{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load metadata.jsonl\n",
    "import json\n",
    "# Load the metadata.jsonl file\n",
    "with open('metadata.jsonl', 'r') as jsonl_file:\n",
    "    json_list = list(jsonl_file)\n",
    "\n",
    "json_QA = []\n",
    "for json_str in json_list:\n",
    "    json_data = json.loads(json_str)\n",
    "    json_QA.append(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "Task ID: 076c8171-9b3b-49b9-a477-244d2a532826\n",
      "Question: The attached file contains a list of vendors in the Liminal Springs mall, along with each vendor’s monthly revenue and the rent they pay the mall. I want you to find the vendor that makes the least money, relative to the rent it pays. Then, tell me what is listed in the “type” column for that vendor.\n",
      "Level: 2\n",
      "Final Answer: Finance\n",
      "Annotator Metadata: \n",
      "  ├── Steps: \n",
      "  │      ├── 1. Open the attached spreadsheet.\n",
      "  │      ├── 2. Write formulas that divide each row’s revenue by its rent. This will tell me how much each vendor makes relative to its rent.\n",
      "  │      ├── 3. Note the value in the type column for the lowest result, Finance.\n",
      "  ├── Number of steps: 3\n",
      "  ├── How long did this take?: 5 minutes\n",
      "  ├── Tools:\n",
      "  │      ├── 1. Microsoft Excel\n",
      "  │      ├── 2. Calculator\n",
      "  └── Number of tools: 2\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "# randomly select 3 samples\n",
    "# {\"task_id\": \"c61d22de-5f6c-4958-a7f6-5e9707bd3466\", \"Question\": \"A paper about AI regulation that was originally submitted to arXiv.org in June 2022 shows a figure with three axes, where each axis has a label word at both ends. Which of these words is used to describe a type of society in a Physics and Society article submitted to arXiv.org on August 11, 2016?\", \"Level\": 2, \"Final answer\": \"egalitarian\", \"file_name\": \"\", \"Annotator Metadata\": {\"Steps\": \"1. Go to arxiv.org and navigate to the Advanced Search page.\\n2. Enter \\\"AI regulation\\\" in the search box and select \\\"All fields\\\" from the dropdown.\\n3. Enter 2022-06-01 and 2022-07-01 into the date inputs, select \\\"Submission date (original)\\\", and submit the search.\\n4. Go through the search results to find the article that has a figure with three axes and labels on each end of the axes, titled \\\"Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation\\\".\\n5. Note the six words used as labels: deontological, egalitarian, localized, standardized, utilitarian, and consequential.\\n6. Go back to arxiv.org\\n7. Find \\\"Physics and Society\\\" and go to the page for the \\\"Physics and Society\\\" category.\\n8. Note that the tag for this category is \\\"physics.soc-ph\\\".\\n9. Go to the Advanced Search page.\\n10. Enter \\\"physics.soc-ph\\\" in the search box and select \\\"All fields\\\" from the dropdown.\\n11. Enter 2016-08-11 and 2016-08-12 into the date inputs, select \\\"Submission date (original)\\\", and submit the search.\\n12. Search for instances of the six words in the results to find the paper titled \\\"Phase transition from egalitarian to hierarchical societies driven by competition between cognitive and social constraints\\\", indicating that \\\"egalitarian\\\" is the correct answer.\", \"Number of steps\": \"12\", \"How long did this take?\": \"8 minutes\", \"Tools\": \"1. Web browser\\n2. Image recognition tools (to identify and parse a figure with three axes)\", \"Number of tools\": \"2\"}}\n",
    "\n",
    "import random\n",
    "# random.seed(42)\n",
    "random_samples = random.sample(json_QA, 1)\n",
    "for sample in random_samples:\n",
    "    print(\"=\" * 50)\n",
    "    print(f\"Task ID: {sample['task_id']}\")\n",
    "    print(f\"Question: {sample['Question']}\")\n",
    "    print(f\"Level: {sample['Level']}\")\n",
    "    print(f\"Final Answer: {sample['Final answer']}\")\n",
    "    print(f\"Annotator Metadata: \")\n",
    "    print(f\"  ├── Steps: \")\n",
    "    for step in sample['Annotator Metadata']['Steps'].split('\\n'):\n",
    "        print(f\"  │      ├── {step}\")\n",
    "    print(f\"  ├── Number of steps: {sample['Annotator Metadata']['Number of steps']}\")\n",
    "    print(f\"  ├── How long did this take?: {sample['Annotator Metadata']['How long did this take?']}\")\n",
    "    print(f\"  ├── Tools:\")\n",
    "    for tool in sample['Annotator Metadata']['Tools'].split('\\n'):\n",
    "        print(f\"  │      ├── {tool}\")\n",
    "    print(f\"  └── Number of tools: {sample['Annotator Metadata']['Number of tools']}\")\n",
    "print(\"=\" * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "### build a vector database based on the metadata.jsonl\n",
    "# https://python.langchain.com/docs/integrations/vectorstores/supabase/\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from langchain_huggingface import HuggingFaceEmbeddings\n",
    "from pinecone import Pinecone, ServerlessSpec\n",
    "\n",
    "\n",
    "\n",
    "load_dotenv()\n",
    "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\") #  dim=768\n",
    "# Initialize Pinecone\n",
    "pc = Pinecone(api_key=os.getenv(\"PINECONE_API_KEY\"))\n",
    "index_name = \"agent-metadata\"\n",
    "if index_name not in pc.list_indexes().names():\n",
    "    pc.create_index(\n",
    "        index_name,\n",
    "        dimension=768,\n",
    "        metric=\"cosine\",\n",
    "        spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\")\n",
    "    )\n",
    "index = pc.Index(index_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Uploaded 165 vectors to Pinecone.\n"
     ]
    }
   ],
   "source": [
    "docs = []\n",
    "for sample in json_QA:\n",
    "    content = f\"Question : {sample['Question']}\\n\\nFinal answer : {sample['Final answer']}\"\n",
    "    doc = {\n",
    "        \"content\" : content,\n",
    "        \"metadata\" : { # meatadata的格式必须时source键，否则会报错\n",
    "            \"source\" : sample['task_id']\n",
    "        },\n",
    "        \"embedding\" : embeddings.embed_query(content),\n",
    "    }\n",
    "    docs.append(doc)\n",
    "\n",
    "# wrap the metadata.jsonl's questions and answers into a list of document\n",
    "pinecone_vectors = []\n",
    "for doc in docs:\n",
    "    pinecone_vectors.append({\n",
    "        \"id\": doc[\"metadata\"][\"source\"],  # must be a unique string\n",
    "        \"values\": doc[\"embedding\"],\n",
    "        \"metadata\": {\n",
    "            \"content\": doc[\"content\"]\n",
    "        }\n",
    "    })\n",
    "\n",
    "# Upload in batches\n",
    "BATCH_SIZE = 100\n",
    "for i in range(0, len(pinecone_vectors), BATCH_SIZE):\n",
    "    batch = pinecone_vectors[i:i + BATCH_SIZE]\n",
    "    index.upsert(batch)\n",
    "\n",
    "print(f\"✅ Uploaded {len(pinecone_vectors)} vectors to Pinecone.\")\n",
    "\n",
    "\n",
    "# ALTERNATIVE : Save the documents (a list of dict) into a csv file, and manually upload it to Supabase\n",
    "# import pandas as pd\n",
    "# df = pd.DataFrame(docs)\n",
    "# df.to_csv('supabase_docs.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ALTERNATIVE : Save the documents (a list of dict) into a csv file, and manually upload it to Supabase\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(docs)\n",
    "df.to_csv('pinecone_docs.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_pinecone import PineconeVectorStore\n",
    "vectorstore = PineconeVectorStore(\n",
    "    index=index,\n",
    "    embedding=embeddings,\n",
    "    text_key=\"content\"\n",
    ")\n",
    "\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(id='840bfca7-4f7b-481a-8794-c560c340185d', metadata={}, page_content='Question : On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?\\n\\nFinal answer : 80GSFC21M0002')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?\"\n",
    "# matched_docs = vector_store.similarity_search(query, 2)\n",
    "docs = retriever.invoke(query)\n",
    "docs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of tools used in all samples:\n",
      "Total number of tools used: 83\n",
      "  ├── web browser: 107\n",
      "  ├── image recognition tools (to identify and parse a figure with three axes): 1\n",
      "  ├── search engine: 101\n",
      "  ├── calculator: 34\n",
      "  ├── unlambda compiler (optional): 1\n",
      "  ├── a web browser.: 2\n",
      "  ├── a search engine.: 2\n",
      "  ├── a calculator.: 1\n",
      "  ├── microsoft excel: 5\n",
      "  ├── google search: 1\n",
      "  ├── ne: 9\n",
      "  ├── pdf access: 7\n",
      "  ├── file handling: 2\n",
      "  ├── python: 3\n",
      "  ├── image recognition tools: 12\n",
      "  ├── jsonld file access: 1\n",
      "  ├── video parsing: 1\n",
      "  ├── python compiler: 1\n",
      "  ├── video recognition tools: 3\n",
      "  ├── pdf viewer: 7\n",
      "  ├── microsoft excel / google sheets: 3\n",
      "  ├── word document access: 1\n",
      "  ├── tool to extract text from images: 1\n",
      "  ├── a word reversal tool / script: 1\n",
      "  ├── counter: 1\n",
      "  ├── excel: 3\n",
      "  ├── image recognition: 5\n",
      "  ├── color recognition: 3\n",
      "  ├── excel file access: 3\n",
      "  ├── xml file access: 1\n",
      "  ├── access to the internet archive, web.archive.org: 1\n",
      "  ├── text processing/diff tool: 1\n",
      "  ├── gif parsing tools: 1\n",
      "  ├── a web browser: 7\n",
      "  ├── a search engine: 7\n",
      "  ├── a speech-to-text tool: 2\n",
      "  ├── code/data analysis tools: 1\n",
      "  ├── audio capability: 2\n",
      "  ├── pdf reader: 1\n",
      "  ├── markdown: 1\n",
      "  ├── a calculator: 5\n",
      "  ├── access to wikipedia: 3\n",
      "  ├── image recognition/ocr: 3\n",
      "  ├── google translate access: 1\n",
      "  ├── ocr: 4\n",
      "  ├── bass note data: 1\n",
      "  ├── text editor: 1\n",
      "  ├── xlsx file access: 1\n",
      "  ├── powerpoint viewer: 1\n",
      "  ├── csv file access: 1\n",
      "  ├── calculator (or use excel): 1\n",
      "  ├── computer algebra system: 1\n",
      "  ├── video processing software: 1\n",
      "  ├── audio processing software: 1\n",
      "  ├── computer vision: 1\n",
      "  ├── google maps: 1\n",
      "  ├── access to excel files: 1\n",
      "  ├── calculator (or ability to count): 1\n",
      "  ├── a file interface: 3\n",
      "  ├── a python ide: 1\n",
      "  ├── spreadsheet editor: 1\n",
      "  ├── tools required: 1\n",
      "  ├── b browser: 1\n",
      "  ├── image recognition and processing tools: 1\n",
      "  ├── computer vision or ocr: 1\n",
      "  ├── c++ compiler: 1\n",
      "  ├── access to google maps: 1\n",
      "  ├── youtube player: 1\n",
      "  ├── natural language processor: 1\n",
      "  ├── graph interaction tools: 1\n",
      "  ├── bablyonian cuniform -> arabic legend: 1\n",
      "  ├── access to youtube: 1\n",
      "  ├── image search tools: 1\n",
      "  ├── calculator or counting function: 1\n",
      "  ├── a speech-to-text audio processing tool: 1\n",
      "  ├── access to academic journal websites: 1\n",
      "  ├── pdf reader/extracter: 1\n",
      "  ├── rubik's cube model: 1\n",
      "  ├── wikipedia: 1\n",
      "  ├── video capability: 1\n",
      "  ├── image processing tools: 1\n",
      "  ├── age recognition software: 1\n",
      "  ├── youtube: 1\n"
     ]
    }
   ],
   "source": [
    "# list of the tools used in all the samples\n",
    "from collections import Counter, OrderedDict\n",
    "\n",
    "tools = []\n",
    "for sample in json_QA:\n",
    "    for tool in sample['Annotator Metadata']['Tools'].split('\\n'):\n",
    "        tool = tool[2:].strip().lower()\n",
    "        if tool.startswith(\"(\"):\n",
    "            tool = tool[11:].strip()\n",
    "        tools.append(tool)\n",
    "tools_counter = OrderedDict(Counter(tools))\n",
    "print(\"List of tools used in all samples:\")\n",
    "print(\"Total number of tools used:\", len(tools_counter))\n",
    "for tool, count in tools_counter.items():\n",
    "    print(f\"  ├── {tool}: {count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"\"\"\n",
    "You are a helpful assistant tasked with answering questions using a set of tools.\n",
    "If the tool is not available, you can try to find the information online. You can also use your own knowledge to answer the question. \n",
    "You need to provide a step-by-step explanation of how you arrived at the answer.\n",
    "==========================\n",
    "Here is a few examples showing you how to answer the question step by step.\n",
    "\"\"\"\n",
    "for i, samples in enumerate(random_samples):\n",
    "    system_prompt += f\"\\nQuestion {i+1}: {samples['Question']}\\nSteps:\\n{samples['Annotator Metadata']['Steps']}\\nTools:\\n{samples['Annotator Metadata']['Tools']}\\nFinal Answer: {samples['Final answer']}\\n\"\n",
    "system_prompt += \"\\n==========================\\n\"\n",
    "system_prompt += \"Now, please answer the following question step by step.\\n\"\n",
    "\n",
    "# save the system_prompt to a file\n",
    "with open('system_prompt.txt', 'w') as f:\n",
    "    f.write(system_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "You are a helpful assistant tasked with answering questions using a set of tools.\n",
      "If the tool is not available, you can try to find the information online. You can also use your own knowledge to answer the question. \n",
      "You need to provide a step-by-step explanation of how you arrived at the answer.\n",
      "==========================\n",
      "Here is a few examples showing you how to answer the question step by step.\n",
      "\n",
      "Question 1: The attached file contains a list of vendors in the Liminal Springs mall, along with each vendor’s monthly revenue and the rent they pay the mall. I want you to find the vendor that makes the least money, relative to the rent it pays. Then, tell me what is listed in the “type” column for that vendor.\n",
      "Steps:\n",
      "1. Open the attached spreadsheet.\n",
      "2. Write formulas that divide each row’s revenue by its rent. This will tell me how much each vendor makes relative to its rent.\n",
      "3. Note the value in the type column for the lowest result, Finance.\n",
      "Tools:\n",
      "1. Microsoft Excel\n",
      "2. Calculator\n",
      "Final Answer: Finance\n",
      "\n",
      "==========================\n",
      "Now, please answer the following question step by step.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# load the system prompt from the file\n",
    "with open('system_prompt.txt', 'r') as f:\n",
    "    system_prompt = f.read()\n",
    "print(system_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dotenv\n",
    "from langgraph.graph import MessagesState, START, StateGraph\n",
    "from langgraph.prebuilt import tools_condition\n",
    "from langgraph.prebuilt import ToolNode\n",
    "from langchain_groq import ChatGroq\n",
    "from langchain_huggingface import HuggingFaceEmbeddings\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "from langchain_community.document_loaders import WikipediaLoader\n",
    "from langchain_community.document_loaders import ArxivLoader\n",
    "from langchain.tools.retriever import create_retriever_tool\n",
    "from langchain_core.messages import HumanMessage, SystemMessage\n",
    "from langchain_core.tools import tool\n",
    "from pinecone import Pinecone, ServerlessSpec\n",
    "from langchain_pinecone import PineconeVectorStore\n",
    "\n",
    "\n",
    "# Define the retriever from supabase\n",
    "load_dotenv()\n",
    "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\") #  dim=768\n",
    "\n",
    "\n",
    "# Initialize Pinecone\n",
    "pc = Pinecone(api_key=os.getenv(\"PINECONE_API_KEY\"))\n",
    "index_name = \"agent-metadata\"\n",
    "if index_name not in pc.list_indexes().names():\n",
    "    pc.create_index(\n",
    "        index_name,\n",
    "        dimension=768,\n",
    "        metric=\"cosine\",\n",
    "        spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\")\n",
    "    )\n",
    "index = pc.Index(index_name)\n",
    "\n",
    "vector_store = PineconeVectorStore(\n",
    "    index=index,\n",
    "    embedding=embeddings,\n",
    "    text_key=\"content\"\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "question_retrieve_tool = create_retriever_tool(\n",
    "    vector_store.as_retriever(),\n",
    "    \"Question Retriever\",\n",
    "    \"Find similar questions in the vector database for the given question.\",\n",
    ")\n",
    "\n",
    "@tool\n",
    "def multiply(a: int, b: int) -> int:\n",
    "    \"\"\"Multiply two numbers.\n",
    "\n",
    "    Args:\n",
    "        a: first int\n",
    "        b: second int\n",
    "    \"\"\"\n",
    "    return a * b\n",
    "\n",
    "@tool\n",
    "def add(a: int, b: int) -> int:\n",
    "    \"\"\"Add two numbers.\n",
    "    \n",
    "    Args:\n",
    "        a: first int\n",
    "        b: second int\n",
    "    \"\"\"\n",
    "    return a + b\n",
    "\n",
    "@tool\n",
    "def subtract(a: int, b: int) -> int:\n",
    "    \"\"\"Subtract two numbers.\n",
    "    \n",
    "    Args:\n",
    "        a: first int\n",
    "        b: second int\n",
    "    \"\"\"\n",
    "    return a - b\n",
    "\n",
    "@tool\n",
    "def divide(a: int, b: int) -> int:\n",
    "    \"\"\"Divide two numbers.\n",
    "    \n",
    "    Args:\n",
    "        a: first int\n",
    "        b: second int\n",
    "    \"\"\"\n",
    "    if b == 0:\n",
    "        raise ValueError(\"Cannot divide by zero.\")\n",
    "    return a / b\n",
    "\n",
    "@tool\n",
    "def modulus(a: int, b: int) -> int:\n",
    "    \"\"\"Get the modulus of two numbers.\n",
    "    \n",
    "    Args:\n",
    "        a: first int\n",
    "        b: second int\n",
    "    \"\"\"\n",
    "    return a % b\n",
    "\n",
    "@tool\n",
    "def wiki_search(query: str) -> str:\n",
    "    \"\"\"Search Wikipedia for a query and return maximum 2 results.\n",
    "    \n",
    "    Args:\n",
    "        query: The search query.\"\"\"\n",
    "    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()\n",
    "    formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
    "        [\n",
    "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content}\\n</Document>'\n",
    "            for doc in search_docs\n",
    "        ])\n",
    "    return {\"wiki_results\": formatted_search_docs}\n",
    "\n",
    "@tool\n",
    "def web_search(query: str) -> str:\n",
    "    \"\"\"Search Tavily for a query and return maximum 3 results.\n",
    "    \n",
    "    Args:\n",
    "        query: The search query.\"\"\"\n",
    "    search_docs = TavilySearchResults(max_results=3).invoke(query=query)\n",
    "    formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
    "        [\n",
    "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content}\\n</Document>'\n",
    "            for doc in search_docs\n",
    "        ])\n",
    "    return {\"web_results\": formatted_search_docs}\n",
    "\n",
    "@tool\n",
    "def arvix_search(query: str) -> str:\n",
    "    \"\"\"Search Arxiv for a query and return maximum 3 result.\n",
    "    \n",
    "    Args:\n",
    "        query: The search query.\"\"\"\n",
    "    search_docs = ArxivLoader(query=query, load_max_docs=3).load()\n",
    "    formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
    "        [\n",
    "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content[:1000]}\\n</Document>'\n",
    "            for doc in search_docs\n",
    "        ])\n",
    "    return {\"arvix_results\": formatted_search_docs}\n",
    "\n",
    "@tool\n",
    "def similar_question_search(question: str) -> str:\n",
    "    \"\"\"Search the vector database for similar questions and return the first results.\n",
    "    \n",
    "    Args:\n",
    "        question: the question human provided.\"\"\"\n",
    "    matched_docs = vector_store.similarity_search(query, 3)\n",
    "    formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
    "        [\n",
    "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content[:1000]}\\n</Document>'\n",
    "            for doc in matched_docs\n",
    "        ])\n",
    "    return {\"similar_questions\": formatted_search_docs}\n",
    "\n",
    "tools = [\n",
    "    multiply,\n",
    "    add,\n",
    "    subtract,\n",
    "    divide,\n",
    "    modulus,\n",
    "    wiki_search,\n",
    "    web_search,\n",
    "    arvix_search,\n",
    "    question_retrieve_tool\n",
    "]\n",
    "\n",
    "llm = ChatGroq(model=\"llama-3.3-70b-versatile\")\n",
    "llm_with_tools = llm.bind_tools(tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the system prompt from the file\n",
    "with open('system_prompt.txt', 'r') as f:\n",
    "    system_prompt = f.read()\n",
    "\n",
    "\n",
    "# System message\n",
    "sys_msg = SystemMessage(content=system_prompt)\n",
    "\n",
    "# Node\n",
    "def assistant(state: MessagesState):\n",
    "    \"\"\"Assistant node\"\"\"\n",
    "    return {\"messages\": [llm_with_tools.invoke([sys_msg] + state[\"messages\"])]}\n",
    "\n",
    "# Build graph\n",
    "builder = StateGraph(MessagesState)\n",
    "builder.add_node(\"assistant\", assistant)\n",
    "builder.add_node(\"tools\", ToolNode(tools))\n",
    "builder.add_edge(START, \"assistant\")\n",
    "builder.add_conditional_edges(\n",
    "    \"assistant\",\n",
    "    # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools\n",
    "    # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END\n",
    "    tools_condition,\n",
    ")\n",
    "builder.add_edge(\"tools\", \"assistant\")\n",
    "\n",
    "# Compile graph\n",
    "graph = builder.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "display(Image(graph.get_graph(xray=True).draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "question = \"\"\n",
    "messages = [HumanMessage(content=question)]\n",
    "messages = graph.invoke({\"messages\": messages})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "I'm happy to help you with your question. However, I don't see a question provided. Could you please provide the question you would like me to answer? I'll do my best to provide a step-by-step explanation of how I arrive at the answer.\n"
     ]
    }
   ],
   "source": [
    "for m in messages['messages']:\n",
    "    m.pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ai",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}