2024_hw1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9c6de35d-30d1-447b-b5bc-795525dd947d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Q1. Running Elastic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb1b5e3a-f717-43d3-ace1-d7fdcb5c92a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"name\" : \"025a6d95ab4d\",\n",
      "  \"cluster_name\" : \"docker-cluster\",\n",
      "  \"cluster_uuid\" : \"gtiCqXoIQ6iwSmDSx2UuGw\",\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": "code",
   "execution_count": 92,
   "id": "734f3f1c-4fe9-4218-9e63-296686ceb94f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# docker run -it --rm --name elasticsearch -p 9200:9200 -p 9300:9300 -e \"discovery.type=single-node\" -e \"xpack.security.enabled=false\" docker.elastic.co/elasticsearch/elasticsearch:8.4.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "864d7116-88ba-4c32-aaac-bbab1a9a2e2c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\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",
    "documents = []\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": "markdown",
   "id": "cc4d3c12-e773-4708-a4bc-ca57111f05fe",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Q2. Indexing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9a8d91-4cc3-4c00-a13e-2a6e0d76d430",
   "metadata": {},
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ec27c0-da88-4dad-92d6-882c83f3eeae",
   "metadata": {},
   "source": [
    "### Q3. Searching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60893673-a1fa-4e09-a03c-7aef197d775f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch\n",
    "from elasticsearch.helpers import bulk\n",
    "import requests\n",
    " \n",
    "# Create an Elasticsearch client instance\n",
    "es = Elasticsearch([{'host': 'localhost', 'port': 9200, 'scheme': 'http'}], request_timeout=30)\n",
    " \n",
    "# URL of the documents JSON file\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",
    "# Prepare documents for bulk indexing\n",
    "documents = []\n",
    "for course in documents_raw:\n",
    "    course_name = course['course']\n",
    "    for doc in course['documents']:\n",
    "        doc['course'] = course_name\n",
    "        documents.append({\n",
    "            \"_index\": \"documents_index\",\n",
    "            \"_source\": doc\n",
    "        })\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37d48a38-fc6c-4634-b031-0fbe7c3dc41a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the index settings and mappings\n",
    "index_name = \"documents_index\"\n",
    "index_body = {\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",
    "# Create the index with the defined settings and mappings\n",
    "if not es.indices.exists(index=index_name):\n",
    "    es.indices.create(index=index_name, body=index_body)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "507fbef9-aaa3-4ea5-bc08-3f1df7d24dd6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "{'took': 40, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 865, 'relation': 'eq'}, 'max_score': 84.050095, 'hits': [{'_index': 'documents_index', '_id': 'vFjASZAByWajB53U-iDv', '_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'}}]}}\n",
      "Top score: 84.050095\n"
     ]
    }
   ],
   "source": [
    "# Index documents using bulk API\n",
    "bulk(es, documents)\n",
    " \n",
    "# Refresh the index to make sure all documents are searchable\n",
    "# es.indices.refresh(index=\"documents_index\")\n",
    "print(es.indices.exists(index=\"documents_index\"))\n",
    "# Define the search query\n",
    "# query = {\n",
    "#     \"query\": {\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",
    "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",
    "# Execute the search query\n",
    "response = es.search(index=\"documents_index\", body=query)\n",
    "print(response)\n",
    "# Get the score for the top-ranking result\n",
    "top_score = response['hits']['hits'][0]['_score']\n",
    "print(f\"Top score: {top_score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fb25eabf-b6c4-45fe-93b8-ebb1c11e1c38",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'acknowledged': True})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# result = es.search(index=index_name, body={\"query\": {\"match_all\": {}}})\n",
    "# print(len(result['hits']['hits']))\n",
    "# es.indices.delete(index=index_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bbb1bb3-6d73-4897-b6d2-355bf2a0378d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Q4. Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4212c89a-08d3-4542-9bf2-9686adf4ccea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01f3a255-4974-4c01-9650-41f9ac2fe371",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 84.17781 {'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'}\n",
      "\n",
      " 84.17781 {'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'}\n",
      "\n",
      " 51.134113 {'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'}\n",
      "\n",
      "\n",
      "The 3rd question returned by the search engine is: How do I copy files from my local machine to docker container?\n"
     ]
    }
   ],
   "source": [
    "# Index documents using bulk API\n",
    "# bulk(es, documents)\n",
    "\n",
    "# Refresh the index to make sure all documents are searchable\n",
    "# es.indices.refresh(index=\"documents_index\")\n",
    "# Define the search query\n",
    "q4_search_query = {\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",
    "    \"size\": 3\n",
    "}\n",
    "\n",
    "# Perform the search\n",
    "response = es.search(index=\"documents_index\", body=q4_search_query)\n",
    "\n",
    "for hit in response['hits']['hits']:\n",
    "    print(\"\\n\", hit['_score'], hit['_source'])\n",
    "# print(response['hits']['hits']['_score'])\n",
    "# Extract the 3rd question from the search results\n",
    "third_question = response['hits']['hits'][2]['_source']['question']\n",
    "print(f\"\\n\\nThe 3rd question returned by the search engine is: {third_question}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c99b87f-f169-45f0-9309-30e28bc34bc2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# result = es.delete_by_query(index=index_name, body={\"query\": {\"match_all\": {}}})\n",
    "# print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87a01f32-0152-4504-a74b-74edfbea4cee",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Q5. Building a prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6851e4b7-2026-487c-9a0f-f38b31c77edb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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: How do I execute a command in a running docker container?\n",
      "\n",
      "CONTEXT:\n",
      "Q: How do I debug a docker container?\n",
      "A: Launch the container image in interactive mode and overriding the entrypoint, so that it starts a bash command.\n",
      "docker run -it --entrypoint bash <image>\n",
      "If the container is already running, execute a command in the specific container:\n",
      "docker ps (find the container-id)\n",
      "docker exec -it <container-id> bash\n",
      "(Marcos MJD)\n",
      "\n",
      "Q: How do I copy files from my local machine to docker container?\n",
      "A: You can copy files from your local machine into a Docker container using the docker cp command. Here's how to do it:\n",
      "To 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:\n",
      "docker cp /path/to/local/file_or_directory container_id:/path/in/container\n",
      "Hrithik Kumar Advani\n",
      "\n",
      "Q: How do I copy files from a different folder into docker container’s working directory?\n",
      "A: You can copy files from your local machine into a Docker container using the docker cp command. Here's how to do it:\n",
      "In the Dockerfile, you can provide the folder containing the files that you want to copy over. The basic syntax is as follows:\n",
      "COPY [\"src/predict.py\", \"models/xgb_model.bin\", \"./\"]\t\t\t\t\t\t\t\t\t\t\tGopakumar Gopinathan\n",
      "1462\n"
     ]
    }
   ],
   "source": [
    "results = [{'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",
    "context = \"\"\n",
    "context_template = context_template = \"\"\"\n",
    "Q: {question}\n",
    "A: {text}\n",
    "\"\"\".strip()\n",
    "for doc in results:\n",
    "    context = context + context_template.format(question=doc['question'], text=doc['text']).strip() + \"\\n\\n\"\n",
    "#print(context)\n",
    "\n",
    "\n",
    "question = \"How do I execute a command in a running docker container?\"\n",
    "\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",
    "\"\"\".format(question=question, context=context).strip()\n",
    "\n",
    "print(prompt_template)\n",
    "print(len(prompt_template))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e91528c-bbbb-4b9b-96b3-918754f27174",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Q6. Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05051d59-c45c-4230-8e8f-a899a4247071",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[63842, 261, 4165, 14029, 29186, 13, 30985, 290, 150339, 4122, 402, 290, 31810, 8099, 591, 290, 40251, 7862, 558, 8470, 1606, 290, 19719, 591, 290, 31810, 8099, 1261, 55959, 290, 150339, 364, 107036, 25, 3253, 621, 357, 15792, 261, 6348, 306, 261, 6788, 62275, 9282, 1715, 10637, 50738, 734, 48, 25, 3253, 621, 357, 15199, 261, 62275, 9282, 3901, 32, 25, 41281, 290, 9282, 3621, 306, 25383, 6766, 326, 151187, 290, 7251, 4859, 11, 813, 484, 480, 13217, 261, 38615, 6348, 558, 68923, 2461, 533, 278, 2230, 7962, 4859, 38615, 464, 3365, 523, 3335, 290, 9282, 382, 4279, 6788, 11, 15792, 261, 6348, 306, 290, 4857, 9282, 734, 68923, 10942, 350, 6555, 290, 9282, 26240, 446, 68923, 25398, 533, 278, 464, 6896, 26240, 29, 38615, 198, 6103, 277, 10732, 391, 79771, 1029, 48, 25, 3253, 621, 357, 5150, 6291, 591, 922, 2698, 7342, 316, 62275, 9282, 3901, 32, 25, 1608, 665, 5150, 6291, 591, 634, 2698, 7342, 1511, 261, 91238, 9282, 2360, 290, 62275, 27776, 6348, 13, 44257, 1495, 316, 621, 480, 734, 1385, 5150, 261, 1974, 503, 12552, 591, 634, 2698, 7342, 1511, 261, 6788, 91238, 9282, 11, 481, 665, 1199, 290, 2700, 68923, 27776, 6348, 62102, 623, 9439, 45440, 382, 472, 18183, 734, 68923, 27776, 820, 4189, 72231, 52214, 51766, 15400, 35850, 9282, 1537, 27975, 4189, 26985, 190543, 198, 106096, 437, 507, 70737, 15241, 3048, 279, 48, 25, 3253, 621, 357, 5150, 6291, 591, 261, 2647, 15610, 1511, 62275, 9282, 802, 4113, 12552, 3901, 32, 25, 1608, 665, 5150, 6291, 591, 634, 2698, 7342, 1511, 261, 91238, 9282, 2360, 290, 62275, 27776, 6348, 13, 44257, 1495, 316, 621, 480, 734, 637, 290, 91238, 2318, 11, 481, 665, 3587, 290, 15610, 15683, 290, 6291, 484, 481, 1682, 316, 5150, 1072, 13, 623, 9439, 45440, 382, 472, 18183, 734, 128701, 9129, 7205, 8138, 21369, 17311, 672, 392, 13123, 22739, 9320, 10928, 69422, 672, 9633, 2601, 14973, 22713, 167296, 30463, 499, 137058, 22064]\n",
      "322\n"
     ]
    }
   ],
   "source": [
    "import tiktoken\n",
    "\n",
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")\n",
    "encoded_prompt = encoding.encode(prompt_template)\n",
    "\n",
    "# Print the encoded tokens\n",
    "print(encoded_prompt)\n",
    "print(len(encoded_prompt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec37e683-4db5-4e13-a4a5-e1e59d201666",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b362b7e-c666-4e4c-8406-94fba812aad1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "kernel": "py311",
   "name": "tf2-gpu.2-15.m122",
   "type": "gcloud",
   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf2-gpu.2-15:m122"
  },
  "kernelspec": {
   "display_name": "py311 (Local)",
   "language": "python",
   "name": "py311"
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  "language_info": {
   "codemirror_mode": {
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    "version": 3
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