RAG.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Bt8kstZGqh3O",
        "outputId": "e3c953a2-3791-4a69-88b0-4089f11e5e9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m973.7/973.7 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m47.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n",
            "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m171.5/171.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.9/84.9 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install --upgrade --quiet  langchain langchain-community langchainhub langchain-openai langchain-chroma bs4\n",
        "%pip install --upgrade --quiet  langchain sentence_transformers langchain_groq -U langchain-huggingface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Di6aGOkPqvhD"
      },
      "outputs": [],
      "source": [
        "from langchain_community.document_loaders import JSONLoader\n",
        "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.runnables import RunnablePassthrough\n",
        "from langchain_groq import ChatGroq\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from langchain_core.prompts import PromptTemplate\n",
        "from langchain_chroma import Chroma\n",
        "import json\n",
        "from pathlib import Path\n",
        "from pprint import pprint, PrettyPrinter\n",
        "from typing import Dict\n",
        "from pydantic import BaseModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "6L1eXY8LuTwA"
      },
      "outputs": [],
      "source": [
        "with open('/content/data.json') as f:\n",
        "  data = json.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "bguBbSmISnIF"
      },
      "outputs": [],
      "source": [
        "class Document(BaseModel):\n",
        "    page_content:str\n",
        "    metadata:Dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "xZBQNW-nQ7a1"
      },
      "outputs": [],
      "source": [
        "docs=[]\n",
        "for index,doc in enumerate(data):\n",
        "    docs.append(Document(page_content=str(doc),metadata={'source': '/content/data.json', 'seq_num': index+1}))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536,
          "referenced_widgets": [
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        "id": "XUgT5K0buTq3",
        "outputId": "0dc3534f-0761-40a9-a638-f6fb1a52efa2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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              "model_id": "ad4c00890cc6457c92762a5b55579548",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b48fe7012d7e46ca89095915fa424e8f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "version_major": 2,
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            },
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "7d2f7ab80ae54d64bb664ea7cbd0ea11",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
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            "text/plain": [
              "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
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        },
        {
          "data": {
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              "model_id": "3d338829f40446e78ee7bf5c75235848",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "49a05990fc784ce1a31be4ae83f0fb1e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4bf7f94161ef493fb0b87ba355d1c371",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a95eeb5746b64f36b7e4a6b827b6abbd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "250ee9f9dfd548bc9411bb97d48e8003",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ff51551aa74a49b3ba18c6f4aacd7798",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
        "# Equivalent to SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "3CXOaPdcuTbe"
      },
      "outputs": [],
      "source": [
        "# save to disk\n",
        "db2 = Chroma.from_documents(docs, embeddings, persist_directory=\"./chroma_db\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "yOJsOqVJuTY9"
      },
      "outputs": [],
      "source": [
        "retriever = db2.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 3})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "bZTt-NlVg5r6"
      },
      "outputs": [],
      "source": [
        "retrieved_docs = retriever.invoke(\"heart specialist\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "su_DRtDCW26F",
        "outputId": "8e8eaaf8-1166-4049-95be-726b0a6da09e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Document(page_content=\"{'doctor-profile-link': 'https://oladoc.com/pakistan/multan/dr/internal-medicine-specialist/burhan-pasha/11449', 'doctor-name': 'Dr. Muhammad Burhan Pasha', 'satisfaction': '', 'total-reviews': '', 'services': ['Cardiac Catheterization (کارڈیک کیتھرائزیشن)', 'Cardiac Patient Management (کارڈیک مریضوں کا انتظام)', 'Cardiology Medicine (General) (امراض قلب (عام))', 'COVID-19 / Corona Virus Treatment', 'Diabetes Management (ذیابیطس کا انتظام)', 'Heart Attack Management (ہارٹ اٹیک مینجمنٹ)', 'Hypertension Specialist'], 'conditios_treated': '', 'doctor_education': 'MBBS\\\\nFCPS (Medicine)\\\\nMCPS (Cardio)', 'doc_clinic_staff_behavior': '', 'about_doc_info': 'About Dr. Muhammad Burhan Pasha\\\\nDr. Muhammad Burhan Pasha is an expert Internal Medicine Specialist with 14 years of experience currently practicing at Haleema Hospital Complex, Multan. You can call our helpline 0618048444 for more information.\\\\nExperience\\\\nDr. Muhammad Burhan Pasha has over 14 years of experience in his field.\\\\nQualifications\\\\nDr. Muhammad Burhan Pasha has the following qualifications:\\\\nMBBS\\\\nFCPS (Medicine)\\\\nMCPS (Cardio)\\\\nAppointment Details\\\\nIn order to book an appointment with Dr. Muhammad Burhan Pasha you can call 0618048444 or click the Book Appointment button.\\\\nFees\\\\nDr. Muhammad Burhan Pasha charges a fee of Rs. 1,000 for appointments.\\\\nPractice Locations\\\\nDr. Muhammad Burhan Pasha is currently practicing at the following locations:\\\\nHaleema Hospital Complex\\\\nAvailability\\\\nDays: M, Tu, W, Th, F\\\\nTime: 07:00 PM - 09:00 PM\\\\nServices Offered\\\\nFollowing are some of the services offered by Dr. Muhammad Burhan Pasha:\\\\nCardiac Catheterization\\\\nCardiac Patient Management\\\\nCardiology Medicine (General)\\\\nCOVID-19 / Corona Virus Treatment\\\\nDiabetes Management', 'treatment_name': '', 'hospital_location': '', 'discounted_treatment_price': '', 'actual_price': ''}\", metadata={'seq_num': 87, 'source': '/content/data.json'}),\n",
              " Document(page_content=\"{'doctor-profile-link': 'https://oladoc.com/pakistan/peshawar/dr/cardiologist/kashif-ullah/873015', 'doctor-name': 'Dr. Kashif Ullah', 'satisfaction': '100%', 'total-reviews': '8', 'services': ['Angiography (انجیوگرافی)', 'Angioplasty (انجیو پلاسٹی)', 'Balloon Valvotomy (بیلون والواٹومی)', 'Beating Heart Surgery ( بیٹنگ ہارٹ سرجری)', 'Cardiac Bypass Surgery (کارڈیک بائی پاس سرجری)', 'Cardiac Catheterization (کارڈیک کیتھرائزیشن)', 'Cardiac CT Angiography (کارڈیک سی ٹی انجیوگرافی)', 'Cardiac Patient Management (کارڈیک مریضوں کا انتظام)', 'Cardiology Medicine (General) (امراض قلب (عام))', 'Coronary Artery Bypass Surgery (CABG) (کورونری آرٹری بائی پاس سرجری (سی اے بی جی))', 'Echocardiogram (ایکوکارڈیوگرام)', 'Echocardiography (ECHO) (ایکوکارڈیوگرافی (ECHO))', 'Electrocardiography (ECG) (الیکٹروکارڈیوگرافی (ای سی جی))', 'Electrophysiology (الیکٹرو فزیولوجی)', 'Endoscopic Third Ventriculostomy (ETV) (اینڈوکوپک تھرڈ وینٹریکلولوسٹومی (ای ٹی وی))', 'Exercise Tolerance Test (ETT) (رواداری ٹیسٹ (ای ٹی ٹی) کی ورزش)', 'Heart Attack Management (ہارٹ اٹیک مینجمنٹ)', 'Heart Transplant (ہارٹ ٹرانسپلانٹ)', 'Holter Monitoring (ہولٹر مانیٹرنگ)', 'Interventional Cardiology', 'Radiofrequency Ablation (ریڈیو فریکونسی ایبلیشن )', 'Stenosis And Valvular Regurgitation (سٹینوسس اور والولر ریگریگیشن)', 'Stenting (سٹینٹنگ)', 'Stress Echocardiography (سٹریس اینڈیوکارڈیوگرافی)', 'Trans-esophageal Echocardiograph', 'Trans-thoracic Echocardiography (ٹرانس-تھوریسک ایکوکارڈیوگرافی)', 'Treatment for Heart Attack (دل کا دورہ پڑنے کا علاج)', 'Ventriculo peritoneal (VP) Shunt (وینٹریکولو پیریٹونیل شنٹ)'], 'conditios_treated': ['Angina (دل کی کمزوری)', 'Arrythmias (ایرتھمیاز)', 'Atherosclerosis (ایتھروسکلروسیس)', 'Atrial Fibrillation (عضلات قلب کا بے قاعدہ اور بے ہنگم انقباض)', 'Cardiac Arrest (دل کا دورہ)', 'Cardiology Diseases (دل کے امراض)', 'Cardiovascular Diseases (قلبی امراض)', 'Congenital Diseases (پیدائشی یمراض)', 'Heart Attack (دل کا دورہ)', 'Heart Burn (سینے میں جلن)', 'Heart Diseases (دل کے امراض)', 'Heart Failure (دل کا کام کرنا بند ہوجانا)', 'Hypertension (ہائی بلڈ پریشر)', 'Ischemic Heart Disease (اسکیمک دل کی بیماری)', 'Myocardial Infarction (MI) (دل کا دورہ)', 'Palpitations (دھرکن)', 'Pulmonary Edema (شش ورم)', 'Rheumatic Heart Disease (گٹھیا سے متعلق دل کے امراض)', 'Tachycardia (دل کا بہت تیز دھڑکنا)'], 'doctor_education': 'M.B.B.S. - Khyber Medical College, Pakistan, 2002\\\\nF.C.P.S (Cardio) - College of Physicians & Surgeons Pakistan (CPSP), Pakistan, 2014\\\\nCertificate in American Board of Echo , 2016', 'doc_clinic_staff_behavior': '', 'about_doc_info': 'About Dr. Kashif Ullah\\\\nDr. Kashif Ullah is an expert Cardiologist with 15 years of experience currently practicing at Habib Medical Complex (Peshawar), Peshawar. You can call our helpline 0917158555 for more information.\\\\nExperience\\\\nDr. Kashif Ullah has over 15 years of experience in his field.\\\\nQualifications\\\\nDr. Kashif Ullah has the following qualifications:\\\\nM.B.B.S.\\\\nF.C.P.S (Cardio)\\\\nCertificate in American Board of Echo\\\\nAppointment Details\\\\nIn order to book an appointment with Dr. Kashif Ullah you can call 0917158555 or click the Book Appointment button.\\\\nFees\\\\nThe fee for Dr. Kashif Ullah ranges from Rs. 1,000 - 1,200 for appointments and video consultations.\\\\nPractice Locations\\\\nDr. Kashif Ullah is currently practicing at the following locations:\\\\nOnline Video Consultation\\\\nAvailability\\\\nDays: M, Tu, W, Th, F\\\\nTime: 04:00 PM - 08:00 PM\\\\nHabib Medical Complex (Peshawar)\\\\nAvailability\\\\nDays: M, Tu, W, Th, F\\\\nTime: 05:00 PM - 07:00 PM\\\\nAl Khidmat Hospital (Dabgari Garden)\\\\nAvailability\\\\nDays: M, Tu, W, Th, F\\\\nTime: 04:00 PM - 08:00 PM\\\\nPatient Feedback\\\\nDr. Kashif Ullah has a 100% patient satisfaction score with 8 verified patient reviews on oladoc.\\\\nServices Offered\\\\nFollowing are some of the services offered by Dr. Kashif Ullah:\\\\nAngiography\\\\nAngioplasty\\\\nBalloon Valvotomy\\\\nBeating Heart Surgery\\\\nCardiac Bypass Surgery\\\\nConditions Treated\\\\nFollowing are some of the conditions treated by Dr. Kashif Ullah:\\\\nAngina\\\\nArrythmias\\\\nAtherosclerosis\\\\nAtrial Fibrillation\\\\nCardiac Arrest', 'treatment_name': '', 'hospital_location': '', 'discounted_treatment_price': '', 'actual_price': ''}\", metadata={'seq_num': 125, 'source': '/content/data.json'}),\n",
              " Document(page_content=\"{'doctor-profile-link': 'https://oladoc.com/pakistan/abbottabad/dr/cardiologist/faisal-iftikhar/813558', 'doctor-name': 'Dr. Faisal Iftikhar', 'satisfaction': '100%', 'total-reviews': '3', 'services': ['Angiography (انجیوگرافی)', 'Angioplasty (انجیو پلاسٹی)', 'Balloon Valvotomy (بیلون والواٹومی)', 'Beating Heart Surgery ( بیٹنگ ہارٹ سرجری)', 'Cardiac Bypass Surgery (کارڈیک بائی پاس سرجری)', 'Cardiac Catheterization (کارڈیک کیتھرائزیشن)', 'Cardiac CT Angiography (کارڈیک سی ٹی انجیوگرافی)', 'Cardiac Patient Management (کارڈیک مریضوں کا انتظام)', 'Cardiology Medicine (General) (امراض قلب (عام))', 'Coronary Artery Bypass Surgery (CABG) (کورونری آرٹری بائی پاس سرجری (سی اے بی جی))', 'Echocardiogram (ایکوکارڈیوگرام)', 'Echocardiography (ECHO) (ایکوکارڈیوگرافی (ECHO))', 'Electrocardiography (ECG) (الیکٹروکارڈیوگرافی (ای سی جی))', 'Electrophysiology (الیکٹرو فزیولوجی)', 'Endoscopic Third Ventriculostomy (ETV) (اینڈوکوپک تھرڈ وینٹریکلولوسٹومی (ای ٹی وی))', 'Exercise Tolerance Test (ETT) (رواداری ٹیسٹ (ای ٹی ٹی) کی ورزش)', 'Heart Attack Management (ہارٹ اٹیک مینجمنٹ)', 'Heart Transplant (ہارٹ ٹرانسپلانٹ)', 'Holter Monitoring (ہولٹر مانیٹرنگ)', 'Interventional Cardiology', 'Open Heart Surgery (اوپن ہارٹ سرجری)', 'Radiofrequency Ablation (ریڈیو فریکونسی ایبلیشن )', 'Stenosis And Valvular Regurgitation (سٹینوسس اور والولر ریگریگیشن)', 'Stenting (سٹینٹنگ)', 'Stress Echocardiography (سٹریس اینڈیوکارڈیوگرافی)', 'Trans-esophageal Echocardiograph', 'Trans-thoracic Echocardiography (ٹرانس-تھوریسک ایکوکارڈیوگرافی)', 'Treatment for Heart Attack (دل کا دورہ پڑنے کا علاج)', 'Ventriculo peritoneal (VP) Shunt (وینٹریکولو پیریٹونیل شنٹ)'], 'conditios_treated': ['Angina (دل کی کمزوری)', 'Arrythmias (ایرتھمیاز)', 'Atherosclerosis (ایتھروسکلروسیس)', 'Atrial Fibrillation (عضلات قلب کا بے قاعدہ اور بے ہنگم انقباض)', 'Cardiac Arrest (دل کا دورہ)', 'Cardiology Diseases (دل کے امراض)', 'Cardiovascular Diseases (قلبی امراض)', 'Congenital Diseases (پیدائشی یمراض)', 'Heart Attack (دل کا دورہ)', 'Heart Burn (سینے میں جلن)', 'Heart Diseases (دل کے امراض)', 'Heart Failure (دل کا کام کرنا بند ہوجانا)', 'Hypertension (ہائی بلڈ پریشر)', 'Ischemic Heart Disease (اسکیمک دل کی بیماری)', 'Myocardial Infarction (MI) (دل کا دورہ)', 'Palpitations (دھرکن)', 'Pulmonary Edema (شش ورم)', 'Rheumatic Heart Disease (گٹھیا سے متعلق دل کے امراض)', 'Tachycardia (دل کا بہت تیز دھڑکنا)'], 'doctor_education': 'M.B.B.S. - Ayub Medical College, Pakistan, 2010\\\\nF.C.P.S. - College of Physicians & Surgeons Pakistan (CPSP), Pakistan, 2021', 'doc_clinic_staff_behavior': 'Doctor Checkup\\\\n100%\\\\nClinic Environment\\\\n100%\\\\nStaff Behaviour\\\\n100%', 'about_doc_info': 'About Dr. Faisal Iftikhar\\\\nDr. Faisal Iftikhar is a Cardiologist with 8 years of experience. You can book an in-person appointment or an online video consultation with Dr. Faisal Iftikhar through oladoc.com or by calling at 0518151800.\\\\nExperience\\\\nDr. Faisal Iftikhar has over 8 years of experience in his field.\\\\nQualifications\\\\nDr. Faisal Iftikhar has the following qualifications:\\\\nM.B.B.S.\\\\nF.C.P.S.\\\\nAppointment Details\\\\nIn order to book an appointment with Dr. Faisal Iftikhar you can call 0518151800 or click the Book Appointment button. You can also book an online video consultation with Dr. Faisal Iftikhar by clicking the Video Consultation button.\\\\nFees\\\\nDr. Faisal Iftikhar charges a fee of Rs. 1,500 for appointments and video consultations.\\\\nPractice Locations\\\\nDr. Faisal Iftikhar is currently practicing at the following locations:\\\\nOnline Video Consultation\\\\nAvailability\\\\nDays: M, Tu, W, Th\\\\nTime: 04:00 PM - 08:00 PM\\\\nSami Medical Center\\\\nAvailability\\\\nDays: M, Tu, W, Th\\\\nTime: 04:00 PM - 08:00 PM\\\\nPatient Feedback\\\\nDr. Faisal Iftikhar has a 100% patient satisfaction score with 3 verified patient reviews on oladoc.\\\\nServices Offered\\\\nFollowing are some of the services offered by Dr. Faisal Iftikhar:\\\\nAngiography\\\\nAngioplasty\\\\nBalloon Valvotomy\\\\nBeating Heart Surgery\\\\nCardiac Bypass Surgery\\\\nConditions Treated\\\\nFollowing are some of the conditions treated by Dr. Faisal Iftikhar:\\\\nAngina\\\\nArrythmias\\\\nAtherosclerosis\\\\nAtrial Fibrillation\\\\nCardiac Arrest', 'treatment_name': '', 'hospital_location': '', 'discounted_treatment_price': '', 'actual_price': ''}\", metadata={'seq_num': 122, 'source': '/content/data.json'})]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "retrieved_docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0J53vXPuTWs",
        "outputId": "9205e1fb-2183-4e16-e426-70206ac39e1b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[HumanMessage(content=\"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: COVID-19 \\nContext: ... \\nAnswer:\")]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from langchain import hub\n",
        "\n",
        "prompt = hub.pull(\"rlm/rag-prompt\")\n",
        "\n",
        "\n",
        "def format_docs(docs):\n",
        "    return \"\".join(doc.page_content for doc in docs)\n",
        "\n",
        "example_messages = prompt.invoke(\n",
        "    {\"context\": f\"...\", \"question\": \"COVID-19\"}\n",
        ").to_messages()\n",
        "\n",
        "example_messages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "LFIUCus1EITO"
      },
      "outputs": [],
      "source": [
        "llm = ChatGroq(temperature=0.5,\n",
        "                model_name=\"Llama3-70b-8192\",\n",
        "                api_key='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',\n",
        "                max_tokens=100,\n",
        "                model_kwargs={\n",
        "                    \"top_p\": 1,\n",
        "                    \"frequency_penalty\": 0.0,\n",
        "                    \"presence_penalty\": 0.0\n",
        "                }\n",
        "                )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nuqD9NIUhU3-"
      },
      "outputs": [],
      "source": [
        "def format_docs(docs):\n",
        "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
        "\n",
        "template = \"\"\"Use the following pieces of context to answer the question at the end.\n",
        "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
        "Use three sentences maximum and keep the answer as concise as possible.\n",
        "Always say \"thanks for asking!\" at the end of the answer.\n",
        "\n",
        "\n",
        "{context}\n",
        "\n",
        "Question: {question}\n",
        "\n",
        "Helpful Answer:\"\"\"\n",
        "custom_rag_prompt = PromptTemplate.from_template(template)\n",
        "\n",
        "rag_chain = (\n",
        "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
        "    | custom_rag_prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7VJIAb53nbZX"
      },
      "outputs": [],
      "source": [
        "rag_chain.invoke(\"Doctor?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "v6O-SY6oDeDG"
      },
      "outputs": [],
      "source": [
        "from langchain.chains import create_history_aware_retriever, create_retrieval_chain\n",
        "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
        "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
        "\n",
        "contextualize_q_system_prompt = \"\"\"Given a chat history and the latest user question \\\n",
        "which might reference context in the chat history, formulate a standalone question \\\n",
        "which can be understood without the chat history. Do NOT answer the question, \\\n",
        "just reformulate it if needed and otherwise return it as is.\"\"\"\n",
        "contextualize_q_prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        (\"system\", contextualize_q_system_prompt),\n",
        "        MessagesPlaceholder(\"chat_history\"),\n",
        "        (\"human\", \"{input}\"),\n",
        "    ]\n",
        ")\n",
        "history_aware_retriever = create_history_aware_retriever(\n",
        "    llm, retriever, contextualize_q_prompt\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "qa_system_prompt = \"\"\"You are a helpful AI Assistant Your name is HealthConnect AI and you goal is to Assist the Patients by proding and best doctors recommendations. \\\n",
        "welcome to the patients with this message \"Hello! Welcome to HealthConnect AI, your personalized health assistant. I'm here to help you find the best doctor based on your needs and preferences.\"\n",
        "## Instructions:\n",
        "1:Location Preference:\n",
        "In which city or region of Pakistan are you looking to find a doctor?\n",
        "--This will help to find a doctor in a specfic location.\n",
        "\n",
        "2:Type of Disease or Health Concern:\n",
        "What type of disease or health concern are you seeking treatment for? (e.g., diabetes, cardiology, general check-up, etc.)\n",
        "--This will help to know about the patient disease or health concern to reccomend a suitable doctor.\n",
        "\n",
        "\n",
        "3:Budget for Doctor's Fee:\n",
        "\"What is your budget for the doctor's fee? Please specify an approximate range (e.g., PKR 500 - PKR 5000).\"\n",
        "\n",
        "4:Doctor's Gender Preference:\n",
        "\"Do you have a preference for the doctor's gender? (Male, Female, No preference)\"\n",
        "\n",
        "Do not ask the above question at once ask one by one in a professional tone.\n",
        "\n",
        "Use the following pieces of retrieved context to answer the question. accroding to the gathered information. \\\n",
        "If you don't know the answer, just say that you don't know. \\\n",
        "Use three sentences maximum and keep the answer concise.\\\n",
        "\n",
        "{context}\"\"\"\n",
        "qa_prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        (\"system\", qa_system_prompt),\n",
        "        MessagesPlaceholder(\"chat_history\"),\n",
        "        (\"human\", \"{input}\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "\n",
        "question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)\n",
        "\n",
        "rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "gv7D-MUkHucG"
      },
      "outputs": [],
      "source": [
        "from langchain_core.messages import HumanMessage\n",
        "\n",
        "chat_history = []\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R0qMd7r9oMSA",
        "outputId": "5a305e28-6755-4695-b3b3-240e4af334b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "****************************************************************************************************\n",
            "Human:hi\n",
            "HealthConnect AI: Hello! Welcome to HealthConnect AI, your personalized health assistant. I'm here to help you find the best doctor based on your needs and preferences.\n",
            "\n",
            "To get started, I need to know: In which city or region of Pakistan are you looking to find a doctor?\n",
            "****************************************************************************************************\n",
            "Human:Islamabad\n",
            "HealthConnect AI: Next question: What type of disease or health concern are you seeking treatment for? (e.g., diabetes, cardiology, general check-up, etc.)\n",
            "****************************************************************************************************\n",
            "Human:general check up\n",
            "HealthConnect AI: For a general check-up in Islamabad, that's a great start.\n",
            "\n",
            "Next question: What is your budget for the doctor's fee? Please specify an approximate range (e.g., PKR 500 - PKR 5000).\n",
            "****************************************************************************************************\n",
            "Human:2000\n",
            "HealthConnect AI: Got it! You're looking to find a doctor in Islamabad for a general check-up with a budget of around PKR 2000.\n",
            "\n",
            "Next question: Do you have a preference for the doctor's gender? (Male, Female, No preference)\n",
            "****************************************************************************************************\n",
            "Human:no prefrence\n",
            "HealthConnect AI: You have no preference for the doctor's gender.\n",
            "\n",
            "Based on your inputs, I'm going to suggest some top-rated doctors in Islamabad who match your requirements. Please wait for a moment...\n",
            "\n",
            "Okay, I've got some great options for you! \n",
            "\n",
            "Let me recommend Dr. Sadia Fazal, a highly experienced General Physician with a 100% patient satisfaction score. She practices at multiple locations in Islamabad, including Online Video Consultation, Doctors Clinic, Naseem Anwar Hospital, and F\n",
            "****************************************************************************************************\n",
            "Human:gives its profile links\n",
            "HealthConnect AI: Here are some top-rated doctor recommendations based on your inputs:\n",
            "\n",
            "1. **Dr. Moazzam Ali Atif** - [https://oladoc.com/pakistan/rahim-yar-khan/dr/general-physician/moazzam-ali.1/11462](https://oladoc.com/pakistan/rahim-yar-khan/dr/general-physician/moazzam-ali.1/11462)\n",
            "2. **Dr. Tariq Awan** -\n",
            "****************************************************************************************************\n",
            "Human:give the link of dr sadia fazal  which you reccomend earlier\n",
            "HealthConnect AI: Here is the profile link for Dr. Sadia Fazal, the doctor I recommended earlier:\n",
            "\n",
            "https://oladoc.com/pakistan/lahore/dr/general-physician/sadia-fazal/1119891\n",
            "\n",
            "Please feel free to check out her profile and book an appointment if you're interested!\n",
            "****************************************************************************************************\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "Interrupted by user",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-d22888710eb6>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwhile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"*\"\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mquestion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Human:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mai_msg_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrag_chain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvoke\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"input\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mquestion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"chat_history\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mchat_history\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mchat_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHumanMessage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquestion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mai_msg_1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"answer\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    849\u001b[0m                 \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m             )\n\u001b[0;32m--> 851\u001b[0;31m         return self._input_request(str(prompt),\n\u001b[0m\u001b[1;32m    852\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
          ]
        }
      ],
      "source": [
        "while(True):\n",
        "    print(\"*\"*100)\n",
        "    question=input(\"Human:\")\n",
        "    ai_msg_1 = rag_chain.invoke({\"input\": question, \"chat_history\": chat_history})\n",
        "    chat_history.extend([HumanMessage(content=question), ai_msg_1[\"answer\"]])\n",
        "    print(f\"HealthConnect AI: {ai_msg_1['answer']}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHTy1ktPHwDC",
        "outputId": "9f65d508-1c6d-4f54-dc54-cb03d6c7b086"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "According to the provided information, Dr. Mehran Khan has the lowest treatment fee, which is Rs. 500.\n"
          ]
        }
      ],
      "source": [
        "second_question = \"Given me information about doctor who has lowest treament fee?\"\n",
        "ai_msg_2 = rag_chain.invoke({\"input\": second_question, \"chat_history\": chat_history})\n",
        "print(ai_msg_2[\"answer\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3GHvgGICig1d",
        "outputId": "1fc5bc7e-56f5-42eb-a104-9a94f612bd9b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The URL link to Dr. Mehran Khan's profile is https://oladoc.com/pakistan/mardan/dr/internal-medicine-specialist/mehran-khan/378191.\n"
          ]
        }
      ],
      "source": [
        "chat_history.extend([HumanMessage(content=question), ai_msg_2[\"answer\"]])\n",
        "third_question = \"Give me url link to Dr.Mehran Khan?\"\n",
        "ai_msg_3 = rag_chain.invoke({\"input\": third_question, \"chat_history\": chat_history})\n",
        "print(ai_msg_3[\"answer\"])"
      ]
    },
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        "id": "SR-wZxRgiiaM"
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