MISTRAL_API_TUTORIAL_Open_Mixtral_8x22b.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "5axbl8pdofkY",
        "6MaAV5xQuK5x",
        "rrmBFPAXuTWH",
        "R0jhm6tSveUu",
        "r8bzkoN7voUK",
        "YsKBPEATwKag",
        "PmaEADQowwNQ"
      ],
      "machine_shape": "hm",
      "gpuType": "L4",
      "authorship_tag": "ABX9TyNAu0gNHn82yZzTqDL0gCyF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/frank-morales2020/MLxDL/blob/main/MISTRAL_API_TUTORIAL_Open_Mixtral_8x22b.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://docs.mistral.ai/platform/client/\n",
        "\n",
        "https://github.com/mistralai/client-python"
      ],
      "metadata": {
        "id": "HFSQBSaVuYEu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dependencies and Settings"
      ],
      "metadata": {
        "id": "NesDaI4OICGe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eoPAKz9ycOCu",
        "outputId": "ea5ea572-fac0-4f85-c4df-5439425bbf46"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Apr 20 14:10:58 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA L4                      Off | 00000000:00:03.0 Off |                    0 |\n",
            "| N/A   58C    P8              14W /  72W |      1MiB / 23034MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Installing Modules"
      ],
      "metadata": {
        "id": "S2X7iLVUPIrN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install colab-env --quiet\n",
        "!pip install mistralai --quiet\n",
        "\n",
        "# Install Hugging Face libraries\n",
        "!pip install  --upgrade transformers datasets accelerate evaluate bitsandbytes --quiet\n",
        "from datasets import load_dataset"
      ],
      "metadata": {
        "id": "ghqAXWE-xJa_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing Modules"
      ],
      "metadata": {
        "id": "k9lF1C1DNgKm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import mistralai\n",
        "from mistralai.client import MistralClient\n",
        "from mistralai.models.chat_completion import ChatMessage\n",
        "import os\n",
        "import colab_env\n",
        "import json"
      ],
      "metadata": {
        "id": "a87Hog_P8h9P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MISTRAL API SETTINGS"
      ],
      "metadata": {
        "id": "9V3yq8LLuqdT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "api_key = os.environ[\"MISTRAL_API_KEY\"]\n",
        "client = MistralClient(api_key=api_key)"
      ],
      "metadata": {
        "id": "NnLef38upn75"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Function Definitions"
      ],
      "metadata": {
        "id": "5axbl8pdofkY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def mistral_chat(prompt,model,client,stream):\n",
        "  client = MistralClient(api_key=api_key)\n",
        "  messages = [ChatMessage(role=\"user\", content=prompt)]\n",
        "  if stream==True:\n",
        "     print(\"Streaming\")\n",
        "     print()\n",
        "     print('Answer: ')\n",
        "     for chunk in client.chat_stream(\n",
        "        model=model,\n",
        "        messages=messages,\n",
        "        ):\n",
        "        if chunk.choices[0].delta.content is not None:\n",
        "            print(chunk.choices[0].delta.content, end=\"\")\n",
        "  else:\n",
        "\n",
        "     #print(\"No streaming\")\n",
        "     chat_response = client.chat(model=model,messages=messages, safe_mode=True,temperature=0.9)\n",
        "     #print(chat_response.choices[0].message.content)\n",
        "     return chat_response"
      ],
      "metadata": {
        "id": "CQPXU56gJGNS"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_prompt_formats(sample):\n",
        "    \"\"\"\n",
        "    Format various fields of the sample ('instruction','output')\n",
        "    Then concatenate them using two newline characters\n",
        "    :param sample: Sample dictionnary\n",
        "    \"\"\"\n",
        "    INTRO_BLURB = \"Below is an instruction that describes a task. Write a response that appropriately completes the request.\"\n",
        "    INSTRUCTION_KEY = \"### Instruct: Summarize the below conversation.\"\n",
        "    RESPONSE_KEY = \"### Output:\"\n",
        "    END_KEY = \"### End\"\n",
        "\n",
        "    blurb = f\"\\n{INTRO_BLURB}\"\n",
        "    instruction = f\"{INSTRUCTION_KEY}\"\n",
        "    input_context = f\"{sample['dialogue']}\" if sample[\"dialogue\"] else None\n",
        "    response = f\"{RESPONSE_KEY}\\n{sample['summary']}\"\n",
        "    end = f\"{END_KEY}\"\n",
        "\n",
        "    parts = [part for part in [blurb, instruction, input_context, response, end] if part]\n",
        "\n",
        "    formatted_prompt = \"\\n\\n\".join(parts)\n",
        "    sample[\"text\"] = formatted_prompt\n",
        "\n",
        "    return sample"
      ],
      "metadata": {
        "id": "LY2NjeYtpGet"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Managenment"
      ],
      "metadata": {
        "id": "Rwwy34zSFHD2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model list\n"
      ],
      "metadata": {
        "id": "mhLS0Qm6ExFh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import date\n",
        "date.fromisoformat('2024-04-17')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JLogBVEN4_e",
        "outputId": "8cdd8807-6a7c-4469-a30e-a85163966c0e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.date(2024, 4, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://docs.mistral.ai/getting-started/models/\n",
        "\n",
        "\n",
        "open-mixtral-8x22b: A bigger sparse mixture of experts model. As such, it leverages up to 176B parameters but only uses about 39B during inference, leading to better inference throughput at the cost of more vRAM. Learn more on the dedicated blog post"
      ],
      "metadata": {
        "id": "NgkyDoD1O5Gg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "{\n",
        "  \"object\": \"string\",\n",
        "  \"data\": [\n",
        "    {\n",
        "      \"id\": \"string\",\n",
        "      \"object\": \"string\",\n",
        "      \"created\": 0,\n",
        "      \"owned_by\": \"string\"\n",
        "    }\n",
        "  ]\n",
        "}"
      ],
      "metadata": {
        "id": "p0EcqJQc41fd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_list=client.list_models()\n",
        "model_list.data[5].id\n",
        "\n",
        "for model in model_list.data:\n",
        "    print(model.id)\n",
        "    #print(model.created)\n",
        "    #print(model.owned_by)"
      ],
      "metadata": {
        "id": "kJGSPIMaDBPv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a679006-0d01-479a-811f-49124447d054"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "open-mistral-7b\n",
            "mistral-tiny-2312\n",
            "mistral-tiny\n",
            "open-mixtral-8x7b\n",
            "open-mixtral-8x22b\n",
            "open-mixtral-8x22b-2404\n",
            "mistral-small-2312\n",
            "mistral-small\n",
            "mistral-small-2402\n",
            "mistral-small-latest\n",
            "mistral-medium-latest\n",
            "mistral-medium-2312\n",
            "mistral-medium\n",
            "mistral-large-latest\n",
            "mistral-large-2402\n",
            "mistral-embed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model=model_list.data[11].id # mistral-large-latest\n",
        "model=model_list.data[4].id\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5uO56886bzN0",
        "outputId": "7bacdd4a-95a0-4fb8-cda5-78cb2b89a59f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'open-mixtral-8x22b'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# FUNCTION CALLING"
      ],
      "metadata": {
        "id": "m1j2J7NMuDaH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import functools\n",
        "import json\n",
        "import os\n",
        "\n",
        "import pandas as pd\n",
        "from mistralai.client import MistralClient\n",
        "from mistralai.models.chat_completion import ChatMessage, Function\n",
        "\n",
        "# Assuming we have the following data\n",
        "data = {\n",
        "    \"transaction_id\": [\"T1001\", \"T1002\", \"T1003\", \"T1004\", \"T1005\"],\n",
        "    \"customer_id\": [\"C001\", \"C002\", \"C003\", \"C002\", \"C001\"],\n",
        "    \"payment_amount\": [125.50, 89.99, 120.00, 54.30, 210.20],\n",
        "    \"payment_date\": [\"2021-10-05\", \"2021-10-06\", \"2021-10-07\", \"2021-10-05\", \"2021-10-08\"],\n",
        "    \"payment_status\": [\"Paid\", \"Unpaid\", \"Paid\", \"Paid\", \"Pending\"],\n",
        "}\n",
        "\n",
        "# Create DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "\n",
        "def retrieve_payment_status(df: pd.DataFrame, transaction_id: str) -> str:\n",
        "    if transaction_id in df.transaction_id.values:\n",
        "        return json.dumps({\"status\": df[df.transaction_id == transaction_id].payment_status.item()})\n",
        "    else:\n",
        "        return json.dumps({\"status\": \"error - transaction id not found.\"})\n",
        "\n",
        "\n",
        "def retrieve_payment_date(df: pd.DataFrame, transaction_id: str) -> str:\n",
        "    if transaction_id in df.transaction_id.values:\n",
        "        return json.dumps({\"date\": df[df.transaction_id == transaction_id].payment_date.item()})\n",
        "    else:\n",
        "        return json.dumps({\"status\": \"error - transaction id not found.\"})\n",
        "\n",
        "\n",
        "names_to_functions = {\n",
        "    \"retrieve_payment_status\": functools.partial(retrieve_payment_status, df=df),\n",
        "    \"retrieve_payment_date\": functools.partial(retrieve_payment_date, df=df),\n",
        "}\n",
        "\n",
        "\n",
        "tools = [\n",
        "    {\n",
        "        \"type\": \"function\",\n",
        "        \"function\": Function(\n",
        "            name=\"retrieve_payment_status\",\n",
        "            description=\"Get payment status of a transaction id\",\n",
        "            parameters={\n",
        "                \"type\": \"object\",\n",
        "                \"required\": [\"transaction_id\"],\n",
        "                \"properties\": {\"transaction_id\": {\"type\": \"string\", \"description\": \"The transaction id.\"}},\n",
        "            },\n",
        "        ),\n",
        "    },\n",
        "    {\n",
        "        \"type\": \"function\",\n",
        "        \"function\": Function(\n",
        "            name=\"retrieve_payment_date\",\n",
        "            description=\"Get payment date of a transaction id\",\n",
        "            parameters={\n",
        "                \"type\": \"object\",\n",
        "                \"required\": [\"transaction_id\"],\n",
        "                \"properties\": {\"transaction_id\": {\"type\": \"string\", \"description\": \"The transaction id.\"}},\n",
        "            },\n",
        "        ),\n",
        "    },\n",
        "]\n",
        "\n",
        "\n",
        "api_key = os.environ[\"MISTRAL_API_KEY\"]\n",
        "\n",
        "client = MistralClient(api_key=api_key)\n",
        "\n",
        "messages = [ChatMessage(role=\"user\", content=\"What's the status of my transaction?\")]\n",
        "\n",
        "response = client.chat(model=model, messages=messages, tools=tools)\n",
        "\n",
        "print(response.choices[0].message.content)\n",
        "\n",
        "messages.append(ChatMessage(role=\"assistant\", content=response.choices[0].message.content))\n",
        "messages.append(ChatMessage(role=\"user\", content=\"My transaction ID is T1001.\"))\n",
        "\n",
        "response = client.chat(\n",
        "    model=model, messages=messages, tools=tools\n",
        ")\n",
        "\n",
        "tool_call = response.choices[0].message.tool_calls[0]\n",
        "function_name = tool_call.function.name\n",
        "function_params = json.loads(tool_call.function.arguments)\n",
        "\n",
        "print(f\"calling function_name: {function_name}, with function_params: {function_params}\")\n",
        "\n",
        "function_result = names_to_functions[function_name](**function_params)\n",
        "\n",
        "messages.append(response.choices[0].message)\n",
        "messages.append(ChatMessage(role=\"tool\", name=function_name, content=function_result))\n",
        "\n",
        "response = client.chat(model=model, messages=messages, tools=tools)\n",
        "\n",
        "print(f\"{response.choices[0].message.content}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UxMKTQcelnZO",
        "outputId": "d370c834-04d8-4c69-f5cd-ca6160b09a77"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To provide you with the status of your transaction, I need the transaction ID. Please provide me with that information and I will use the \"retrieve_payment_status\" function to get the details.\n",
            "\n",
            "Assistant Function: retrieve_payment_status({\"transaction_id\": \"<user_provided_transaction_id>\"})\n",
            "calling function_name: retrieve_payment_status, with function_params: {'transaction_id': 'T1001'}\n",
            "The status of your transaction with ID T1001 is \"Paid\".\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# JSON FORMAT"
      ],
      "metadata": {
        "id": "6MaAV5xQuK5x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chat_response = client.chat(\n",
        "        model=model,\n",
        "        response_format={\"type\": \"json_object\"},\n",
        "        messages=[ChatMessage(role=\"user\", content=\"What is the best French cheese?\")],\n",
        "\n",
        "    )\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "foDqw7Polgi7",
        "outputId": "97dc92f9-7caa-490e-850b-ab7f5bc4031f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"\": \"There isn't a definitive answer to which French cheese is the best, as it depends on personal preference. However, some popular and highly-rated French cheeses include:\n",
            "\n",
            "1. Brie de Meaux: A soft cow's milk cheese with a creamy texture and mild, buttery flavor.\n",
            "2. Roquefort: A tangy, creamy blue cheese made from sheep's milk.\n",
            "3. Comté: A firm, nutty-tasting cow's milk cheese with a complex flavor profile.\n",
            "4. Camembert: A soft, creamy cheese with a strong, earthy flavor.\n",
            "5. Époisses: A soft, pungent cheese with a strong aroma and rich, slightly salty flavor.\n",
            "\n",
            "Ultimately, the best French cheese for you will depend on your individual taste preferences.\"}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Embeddings"
      ],
      "metadata": {
        "id": "rrmBFPAXuTWH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings_response = client.embeddings(\n",
        "        model=\"mistral-embed\",\n",
        "        input=[\"What is the best French cheese?\"] * 10,\n",
        "    )\n",
        "\n",
        "#print(embeddings_response)"
      ],
      "metadata": {
        "id": "XrcxS70jkyxw"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dialogue Summarization"
      ],
      "metadata": {
        "id": "6cX_VnGxoyJ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### DATASET for Dialogue Summarization\n",
        "huggingface_dataset_name = \"neil-code/dialogsum-test\"\n",
        "dataset = load_dataset(huggingface_dataset_name)"
      ],
      "metadata": {
        "id": "90oTDGQVMSmc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oNfUqi-ae0l",
        "outputId": "b5cbfc7c-c93a-455d-d01a-6e7543d584fa"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
            "        num_rows: 1999\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
            "        num_rows: 499\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
            "        num_rows: 499\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print(\"Preprocessing dataset dialogsum_test\")\n",
        "dataset_dialogsum_test = dataset.map(create_prompt_formats)#, batched=True)"
      ],
      "metadata": {
        "id": "fxbsc-gJNrzU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#query = dataset_dialogsum_test['validation'][400]['dialogue']\n",
        "query = dataset_dialogsum_test['test'][400]['dialogue']\n",
        "query = \"Summarize.\\n %s\"%query"
      ],
      "metadata": {
        "id": "TyM4uW-FOCRU"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "#print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9DkARB50OoFD",
        "outputId": "ad417696-030f-478c-b49c-54c823c23bff"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: Summarize.\n",
            " #Person1#: It was a heavy storm last night, wasn't it?\n",
            "#Person2#: It certainly was. The wind broke several windows. What weather!\n",
            "#Person1#: Do you know that big tree in front of my house? One of the biggest branches came down in the night.\n",
            "#Person2#: Really? Did it do any damage to your home?\n",
            "#Person1#: Thank goodness! It is far away from that.\n",
            "#Person2#: I really hate storms. It's about time we had some nice spring weather.\n",
            "#Person1#: It's April, you know. The flowers are beginning to blossom.\n",
            "#Person2#: Yes, that's true. But I still think the weather is terrible.\n",
            "#Person1#: I suppose we should not complain. We had a fine March after all.\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Answer: \n",
            "Person 1 initiated a conversation about the heavy storm that occurred the previous night, which resulted in broken windows due to strong winds. Person 1 also mentioned that a large branch of a tree near their house fell during the night. Fortunately, it did not cause any damage to their home. Person 2 expressed dislike for storms and expressed a longing for pleasant spring weather. Person 1 responded by acknowledging that it's April and flowers are beginning to bloom. However, Person 2 still thought the weather was poor. Person 1 concluded the conversation by suggesting that they should not complain since they had experienced a fine March.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Text to SQL"
      ],
      "metadata": {
        "id": "R0jhm6tSveUu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Text to SQL #1"
      ],
      "metadata": {
        "id": "ZlBryezOIx-M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "#model = \"mistral-large-latest\"\n",
        "query = \"What is the average grid for competitors with over 22 laps and time/retired of +17.276? Answer me as SQL and explain your solutions.\"\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7AT0uhsHrIu",
        "outputId": "de9edea9-1e09-48fa-bd9a-a9fa2be08965"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: What is the average grid for competitors with over 22 laps and time/retired of +17.276? Answer me as SQL and explain your solutions.\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "To answer your question, I'll need to make some assumptions since the context isn't fully provided. I'll assume that we have a table named `competitors` with the following columns: `lap_count` for the number of laps, `time_ retired` for the time or the retirement flag, and `competitor_id` as the primary key.\n",
            "\n",
            "To find the average grid position for competitors with over 22 laps and a time/retirement of +17.276, I'll also assume that there's another table named `grids` with columns `competitor_id`, `grid_position`, and `average_grid_position`. The `average_grid_position` is the result we want.\n",
            "\n",
            "To get the desired result, we'll join the `competitors` and `grids` tables based on the `competitor_id` and filter the data based on your conditions. The SQL command would look something like this:\n",
            "\n",
            "```SQL\n",
            "SELECT AVG(g.grid_position) AS average_grid_position\n",
            "FROM competitors AS c\n",
            "JOIN grids AS g ON c.competitor_id = g.competitor_id\n",
            "WHERE c.lap_count > 22 AND (c.time_retired = '+17.276' OR c.time_retired IS NULL);\n",
            "```\n",
            "\n",
            "In the query above, I'm joining the `competitors` and `grids` tables on the `competitor_id` column. Then, I'm applying the conditions for competitors with over 22 laps and time/retirement of +17.276. In this context, I assume that `NULL` in the `time_retired` column indicates a competitor who hasn't retired yet.\n",
            "\n",
            "Finally, I calculate the average grid position for the filtered competitors using the `AVG()` function.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Text to SQL #2"
      ],
      "metadata": {
        "id": "T6RyoJq0Jvqk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query =  \"What was the margin of victory for the Players Championship tournament? Answer me as SQL and explain your solutions.\"\n",
        "\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DrbhuFQJDgw",
        "outputId": "80fba7c9-d137-446b-d0c6-e42eb8678687"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: What was the margin of victory for the Players Championship tournament? Answer me as SQL and explain your solutions.\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "I cannot provide real-time data or precise historical data without access to a specific database. However, I can create an SQL query based on hypothetical data to show how one might find the margin of victory for a golf tournament like The Players Championship.\n",
            "\n",
            "Assuming there is a table named 'tournaments' with the following columns: 'tournament_name', 'year', 'winner', 'second_place', 'winner_score', and 'second_place_score', the SQL query would look like this:\n",
            "\n",
            "```sql\n",
            "SELECT\n",
            "    tournament_name,\n",
            "    year,\n",
            "    winner,\n",
            "    second_place,\n",
            "    winner_score,\n",
            "    second_place_score,\n",
            "    (winner_score - second_place_score) AS margin_of_victory\n",
            "FROM\n",
            "    tournaments\n",
            "WHERE\n",
            "    tournament_name = 'The Players Championship';\n",
            "```\n",
            "\n",
            "This SQL query will give you a list of all The Players Championship tournaments in your hypothetical database along with the winner, second place, their scores, and the margin of victory (winner's score - second place's score).\n",
            "\n",
            "Please note that SQL queries are based on the structure, names, and data types of your tables and columns, and they may need to be adjusted accordingly. Also, SQL is case-insensitive for keywords and identifiers, but the casing of data might be important depending on the database system and collation settings.\n",
            "\n",
            "Lastly, this solution assumes that there are no ties for first or second place, and that the margin of victory is based only on the scores of the winner and the second-place golfer.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question Answering Dataset - CASE - SQUAD2.0"
      ],
      "metadata": {
        "id": "r8bzkoN7voUK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'What was the first album Beyoncé released as a solo artist?'\n",
        "\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCc0mqzlGe-J",
        "outputId": "03899f47-3256-4ae4-a6cd-81ff78eb835a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: What was the first album Beyoncé released as a solo artist?\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "The first album Beyoncé released as a solo artist was \"Dangerously in Love\" in 2003. This album was a massive success and featured popular songs like \"Crazy in Love\" and \"Baby Boy\". It established Beyoncé as a successful solo artist and solidified her transition from her previous group, Destiny's Child.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AIRLINES CASES"
      ],
      "metadata": {
        "id": "M7ZWdXHUvxoE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'tell me the best route by airplane between nyc and shanghai, suggest me the best airline for it'\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qs70Od3nsOZb",
        "outputId": "3b522285-ffe1-4758-ba76-d1ab36196575"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: tell me the best route by airplane between nyc and shanghai, suggest me the best airline for it\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "Based on your request, here's the information you need:\n",
            "\n",
            "1. **Route**: New York City (JFK) to Shanghai (PVG) is the most common and direct route between these two cities. The total flight time is around 15 hours, depending on the specific flight and any layovers.\n",
            "\n",
            "2. **Best Airlines**: While the \"best\" airline can vary based on individual preferences and factors like price, comfort, and service, here are some top-rated airlines that frequently fly this route:\n",
            "\n",
            "   - **Air China**: Air China operates non-stop flights from JFK to PVG with competitive prices. They offer three classes: Economy, Premium Economy, and Business.\n",
            "\n",
            "   - **China Eastern Airlines**: China Eastern also offers non-stop flights from JFK to PVG. They have a good reputation for customer service and offer Economy, Premium Economy, and Business classes.\n",
            "\n",
            "   - **United Airlines**: United operates direct flights to Shanghai from Newark Liberty International Airport (EWR), which is near New York City. They offer Economy, Premium Economy, and Business (named Polaris) classes.\n",
            "\n",
            "   - **Delta Airlines**: Delta operates direct flights from JFK to PVG, offering Economy, Premium Economy (Delta Comfort+), and Business (Delta One) classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'tell me the airline in the world. Please included canada in the list'\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfvmIHeBtRdH",
        "outputId": "91fb7ce1-90a1-4388-d615-3a0b32111566"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: tell me the airline in the world. Please included canada in the list\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "I'm delighted to assist you! However, it's important to clarify that there are numerous airlines across the globe, including Canada. Here's a brief list of some major airlines from different regions:\n",
            "\n",
            "1. United States: American Airlines, Delta Air Lines, United Airlines\n",
            "2. Canada: Air Canada, WestJet, Air Transat\n",
            "3. Europe: British Airways (UK), Lufthansa (Germany), Air France (France)\n",
            "4. Asia: Emirates (United Arab Emirates), Singapore Airlines (Singapore), Qatar Airways (Qatar)\n",
            "5. Australia: Qantas, Jetstar Airways\n",
            "6. Africa: Ethiopian Airlines (Ethiopia), South African Airways (South Africa)\n",
            "7. South America: LATAM Airlines Group (Chile), Aerolíneas Argentinas (Argentina)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'tell me the best airline in the world with the best pilots.'\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HI2HRiHytixs",
        "outputId": "dc835d27-4088-4bfa-ccf3-6579cf216875"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: tell me the best airline in the world with the best pilots.\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "While it's challenging to definitively state the \"best\" airline in the world, as this can depend on various factors such as passenger experience, safety records, and pilot training programs, a few airlines are consistently recognized for their high standards.\n",
            "\n",
            "For instance, Singapore Airlines is often ranked highly by industry experts and passengers alike for its exceptional service, modern fleet, and commitment to safety. The airline has a comprehensive pilot training program that includes simulator training for various scenarios, ensuring their pilots are well-prepared for any situation.\n",
            "\n",
            "Another airline known for its excellent pilots is Qantas, based in Australia. Qantas has a reputation for safety and is often cited as one of the world's safest airlines. They have a rigorous pilot selection and training process, and their pilots undergo regular assessments and proficiency checks.\n",
            "\n",
            "Emirates is another airline that is highly regarded for its pilot training, safety, and operational efficiency. They have a diverse range of pilots from around the world, all of whom are trained in their state-of-the-art facilities.\n",
            "\n",
            "It's important to note that all airlines have rigorous standards for pilot training and safety, and these three are just examples of airlines that are often recognized for their high standards.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'tell me about the Aircraft Ulization, how to calculate properly'\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CklKzT5vt_V7",
        "outputId": "a89cea3a-5a63-4011-f56c-cea539fe7ff5"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: tell me about the Aircraft Ulization, how to calculate properly\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "Aircraft utilization is a metric used in the aviation industry to measure how often an aircraft is used. It's typically expressed as a percentage and is calculated by dividing the total aircraft flight hours by the total hours in a given period, then multiplying by 100 to get a percentage.\n",
            "\n",
            "The formula is as follows:\n",
            "\n",
            "Aircraft Utilization (%) = (Total Flight Hours / Total Hours in Period) * 100\n",
            "\n",
            "For example, if an aircraft was flown for 500 hours in a month that has 720 hours (30 days), the aircraft utilization would be:\n",
            "\n",
            "Aircraft Utilization (%) = (500 hours / 720 hours) * 100 = 69.44%\n",
            "\n",
            "This means the aircraft was used approximately 69.44% of the time during that month.\n",
            "\n",
            "However, it's important to note that \"utilization\" can be defined differently based on context. Some may consider block time (the time from when the aircraft's brakes are released on departure to the time they are set at the destination) instead of flight hours. Others may include scheduled maintenance in their calculations. Therefore, it's always a good idea to clarify what's included in the \"utilization\" metric.\n",
            "\n",
            "Also, high aircraft utilization isn't always the goal. Airlines and operators must balance utilization with other factors like maintenance schedules, crew availability, and passenger demand. Too much utilization without proper maintenance can lead to safety issues, while too little can result in inefficient use of resources.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'tell me about the best Flight Plan Calculations - Enroute calculations'\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0GY-J98uFFh",
        "outputId": "cd71804d-560c-4a8f-8c36-6a3e2fa6e9b1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: tell me about the best Flight Plan Calculations - Enroute calculations\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "Flight plan calculations involve several complex aspects, and the \"best\" calculations often depend on the specific flight, aircraft, and conditions. However, here are some key elements typically included in enroute flight plan calculations:\n",
            "\n",
            "1. **Wind and Temperature Calculations**: These are used to determine the impact of wind and temperature on aircraft performance, such as groundspeed, true airspeed, and fuel consumption. This information is crucial for planning and executing a flight efficiently and safely.\n",
            "\n",
            "2. **Altitude Calculations**: The optimal cruising altitude is determined based on factors like aircraft performance, weather conditions, and air traffic control (ATC) clearances. This calculation is important for fuel efficiency and passenger comfort.\n",
            "\n",
            "3. **Routing Calculations**: These involve determining the most efficient and safe flight path. This typically takes into account airspace restrictions, navigational aids, and preferred routing designated by ATC.\n",
            "\n",
            "4. **Fuel Calculations**: This involves determining the amount of fuel needed for the flight, including reserves. Factors considered include the distance to be traveled, aircraft performance, weather conditions, and ATC routing.\n",
            "\n",
            "5. **Time Calculations**: Estimated time enroute (ETE) is calculated based on factors like distance, speed, wind, and routing. This helps in planning departure and arrival times, coordinating with ATC, and ensuring compliance with crew duty time regulations.\n",
            "\n",
            "6. **Performance Calculations**: These help determine the aircraft's performance capabilities under given conditions. This includes parameters like climb rate, cruise speed, and landing distance.\n",
            "\n",
            "7. **Weight and Balance Calculations**: These ensure the aircraft is loaded within its weight and balance limits for safe operation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'tell me more about the best flight planning computer calculation of ForeFlight'\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhzOnAvzufIb",
        "outputId": "1aa2699c-c897-4f61-b671-03c708b9b3da"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: tell me more about the best flight planning computer calculation of ForeFlight\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "ForeFlight is a popular and highly regarded flight planning and navigation application used by pilots around the world. Its flight planning features are renowned for their accuracy, convenience, and user-friendly interface. Here are some key aspects of ForeFlight's flight planning computer calculation:\n",
            "\n",
            "1. Route Planning: ForeFlight enables pilots to plan their flights by creating and modifying routes. The app automatically calculates the distance, expected fuel burn, and time en route based on the aircraft's performance data and prevailing winds. You can also compare different routes and altitudes to find the most efficient and safe flight path.\n",
            "\n",
            "2. Winds Aloft: The app automatically integrates current and forecasted wind information (both direction and speed) at different altitudes, allowing pilots to optimize their route, altitude, and fuel consumption. This feature ensures accurate calculations of flight time, fuel burn, and groundspeed.\n",
            "\n",
            "3. Aircraft Performance Profiles: ForeFlight provides performance profiles for various aircraft types. These profiles, created by the ForeFlight team, take into account factors such as aircraft weight, fuel capacity, and engine performance. They allow for precise calculations of required takeoff distance, landing distance, and climb performance.\n",
            "\n",
            "4. Weight and Balance: ForeFlight includes a weight and balance calculator, which allows pilots to input the weight of passengers, luggage, and fuel to ensure they stay within the aircraft's weight and balance limits.\n",
            "\n",
            "5. Fuel Planning: ForeFlight's fuel planning features allow pilots to calculate the required fuel for a given trip, accounting for reserve fuel requirements, expected winds, and aircraft performance. This feature is instrumental in ensuring safe and efficient flights.\n",
            "\n",
            "6. ETE and ETA: ForeFlight calculates the Estimated Time Enroute (ETE) and Estimated Time of Arrival (ETA) to help pilots stay on schedule and avoid delays.\n",
            "\n",
            "7. Auto-Routing: ForeFlight's auto-routing feature automatically generates an efficient and safe route based on a pilot's departure and arrival airports, aircraft performance, and forecasted weather conditions.\n",
            "\n",
            "In summary, ForeFlight is an indispensable tool for pilots, as it greatly simplifies flight planning and navigation. Its comprehensive features, accurate data, and user-friendly interface make it one of the best flight planning applications available today.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'tell me more about ForeFlight'\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xxe7ubK0uyDW",
        "outputId": "962ef238-c6e7-446e-a0af-175438667713"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: tell me more about ForeFlight\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "ForeFlight is a popular electronic flight bag application designed for pilots, flight crews, and flight service providers. It provides powerful tools for flight planning, weather, airport information, document management, and synthetic vision.\n",
            "\n",
            "Features of ForeFlight include:\n",
            "\n",
            "1. Flight planning: ForeFlight allows pilots to create, file, and amend flight plans, as well as manage aircraft performance profiles.\n",
            "\n",
            "2. Weather: ForeFlight provides access to a wide range of weather information, including radar, satellite, winds aloft, METARs, and TAFs.\n",
            "\n",
            "3. Airports: ForeFlight offers detailed information on airports, including runway diagrams, frequencies, procedures, and fuel prices.\n",
            "\n",
            "4. Charts: ForeFlight has a large library of aviation charts, including sectionals, IFR enroute charts, approach plates, and airport diagrams.\n",
            "\n",
            "5. Synthetic vision: ForeFlight's synthetic vision feature provides a 3D view of the aircraft's position, terrain, and obstacles.\n",
            "\n",
            "6. Logbook: ForeFlight includes a digital logbook, allowing pilots to track flight time and currency.\n",
            "\n",
            "7. Document management: ForeFlight allows pilots to store and access important documents, such as aircraft manuals and FAA regulations.\n",
            "\n",
            "8. Connectivity: ForeFlight offers connectivity to a variety of avionics, including ADS-B weather and traffic, and GPS position.\n",
            "\n",
            "ForeFlight is available on iOS devices, such as iPhones and iPads, and is compatible with a variety of aviation hardware, such as ADS-B receivers and GPS devices. It is used by pilots and aviation professionals around the world for flight planning, weather, and navigation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'Tell me more about the best flight planning computer calculation of ForeFlight, it is cloud-based? which computer language they are using?'\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-KK63SFvJb0",
        "outputId": "b718b3c5-eaa2-41b1-c559-e330ab148d92"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: Tell me more about the best flight planning computer calculation of ForeFlight, it is cloud-based? which computer language they are using?\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "ForeFlight is a popular flight planning application used by pilots worldwide. It provides a range of features including aviation charts, weather information, and flight planning tools. However, as a assistant, I don't have real-time access to specific company data or internal details, but I can share some general insights based on publicly available information.\n",
            "\n",
            "ForeFlight's flight planning computer calculation is designed to help pilots plan their flights efficiently and accurately. It takes into account various factors such as aircraft performance, weather conditions, airspace restrictions, and airport information. The application uses complex algorithms to calculate the most efficient and safe flight route, considering factors like fuel burn, time enroute, and wind effects.\n",
            "\n",
            "As for the technology stack, ForeFlight is a cloud-based application, which means it relies on remote servers hosted on the internet to store and process data. This architecture allows pilots to access the latest data and updates in real-time from anywhere in the world.\n",
            "\n",
            "While I don't have specific information about the programming languages used by ForeFlight, it's common for modern cloud-based applications to use a combination of languages such as Python, Java, JavaScript, and Swift for iOS development. However, this is just speculation, and the actual languages used could be different. For more precise information, I would recommend reaching out to ForeFlight's support directly.\n",
            "\n",
            "Lastly, it's important to note that ForeFlight and other flight planning tools are just aids and should be used in conjunction with sound judgment and airmanship. Safe flying requires continuous situational awareness and adaptation to changing conditions, which no application can fully account for.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Case-based reasoning"
      ],
      "metadata": {
        "id": "YsKBPEATwKag"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"I bought a computer for $900, sold it for $1200, repurchased it for $1300, and sold it again for $1600. how much did I earn? Take in consideration the money for the repurchased too.\"\n",
        "\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1EY6bG37jUEc",
        "outputId": "2f826958-fa17-45b9-8198-d336c2941b99"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: I bought a computer for $900, sold it for $1200, repurchased it for $1300, and sold it again for $1600. how much did I earn? Take in consideration the money for the repurchased too.\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "You bought the computer for $900 and sold it for $1200, making a profit of $300. Then, you repurchased it for $1300 and sold it again for $1600, making another profit of $300. However, you also had the cost of repurchasing the computer for $1300.\n",
            "\n",
            "So if we add up all the money that changed hands:\n",
            "First sale: +$1200\n",
            "Repurchase: -$1300\n",
            "Second sale: +$1600\n",
            "\n",
            "And subtract the initial purchase price:\n",
            "Total: +$1200 - $1300 + $1600 - $900 = $500\n",
            "\n",
            "Therefore, after accounting for the repurchase, you ended up with a net gain of $500.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"I bought an ice cream for 6 kids. Each cone was $1.25 and I paid with a $10 bill. How many dollars did I get back? Explain first before answering.\"\n",
        "\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-llUfOheRo0t",
        "outputId": "0410facc-482b-4b85-9197-8404e77efe10"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: I bought an ice cream for 6 kids. Each cone was $1.25 and I paid with a $10 bill. How many dollars did I get back? Explain first before answering.\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "Sure, I'd be happy to help with that!\n",
            "\n",
            "First, let's calculate the total cost of the ice cream cones. If each cone costs $1.25 and you bought 6 cones, then the total cost would be:\n",
            "\n",
            "6 cones * $1.25/cone = $7.50\n",
            "\n",
            "Now, you've paid for the ice cream with a $10 bill. To find out how much money you get back, we subtract the total cost of the ice cream from the amount you paid:\n",
            "\n",
            "$10.00 - $7.50 = $2.50\n",
            "\n",
            "So, you would get back $2.50 in change.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Case-based Knowledge"
      ],
      "metadata": {
        "id": "PmaEADQowwNQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query='can we compare voltaire and victor hugo? who is better poet?'\n",
        "#model = \"mistral-large-latest\"\n",
        "\n",
        "\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ow1RigdcsNCp",
        "outputId": "40cc1275-2c64-4f53-8c66-816e38134f38"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: can we compare voltaire and victor hugo? who is better poet?\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n",
            "\n",
            "Answer: \n",
            "Voltaire and Victor Hugo were both influential French writers, philosophers, and poets who left profound impacts on literature, politics, and culture. However, it is not a matter of who was \"better\" as they had different styles, themes, and contexts while writing.\n",
            "\n",
            "Voltaire (1694-1778) was a prominent figure of the Enlightenment period, known for his wit, intelligence, and advocacy for freedom of speech and religious tolerance. His poems often carried satire, humor, and criticism of the church and the aristocracy. Famous works include \"La Henriade\" and \"Le Mondain.\"\n",
            "\n",
            "Victor Hugo (1802-1885) was a leading Romantic writer, best known for his novels such as \"Les Misérables\" and \"The Hunchback of Notre-Dame.\" His poetry was deeply emotional and rich, filled with philosophical and moral themes. His most famous poetic works include \"Les Contemplations\" and \"La Légende des siècles.\"\n",
            "\n",
            "As seen, both Voltaire and Hugo were significant in their own rights. It is best to enjoy their works separately and appreciate their unique contributions to literature rather than attempting to rank them as \"better\" or \"worse\" than each other.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query='who is the best French Poet?'\n",
        "#model =  \"open-mistral-7b\"\n",
        "#model = \"mistral-large-latest\"\n",
        "\n",
        "\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y71lC549KkKZ",
        "outputId": "00c77992-21a6-4d60-a49c-f7dac269e26c"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: who is the best French Poet?\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Answer: \n",
            "There isn't a definitive answer to who the \"best\" French poet is, as this can be subjective and depends on personal preferences. However, some of the most celebrated French poets include Charles Baudelaire, Victor Hugo, Arthur Rimbaud, Paul Verlaine, and Guillaume Apollinaire. Each poet has made significant contributions to French literature in their unique styles and themes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Image to TEXT"
      ],
      "metadata": {
        "id": "VjyCgznMvHX8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://images.hindustantimes.com/auto/img/2023/07/23/1600x900/Tesla_Cybertruck_1688887534001_1690087911053.jpeg -O tesla.jpeg"
      ],
      "metadata": {
        "id": "K9T-IKqzTZSH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f8fbf62-d9f9-4f53-c6c0-280cc6b648a2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-04-20 14:16:00--  https://images.hindustantimes.com/auto/img/2023/07/23/1600x900/Tesla_Cybertruck_1688887534001_1690087911053.jpeg\n",
            "Resolving images.hindustantimes.com (images.hindustantimes.com)... 23.1.102.121, 2600:1406:4c00:287::1833, 2600:1406:4c00:2b0::1833\n",
            "Connecting to images.hindustantimes.com (images.hindustantimes.com)|23.1.102.121|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 216736 (212K) [image/jpeg]\n",
            "Saving to: ‘tesla.jpeg’\n",
            "\n",
            "tesla.jpeg          100%[===================>] 211.66K  --.-KB/s    in 0.05s   \n",
            "\n",
            "2024-04-20 14:16:01 (3.90 MB/s) - ‘tesla.jpeg’ saved [216736/216736]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query=\"Explain this image: https://images.hindustantimes.com/auto/img/2023/07/23/1600x900/Tesla_Cybertruck_1688887534001_1690087911053.jpeg\"\n",
        "\n",
        "chat_response=mistral_chat(query,model,client,stream=False)\n",
        "print()\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "print(\"No streaming\")\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "\n",
        "image_path = '/content/tesla.jpeg'\n",
        "testim = cv2.imread(image_path)\n",
        "plt.imshow(testim)\n",
        "plt.show()\n",
        "print()\n",
        "print('Answer: ')\n",
        "print(chat_response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "HhyUvczmNrib",
        "outputId": "ee9ba283-dd97-41c7-a575-e47d2be9447e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: Explain this image: https://images.hindustantimes.com/auto/img/2023/07/23/1600x900/Tesla_Cybertruck_1688887534001_1690087911053.jpeg\n",
            "--------------------------------------------------------------------------------\n",
            "No streaming\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Answer: \n",
            "The image you've provided is a photo of the Tesla Cybertruck, an all-electric battery-powered pickup truck manufactured by Tesla, Inc. This vehicle is unique due to its unconventional design, which features a stainless steel, angular exterior that is more in line with futuristic concept cars than traditional pickup trucks.\n",
            "\n",
            "The Cybertruck boasts several innovative features, including superb towing capacity, a spacious interior, and the capability to accommodate a variety of cargo and equipment. It is powered by a rechargeable battery, contributing to its environmentally-friendly reputation as an electric vehicle.\n",
            "\n",
            "The image appears to show the vehicle in a desert environment, highlighting its rugged capabilities. It's important to note that the actual performance and features of the Cybertruck may vary based on the specific model and configuration.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CHAT WITH streaming\n"
      ],
      "metadata": {
        "id": "bUeM_CynH0yE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query=\"What is the best French restaurant in Paris?\"\n",
        "#model = \"mistral-large-latest\"\n",
        "\n",
        "\n",
        "print()\n",
        "print(\"-\" * 80)\n",
        "print('Question: %s'%query)\n",
        "print(\"-\" * 80)\n",
        "#print('Answer: ')\n",
        "chat_response=mistral_chat(query,model,client,stream=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cohNToVWzG6x",
        "outputId": "1a2af31b-2e5f-47c5-c140-177b51d7578e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Question: What is the best French restaurant in Paris?\n",
            "--------------------------------------------------------------------------------\n",
            "Streaming\n",
            "\n",
            "Answer: \n",
            "Choosing the \"best\" French restaurant in Paris can be quite subjective as it depends on personal preferences. However, one highly acclaimed restaurant is \"Le Meurice,\" located in the luxurious Le Meurice Hotel. It's a Michelin-starred restaurant led by Chef Alain Ducasse, known for its gourmet French cuisine and elegant atmosphere.\n",
            "\n",
            "Another top-rated restaurant is \"L'Arpège,\" run by Chef Alain Passard, which is also a Michelin-starred establishment. It's known for its innovative vegetable dishes, as well as its seafood and meat offerings.\n",
            "\n",
            "Lastly, \"L'Ambroisie\" in the heart of the Marais district is another excellent choice. It's a three-Michelin-starred restaurant, and Chef Bernard Pacaud's menu is a testament to traditional French cooking techniques.\n",
            "\n",
            "Please note that these restaurants are high-end and reservations are often required. Bon appétit!"
          ]
        }
      ]
    }
  ]
}