llm-zoomcamp-hw1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
      "100   539  100   539    0     0  25156      0 --:--:-- --:--:-- --:--:-- 25666\r\n",
      "    \"build_hash\" : \"42f05b9372a9a4a470db3b52817899b99a76ee73\",\r\n"
     ]
    }
   ],
   "source": [
    "!curl localhost:9200 | grep build_hash"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch\n",
    "\n",
    "es_client = Elasticsearch('http://localhost:9200')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'course-questions'})"
     },
     "execution_count": 11,
     "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",
    "index_name = \"course-questions\"\n",
    "\n",
    "es_client.indices.create(index=index_name, body=index_settings)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 948/948 [00:03<00:00, 279.67it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=index_name, document=doc)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "query = 'How do I execute a command in a running docker container'\n",
    "\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'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "56.79628"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response['hits']['hits'][0].get('_score')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "query = 'How do I execute a command in a running docker container'\n",
    "\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\": \"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'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "'How do I debug a docker container?'"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_docs[2].get('question')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "context_template = \"\"\"\n",
    "Q: {question}\n",
    "A: {text}\n",
    "\"\"\".strip()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "'Q: {question}\\nA: {text}'"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "context_template"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [],
   "source": [
    "def format_record(record):\n",
    "    return context_template.format(question=record['question'], text=record['text'])\n",
    "\n",
    "formatted_records = [format_record(record) for record in result_docs]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [],
   "source": [
    "question = 'How do I execute a command in a running docker container?'\n",
    "context = formatted_records"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [],
   "source": [
    "prompt = f\"\"\"\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()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "data": {
      "text/plain": "2159"
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prompt)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "data": {
      "text/plain": "505"
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tiktoken\n",
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")\n",
    "tokens = encoding.encode(prompt)\n",
    "num_tokens = len(tokens)\n",
    "num_tokens"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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   "file_extension": ".py",
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