01-intro/homework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5cdae3c0-aac9-496f-92a9-4fd49709cb03",
   "metadata": {},
   "source": [
    "# 1. Running Elastic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f43a536a-4025-4ced-a375-f2abec38b32e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"name\" : \"d98126bd33d8\",\n",
      "  \"cluster_name\" : \"docker-cluster\",\n",
      "  \"cluster_uuid\" : \"w1i8LxYJSrybDGxbQw7XwA\",\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": "058f3d77-6599-4771-810c-75c5a6ec176c",
   "metadata": {},
   "source": [
    "## What's the version.build_hash value?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aad8716c-786e-4d85-830f-ed6803109883",
   "metadata": {},
   "source": [
    "42f05b9372a9a4a470db3b52817899b99a76ee73"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3902ab6a-3681-4f53-ad83-fe09806ee3cd",
   "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": "markdown",
   "id": "99dd5fb5-1056-4bc9-9ec2-0983a43b8bcb",
   "metadata": {},
   "source": [
    "# Q2. Indexing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "deb68f38-7157-4b95-a493-2e5f308e2ac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90886cac-5f64-4927-b08c-c9663c5cf1f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_client = Elasticsearch('http://localhost:9200') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0808b755-dabf-4064-bca6-b24601adab27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'course-questions'})"
      ]
     },
     "execution_count": 9,
     "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": "markdown",
   "id": "7651d2e6-819c-4277-b3e2-c90780381e1a",
   "metadata": {},
   "source": [
    "## Which function do you use for adding your data to elastic?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8469c74c-0384-4f5c-8e57-b5a5c3e6c2c6",
   "metadata": {},
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "759a812a-e1bf-4434-b946-08b9f4cf6527",
   "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": 12,
   "id": "b7dec894-c9cc-4456-9843-1de9c7108172",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [00:31<00:00, 30.56it/s]\n"
     ]
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "673736fc-55c0-40d2-b4fa-697451fd8e52",
   "metadata": {},
   "source": [
    "# Q3. Searching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2292abaa-6f50-4e5f-a2e3-6bf574107a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'How do I execute a command in a running docker container?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a8b75db-18d4-4fdb-86b1-6f54f3e1cd01",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72c1ba1d-b29e-4342-838a-7f80bba1402b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'took': 429, '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': 'tptmUZAB-9VKHLRCAwpE', '_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'}}, {'_index': 'course-questions', '_id': 'xJtlUZAB-9VKHLRCwwj8', '_score': 75.54128, '_source': {'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>', 'section': 'Module 1: Docker and Terraform', 'question': 'PGCLI - running in a Docker container', 'course': 'data-engineering-zoomcamp'}}, {'_index': 'course-questions', '_id': '5ZtmUZAB-9VKHLRCKgsJ', '_score': 72.08518, '_source': {'text': 'If you are trying to run Flask gunicorn & MLFlow server from the same container, defining both in Dockerfile with CMD will only run MLFlow & not Flask.\\nSolution: Create separate shell script with server run commands, for eg:\\n> \\tscript1.sh\\n#!/bin/bash\\ngunicorn --bind=0.0.0.0:9696 predict:app\\nAnother script with e.g. MLFlow server:\\n>\\tscript2.sh\\n#!/bin/bash\\nmlflow server -h 0.0.0.0 -p 5000 --backend-store-uri=sqlite:///mlflow.db --default-artifact-root=g3://zc-bucket/mlruns/\\nCreate a wrapper script to run above 2 scripts:\\n>\\twrapper_script.sh\\n#!/bin/bash\\n# Start the first process\\n./script1.sh &\\n# Start the second process\\n./script2.sh &\\n# Wait for any process to exit\\nwait -n\\n# Exit with status of process that exited first\\nexit $?\\nGive executable permissions to all scripts:\\nchmod +x *.sh\\nNow we can define last line of Dockerfile as:\\n> \\tDockerfile\\nCMD ./wrapper_script.sh\\nDont forget to expose all ports defined by services!', 'section': 'Module 4: Deployment', 'question': 'Running multiple services in a Docker container', 'course': 'mlops-zoomcamp'}}, {'_index': 'course-questions', '_id': '1ZtmUZAB-9VKHLRCBwoq', '_score': 51.04628, '_source': {'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'}}, {'_index': 'course-questions', '_id': '1ptmUZAB-9VKHLRCBwpM', '_score': 49.938507, '_source': {'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'}}]}})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c06b45b-8079-49ab-af21-3a234db5c52b",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_docs = []\n",
    "\n",
    "for hit in response['hits']['hits']:\n",
    "    result_docs.append((hit['_source'], hit['_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "635e04e2-6a48-4f11-b5d9-42f9f312ed5d",
   "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'}, 84.050095)\n"
     ]
    }
   ],
   "source": [
    "print(result_docs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e89a4632-a959-41c3-8e30-212126ebf572",
   "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",
       " 84.050095)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(result_docs, key=lambda h: h[1], reverse=True)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46c9301f-775f-43d1-84e0-91978deeb289",
   "metadata": {},
   "source": [
    "## What's the score for the top ranking result?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "886812cf-f165-4194-9acb-53b4638c6558",
   "metadata": {},
   "source": [
    "84.05"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb90a2e7-a91f-4bd7-b897-24894dcdb27f",
   "metadata": {},
   "source": [
    "# Q4. Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2e629d27-0c89-4ae6-9113-6e73648db955",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "504f892b-f039-40ff-b479-b9eaaaf0a120",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_docs = []\n",
    "\n",
    "for hit in response['hits']['hits']:\n",
    "    result_docs.append((hit['_source'], hit['_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2facc089-c436-4ede-a203-b621b937d974",
   "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",
       "  84.050095),\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",
       "  51.04628),\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",
       "  49.938507)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_docs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548db9ce-6a25-4db6-b9a6-7881dfadbc88",
   "metadata": {},
   "source": [
    "## What's the 3rd question returned by the search engine?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9bdb1f-6bfa-4564-93eb-0821ec1341a9",
   "metadata": {},
   "source": [
    "How do I copy files from a different folder into docker container’s working directory?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2c1d53-3ec4-4864-981e-e18932982a23",
   "metadata": {},
   "source": [
    "# Q5. Building a prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "268493eb-a3f8-41bf-9f11-cc3a0ef6c5df",
   "metadata": {},
   "outputs": [],
   "source": [
    "context_template = \"\"\"\n",
    "Q: {question}\n",
    "A: {text}\n",
    "\"\"\".strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8bbbb69e-7889-4ea2-96ad-130a16e66741",
   "metadata": {},
   "outputs": [],
   "source": [
    "context = \"\"\n",
    "    \n",
    "for doc in result_docs:\n",
    "    context = context + context_template.format(question=doc[0]['question'], text=doc[0]['text'])+\"\\n\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fb319a96-1ff4-49a4-976e-9134d398ce84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5879e076-fa59-4351-b14a-eedb61421aab",
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c889c9e7-0e1f-417d-b99c-24ef0638d4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = prompt_template.format(question=query, context=context).strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3240499c-626f-4f45-b4fb-ab1fe55de4b8",
   "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"
     ]
    }
   ],
   "source": [
    "print(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6afe9f81-2f2f-4d00-8d00-c6fecd016a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1462"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "952debf8-0781-4c01-9f63-2185f6c7cecb",
   "metadata": {},
   "source": [
    "## What's the length of the resulting prompt? (use the len function)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e080e0df-f5cc-47d7-9531-346c3381c51f",
   "metadata": {},
   "source": [
    "1462"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2882a40b-7792-481c-9567-0794329f7e64",
   "metadata": {},
   "source": [
    "# Q6. Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f1fd8c4c-8ec9-4b15-b018-568288c4e974",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tiktoken\n",
      "  Downloading tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\n",
      "Collecting regex>=2022.1.18 (from tiktoken)\n",
      "  Downloading regex-2024.5.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (40 kB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m856.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: requests>=2.26.0 in /home/codespace/.local/lib/python3.10/site-packages (from tiktoken) (2.32.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/python/3.10.13/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (2024.2.2)\n",
      "Downloading tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading regex-2024.5.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (775 kB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m775.1/775.1 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: regex, tiktoken\n",
      "Successfully installed regex-2024.5.15 tiktoken-0.7.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8546ee0e-7898-447c-9029-6862e1bca933",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7e2016cb-8342-433b-897f-eb2687afbbfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "640aad68-a444-471e-a3fc-e76d98607f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = encoding.encode(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c5b944b7-9589-426d-b6de-e2ab73c92a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "322"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1bfe062-abff-4974-ab8a-78ba6542fd7d",
   "metadata": {},
   "source": [
    "## How many tokens does our prompt have?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d07f51-5e89-4372-986a-b64d8fba453e",
   "metadata": {},
   "source": [
    "322"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ad6a5225-0294-40fe-928d-006938f8e736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b\"You're\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoding.decode_single_token_bytes(63842)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b9be16d-0f32-4b0b-ace9-fc1c7476f3dc",
   "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": "3.10.13"
  }
 },
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