01-rag-intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42e20f86-a419-4c02-aed3-891f183ec8da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-07-02 02:31:29--  https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3832 (3.7K) [text/plain]\n",
      "Saving to: ‘minsearch.py’\n",
      "\n",
      "minsearch.py        100%[===================>]   3.74K  --.-KB/s    in 0s      \n",
      "\n",
      "2024-07-02 02:31:29 (13.5 MB/s) - ‘minsearch.py’ saved [3832/3832]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7c4751cd-2b75-42e9-86b2-dbaf65039355",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-07-02 02:57:32--  https://raw.githubusercontent.com/DataTalksClub/llm-zoomcamp/main/01-intro/documents.json\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.108.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 658332 (643K) [text/plain]\n",
      "Saving to: ‘documents.json’\n",
      "\n",
      "documents.json      100%[===================>] 642.90K  3.16MB/s    in 0.2s    \n",
      "\n",
      "2024-07-02 02:57:33 (3.16 MB/s) - ‘documents.json’ saved [658332/658332]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/DataTalksClub/llm-zoomcamp/main/01-intro/documents.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f739e274-86cb-4c64-aa61-9824373ed3da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import minsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48d40ccb-dd31-4d7f-971b-28303f57db3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a868363-6a06-4edd-8e26-c648ca6ec403",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('documents.json','rt') as f_in:\n",
    "    docs_raw = json.load(f_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8209257-e31a-45db-ab8d-71e4c8c4d51e",
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = []\n",
    "\n",
    "for course_dict in docs_raw:\n",
    "    for doc in course_dict[\"documents\"]:\n",
    "        doc['course'] = course_dict['course']\n",
    "        documents.append(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c67b24ac-8f3e-4ef9-92a3-63d26cf0ed15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'In this section of the course, the 5432 port of pgsql is mapped to your computer’s 5432 port. Which means you can access the postgres database via pgcli directly from your computer.\\nSo No, you don’t need to run it inside another container. Your local system will do.',\n",
       " 'section': 'Module 1: Docker and Terraform',\n",
       " 'question': 'PGCLI - INKhould we run pgcli inside another docker container?',\n",
       " 'course': 'data-engineering-zoomcamp'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3e45615e-d59b-4f59-bbfb-9fbbd2f1f267",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = minsearch.Index(\n",
    "    text_fields=[\"question\",\"text\",\"section\"], \n",
    "    keyword_fields=[\"course\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a346b08e-acab-47c2-93e4-b8115ad344d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<minsearch.Index at 0x7d9702048a90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "35308de0-d7db-45d3-bfd7-0699e2dc3654",
   "metadata": {},
   "outputs": [],
   "source": [
    "q = \"can i join the course , if it has already started?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "32ee4d10-ff2f-42dc-9af9-c7b872821ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "boost={'questions': 3.0,'section': 0.5}\n",
    "results = index.search(\n",
    "    query=q,\n",
    "    filter_dict={'course':'data-engineering-zoomcamp'},\n",
    "    boost_dict=boost,\n",
    "    num_results=5\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2d6c3f04-dcd1-49bb-9adb-af001b6a3e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': \"The purpose of this document is to capture frequently asked technical questions\\nThe exact day and hour of the course will be 15th Jan 2024 at 17h00. The course will start with the first  “Office Hours'' live.1\\nSubscribe to course public Google Calendar (it works from Desktop only).\\nRegister before the course starts using this link.\\nJoin the course Telegram channel with announcements.\\nDon’t forget to register in DataTalks.Club's Slack and join the channel.\",\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - When will the course start?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': \"Yes, even if you don't register, you're still eligible to submit the homeworks.\\nBe aware, however, that there will be deadlines for turning in the final projects. So don't leave everything for the last minute.\",\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - Can I still join the course after the start date?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': 'Yes, we will keep all the materials after the course finishes, so you can follow the course at your own pace after it finishes.\\nYou can also continue looking at the homeworks and continue preparing for the next cohort. I guess you can also start working on your final capstone project.',\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - Can I follow the course after it finishes?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': 'Yes, the slack channel remains open and you can ask questions there. But always sDocker containers exit code w search the channel first and second, check the FAQ (this document), most likely all your questions are already answered here.\\nYou can also tag the bot @ZoomcampQABot to help you conduct the search, but don’t rely on its answers 100%, it is pretty good though.',\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - Can I get support if I take the course in the self-paced mode?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': 'Star the repo! Share it with friends if you find it useful ❣️\\nCreate a PR if you see you can improve the text or the structure of the repository.',\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'How can we contribute to the course?',\n",
       "  'course': 'data-engineering-zoomcamp'}]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2764f701-1793-4bd1-ab56-be575fec4fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "db0ff68d-7df4-4fde-afce-24266fbbff7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = OpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "16d92932-1727-4ddf-9703-00dfcab0e8fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "q = \"Will I get certification post completion?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "97390b43-b518-4e2a-a6d0-b8fdbc3c87c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model='gpt-3.5-turbo',\n",
    "    messages=[{'role':'user','content':q}]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e3669712-7203-4392-b01d-b8fb714515e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'It depends on the course or program you are completing. Some courses or programs offer certification upon completion, while others do not. It is important to check the details of the course or program you are interested in to determine if certification is provided.'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3f42f766-b9c5-46b7-95fe-97dbfb846dbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt_template= \"\"\"\n",
    "You're a course teaching assistant. Answer the QUESTION based on the CONTEXT from FAQ database.\n",
    "Use only the facts from the CONTEXT when answering the QUESTION.\n",
    "If the CONTEXT doesn't contain the answer, output NONE\n",
    "\n",
    "QUESTION: {question}\n",
    "\n",
    "CONTEXT:\n",
    "{context}\n",
    "\"\"\".strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5d81361b-b949-45bb-886c-c1cbd4de0904",
   "metadata": {},
   "outputs": [],
   "source": [
    "context=\"\"\n",
    "\n",
    "for doc in results:\n",
    "    context = context + f\"section : {doc['section']}\\nquestion: {doc['question']}\\nanswer:{doc['text']}|n|n\"\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b84ab0d3-7102-4270-ae11-9f288fcef666",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = prompt_template.format(question=q,context=context).strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ab985726-0852-4bb1-9b7f-37379bf124f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You're a course teaching assistant. Answer the QUESTION based on the CONTEXT from FAQ database.\n",
      "Use only the facts from the CONTEXT when answering the QUESTION.\n",
      "If the CONTEXT doesn't contain the answer, output NONE\n",
      "\n",
      "QUESTION: Will I get certification post completion?\n",
      "\n",
      "CONTEXT:\n",
      "section : General course-related questions\n",
      "question: Course - When will the course start?\n",
      "answer:The purpose of this document is to capture frequently asked technical questions\n",
      "The exact day and hour of the course will be 15th Jan 2024 at 17h00. The course will start with the first  “Office Hours'' live.1\n",
      "Subscribe to course public Google Calendar (it works from Desktop only).\n",
      "Register before the course starts using this link.\n",
      "Join the course Telegram channel with announcements.\n",
      "Don’t forget to register in DataTalks.Club's Slack and join the channel.|n|nsection : General course-related questions\n",
      "question: Course - Can I still join the course after the start date?\n",
      "answer:Yes, even if you don't register, you're still eligible to submit the homeworks.\n",
      "Be aware, however, that there will be deadlines for turning in the final projects. So don't leave everything for the last minute.|n|nsection : General course-related questions\n",
      "question: Course - Can I follow the course after it finishes?\n",
      "answer:Yes, we will keep all the materials after the course finishes, so you can follow the course at your own pace after it finishes.\n",
      "You can also continue looking at the homeworks and continue preparing for the next cohort. I guess you can also start working on your final capstone project.|n|nsection : General course-related questions\n",
      "question: Course - Can I get support if I take the course in the self-paced mode?\n",
      "answer:Yes, the slack channel remains open and you can ask questions there. But always sDocker containers exit code w search the channel first and second, check the FAQ (this document), most likely all your questions are already answered here.\n",
      "You can also tag the bot @ZoomcampQABot to help you conduct the search, but don’t rely on its answers 100%, it is pretty good though.|n|nsection : General course-related questions\n",
      "question: How can we contribute to the course?\n",
      "answer:Star the repo! Share it with friends if you find it useful ❣️\n",
      "Create a PR if you see you can improve the text or the structure of the repository.|n|n\n"
     ]
    }
   ],
   "source": [
    "print(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e4dcb390-5a17-47fb-b08a-8967c280702f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Yes, you will get certification upon completion of the course.'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model='gpt-3.5-turbo',\n",
    "    messages=[{'role':'user','content': prompt}]\n",
    ")\n",
    "\n",
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0c33d869-4b04-47f4-b0bb-164cfe360791",
   "metadata": {},
   "outputs": [],
   "source": [
    "def search(query):\n",
    "    boost={'questions': 3.0,'section': 0.5}\n",
    "    results = index.search(\n",
    "    query=query,\n",
    "    filter_dict={'course':'data-engineering-zoomcamp'},\n",
    "    boost_dict=boost,\n",
    "    num_results=5    \n",
    ")\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d0b8cfb-a5e2-4209-9264-67d65962e2c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d338e82e-aefe-4e72-806c-1143f736fdfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_prompt(query,search_results):\n",
    "    prompt_template= \"\"\"\n",
    "You're a course teaching assistant. Answer the QUESTION based on the CONTEXT from FAQ database.\n",
    "Use only the facts from the CONTEXT when answering the QUESTION.\n",
    "\n",
    "QUESTION: {question}\n",
    "\n",
    "CONTEXT:\n",
    "{context}\n",
    "\"\"\".strip()\n",
    "    context=\"\"\n",
    "\n",
    "    for doc in results:\n",
    "        context = context + f\"section : {doc['section']}\\nquestion: {doc['question']}\\nanswer:{doc['text']}|n|n\"\n",
    "    prompt = prompt_template.format(question=query,context=context).strip()\n",
    "\n",
    "    return prompt\n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1976276d-d663-4607-9760-e58674fab199",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5223393e-fab0-41e1-abad-009617a2c4d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9d5607f7-99da-480f-bfcc-5ff957020f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def llm(prompt):\n",
    "    response = client.chat.completions.create(\n",
    "    model='gpt-3.5-turbo',\n",
    "    messages=[{'role':'user','content': prompt}]\n",
    ")\n",
    "\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8f9c435f-f36c-41ec-8965-d7b6111e86e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rag(query):\n",
    "    query = query\n",
    "    search_results  = search(query)\n",
    "    prompt = build_prompt(query,search_results)\n",
    "    answer = llm(prompt)\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "3dc34bf4-bc47-4ae7-a02d-6be406dd5c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'No, if the course has already started, you may not be able to enroll. It is best to contact the course instructor or coordinator for more information.'"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rag(\"the course has already started , Can i Still enroll?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28fc078e-8e19-43dc-8338-f40f0c707b26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "36a61286-4a6b-4d79-b36b-f49f7e055474",
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "c636d4c1-4860-41c0-b98b-6b5f5814f055",
   "metadata": {},
   "outputs": [],
   "source": [
    "#When hosted in CLoud , we need to provide that Url over here\n",
    "es_client = Elasticsearch('http://localhost:9200')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "09068b21-d5c9-492f-9825-55e8bda6d8ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'name': 'fcb9f783d4f8', 'cluster_name': 'docker-cluster', 'cluster_uuid': 'sUP13sPaQqmKdym9T6P0uw', 'version': {'number': '8.4.3', 'build_flavor': 'default', 'build_type': 'docker', 'build_hash': '42f05b9372a9a4a470db3b52817899b99a76ee73', 'build_date': '2022-10-04T07:17:24.662462378Z', 'build_snapshot': False, 'lucene_version': '9.3.0', 'minimum_wire_compatibility_version': '7.17.0', 'minimum_index_compatibility_version': '7.0.0'}, 'tagline': 'You Know, for Search'})"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es_client.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "98a8dfc1-7a31-42d9-8afe-a47671662808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'course-questions'})"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_settings = {\n",
    "    \"settings\": {\n",
    "        \"number_of_shards\": 1,\n",
    "        \"number_of_replicas\": 0\n",
    "    },\n",
    "    \"mappings\": {\n",
    "        \"properties\": {\n",
    "            \"text\": {\"type\": \"text\"},\n",
    "            \"section\": {\"type\": \"text\"},\n",
    "            \"question\": {\"type\": \"text\"},\n",
    "            \"course\": {\"type\": \"keyword\"} \n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "index_name = \"course-questions\"\n",
    "\n",
    "es_client.indices.create(index = index_name,body=index_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "12d0ded2-f14b-4c68-acec-18e84712d2cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': \"The purpose of this document is to capture frequently asked technical questions\\nThe exact day and hour of the course will be 15th Jan 2024 at 17h00. The course will start with the first  “Office Hours'' live.1\\nSubscribe to course public Google Calendar (it works from Desktop only).\\nRegister before the course starts using this link.\\nJoin the course Telegram channel with announcements.\\nDon’t forget to register in DataTalks.Club's Slack and join the channel.\",\n",
       " 'section': 'General course-related questions',\n",
       " 'question': 'Course - When will the course start?',\n",
       " 'course': 'data-engineering-zoomcamp'}"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "5a439d30-5c72-4ad9-a0a9-a85722fbe65f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "edce1a8c-43a8-4a28-a50a-53f967b1b2dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b68dbb50eeb44ca48636ce07ccac6485",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/948 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    es_client.index(index = index_name,document=doc)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0b0993e2-3da5-4b5f-8846-d4da31c87ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"I just discovered teh course, Can i still join\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "5c7bb409-0972-4181-a26a-2ba9eb99aa4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def elastic_search(query):\n",
    "\n",
    "    search_query = {\n",
    "    \"size\": 5,\n",
    "    \"query\": {\n",
    "        \"bool\": {\n",
    "            \"must\": {\n",
    "                \"multi_match\": {\n",
    "                    \"query\": query,\n",
    "                    \"fields\": [\"question^4\", \"text\", \"section\"],\n",
    "                    \"type\": \"best_fields\"\n",
    "                }\n",
    "            },\n",
    "            \"filter\": {\n",
    "                \"term\": {\n",
    "                    \"course\": \"data-engineering-zoomcamp\"\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "    response = es_client.search(index = index_name,body = search_query)\n",
    "    result_docs = []\n",
    "    for hit in response['hits']['hits']:\n",
    "        result_docs.append(hit[\"_source\"])\n",
    "    return result_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "9d2fa85f-6bf2-422c-bd04-d435f24bf4fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': 'In case running pgcli  locally causes issues or you do not want to install it locally you can use it running in a Docker container instead.\\nBelow the usage with values used in the videos of the course for:\\nnetwork name (docker network)\\npostgres related variables for pgcli\\nHostname\\nUsername\\nPort\\nDatabase name\\n$ docker run -it --rm --network pg-network ai2ys/dockerized-pgcli:4.0.1\\n175dd47cda07:/# pgcli -h pg-database -U root -p 5432 -d ny_taxi\\nPassword for root:\\nServer: PostgreSQL 16.1 (Debian 16.1-1.pgdg120+1)\\nVersion: 4.0.1\\nHome: http://pgcli.com\\nroot@pg-database:ny_taxi> \\\\dt\\n+--------+------------------+-------+-------+\\n| Schema | Name             | Type  | Owner |\\n|--------+------------------+-------+-------|\\n| public | yellow_taxi_data | table | root  |\\n+--------+------------------+-------+-------+\\nSELECT 1\\nTime: 0.009s\\nroot@pg-database:ny_taxi>',\n",
       "  'section': 'Module 1: Docker and Terraform',\n",
       "  'question': 'PGCLI - running in a Docker container',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': 'You can check the version of your local spark using spark-submit --version. In the build.sh file of the Python folder, make sure that SPARK_VERSION matches your local version. Similarly, make sure the pyspark you pip installed also matches this version.',\n",
       "  'section': 'Module 6: streaming with kafka',\n",
       "  'question': 'How do I check compatibility of local and container Spark versions?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': \"There are 3 Zoom Camps in a year, as of 2024. However, they are for separate courses:\\nData-Engineering (Jan - Apr)\\nMLOps (May - Aug)\\nMachine Learning (Sep - Jan)\\nThere's only one Data-Engineering Zoomcamp “live” cohort per year, for the certification. Same as for the other Zoomcamps.\\nThey follow pretty much the same schedule for each cohort per zoomcamp. For Data-Engineering it is (generally) from Jan-Apr of the year. If you’re not interested in the Certificate, you can take any zoom camps at any time, at your own pace, out of sync with any “live” cohort.\",\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - how many Zoomcamps in a year?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': \"It is recommended by the Docker do\\n[Windows 10 / 11 Home Edition] If you're running a Home Edition, you can still make it work with WSL2 (Windows Subsystem for Linux) by following the tutorial here\\nIf even after making sure your WSL2 (or Hyper-V) is set up accordingly, Docker remains stuck, you can try the option to Reset to Factory Defaults or do a fresh install.\",\n",
       "  'section': 'Module 1: Docker and Terraform',\n",
       "  'question': 'Should I run docker commands from the windows file system or a file system of a Linux distribution in WSL?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': \"No, you can only get a certificate if you finish the course with a “live” cohort. We don't award certificates for the self-paced mode. The reason is you need to peer-review capstone(s) after submitting a project. You can only peer-review projects at the time the course is running.\",\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Certificate - Can I follow the course in a self-paced mode and get a certificate?',\n",
       "  'course': 'data-engineering-zoomcamp'}]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "083a3726-7edc-41fc-b653-9d1ec16298ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"How do I execute a command in a running docker container?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "d91465a8-dc9a-40c9-958f-e3d95db3bc61",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rag(query):\n",
    "    query = query\n",
    "    search_results  = elastic_search(query)\n",
    "    prompt = build_prompt(query,search_results)\n",
    "    answer = llm(prompt)\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "40e34580-f586-4ea2-8661-3801f450a4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'To execute a command in a running docker container, first make sure that your Kafka broker docker container is working by using `docker ps` to confirm its status. If it is not working, you can start all the instances by navigating to the docker-compose YAML file folder and running `docker-compose up -d`.'"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rag(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "e82ade9b-3c43-4500-90dd-2361bd7d4b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': 'In case running pgcli  locally causes issues or you do not want to install it locally you can use it running in a Docker container instead.\\nBelow the usage with values used in the videos of the course for:\\nnetwork name (docker network)\\npostgres related variables for pgcli\\nHostname\\nUsername\\nPort\\nDatabase name\\n$ docker run -it --rm --network pg-network ai2ys/dockerized-pgcli:4.0.1\\n175dd47cda07:/# pgcli -h pg-database -U root -p 5432 -d ny_taxi\\nPassword for root:\\nServer: PostgreSQL 16.1 (Debian 16.1-1.pgdg120+1)\\nVersion: 4.0.1\\nHome: http://pgcli.com\\nroot@pg-database:ny_taxi> \\\\dt\\n+--------+------------------+-------+-------+\\n| Schema | Name             | Type  | Owner |\\n|--------+------------------+-------+-------|\\n| public | yellow_taxi_data | table | root  |\\n+--------+------------------+-------+-------+\\nSELECT 1\\nTime: 0.009s\\nroot@pg-database:ny_taxi>',\n",
       "  'section': 'Module 1: Docker and Terraform',\n",
       "  'question': 'PGCLI - running in a Docker container',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': 'You can check the version of your local spark using spark-submit --version. In the build.sh file of the Python folder, make sure that SPARK_VERSION matches your local version. Similarly, make sure the pyspark you pip installed also matches this version.',\n",
       "  'section': 'Module 6: streaming with kafka',\n",
       "  'question': 'How do I check compatibility of local and container Spark versions?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': \"There are 3 Zoom Camps in a year, as of 2024. However, they are for separate courses:\\nData-Engineering (Jan - Apr)\\nMLOps (May - Aug)\\nMachine Learning (Sep - Jan)\\nThere's only one Data-Engineering Zoomcamp “live” cohort per year, for the certification. Same as for the other Zoomcamps.\\nThey follow pretty much the same schedule for each cohort per zoomcamp. For Data-Engineering it is (generally) from Jan-Apr of the year. If you’re not interested in the Certificate, you can take any zoom camps at any time, at your own pace, out of sync with any “live” cohort.\",\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - how many Zoomcamps in a year?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': \"It is recommended by the Docker do\\n[Windows 10 / 11 Home Edition] If you're running a Home Edition, you can still make it work with WSL2 (Windows Subsystem for Linux) by following the tutorial here\\nIf even after making sure your WSL2 (or Hyper-V) is set up accordingly, Docker remains stuck, you can try the option to Reset to Factory Defaults or do a fresh install.\",\n",
       "  'section': 'Module 1: Docker and Terraform',\n",
       "  'question': 'Should I run docker commands from the windows file system or a file system of a Linux distribution in WSL?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': \"No, you can only get a certificate if you finish the course with a “live” cohort. We don't award certificates for the self-paced mode. The reason is you need to peer-review capstone(s) after submitting a project. You can only peer-review projects at the time the course is running.\",\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Certificate - Can I follow the course in a self-paced mode and get a certificate?',\n",
       "  'course': 'data-engineering-zoomcamp'}]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic_search(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "5a035469-c9c5-43bc-b615-db7b095e555d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'How do I execute a command in a running docker container?'"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "490273e8-3ae4-44ce-92d8-e7b71cdaa75a",
   "metadata": {},
   "outputs": [],
   "source": [
    "search_query = {\n",
    "\"size\": 3,\n",
    "\"query\": {\n",
    "    \"bool\": {\n",
    "        \"must\": {\n",
    "            \"multi_match\": {\n",
    "                \"query\": query,\n",
    "                \"fields\": [\"question^4\", \"text\"],\n",
    "                \"type\": \"best_fields\"\n",
    "            }\n",
    "        },\n",
    "        \"filter\": {\n",
    "            \"term\": {\n",
    "                \"course\": \"machine-learning-zoomcamp\"\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "}\n",
    "response = es_client.search(index = index_name,body = search_query)\n",
    "\n",
    "result_docs = []\n",
    "for hit in response['hits']['hits']:\n",
    "    result_docs.append(hit[\"_source\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "36b4d9bd-a0ff-443c-b4b7-ae13cf7eda43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'You can copy files from your local machine into a Docker container using the docker cp command. Here\\'s how to do it:\\nIn the Dockerfile, you can provide the folder containing the files that you want to copy over. The basic syntax is as follows:\\nCOPY [\"src/predict.py\", \"models/xgb_model.bin\", \"./\"]\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tGopakumar Gopinathan',\n",
       " 'section': '5. Deploying Machine Learning Models',\n",
       " 'question': 'How do I copy files from a different folder into docker container’s working directory?',\n",
       " 'course': 'machine-learning-zoomcamp'}"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_docs[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "29cf6149-4bfe-4483-bae2-a019598636b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_prompt(query,search_results):\n",
    "    prompt_template = \"\"\"\n",
    "You're a course teaching assistant. Answer the QUESTION based on the CONTEXT from the FAQ database.\n",
    "Use only the facts from the CONTEXT when answering the QUESTION.\n",
    "\n",
    "QUESTION: {question}\n",
    "\n",
    "CONTEXT:\n",
    "{context}\n",
    "\"\"\".strip()\n",
    "    context=\"\"\n",
    "\n",
    "    for doc in results:\n",
    "        context = context + f\"\"\"\n",
    "Q: {doc['question']}\n",
    "A: {doc['text']}\n",
    "\\n\\n\"\"\".strip()\n",
    "    prompt = prompt_template.format(question=query,context=context).strip()\n",
    "\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "ded2c884-60e9-458e-be5c-1a90d2d64fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = build_prompt(query,result_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "f773e4be-783a-4740-9eb1-96506222081f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2475"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "daaeb9a6-e6e0-4ba5-a789-da20620200ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"You're a course teaching assistant. Answer the QUESTION based on the CONTEXT from the FAQ database.\\nUse only the facts from the CONTEXT when answering the QUESTION.\\n\\nQUESTION: How do I execute a command in a running docker container?\\n\\nCONTEXT:\\nQ: Module “kafka” not found when trying to run producer.py\\nA: Solution from Alexey: create a virtual environment and run requirements.txt and the python files in that environment.\\nTo create a virtual env and install packages (run only once)\\npython -m venv env\\nsource env/bin/activate\\npip install -r ../requirements.txt\\nTo activate it (you'll need to run it every time you need the virtual env):\\nsource env/bin/activate\\nTo deactivate it:\\ndeactivate\\nThis works on MacOS, Linux and Windows - but for Windows the path is slightly different (it's env/Scripts/activate)\\nAlso the virtual environment should be created only to run the python file. Docker images should first all be up and running.Q: Error while running python3 stream.py worker\\nA: If you get an error while running the command python3 stream.py worker\\nRun pip uninstall kafka-python\\nThen run pip install kafka-python==1.4.6\\nWhat is the use of  Redpanda ?\\nRedpanda: Redpanda is built on top of the Raft consensus algorithm and is designed as a high-performance, low-latency alternative to Kafka. It uses a log-centric architecture similar to Kafka but with different underlying principles.\\nRedpanda is a powerful, yet simple, and cost-efficient streaming data platform that is compatible with Kafka® APIs while eliminating Kafka complexity.Q: Java Kafka: How to run producer/consumer/kstreams/etc in terminal\\nA: In the project directory, run:\\njava -cp build/libs/<jar_name>-1.0-SNAPSHOT.jar:out src/main/java/org/example/JsonProducer.javaQ: ModuleNotFoundError: No module named 'avro'\\nA: ✅SOLUTION: pip install confluent-kafka[avro].\\nFor some reason, Conda also doesn't include this when installing confluent-kafka via pip.\\nMore sources on Anaconda and confluent-kafka issues:\\nhttps://github.com/confluentinc/confluent-kafka-python/issues/590\\nhttps://github.com/confluentinc/confluent-kafka-python/issues/1221\\nhttps://stackoverflow.com/questions/69085157/cannot-import-producer-from-confluent-kafkaQ: kafka.errors.NoBrokersAvailable: NoBrokersAvailable\\nA: If you have this error, it most likely that your kafka broker docker container is not working.\\nUse docker ps to confirm\\nThen in the docker compose yaml file folder, run docker compose up -d to start all the instances.\""
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de53609-3d03-4715-9a16-ba0f3f1254f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c59c6eaf-b6f5-4adf-9df0-a05743ef3d1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def elastic_search(query):\n",
    "\n",
    "    search_query = {\n",
    "    \"size\": 5,\n",
    "    \"query\": {\n",
    "        \"bool\": {\n",
    "            \"must\": {\n",
    "                \"multi_match\": {\n",
    "                    \"query\": query,\n",
    "                    \"fields\": [\"question^4\", \"text\", \"section\"],\n",
    "                    \"type\": \"best_fields\"\n",
    "                }\n",
    "            },\n",
    "            \"filter\": {\n",
    "                \"term\": {\n",
    "                    \"course\": \"data-engineering-zoomcamp\"\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "    response = es_client.search(index = index_name,body = search_query)\n",
    "    result_docs = []\n",
    "    for hit in response['hits']['hits']:\n",
    "        result_docs.append(hit[\"_source\"])\n",
    "    return result_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "9a46ea59-b4ec-44f5-ac4b-70e05d128a5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tiktoken in /usr/local/python/3.10.13/lib/python3.10/site-packages (0.7.0)\n",
      "Requirement already satisfied: regex>=2022.1.18 in /usr/local/python/3.10.13/lib/python3.10/site-packages (from tiktoken) (2024.5.15)\n",
      "Requirement already satisfied: requests>=2.26.0 in /home/codespace/.local/lib/python3.10/site-packages (from tiktoken) (2.32.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (2024.2.2)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "86273282-0efb-4c67-8b40-e4fcee76ca68",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "8319b9b8-8b28-475b-a119-96b1996578dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding = tiktoken.encoding_for_model(\"gpt-3.5-turbo\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "87593a7a-4f0b-44b6-a47a-6df9d3679aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "564"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(encoding.encode(prompt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23fe19c3-c721-4806-8dd6-7aa629057181",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
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