notebooks/llmu/RAG_with_Connectors.ipynb

{
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      "id": "v_eWsiiMfu2q",
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      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/llmu/RAG_with_Connectors.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9e30089",
      "metadata": {
        "id": "f9e30089"
      },
      "source": [
        "# RAG with Connectors\n",
        "\n",
        "This notebook shows how to build a RAG-powered chatbot with Cohere's Chat endpoint using connectors. The chatbot can extract relevant information from external documents and produce verifiable, inline citations in its responses.\n",
        "\n",
        "Read the accompanying [article here](https://txt.cohere.com/rag-connectors/).\n",
        "\n",
        "Connectors are ways of connecting to data sources. These data sources could be internal documents, document databases, the broader internet, or any other source of context which can inform the replies generated by the model.\n",
        "\n",
        "We'll use the web search connector, a Cohere-managed connector that you can use without additional setup.\n",
        "\n",
        "The diagram below provides an overview of what we’ll build."
      ]
    },
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wftWpBmmuwfJ8ZV5SCaqffpVLU9Grzfm0qmJoqoDVx5znJRVHB2keX+a/zf4y/6mSVa/KOmp9OY5qHtq+HuoyB3UZa9vKaDn23Pf444/fdX4zX/PctP1vopL5adNCMffJOcXm6zj/HavyCgBPJIAFACxLORGQEy3NkzWD5KRBTpospIR2SnPn4l/zhFSlPpacFMvJjjYnWHKSo36ycJgdvQ9qft+oOchJNRcbAWB5mYVwddYX41yMypqouV7Jz2mF0k8CU3X9WtAMk/vXx9d2vQgAy1mzjfGgAFbzfbNq21YPYFWP73fOobndYSGOBEvqqmDNOHL+JWuH+tjSym3c8z/Zzrhhm4RiquBPJcc0qXMxzQ8ZjhvEzxzUn8977rmn7/1ynqvZ/q5qwTiOzMcRRxyx23YHBdKaH2rMWm+cgFnu22xFOOg12UXVBnEcmcPmOcO25wYruW/2Ww9P5bU9rO1kv220CV/Vx9hsRZjwngAWADzR6gIAsMwkCJVPg41zMS0nYXJyJkGn+smZNnLyJPvLCZBB4aumnHTJSafmycSFqvbfZQ4AACZh3AujuRDWvGCUT9oPkouFuWhU3aoLv201L3KNu/YDgOWoGabI+2O/cxwJXtRVgZZmJatmYLraZv19N+//w0IjzdDHuGGVSrOqU7OaZhvjrm8ix9YM6XTZdxvNqu9t5DxYPshX3QaFcprnlsYN8NQf1wzYDzovVu2juiVgNI5+r61+1WO76Hr8qTDW3M44obJKXs/1Y6uql7U17mu5quhf17ZSPwCsNCpgAQDLSk7c9DsJUp0sqE7U5cRUThY0TxgkGJXfpaR3m0+O5b4pH97vJFcen/3l66A2ghlrxjJuqfBhcgzjfgoPAGASsu7pUlmgWTVj2AXEXLgbVB2rjfXr1xcAYHfVOYx6OCjnMZqBp3oYp/6en3Mu1fmP6rFN41S/SoCruR7oWr0oj2seW7Y/qN1eU9YdXds8Zx/N81Tj7HuY5jHl/FRa/U26JXUz3LOQKlIJHDUrpfWT11KX0Nti6HL8eS03g2xdjy9zk/OI9Q8sjPOBhC7jz3+rQlcAMJoAFgCwbOSkU/OkVk6S5VNyg04+5TEJbdVLqeeEQn436tN1eWy/8FVOJqUyQ/OERu6Xk0zZdv0EWbXvQSGsbG/Q+FOxq3lytPnpwYwjJ0oOO+ywXaGyfOpP+0EAYNK6tiJpBt9VpQKAxZf38fo5hn4VgwYFsCLnYKpzHFXrvfp7fLPC5bB1Q7+w1kKCRRlr89xPW+NW26yrwun1eZtUkCXni+rnwaoPCWZ/GXPVlnChLaoHtZ3sotnqsqq0NunQ2DR1Of5+z/lCWvh1/UBCFbQcV/Mx06jiBgDLgQAWALBsNINH+TThu9/97qGPyQmEBJ8STqo/Po/LJ9EGnaTKCaJm+Kr6BNqgEyD5e06O5e+pUFX/BGFOAuYEWYJb/bQNS5177rm7ToLkMTkGQSsAWJ76rVOW+gLWQi/wLVTV2igXufI1F46rljzV74S7AKC/5hqiGRppBnGabQebgZLcv36OpLm95uOH7Ts/H3HEEaWr5vv/OOuBha6tRs1rVznvlfNJ9TBMvs/v6mGzeoX2qkJ85r7NcfUL2uTDgDn/NCld1q/19d3WrVvLbY313m1TDAh1eT30G89CAlhdLcU+AWAlEcACAJaN5onAcdrw5ROD11577W7bSEBqUEn497znPU84eZIy723CTjnhlRNhuSBY31/Gm0/RLeTCZTWmBK/6tWIEAJaPfq30ljpctNgBrBxv1mxZU+WC2zQvtgHActesTtQMCqV1XqVf2+Gc06ifi8l5lnoAq/k+PSzI0m9NM8n3+aUMYE1qvZbn4Jprrul9QHDY3FTBpKifh8q4cu5o0IcBo992lyLQnnHn9Vet+ZZSl/Vus5rcPFX8AgDaW10AAJaJcU7k9dMMWw06eZXfNytrdak0lWpZ9ZM21QXEhWqWoAcAlqd+a49ma5/lKuumVF5IJYxc6E2bZ+ErAFiY5tqi+d5aD770q6TTbHfX/KBcv7aCLEzmfPPmzb1zTOPOZ57fnEPKempW11F5DWV8CZnlXNxSh68AAIZRAQsAWLZyUmack0/5VOaOHTtG3i8nf5qf9MsJq3HlJFkeVw9z5eLhoKpbbSUMBgAsf9VFzvq6JOufYVUM2qhC4dV2s5+FbnOScoFwWKWHjDdrwKrdTlpNVy138jVzlMcDALtrri2qVm75fb6vB6rqla3q8vuq/V3VDq56/62vWcYNC+X8ySTPdyx12+RJy/zklucrc51Qfr6vWvNVX/vJ344//vhy4403PuHDjP3mKVW3JlnBadC2LrzwwoEfMKzWedX6rrney9+y3muGAJfKcnu9AQD9CWABAMtGTrLUPwl39tln99oCTrqsd7NKVU5wdd1HyvPXA1j1k5Nd5ESnMuYAsHJk/VO/sJQw98UXX1wWIu2Cmq2cF9omeZL6ha+yHktIrLroBgB0k3MK9XMr+b75u2i2K6ycfPLJuwJYkXVKzlU0W7CNCmD1a9u3VOc7qjlYyOPrpnkc2XZu/QJyVSvC3N7znvfstp7K33IeLeGqun7rqirgNE15DTXDV3nNZL2Xdd+8rfearcOrQJx1KwAsL1oQAgDLRvPkUk4o5RN8OYGUi5HNT1t21TxxlpOLXTVPOFafSuxq3DaIAMB8a65/so5Y6Cf9s26qa7YTWkq5GNdcK+VCYdruZB3UZpxbt24tAEB/zfMKVXvjq666atfv8n476PxD8/cJdse47Qebf1/K1nPN8Ni4mmuXpWq9WD1vCdqnbWEzcN+v4nu/dWD1nE5Tql/VZc2bCl0Zc9t16Sy1Vez3nGunCADLjwAWALBspHVfv09I5kLdGWec0QtjPfnJT+7d8n2qJ5x77rm9ilZtT3pke82TUdnGEUcc0emWcTQt5ARRSq4DACtHv9aAzWqd42oGuGYp4F2/+BupgDDu+GalFQ0AzKLmeZXqHEX9vMmwAFFVgalSBbvbVtAa9PeFhszz+Jwfqm7jnHtphtPH3e+4x75Y0tKxGWbqNy/NtdZC5qN6fPU89NtW5qs5jnErvFbtF2dFvyqtCwmyNc9FLnT9DwBMhgAWALBs5ERGKiCMKoNelVzPibu0/8uFuwSh2pyw6FdBq6pa1fXWtJCqDEqXA8DK0q8CRS5mdb1A2e+CZL+Q11JprsW6hMMWetEQAJazZpXvqipSfW3Rr71dXf3vVau1catA9atsuZCQSaqj12/NUPcwOfaua6tmJaeYRLg94Z16ACctBceV+e1Xmb2puRbMXHQNN+Wx+ZBk/blo6teuctyWh7MYuM/5x7qck+xSqT9zn8fWzy02WxwCAEtDAAsAWFZyQiZl1NOGZtyS7jlhkZMhOXE1qCLWJFoYjuKkCQAwjlQvaMrFrHHXLVkLNS8SZm01SxWwFlrJYNyKFwCw0vSrgNUMLzdDWk3NCk8JOzUraLX5AFmzRV7ex7u0bcv4m4Gc008/vYyjX5BqlKrqVl3OO40bJupnx44duwVwugbMm+vFfs9LAnXN3/cLTrXRDNH1C/Pdc889ZSFyTF2er2lrvua6jrPfen1UKBIAWBwCWADAVEwqqNR1OzmhdeONN/bCWFdccUW54IILdrWoGXWiLyeu0p6w38W5fo9L1a3sZ1I3J00AgHFkfdO8QDlsPdNP1lypRtC8f79w11JqBuzHuQibY0u7FgBgsJz3aAaE6qGZflWTmprnNZohpLYBpLe85S1PuG+/9cow/d7/u4SgEuAaZx1RrcWaJrW2ap7byvjGrRDWbI847Lltjjv7Gzc8lPs3Xwv95qP53GSM45wfzPM0i4H7rNmb/22kktU4z1sqnTXncJaq1QLASieABQBMxGGHHbbbz/3Ky3fRvKjW/BTlKNWnwHJCJ1WxEpZKMCufpsstP1988cVPOLmT8ff7NF+/AFbum8dP6gYAMK6sdfpdrMqFv1EXdXIBLe2Ym+uuXJxstkpZav3aLbYJYVXHmHVbcz23kA8O5LGLUSEVABZT8/22Xj2qTWXMZoirWX2qbcXybCfncurGCZlnv837ZlzjhqCqtUPCMjlXNGrf/fYb/dZrXWVMzQB+fm4b5ukXEBv2gcBsu/nc58OOCTuNWgvl77lf7l/XL2AX/T442SbsVX2goAooTXLNNyn9zkFmvT3q+Ko5bD7n2VZzXgGApbNHAQCYgH4nz6699toFnVhqnqCLSQaUciImJ4+qqhE5kZGTafX9Ny/SVScR6yfRtmzZUgAAllLWKAmWNy/2VS2Wc2GmqgSadsdbt27d1a6m30XErHdygWjW5BP+OZbqAlq+5pizhktbl/q6LX9LOCsXtOrrylzIzcW5+v1yMbVqp5RtDLoA2VzzVvsfdsEyc6kyAQDzZFhAqm3rvrw31s+x1I3z4bqsXxJcqgdUsnY54ogjevvILdur1gB5b875qH5tByNV0sc9t1RfOyTck+1mXJmLbCv7HrXf3H/SQZkEmOrtlTOGrPuy/6w9+oXlct+EtPLc1ANJbYJpmbsE2utrx2wnx5x9ZZ/1uc06LHOSMTbDT3mNDXp9VOGy+nxV480xN1+f/Y4pr4uMpb6P6vushSNjrsabMfY7v9cvtJUqVIMq6zfnoCl/yzxm/Vjfdo41Y6heV83Xc785rNb/AMAM2QEAMCE7/8d/R5YX1W3nSYMdC7HzpNFu29t5kmLHtDWPYeeJjB2jxrXQ49x54mXHzpN5vVu+H0fmZNR4AYCVY/PmzU9YH4x723lRq7edNnZeLNrtsVknddFcXw3bzsUXXzx07FmbZQ6a67rc8tjIfYbNwSD33HNP3+2OugHAPLnyyisHvqfdeOONrbaRcxwL3Ubd+eefv6D1Td6/254z6fc+Pmz9MeqWdUfWENMwau2Xv1W3QWuY/L7tc5L9Zb21kOdi48aNI+cjfx90XBlv5jTj6Hef/D6Pz/M9bByXXnrprv0tdP1c3bI2biPzvZB95rF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      ]
    },
    {
      "cell_type": "markdown",
      "id": "5IdmW-I9NXpq",
      "metadata": {
        "id": "5IdmW-I9NXpq"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "r2jcKQ6iLefn",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2jcKQ6iLefn",
        "outputId": "acb51f35-43a2-4567-d8e6-913f00d57df6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.2/117.2 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.8/77.8 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "! pip install cohere -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "90f134ba",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "90f134ba",
        "outputId": "f2236cef-f274-4100-dbcd-333b826f5ee8"
      },
      "outputs": [],
      "source": [
        "import uuid\n",
        "import cohere\n",
        "from cohere import ChatConnector\n",
        "from typing import List\n",
        "\n",
        "co = cohere.Client(\"COHERE_API_KEY\") # Get your API key here: https://dashboard.cohere.com/api-keys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "EavECqgqNJ8g",
      "metadata": {
        "cellView": "form",
        "id": "EavECqgqNJ8g"
      },
      "outputs": [],
      "source": [
        "#@title Enable text wrapping in Google Colab\n",
        "\n",
        "from IPython.display import HTML, display\n",
        "\n",
        "def set_css():\n",
        "  display(HTML('''\n",
        "  <style>\n",
        "    pre {\n",
        "        white-space: pre-wrap;\n",
        "    }\n",
        "  </style>\n",
        "  '''))\n",
        "get_ipython().events.register('pre_run_cell', set_css)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d319ece1",
      "metadata": {
        "id": "d319ece1"
      },
      "source": [
        "# Create a chatbot\n",
        "\n",
        "In connector mode, most of the implementation is taken care of by the endpoint, including deciding whether to retrieve information, generating queries, retrieving documents, chunking and reranking documents (post-retrieval), and generating the response. This greatly simplifies our code.\n",
        "\n",
        "The `Chatbot` class below handles the interaction between the user and chatbot.  We define the connector for the chatbot to use with the attribute `self.connectors`. In this notebook, we will use Cohere's `“web-search”` connector, which runs searches against a browser in safe mode.\n",
        "\n",
        "The run() method contains the logic for getting the user message, displaying the chatbot response with citations, along with a way for the user to end the conversation.\n",
        "\n",
        "Then, the chatbot responds to the user message.  We call `co.chat()` and supply a `connectors` parameter to make the chatbot component use connector mode.  All of the remaining implementation is taken care of by the endpoint, up to generating the response.\n",
        "\n",
        "We also pass the `conversation_id` parameter, which retains the interactions between the user and the chatbot in the same conversation thread. We enable the `stream` parameter so we can stream the chatbot response.\n",
        "\n",
        "We then print the chatbot's response.  In the case that the external information was used to generate a response, we also display documents and in-line citations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "e52d521d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "e52d521d",
        "outputId": "b0f90f1c-17c8-46fa-d471-b11059767ede"
      },
      "outputs": [],
      "source": [
        "class Chatbot:\n",
        "    def __init__(self, connectors: List[str]):\n",
        "        \"\"\"\n",
        "        Initializes an instance of the Chatbot class.\n",
        "\n",
        "        \"\"\"\n",
        "        self.conversation_id = str(uuid.uuid4())\n",
        "        self.connectors = [ChatConnector(id=connector) for connector in connectors]\n",
        "\n",
        "    def run(self):\n",
        "        \"\"\"\n",
        "        Runs the chatbot application.\n",
        "\n",
        "        \"\"\"\n",
        "        while True:\n",
        "            # Get the user message\n",
        "            message = input(\"User: \")\n",
        "\n",
        "            # Typing \"quit\" ends the conversation\n",
        "            if message.lower() == \"quit\":\n",
        "                print(\"Ending chat.\")\n",
        "                break\n",
        "            else:                       # If using Google Colab, remove this line to avoid printing the same thing twice\n",
        "              print(f\"User: {message}\") # If using Google Colab, remove this line to avoid printing the same thing twice\n",
        "\n",
        "            # Generate response\n",
        "            response = co.chat_stream(\n",
        "                    message=message,\n",
        "                    model=\"command-r-plus\",\n",
        "                    conversation_id=self.conversation_id,\n",
        "                    connectors=self.connectors,\n",
        "            )\n",
        "\n",
        "            # Print the chatbot response, citations, and documents\n",
        "            print(\"\\nChatbot:\")\n",
        "            citations = []\n",
        "            cited_documents = []\n",
        "\n",
        "            # Display response\n",
        "            for event in response:\n",
        "                if event.event_type == \"text-generation\":\n",
        "                    print(event.text, end=\"\")\n",
        "                elif event.event_type == \"citation-generation\":\n",
        "                    citations.extend(event.citations)\n",
        "                elif event.event_type == \"stream-end\":\n",
        "                    cited_documents = event.response.documents\n",
        "\n",
        "            # Display citations and source documents\n",
        "            if citations:\n",
        "              print(\"\\n\\nCITATIONS:\")\n",
        "              for citation in citations:\n",
        "                print(citation)\n",
        "\n",
        "              print(\"\\nDOCUMENTS:\")\n",
        "              for document in cited_documents:\n",
        "                print({'id': document['id'],\n",
        "                      'snippet': document['snippet'][:400] + '...',\n",
        "                      'title': document['title'],\n",
        "                      'url': document['url']})\n",
        "\n",
        "            print(f\"\\n{'-'*100}\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "rJbwGPksPfh0",
      "metadata": {
        "id": "rJbwGPksPfh0"
      },
      "source": [
        "# Run the chatbot"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c755140c",
      "metadata": {
        "id": "c755140c"
      },
      "source": [
        "We can now run the chatbot.  For this, we create the instance of `Chatbot` using Cohere's managed web-search connector.  Then we run the chatbot by invoking the `run()` method.\n",
        "\n",
        "The format of each citation is:\n",
        "- `start`: The starting point of a span where one or more documents are referenced\n",
        "- `end`: The ending point of a span where one or more documents are referenced\n",
        "- `text`: The text representing this span\n",
        "- `document_ids`: The IDs of the documents being referenced (`doc_0` being the ID of the first document passed to the `documents` creating parameter in the endpoint call, and so on)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "99e5005b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        },
        "id": "99e5005b",
        "outputId": "4609e72c-df6f-4c77-8132-cc0e73b80eee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "User: What is Cohere's LLM University\n",
            "\n",
            "Chatbot:\n",
            "Cohere's LLM University (LLMU) is a set of comprehensive learning resources for anyone interested in natural language processing (NLP), from beginners to advanced learners. The curriculum covers everything from the basics of LLMs to the most advanced topics, including generative AI. The course is designed to give learners a solid foundation in NLP and help them develop their own applications.\n",
            "\n",
            "CITATIONS:\n",
            "start=24 end=30 text='(LLMU)' document_ids=['web-search_0', 'web-search_1']\n",
            "start=36 end=75 text='set of comprehensive learning resources' document_ids=['web-search_1']\n",
            "start=101 end=134 text='natural language processing (NLP)' document_ids=['web-search_0', 'web-search_1']\n",
            "start=141 end=172 text='beginners to advanced learners.' document_ids=['web-search_0', 'web-search_1']\n",
            "start=177 end=187 text='curriculum' document_ids=['web-search_0', 'web-search_1']\n",
            "start=215 end=229 text='basics of LLMs' document_ids=['web-search_0', 'web-search_1']\n",
            "start=237 end=283 text='most advanced topics, including generative AI.' document_ids=['web-search_1']\n",
            "start=326 end=349 text='solid foundation in NLP' document_ids=['web-search_0', 'web-search_1']\n",
            "start=364 end=395 text='develop their own applications.' document_ids=['web-search_0', 'web-search_1']\n",
            "\n",
            "DOCUMENTS:\n",
            "{'id': 'web-search_0', 'snippet': 'Guides and ConceptsAPI ReferenceRelease NotesApplication ExamplesLLMU\\n\\nCoralDashboardDocumentationPlaygroundCommunityLog In\\n\\nCoralDashboardDocumentationPlaygroundCommunityLog In\\n\\nWelcome to LLM University!\\n\\nWelcome to LLM University by Cohere!\\n\\nWe’re so happy that you’ve chosen to learn Natural Language Processing and Large Language Models with us.\\n\\nOur comprehensive curriculum aims to give you a ...', 'title': 'LLM University (LLMU) | Cohere', 'url': 'https://docs.cohere.com/docs/llmu'}\n",
            "{'id': 'web-search_1', 'snippet': \"Introducing LLM University — Your Go-To Learning Resource for NLP🎓\\n\\nDiscover our comprehensive NLP curriculum at LLM University. From the fundamentals of LLMs all the way to the most advanced topics, including generative AI\\n\\nWe're excited to announce the launch of LLM University (LLMU), a set of comprehensive learning resources for anyone interested in natural language processing (NLP), from begin...\", 'title': 'Introducing LLM University — Your Go-To Learning Resource for NLP🎓', 'url': 'https://txt.cohere.com/llm-university/'}\n",
            "\n",
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "Ending chat.\n"
          ]
        }
      ],
      "source": [
        "# Define the connector\n",
        "connectors = [\"web-search\"]\n",
        "\n",
        "# Create an instance of the Chatbot class\n",
        "chatbot = Chatbot(connectors)\n",
        "\n",
        "# Run the chatbot\n",
        "chatbot.run()"
      ]
    }
  ],
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