MinSearch/HomeWork1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd33b477-e84f-476f-be14-54f5ea44da85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-07-01 15:52:27--  https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3832 (3.7K) [text/plain]\n",
      "Saving to: ‘minsearch.py.9’\n",
      "\n",
      "minsearch.py.9      100%[===================>]   3.74K  --.-KB/s    in 0s      \n",
      "\n",
      "2024-07-01 15:52:28 (51.0 MB/s) - ‘minsearch.py.9’ saved [3832/3832]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5f33cfa-e0aa-4d96-9edf-a9848ea2515f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import minsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9bfb0f95-9965-4ba9-9697-911929a6318b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed6870dc-a338-4fbd-90d9-808ce0b4912f",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('documents.json','rt') as f_in:\n",
    "    docs_raw = json.load(f_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c182002d-fa1a-42a2-a5fc-8f9ace2c6cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#docs_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32778d74-aff1-4aba-98ee-c003761691dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce364d4f-1a47-452d-8285-831c78361692",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 8,
   "id": "5c5c3237-411c-4b93-9db3-6f136b8eb486",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b9235d73-a2f3-4b79-ac86-e57c6ac60578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<minsearch.Index at 0x783d4606fa30>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = minsearch.Index(\n",
    "    text_fields=[\"question\", \"text\", \"section\"],\n",
    "    keyword_fields=[\"course\"]\n",
    ")\n",
    "\n",
    "index.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8905bde9-2632-4b1d-9c73-e173a920da4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\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",
      "}\n",
      "{\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",
      "}\n",
      "{\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\\u2019t 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",
      "}\n",
      "{\n",
      "  \"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  \\u201cOffice 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\\u2019t 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",
      "}\n",
      "{\n",
      "  \"text\": \"You can start by installing and setting up all the dependencies and requirements:\\nGoogle cloud account\\nGoogle Cloud SDK\\nPython 3 (installed with Anaconda)\\nTerraform\\nGit\\nLook over the prerequisites and syllabus to see if you are comfortable with these subjects.\",\n",
      "  \"section\": \"General course-related questions\",\n",
      "  \"question\": \"Course - What can I do before the course starts?\",\n",
      "  \"course\": \"data-engineering-zoomcamp\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "query = \"Can I join the course if it has already started?\"\n",
    "\n",
    "filter_dict = {\"course\": \"data-engineering-zoomcamp\"}\n",
    "boost_dict = {\"question\": 3}\n",
    "\n",
    "results = index.search(query, filter_dict, boost_dict, num_results=5)\n",
    "\n",
    "for result in results:\n",
    "    print(json.dumps(result, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9cefbf99-671c-4b7b-990c-d4d0ab5e9573",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d5317e3-4617-4503-83d2-a7073442d0d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = OpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba566750-49e7-4a09-adc1-fd4c5fa2d7c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "q = 'the course has already started, can I still enroll?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "520584a5-d979-42a2-85e8-9d164f39bace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"It's possible to enroll in a course that has already started, but it depends on various factors, such as the policies of the institution or the flexibility of the course. Here are some steps you can take to find out:\\n\\n1. **Contact the Instructor or Course Coordinators:** Reach out to the professor or course coordinator directly through email or the institution's communication channels. Explain your situation and express your interest in joining the course.\\n\\n2. **Check Online Portals:** Many institutions have online registration systems where you can see if late enrollment is an option. Sometimes, there will be a specific deadline up to which late registrations are accepted.\\n\\n3. **Review the Syllabus:** Look at the course syllabus, if available, to understand what you have missed so far and assess if you can catch up.\\n\\n4. **Institution’s Policies:** Some institutions have specific policies and procedures for late enrollment. Check the student handbook or the institution’s website for detailed information.\\n\\n5. **Attend a Class:** If possible, attend a class session or sit in to get a feel for the course material and decide if you can handle the workload.\\n\\n6. **Catch Up on Missed Work:** Be prepared to put in extra effort to catch up on any missed lectures, assignments, or readings.\\n\\nBy taking these steps, you can determine if it's feasible for you to enroll late and what you need to do to get caught up with the rest of the class.\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model='gpt-4o',\n",
    "    messages=[{\"role\": \"user\", \"content\": q}]\n",
    ")\n",
    "\n",
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aa867bd4-b7c0-46bf-afb6-3bd690a1d7c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def search(query):\n",
    "    boost = {'question': 3.0, 'section': 0.5}\n",
    "\n",
    "    results = index.search(\n",
    "        query=query,\n",
    "        filter_dict={'course': 'data-engineering-zoomcamp'},\n",
    "        boost_dict=boost,\n",
    "        num_results=5\n",
    "    )\n",
    "\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aac709da-4f09-437d-9d74-0c36f63103f3",
   "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",
    "\n",
    "    context = \"\"\n",
    "    \n",
    "    for doc in search_results:\n",
    "        context = context + f\"section: {doc['section']}\\nquestion: {doc['question']}\\nanswer: {doc['text']}\\n\\n\"\n",
    "    \n",
    "    prompt = prompt_template.format(question=query, context=context).strip()\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b7e6c0a-599c-44a0-b381-92478c5629c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def llm(prompt):\n",
    "    response = client.chat.completions.create(\n",
    "        model='gpt-4o',\n",
    "        messages=[{\"role\": \"user\", \"content\": prompt}]\n",
    "    )\n",
    "    \n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c822064f-b2d6-4aa2-bbab-c8898a9180c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'how do I run kafka?'\n",
    "\n",
    "def rag(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": 20,
   "id": "43121cb5-1e53-417c-95d5-70d8e67b501a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'To run Kafka, here are instructions depending on whether you are using Java or Python:\\n\\n### For Java:\\nTo run a Kafka producer, consumer, or KStreams application in the terminal, navigate to your project directory and execute the following command:\\n```bash\\njava -cp build/libs/<jar_name>-1.0-SNAPSHOT.jar:out src/main/java/org/example/JsonProducer.java\\n```\\nReplace `<jar_name>` with the actual name of your JAR file.\\n\\n### For Python:\\nIf you need to run a Python Kafka producer, follow these steps to resolve potential module issues by using a virtual environment:\\n1. **Create a virtual environment and install packages:**\\n   ```bash\\n   python -m venv env\\n   source env/bin/activate\\n   pip install -r ../requirements.txt\\n   ```\\n2. **Activate the virtual environment (run this command each time you use the virtual environment):**\\n   ```bash\\n   source env/bin/activate\\n   ```\\n   Note: On Windows, the path for activation is slightly different:\\n   ```bash\\n   env\\\\Scripts\\\\activate\\n   ```\\n3. **Deactivate the virtual environment (when done):**\\n   ```bash\\n   deactivate\\n   ```\\nAdditionally, make sure all Docker images are up and running if you are utilizing Docker for your services.'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rag(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea157c05-5f01-44b2-8aa1-a64a08f29b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Yes, you can still enroll in the course after it has started. Even if you don't register by the start date, you are still eligible to submit the homeworks. However, keep in mind that there will be deadlines for the final projects, so it's advisable not to procrastinate.\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rag('the course has already started, can I still enroll?')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad7a1f2d-150d-425a-abb4-fc068cfacd1d",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dab37e64-bba8-4f46-8a91-5cbd5b18f54a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "359ae49e-9686-414a-b97b-4bbe7c932308",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_client = Elasticsearch('http://localhost:9200') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5878b25a-a877-4267-9ff5-1e8879b8a5a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1ee3003b-72f0-4a69-9349-97b52ec62224",
   "metadata": {},
   "outputs": [
    {
     "ename": "BadRequestError",
     "evalue": "BadRequestError(400, 'resource_already_exists_exception', 'index [course-questions/ytXK35IOTFOZtqY4hST55w] already exists')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[24], line 18\u001b[0m\n\u001b[1;32m      1\u001b[0m index_settings \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msettings\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[1;32m      3\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumber_of_shards\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     13\u001b[0m     }\n\u001b[1;32m     14\u001b[0m }\n\u001b[1;32m     16\u001b[0m index_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcourse-questions\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 18\u001b[0m \u001b[43mes_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_settings\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/utils.py:446\u001b[0m, in \u001b[0;36m_rewrite_parameters.<locals>.wrapper.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    443\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m    444\u001b[0m             \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 446\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mapi\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/indices.py:553\u001b[0m, in \u001b[0;36mIndicesClient.create\u001b[0;34m(self, index, aliases, error_trace, filter_path, human, mappings, master_timeout, pretty, settings, timeout, wait_for_active_shards, body)\u001b[0m\n\u001b[1;32m    551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m __body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    552\u001b[0m     __headers[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperform_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[return-value]\u001b[39;49;00m\n\u001b[1;32m    554\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPUT\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    555\u001b[0m \u001b[43m    \u001b[49m\u001b[43m__path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    556\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__query\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    557\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    558\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__body\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    559\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mindices.create\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    560\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_parts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__path_parts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    561\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:423\u001b[0m, in \u001b[0;36mNamespacedClient.perform_request\u001b[0;34m(self, method, path, params, headers, body, endpoint_id, path_parts)\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mperform_request\u001b[39m(\n\u001b[1;32m    411\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    412\u001b[0m     method: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Use the internal clients .perform_request() implementation\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;66;03m# so we take advantage of their transport options.\u001b[39;00m\n\u001b[0;32m--> 423\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperform_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    424\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    425\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    426\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    427\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    428\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    429\u001b[0m \u001b[43m        \u001b[49m\u001b[43mendpoint_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mendpoint_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath_parts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath_parts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:271\u001b[0m, in \u001b[0;36mBaseClient.perform_request\u001b[0;34m(self, method, path, params, headers, body, endpoint_id, path_parts)\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mperform_request\u001b[39m(\n\u001b[1;32m    256\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    257\u001b[0m     method: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    264\u001b[0m     path_parts: Optional[Mapping[\u001b[38;5;28mstr\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    265\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ApiResponse[Any]:\n\u001b[1;32m    266\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_otel\u001b[38;5;241m.\u001b[39mspan(\n\u001b[1;32m    267\u001b[0m         method,\n\u001b[1;32m    268\u001b[0m         endpoint_id\u001b[38;5;241m=\u001b[39mendpoint_id,\n\u001b[1;32m    269\u001b[0m         path_parts\u001b[38;5;241m=\u001b[39mpath_parts \u001b[38;5;129;01mor\u001b[39;00m {},\n\u001b[1;32m    270\u001b[0m     ) \u001b[38;5;28;01mas\u001b[39;00m otel_span:\n\u001b[0;32m--> 271\u001b[0m         response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_perform_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    272\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    274\u001b[0m \u001b[43m            \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    275\u001b[0m \u001b[43m            \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    276\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    277\u001b[0m \u001b[43m            \u001b[49m\u001b[43motel_span\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43motel_span\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    278\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    279\u001b[0m         otel_span\u001b[38;5;241m.\u001b[39mset_elastic_cloud_metadata(response\u001b[38;5;241m.\u001b[39mmeta\u001b[38;5;241m.\u001b[39mheaders)\n\u001b[1;32m    280\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:352\u001b[0m, in \u001b[0;36mBaseClient._perform_request\u001b[0;34m(self, method, path, params, headers, body, otel_span)\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mKeyError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m    350\u001b[0m             \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 352\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTP_EXCEPTIONS\u001b[38;5;241m.\u001b[39mget(meta\u001b[38;5;241m.\u001b[39mstatus, ApiError)(\n\u001b[1;32m    353\u001b[0m         message\u001b[38;5;241m=\u001b[39mmessage, meta\u001b[38;5;241m=\u001b[39mmeta, body\u001b[38;5;241m=\u001b[39mresp_body\n\u001b[1;32m    354\u001b[0m     )\n\u001b[1;32m    356\u001b[0m \u001b[38;5;66;03m# 'X-Elastic-Product: Elasticsearch' should be on every 2XX response.\u001b[39;00m\n\u001b[1;32m    357\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verified_elasticsearch:\n\u001b[1;32m    358\u001b[0m     \u001b[38;5;66;03m# If the header is set we mark the server as verified.\u001b[39;00m\n",
      "\u001b[0;31mBadRequestError\u001b[0m: BadRequestError(400, 'resource_already_exists_exception', 'index [course-questions/ytXK35IOTFOZtqY4hST55w] already exists')"
     ]
    }
   ],
   "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": 25,
   "id": "b115f316-e6f6-43ff-a182-bfbf7936e1f3",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a06a667e-3eef-4dfb-9471-e4bb8ccd81e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/python/3.10.13/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "67be9906-0f7b-40a4-b532-7adadbf707a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 948/948 [00:24<00:00, 39.28it/s]\n"
     ]
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0fa9f637-c80e-4eb8-9d46-30841867852e",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'I just disovered the course. Can I still join it?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fe470017-8bd8-4efb-908b-d24fc1acdf64",
   "metadata": {},
   "outputs": [],
   "source": [
    "def elastic_search(query):\n",
    "    search_query = {\n",
    "        \"size\": 5,\n",
    "        \"query\": {\n",
    "            \"bool\": {\n",
    "                \"must\": {\n",
    "                    \"multi_match\": {\n",
    "                        \"query\": query,\n",
    "                        \"fields\": [\"question^3\", \"text\", \"section\"],\n",
    "                        \"type\": \"best_fields\"\n",
    "                    }\n",
    "                },\n",
    "                \"filter\": {\n",
    "                    \"term\": {\n",
    "                        \"course\": \"data-engineering-zoomcamp\"\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    response = es_client.search(index=index_name, body=search_query)\n",
    "    \n",
    "    result_docs = []\n",
    "    \n",
    "    for hit in response['hits']['hits']:\n",
    "        result_docs.append(hit['_source'])\n",
    "    \n",
    "    return result_docs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c3bfdbb5-c107-4b1e-a5fd-059feb426b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rag(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": 31,
   "id": "b691c7bf-7367-4ba3-98ce-31da57cf9180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Yes, you can still join the course even if you discovered it after the start date. You are eligible to submit the homework, but be mindful of the deadlines for turning in the final projects.'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rag(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "927e76b6-0752-48df-ab12-7bf8a09270a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests \n",
    "\n",
    "docs_url = 'https://github.com/DataTalksClub/llm-zoomcamp/blob/main/01-intro/documents.json?raw=1'\n",
    "docs_response = requests.get(docs_url)\n",
    "documents_raw = docs_response.json()\n",
    "\n",
    "documents = []\n",
    "\n",
    "for course in documents_raw:\n",
    "    course_name = course['course']\n",
    "\n",
    "    for doc in course['documents']:\n",
    "        doc['course'] = course_name\n",
    "        documents.append(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5a013d6f-c4a9-4766-8069-f19ec81eadea",
   "metadata": {},
   "outputs": [
    {
     "ename": "BadRequestError",
     "evalue": "BadRequestError(400, 'resource_already_exists_exception', 'index [query-questions_in/mYpCuVYaQB2MZnnsfRBxsA] already exists')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[35], line 17\u001b[0m\n\u001b[1;32m      1\u001b[0m ques_index_settings \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msettings\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[1;32m      3\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumber_of_shards\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m     }\n\u001b[1;32m     13\u001b[0m }\n\u001b[1;32m     15\u001b[0m ques_index_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquery-questions_in\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 17\u001b[0m \u001b[43mes_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mques_index_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mques_index_settings\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/utils.py:446\u001b[0m, in \u001b[0;36m_rewrite_parameters.<locals>.wrapper.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    443\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m    444\u001b[0m             \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 446\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mapi\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/indices.py:553\u001b[0m, in \u001b[0;36mIndicesClient.create\u001b[0;34m(self, index, aliases, error_trace, filter_path, human, mappings, master_timeout, pretty, settings, timeout, wait_for_active_shards, body)\u001b[0m\n\u001b[1;32m    551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m __body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    552\u001b[0m     __headers[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperform_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[return-value]\u001b[39;49;00m\n\u001b[1;32m    554\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPUT\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    555\u001b[0m \u001b[43m    \u001b[49m\u001b[43m__path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    556\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__query\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    557\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    558\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__body\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    559\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mindices.create\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    560\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_parts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__path_parts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    561\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:423\u001b[0m, in \u001b[0;36mNamespacedClient.perform_request\u001b[0;34m(self, method, path, params, headers, body, endpoint_id, path_parts)\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mperform_request\u001b[39m(\n\u001b[1;32m    411\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    412\u001b[0m     method: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Use the internal clients .perform_request() implementation\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;66;03m# so we take advantage of their transport options.\u001b[39;00m\n\u001b[0;32m--> 423\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperform_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    424\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    425\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    426\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    427\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    428\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    429\u001b[0m \u001b[43m        \u001b[49m\u001b[43mendpoint_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mendpoint_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath_parts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath_parts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:271\u001b[0m, in \u001b[0;36mBaseClient.perform_request\u001b[0;34m(self, method, path, params, headers, body, endpoint_id, path_parts)\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mperform_request\u001b[39m(\n\u001b[1;32m    256\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    257\u001b[0m     method: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    264\u001b[0m     path_parts: Optional[Mapping[\u001b[38;5;28mstr\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    265\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ApiResponse[Any]:\n\u001b[1;32m    266\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_otel\u001b[38;5;241m.\u001b[39mspan(\n\u001b[1;32m    267\u001b[0m         method,\n\u001b[1;32m    268\u001b[0m         endpoint_id\u001b[38;5;241m=\u001b[39mendpoint_id,\n\u001b[1;32m    269\u001b[0m         path_parts\u001b[38;5;241m=\u001b[39mpath_parts \u001b[38;5;129;01mor\u001b[39;00m {},\n\u001b[1;32m    270\u001b[0m     ) \u001b[38;5;28;01mas\u001b[39;00m otel_span:\n\u001b[0;32m--> 271\u001b[0m         response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_perform_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    272\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    274\u001b[0m \u001b[43m            \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    275\u001b[0m \u001b[43m            \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    276\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    277\u001b[0m \u001b[43m            \u001b[49m\u001b[43motel_span\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43motel_span\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    278\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    279\u001b[0m         otel_span\u001b[38;5;241m.\u001b[39mset_elastic_cloud_metadata(response\u001b[38;5;241m.\u001b[39mmeta\u001b[38;5;241m.\u001b[39mheaders)\n\u001b[1;32m    280\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:352\u001b[0m, in \u001b[0;36mBaseClient._perform_request\u001b[0;34m(self, method, path, params, headers, body, otel_span)\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mKeyError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m    350\u001b[0m             \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 352\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTP_EXCEPTIONS\u001b[38;5;241m.\u001b[39mget(meta\u001b[38;5;241m.\u001b[39mstatus, ApiError)(\n\u001b[1;32m    353\u001b[0m         message\u001b[38;5;241m=\u001b[39mmessage, meta\u001b[38;5;241m=\u001b[39mmeta, body\u001b[38;5;241m=\u001b[39mresp_body\n\u001b[1;32m    354\u001b[0m     )\n\u001b[1;32m    356\u001b[0m \u001b[38;5;66;03m# 'X-Elastic-Product: Elasticsearch' should be on every 2XX response.\u001b[39;00m\n\u001b[1;32m    357\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verified_elasticsearch:\n\u001b[1;32m    358\u001b[0m     \u001b[38;5;66;03m# If the header is set we mark the server as verified.\u001b[39;00m\n",
      "\u001b[0;31mBadRequestError\u001b[0m: BadRequestError(400, 'resource_already_exists_exception', 'index [query-questions_in/mYpCuVYaQB2MZnnsfRBxsA] already exists')"
     ]
    }
   ],
   "source": [
    "ques_index_settings = {\n",
    "    \"settings\": {\n",
    "        \"number_of_shards\": 1,\n",
    "        \"number_of_replicas\": 0\n",
    "    },\n",
    "    \"mappings\": {\n",
    "        \"properties\": {\n",
    "            \"text\": {\"type\": \"text\"},\n",
    "            \"course\": {\"type\": \"keyword\"} ,\n",
    "             \"question\": {\"type\": \"text\"}\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "ques_index_name = \"query-questions_in\"\n",
    "\n",
    "es_client.indices.create(index=ques_index_name, body=ques_index_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d6168f9e-1c78-42ab-aa8a-f7bc8532943c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tqdm in /usr/local/python/3.10.13/lib/python3.10/site-packages (4.66.4)\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"
     ]
    }
   ],
   "source": [
    "!pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "166196d4-97a6-49d6-a677-4c105a72d647",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/python/3.10.13/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad2c9c33-ada8-404c-8f16-ea4a8f73915a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 948/948 [00:23<00:00, 40.72it/s]\n"
     ]
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=ques_index_name, document=doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7e23654c-5570-4fda-a14f-863b9235a832",
   "metadata": {},
   "outputs": [],
   "source": [
    "ques=\"How do I execute a command in a running docker container?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d719f25e-4438-4bf5-8eec-8a6b18d8bbf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def home_elastic_search(query):\n",
    "    search_query = {\n",
    "        \"size\": 5, \n",
    "        \"query\": {\n",
    "            \"bool\": {\n",
    "                \"must\": {\n",
    "                    \"multi_match\": {\n",
    "                        \"query\": query,\n",
    "                        \"fields\": [\"question^4\", \"text\"],\n",
    "                        \"type\": \"best_fields\"\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "       \n",
    "    }\n",
    "\n",
    "    response = es_client.search(index=ques_index_name, body=search_query)\n",
    "    \n",
    "    #result_docs = []\n",
    "    \n",
    "    #for hit in response['hits']['hits']:\n",
    "        #result_docs.append(hit['_source'])\n",
    "    \n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e7468289-5e39-4656-afd5-9735037e9778",
   "metadata": {},
   "outputs": [],
   "source": [
    "def home_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",
    "\n",
    "    context = \"\"\n",
    "    \n",
    "    for doc in search_results:\n",
    "        context = context + f\"question: {doc['question']}\\nanswer: {doc['text']}\\n\\n\"\n",
    "    \n",
    "    prompt = prompt_template.format(question=query, context=context).strip()\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "097bc484-d96c-4194-bea2-1cf8692d37ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def home_llm(prompt):\n",
    "    response = client.chat.completions.create(\n",
    "        model='gpt-4o',\n",
    "        messages=[{\"role\": \"user\", \"content\": prompt}]\n",
    "    )\n",
    "    \n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b80806c6-bb51-4623-b5de-c265c3bcca52",
   "metadata": {},
   "outputs": [],
   "source": [
    "search_results = home_elastic_search(ques)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "851f0abb-2e08-4e57-8b01-894bd8d12775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'took': 14, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 865, 'relation': 'eq'}, 'max_score': 84.050095, 'hits': [{'_index': 'query-questions_in', '_id': 'oJsFb5ABAGS7IfVSZlRn', '_score': 84.050095, '_source': {'text': 'Launch the container image in interactive mode and overriding the entrypoint, so that it starts a bash command.\\ndocker run -it --entrypoint bash <image>\\nIf the container is already running, execute a command in the specific container:\\ndocker ps (find the container-id)\\ndocker exec -it <container-id> bash\\n(Marcos MJD)', 'section': '5. Deploying Machine Learning Models', 'question': 'How do I debug a docker container?', 'course': 'machine-learning-zoomcamp'}}, {'_index': 'query-questions_in', '_id': 'rpsFb5ABAGS7IfVSN1JT', '_score': 75.54128, '_source': {'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>', 'section': 'Module 1: Docker and Terraform', 'question': 'PGCLI - running in a Docker container', 'course': 'data-engineering-zoomcamp'}}, {'_index': 'query-questions_in', '_id': 'z5sFb5ABAGS7IfVSglXh', '_score': 72.08518, '_source': {'text': 'If you are trying to run Flask gunicorn & MLFlow server from the same container, defining both in Dockerfile with CMD will only run MLFlow & not Flask.\\nSolution: Create separate shell script with server run commands, for eg:\\n> \\tscript1.sh\\n#!/bin/bash\\ngunicorn --bind=0.0.0.0:9696 predict:app\\nAnother script with e.g. MLFlow server:\\n>\\tscript2.sh\\n#!/bin/bash\\nmlflow server -h 0.0.0.0 -p 5000 --backend-store-uri=sqlite:///mlflow.db --default-artifact-root=g3://zc-bucket/mlruns/\\nCreate a wrapper script to run above 2 scripts:\\n>\\twrapper_script.sh\\n#!/bin/bash\\n# Start the first process\\n./script1.sh &\\n# Start the second process\\n./script2.sh &\\n# Wait for any process to exit\\nwait -n\\n# Exit with status of process that exited first\\nexit $?\\nGive executable permissions to all scripts:\\nchmod +x *.sh\\nNow we can define last line of Dockerfile as:\\n> \\tDockerfile\\nCMD ./wrapper_script.sh\\nDont forget to expose all ports defined by services!', 'section': 'Module 4: Deployment', 'question': 'Running multiple services in a Docker container', 'course': 'mlops-zoomcamp'}}, {'_index': 'query-questions_in', '_id': 'v5sFb5ABAGS7IfVSaVRT', '_score': 51.04628, '_source': {'text': \"You can copy files from your local machine into a Docker container using the docker cp command. Here's how to do it:\\nTo copy a file or directory from your local machine into a running Docker container, you can use the `docker cp command`. The basic syntax is as follows:\\ndocker cp /path/to/local/file_or_directory container_id:/path/in/container\\nHrithik Kumar Advani\", 'section': '5. Deploying Machine Learning Models', 'question': 'How do I copy files from my local machine to docker container?', 'course': 'machine-learning-zoomcamp'}}, {'_index': 'query-questions_in', '_id': 'wJsFb5ABAGS7IfVSaVRr', '_score': 49.938507, '_source': {'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', 'section': '5. Deploying Machine Learning Models', 'question': 'How do I copy files from a different folder into docker container’s working directory?', 'course': 'machine-learning-zoomcamp'}}]}})"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fe3b0ff3-ca9d-4603-ad3e-bf8eb50d30b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ml_elastic_search(query):\n",
    "    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",
    "\n",
    "    response = es_client.search(index=ques_index_name, body=search_query)\n",
    "    \n",
    "    result_docs = []\n",
    "    \n",
    "    for hit in response['hits']['hits']:\n",
    "        result_docs.append(hit['_source'])\n",
    "    \n",
    "    return result_docs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5afe1fac-cc09-4b58-96c8-282776a3a72c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_search_results = ml_elastic_search(ques)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f8b52598-e6ed-4f9b-851a-2c1d8e559af9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': 'Launch the container image in interactive mode and overriding the entrypoint, so that it starts a bash command.\\ndocker run -it --entrypoint bash <image>\\nIf the container is already running, execute a command in the specific container:\\ndocker ps (find the container-id)\\ndocker exec -it <container-id> bash\\n(Marcos MJD)',\n",
       "  'section': '5. Deploying Machine Learning Models',\n",
       "  'question': 'How do I debug a docker container?',\n",
       "  'course': 'machine-learning-zoomcamp'},\n",
       " {'text': \"You can copy files from your local machine into a Docker container using the docker cp command. Here's how to do it:\\nTo copy a file or directory from your local machine into a running Docker container, you can use the `docker cp command`. The basic syntax is as follows:\\ndocker cp /path/to/local/file_or_directory container_id:/path/in/container\\nHrithik Kumar Advani\",\n",
       "  'section': '5. Deploying Machine Learning Models',\n",
       "  'question': 'How do I copy files from my local machine to docker container?',\n",
       "  'course': 'machine-learning-zoomcamp'},\n",
       " {'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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_search_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7f393c3a-4adb-415e-a2db-f773daad89a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'How do I debug a docker container?'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_search_results[0]['question']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "95a0e32c-f46c-4969-9740-f425872664fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ml_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",
    "\n",
    "    context = \"\"\n",
    "    cntr =0\n",
    "    for doc in search_results:\n",
    "        cntr =  cntr+1\n",
    "        if(cntr <3):\n",
    "            context = context + f\"Q: {doc['question']}\\nA: {doc['text']}\\n\\n\"\n",
    "        else:\n",
    "            context = context + f\"Q: {doc['question']}\\nA: {doc['text']}\"\n",
    "    context = context.strip()\n",
    "    print(context)\n",
    "    \n",
    "    prompt = prompt_template.format(question=query, context=context).strip()\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "37eee584-e393-4197-80ee-7858c9022945",
   "metadata": {},
   "outputs": [],
   "source": [
    "context_template = \"\"\"\n",
    "Q: {question}\n",
    "A: {text}\n",
    "\"\"\".strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "194bb77b-0396-47a4-af2d-01ba6e0f6ed4",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'context' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[91], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mcontext\u001b[49m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'context' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b7befd0c-2be9-4aec-84e9-2e6907289fc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q: How do I debug a docker container?\n",
      "A: Launch the container image in interactive mode and overriding the entrypoint, so that it starts a bash command.\n",
      "docker run -it --entrypoint bash <image>\n",
      "If the container is already running, execute a command in the specific container:\n",
      "docker ps (find the container-id)\n",
      "docker exec -it <container-id> bash\n",
      "(Marcos MJD)\n",
      "\n",
      "Q: How do I copy files from my local machine to docker container?\n",
      "A: You can copy files from your local machine into a Docker container using the docker cp command. Here's how to do it:\n",
      "To copy a file or directory from your local machine into a running Docker container, you can use the `docker cp command`. The basic syntax is as follows:\n",
      "docker cp /path/to/local/file_or_directory container_id:/path/in/container\n",
      "Hrithik Kumar Advani\n",
      "\n",
      "Q: How do I copy files from a different folder into docker container’s working directory?\n",
      "A: You can copy files from your local machine into a Docker container using the docker cp command. Here's how to do it:\n",
      "In the Dockerfile, you can provide the folder containing the files that you want to copy over. The basic syntax is as follows:\n",
      "COPY [\"src/predict.py\", \"models/xgb_model.bin\", \"./\"]\t\t\t\t\t\t\t\t\t\t\tGopakumar Gopinathan\n"
     ]
    }
   ],
   "source": [
    " prompt = ml_build_prompt(ques, ml_search_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f4ee2a24-8f7e-46a8-ad3d-b91a0cf0f022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1463"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "27f73d37-f71e-48b3-adff-6693f0007fe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tiktoken\n",
      "  Downloading tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\n",
      "Collecting regex>=2022.1.18 (from tiktoken)\n",
      "  Downloading regex-2024.5.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (40 kB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m446.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mMB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement 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 /usr/local/python/3.10.13/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (2.0.7)\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",
      "Downloading tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading regex-2024.5.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (775 kB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m775.1/775.1 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: regex, tiktoken\n",
      "Successfully installed regex-2024.5.15 tiktoken-0.7.0\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": 66,
   "id": "4b22c76c-0b4d-476a-80b1-a265a56d2e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiktoken\n",
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6a7b5ec5-777d-491b-b737-98abd1cc1bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def num_tokens_from_string(string: str, encoding_name: str) -> int:\n",
    "    \"\"\"Returns the number of tokens in a text string.\"\"\"\n",
    "    encoding = tiktoken.encoding_for_model(\"gpt-4o\")\n",
    "    num_tokens = len(encoding.encode(string))\n",
    "    return num_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "98748061-d8b9-464c-9055-1bf92833de46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "323"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_tokens_from_string(prompt, \"cl100k_base\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d74bd472-7a08-4494-a8ce-c7a138638055",
   "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"
  }
 },
 "nbformat": 4,
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}