10_2_3_Complete_Tool_Use_Workflow.ipynb

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    "# Appendix 10.2.3: The complete tool use workflow\n",
    "\n",
    "## Learning goals\n",
    "\n",
    "* Understand the entire tool use workflow\n",
    "* Write properly structured `tool_result` messages\n",
    "* Implement a chatbot that utilizes tool use\n",
    "\n",
    "In this lesson, we're going to implement the \"full\" 4-step tool use workflow we covered earlier.  So far, we've seen that Claude has used tools, but we haven't sent follow up requests to Claude that contain the result of our tool functionality.  Here's a recap of the full 4-step process: \n",
    "\n",
    "1. **Provide Claude with tools and a user prompt:** (API request)\n",
    "    * Define the set of tools you want Claude to have access to, including their names, descriptions, and input schemas.\n",
    "    * Provide a user prompt that may require the use of one or more of these tools to answer, such as \"How many shares of General Motors can I buy with $500?\"\n",
    "\n",
    "2. **Claude uses a tool:** (API response)\n",
    "    * Claude assesses the user prompt and decides whether any of the available tools would help with the user's query or task. If so, it also decides which tool(s) to use and with what input(s).\n",
    "    * Claude outputs a properly formatted tool use request.\n",
    "    * The API response will have a `stop_reason` of `tool_use`, indicating that Claude wants to use an external tool.\n",
    "\n",
    "3. **Extract tool input(s), run code, and return results:** (API request)\n",
    "    * On the client side, you should extract the tool name and input from Claude's tool use request.\n",
    "    * Run the actual tool code on the client side.\n",
    "    * Return the results to Claude by continuing the conversation with a new user message containing a `tool_result` content block.\n",
    "\n",
    "4. **Claude uses the tool result to formulate a response:** (API response)\n",
    "    * After receiving the tool results, Claude will use that information to formulate its final response to the original user prompt.\n",
    "\n",
    "In this lesson, we'll go through the entire process. \n",
    "\n",
    "Here's a diagram with a general overview of the process:\n",
    "\n",
    "![tool_use_flow.png](./images/tool_use_flow.png)"
   ]
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fFo2xdOkS7Nn9PcaOHYNtW7dgvbe4PG5w8B1cuFi8QE5pW/Pdapkg55EC5UfdR45Aw4ZOAABNTU0sWDBfFICMiorCpUvSe5CVlSUTDA8NDRWy5czNzODltQ5bt2wWvnr06KH261KkQoUK2P39LuHLocCazK6uLqL9q1atVDCSrDVr1yEtLQ0SiQR79+wWrR2rq6uLrVs2Y8rkSXB3H4nnBUrTLlm6DIB0zeEd231EQTdTUxMcPnIIBgYGePfuHVasXKVwDhKJBFu3bhatFaqnpwdvLy8YGxsBAK7fuFHsayrMps1bkJ2dDW1tbezft1cI/gKAiUllHDp4AHp6emo51+JFC2XWeJ4yeZKQYXzt2nV5h5Xq+D///AvCwsIAAAsXLhAFfwFg9ixP9OnjVqJ5lcSC+fNkMutnec4UXnC44icbnM9bN1oikWDTpo0yL6l8883X6N6tGwDlP+/Xb9gIALC1tcXOHdtFYzdq1BDeXsUfr2uXLvj5p+No+8UXqFqlSpktPxAZGYnde6RrhA8fPkwU/AWka5vv2bMbY8aMhkllE+Tk5BR77NXfrQEAjB0zWhT8BQADAwOsXSPdHxx8BydP/lmSyyAiIiIiIiIqFmYAExERFSEhIQE//HC8vKdR6vz8ruLmzQC0aNG8vKdSLCEh9/D6dZKobcrUyaLMKkdHR3Tr1g0nT54U2vJnuBbMdtXT08OkiRNEbe7uI7F8xQpRJvE1/2syAaOyZGlpiRXLl4lKN+d58EC8Durvf5zEhYuXRG1xcS9F2+EPH6I3ekFPTw8NGzohICBQ2HfkyFEcOXIUJiaVUbNmTdStWxd169bF4EEDYWlpqb6LKme3bt0CAPTo0R21atWS2a+trQ0vOUGu+PgEYe3ZUaPc5Wb2VTE3R9++X+HAgYO4eTNAZn8eBwcH1K4tW9LbxKQynBydcPnKFTx5/KS4l1SovOBnxw4dYGtrK7O/WrVq6NPHDceO/VDic3XqLH8NXlcXF/j5XcWTJ0+QmZkJiURSZuOHhoYCkAbnPNzd5R4/fvw4UTZ9WerevZvc9nZt2yIgIBCPHz9GTk6O6HkLvnMHgDRIe/fuXbnH17SqCQAIDb1f7HuenJyMv//+G4A0+1feM+7m1huWlpZy1+0taMiQwaJ1x8tKUFAwsrKyAACjR8mu0wxIg9MF1y8uSkxMDP755x8AgImpKc6eOyfTJzc3F8bGRkhKSsa90NByfbmAiIiIiIiI/n9gAJiIiKgImzZvQUa+kreqqFOnDlq1agVzMzN8ZvAZUlJSEBMTi8DAQOEX6x+DzVu24EiLQ+U9jWKJfRkr01bv889l2uo62IsCwC9e/Fsy+GWseAxra2tUrFhR1CaRSGBraysKAMfEyp5bXaTPSkthOzHxlVBeOM/QIYPlBn8zMjLw6NEjUduzZ8+KPGf+YxYsmI++fb8WAiX555GY+ArBwdIg06pVqzFzxnQsWqR8mdyPTXZ2Nh4+lN6DukqWQc8L/gLSDGBF8sqrR0REICUlBYaGhjJ9ahQSULewlK5HGp+QoLCPMvL+zAtbt9W+QIa1KgwMDBSW4s5bYzUrKwuvk5LkrmlbWuM/+rBuuI1Nbejr68s9vl69ekrPRx309PRQNV9Gdn41atQAIC1LnpCYKLqmvJcD7t+/jx49ehV6jqysLDx9+rRYZf+fPPn37yhFPx+amppwsLcvVgDYysqqyD6lIfzhQwAf5upQ8mc7z6N8a9AvWrS4yP5PnqjnJQ4iIiIiIiKiwjAATEREVIioqCj89NNPKh/foUN7TJo4CfXrKw4k+Pv7w8vbG6Gh91U+j7oEBATgyhU/uLq6lPdU1Ob9+/eibVXKjapjjOJq3rwZdn+/S9jOzc1FGxdX0fqaW7Zug4eHO2rWrCk6VkNDQyazrlmzpjAyNCr0nPnL/3bt0gVHDh/CosVLRMHNgtLT07Fi5SrY2dlhwID+CvsVvHcFtz8GSUnJQulrI2NjpY5NyBeQNTZSfGzlSpVEx8gLAGvrKP6nef5S5iX19u1bJCUlAwCM882roEqVlLsX8mhrK17rVB3XpOr4eX9upiamCvuYVK4MTU1N5Obmqj5BFVSoIFGYIVtYxm7iq0QAgI2NDZo2bVLkeQq+5KFIfEK88L2RseLPkkqVFT9L+ZXW+ulFSfjwEo+BgQF0dXXVNu6rxFfC9z169ICBQeFrFNvIyfInIiIiIiIiUjcGgImIiArh5eWt1DqA+U2fPg3jxo4tMljYunVrODs749Chw1i5SvH6oGVlnZfXJxEArla1mkxb2IMHqFZN3B7+UJwRW63av8HOqgX6Pnv2DGlpaTAwMBDaMjMzZTK2qlapovK8laWpqYlVq1aic+euQltaWhoWLV6C/fv2ivpKJBLY2dkJ5W0BYOKECXKzhQvTp48bevXqiUuXLyM4+A4ePHiA2JhYPPn7b0RERIj6Hj16TAgA51+7Ns+LmBhRJmVUdLRMHzMFGZzKSktLU+k4U1MTVDE3x8u4OEQXI4MxPxubf4M50rWS5Qfenj9/DkCa3VkwcF/W9PX1YWFhgejoaMTExCjsFx0l+2f1X1GrVi34+19D5IfSvfI8f/68zIO/JWFjY4M7d+7CqmZNHDp4QG3jWucriR7zopDnRc7P9sekjq0NACAlJQWJia9E63yXRP7PgClTJqFd27ZqGZeIiIiIiIioJEovfYWIiOgTFx8fj1OnT6t0bNeuXTB+3DhR8Dc6OhqXLl/GiRMn4O/vj/j4f7OqtLS0MGLEcIwdO6bE8y6p8PBw3PmwluTHzNGxgcwv8Ldu2SYK2IeEhODMmTOiPq1btZL7PSDNjNzms13UtnfvPlH5ZwBo3aZ1ieaurLZffIG+fb8StR09egzXrl2X6VuwtOmhQ4dl+kRERCAsLEz4khcE1NLSQof27eE5cwb27P4evr4n8TA8TCaY/CBflnCtWrVkSmhv2rgZb968ASANvGzcsEm039TURCZoX1wFX664cP4Cnj59qtJYn38oH37V31+p4+zs7IRMUz8/P4X9rl6VjuvgYK/WbN788t+Pt2/TC+1r92Hd35s3byrsU9h6xZ86Oztp6etnz57JvNSQ58oVxX+exZX3Z/L27dsSj1UU+w/XFBQchJSUFLWNa21tLWTtKnomkpKScf9+mNrOmZ8yz3Vh8pe7vnr1aonmlF/+z4C8n3MiIiIiIiKi8sYAMBERkQJ+fn4qlas1MTHBooULhRKeGRkZWLZsOdp36IjRo8dgzrdzMWKkO9q174DvvlsjKsM5buxYmJjIZlGWtbPnzpX3FIqkra2NCRMmiNp8T51Cr15u8Nm+HUuXLkP37j1F99fQ0BDu7iOF7SZNGqND+/aiMRYuXISx48Zj167vMXnKVMyY6Sna36RJ43LJ8FqxfBn09PREbXPnzZN5RkeOHCHaPn/hAgYNHoJLly/jzp27WLlqNep+Xh8NGzURvv74Q7pG8q1bt2Hv8Lnoa+u2bUIWZE5OjkwJWguL6sL3mpqaaNpEnAH719mzsLN3QBsXV9g71MX5CxdE+1s0b6H8zfigRs0aou3Yly/xeb0GqG1ji3r1HdGtW49ij9XbTbpmalBQMH7//Q+Z/c+fP0e16hYwqGiEHTt2Cu26urro2VN6nn37D8hdA/Xc+fO4dPkyAMDNza3Yc1JW3jrBAHDnbuEvcbRuLX354e7dEPz5558y+y9fuYLLV66od4IfEZc2bQBI139euWq1zP709HRs2LhJpl1ZFtWlPx9Pn0bg9eukEo9XmD59+kBbWxtJSclYt85Lbp/AwFvw9JwFT89ZwosZRZFIJMLLMocOH5b7wsg2Hx8kJyerPvlCKPNcF6ZF8+bC+sNe3t5yS2DPmjUbBhWNUNvGttjVP3R1ddG7t/Tzw8fHBy/j4mT6vH//Hqu/WwNPz1n49dffVL4GIiIiIiIiouJiAJiIiEgBVbO/unbtIippu87LC4cOH5YpJZqRkYE9e/di/YaNQpuBgQE6deqk2oTV6OzZjz8ADACzZ3mKygsD0mDbtGkzsHLVasTly7IGgEWLFgoBAEC6Zu7Gjeuhr68v6rdv335MmjwFO3fuQnZ2ttCura2NzZs2lVoGZ2FsbGwwccJ4Udv16zdw9OgxUVunjh1l1uT9+edf0LlzVzRv4YylS5eJnsWaNWuib9++AIDGjRvB3NwMERERwteMGZ6oVas2WrZqjSpVq2P/fnFp2VYtW4q2V6xYLnN/4uMTEBAQiMR8a2UC0sDS8uVLlbgLYk0aN5Zpy83NRVRUFB4/fowXhZQ3LmjM6NFo0KABAGCkuwcOHTqMlJQUZGRk4MLFi+jVuw8SE1/B3NwcAwYMEB27csVy6OrqIiUlBR06dsbpM2eQmpqKuPh47N9/AP37S7Omra2tMW3qFJWvtygN6tcXMjUnTpwMn+3bceZ//8PFS5fg739N1Hfq1ClCKfMRIz1w5MhRJCcn482bNzhx4lcMGDCwXJ7zstKmTWt8+WUfAMDBg4cwYeIkREREIDc3F3fu3EXPXr1x/37J12V3cnICAKSmpqJvv344fPgIzp0/j4uXLiEsTL0Zsw0bOmHYsKEAgDVr12Hu3HlC6fGkpGTs338Afb78Epu3bEXEs0h89lnha9Xmt2jRQgDSLP7OXbrhyhU/ZGZm4mVcHNasXYfly1eU2vOizHNdGH19fSxbugSANJO5X79v8ODBAwBATEwM5s9fgE2btyAzMxMTJoyHlpbi9aULWrF8GXR1dZGY+AodOnTChYsXkZ6ejpycHISEhGDgoMFYvHgJfLbvgIWFRdEDEhEREREREZUQA8BERERyZGVlqZz91qhhI+H7+PgEuSV48zt8+LAoE8ne3k6l86pTRESEyqV0y1KFChWwb+8eUWlPRUaN8pAJoAKAvb09dmz3QaVKxoUeX7FiRWzauAHNmjVVeb4lNWfObFSrWlXUtmTpMplMvnXr1spkNstjbGyEA/v3CWv3ampq4odjR2FtbS3qF/vyJW7fli0r26RJY8ybN1fU1qxZUyxZslgI2Ciiq6uLNWtWo379+kXOUxF395Fo1Kihysfnp6Ojgx3bfWBpaYm0tDR4jBqNatUtYWJqjq5duyMsLAyGhobYscMHxsZGomNtbW2xccN66OnpISIiAm5uX6JK1eqwtKyJMWPHITU1FVWrVMHOndtlXjZQpxo1amDypIkApKWNp02bgd69+6BLl25w9xgl6mtkZIQ1a76DRCJBSkoKRrp7oFp1S1SpWh0DBg5CZmaWTDb5f82qlStQ68P6trt374G9w+eoVNkUzVs4w8/vKiZPmlTic3z9dT+0bOkMQFoe2N1jFLp374kuXbqJXv5Rl2XLlsLFRZrd7L1+A2zq2MHE1BzmVapizNhxiI9PQO3ateHjs1Wpcdu0aY0J46Wfnw8fPkTHTp1hZl4VNWpYYeHCRahbty46l9LLS8o810UZMKA/Ro+WHnP6zBk4NWwM40omsKpVG+u8vAEA3bt1U/pFjTp16mDzpo3Q19fHo0eP0LVrd5iZV4WRcWU0bdYCJ078CgBYvHgRmjdvptTYRERERERERKpgAJiIiEiO69dvIC0tTaVj//zzT8yZ8y3mzPkWs2bPlsn8Lejdu3eIjn4hbOsXKPNbXj6VLOCGDZ1w47o/pk+bCgMDA5n9devWha/vSfhs26owo2vAgP64FRiAXr16yWSxaWpqolPHjrhx3V8IHJQXIyMjLFy4QNQWGRkpE0iqYm6OU6f+xOZNG4Usz/x0dHQwfPgw3L0TLJQCzmNhYYHr16T308zMVO48zM3MsGD+PJz84w+593z2LE/4X/VDh/btZQKeBgYG6NSxI675+2FigRLeytLR0cH5c2exYP48NG/eTAhkq6pZs6a4du0qevSQlnTOyspCZmYmAKBlS2f4XbmkMMjl7j4SF86fQ8OG0ozP/JnjXbt0wfXr/mj7xRclml9xrF69Crt27kDr1q1Qxdy80L4DBw7AX/87g7p16wKQzjkzMxN2dnb4+ecf8fnndUt9vuXJxsYG1/yvws2tt7DObHp6OvT19bFw4QIsW7ZE6JtX0l9Zmpqa8P3zJOZ+OweNGzeCoaGhOqaukLmZGc6cPoXZszyF9bhTU1MBSDPux4wZjRvXrxX5bMizceN6bPfZJnwupKdL1+Pt0L49fvv1F+jpl97fXco814XR0NDAtq1bsGf398I4eeszGxkZYcmSxThx4meVsplHjBiOC+fPoXFj6UtgmZmZwueHg4MDTp78HXNmz1J57kRERERERETK0MjMSFd+cUMqNxfGlt66cUREn5L2O38v1fEXLlqMH374oVTPkUdTUxN3goOE9V03bNgIn+3bizyufv166Nu3L5o2bQozU1NkZmYiITERAQEBOHHiBB49elyieTk5OeHnn34s0RhlLT09HbdvByE+Ph4VdCvApnZt2NvbKzVGfHwCQu6FIOl1EoyMjVCvXj2ZrNtPSW5uLu7eDcGLF9HIzMyCiYkJGjVqKASHijo2NDQUz6OikJaaBjNzM9SwtETNmjVRoUKFYp0/JycHkZGR+Of5c9SsUQO1atUSgm0fs6ioKDwID4eGhgZsateWyYouzKNHjxDx7Bl0dHTgYG+P6tWrF31QOYuJiUHo/fuoXq0a7O3t/9Pln+VJTk7G7aAgaGpqwsnRCZUqGSMiIgL2Dp8DAH768Tjc3HqX8yyV8+bNG9y5cxdJSa9hZGQMR8cGaglAv3//Ho8fP0bkP/+gjo2NUj8bH5OsrCwEBQUjITEBJpVN4OTkKLPOuqoeP36MpxER0NDQQM0aNYpVpYKIiIg+Xvx9LBGRVGn/PpbUiwHgTwz/wUFEJFXa/+Do1dsN4eHhpXqOPH36uGHd2rXC9tBhw3Djxk2F/bW0tDB//jwMGjhQYUZrVlYW9u7dBy9vb5XnJZFIcD/0nsrHExF9yk6ePIm+/b4BAATdDixRuXIiIiIi+nTx97FERFIMAH9a/n+92k9ERFRMCQkJZXKejh07YN7cf9dQvXbtWqHBXwDYtnUrOnQofH1XHR0djB07Bvqf6WPZsuUqzS0zMxPp6elqy4giIvpYHD16DBcuXoSGhgaWLF4ECwsL0f6srCx4ea8HAFhaWsLW1rY8pklEREREREREpBIGgImIiArIzc3F69ev1Tbe11/3g6Ojo7CtoaEBYyMj1LK2hr2dndAe8ewZ5s2fX+hY/b/5RhT8jYyMxMFDh3H9+nVUqCBBo4aN4OHhLgQzBg8ahPPnL8Df31+lub9+/ZoBYCL6z3FwcMD4CRORnp6OixcvYerUyWjerDk++0wfDx8+wsZNm3DzZgAAYNHCBcUueU5ERERERERE9DFgAJiIiKiAN2/eICcnR23jObdwRu/evQrt89fZs1i8eEmhmccGBgaYPHmSsB0WFobRY8YiLi5OaAsNvY9ff/sNB/bvg6OjIzQ1NTHLc6bKAeCU1FR8/KuXEhEpp3HjRtjusw2Tp0zFP//8g5kzZ8nt5+HhjmHDhpbx7IiIiIiIiIiISkazvCdARET0sUlNTS3zc7q6uGDypEnQ0dFR2GeUhweqVKkCQJqlvHDRYlHwN09aWho8PWchMDAQgYGBePv2LUxNTVWaV1o53AsiorIwaNBA3Am+jREjhqPqh89WALCyskK3rl3x228nsN1nGzQ1+V8mIiIiIiIiIvq0MAOYiIiogLdv36p1vICAAGRmZgrbGhpApUqVUL16ddja2kJLSwu6uroYNGgg6tSxgbvHKGRkZMiM4/qFq/C9v/81hISEKDxnxLNnGDR4SInn/kqNpbCJiD42NWrUwK6dOwBIX/7Jzc2FkZFROc+KiIiIiIiIiKhkGAAmIiIq4G16ulrHO/7jjzj+449y9znY22PWrFlwdXUBADRv3hweHh7w8fER9dPU1ISDvb2wHXwnWK1zVCQ1JaVMzkNEVN4qVqxY3lMgIiIiIiIiIlIL1jMjIiIqQFtLq8zOFf7wISZMnIiwsDChzcN9JHR1dUX9TExMROWhnz9/Xibz09GRlMl5iIiIiIiIiIiIiEg9GAAmIiIqQE9fv0zPl5GRgQMHDgrbhoaGsLOzE/XRLzCnslqnuGJFgzI5DxERERERERERERGpBwPAREREBejr6ZX5OUPvh4q2bW1tRdtxcXGibQsLy1KfE8CSqERERERERERERESfGq4BTEREVEDBbFtltGvXDgMH9Be2FyxcJBO8leezz8SZtoaG4sBreno64uLiYG5uDgCwsqqp8hyVYcAAMBEREREREREREdEnhRnAREREBZQkABwfH4d27doJX199+WWxjmvcqJFo++XLlzJ9goODhe+7de0qs05wQc7OLdDS2RktnZ1VzuQ1ZACYiIiIiIiIiIiI6JPCADAREVEBWlpaKgdMQ0PvIz4+XtgeN24sHB0dCz3GysoKY8aMFrYzMzPh739Npt+hw4eRm5sLADAzM4P7yJEKx+z/zTc4dPAgDh48gF27dkJTU0PZSwEAVKpUSaXjiIiIiIiIiIiIiKh8MABMREQkR8OGTiofe/TYMeH7zz77DHv37MGE8eNlMna1tLQwaNBAHNi/D5UrVxbaT506jeTkZJlxb94MwKnTp4XtKVMmY5SHh0y/fv36Yv78ecL2iRO/Ijk5RenrsLCoDr1yWA+ZiIiIiIiIiIiIiFTHNYCJiIjkcHVxhZ/fVZWO3blzF5xbOKNFi+YAACMjQ0yfPg2TJ09CdHQ04uPjUblyZVSrVk0mwBoVFYV1Xl4Kx/by8kLbL76AgYEBtLS0MGfObAwZMhh374ZAU1MT9vZ2sLa2FvpHR7+Al7e3Stfh4uKq0nFEREREREREREREVH6YAUxERCSHq6uLysdmZWVh4qSJOHfuvKhdW1sbVlZWaNq0KWrXri0T/A0LC4O7uwfi4uIUjh0d/QLjxo8XZQhbWFige/du6Nq1iyj4m5CQgKnTpiE1NVWl6yjJPSAiIiIiIiIiIiKi8sEAMBERkRy1a9cWBVOVlZycgvETJmDatOkICgpGTk6O3H7v37/Hw0ePsGr1anzVtx8inj0rcuybNwPg1qcP/vT1RUZGhsz+9PR0/PHHSfT58ivcvXtXpfnr6OjAuUULlY6l0pGeno6AgEAEBAQiKUm2RLgy+n39DaysrDF+wkQ1za7s+PldhZWVNaysrBEVFVVq54mKisK0aTPQqVMX1LG1F86Z9/Xq1etSOzcVT25urvAzkZCQWN7TKVVJScnCtWZmZhbaNzHxFVq3cUHrNi744YfjapvD9es3hOc/PDxcbeMSERERERERkfqxBDQREZECrq4uiIiIKNEYvqdOwffUKVSuXBlNmzaFqakJDAwM8PbNWyQkJiAoKLjQjF9FoqNfYPr0GTAyMkKrVi1RtWpVZGVm4eXLl7hx86bKWb95nJ1boGLFiiUag9QrIiICbT6U5f7991/RrWtXlcdKTUlFTGws0tLS1DW9MpOdk42Y2FgA0gBgabh16za6dO1W6M/R+/fvS+XcVHzh4eHCz8S5s3/9p6sWHD9+HJOnTIWenh7i42IL7RtyLwSBgbcAAOZVzNU2h+A7wYiJjYW+vj7q1KmjtnGJiIiIiIiISP0YACYiIlLA1cUVBw4cVMtYr169wl9//aWWsfJLTk7G6dNn1D6uK9f/pf/HZs+Zg9TUVGhra8PdfSRcXVxQ0VD8QoShIV+QKG937vxb4cDJybEcZ1L67oaEAAAaNKgPiURSeN+7IcL3Deo3UN8cPozr5OQIbW3+N5KIiIiIiIjoY8b/uRMRESng7NwCEomkyHKb/0X/5Uw6osK8e/cON27cBAB4eLhjy+ZN5TwjUiSvxL29vT2MjIzKeTalK+9aHR2LDnTfu3cPAGBlZQUzM1O1z8HJyUltYxIRERERERFR6eAawERERApIJBKMGDG8vKdR5jp27IDatWuX9zSIysU///yD7OxsAICrC1+E+JiFhEgDnf/17N+MjAzcuxcKAHBsUHRG7+ZNGxEfF4vgoFtqm8O7d+8QGnofAOBUjCA0EREREREREZWv/wMAAP//7N1nWBRXF8DxPwgoCHaKYAMiIFY0xRJ77IVi19hiSyxJTDTRWJKY2DX2NHuNGhXbay/YKwgWUKwoAooKG5XO8n5YdsKyC4KiqDm/TzD3zsyZWXYfnj1zzpUEsBBCCJGNTwcNolSpvKuget0VKFCA4V8Oz+8whMg32uQvgKWVZT5GIrKTlpZGwLkAIGdVsW+yS5eCSUhIAHJWfVu4cGGKFi2KpWXe/f1euHBR6Ybxtt9vIYQQQgghhBDibSAtoIUQQohsWFlZMWjgQCZOmpTfobwSnu3b4+JS8aUcOyYmlv/9738AeHjUoHLlynpzVCoVv//xJwA+3l5UrKgfy82bNzl27DgAnTp1pGDBgjrj8fHxbNrky7Hjx1GpVFSqVAmPGtVp3rw5pqamL3QNly5d4tDhwwQFncfG2prGTRrTpHFjIiMj2b//AABt2rShePFiBvdPS0tjz969HDxwkKh79yhapCg1a3rg7e1FkSJF9OYfOXKUsLAwAO5GRCjb/Q768fDBQ525np7tsbJ6vnVpIyIiWLV6DcHBwZQqWZJatWrRpk1rgzFldu5cIMeOH+PSpWDKlClD82bNeO+9d7l8+TJnz/oD0L17N4yNDT93+OhRDGvXrSUg4BxPnz7Fwd6ej5p9RMsWLZ7rWgB27tql3J933nmH2rU/yHZ+Tu+zhYUFPj7eyu+79+wh+n40DmUcaNyoEWlpaRw/foLr168Tde8eaWlp2NrYGOwk8PTpU7Zs2crp02eIiY3BxtqaRo0b0bJFCwoUKGAwzrS0NFavXgNAzZoeuLu7c9DPj/379nM/OpqyZcvi2b6dToJOrVann+c0j588oWqVKjRs2AA3N7dn3cZnevDgIev/Xk9AwDkSExNxdXWld6+elC1blhs3bnD8+AkAunTpbPC9p1ar2btvH3v37CUiMhIHe3sqV65M586dsLCw0Jvvd+gQ4XfCAYiJjSEmJhaA+/fusWrVap25PXp0x8jI6IWvMbPo6AesXbeWwMAgUlNTcXd3p2+fPlhblyIg4BzBwcGYm5vToYOPzn4ZPyPatWtL0aJFCQwM4vLly4TfvUtqaioAPT/ugb29PevX/60kXE+fOaMc5/z581y/fl353cbWhubNmim/R927x769+4CsP4vi4+PZsmUrR48d48GDB5QqVYoP69XD29tL7/M043kBTE1NqVJF/7Mbcv96CiGEEEIIIYQQ4uUxSkqMT8vvIETOHRjkmd8hCCHEa6HJH1te2bkSExNp2ao14eHhr+yc+aFgwYLs3rUTBweHl3L85ORkHB2duR8djY+PN2v/WqM3Z+XKVfTrPwCA4V9+wdSpU/TmfPXVCOYvWECVKlUI8D+jM3bt2jV69e6jJB4zqlevLkuXLKZChQq5jl2tVjN9xkwmTPiJ5ORknbE+fXrj4+NN+/ZeAJw9c8pghVx09AMGDhzE/3bs0Bt75513WLJ4kV6isk/fT1iz5q8cxXg55FKOW3e3aNGKg35+dOnSGR9vbwYO+hSVSqUzx83NjeXLluLhUcPgMVJSUvjhhx+ZNn2GznZjY2NGj/qWUtalGD78awAe/xNrMLF04sRJ+vT9hJs3b+qNeXt78ftvv+klsA76+dGiRSsArl29Qrly5XTGv//+ByZPmQpAo4YNWbdubZYJea2c3mdHR0euXA5WftfeR0/P9oz57jsGDBxIUNB5nX3ee+9djh09orMtMDCI3n36EhISoneOpk2asGTJIkqXLq03lpqairmFpqpzypTJXLt2jUWLFuvNGzZ0KNOnT0Wl+ocOHTty9OgxnfFChQoxbtxYRnz91XMnSQ8dPkyfPp9w9+5dne0WFhasXLGMoPMXmDDhJ+xsbbl9+5be/vejo/mkbz/27N2rN+bq6sofv/9G3bp1dLbXrfehwfe2of0vnA/M3QXlwJEjR/m4Zy8iIyN1tltbl8J30ybmzJ3L339voHGjRuzevVNnzuLFS/hs8BBMTEw4e+YUX375FX6HDumdI+JuOAUKFMDWTv/1N6R//378umC+8vuyZcsZOOhTjI2Nib4fpfdQSGhoKD0+7qn3dwpQt24dVq1cQZkyZfTGPv/iS37//Q88PGpw6uQJvfHneT2FEEIIId4U8n2sEEJovMrvY8WLkwpgIYQQ4hkKFizIsKFD+HbU6PwO5aXq3bvXS0v+gqZyzNPLk4ULF7Fnz16ePHmi16J085atys++m7cwZcpknQRVWloaW7dtA9CpxAQICwujfoOGPHz4CDtbW7p160q58uW4fu0GK1au5Nix49Sv35CAAH+srXPX1nvMmLHM/GUWoKkKr1unDsWKF+PIkaMsW7acmzf0E5gZPXz4iHof1ufWrVsAVKlSBQ+PGoSFhXH69BmuXbvGR82as2HDep3K19J2dlRNX/MzMTGR0NBQQJOIzHzvnqe6+dq1a/Tp+wlGRkZ81LQpjk6OBAQE4O8fwOXLl2n6UTP8z57G0dFRb9/hX33NH+nV2kWKFKH2Bx9gamrK8RPHmThpMvXrf5jtuQ/6+dGunSdJSUlYWlpS/8MPKVO2DOfOnePsWX98fTcTGRnJwQP7s6yIzSg1NZVhn3+hJEQ7dPBh6ZLFFCpU6Jn75vQ+21hbG9w/NiaW9u09ibp3T9lmamqKkZGRXuyHDx+hvacXcXFxmJmZ8d577+Ls7ExQUBBBQefZf+AAtevU48Txo9jb22cZ86pVq7l48SJubm7UqFGd+/fuc+LkSeLj45k3fz6l7Utz7Ogxjh49hqurKx4eNYi+H82JkyeJi4tjzJixWJcq9VzrnK9evYaBgz4lOTkZCwsL6tapQ8WKFTl95jT+/gH06z+ASpUqAeDh4aG3f0hICO09vQkLC6No0aL06tmTSpXciIyMZOHCRVy5coXuPT7mXIC/krxPTU3FyMhIeSAhPDyc6OgHmJub4+bmqnP8Rg0b5vqanmX9+r/pP2AgCQkJmJub06B+fcqVL4ef3yGuXr1Kv/4DMrRI1l+nNyi9grZcuXJ07NSFa9euKWNmZmYAlC9fnlKlSuLvH6Dz4EVIyGUSEhKwsbbGoYzuZ3TDBg10fteui+zm5qaX/D1y5CgdO3UiJiYWU1NTPvjgfapXq05ISAgHDh7k+PETtGrdlrNnTuk9sBEUFAQYbkH9PK+nEEIIIYQQQgghXi6pAH7DyBNnQgih8aqfOEtNTcXT04sr6Ymht42lpSVHDh/K0zUjDTlw8CAtW7YG4K81q3XapMbGqihfwZH4+Hhl2yG/g9SpU1v5/fjxEzRq3ASAoMAAJckE/1ZxVqxYkYMH9+sk60JDQ6nfoCExMbEMGTyYWbNm5jjmq1evUuvd90lISKBy5cps3LBeqbSNj4/nk3792bhxkzLfUAWwtmrZ1NSUX2bOYNCggcrYuXOBdOrchdu3b1OpUiXOnD6pJIQyCg4OpoZHLQC2bPGlVcuWOb6GzLSVq6BJSP29fp1OwumPP/5k+Fdfk5KSQpcunVm5YrnO/pcuXeK992uTkpLCe++9y4YNf1Pazg7QJLu7dO3K4cP/Vr1mrgBOTU2lTt16BAYGYW9vj++mjTrnX7x4CUOGDkOtVjN/3lwGDhygjBmqAE5ISKB3n774+m4GYPBnn/HLLzOybDudndzc54z3sbSdHcO/Gk6b1q2wt7encOHCevNTU1P5sH4D/P0DsLO1Ze3av3SqIlevXsNng4dorqd3Lxb++Yfe/toKYICxY75j3LixykMSQUHn8fL24e7du5iZmZGUlMTYMd8xduwY5V5cuHABTy8fwsPDsbe3JyT4Iubm5jm+P48exVCtWnXuR0fj7OzM1i2+Sqv2tLQ0Ro0azazZc5T5o779hgkTftQ5RqtWbdh/4ACurq7s2vk/nQdPbt++Ta1330elUjFyxNdMnPizwTjatm3Pnr17ad2qFZs3bzI4J6/ExqqoVq06Uffu4eTkxPZtW3jnnXcAzWdAt2492LHz34rfxYsW0rPnxzrHaNioMSdOnASgVq2afPH559Su/QF2dnbZPqSQkpJCKWtb4uLimDjxZ0aO+DrbWD9q1pzDh4/QvXs3li1donOc2nXqcf78eUqWLMG6tWtp0KC+Mr569Rr6ftIPgFmzZjJk8GBlLDk5GWsbO+Li4vTGIG9eTyGEEEKI15l8HyuEEBpSAfxmyf23YkIIIcR/UIECBZgxc8ZLT5DmB2NjY2ZMn/5Krq1hgwZKVeOWrVt1xrZv3058fDx16tSmRg1NlZmvr6/OnC1bNP9oVq9eTSf5Gxoaytq16wCYNPFnvUpNFxcXRowYAcDCRYu4d/9+jmOePWcuCQkJgCaxk7HNsrm5OUsWL8q2UjMsLIw//tRUyvbu3Usn+Qua9ZBnpyekQ0JCWLZsud4xXqaZM6brtXkeNGigksBat249V69e1RmfM3ceKSkpmJiYsGzpEiX5C1CyZAlWrliebVJx3br1BAZqKgonT5qod/5+/T5h+vSpDBw4gITExGzjV6lUeHn5KMnfCRN+ZPbsX54r+fu8TExMWLVqJV9+8TkVK1Y0mPwFTRWpv38AoGnhnLklbo8e3fns00GAph16cHCw3jG0PDxq6CR/QfO+GD3qWwCSkpKoWdODcePG6tyLqlWr8u03IwHN2s8hIZdzda2TJ0/mfnQ0pqambNm8SWedbiMjI3788Qed90PmhyE2bfJl/wHNWrgrli/T6zpQrlw5Pu7RA4AzZ85mGce/Fan67dbz2rRp04i6dw8TExN8N21Qkr+g+QyYPn2qzj3Wfn5pJScnKy2Xa9Sozt49u+natQsVKlR4ZoV6SEgIcXFxBo+bWWpqqvK+ynzfFy5apKzj++cfv+skf0Hztzd2zHd06ODD06dxWcZQrerLeT2FEEIIIYQQQgiRt6QFtBBCCJFDbq6uzJ83l/4DBpKSkpLf4eSZ0aNG0bRpk1dyrgIFCuDj7c38BQvYuXMX8fHxSqLQd7Mmuevl5UVCQgKBgUFs3rKVqVOnKEmurdu2A+Dj46Nz3HPnAlGr1RgbG2NkZMTeffv0zl0kvR1qUlISwcHB2NrY5CjmixcvAtCkcWNq1tRvZ2tubk6/T/ry088TDe7v7x+grBs8cMAAg3PatGmDs7Mz169f5/SZMzoVry+To6Mj7du3Mzj22aefsnTpMgAuXQrWSfRpE5MfNW2qs12rdOnSeHl58tdfaw0eW5sEtbWxoVOnjgbnDBs69JnxR0RE0qVrN/z9AzAzM2PB/Hn07t3rmfvlNVdX12e2vIZ/E2B2trZ07tzJ4JxPPx3ErNlzUKvVnD59Bnd3d4PzGjVsaHD93oYN/20J3KRxY4NzMsZ65coVg3/XhiQnJ7M0/QGFTp064uLiojenUKFCfPhhPdav/xvQT1r++ttvALi7u/Pg4QOD71VtzJeCLxmMIywsTHmIw1BL4ryUkpLCosWatuI+Pt46D55oVaxYkUqVKnHp0iUsLCxwc3PTGc+YQP1q+PBcPWyjTejCs681NDSUf/75J32ubqJ2w4aNANSs6UHbtm0N7j9+/DiD27XJa2NjY73j5sXrKYQQQgghhBBCiLwnCWAhhBAiF+rVq8fPP01g1Ojv8juUPNG7d6/nWgP0RXh7ezF/wQJUKhX79u2jXbt2xMZqfgbo4ONNQkIC33//A7du3eLEiZPUrVuHs2f9uX79uuYYXrotuK6lb1er1XTs1PmZMVy7eo3GjRrlKN7LlzUVklkl4p41pm0bbmxsrLdWqZaRkRFubm5cv35dWX/2VXBzdTWYIASoVMkNExMTUlJSCM1UAayN0dVVPwGo5epq+Frh33vq6uaKicnz/zvauXMXou7dw8rKilWrVrxQW+wXUaF8+RzNu3LlCgAuri5ZXneFChWwsrLi8ePH2f4t2JUubXB7xurb0vaG55Qu/e8cbcIwJ86dC1TmZ1yrOjNbG1sAihcvhrOzs7I9Pj6ekydPAZqHCNq0MfzwwbNoE5Lw7KrYFxUYGERsrAqANq1bZznPztaWS5cuUb16Nb31uLVJXGNjY1pkc98M0VbtOjo6ZrkGtVbG+1K9mu590T60kdVDATmJwdXVlSJFiijb8+r1FEIIIYQQQgghRN6TBLAQQgiRSx06dOBuRATz5s3P71BeSNOmTfhu9OhXft569epSoUIFbt26xeYtW2nXrp3S/rl+/Q8pV64coFkn098/AF9fX+rWrcPW9JbRtWrV1Kuwe/TwEQBWVla0bt3qmTEULVo0R7HGx8cTExMLQImSJbKcl93Yg+gHgGad5ezavRYvXgyAe/dy3p76RRVLP6chBQsWxMrKkpiYWB5ERyvb4+LilIRYseLFs9y/eDbHjn6gOV7RolnPyYm49PWiExISiHkU80LHehEmpjn7lzon121kZESJEiV4/Pgx9zPc98yyanGdcXvWc3KXANSKiopSfnZ0dMxyXmRUJAAeNTx0ko1RUVEkJSUB0LxZM+xK2xncXyurhGdQekKyVKmS2caRFyIiIpSfM7Z/zywy/d4YqtLVJlDd3NyyfV8YcuGCpgNBThLd2rbYzs7OlCpVUtkeERHBw/TPyOe5XxnbV2eUV6+nEEIIIYQQQggh8p4kgIUQQojn8PmwYURERLJx48b8DuW5VKtWjTmzZ7/SdVK1jI2N8fbyZNbsOezYsYOkpCQ2b9G2f/63stfb2xt//wClDfSWrdvS53jpHdPJWZPUSExMZNHCPylYsGCexGpubk65cuW4ffs2d+7cyXLendtZj2lj++eff4iJic0yARR+JxyAihnWF33ZwsPDsxx79ChGSX5nrOK0sLDAwcGBu3fvEhkZmeX+d8PvZjlWsWJFAgODCA/P+r7lxF9/rWbYsC+4ceMGn/TrT1xcHP3793uhY75Mzs7OBAYGERGR9b1JSkri7l3N+Duv8G8hJzK2vs/uYYYrVzSVy1WrVtHZnpaWpvzcv38/nfd7bmgTqtWrVc91NWtu5eSa4+LiuHHjBgDVqlbVG9cmUHO7XnFaWhrnAs9pjlvt2ftmdR61Wq38nNvPfLVazbnAQIMx5NXrKYQQQgghhBBCiLz36r/1FUIIId4SUyZPYty4sRQoUCC/Q8mVzp06sX7d2jxLkj6PDh06APDw4SM2bfJl7959GBsb4+Ptrczp2EGzzm9YWBgLFy4iJCQEAB9v/QSwtt1wUlISx0+cyNNYXdPXOT1x4iSpqakG5xw9dizr/TO0Qj5y5IjBObGxKgKDNEmWrNpEZ0zcxMfFZx90DgUFnedRFpWzhw4dUn6u6KK7zq9L+rq/p06dyvLYp06dznLMLf2eXL58RamcfB6uLi7s3bMLd3d31Go1g4cMZe5rXJmv/VsKDg4h6t49g3OOHjumJB3dsmmjnR8yVo/evHnT4JwjR45y4cIFQL8atnz58koS9Xp6wvR53Lp1C4By5cs99zFyKuM1a1vQZ7Zy5SoSEhIA/WtWq9UEplfm5iSJm9GDBw+VhzDKlS37zPlB54MMxuDg4IBV+hro165dy1UMV69eRaVSpR9XN/68ej2FEEIIIYQQQgiR9yQBLIQQQryAXj17smTxIiwtLfM7lGcyNTVl4sSfmTjx53xPWr///ntUTE8ijhr9HfHx8TSoXx8HBwdljpOTEx988D4A48aP19svo7p16lA+fR3WSRMnG0zUPnoUw8iR3zBixEilgjAn6jeoD2jWrV25cpXeeGhoKGvW/JXl/nVq11baWs+YOVNpmZrR7NmzlbbK7dobXkezTJkyys/aVq8v6p9//mHWrFl62xMTE5k+YwagabPrUcNDZ7xevbrpcZxn+/btevsfOnyYQ4cPZ3nedu3aYWJiQnx8PL/M/EVvPDk5mYaNGmNpVZSevbJfo7ps2bLs3r2TWrVqAjBixEimTJ2W7T75pX379hgbGxMfH8/MGTP1xlNSUpg+TXPfbW1saNiw4asOMVvOzk7KgwirVq/WG4+Li2PChJ+U3zO3DC5QoABVqlQGYPXqNTqVqVpXr15lxIiRjBgxUqf9ckYJCYkAxMbGPt+F5IKzs5OyXvOKFSv1xiOjopg9Zy6g+YzVXp9WaGiokkDN7XrFiYkJys+xquyv9ebNm0Snt5vPnKg1MjJS4vpr7TolWZ3RyJHf4OrmTpOmH+lU9ma3rnBevZ5CCCGEEEIIIYTIe5IAFkIIIV5Q3bp12bjh72zXh8xvxYoVY83qVXTu1Cm/Q1F08NFU+2qTAt4++pW93ukVwdrkqKH2z6BpS/z9+HGAJvno7d2BCxcuoFariY+PZ9/+/bRq3Zo5c+excZNvrtbBHDZ0iJLAHTrsc+YvWMCDBw9JSEhg9549tGjZ2mBSV6tw4cJKbCdPnsLT05szZ86SlJTE9evXGTNmLJMmTwGgU6eONGzQwOBxLC0tcXd3B2De/AVMmPAT27dv56CfHwf9/IiPf76q4KnTpjNu3HjCw8NRq9UEBZ2nfXsvzp71B+Cbb77Ra1v9xRefY2drC0Cfvv1YvXoNKpWKp0+fsmmTL127dlOSZobUqFGdvn37ADBn7jzGjh1HZGQkarWa4OBgOnfpxokTJzVVvZ999sxrsLWxYeeOHTRIT9aPH/8948aNf57b8VLVrOlB7969AM11f/31SEJDQ0lOTiYg4BwdO3Zm/4EDAHw3ZnSu14t92YoUKULXrl0A2Lp1G+PHf09srIrU1FTOnDmLp6c3R44eBTTvycxrdQN8/dVXAFy8eJHeffoqbcQTExPZuHETrVq3Ze68+Vy8eAl7e3uDcTikb9++/X+MGjWabdu2cejwYU6fPvNSrrlbt64A7Ni5k9GjvyMmJpbk5GT8Dh2iVas2SjV0lSqVMTc319k/MPDfhzUyJ1Bzcu5ixTTrlU+bOp3Zc+aye88eDh8+QmhoaJbnqWFgHWLtfY+IiKBb94+Vz927d+/y6WeDmTN3Hjdv3uSTvn112mprHzZxdHTE2rpUlsd9kddTCCGEEEIIIYQQec8oKTE+7dnTxOviwCBZW0sIIQCa/LElv0PQ8+TJE4YMHcbx48fzOxQdbm5u/P7bbzg4vF5fvgcGBvH+B7UBMDEx4fr1q5S2s9OZExYWRkWXf5NIl0MuZZloT0tLY9jnX/DnnwuVbZaWlsTFxSmVaRYWFvj6bqRxo0a5inXbtm307NWHuLg4ZVuhQoVISEjAxMSEQQMHsuDXXwE4e+aUwbUyBw8ZyuLFS5RtJiYmOuuLvvtuLdavW6tT6ZvZ9u3b8elgOImf3b3JrEWLVhz086NlixbcCgvj8uXLgGbN44yJ5CaNG7N162bMzMz0jvHXX2sZMHCQkvw2MTHB2NiYpKQkrKys6Nq1CwsXLgLg8T+xei3HHzx4SKfOnTl27N/3i4WFhc49/vHHHxg96lud/Q76+dGiRSsArl29oiTnAZ4+fUq3bj3YtXs3AMOGDmXGjGm5Wic2ODiYGh61ANiyxZdWLVtmOVd7Hz092/P3+nU5Ov796Gi6dOmqc92Z/xa6d+/GooV/6iXRU1NTMbfQdBuYPn0aX3w+TO/4T58+pXgJTaJu1qyZDBk8WG+OSqXC2kbzXps3dw6DBg3MUeygaYNcu049parV2NgYKysrVCoVdra2VKlShX3791O3bh38Dh4weIyOnTqzNX1NbwAba2sexcQo98DV1ZV9+/Zga2NjcP+9+/bh6emtc88AmjdrxvbtW3N8LTl148YNatepqzyIYmJigrm5OY8fP6ZJ48ao/lHh7x9Anz69+fOP33X2HT36O2b+MgtnZ2dCgi/m+twTJvzEzxMnGdj+I6O+/Ub5/ccfJzBx0mRKly5N2C3D7Zgz3/eSJUvw8OEj5fdhQ4cyc+Z0nX3atm3Pnr178fb2Yt1aw50OXvT1FEIIIYR43cn3sUIIofE6fh8rsiYVwEIIIUQesbS0ZOmSxcyfN9dgm+JXzdramnFjx7Bp44bXLvkLmirQypU17UMbNmigl/wFzRqT2nb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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a diagram explaining how our script will work: \n",
    "\n",
    "![wikipedia_diagram.png](attachment:wikipedia_diagram.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Here are some real exchanges that illustrate the end result we're hoping to achieve: \n",
    "\n",
    "> **User**: As of 2024 who has more oscars, Christopher Nolan or Ben Stiller?\n",
    "\n",
    "> *Claude wants to get an article for Christopher Nolan* (tool use)\n",
    "\n",
    "> *Claude wants to get an article for Ben Stiller* (tool use)\n",
    "\n",
    "> **Claude**: As of 2024, filmmaker Christopher Nolan has won 2 Academy Awards for Best Director and Best Picture for his 2023 film Oppenheimer. On the other hand, actor and comedian Ben Stiller has not won any Oscars in his career so far, although he has won other awards like Emmys and MTV Movie Awards. So to directly answer the question, as of 2024, Christopher Nolan has won more Oscars (2) than Ben Stiller, who has not won any.\n",
    "\n",
    "Here's another example:\n",
    "\n",
    "> **User:** Who won the 2024 Masters Tournament?\n",
    "\n",
    "> *Claude wants to get an article for 2024 Masters Tournament*  (tool use)\n",
    "\n",
    "> **Claude:** Scottie Scheffler won the 2024 Masters Tournament, his second Masters victory. He finished four strokes ahead of runner-up Ludvig Åberg, who was making his debut at the Masters.\n",
    "\n",
    "If we ask Claude a question that it can answer without needing to use our tool, it should just answer immediately without using our Wikipedia search tool:\n",
    "\n",
    "> **User:** Who won the 2021 Masters?\n",
    "\n",
    "> **Claude:** Hideki Matsuyama won the 2021 Masters Tournament, becoming the first Japanese man to win a major golf championship. He finished with a score of 278 (-10) to claim the green jacket at Augusta National Golf Club.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a look at what the `messages` list looks like by the end of this process:\n",
    "\n",
    "![messages_diagram.png](./images/br_messages_diagram.png)\n",
    "\n",
    "The main new topic in this lesson is how to properly format our `tool_result` user message when responding to Claude's `tool_use` message."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the complete workflow\n",
    "\n",
    "### 1. Defining our Wikipedia search function\n",
    "\n",
    "Before we do anything with Claude, let's write a simple function to search Wikipedia.  The following function uses the `wikipedia` package to search for matching wikipedia pages based on a search term.  To keep things simple, we take the first returned page title and then use that to access the corresponding page content.\n",
    "\n",
    "Note: this simple function assumes we find a Wikipedia article. To keep things brief, the function has no error handling which is not a great idea in the real world!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import json\n",
    "from datetime import datetime\n",
    "from botocore.exceptions import ClientError\n",
    "\n",
    "# Import the hints module from the utils package\n",
    "from utils import hints\n",
    "\n",
    "session = boto3.Session()\n",
    "region = session.region_name\n",
    "\n",
    "bedrock_client = boto3.client(service_name = 'bedrock-runtime', region_name = region,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import wikipedia\n",
    "\n",
    "def get_article(search_term):\n",
    "    results = wikipedia.search(search_term)\n",
    "    first_result = results[0]\n",
    "    page = wikipedia.page(first_result, auto_suggest=False)\n",
    "    return page.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "article = get_article(\"Superman\")\n",
    "print(article[:500]) # article is very long, so let's just print a preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "article = get_article(\"Zendaya\")\n",
    "print(article[:500]) # article is very long, so let's just print a preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Writing the tool definition\n",
    "Next up, we need to define our tool using the proper JSON Schema format.  This is a very simple tool definition because the function expects a single argument: the search term string.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "toolConfig = {\n",
    "  \"tools\": [\n",
    "    {\n",
    "      \"toolSpec\": {\n",
    "        \"name\": \"get_article\",\n",
    "        \"description\": \"A tool to retrieve an up to date Wikipedia article.\",\n",
    "        \"inputSchema\": {\n",
    "          \"json\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "              \"search_term\": {\n",
    "                \"type\": \"string\",\n",
    "                \"description\": \"The search term to find a wikipedia article by title\"\n",
    "              }\n",
    "            },\n",
    "            \"required\": [\"search_term\"]\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  ]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Provide Claude with the tool and user prompt\n",
    "\n",
    "Next, we'll tell Claude it has access to the Wikipedia search tool and ask it to answer a question we know it cannot answer without the tool, like \"Who won the 2024 Masters Tournament?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "modelId = \"anthropic.claude-3-sonnet-20240229-v1:0\"\n",
    "messages = [{\"role\": \"user\", \"content\": [{\"text\": \"who won the 2024 Masters Tournament?\"}]}]\n",
    "\n",
    "converse_api_params = {\n",
    "    \"modelId\": modelId,\n",
    "    \"messages\": messages,\n",
    "    \"inferenceConfig\": {\"temperature\": 0.0, \"maxTokens\": 4096},\n",
    "    \"toolConfig\": toolConfig\n",
    "}\n",
    "\n",
    "response = bedrock_client.converse(**converse_api_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Claude uses the tool (API response)\n",
    "\n",
    "Let's look at the response we got back.  Claude wants to use our tool!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "response['output']['message']['content']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude's response contains 2 blocks: \n",
    "\n",
    "* A `text` value with the text \"Okay, let me use the available tool to try and find information on who won the 2024 Masters Tournament on Wikipedia:\"\n",
    "\n",
    "```\n",
    "[{'text': 'Okay, let me search Wikipedia for information on the 2024 Masters Tournament winner:'},\n",
    " {'toolUse': {'toolUseId': 'tooluse_8kC2QECHRYePqW0BS96_9Q',\n",
    "   'name': 'get_article',\n",
    "   'input': {'search_term': '2024 Masters Tournament'}}}]\n",
    "```\n",
    "\n",
    "* A `toolUse` value calling our `get_article` tool with the `search_term` \"2024 Masters Tournament\"\n",
    "\n",
    "```\n",
    "{'toolUse': {'toolUseId': 'tooluse_8kC2QECHRYePqW0BS96_9Q',\n",
    "   'name': 'get_article',\n",
    "   'input': {'search_term': '2024 Masters Tournament'}}}\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Extract tool input(s), run code, and return results (API request)\n",
    "\n",
    "Now that Claude has responded telling us it wants to use a tool, it's time for us to actually run the underlying functionality AND respond back to Claude with the corresponding Wikipedia page content.\n",
    "\n",
    "**We need to pay special attention to make sure we update our `messages` list**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll begin by updating our `messages` list to include Claude's most recent response:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "messages.append({\"role\": \"assistant\", \"content\": response['output']['message']['content']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "messages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll extract the specific tool and arguments that Claude wants to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# This is a simple, but brittle way of getting the tool use information\n",
    "# We're simply taking the last block from Claude's response.\n",
    "tool_use = response['output']['message']['content'][-1]\n",
    "tool_id = tool_use['toolUse']['toolUseId']\n",
    "tool_name = tool_use['toolUse']['name']\n",
    "tool_inputs = tool_use['toolUse']['input']\n",
    "\n",
    "print(\"Tool Id: \", tool_id)\n",
    "print(\"Tool name: \", tool_name)\n",
    "print(\"Tool input\", tool_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll make sure Claude is calling the `get_article` tool we're expecting.  We'll take the `search_term` Claude came up with and pass that to the `get_article` function we wrote earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if tool_name == \"get_article\":\n",
    "    search_term = tool_inputs[\"search_term\"]\n",
    "    wiki_result = get_article(search_term)\n",
    "    print(f\"Searching Wikipedia for: {search_term}\")\n",
    "    print(\"WIKIPEDIA PAGE CONTENT:\")\n",
    "    print(wiki_result[:500]) #just printing a small bit of the article because it's so long"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've executed the function Claude wanted us to call, it's time to respond back to Claude with the Wikipedia page data.\n",
    "\n",
    "As we know, when Claude wants to use a tool, it responds to us with a `stop_reason` of `tool_use` and one or more `tool_use` content blocks in the API response that include:\n",
    "* `id`: A unique identifier for this particular tool use block. This will be used to match up the tool results later.\n",
    "* `name`: The name of the tool being used.\n",
    "* `input`: An object containing the input being passed to the tool, conforming to the tool's `input_schema`.\n",
    "\n",
    "Once we have executed our underlying tool function, we need to respond back to Claude with a particular format as well. Specifically, to continue the conversation, we need to send a new message with the **role of `user`** and a content block **containing the `tool_result` type**, along with the following information:\n",
    "* `toolUseId`: The id of the tool use request this is a result for.\n",
    "* `content`: The result of the tool, as a string (e.g. \"content\": \"15 degrees\") or list of nested content blocks (e.g. \"content\": [{\"type\": \"text\", \"text\": \"15 degrees\"}]\\). \n",
    "* `status` (optional): Set to **success** or **error** based on the tool execution results.\n",
    "\n",
    "Here's an example of what a properly formatted `toolResult` message looks like: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```json\n",
    "{\n",
    "    \"role\": \"user\",\n",
    "    \"content\": [\n",
    "        {\n",
    "            \"toolResult\": {\n",
    "                \"toolUseId\": \"tooluse_kZJMlvQmRJ6eAyJE5GIl7Q\",\n",
    "                \"content\": [\n",
    "                    {\n",
    "                        \"json\": {\n",
    "                            \"song\": \"Elemental Hotel\",\n",
    "                            \"artist\": \"8 Storey Hike\"\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do this now for our Wikipedia search example. We need to form a properly constructed tool response message to send our Wikipedia search result back to Claude:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tool_response = {\n",
    "    \"role\": \"user\",\n",
    "    \"content\": [\n",
    "        {\n",
    "            \"toolResult\": {\n",
    "                \"toolUseId\": tool_id,\n",
    "                \"content\": [\n",
    "                    {\n",
    "                        \"text\": wiki_result\n",
    "                        }\n",
    "                ]\n",
    "            }\n",
    "        }\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Notice the long wikipedia article content!\n",
    "tool_response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to add our `tool_response` message to our messages list: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "messages.append(tool_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "messages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our `messages` list now looks like this: \n",
    "\n",
    "* User: Who won the 2024 Masters Tournament?\n",
    "* Assistant: I want to use the `get_article` tool with `search_term` \"2024 Masters Tournament\"\n",
    "* User: Here's the tool result that contains the Wikipedia article you asked for\n",
    "\n",
    "Here's a diagram that illustrates this.\n",
    "\n",
    "![wiki_messages.png](./images/br_wiki_messages.png)\n",
    "\n",
    "Note that the initial `id` matches the `tool_use_id` in our follow up user message\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Claude uses the tool result to formulate a response: (API response)\n",
    "\n",
    "Finally, we can use our updated `messages` list and send a new request to Claude:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "converse_api_params = {\n",
    "    \"modelId\": modelId,\n",
    "    \"messages\": messages,\n",
    "    \"inferenceConfig\": {\"temperature\": 0.0, \"maxTokens\": 1000},\n",
    "    \"toolConfig\": toolConfig #The toolConfig which contains our article_search_tool details\n",
    "}\n",
    "\n",
    "follow_up_response = bedrock_client.converse(**converse_api_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "follow_up_response['output']['message']['content'][0]['text']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Claude now has the information needed to answer the initial question and responds back with:\n",
    "\n",
    "> 'According to the information provided, Scottie Scheffler won the 2024 Masters Tournament.According to the information provided, Scottie Scheffler won the 2024 Masters Tournament...'\n",
    "\n",
    "We have now completed all 4 steps of the process! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving the code\n",
    "\n",
    "At a bare minimum, we probably want to put all the above code into a reusable function so we can try it out a few times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def answer_question(question):\n",
    "    messages = [{\"role\": \"user\", \"content\": [{\"text\": question}]}]\n",
    "\n",
    "    converse_api_params = {\n",
    "        \"modelId\": modelId,\n",
    "        \"messages\": messages,\n",
    "        \"inferenceConfig\": {\"temperature\": 0.0, \"maxTokens\": 4096},\n",
    "        \"toolConfig\": toolConfig #The toolConfig which contains our article_search_tool details\n",
    "    }\n",
    "\n",
    "    response = bedrock_client.converse(**converse_api_params)\n",
    "\n",
    "    if(response['stopReason'] == \"tool_use\"):\n",
    "        tool_use = response['output']['message']['content'][-1]\n",
    "        tool_id = tool_use['toolUse']['toolUseId']\n",
    "        tool_name = tool_use['toolUse']['name']\n",
    "        tool_inputs = tool_use['toolUse']['input']\n",
    "        #Add Claude's tool use call to messages:\n",
    "        messages.append({\"role\": \"assistant\", \"content\": response['output']['message']['content']})\n",
    "\n",
    "        if tool_name == \"get_article\":\n",
    "            search_term = tool_inputs[\"search_term\"]\n",
    "            print(f\"Claude wants to get an article for: {search_term}\")\n",
    "            wiki_result = get_article(search_term) #get wikipedia article content\n",
    "            #construct our tool_result message\n",
    "            tool_response = {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": [\n",
    "                    {\n",
    "                        \"toolResult\": {\n",
    "                            \"toolUseId\": tool_id,\n",
    "                            \"content\": [\n",
    "                                {\"text\": wiki_result}\n",
    "                            ]\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "            messages.append(tool_response)\n",
    "            #respond back to Claude\n",
    "            converse_api_params = {\n",
    "                \"modelId\": modelId,\n",
    "                \"messages\": messages,\n",
    "                \"inferenceConfig\": {\"temperature\": 0.0, \"maxTokens\": 4096},\n",
    "                \"toolConfig\": toolConfig  #The toolConfig which contains our article_search_tool details\n",
    "            }\n",
    "\n",
    "            response = bedrock_client.converse(**converse_api_params)\n",
    "\n",
    "            print(\"Claude's final answer:\")\n",
    "            print(response['output']['message']['content'][0]['text'])\n",
    "\n",
    "    else:\n",
    "        print(\"Claude did not call our tool\")\n",
    "        print(response['output']['message']['content'][0]['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "answer_question(\"Who won the 2024 F1 Australian Grand Prix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "answer_question(\"Who stars in the movie Challengers?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Let's try an example that Claude should NOT need our tool to answer:\n",
    "answer_question(\"Who wrote the book 'The Life of Pi'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that Claude called our Wikipedia tool to help answer this last question, even though Claude already knows the answer.  \"Life of Pi\" was published in 2001, long before Claude's training cutoff!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving our prompt\n",
    "\n",
    "As we saw in a previous lesson, sometimes Claude is overly eager to use tools.  An easy wasy to fix this is through the system prompt.\n",
    "\n",
    "\n",
    "We could add a system prompt that looks something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"\"\"\n",
    "    You will be asked a question by the user. \n",
    "    If answering the question requires data you were not trained on, you can use the get_article tool to get the contents of a recent wikipedia article about the topic. \n",
    "    If you can answer the question without needing to get more information, please do so. \n",
    "    Only call the tool when needed. \n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's update our function to use this new system prompt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def answer_question(question):\n",
    "    system_prompt = \"\"\"\n",
    "    You will be asked a question by the user. \n",
    "    If answering the question requires data you were not trained on, you must use the get_article tool to get the contents of a recent wikipedia article about the topic. \n",
    "    If you can answer the question without needing to get more information, please do so. \n",
    "    Only call the tool when needed. \n",
    "    \"\"\"\n",
    "    messages = [{\"role\": \"user\", \"content\": [{\"text\": question}]}]\n",
    "\n",
    "    converse_api_params = {\n",
    "        \"modelId\": modelId,\n",
    "        \"system\": [{\"text\": system_prompt}],\n",
    "        \"messages\": messages,\n",
    "        \"inferenceConfig\": {\"temperature\": 0.0, \"maxTokens\": 4096},\n",
    "        \"toolConfig\": toolConfig  #The toolConfig which contains our article_search_tool details\n",
    "    }\n",
    "\n",
    "    response = bedrock_client.converse(**converse_api_params)\n",
    "\n",
    "    if(response['stopReason'] == \"tool_use\"):\n",
    "        tool_use = response['output']['message']['content'][-1]\n",
    "        tool_id = tool_use['toolUse']['toolUseId']\n",
    "        tool_name = tool_use['toolUse']['name']\n",
    "        tool_inputs = tool_use['toolUse']['input']\n",
    "        #Add Claude's tool use call to messages:\n",
    "        messages.append({\"role\": \"assistant\", \"content\": response['output']['message']['content']})\n",
    "\n",
    "        if tool_name == \"get_article\":\n",
    "            search_term = tool_inputs[\"search_term\"]\n",
    "            print(f\"Claude wants to get an article for: {search_term}\")\n",
    "            wiki_result = get_article(search_term) #get wikipedia article content\n",
    "            #construct our tool_result message\n",
    "            tool_response = {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": [\n",
    "                    {\n",
    "                        \"toolResult\": {\n",
    "                            \"toolUseId\": tool_id,\n",
    "                            \"content\": [\n",
    "                                {\"text\": wiki_result}\n",
    "                            ]\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "            messages.append(tool_response)\n",
    "            #respond back to Claude\n",
    "            converse_api_params = {\n",
    "                \"modelId\": modelId,\n",
    "                \"system\": [{\"text\": system_prompt}],\n",
    "                \"messages\": messages,\n",
    "                \"inferenceConfig\": {\"temperature\": 0.0, \"maxTokens\": 4096},\n",
    "                \"toolConfig\": toolConfig  #The toolConfig which contains our article_search_tool details\n",
    "            }\n",
    "\n",
    "            response = bedrock_client.converse(**converse_api_params)\n",
    "\n",
    "            print(\"Claude's final answer:\")\n",
    "            print(response['output']['message']['content'][0]['text'])\n",
    "\n",
    "    else:\n",
    "        print(\"Claude did not call our tool\")\n",
    "        print(response['output']['message']['content'][0]['text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try asking the same question:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_question(\"Who wrote the book The Life of Pi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It worked! Claude did not use our tool when it wasn't needed.  Let's make sure it still works when answering questions that do require recent knowledge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_question(\"Who wrote the score for the movie Challengers?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's working as intended in both situations! Now let's work on getting Claude's response to be a little more succinct.  It's great that Claude is explaining HOW it came up with the correct answer, but it's a little verbose. Let's do a little basic prompt engineering to fix that.  \n",
    "\n",
    "Let's try this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = f\"\"\"\n",
    "    Answer the following question <question>Who wrote the movie Poor Things?</question>\n",
    "    When you can answer the question, keep your answer as short as possible and enclose it in <answer> tags\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's our function updated with the new prompt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def answer_question(question):\n",
    "    system_prompt = \"\"\"\n",
    "    You will be asked a question by the user. \n",
    "    If answering the question requires data you were not trained on, you must use the get_article tool to get the contents of a recent wikipedia article about the topic. \n",
    "    If you can answer the question without needing to get more information, please do so. \n",
    "    Only call the tool when needed.\n",
    "    \"\"\"\n",
    "    prompt = f\"\"\"\n",
    "    Answer the following question <question>{question}</question>\n",
    "    When you can answer the question, keep your answer as short as possible and enclose it in <answer> tags\n",
    "    \"\"\"\n",
    "    messages = [{\"role\": \"user\", \"content\": [{\"text\": prompt}]}]\n",
    "\n",
    "    converse_api_params = {\n",
    "        \"modelId\": modelId,\n",
    "        \"system\": [{\"text\": system_prompt}],\n",
    "        \"messages\": messages,\n",
    "        \"additionalModelRequestFields\": {\"max_tokens\": 4096},\n",
    "        \"toolConfig\": toolConfig  #The toolConfig which contains our article_search_tool details\n",
    "    }\n",
    "\n",
    "    response = bedrock_client.converse(**converse_api_params)\n",
    "\n",
    "    if(response['stopReason'] == \"tool_use\"):\n",
    "        tool_use = response['output']['message']['content'][-1]\n",
    "        tool_id = tool_use['toolUse']['toolUseId']\n",
    "        tool_name = tool_use['toolUse']['name']\n",
    "        tool_inputs = tool_use['toolUse']['input']\n",
    "        #Add Claude's tool use call to messages:\n",
    "        messages.append({\"role\": \"assistant\", \"content\": response['output']['message']['content']})\n",
    "\n",
    "        if tool_name == \"get_article\":\n",
    "            search_term = tool_inputs[\"search_term\"]\n",
    "            print(f\"Claude wants to get an article for: {search_term}\")\n",
    "            wiki_result = get_article(search_term) #get wikipedia article content\n",
    "            #construct our tool_result message\n",
    "            tool_response = {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": [\n",
    "                    {\n",
    "                        \"toolResult\": {\n",
    "                            \"toolUseId\": tool_id,\n",
    "                            \"content\": [\n",
    "                                {\"text\": wiki_result}\n",
    "                            ]\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "            messages.append(tool_response)\n",
    "            #respond back to Claude\n",
    "            converse_api_params = {\n",
    "                \"modelId\": \"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
    "                \"system\": [{\"text\": system_prompt}],\n",
    "                \"messages\": messages,\n",
    "                \"additionalModelRequestFields\": {\"max_tokens\": 4096},\n",
    "                \"toolConfig\": toolConfig  #The toolConfig which contains our article_search_tool details\n",
    "            }\n",
    "\n",
    "            response = bedrock_client.converse(**converse_api_params)\n",
    "\n",
    "            print(\"Claude's final answer:\")\n",
    "            print(response['output']['message']['content'][0]['text'])\n",
    "\n",
    "    else:\n",
    "        print(\"Claude did not call our tool\")\n",
    "        print(response['output']['message']['content'][0]['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_question(\"Who wrote the score for the film Challengers?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "answer_question(\"Who won the 2024 F1 Australian Grand Prix?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_question(\"How many legs does an octopus have?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better! Claude is now responding with answers without a bunch of additional \"thinking\" and explanation about how it arrived at the answer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Can you update this code so that it fulfills the following requirements:\n",
    "* Claude might not get enough information from the first Wikipedia page we respond with.  We haven't handled that situation yet.  Imagine we ask Claude \"How many Oscars does Christopher Nolan have?  Does he have more than the number of Emmy's that Ben Stiller has?\"  Claude would need to search Christopher Nolan's Wikipedia page AND Ben Stiller's page, likely one after another.  Our code currently does not allow this, so let's build in that functionality! **Hint: use a loop!**  \n",
    "* Extract the answer from the `<answer></answer>` tags Claude currently responds with so that you only print out the actual answer content.\n",
    "* Can you turn this into a full command-line chatbot that continuously asks a user to enter a query and then responds with the answer over and over until a user quits the program? The output could look something like this: \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a screenshot of an example conversation session: \n",
    "\n",
    "![exercise_conversation.png](./images/exercise_conversation.png)"
   ]
  }
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