Question_&_Answering_System.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4rZIfEXp3fK",
        "outputId": "b0bf3f61-f1c8-416e-dc9e-8423055c4961"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at ./content/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('./content/')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## installing required modules\n",
        "\n",
        "!pip install PdfReader\n",
        "!pip install langchain\n",
        "!pip install PyPDF2\n",
        "!pip install InstructorEmbedding\n",
        "!pip install sentence_transformers\n",
        "!pip install faiss\n",
        "!pip install faiss-gpu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ClF6dyTSqG6f",
        "outputId": "54043b9f-de21-4d6f-d01c-253ac6d66d27"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting PdfReader\n",
            "  Downloading pdfreader-0.1.12.tar.gz (2.9 MB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting bitarray>=1.1.0 (from PdfReader)\n",
            "  Downloading bitarray-2.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (287 kB)\n",
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            "\u001b[?25hRequirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.10/dist-packages (from PdfReader) (9.4.0)\n",
            "Collecting pycryptodome>=3.9.9 (from PdfReader)\n",
            "  Downloading pycryptodome-3.19.0-cp35-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
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            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from PdfReader) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->PdfReader) (1.16.0)\n",
            "Building wheels for collected packages: PdfReader\n",
            "  Building wheel for PdfReader (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PdfReader: filename=pdfreader-0.1.12-py3-none-any.whl size=134538 sha256=d42f53bbe1658461aeb7068954b6e2895320b86209ae317769ffaaf0359f2eb6\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/7d/f1/8b8a503d8b444a82e4c3c8b1230c02316c7579b90542de4495\n",
            "Successfully built PdfReader\n",
            "Installing collected packages: bitarray, pycryptodome, PdfReader\n",
            "Successfully installed PdfReader-0.1.12 bitarray-2.8.3 pycryptodome-3.19.0\n",
            "Collecting langchain\n",
            "  Downloading langchain-0.0.344-py3-none-any.whl (1.9 MB)\n",
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            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain)\n",
            "  Downloading dataclasses_json-0.6.3-py3-none-any.whl (28 kB)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Collecting langchain-core<0.1,>=0.0.8 (from langchain)\n",
            "  Downloading langchain_core-0.0.8-py3-none-any.whl (177 kB)\n",
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            "\u001b[?25hCollecting langsmith<0.1.0,>=0.0.63 (from langchain)\n",
            "  Downloading langsmith-0.0.68-py3-none-any.whl (48 kB)\n",
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            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
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            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain)\n",
            "  Downloading marshmallow-3.20.1-py3-none-any.whl (49 kB)\n",
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            "\u001b[?25hCollecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.7,>=0.5.7->langchain)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Collecting jsonpointer>=1.9 (from jsonpatch<2.0,>=1.33->langchain)\n",
            "  Downloading jsonpointer-2.4-py2.py3-none-any.whl (7.8 kB)\n",
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            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: mypy-extensions, marshmallow, jsonpointer, typing-inspect, langsmith, jsonpatch, langchain-core, dataclasses-json, langchain\n",
            "Successfully installed dataclasses-json-0.6.3 jsonpatch-1.33 jsonpointer-2.4 langchain-0.0.344 langchain-core-0.0.8 langsmith-0.0.68 marshmallow-3.20.1 mypy-extensions-1.0.0 typing-inspect-0.9.0\n",
            "Collecting PyPDF2\n",
            "  Downloading pypdf2-3.0.1-py3-none-any.whl (232 kB)\n",
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            "\u001b[?25hInstalling collected packages: PyPDF2\n",
            "Successfully installed PyPDF2-3.0.1\n",
            "Collecting InstructorEmbedding\n",
            "  Downloading InstructorEmbedding-1.0.1-py2.py3-none-any.whl (19 kB)\n",
            "Installing collected packages: InstructorEmbedding\n",
            "Successfully installed InstructorEmbedding-1.0.1\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence_transformers) (1.3.0)\n",
            "Building wheels for collected packages: sentence_transformers\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=a6a0bc49b42876bb9a7c0501f6282c97931a20af6ac0fed6c5098c60d4ef6b5f\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence_transformers\n",
            "Installing collected packages: sentencepiece, sentence_transformers\n",
            "Successfully installed sentence_transformers-2.2.2 sentencepiece-0.1.99\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement faiss (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for faiss\u001b[0m\u001b[31m\n",
            "\u001b[0mCollecting faiss-gpu\n",
            "  Downloading faiss_gpu-1.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (85.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.5/85.5 MB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: faiss-gpu\n",
            "Successfully installed faiss-gpu-1.7.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## import required libraries\n",
        "\n",
        "from PyPDF2 import PdfReader\n",
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "from langchain.chains import ConversationalRetrievalChain\n",
        "from langchain.chains import RetrievalQA\n",
        "from langchain.llms import HuggingFaceHub\n",
        "import os\n",
        "from langchain.prompts.prompt import PromptTemplate"
      ],
      "metadata": {
        "id": "z-KkSU2-qhuP"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## extracting text from pdf files\n",
        "def get_pdf_text(pdf_docs):\n",
        "    text = \"\"\n",
        "    for pdf in pdf_docs:\n",
        "        pdf_reader = PdfReader(pdf)\n",
        "        for page in pdf_reader.pages:\n",
        "            text += page.extract_text()\n",
        "    return text\n",
        "\n",
        "## creating overlapping text chunks\n",
        "def get_text_chunks(text):\n",
        "    text_splitter = CharacterTextSplitter(\n",
        "        separator=\"\\n\",\n",
        "        chunk_size=1000,\n",
        "        chunk_overlap=200,\n",
        "        length_function=len\n",
        "    )\n",
        "    chunks = text_splitter.split_text(text)\n",
        "    return chunks\n",
        "\n",
        "## creating embeddings for chunks of text\n",
        "def get_vectorstore(text_chunks):\n",
        "    #embeddings = OpenAIEmbeddings()\n",
        "    embeddings = HuggingFaceInstructEmbeddings(model_name=\"hkunlp/instructor-xl\")\n",
        "    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)\n",
        "    return vectorstore\n",
        "\n",
        "## ceating a retrival llm chain\n",
        "def retrieval_qa_chain(db,return_source_documents):\n",
        "    llm = HuggingFaceHub(repo_id=\"tiiuae/falcon-7b-instruct\", model_kwargs={\"temperature\":0.6,\"max_length\":500, \"max_new_tokens\":700})\n",
        "    qa_chain = RetrievalQA.from_chain_type(llm=llm,\n",
        "                                       chain_type='stuff',\n",
        "                                       retriever=db,\n",
        "                                       return_source_documents=return_source_documents,\n",
        "                                       )\n",
        "    return qa_chain\n"
      ],
      "metadata": {
        "id": "AfINGHCfqoCW"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## DATA VECTORIZATION AND INDEX CREATION\n",
        "\n",
        "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = \"<REPLACE WITH YOUR API TOKEN>\"\n",
        "path_to_pdf = ['/content/content/MyDrive/2/Lecture01.pdf']\n",
        "\n",
        "raw_text = get_pdf_text(path_to_pdf)\n",
        "\n",
        "# get the text chunks\n",
        "text_chunks = get_text_chunks(raw_text)\n",
        "\n",
        "# create vector store\n",
        "vectorstore = get_vectorstore(text_chunks)\n",
        "\n",
        "## creating a db with similarity search and obtaining top 3 most matched vectors of all the vectors present in vector index\n",
        "db = vectorstore.as_retriever(search_kwargs={'k': 3})\n",
        "\n",
        "## passing database to bot as input and initializing the bot\n",
        "\n",
        "bot = retrieval_qa_chain(db,True)"
      ],
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            "/usr/local/lib/python3.10/dist-packages/InstructorEmbedding/instructor.py:7: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
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              "model_id": "7eca50ae137349bc92d1673fb256a7b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbbfbfa128dc4a45a610825210dfbd1d"
            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/461 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ee7e726d02b434481449148d0b276ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "load INSTRUCTOR_Transformer\n",
            "max_seq_length  512\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:127: FutureWarning: '__init__' (from 'huggingface_hub.inference_api') is deprecated and will be removed from version '1.0'. `InferenceApi` client is deprecated in favor of the more feature-complete `InferenceClient`. Check out this guide to learn how to convert your script to use it: https://huggingface.co/docs/huggingface_hub/guides/inference#legacy-inferenceapi-client.\n",
            "  warnings.warn(warning_message, FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## passing query to llm\n",
        "\n",
        "query = \"What is a Database?\"\n",
        "sol=bot(query)\n",
        "\n",
        "## answer giveb by llm\n",
        "\n",
        "print(sol['result'])\n",
        "\n",
        "print(sol['source_documents'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fyoED0BLssKg",
        "outputId": "143fa5d4-63cd-4c2e-ca32-68f322c043c4"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "A database system is a combination of the database and the DBMS.\n",
            "[Document(page_content='1 \\n \\n \\nWhat is a Database?  \\n• Collection of related  data organized to facilitate retrieval, management and updating  \\no Data – known facts that can be recorded and that have implicit meaning  \\n \\n• Properties  \\no Represents some aspects of the real world – miniworld (UoD)  \\n\\uf0a7 Changes in UoD are reflected in the database  \\no Logically coherent collection of data with inherent meaning  \\no Designed, built and populated for some specific purpose  \\n\\uf0a7 Intended user group  \\n\\uf0a7 Set of applications  \\n \\nDatabase Management System(DBMS)  \\n• A collec tion of programs that enables a user to  create  and maintain  the database  \\no Defining  \\n\\uf0a7 Specifying the types of data, structures, constraints  \\no Constructing  \\n\\uf0a7 Storing the data on a storage medium  \\no Manipulating  \\n\\uf0a7 Retrieving specific data , updating , generating reports  \\n \\nDBMS classification  \\n• Data model  \\no Relational  \\no Object  \\no Hierarchical  \\no Network  \\n\\uf0a7 Most commonly used - relational model  \\n \\n• Number of users'), Document(page_content='• Enforcing integrity constraints  \\nParticipants of a DBMS  \\nDatabase Administrators  \\n• The database administrator (DBA) is a person (or group of persons) responsible for the \\noverall control of the entire database  system. Specifically, the DBA does the following:  3 \\n o Deciding on the information content of the database.  \\no Deciding on the storage structure and access strategy.  \\no Defining strategy for backup and recovery.  \\no Defining authorization checks and validation p rocedures.  \\no Liaising with users.  \\no Acquiring necessary software and hardware resources.  \\no Monitoring performance and responding to changes in requirements.  \\n \\nDatabase Designers  \\n \\n• The database designers are responsible for defining the content, the structu re, the \\nconstraints, and functions or transactions against the database.  \\n• They must communicate with the end -users and understand their needs.  \\n \\nSoftware Engineers  \\n \\n• This includes systems analysts and application programmers.'), Document(page_content='DBMS classification  \\n• Data model  \\no Relational  \\no Object  \\no Hierarchical  \\no Network  \\n\\uf0a7 Most commonly used - relational model  \\n \\n• Number of users  \\no Single user  \\n\\uf0a7 Only one user at a given time  \\no Multi -user \\n\\uf0a7 Supports multiple users at the same time  \\n \\n• Number of cites over which the database is distributed  \\no Centralized  \\n\\uf0a7 Stored at a single site  \\no Distributed  \\n\\uf0a7 Distributed over many sites connected by a network  \\n \\n• General purpose  \\no Can be used f or various applications               Lecture 1                   Database Management Systems2 \\n • Special purpose  \\no Built for specific application  \\n\\uf0a7 Air line reservations  \\n\\uf0b7 Large number of concurrent transactions need to be performed \\nwithout delays  \\n \\nDatabase system  \\n• A database system is a combination of the database and the DBMS  \\n \\nCharacteristics of database approach  \\n• In the conventional file processing environment, each user defines, and implements his \\nown files for specific applications')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normal falcon without context\n",
        "\n",
        "llm = HuggingFaceHub(repo_id=\"tiiuae/falcon-7b-instruct\", model_kwargs={\"temperature\":0.7,\"max_length\":500, \"max_new_tokens\":700})\n",
        "\n",
        "\n",
        "\n",
        "llm(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "fjHaItqos7V3",
        "outputId": "a5d4e194-f09c-4040-e50e-e155056a46ac"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:127: FutureWarning: '__init__' (from 'huggingface_hub.inference_api') is deprecated and will be removed from version '1.0'. `InferenceApi` client is deprecated in favor of the more feature-complete `InferenceClient`. Check out this guide to learn how to convert your script to use it: https://huggingface.co/docs/huggingface_hub/guides/inference#legacy-inferenceapi-client.\n",
            "  warnings.warn(warning_message, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nA database is a collection of information stored on a computer system. A database consists of a set of databases, which are used to store different types of data. The database consists of a set of tables, which are used to store and retrieve data. A database is used to store, retrieve, and manipulate large amounts of data in an efficient manner.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ques=['what is Database Management System',\n",
        "      'what are the DBMS classifications',\n",
        "      'what is Database system',\n",
        "      'what are the Advantages of a DBMS?',\n",
        "      ' what are the Characteristics of database approach',\n",
        "      'How to Design databases']\n",
        "\n",
        "\n",
        "\n",
        "sol=bot(ques[0])\n",
        "print(ques[0])\n",
        "print(sol['result'])\n",
        "print(\" \")\n",
        "\n",
        "sol=bot(ques[1])\n",
        "print(ques[1])\n",
        "print(sol['result'])\n",
        "print(\" \")\n",
        "\n",
        "sol=bot(ques[2])\n",
        "print(ques[2])\n",
        "print(sol['result'])\n",
        "print(\" \")\n",
        "\n",
        "sol=bot(ques[3])\n",
        "print(ques[3])\n",
        "print(sol['result'])\n",
        "print(\" \")\n",
        "\n",
        "sol=bot(ques[4])\n",
        "print(ques[4])\n",
        "print(sol['result'])\n",
        "print(\" \")\n",
        "\n",
        "sol=bot(ques[5])\n",
        "print(ques[5])\n",
        "print(sol['result'])\n",
        "print(\" \")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-AaBQArtH9X",
        "outputId": "f589ffc1-1ee2-4c1b-da7b-9084f0b1fb03"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "what is Database Management System\n",
            "\n",
            "The answer is \"A Database Management System (DBMS) is a software application that is used to store, retrieve, and manipulate data. It is designed to provide a user-friendly interface to the user, allowing them to easily create, update, and maintain their own data without having to build their own database from scratch. DBMSs are commonly used in businesses, healthcare, and government to store and manage their data. They can also be used in personal applications, such as web development, to store and retrieve data from a web server. In summary, a DBMS is a software application that is used to store, retrieve, and manipulate data.\"\n",
            " \n",
            "what are the DBMS classifications\n",
            "\n",
            "The DBMS classifications are relational databases, object databases, hierarchical databases, and network databases.\n",
            " \n",
            "what is Database system\n",
            "\n",
            "\n",
            "The correct answer to the question \"What is a Database?\" is \"A database is a collection of related data organized to facilitate retrieval, management, and updating.\" \n",
            "\n",
            "A database system is a combination of the database and the DBMS, where the database is the collection of data that is organized and stored in a specific way, while the DBMS is the software that enables users to create, maintain, and manipulate the database. \n",
            "\n",
            "The characteristics of a database approach include the user defining the database, it being a combination of the conventional file processing environment and the DBMS, and the database being a collection of data organized in a specific way. \n",
            "\n",
            "The main components of a DBMS are the database administrator, the database design, and the DBMS classification that includes relational, object-oriented, and hierarchical databases. \n",
            "\n",
            "In summary, a database system is a collection of related data organized to facilitate retrieval, management, and updating, and it includes the database that is organized in a specific way, the DBMS that enables users to create, maintain, and manipulate the database, and the DBMS classification that includes relational, object-oriented, and hierarchical databases.\n",
            " \n",
            "what are the Advantages of a DBMS?\n",
            "\n",
            "<pre><code>• Controlling redundancy\n",
            "• Restricting unauthorized access\n",
            "• Providing multiple user interfaces\n",
            "• Providing backup and recovery\n",
            "• Providing storage structures for efficient query processing\n",
            "• Enforcing integrity constraints\n",
            "• Enforcing consistency preservation\n",
            "• Adding more users to the database\n",
            "• Database designers create the database design\n",
            "• Software engineers create the software for the database system\n",
            "• DBMS classification: Relational, Object, Hierarchical, Network\n",
            "• DBMS classification: Single user, Multi-user\n",
            "• DBMS classification: Centralized, Distributed\n",
            "• DBMS classification: General purpose, Special purpose\n",
            "• DBMS classification: Built for specific application, Built for a specific purpose\n",
            "• DBMS classification: Number of users, Number of users at a time, Number of users over time\n",
            "• DBMS classification: Enforcing integrity constraints, Enforcing consistency preservation, Enforcing backward compatibility\n",
            "• DBMS classification: Database, DB, DBMS, DBMS, DBMS\n",
            "</code></pre>\n",
            " \n",
            " what are the Characteristics of database approach\n",
            "\n",
            "\n",
            "Characteristics of database approach\n",
            "\n",
            "1. Self-describing nature\n",
            "2. Program-data independence\n",
            "3. Program-data freedom\n",
            "4. Support for multiple views\n",
            "5. Multiuser transaction processing\n",
            "6. Database transactions\n",
            "7. Database system\n",
            "8. Characteristics of database approach\n",
            "9. Isolation\n",
            "10. Consistency preservation\n",
            "11. Durability or permanency\n",
            "12. Controlling redundancy\n",
            "13. Restricting unauthorized access\n",
            "14. Providing multiple user interfaces\n",
            "15. Providing backup and recovery\n",
            "16. Providing storage structures for efficient query processing\n",
            "17. Enforcing integrity constraints\n",
            "18. Representing complex relationships among data\n",
            "19. Enforcing integrity constraints\n",
            "20. Managing database growth\n",
            "21. Database integrity\n",
            "22. Database security\n",
            "23. Database reliability\n",
            "24. Database scalability\n",
            "25. Database performance\n",
            "\n",
            "The DBMS classification is based on the number of users. There can be a single user, multiple users, or a distributed database system. The DBMS classification can be based on the number of users or the number of concurrent transactions.\n",
            " \n",
            "How to Design databases\n",
            "\n",
            "\n",
            "1. What are the different types of databases?\n",
            "2. What are the key design principles for database systems?\n",
            "3. What are the components of a database system?\n",
            "4. What are the different types of database normalization?\n",
            "5. What are the different types of database relationships?\n",
            "6. What are the different types of database search algorithms?\n",
            "7. What are the design considerations for database security?\n",
            "8. What are the different types of database backup and recovery strategies?\n",
            "9. What are the different types of database indexing?\n",
            "10. What are the different types of database management systems?\n",
            " \n"
          ]
        }
      ]
    }
  ]
}