01-intro/01-intro-homework.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "id": "c4d5cc0c-d29c-452e-88f5-f48cf7824b43",
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   "source": [
    "## Q1. Running Elastic\n",
    "### Run Elastic Search 8.4.3, and get the cluster information. If you run it on localhost, this is how you do it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff8c3412-5c52-4967-86d1-870e68a20cdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"name\" : \"e0fbf257da75\",\n",
      "  \"cluster_name\" : \"docker-cluster\",\n",
      "  \"cluster_uuid\" : \"qxl2dvwIRfG9GZNqXEABJA\",\n",
      "  \"version\" : {\n",
      "    \"number\" : \"8.4.3\",\n",
      "    \"build_flavor\" : \"default\",\n",
      "    \"build_type\" : \"docker\",\n",
      "    \"build_hash\" : \"42f05b9372a9a4a470db3b52817899b99a76ee73\",\n",
      "    \"build_date\" : \"2022-10-04T07:17:24.662462378Z\",\n",
      "    \"build_snapshot\" : false,\n",
      "    \"lucene_version\" : \"9.3.0\",\n",
      "    \"minimum_wire_compatibility_version\" : \"7.17.0\",\n",
      "    \"minimum_index_compatibility_version\" : \"7.0.0\"\n",
      "  },\n",
      "  \"tagline\" : \"You Know, for Search\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!curl localhost:9200"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d908be9-b8c7-4785-bd50-5bd384a21981",
   "metadata": {},
   "source": [
    "### Getting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44edfc13-0d4d-4dfc-8752-dee096f58a78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a3872ce-93e7-40ef-b1da-bf62c0655ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 4,
   "id": "fba0f19b-6a4e-46e6-bb34-549f77b7830e",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "917c53cb-d8f3-478f-bf53-b17322f253c8",
   "metadata": {},
   "source": [
    "## Q2. Indexing the data\n",
    "### Index the data in the same way as was shown in the course videos. Make the course field a keyword and the rest should be text.Don't forget to install the ElasticSearch client for Python:\n",
    "### Which function do you use for adding your data to elastic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "90a55c6e-8d66-47c4-966a-9296eeae03a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "531cdb8a-3ce3-4d93-9645-31177bca51b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_client = Elasticsearch('http://localhost:9200')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0db367ca-b457-44ef-b98c-73825e8bf23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'hw-faq-questions'})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_settings = {\n",
    "    \"settings\": {\n",
    "        \"number_of_shards\": 1,\n",
    "        \"number_of_replicas\": 0\n",
    "    },\n",
    "    \"mappings\": {\n",
    "        \"properties\": {\n",
    "            \"text\": {\"type\": \"text\"},\n",
    "            \"section\": {\"type\": \"text\"},\n",
    "            \"question\": {\"type\": \"text\"},\n",
    "            \"course\": {\"type\": \"keyword\"} \n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "index_name = \"hw-faq-questions\"\n",
    "\n",
    "es_client.indices.create(index=index_name, body=index_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0dcf18e5-4f61-418b-ad4c-6e688c1bebdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/codespace/miniconda3/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": 9,
   "id": "40e2c3a3-1dde-4fd7-87c7-3fa059e2926b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [00:18<00:00, 50.47it/s]\n"
     ]
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585388ff-5c10-47e3-8184-a88aba95c75e",
   "metadata": {},
   "source": [
    "## Q3. Searching\n",
    "### What's the score for the top ranking result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9dbf134-cdfc-4ae7-8cf1-736f38fc1fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def 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=index_name, body=search_query)\n",
    "    \n",
    "    result_docs = []\n",
    "    scores = []\n",
    "    \n",
    "    for hit in response['hits']['hits']:\n",
    "        result_docs.append(hit['_source'])\n",
    "        scores.append(hit['_score'])\n",
    "    \n",
    "    return result_docs, scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e0bb1c12-694e-4de2-bbf7-117a3954eb67",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"How do I execute a command in a running docker container?\"\n",
    "elastic_search_result, scores = elastic_search(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a4a7c64-9b39-4474-b135-c51fd74ffc23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84.050095\n"
     ]
    }
   ],
   "source": [
    "print(max(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1fae61-2922-4e7d-9659-652770f34301",
   "metadata": {},
   "source": [
    "## Q4. Filtering\n",
    "### Return 3 results. What's the 3rd question returned by the search engine?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80ee0757-31fb-4b7d-aa68-8a8e53bdc0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'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'}, {'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'}, {'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'}]\n"
     ]
    }
   ],
   "source": [
    "print(elastic_search_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e479a1d-916e-429e-b3b7-468c28f6189e",
   "metadata": {},
   "source": [
    "## Q5. Building a prompt\n",
    "### What's the length of the resulting prompt? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "553f1e28-a476-46cb-8755-add1d8cb9489",
   "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": 15,
   "id": "8d14fc39-ce74-4fbc-b15f-cb89b521f2b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of prompt: 1637\n"
     ]
    }
   ],
   "source": [
    "prompt = build_prompt(query, elastic_search_result)\n",
    "prompt_len = len(prompt)\n",
    "print(f\"length of prompt: {prompt_len}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c4e902-ed36-4746-b74e-9e3e17422f26",
   "metadata": {},
   "source": [
    "## Q6. Tokens\n",
    "### How many tokens does our prompt have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f6bd11dd-535b-4abc-b38a-1a457f8367c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1158e1c3-3744-4123-882a-2d92c09c3238",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")\n",
    "token_list = encoding.encode(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b494208-bb1e-494b-87e4-960da79a9d24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "token count 356\n"
     ]
    }
   ],
   "source": [
    "token_count = len(token_list)\n",
    "print(f\"token count {token_count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ff6a22-6781-4c55-a246-6e878f98e414",
   "metadata": {},
   "source": [
    "## Done"
   ]
  }
 ],
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