi', 'type': 'tool_call'}\n",
      "Back to the model!\n"
     ]
    }
   ],
   "source": [
    "messages = [HumanMessage(content=\"What is the weather in SF and LA?\")]\n",
    "result = abot.graph.invoke({\"messages\": messages})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "90526368-400c-4aae-aa1b-fa6defe5b3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The current weather in San Francisco is light rain with a temperature of 55.9°F, and in Los Angeles, it is clear with a temperature of 57.0°F.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['messages'][-1].content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cc0f93c6-696a-4738-8af9-fadf0949adad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling: {'name': 'tavily_search_results_json', 'args': {'query': 'Super Bowl 2024 winner'}, 'id': 'call_QHaijYKppvTXimPBEdrfddae', 'type': 'tool_call'}\n",
      "Back to the model!\n",
      "Calling: {'name': 'tavily_search_results_json', 'args': {'query': 'Kansas City Chiefs headquarters location'}, 'id': 'call_m3XNO1vCVrAyBVj9CC5KVZYY', 'type': 'tool_call'}\n",
      "Calling: {'name': 'tavily_search_results_json', 'args': {'query': 'GDP of Missouri 2024'}, 'id': 'call_X8jc328ogY1GuBBPVLBtjX2k', 'type': 'tool_call'}\n",
      "Back to the model!\n"
     ]
    }
   ],
   "source": [
    "# Note, the query was modified to produce more consistent results. \n",
    "# Results may vary per run and over time as search information and models change.\n",
    "\n",
    "query = \"Who won the super bowl in 2024? In what state is the winning team headquarters located? \\\n",
    "What is the GDP of that state? Answer each question.\" \n",
    "messages = [HumanMessage(content=query)]\n",
    "\n",
    "model = ChatOpenAI(model=\"gpt-4o\")  # requires more advanced model\n",
    "abot = Agent(model, [tool], system=prompt)\n",
    "result = abot.graph.invoke({\"messages\": messages})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e89c1330-6f23-4ac4-ad5e-b7050f9ed599",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. The Kansas City Chiefs won the Super Bowl in 2024 by defeating the San Francisco 49ers with a score of 25-22.\n",
      "\n",
      "2. The headquarters of the Kansas City Chiefs is located in Kansas City, Missouri.\n",
      "\n",
      "3. As of 2023, Missouri had a real GDP of $348.5 billion. Information specific to 2024 was not directly available, but Missouri was noted for experiencing solid economic growth and having the sixth lowest cost of living in the United States for the third quarter of 2024.\n"
     ]
    }
   ],
   "source": [
    "print(result['messages'][-1].content)"
   ]
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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