{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import LLMMathChain\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain.agents import AgentExecutor, create_openai_tools_agent\n",
    "from langchain import hub\n",
    "from pydantic import BaseModel, Field\n",
    "from langchain.tools import BaseTool\n",
    "from typing import Type\n",
    "from hellocomputer.config import settings\n",
    "from hellocomputer.models import AvailableModels\n",
    "\n",
    "# Get the prompt to use - you can modify this!\n",
    "prompt = hub.pull(\"hwchase17/openai-tools-agent\")\n",
    "\n",
    "llm = ChatOpenAI(\n",
    "    base_url=settings.llm_base_url,\n",
    "    api_key=settings.llm_api_key,\n",
    "    model=AvailableModels.firefunction_2,\n",
    "    temperature=0.5,\n",
    "    max_tokens=256,\n",
    ")\n",
    "\n",
    "math_llm = ChatOpenAI(\n",
    "    base_url=settings.llm_base_url,\n",
    "    api_key=settings.llm_api_key,\n",
    "    model=AvailableModels.mixtral_8x7b,\n",
    "    temperature=0.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CalculatorInput(BaseModel):\n",
    "    query: str = Field(description=\"should be a math expression\")\n",
    "\n",
    "class CustomCalculatorTool(BaseTool):\n",
    "    name: str = \"Calculator\"\n",
    "    description: str = \"Tool to evaluate mathemetical expressions\"\n",
    "    args_schema: Type[BaseModel] = CalculatorInput\n",
    "\n",
    "    def _run(self, query: str) -> str:\n",
    "        \"\"\"Use the tool.\"\"\"\n",
    "        return LLMMathChain.from_llm(llm=math_llm, verbose=True).invoke(query)\n",
    "\n",
    "    async def _arun(self, query: str) -> str:\n",
    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
    "        return LLMMathChain.from_llm(llm=math_llm, verbose=True).ainvoke(query)\n",
    "\n",
    "tools = [\n",
    "  CustomCalculatorTool()\n",
    "]\n",
    "\n",
    "agent = create_openai_tools_agent(llm, tools, prompt)\n",
    "\n",
    "agent = AgentExecutor(agent=agent, tools=tools, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThe capital of USA is Washington, D.C.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "{'input': 'What is the capital of USA?', 'output': 'The capital of USA is Washington, D.C.'}\n"
     ]
    }
   ],
   "source": [
    "print(agent.invoke({\"input\": \"What is the capital of USA?\"}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `Calculator` with `{'query': '100/25'}`\n",
      "\n",
      "\n",
      "\u001b[0m\n",
      "\n",
      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
      "100/25\u001b[32;1m\u001b[1;3m```text\n",
      "100 / 25\n",
      "```\n",
      "...numexpr.evaluate(\"100 / 25\")...\n",
      "\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3m4.0\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\u001b[36;1m\u001b[1;3m{'question': '100/25', 'answer': 'Answer: 4.0'}\u001b[0m\u001b[32;1m\u001b[1;3mThe answer is 4.0.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "{'input': 'What is 100 divided by 25?', 'output': 'The answer is 4.0.'}\n"
     ]
    }
   ],
   "source": [
    "print(agent.invoke({\"input\": \"What is 100 divided by 25?\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now let's modify this code and make it a Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langgraph.prebuilt import ToolNode\n",
    "from langchain_core.messages import AIMessage\n",
    "\n",
    "tool_node = ToolNode(tools=tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
      "50 / 2\u001b[32;1m\u001b[1;3m```text\n",
      "50 / 2\n",
      "```\n",
      "...numexpr.evaluate(\"50 / 2\")...\n",
      "\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3m25.0\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'messages': [ToolMessage(content='{\"question\": \"50 / 2\", \"answer\": \"Answer: 25.0\"}', name='Calculator', tool_call_id='tool_call_id')]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call the tools node manually\n",
    "\n",
    "message_with_single_tool_call = AIMessage(\n",
    "    content=\"\",\n",
    "    tool_calls=[\n",
    "        {\n",
    "            \"name\": \"Calculator\",\n",
    "            \"args\": {\"query\": \"50 / 2\"},\n",
    "            \"id\": \"tool_call_id\",\n",
    "            \"type\": \"tool_call\",\n",
    "        }\n",
    "    ],\n",
    ")\n",
    "\n",
    "tool_node.invoke({\"messages\": [message_with_single_tool_call]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bind the tools to the agent so it knows what to call\n",
    "\n",
    "agent = ChatOpenAI(\n",
    "    base_url=\"https://api.fireworks.ai/inference/v1\",\n",
    "    api_key=\"bGWp5ErQNI7rP8GOcGBJmyC5QMV7z8UdBpLAseTaxhbAk6u1\",\n",
    "    model=\"accounts/fireworks/models/firefunction-v2\",\n",
    "    temperature=0.5,\n",
    "    max_tokens=256,\n",
    ").bind_tools(tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'Calculator',\n",
       "  'args': {'query': '234/7'},\n",
       "  'id': 'call_mP5fctn8N6vilM1Yewb5QxRY',\n",
       "  'type': 'tool_call'}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.invoke(\"234/7\").tool_calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Literal\n",
    "\n",
    "from langgraph.graph import StateGraph, MessagesState\n",
    "\n",
    "\n",
    "def should_continue(state: MessagesState) -> Literal[\"tools\", \"__end__\"]:\n",
    "    messages = state[\"messages\"]\n",
    "    last_message = messages[-1]\n",
    "    if last_message.tool_calls:\n",
    "        return \"tools\"\n",
    "    return \"__end__\"\n",
    "\n",
    "\n",
    "def call_model(state: MessagesState):\n",
    "    messages = state[\"messages\"]\n",
    "    response = agent.invoke(messages)\n",
    "    return {\"messages\": [response]}\n",
    "\n",
    "\n",
    "workflow = StateGraph(MessagesState)\n",
    "\n",
    "# Define the two nodes we will cycle between\n",
    "workflow.add_node(\"agent\", call_model)\n",
    "workflow.add_node(\"tools\", tool_node)\n",
    "\n",
    "workflow.add_edge(\"__start__\", \"agent\")\n",
    "workflow.add_conditional_edges(\n",
    "    \"agent\",\n",
    "    should_continue,\n",
    ")\n",
    "workflow.add_edge(\"tools\", \"agent\")\n",
    "\n",
    "app = workflow.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "What's twelve times twelve?\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  Calculator (call_dEdudo8txWMpLKMek070TIWd)\n",
      " Call ID: call_dEdudo8txWMpLKMek070TIWd\n",
      "  Args:\n",
      "    query: 12 * 12\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
      "12 * 12\u001b[32;1m\u001b[1;3m```text\n",
      "12 * 12\n",
      "```\n",
      "...numexpr.evaluate(\"12 * 12\")...\n",
      "\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3m144\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: Calculator\n",
      "\n",
      "{\"question\": \"12 * 12\", \"answer\": \"Answer: 144\"}\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "The answer is 144.\n"
     ]
    }
   ],
   "source": [
    "for chunk in app.stream(\n",
    "    {\"messages\": [(\"human\", \"What's twelve times twelve?\")]}, stream_mode=\"values\"\n",
    "):\n",
    "    chunk[\"messages\"][-1].pretty_print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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