{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yuurmT9VeEbZ"
   },
   "source": [
    "### **If you are using Colab for free, we highly recommend you active the T4 GPU hardware accelerator. Our models are designed to run with at least 16GB of RAM, activating T4 will grant the notebook 16GB of GDDR6 RAM as opposed to the 13GB Colab gives automatically.**\n",
    "### **To active T4:**\n",
    "### **1. click on the \"Runtime\" tab**\n",
    "### **2. click on \"Change runtime type\"**\n",
    "### **3. select T4 GPU under Hardware Accelerator**\n",
    "### **NOTE: there is a weekly usage limit on using T4 for free**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p-3xdQQReK1R"
   },
   "source": [
    "# **Fast Start Example 8 - Agents** #\n",
    "This example shows how to build locally-running Agents deploying multiple small specialized function-calling models as tools with an integrated work management, process and journaling capability using the LLMfx class.\n",
    "\n",
    "This example shows a workflow receiving a customer transcript, and having an agent run through a series of analytical and classification steps.\n",
    "\n",
    "    1.  Create an agent using the LLMfx class.\n",
    "    2.  Load multiple specialized tools for the agent.\n",
    "    3.  Execute a series of function-calls.\n",
    "    4.  Generate a consolidated automatic dictionary report.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZZeZz22aaxI0"
   },
   "source": [
    "# Create agent and load tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a4nBJr5SdVi6",
    "outputId": "2612cc14-1e33-4964-ac7f-89bce5fdfc83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: psycopg-binary, psycopg, pgvector, jmespath, dnspython, colorama, pymongo, botocore, s3transfer, boto3, llmware\n",
      "Successfully installed boto3-1.34.146 botocore-1.34.146 colorama-0.4.6 dnspython-2.6.1 jmespath-1.0.1 llmware-0.3.3 pgvector-0.2.4 psycopg-3.1.17 psycopg-binary-3.1.17 pymongo-4.8.0 s3transfer-0.10.2\n"
     ]
    }
   ],
   "source": [
    "!pip install llmware"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "mYZw-HAr3HjW"
   },
   "outputs": [],
   "source": [
    "from llmware.agents import LLMfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4CqHWBz83I_x",
    "outputId": "d71f2b76-3421-4227-ef45-59560a9d894c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:llmware.agents:Agent - Setting up LLMWare Workspace.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "update: Launching LLMfx process\n",
      "step - \t1 - \tcreating object - ready to start processing.\n",
      "step - \t2 - \tloading new processing text - 1 new entries\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.',\n",
       "  'file_source': 'NA',\n",
       "  'page_num': 'NA'}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent = LLMfx()\n",
    "\n",
    "customer_transcript = \"My name is Michael Jones, and I am a long-time customer.  \" \\\n",
    "                      \"The Mixco product is not working currently, and it is having a negative impact \" \\\n",
    "                      \"on my business, as we can not deliver our products while it is down. \" \\\n",
    "                      \"This is the fourth time that I have called.  My account number is 93203, and \" \\\n",
    "                      \"my user name is mjones. Our company is based in Tampa, Florida.\"\n",
    "\n",
    "agent.load_work(customer_transcript)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QuPgiTmKD8Hm"
   },
   "source": [
    "Load individual SLIM tool or multiple tools list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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      "791bdd214d724280aebb0379fd756495",
      "0f20d82d76d94d8ab69b544ed2f9289f",
      "0e5aaae852904e65bb4a53cc0b401319",
      "0939a52140064a4ba8fb3768d3868dc2",
      "b84b4be62bda46d2b9c2e1f13cf65638",
      "b54c55fed6724027a33f716928b2c24f",
      "62f0e021814945018275ecc4a70c0bbd",
      "8ce8c907ebc54090b825584438af92be",
      "0b6fa7ade10f45c99baadfa202b03797",
      "2df0ce4207864d46aa69f95a9c4551f2",
      "f61ece76017c47e185784df85f15bb8a",
      "c886b1aad38b4a1090934425bd9fbc7e",
      "18b60891f53b4d75b69f927305420295",
      "b26443fcac344b80a9a9395bf9f8e027",
      "15cf861836694d6bbeb3f28faeb621e8"
     ]
    },
    "id": "kYaaIJ3pD1jE",
    "outputId": "351c5309-3545-4737-e91c-c0daf96229ab"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:llmware.models:ModelCatalog - load_model - fetching model - slim-sentiment-tool - from remote repository using pull_snapshot_from_hf - this may take a couple of minutes the first time.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t3 - \tloading tool - sentiment\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
      "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c50e606ff8ea41bfb369f6ccf368d238",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "649f1a0e43e040249cfaaece29a921eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8903cd5eab8848ccacb4f2483af6fe09",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db49ca4e13f146c9a4e13e3fb7a12de0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c2dc9b09f204508a6f0f6f2e2b74997",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:llmware.models:ModelCatalog - load_model - fetching model - slim-ner-tool - from remote repository using pull_snapshot_from_hf - this may take a couple of minutes the first time.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t4 - \tloading tool - ner\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
      "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e36ee9b5bd324c01a69aec529d8d93a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9038edccd14948b4b38c07d92c963053",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb3b917992324c0dadbeb84f5355327a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "289163c444ca410591a39e4d58ce66c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b147f5fe70642f3aab7440ca3cb6f81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:llmware.models:ModelCatalog - load_model - fetching model - slim-emotions-tool - from remote repository using pull_snapshot_from_hf - this may take a couple of minutes the first time.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t5 - \tloading tool - emotions\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
      "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d4bee213c2d49b9bb7b368fbb0de8fc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "557f25962c71458fac4ed58c5f7b47aa",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c32c4eeb2dac41a7a3af225004e26ee9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20049e23b6fd428684bb285e7ace9cdb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b65402522dcd4ec7972c1592674450f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:llmware.models:ModelCatalog - load_model - fetching model - slim-topics-tool - from remote repository using pull_snapshot_from_hf - this may take a couple of minutes the first time.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t6 - \tloading tool - topics\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
      "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb2ea20cf5ed4f6f9e1d57defa8b9119",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cf2636cff904136a5b34d32c880b665",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f923e8ca320547d5b0d060e48d5ecdd6",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3a97e8eb12046bb80331910dcca903d",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e454cf9ec174950a1f03462d61c6be6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:llmware.models:ModelCatalog - load_model - fetching model - slim-intent-tool - from remote repository using pull_snapshot_from_hf - this may take a couple of minutes the first time.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t7 - \tloading tool - intent\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
      "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db1f23e7ce1c447e9cf0a6ab7e2e114f",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "206dd102490d416cb9b16c72ed0f12ff",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c4b5d738e3f42dcb6c007891441593e",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4b7776484b4488993bc980caa955065",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6a8151d21fd4bd7bf44e98734536e88",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:llmware.models:ModelCatalog - load_model - fetching model - slim-tags-tool - from remote repository using pull_snapshot_from_hf - this may take a couple of minutes the first time.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t8 - \tloading tool - tags\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
      "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c649b4585e8e4384834b80c1d15e092c",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f596894527c40b2896333c7b6d2c79d",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc31e7a57a5440edbca3f90cf0da6ccc",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47f736072db04c78942260a46adf5aea",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a71cdbbb57c472c80ddb1b039ec727f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:llmware.models:ModelCatalog - load_model - fetching model - slim-ratings-tool - from remote repository using pull_snapshot_from_hf - this may take a couple of minutes the first time.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t9 - \tloading tool - ratings\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
      "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "577cc7116f07412f90140568819b7b75",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06ad1a71c8a0417998f10b636dd9cd91",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08f67e915bd94d06a963fb78f43b233d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "def65225ded24775b7041ed2978dbeee",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fda01e5fff3f4de49201dc6ab49bab68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "slim-ratings.gguf:   0%|          | 0.00/669M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:llmware.models:ModelCatalog - load_model - fetching model - bling-answer-tool - from remote repository using pull_snapshot_from_hf - this may take a couple of minutes the first time.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t10 - \tloading tool - answer\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
      "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6eae20c34cc94a45aecc2f9bad71ad94",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec4405e0d3854a248e37c6cfb40c98ff",
       "version_major": 2,
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      },
      "text/plain": [
       "README.md:   0%|          | 0.00/1.50k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d81d7cae6d1e42e3a6e049b4655c892e",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c03d7cd927e242999e4b7a6736bd22ef",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b84b4be62bda46d2b9c2e1f13cf65638",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<llmware.agents.LLMfx at 0x7bacb0295540>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.load_tool(\"sentiment\")\n",
    "agent.load_tool(\"ner\")\n",
    "\n",
    "agent.load_tool_list([\"emotions\", \"topics\", \"intent\", \"tags\", \"ratings\", \"answer\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pzi2QDb4ahJJ"
   },
   "source": [
    "# Deploy tools and run analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U9AvagPPbGsL"
   },
   "source": [
    "Assess \"soft skills\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 641,
     "referenced_widgets": [
      "6e8c017741b94c4991efd41a2d049f5e",
      "af1e6c37f2654cf2a492357174e52333",
      "fe8dec363a754b089390b5c674c3abef",
      "c05a5349a5134d10882e1a3fd23ef0ad",
      "28b24e3acdd6465eb3c6bdd6b6fe9091",
      "b452f05bdeb74b9c994b41e5c7dd66a1",
      "e907a49e3eb84a0fb0521fdea9e8283f",
      "0ff642fa936342e0b252a5ba462a568a",
      "099a0e7a05c94a0d9a9208442d8b1ae6",
      "fb3908d3a26a41da874bbd8f12491067",
      "d23552be1c5f4a43802148f4dcfb70a8"
     ]
    },
    "id": "kxIk32w0ahUj",
    "outputId": "2774cae4-8435-4d09-c41f-619ff41a6ea1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t11 - \texecuting function call - deploying - sentiment \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:llmware.models:update: LocalTokenizer - need to fetch tokenizer - tokenizer_tl.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t12 - \texecuting function call - getting response - sentiment\n",
      "\t\t\t\t -- llm_response - {'sentiment': ['negative']}\n",
      "\t\t\t\t -- output type - dict\n",
      "\t\t\t\t -- usage - {'input': 107, 'output': 8, 'total': 115, 'metric': 'tokens', 'processing_time': 1.458883285522461, 'type': 'dict'}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e8c017741b94c4991efd41a2d049f5e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_tl.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t13 - \tanalyzing response - sentiment\n",
      "\t\t\t\t -- confidence score - 0.801\n",
      "\t\t\t\t -- analyzing response - \u001b[32msent\u001b[32miment\u001b[32m':\u001b[32m ['\u001b[32mnegative\u001b[32m']\u001b[32m}\u001b[39m\n",
      "\t\t\t\t -- analyzing response - [{'choice_1': ['negative', 0.801, 22198], 'choice_2': ['neut', 0.15, 17821], 'choice_3': ['pos', 0.048, 1066]}]\n",
      "\t\t\t\t -- analyzing response - [('negative', 22198, 0.801), ('neutral', 17821, 0.15), ('positive', 1066, 0.048)]\n",
      "step - \t14 - \texecuting function call - deploying - emotions \n",
      "step - \t15 - \texecuting function call - getting response - emotions\n",
      "\t\t\t\t -- llm_response - {'emotions': ['annoyed']}\n",
      "\t\t\t\t -- output type - dict\n",
      "\t\t\t\t -- usage - {'input': 108, 'output': 10, 'total': 118, 'metric': 'tokens', 'processing_time': 0.6308877468109131, 'type': 'dict'}\n",
      "step - \t16 - \tanalyzing response - emotions\n",
      "\t\t\t\t -- confidence score - 0.383\n",
      "\t\t\t\t -- analyzing response - \u001b[32mem\u001b[32mot\u001b[32mions\u001b[32m':\u001b[32m ['\u001b[33manno\u001b[32myed\u001b[32m']\u001b[32m}\u001b[39m\n",
      "\t\t\t\t -- analyzing response - {'choice_1': ['anno', 0.383, 7665], 'choice_2': ['ang', 0.211, 574], 'choice_3': ['fur', 0.114, 22613]}\n",
      "step - \t17 - \texecuting function call - deploying - intent \n",
      "step - \t18 - \texecuting function call - getting response - intent\n",
      "\t\t\t\t -- llm_response - {'intent': ['customer service']}\n",
      "\t\t\t\t -- output type - dict\n",
      "\t\t\t\t -- usage - {'input': 107, 'output': 8, 'total': 115, 'metric': 'tokens', 'processing_time': 0.5598828792572021, 'type': 'dict'}\n",
      "step - \t19 - \tanalyzing response - intent\n",
      "\t\t\t\t -- confidence score - 0.476\n",
      "\t\t\t\t -- analyzing response - \u001b[32mintent\u001b[32m':\u001b[32m ['\u001b[33mcustomer\u001b[32m service\u001b[32m']\u001b[32m}\u001b[39m\n",
      "\t\t\t\t -- analyzing response - {'choice_1': ['customer', 0.476, 15539], 'choice_2': ['cancel', 0.467, 20713], 'choice_3': ['order', 0.009, 2098]}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'llm_response': {'intent': ['customer service']},\n",
       " 'dict_output': True,\n",
       " 'confidence_score': 0.476}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.sentiment()\n",
    "agent.emotions()\n",
    "agent.intent()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6tP5yzZvbR2g"
   },
   "source": [
    "Different tools and ways to execute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fyE-ukVnbF7h",
    "outputId": "325ad74c-713c-47c0-8bde-28c44ad839e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t20 - \texecuting function call - deploying - ratings \n",
      "step - \t21 - \texecuting function call - getting response - ratings\n",
      "\t\t\t\t -- llm_response - {'rating': ['1']}\n",
      "\t\t\t\t -- output type - dict\n",
      "\t\t\t\t -- usage - {'input': 107, 'output': 7, 'total': 114, 'metric': 'tokens', 'processing_time': 0.5422916412353516, 'type': 'dict'}\n",
      "step - \t22 - \tanalyzing response - ratings\n",
      "\t\t\t\t -- confidence score - 0.574\n",
      "\t\t\t\t -- analyzing response - \u001b[32mrating\u001b[32m':\u001b[32m ['\u001b[33m1\u001b[32m']\u001b[32m}\u001b[39m\n",
      "\t\t\t\t -- analyzing response - [{'choice_1': ['1', 0.574, 29896], 'choice_2': ['2', 0.223, 29906], 'choice_3': ['3', 0.148, 29941]}]\n",
      "step - \t23 - \texecuting function call - deploying - ner \n",
      "step - \t24 - \texecuting function call - getting response - ner\n",
      "\t\t\t\t -- llm_response - {'people': ['Michael Jones', 'mjones'], 'organization': ['Mixco'], 'misc': ['Tampa']}\n",
      "\t\t\t\t -- output type - dict\n",
      "\t\t\t\t -- usage - {'input': 114, 'output': 30, 'total': 144, 'metric': 'tokens', 'processing_time': 1.0823109149932861, 'type': 'dict'}\n",
      "step - \t25 - \tanalyzing response - ner\n",
      "\t\t\t\t -- confidence score - 0.973\n",
      "\t\t\t\t -- analyzing response - \u001b[32mpeople\u001b[32m':\u001b[32m ['\u001b[32mMichael\u001b[32m Jones\u001b[32m',\u001b[32m '\u001b[32mm\u001b[32mj\u001b[32mones\u001b[32m'],\u001b[32m '\u001b[33morgan\u001b[32mization\u001b[32m':\u001b[33m ['\u001b[32mM\u001b[32mix\u001b[32mco\u001b[32m'],\u001b[32m '\u001b[32mm\u001b[32misc\u001b[32m':\u001b[32m ['\u001b[32mT\u001b[32mampa\u001b[33m']\u001b[32m}\u001b[39m\n",
      "\t\t\t\t -- analyzing response - {'choice_1': ['Michael', 0.973, 24083], 'choice_2': ['M', 0.018, 29924], 'choice_3': ['My', 0.007, 3421]}\n",
      "step - \t26 - \texecuting function call - deploying - topics \n",
      "step - \t27 - \texecuting function call - getting response - topics\n",
      "\t\t\t\t -- llm_response - {'topics': ['account numbers']}\n",
      "\t\t\t\t -- output type - dict\n",
      "\t\t\t\t -- usage - {'input': 107, 'output': 9, 'total': 116, 'metric': 'tokens', 'processing_time': 0.5747861862182617, 'type': 'dict'}\n",
      "step - \t28 - \tanalyzing response - topics\n",
      "\t\t\t\t -- confidence score - 0.067\n",
      "\t\t\t\t -- analyzing response - \u001b[32mtop\u001b[32mics\u001b[32m':\u001b[32m ['\u001b[31maccount\u001b[31m numbers\u001b[32m']\u001b[32m}\u001b[39m\n",
      "\t\t\t\t -- analyzing response - {'choice_1': ['account', 0.067, 10149], 'choice_2': ['c', 0.043, 29883], 'choice_3': ['customer', 0.036, 15539]}\n",
      "step - \t29 - \texecuting function call - deploying - tags \n",
      "step - \t30 - \texecuting function call - getting response - tags\n",
      "\t\t\t\t -- llm_response - {'tags': '[\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]'}\n",
      "\t\t\t\t -- output type - dict\n",
      "\t\t\t\t -- usage - {'input': 107, 'output': 30, 'total': 137, 'metric': 'tokens', 'processing_time': 1.078052043914795, 'type': 'dict'}\n",
      "step - \t31 - \tanalyzing response - tags\n",
      "\t\t\t\t -- confidence score - 0.451\n",
      "\t\t\t\t -- analyzing response - \u001b[32mtags\u001b[32m':\u001b[32m '\u001b[32m[\"\u001b[33mM\u001b[32mix\u001b[32mco\u001b[32m\",\u001b[32m \"\u001b[32mT\u001b[32mampa\u001b[32m,\u001b[32m Florida\u001b[32m\",\u001b[32m \"\u001b[33mMy\u001b[32m name\u001b[32m is\u001b[32m Michael\u001b[32m Jones\u001b[33m\",\u001b[32m \"\u001b[32m9\u001b[32m3\u001b[32m2\u001b[32m0\u001b[32m3\u001b[33m\"]\u001b[32m'}\u001b[39m\n",
      "\t\t\t\t -- analyzing response - {'choice_1': ['M', 0.451, 29924], 'choice_2': ['T', 0.319, 29911], 'choice_3': ['My', 0.113, 3421]}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'llm_response': {'people': ['Michael Jones', 'mjones'],\n",
       "   'organization': ['Mixco'],\n",
       "   'misc': ['Tampa']},\n",
       "  'dict_output': True,\n",
       "  'confidence_score': 0.973},\n",
       " {'llm_response': {'topics': ['account numbers']},\n",
       "  'dict_output': True,\n",
       "  'confidence_score': 0.067},\n",
       " {'llm_response': {'tags': '[\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]'},\n",
       "  'dict_output': True,\n",
       "  'confidence_score': 0.451}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.exec_function_call(\"ratings\")\n",
    "\n",
    "agent.exec_multitool_function_call([\"ner\",\"topics\",\"tags\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KSzPfASscH3x",
    "outputId": "7f81cc22-746c-4730-c358-1fda28461a6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t32 - \texecuting function call - deploying - question-answer tool \n",
      "step - \t33 - \texecuting function call - getting response - question - summary\n",
      "\t\t\t\t -- llm_response - Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.\n",
      "\t\t\t\t -- output type - text\n",
      "\t\t\t\t -- usage - {'input': 104, 'output': 65, 'total': 169, 'metric': 'tokens', 'processing_time': 1.9004454612731934}\n",
      "step - \t34 - \texecuting function call - deploying - question-answer tool \n",
      "step - \t35 - \texecuting function call - getting response - question - customer_info\n",
      "\t\t\t\t -- llm_response - Customer: 93203, User: mjones\n",
      "\t\t\t\t -- output type - text\n",
      "\t\t\t\t -- usage - {'input': 110, 'output': 14, 'total': 124, 'metric': 'tokens', 'processing_time': 0.6713261604309082}\n"
     ]
    }
   ],
   "source": [
    "agent.answer(\"What is a short summary?\",key=\"summary\")\n",
    "\n",
    "response = agent.answer(\"What is the customer's account number and user name?\", key=\"customer_info\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MVBtGdRmcOhS"
   },
   "source": [
    "Release from memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Xztvzz6cOEp",
    "outputId": "29eadc91-1f86-47fa-dccb-c3770a25b32c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t36 - \tunloading tool - ner\n",
      "step - \t37 - \tunloading tool - topics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.unload_tool(\"ner\")\n",
    "agent.unload_tool(\"topics\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jd59z3EPcXyT"
   },
   "source": [
    "# Show results\n",
    "1.   Automatically gathered report\n",
    "2.   Activity summary\n",
    "3.   List of gathered responses\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IrHxXQh9eBqR",
    "outputId": "5159a871-22e6-400a-f92a-096ec6d38e8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t38 - \tshowing all gathered reports - [{'sentiment': ['negative'], 'emotions': ['annoyed'], 'intent': ['customer service'], 'rating': ['1'], 'people': ['Michael Jones', 'mjones'], 'organization': ['Mixco'], 'misc': ['Tampa'], 'topics': ['account numbers'], 'tags': '[\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]', 'summary': ['Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.'], 'customer_info': ['Customer: 93203, User: mjones']}]\n",
      "report - 0 - {'sentiment': ['negative'], 'emotions': ['annoyed'], 'intent': ['customer service'], 'rating': ['1'], 'people': ['Michael Jones', 'mjones'], 'organization': ['Mixco'], 'misc': ['Tampa'], 'topics': ['account numbers'], 'tags': '[\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]', 'summary': ['Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.'], 'customer_info': ['Customer: 93203, User: mjones']}\n",
      "\t\t\t\t -- sentiment            - ['negative']                            \n",
      "\t\t\t\t -- emotions             - ['annoyed']                             \n",
      "\t\t\t\t -- intent               - ['customer service']                    \n",
      "\t\t\t\t -- rating               - ['1']                                   \n",
      "\t\t\t\t -- people               - ['Michael Jones', 'mjones']             \n",
      "\t\t\t\t -- organization         - ['Mixco']                               \n",
      "\t\t\t\t -- misc                 - ['Tampa']                               \n",
      "\t\t\t\t -- topics               - ['account numbers']                     \n",
      "\t\t\t\t -- tags                 - [\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]\n",
      "\t\t\t\t -- summary              - ['Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.']\n",
      "\t\t\t\t -- customer_info        - ['Customer: 93203, User: mjones']       \n",
      "\n"
     ]
    }
   ],
   "source": [
    "report = agent.show_report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_B9EZ5PkeESw",
    "outputId": "69acc46f-514c-4bca-832d-0afa8c71b239"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step - \t39 - \tgenerating activity_summary - {'inference_count': 9, 'tools_used': 8, 'tools': ['sentiment', 'ner', 'emotions', 'topics', 'intent', 'tags', 'ratings', 'answer']}\n",
      "update: response analysis:  0 {'step': 13, 'tool': 'sentiment', 'inference': 1, 'llm_response': {'sentiment': ['negative']}, 'confidence_score': 0.801, 'llm_usage': {'input': 107, 'output': 8, 'total': 115, 'metric': 'tokens', 'processing_time': 1.458883285522461, 'type': 'dict'}, 'work_iteration': 0, 'dict_output': True, 'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA', 'logit_analysis': {'ryg_string': \"\\x1b[32msent\\x1b[32miment\\x1b[32m':\\x1b[32m ['\\x1b[32mnegative\\x1b[32m']\\x1b[32m}\\x1b[39m\", 'choices': [{'choice_1': ['negative', 0.801, 22198], 'choice_2': ['neut', 0.15, 17821], 'choice_3': ['pos', 0.048, 1066]}], 'marker_tokens': [('negative', 22198, 0.801), ('neutral', 17821, 0.15), ('positive', 1066, 0.048)], 'low_confidence_choices': [], 'confidence_score': 0.801}}\n",
      "update: response analysis:  1 {'step': 16, 'tool': 'emotions', 'inference': 2, 'llm_response': {'emotions': ['annoyed']}, 'confidence_score': 0.383, 'llm_usage': {'input': 108, 'output': 10, 'total': 118, 'metric': 'tokens', 'processing_time': 0.6308877468109131, 'type': 'dict'}, 'work_iteration': 0, 'dict_output': True, 'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA', 'logit_analysis': {'ryg_string': \"\\x1b[32mem\\x1b[32mot\\x1b[32mions\\x1b[32m':\\x1b[32m ['\\x1b[33manno\\x1b[32myed\\x1b[32m']\\x1b[32m}\\x1b[39m\", 'choices': [{'choice_1': ['anno', 0.383, 7665], 'choice_2': ['ang', 0.211, 574], 'choice_3': ['fur', 0.114, 22613]}, {'choice_1': ['yed', 1.0, 28133], 'choice_2': ['y', 0.0, 29891], 'choice_3': ['ying', 0.0, 5414]}], 'marker_tokens': [], 'low_confidence_choices': [{'choice_1': ['anno', 0.383, 7665], 'choice_2': ['ang', 0.211, 574], 'choice_3': ['fur', 0.114, 22613]}], 'confidence_score': 0.383}}\n",
      "update: response analysis:  2 {'step': 19, 'tool': 'intent', 'inference': 3, 'llm_response': {'intent': ['customer service']}, 'confidence_score': 0.476, 'llm_usage': {'input': 107, 'output': 8, 'total': 115, 'metric': 'tokens', 'processing_time': 0.5598828792572021, 'type': 'dict'}, 'work_iteration': 0, 'dict_output': True, 'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA', 'logit_analysis': {'ryg_string': \"\\x1b[32mintent\\x1b[32m':\\x1b[32m ['\\x1b[33mcustomer\\x1b[32m service\\x1b[32m']\\x1b[32m}\\x1b[39m\", 'choices': [{'choice_1': ['customer', 0.476, 15539], 'choice_2': ['cancel', 0.467, 20713], 'choice_3': ['order', 0.009, 2098]}, {'choice_1': ['service', 0.999, 2669], 'choice_2': ['support', 0.0, 2304], 'choice_3': ['service', 0.0, 5509]}], 'marker_tokens': [], 'low_confidence_choices': [{'choice_1': ['customer', 0.476, 15539], 'choice_2': ['cancel', 0.467, 20713], 'choice_3': ['order', 0.009, 2098]}], 'confidence_score': 0.476}}\n",
      "update: response analysis:  3 {'step': 22, 'tool': 'ratings', 'inference': 4, 'llm_response': {'rating': ['1']}, 'confidence_score': 0.574, 'llm_usage': {'input': 107, 'output': 7, 'total': 114, 'metric': 'tokens', 'processing_time': 0.5422916412353516, 'type': 'dict'}, 'work_iteration': 0, 'dict_output': True, 'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA', 'logit_analysis': {'ryg_string': \"\\x1b[32mrating\\x1b[32m':\\x1b[32m ['\\x1b[33m1\\x1b[32m']\\x1b[32m}\\x1b[39m\", 'choices': [{'choice_1': ['1', 0.574, 29896], 'choice_2': ['2', 0.223, 29906], 'choice_3': ['3', 0.148, 29941]}], 'marker_tokens': [], 'low_confidence_choices': [{'choice_1': ['1', 0.574, 29896], 'choice_2': ['2', 0.223, 29906], 'choice_3': ['3', 0.148, 29941]}], 'confidence_score': 0.574}}\n",
      "update: response analysis:  4 {'step': 25, 'tool': 'ner', 'inference': 5, 'llm_response': {'people': ['Michael Jones', 'mjones'], 'organization': ['Mixco'], 'misc': ['Tampa']}, 'confidence_score': 0.973, 'llm_usage': {'input': 114, 'output': 30, 'total': 144, 'metric': 'tokens', 'processing_time': 1.0823109149932861, 'type': 'dict'}, 'work_iteration': 0, 'dict_output': True, 'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA', 'logit_analysis': {'ryg_string': \"\\x1b[32mpeople\\x1b[32m':\\x1b[32m ['\\x1b[32mMichael\\x1b[32m Jones\\x1b[32m',\\x1b[32m '\\x1b[32mm\\x1b[32mj\\x1b[32mones\\x1b[32m'],\\x1b[32m '\\x1b[33morgan\\x1b[32mization\\x1b[32m':\\x1b[33m ['\\x1b[32mM\\x1b[32mix\\x1b[32mco\\x1b[32m'],\\x1b[32m '\\x1b[32mm\\x1b[32misc\\x1b[32m':\\x1b[32m ['\\x1b[32mT\\x1b[32mampa\\x1b[33m']\\x1b[32m}\\x1b[39m\", 'choices': [{'choice_1': ['Michael', 0.973, 24083], 'choice_2': ['M', 0.018, 29924], 'choice_3': ['My', 0.007, 3421]}, {'choice_1': ['Jones', 0.999, 10920], 'choice_2': [\"',\", 0.001, 742], 'choice_3': ['J', 0.0, 435]}, {'choice_1': [\"',\", 0.936, 742], 'choice_2': [\"'],\", 0.062, 7464], 'choice_3': [\"','\", 0.001, 3788]}, {'choice_1': [\"'\", 0.996, 525], 'choice_2': ['\"', 0.004, 376], 'choice_3': [\"'<\", 0.0, 12801]}, {'choice_1': ['m', 0.824, 29885], 'choice_2': ['M', 0.157, 29924], 'choice_3': ['Michael', 0.005, 24083]}, {'choice_1': ['j', 0.95, 29926], 'choice_2': ['ich', 0.027, 436], 'choice_3': ['ixed', 0.007, 11925]}, {'choice_1': ['ones', 0.999, 2873], 'choice_2': ['ons', 0.0, 787], 'choice_3': ['oles', 0.0, 6544]}, {'choice_1': ['M', 0.989, 29924], 'choice_2': ['My', 0.003, 3421], 'choice_3': ['X', 0.002, 29990]}, {'choice_1': ['ix', 0.993, 861], 'choice_2': ['ike', 0.002, 9345], 'choice_3': ['ic', 0.001, 293]}, {'choice_1': ['co', 1.0, 1111], 'choice_2': ['Co', 0.0, 7967], 'choice_3': ['c', 0.0, 29883]}, {'choice_1': ['T', 0.707, 29911], 'choice_2': ['My', 0.222, 3421], 'choice_3': ['F', 0.025, 29943]}, {'choice_1': ['ampa', 1.0, 20056], 'choice_2': ['amp', 0.0, 1160], 'choice_3': ['or', 0.0, 272]}], 'marker_tokens': [], 'low_confidence_choices': [{'choice_1': ['organ', 0.525, 6388], 'choice_2': ['location', 0.45, 5479], 'choice_3': ['place', 0.011, 6689]}, {'choice_1': [\"['\", 0.578, 6024], 'choice_2': ['[],', 0.408, 19997], 'choice_3': ['[\"', 0.004, 6796]}, {'choice_1': [\"']\", 0.697, 2033], 'choice_2': [\"',\", 0.29, 742], 'choice_3': [',', 0.007, 29892]}], 'confidence_score': 0.973}}\n",
      "update: response analysis:  5 {'step': 28, 'tool': 'topics', 'inference': 6, 'llm_response': {'topics': ['account numbers']}, 'confidence_score': 0.067, 'llm_usage': {'input': 107, 'output': 9, 'total': 116, 'metric': 'tokens', 'processing_time': 0.5747861862182617, 'type': 'dict'}, 'work_iteration': 0, 'dict_output': True, 'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA', 'logit_analysis': {'ryg_string': \"\\x1b[32mtop\\x1b[32mics\\x1b[32m':\\x1b[32m ['\\x1b[31maccount\\x1b[31m numbers\\x1b[32m']\\x1b[32m}\\x1b[39m\", 'choices': [{'choice_1': ['account', 0.067, 10149], 'choice_2': ['c', 0.043, 29883], 'choice_3': ['customer', 0.036, 15539]}, {'choice_1': ['numbers', 0.271, 3694], 'choice_2': ['s', 0.251, 29879], 'choice_3': ['information', 0.127, 2472]}], 'marker_tokens': [], 'low_confidence_choices': [{'choice_1': ['account', 0.067, 10149], 'choice_2': ['c', 0.043, 29883], 'choice_3': ['customer', 0.036, 15539]}, {'choice_1': ['numbers', 0.271, 3694], 'choice_2': ['s', 0.251, 29879], 'choice_3': ['information', 0.127, 2472]}], 'confidence_score': 0.067}}\n",
      "update: response analysis:  6 {'step': 31, 'tool': 'tags', 'inference': 7, 'llm_response': {'tags': '[\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]'}, 'confidence_score': 0.451, 'llm_usage': {'input': 107, 'output': 30, 'total': 137, 'metric': 'tokens', 'processing_time': 1.078052043914795, 'type': 'dict'}, 'work_iteration': 0, 'dict_output': True, 'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA', 'logit_analysis': {'ryg_string': '\\x1b[32mtags\\x1b[32m\\':\\x1b[32m \\'\\x1b[32m[\"\\x1b[33mM\\x1b[32mix\\x1b[32mco\\x1b[32m\",\\x1b[32m \"\\x1b[32mT\\x1b[32mampa\\x1b[32m,\\x1b[32m Florida\\x1b[32m\",\\x1b[32m \"\\x1b[33mMy\\x1b[32m name\\x1b[32m is\\x1b[32m Michael\\x1b[32m Jones\\x1b[33m\",\\x1b[32m \"\\x1b[32m9\\x1b[32m3\\x1b[32m2\\x1b[32m0\\x1b[32m3\\x1b[33m\"]\\x1b[32m\\'}\\x1b[39m', 'choices': [{'choice_1': ['M', 0.451, 29924], 'choice_2': ['T', 0.319, 29911], 'choice_3': ['My', 0.113, 3421]}, {'choice_1': ['ix', 0.953, 861], 'choice_2': ['ike', 0.035, 9345], 'choice_3': ['ic', 0.003, 293]}, {'choice_1': ['co', 0.999, 1111], 'choice_2': ['Co', 0.0, 7967], 'choice_3': ['c', 0.0, 29883]}, {'choice_1': ['\",', 0.975, 613], 'choice_2': ['product', 0.018, 3234], 'choice_3': ['\"', 0.002, 29908]}, {'choice_1': ['\"', 0.996, 376], 'choice_2': ['\"\"', 0.003, 5124], 'choice_3': ['\"-', 0.0, 11663]}, {'choice_1': ['T', 0.873, 29911], 'choice_2': ['My', 0.018, 3421], 'choice_3': ['my', 0.014, 1357]}, {'choice_1': ['ampa', 1.0, 20056], 'choice_2': ['amp', 0.0, 1160], 'choice_3': ['ennessee', 0.0, 21539]}, {'choice_1': [',', 0.765, 29892], 'choice_2': ['\",', 0.234, 613], 'choice_3': ['\"]', 0.0, 3108]}, {'choice_1': ['Florida', 0.996, 13813], 'choice_2': ['F', 0.002, 383], 'choice_3': ['\",', 0.001, 613]}, {'choice_1': ['\",', 0.999, 613], 'choice_2': ['.\",', 0.0, 19602], 'choice_3': ['\"]', 0.0, 3108]}, {'choice_1': ['\"', 0.998, 376], 'choice_2': ['\"\"', 0.001, 5124], 'choice_3': ['\"-', 0.001, 11663]}, {'choice_1': ['My', 0.7, 3421], 'choice_2': ['Michael', 0.265, 24083], 'choice_3': ['M', 0.019, 29924]}, {'choice_1': ['name', 0.998, 1024], 'choice_2': ['Name', 0.001, 4408], 'choice_3': ['name', 0.0, 978]}, {'choice_1': ['is', 0.952, 338], 'choice_2': ['\",', 0.044, 613], 'choice_3': ['\"]', 0.002, 3108]}, {'choice_1': ['Michael', 0.952, 5765], 'choice_2': ['\",', 0.042, 613], 'choice_3': ['\"]', 0.002, 3108]}, {'choice_1': ['Jones', 0.992, 10920], 'choice_2': ['\",', 0.007, 613], 'choice_3': ['\"]', 0.0, 3108]}, {'choice_1': ['\",', 0.619, 613], 'choice_2': [',', 0.355, 29892], 'choice_3': ['\"]', 0.007, 3108]}, {'choice_1': ['\"', 0.997, 376], 'choice_2': ['\"-', 0.002, 11663], 'choice_3': ['\"\"', 0.0, 5124]}, {'choice_1': ['9', 0.877, 29929], 'choice_2': ['M', 0.032, 29924], 'choice_3': ['3', 0.025, 29941]}, {'choice_1': ['3', 1.0, 29941], 'choice_2': ['2', 0.0, 29906], 'choice_3': ['\",', 0.0, 613]}, {'choice_1': ['2', 1.0, 29906], 'choice_2': ['\",', 0.0, 613], 'choice_3': ['3', 0.0, 29941]}, {'choice_1': ['0', 0.999, 29900], 'choice_2': ['\",', 0.001, 613], 'choice_3': ['\"]', 0.0, 3108]}, {'choice_1': ['3', 0.999, 29941], 'choice_2': ['2', 0.0, 29906], 'choice_3': ['4', 0.0, 29946]}], 'marker_tokens': [], 'low_confidence_choices': [{'choice_1': ['M', 0.451, 29924], 'choice_2': ['T', 0.319, 29911], 'choice_3': ['My', 0.113, 3421]}, {'choice_1': ['My', 0.7, 3421], 'choice_2': ['Michael', 0.265, 24083], 'choice_3': ['M', 0.019, 29924]}, {'choice_1': ['\",', 0.619, 613], 'choice_2': [',', 0.355, 29892], 'choice_3': ['\"]', 0.007, 3108]}, {'choice_1': ['\"]', 0.636, 3108], 'choice_2': ['\",', 0.364, 613], 'choice_3': ['\"],', 0.0, 12436]}], 'confidence_score': 0.451}}\n",
      "update: response analysis:  7 {'step': 33, 'tool': 'answer', 'inference': 8, 'llm_response': 'Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.', 'llm_usage': {'input': 104, 'output': 65, 'total': 169, 'metric': 'tokens', 'processing_time': 1.9004454612731934}, 'work_iteration': 0, 'dict_output': False, 'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA'}\n",
      "update: response analysis:  8 {'step': 35, 'tool': 'answer', 'inference': 9, 'llm_response': 'Customer: 93203, User: mjones', 'llm_usage': {'input': 110, 'output': 14, 'total': 124, 'metric': 'tokens', 'processing_time': 0.6713261604309082}, 'work_iteration': 0, 'dict_output': False, 'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA'}\n"
     ]
    }
   ],
   "source": [
    "activity_summary = agent.activity_summary()\n",
    "for i, entries in enumerate(agent.response_list):\n",
    "    print(\"update: response analysis: \", i, entries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7sl396mAemAz",
    "outputId": "9037c612-9649-44da-d06b-16dd537998c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'report': [{'sentiment': ['negative'], 'emotions': ['annoyed'], 'intent': ['customer service'], 'rating': ['1'], 'people': ['Michael Jones', 'mjones'], 'organization': ['Mixco'], 'misc': ['Tampa'], 'topics': ['account numbers'], 'tags': '[\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]', 'summary': ['Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.'], 'customer_info': ['Customer: 93203, User: mjones'], 'source': {'text': 'My name is Michael Jones, and I am a long-time customer.  The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called.  My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida.', 'file_source': 'NA', 'page_num': 'NA'}}], 'activity_summary': {'inference_count': 9, 'tools_used': 8, 'tools': ['sentiment', 'ner', 'emotions', 'topics', 'intent', 'tags', 'ratings', 'answer']}, 'journal': ['creating object - ready to start processing.', 'loading new processing text - 1 new entries', 'loading tool - sentiment', 'loading tool - ner', 'loading tool - emotions', 'loading tool - topics', 'loading tool - intent', 'loading tool - tags', 'loading tool - ratings', 'loading tool - answer', 'executing function call - deploying - sentiment ', \"executing function call - getting response - sentiment\\n\\t\\t\\t\\t -- llm_response - {'sentiment': ['negative']}\\n\\t\\t\\t\\t -- output type - dict\\n\\t\\t\\t\\t -- usage - {'input': 107, 'output': 8, 'total': 115, 'metric': 'tokens', 'processing_time': 1.458883285522461, 'type': 'dict'}\", \"analyzing response - sentiment\\n\\t\\t\\t\\t -- confidence score - 0.801\\n\\t\\t\\t\\t -- analyzing response - \\x1b[32msent\\x1b[32miment\\x1b[32m':\\x1b[32m ['\\x1b[32mnegative\\x1b[32m']\\x1b[32m}\\x1b[39m\\n\\t\\t\\t\\t -- analyzing response - [{'choice_1': ['negative', 0.801, 22198], 'choice_2': ['neut', 0.15, 17821], 'choice_3': ['pos', 0.048, 1066]}]\\n\\t\\t\\t\\t -- analyzing response - [('negative', 22198, 0.801), ('neutral', 17821, 0.15), ('positive', 1066, 0.048)]\", 'executing function call - deploying - emotions ', \"executing function call - getting response - emotions\\n\\t\\t\\t\\t -- llm_response - {'emotions': ['annoyed']}\\n\\t\\t\\t\\t -- output type - dict\\n\\t\\t\\t\\t -- usage - {'input': 108, 'output': 10, 'total': 118, 'metric': 'tokens', 'processing_time': 0.6308877468109131, 'type': 'dict'}\", \"analyzing response - emotions\\n\\t\\t\\t\\t -- confidence score - 0.383\\n\\t\\t\\t\\t -- analyzing response - \\x1b[32mem\\x1b[32mot\\x1b[32mions\\x1b[32m':\\x1b[32m ['\\x1b[33manno\\x1b[32myed\\x1b[32m']\\x1b[32m}\\x1b[39m\\n\\t\\t\\t\\t -- analyzing response - {'choice_1': ['anno', 0.383, 7665], 'choice_2': ['ang', 0.211, 574], 'choice_3': ['fur', 0.114, 22613]}\", 'executing function call - deploying - intent ', \"executing function call - getting response - intent\\n\\t\\t\\t\\t -- llm_response - {'intent': ['customer service']}\\n\\t\\t\\t\\t -- output type - dict\\n\\t\\t\\t\\t -- usage - {'input': 107, 'output': 8, 'total': 115, 'metric': 'tokens', 'processing_time': 0.5598828792572021, 'type': 'dict'}\", \"analyzing response - intent\\n\\t\\t\\t\\t -- confidence score - 0.476\\n\\t\\t\\t\\t -- analyzing response - \\x1b[32mintent\\x1b[32m':\\x1b[32m ['\\x1b[33mcustomer\\x1b[32m service\\x1b[32m']\\x1b[32m}\\x1b[39m\\n\\t\\t\\t\\t -- analyzing response - {'choice_1': ['customer', 0.476, 15539], 'choice_2': ['cancel', 0.467, 20713], 'choice_3': ['order', 0.009, 2098]}\", 'executing function call - deploying - ratings ', \"executing function call - getting response - ratings\\n\\t\\t\\t\\t -- llm_response - {'rating': ['1']}\\n\\t\\t\\t\\t -- output type - dict\\n\\t\\t\\t\\t -- usage - {'input': 107, 'output': 7, 'total': 114, 'metric': 'tokens', 'processing_time': 0.5422916412353516, 'type': 'dict'}\", \"analyzing response - ratings\\n\\t\\t\\t\\t -- confidence score - 0.574\\n\\t\\t\\t\\t -- analyzing response - \\x1b[32mrating\\x1b[32m':\\x1b[32m ['\\x1b[33m1\\x1b[32m']\\x1b[32m}\\x1b[39m\\n\\t\\t\\t\\t -- analyzing response - [{'choice_1': ['1', 0.574, 29896], 'choice_2': ['2', 0.223, 29906], 'choice_3': ['3', 0.148, 29941]}]\", 'executing function call - deploying - ner ', \"executing function call - getting response - ner\\n\\t\\t\\t\\t -- llm_response - {'people': ['Michael Jones', 'mjones'], 'organization': ['Mixco'], 'misc': ['Tampa']}\\n\\t\\t\\t\\t -- output type - dict\\n\\t\\t\\t\\t -- usage - {'input': 114, 'output': 30, 'total': 144, 'metric': 'tokens', 'processing_time': 1.0823109149932861, 'type': 'dict'}\", \"analyzing response - ner\\n\\t\\t\\t\\t -- confidence score - 0.973\\n\\t\\t\\t\\t -- analyzing response - \\x1b[32mpeople\\x1b[32m':\\x1b[32m ['\\x1b[32mMichael\\x1b[32m Jones\\x1b[32m',\\x1b[32m '\\x1b[32mm\\x1b[32mj\\x1b[32mones\\x1b[32m'],\\x1b[32m '\\x1b[33morgan\\x1b[32mization\\x1b[32m':\\x1b[33m ['\\x1b[32mM\\x1b[32mix\\x1b[32mco\\x1b[32m'],\\x1b[32m '\\x1b[32mm\\x1b[32misc\\x1b[32m':\\x1b[32m ['\\x1b[32mT\\x1b[32mampa\\x1b[33m']\\x1b[32m}\\x1b[39m\\n\\t\\t\\t\\t -- analyzing response - {'choice_1': ['Michael', 0.973, 24083], 'choice_2': ['M', 0.018, 29924], 'choice_3': ['My', 0.007, 3421]}\", 'executing function call - deploying - topics ', \"executing function call - getting response - topics\\n\\t\\t\\t\\t -- llm_response - {'topics': ['account numbers']}\\n\\t\\t\\t\\t -- output type - dict\\n\\t\\t\\t\\t -- usage - {'input': 107, 'output': 9, 'total': 116, 'metric': 'tokens', 'processing_time': 0.5747861862182617, 'type': 'dict'}\", \"analyzing response - topics\\n\\t\\t\\t\\t -- confidence score - 0.067\\n\\t\\t\\t\\t -- analyzing response - \\x1b[32mtop\\x1b[32mics\\x1b[32m':\\x1b[32m ['\\x1b[31maccount\\x1b[31m numbers\\x1b[32m']\\x1b[32m}\\x1b[39m\\n\\t\\t\\t\\t -- analyzing response - {'choice_1': ['account', 0.067, 10149], 'choice_2': ['c', 0.043, 29883], 'choice_3': ['customer', 0.036, 15539]}\", 'executing function call - deploying - tags ', 'executing function call - getting response - tags\\n\\t\\t\\t\\t -- llm_response - {\\'tags\\': \\'[\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]\\'}\\n\\t\\t\\t\\t -- output type - dict\\n\\t\\t\\t\\t -- usage - {\\'input\\': 107, \\'output\\': 30, \\'total\\': 137, \\'metric\\': \\'tokens\\', \\'processing_time\\': 1.078052043914795, \\'type\\': \\'dict\\'}', 'analyzing response - tags\\n\\t\\t\\t\\t -- confidence score - 0.451\\n\\t\\t\\t\\t -- analyzing response - \\x1b[32mtags\\x1b[32m\\':\\x1b[32m \\'\\x1b[32m[\"\\x1b[33mM\\x1b[32mix\\x1b[32mco\\x1b[32m\",\\x1b[32m \"\\x1b[32mT\\x1b[32mampa\\x1b[32m,\\x1b[32m Florida\\x1b[32m\",\\x1b[32m \"\\x1b[33mMy\\x1b[32m name\\x1b[32m is\\x1b[32m Michael\\x1b[32m Jones\\x1b[33m\",\\x1b[32m \"\\x1b[32m9\\x1b[32m3\\x1b[32m2\\x1b[32m0\\x1b[32m3\\x1b[33m\"]\\x1b[32m\\'}\\x1b[39m\\n\\t\\t\\t\\t -- analyzing response - {\\'choice_1\\': [\\'M\\', 0.451, 29924], \\'choice_2\\': [\\'T\\', 0.319, 29911], \\'choice_3\\': [\\'My\\', 0.113, 3421]}', 'executing function call - deploying - question-answer tool ', \"executing function call - getting response - question - summary\\n\\t\\t\\t\\t -- llm_response - Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.\\n\\t\\t\\t\\t -- output type - text\\n\\t\\t\\t\\t -- usage - {'input': 104, 'output': 65, 'total': 169, 'metric': 'tokens', 'processing_time': 1.9004454612731934}\", 'executing function call - deploying - question-answer tool ', \"executing function call - getting response - question - customer_info\\n\\t\\t\\t\\t -- llm_response - Customer: 93203, User: mjones\\n\\t\\t\\t\\t -- output type - text\\n\\t\\t\\t\\t -- usage - {'input': 110, 'output': 14, 'total': 124, 'metric': 'tokens', 'processing_time': 0.6713261604309082}\", 'unloading tool - ner', 'unloading tool - topics', 'showing all gathered reports - [{\\'sentiment\\': [\\'negative\\'], \\'emotions\\': [\\'annoyed\\'], \\'intent\\': [\\'customer service\\'], \\'rating\\': [\\'1\\'], \\'people\\': [\\'Michael Jones\\', \\'mjones\\'], \\'organization\\': [\\'Mixco\\'], \\'misc\\': [\\'Tampa\\'], \\'topics\\': [\\'account numbers\\'], \\'tags\\': \\'[\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]\\', \\'summary\\': [\\'Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.\\'], \\'customer_info\\': [\\'Customer: 93203, User: mjones\\']}]\\nreport - 0 - {\\'sentiment\\': [\\'negative\\'], \\'emotions\\': [\\'annoyed\\'], \\'intent\\': [\\'customer service\\'], \\'rating\\': [\\'1\\'], \\'people\\': [\\'Michael Jones\\', \\'mjones\\'], \\'organization\\': [\\'Mixco\\'], \\'misc\\': [\\'Tampa\\'], \\'topics\\': [\\'account numbers\\'], \\'tags\\': \\'[\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]\\', \\'summary\\': [\\'Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.\\'], \\'customer_info\\': [\\'Customer: 93203, User: mjones\\']}\\n\\t\\t\\t\\t -- sentiment            - [\\'negative\\']                            \\n\\t\\t\\t\\t -- emotions             - [\\'annoyed\\']                             \\n\\t\\t\\t\\t -- intent               - [\\'customer service\\']                    \\n\\t\\t\\t\\t -- rating               - [\\'1\\']                                   \\n\\t\\t\\t\\t -- people               - [\\'Michael Jones\\', \\'mjones\\']             \\n\\t\\t\\t\\t -- organization         - [\\'Mixco\\']                               \\n\\t\\t\\t\\t -- misc                 - [\\'Tampa\\']                               \\n\\t\\t\\t\\t -- topics               - [\\'account numbers\\']                     \\n\\t\\t\\t\\t -- tags                 - [\"Mixco\", \"Tampa, Florida\", \"My name is Michael Jones\", \"93203\"]\\n\\t\\t\\t\\t -- summary              - [\\'Michael Jones is a long-time customer of Mixco, and the company is having a negative impact on his business.  He has called Mixco four times, and the company has not responded to his calls.  His account number is 93203, and his user name is mjones.\\']\\n\\t\\t\\t\\t -- customer_info        - [\\'Customer: 93203, User: mjones\\']       \\n', \"generating activity_summary - {'inference_count': 9, 'tools_used': 8, 'tools': ['sentiment', 'ner', 'emotions', 'topics', 'intent', 'tags', 'ratings', 'answer']}\"]}\n"
     ]
    }
   ],
   "source": [
    "output = {\"report\": report, \"activity_summary\": activity_summary, \"journal\": agent.journal}\n",
    "\n",
    "print(output)"
   ]
  }
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