Module 1/Homework 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4a896e3-4897-45f0-be8b-72c14df4d579",
   "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": 13,
   "id": "5805c977-e048-45b2-a089-a9796ed1f89e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "adb90674-9e5e-485a-86fc-236fa99c0c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_client = Elasticsearch('http://localhost:9200')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43971a0f-8cc3-4933-ba9d-5e9b0f701f2b",
   "metadata": {},
   "source": [
    "# Q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "be88bdf8-672c-49f3-b15a-b528da5255e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'hw1'})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create Index with es\n",
    "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\"} # keyword for filtering\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "index_name = 'hw1'\n",
    "es_client.indices.create(index=index_name, body=index_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1e469a58-22f7-4945-a7fd-225afb30fafb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3c9ea6daa9f44e48e4e59bd17f23b20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/948 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "05cc62cd-ebe5-4a0b-9bfb-bf05c99f3897",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'How do I execute a command in a running docker container?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac03ae61-46bd-415f-b685-1d70295893f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "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'], hit['_score']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a72a5a8-b0b4-4f02-9acb-145f14f66d82",
   "metadata": {},
   "source": [
    "# Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "39f91adb-507d-4be6-8da5-afbb56c07e25",
   "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",
       "  83.27277),\n",
       " ({'text': 'In case running pgcli  locally causes issues or you do not want to install it locally you can use it running in a Docker container instead.\\nBelow the usage with values used in the videos of the course for:\\nnetwork name (docker network)\\npostgres related variables for pgcli\\nHostname\\nUsername\\nPort\\nDatabase name\\n$ docker run -it --rm --network pg-network ai2ys/dockerized-pgcli:4.0.1\\n175dd47cda07:/# pgcli -h pg-database -U root -p 5432 -d ny_taxi\\nPassword for root:\\nServer: PostgreSQL 16.1 (Debian 16.1-1.pgdg120+1)\\nVersion: 4.0.1\\nHome: http://pgcli.com\\nroot@pg-database:ny_taxi> \\\\dt\\n+--------+------------------+-------+-------+\\n| Schema | Name             | Type  | Owner |\\n|--------+------------------+-------+-------|\\n| public | yellow_taxi_data | table | root  |\\n+--------+------------------+-------+-------+\\nSELECT 1\\nTime: 0.009s\\nroot@pg-database:ny_taxi>',\n",
       "   'section': 'Module 1: Docker and Terraform',\n",
       "   'question': 'PGCLI - running in a Docker container',\n",
       "   'course': 'data-engineering-zoomcamp'},\n",
       "  77.09882),\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",
       "  50.83519),\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'},\n",
       "  49.02985),\n",
       " ({'text': \"$ docker exec -it 1e5a1b663052 bash\\nthe input device is not a TTY.  If you are using mintty, try prefixing the command with 'winpty'\\nFix:\\nwinpty docker exec -it 1e5a1b663052 bash\\nA TTY is a terminal interface that supports escape sequences, moving the cursor around, etc.\\nWinpty is a Windows software package providing an interface similar to a Unix pty-master for communicating with Windows console programs.\\nMore info on terminal, shell, console applications hi and so on:\\nhttps://conemu.github.io/en/TerminalVsShell.html\\n(Marcos MJD)\",\n",
       "   'section': '5. Deploying Machine Learning Models',\n",
       "   'question': 'The input device is not a TTY when running docker in interactive mode (Running Docker on Windows in GitBash)',\n",
       "   'course': 'machine-learning-zoomcamp'},\n",
       "  46.26563)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_docs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a40887-b4bf-488b-b42f-064279cbbbbf",
   "metadata": {},
   "source": [
    "# Q4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9ab7ee48-f6dd-4d8a-bd60-df67e4bf67f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "search_query = {\n",
    "        \"size\": 3,\n",
    "        \"query\": {\n",
    "            \"bool\": {\n",
    "                \"must\": {\n",
    "                    \"multi_match\": {\n",
    "                        \"query\": query,\n",
    "                        \"fields\": [\"question^4\", \"text\"],\n",
    "                        \"type\": \"best_fields\"\n",
    "                    }\n",
    "                },\n",
    "                \"filter\": {\n",
    "                    \"term\": {\n",
    "                        \"course\": \"machine-learning-zoomcamp\"\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a25f673-deee-4fe3-82de-860aae623977",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_docs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00368af7-6e00-41ad-8816-492cfb870e33",
   "metadata": {},
   "source": [
    "# Q5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe46b0d7-4e11-4a11-917d-cdb7420d2731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1462"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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 result_docs:\n",
    "    context = context + f\"Q: {doc['question']}\\nA: {doc['text']}\\n\\n\"\n",
    "\n",
    "prompt = prompt_template.format(question=query, context=context).strip()\n",
    "len(prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f21d7c2-026b-4a39-b40f-6eea924b0a5f",
   "metadata": {},
   "source": [
    "# Q6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1482398b-d3a2-4686-a89b-b24c8897f0af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "322"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tiktoken\n",
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")\n",
    "len(encoding.encode(prompt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa4910be",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}