llm_week1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1047211b-7c1f-4f53-9111-9c1f649fc978",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('./documents.json', 'rt') as f_in:\n",
    "    documents_file = json.load(f_in)\n",
    "\n",
    "documents = []\n",
    "\n",
    "for course in documents_file:\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": 7,
   "id": "77736edd-a4c9-4a9b-aac5-de397cb0f8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'name': '658b93c534a1', 'cluster_name': 'docker-cluster', 'cluster_uuid': 'P-ebLFTRQs-acd3ry5nI9Q', '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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from elasticsearch import Elasticsearch\n",
    "\n",
    "es = Elasticsearch(\"http://localhost:9200\")\n",
    "es.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09af6372-236f-4470-a757-3d90b9684cee",
   "metadata": {},
   "outputs": [
    {
     "ename": "BadRequestError",
     "evalue": "BadRequestError(400, 'resource_already_exists_exception', 'index [course-questions/vW87MeYPQmeUuDQcpe5ZbQ] 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[8], line 17\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---> 17\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mes\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\u001b[1;32m     19\u001b[0m response\n",
      "File \u001b[0;32m~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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/vW87MeYPQmeUuDQcpe5ZbQ] 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",
    "response = es.indices.create(index=index_name, body=index_settings)\n",
    "\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "197a9146-eb86-4d90-b532-4037b787de54",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [00:02<00:00, 464.32it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "for doc in tqdm(documents):\n",
    "    es.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ebb168e6-ada9-495a-9eef-ca3703340bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_question = \"How do I join the course after it has started?\"\n",
    "\n",
    "search_query = {\n",
    "    \"size\": 5,\n",
    "    \"query\": {\n",
    "        \"bool\": {\n",
    "            \"must\": {\n",
    "                \"multi_match\": {\n",
    "                    \"query\": user_question,\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",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07a99ce3-ed1e-46c5-9968-2e3f920c1e71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Section: 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 su...\n",
      "\n",
      "Section: 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 su...\n",
      "\n",
      "Section: 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 finishe...\n",
      "\n",
      "Section: 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 finishe...\n",
      "\n",
      "Section: General course-related questions\n",
      "Question: Course - What can I do before the course starts?\n",
      "Answer: You can start by installing and setting up all the dependenc...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "response = es.search(index=index_name, body=search_query)\n",
    "\n",
    "for hit in response['hits']['hits']:\n",
    "    doc = hit['_source']\n",
    "    print(f\"Section: {doc['section']}\")\n",
    "    print(f\"Question: {doc['question']}\")\n",
    "    print(f\"Answer: {doc['text'][:60]}...\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f783e8b2-a2f4-4156-9b83-f538a597a1b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_documents(query, index_name=\"course-questions\", max_results=5):\n",
    "    es = Elasticsearch(\"http://localhost:9200\")\n",
    "    \n",
    "    search_query = {\n",
    "        \"size\": max_results,\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.search(index=index_name, body=search_query)\n",
    "    documents = [hit['_source'] for hit in response['hits']['hits']]\n",
    "    return documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d72475a-40c3-4792-a843-5d4548579a3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Section: 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 su...\n",
      "\n",
      "Section: 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 finishe...\n",
      "\n",
      "Section: General course-related questions\n",
      "Question: Course - What can I do before the course starts?\n",
      "Answer: You can start by installing and setting up all the dependenc...\n",
      "\n",
      "Section: General course-related questions\n",
      "Question: How do I use Git / GitHub for this course?\n",
      "Answer: After you create a GitHub account, you should clone the cour...\n",
      "\n",
      "Section: Workshop 1 - dlthub\n",
      "Question: How do I install the necessary dependencies to run the code?\n",
      "Answer: Answer: To run the provided code, ensure that the 'dlt[duckd...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "user_question = \"How do I join the course after it has started?\"\n",
    "\n",
    "response = retrieve_documents(user_question)\n",
    "\n",
    "for doc in response:\n",
    "    print(f\"Section: {doc['section']}\")\n",
    "    print(f\"Question: {doc['question']}\")\n",
    "    print(f\"Answer: {doc['text'][:60]}...\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ef8cab5-ae38-4b4d-a9d1-02ab90bc4ab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It may be possible to join the course after it has started, but it would depend on the specific policies and guidelines of the course provider. It is recommended to reach out to the instructor or course administrator to inquire about joining and to see if accommodations can be made.\n"
     ]
    }
   ],
   "source": [
    "from openai import OpenAI\n",
    "\n",
    "client = OpenAI()\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"gpt-3.5-turbo\",\n",
    "    messages=[{\"role\": \"user\", \"content\": \"The course already started. Can I still join?\"}]\n",
    ")\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7f0a14b-3c52-40e8-8ae0-d81f6406911d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Section: 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",
      "\n",
      "Section: 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",
      "\n",
      "Section: 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",
      "\n",
      "Section: 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",
      "\n",
      "Section: General course-related questions\n",
      "Question: Course - What can I do before the course starts?\n",
      "Answer: You can start by installing and setting up all the dependencies and requirements:\n",
      "Google cloud account\n",
      "Google Cloud SDK\n",
      "Python 3 (installed with Anaconda)\n",
      "Terraform\n",
      "Git\n",
      "Look over the prerequisites and syllabus to see if you are comfortable with these subjects.\n"
     ]
    }
   ],
   "source": [
    "context_template = \"\"\"\n",
    "Section: {section}\n",
    "Question: {question}\n",
    "Answer: {text}\n",
    "\"\"\".strip()\n",
    "\n",
    "context_docs = retrieve_documents(user_question)\n",
    "\n",
    "context_result = \"\"\n",
    "\n",
    "for doc in context_docs:\n",
    "    doc_str = context_template.format(**doc)\n",
    "    context_result += (\"\\n\\n\" + doc_str)\n",
    "\n",
    "context = context_result.strip()\n",
    "print(context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00999f7c-c8a3-469c-94cd-911e3a03c381",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = f\"\"\"\n",
    "You're a course teaching assistant. Answer the user QUESTION based on CONTEXT - the documents retrieved from our FAQ database. \n",
    "Only use the facts from the CONTEXT. If the CONTEXT doesn't contan the answer, return \"NONE\"\n",
    "\n",
    "QUESTION: {user_question}\n",
    "\n",
    "CONTEXT:\n",
    "\n",
    "{context}\n",
    "\"\"\".strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d01d7d3-3a89-4d83-a1df-a588ea7fef8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"You can still join the course after it has started. However, be aware that there will be deadlines for turning in the final projects. So, don't leave everything for the last minute.\""
      ]
     },
     "execution_count": 19,
     "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",
    "answer = response.choices[0].message.content\n",
    "answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "507b96ff-47bb-4a4d-acb3-83006dc131e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "context_template = \"\"\"\n",
    "Section: {section}\n",
    "Question: {question}\n",
    "Answer: {text}\n",
    "\"\"\".strip()\n",
    "\n",
    "prompt_template = \"\"\"\n",
    "You're a course teaching assistant.\n",
    "Answer the user QUESTION based on CONTEXT - the documents retrieved from our FAQ database.\n",
    "Don't use other information outside of the provided CONTEXT.  \n",
    "\n",
    "QUESTION: {user_question}\n",
    "\n",
    "CONTEXT:\n",
    "\n",
    "{context}\n",
    "\"\"\".strip()\n",
    "\n",
    "\n",
    "def build_context(documents):\n",
    "    context_result = \"\"\n",
    "    \n",
    "    for doc in documents:\n",
    "        doc_str = context_template.format(**doc)\n",
    "        context_result += (\"\\n\\n\" + doc_str)\n",
    "    \n",
    "    return context_result.strip()\n",
    "\n",
    "\n",
    "def build_prompt(user_question, documents):\n",
    "    context = build_context(documents)\n",
    "    prompt = prompt_template.format(\n",
    "        user_question=user_question,\n",
    "        context=context\n",
    "    )\n",
    "    return prompt\n",
    "\n",
    "def ask_openai(prompt, model=\"gpt-3.5-turbo\"):\n",
    "    response = client.chat.completions.create(\n",
    "        model=model,\n",
    "        messages=[{\"role\": \"user\", \"content\": prompt}]\n",
    "    )\n",
    "    answer = response.choices[0].message.content\n",
    "    return answer\n",
    "\n",
    "def qa_bot(user_question):\n",
    "    context_docs = retrieve_documents(user_question)\n",
    "    prompt = build_prompt(user_question, context_docs)\n",
    "    answer = ask_openai(prompt)\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ea724049-f37d-431c-8b0d-9f0952ef8c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Response for the prompt: I'm getting invalid reference format: repository name must be lowercase\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_bot(\"I'm getting invalid reference format: repository name must be lowercase\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3fb5d597-f950-4f31-9554-67032ec847e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: requests in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (2.32.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (from requests) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (from requests) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (from requests) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (from requests) (2024.6.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "02084765-b991-405d-8787-759b08e781e5",
   "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": 107,
   "id": "b7d3d032-cd06-4e21-872c-76ef5a8e2d90",
   "metadata": {},
   "outputs": [
    {
     "ename": "BadRequestError",
     "evalue": "BadRequestError(400, 'resource_already_exists_exception', 'index [course-questions2/ajoR-UKZQ7qfcOskkcEd6g] 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[107], line 16\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     12\u001b[0m     }\n\u001b[1;32m     13\u001b[0m }\n\u001b[1;32m     15\u001b[0m index_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcourse-questions2\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 16\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mes\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\u001b[1;32m     18\u001b[0m response\n",
      "File \u001b[0;32m~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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~/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/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-questions2/ajoR-UKZQ7qfcOskkcEd6g] 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",
    "            \"question\": {\"type\": \"text\"},\n",
    "            \"course\": {\"type\": \"keyword\"} \n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "index_name = \"course-questions2\"\n",
    "response = es.indices.create(index=index_name, body=index_settings)\n",
    "\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "07fbdb3e-02cf-4ef1-ab07-def5da3edaf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [00:02<00:00, 469.71it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "for doc in tqdm(documents):\n",
    "    es.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "705562ef-c24f-4c76-85c7-1a38fde03fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_question = \"How do I execute a command in a running docker container?\"\n",
    "\n",
    "search_query = {\n",
    "    \"size\": 3,\n",
    "    \"query\": {\n",
    "        \"bool\": {\n",
    "            \"must\": {\n",
    "                \"multi_match\": {\n",
    "                    \"query\": user_question,\n",
    "                    \"fields\": [\"question^4\", \"text\", \"course\"],\n",
    "                    \"type\": \"best_fields\"\n",
    "                    }\n",
    "                },\n",
    "                \"filter\": {\n",
    "                    \"term\": {\n",
    "                        \"course\": \"machine-learning-zoomcamp\"\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4ef28948-5a91-4ee0-9d6c-de2c405cff54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question: How do I debug a docker container?\n",
      "Answer: Launch the container image in interactive mode and overriding the entrypoint, so that it starts a ba...\n",
      "\n",
      "Score: 84.17781\n",
      "Question: How do I debug a docker container?\n",
      "Answer: Launch the container image in interactive mode and overriding the entrypoint, so that it starts a ba...\n",
      "\n",
      "Score: 84.17781\n",
      "Question: How do I copy files from my local machine to docker container?\n",
      "Answer: You can copy files from your local machine into a Docker container using the docker cp command. Here...\n",
      "\n",
      "Score: 51.134113\n"
     ]
    }
   ],
   "source": [
    "response = es.search(index=index_name, body=search_query)\n",
    "\n",
    "for hit in response['hits']['hits']:\n",
    "    doc = hit['_source']\n",
    "    print(f\"Question: {doc['question']}\")\n",
    "    print(f\"Answer: {doc['text'][:100]}...\\n\")\n",
    "    print(f\"Score: {hit['_score']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "8183779b-9869-4352-93dc-8c996b554b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_documents(query, index_name=\"course-questions2\", max_results=3):\n",
    "    es = Elasticsearch(\"http://localhost:9200\")\n",
    "    \n",
    "    search_query = {\n",
    "        \"size\": max_results,\n",
    "        \"query\": {\n",
    "            \"bool\": {\n",
    "                \"must\": {\n",
    "                    \"multi_match\": {\n",
    "                        \"query\": query,\n",
    "                        \"fields\": [\"question^4\", \"text\", \"course\"],\n",
    "                        \"type\": \"best_fields\"\n",
    "                    }\n",
    "                },\n",
    "                \"filter\": {\n",
    "                    \"term\": {\n",
    "                        \"course\": \"machine-learning-zoomcamp\"\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    response = es.search(index=index_name, body=search_query)\n",
    "    documents = [hit['_source'] for hit in response['hits']['hits']]\n",
    "    return documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "74df2642-9309-4a4b-b03d-14bc9ef52321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "313"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_question = \"How do I execute a command in a running docker container?\"\n",
    "\n",
    "context_template = \"\"\"\n",
    "Question: {question}\n",
    "Answer: {text}\n",
    "\"\"\".strip()\n",
    "\n",
    "context_docs = retrieve_documents(user_question)\n",
    "\n",
    "context_result = \"\"\n",
    "\n",
    "for doc in context_docs:\n",
    "    doc_str = context_template.format(**doc)\n",
    "    context_result += (\"\\n\\n\" + doc_str)\n",
    "\n",
    "context = context_result.strip()\n",
    "\n",
    "prompt_template = \"\"\"\n",
    "You're a course teaching assistant.\n",
    "Answer the user QUESTION based on CONTEXT - the documents retrieved from our FAQ database.\n",
    "Don't use other information outside of the provided CONTEXT.  \n",
    "\n",
    "QUESTION: {user_question}\n",
    "\n",
    "CONTEXT:\n",
    "\n",
    "{context}\n",
    "\"\"\".strip()\n",
    "\n",
    "def build_prompt(user_question, documents):\n",
    "    prompt = prompt_template.format(\n",
    "        user_question=user_question,\n",
    "        context=context\n",
    "    )\n",
    "    return prompt\n",
    "\n",
    "prompt = build_prompt(user_question, context)\n",
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")\n",
    "enc = encoding.encode(prompt)\n",
    "len(enc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "9987dfaa-ed1c-43ab-8f0c-53b683f2a499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tiktoken\n",
      "  Downloading tiktoken-0.7.0-cp39-cp39-macosx_11_0_arm64.whl.metadata (6.6 kB)\n",
      "Collecting regex>=2022.1.18 (from tiktoken)\n",
      "  Downloading regex-2024.5.15-cp39-cp39-macosx_11_0_arm64.whl.metadata (40 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: requests>=2.26.0 in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (from tiktoken) (2.32.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (from requests>=2.26.0->tiktoken) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (from requests>=2.26.0->tiktoken) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (from requests>=2.26.0->tiktoken) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/rome47/.local/share/virtualenvs/llm-zoomcamp-rag-workshop-4o5KDYXD/lib/python3.9/site-packages (from requests>=2.26.0->tiktoken) (2024.6.2)\n",
      "Downloading tiktoken-0.7.0-cp39-cp39-macosx_11_0_arm64.whl (907 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m907.9/907.9 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hDownloading regex-2024.5.15-cp39-cp39-macosx_11_0_arm64.whl (278 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m278.3/278.3 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \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",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffad8ee2-5628-4f96-a370-47a81467a527",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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