module_1/homework_llmops.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e9d7f938-3b15-47dd-a75f-d25d6d8dc664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-07-01 06:36:54--  https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.108.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3832 (3.7K) [text/plain]\n",
      "Saving to: ‘minsearch.py.4’\n",
      "\n",
      "minsearch.py.4      100%[===================>]   3.74K  --.-KB/s    in 0s      \n",
      "\n",
      "2024-07-01 06:36:55 (16.9 MB/s) - ‘minsearch.py.4’ saved [3832/3832]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "90fcc1cb-ad18-4583-a1b2-81692df7ac77",
   "metadata": {},
   "outputs": [],
   "source": [
    "import minsearch\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "94ebf4cc-4d2a-479c-a8e7-02c5c84c1993",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open ('documents.json', 'rt') as f_in:\n",
    "    docs_raw = json.load(f_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4dabe405-8006-4c1c-ab42-61fc56a6301a",
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = []\n",
    "\n",
    "for course_dict in docs_raw:\n",
    "    for doc in course_dict['documents']:\n",
    "        doc['course'] = course_dict['course']\n",
    "        documents.append(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c1db1f1a-176c-40af-8b37-ec1236e808a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = minsearch.Index(\n",
    "    text_fields=[\"question\",\"text\",\"section\"],\n",
    "    keyword_fields=[\"course\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2119a719-22fd-43f8-85c4-d599eda58c7b",
   "metadata": {},
   "source": [
    "SELECT * WHERE course = 'data-engineering-zoomcamp';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fe1398ea-4c7b-4750-8452-d144faadb3c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<minsearch.Index at 0x72f2c985e710>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "df77154f-b5e7-4f86-9f35-fbe1fcf9efb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5ded2f5d-2f52-41b6-a053-0d2fd3b143a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "API_TOKEN = os.environ.get(\"API_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "55d15a2a-8c61-4daa-86c8-31aa0f6fc9cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = {\"Authorization\": f\"Bearer {API_TOKEN}\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d9cf2f31-d46b-4de2-b0cd-664c4d9a8aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "API_URL = \"https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct\"\n",
    "def askllm(payload):\n",
    "    response = requests.post(API_URL, headers=headers, json=payload)\n",
    "    return response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "92da32e6-8411-4e17-bffe-b1bec10a078c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def search(query):\n",
    "    boost = {'question': 4.0, '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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "58225342-71bf-4ee9-ad55-7a428d5a73e7",
   "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": 96,
   "id": "0ab7cf52-80ed-4e0d-bc00-23dae9f65cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def llm(prompt):\n",
    "    response = askllm({\"inputs\": prompt})\n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "9524892a-2d3b-4c3f-8f82-16c2e40a5453",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 104,
   "id": "e93348e2-28fc-42a8-9fc6-898145a29c7b",
   "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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6401054a-be27-420a-936e-4361841066e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a1cc9189-2a04-4de7-be7b-bdaf37888886",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_client = Elasticsearch('http://localhost:9200')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "8bd6f9c0-8a53-4a3b-ac73-5d0687af26e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'course-questions'})"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_settings = {\n",
    "    \"settings\": {\n",
    "        \"number_of_shards\": 1,\n",
    "        \"number_of_replicas\": 0\n",
    "    },\n",
    "    \"mappings\": {\n",
    "        \"properties\": {\n",
    "            \"text\": {\"type\": \"text\"},\n",
    "            \"section\": {\"type\": \"text\"},\n",
    "            \"question\": {\"type\": \"text\"},\n",
    "            \"course\": {\"type\": \"keyword\"} \n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "index_name = \"course-questions\"\n",
    "\n",
    "es_client.indices.create(index = index_name, body= index_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0455b2bf-9274-4df3-a6b0-f346a38bb0cf",
   "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": 110,
   "id": "40fdc1de-5a8a-478d-91a8-d7134d161aff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [00:24<00:00, 39.09it/s]\n"
     ]
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "141dbd14-ca5d-4709-bafa-363703b00f47",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ac6cfc7b-66a3-43c5-88cc-68d4cf7ae806",
   "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": 113,
   "id": "479d6419-400b-4158-a6ad-e110f9939758",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'I just discovered the course, can I still join?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "849b0758-e912-4e0d-99a6-f6bc49c2bb22",
   "metadata": {},
   "outputs": [],
   "source": [
    "answer = rag(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f14960-c752-45ac-bfa6-bcd384f0c731",
   "metadata": {},
   "source": [
    "Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "ab707ffc-a529-4744-b41f-9026a89b4318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'took': 36, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 865, 'relation': 'eq'}, 'max_score': 84.050095, 'hits': [{'_index': 'course-questions', '_id': 'z_0dbZABXcbFR74fEZmx', '_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'}}]}})"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_query = {\n",
    "        \"size\": 1,\n",
    "        \"query\": {\n",
    "            \"bool\": {\n",
    "                \"must\": {\n",
    "                    \"multi_match\": {\n",
    "                        \"query\": 'How do I execute a command in a running docker container?',\n",
    "                        \"fields\": [\"question^4\", \"text\"],\n",
    "                        \"type\": \"best_fields\"\n",
    "                    }\n",
    "                },\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "es_client.search(index=index_name, body=search_query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f14fa6-0974-4556-ad09-1b926d9f49d3",
   "metadata": {},
   "source": [
    "Question 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "2ecbc0a0-1f67-4ebc-8d0e-dc17bd47c5b6",
   "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": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_query = {\n",
    "        \"size\": 3,\n",
    "        \"query\": {\n",
    "            \"bool\": {\n",
    "                \"must\": {\n",
    "                    \"multi_match\": {\n",
    "                        \"query\": 'How do I execute a command in a running docker container?',\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'])\n",
    "\n",
    "result_docs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e05ebd-1533-48e8-a894-30217ae7bdcf",
   "metadata": {},
   "source": [
    "Question 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5fffb9b8-e47f-49b2-8764-9bb232de3d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "context = \"\"\n",
    "\n",
    "for doc in result_docs:\n",
    "    context = context + f\"Q: {doc['question']}\\nA: {doc['text']}\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "5f973a4d-7650-4978-900d-00b482cf8958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Q: How do I debug a docker container?\\nA: 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)\\nQ: How do I copy files from my local machine to docker container?\\nA: 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\\nQ: How do I copy files from a different folder into docker container’s working directory?\\nA: 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'"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c8ead45d-d11f-4c93-8de6-1f8e6779cf9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'How do I execute a command in a running docker container?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "e73585c1-4d4a-4f78-b4c3-7507b4e1e894",
   "metadata": {},
   "outputs": [],
   "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",
    "prompt = prompt_template.format(question=query, context=context).strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "8e79c2e5-4aec-48e0-bf82-dcf7f6f28c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1461"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33e85167-0769-47b6-8775-fdb4e9c389f0",
   "metadata": {},
   "source": [
    "Question 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "60a4614a-43c7-4ff8-a74d-12b16601280b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "e5bd4eaf-1ea4-4d35-9d91-8a84cad7c2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5835a41d-5510-4378-8feb-59d39d8a4853",
   "metadata": {},
   "source": [
    "Number of Tokens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "75587b6e-00d5-4c80-a254-1542bb428665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "323"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(encoding.encode(prompt))"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
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