Rag & Elastic Search.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "cf41d37e-e217-4669-961a-303b7d36d9b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f21d8add-6cd2-43aa-9670-6557c385e0e7",
   "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": "f320aefd-3da8-48ae-9929-02261a0f1792",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "70c5f1da-5d1a-44b6-8dfd-e368bafd5cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b372444-47c5-45e9-80c6-3d2f7e7d8c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_client = Elasticsearch('http://localhost:9200')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "17df0054-cba4-4ef8-bcd5-6c85120245ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'name': 'e4551fb334f7', 'cluster_name': 'docker-cluster', 'cluster_uuid': 'IiV-FDwGTTeeqC8WMQJMXg', 'version': {'number': '8.4.3', 'build_flavor': 'default', 'build_type': 'docker', 'build_hash': '42f05b9372a9a4a470db3b52817899b99a76ee73', 'build_date': '2022-10-04T07:17:24.662462378Z', 'build_snapshot': False, 'lucene_version': '9.3.0', 'minimum_wire_compatibility_version': '7.17.0', 'minimum_index_compatibility_version': '7.0.0'}, 'tagline': 'You Know, for Search'})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es_client.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ddbbac36-9553-4e80-8958-fb26bdcffdb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'course-questions'})"
      ]
     },
     "execution_count": 23,
     "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",
    "es_client.indices.create(index=index_name, body=index_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0c0ee594-872d-482b-872b-16b1f572a045",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eeea77c4-c8d0-47ca-956a-6520b8726cce",
   "metadata": {},
   "outputs": [],
   "source": [
    "for doc in documents:\n",
    "    es_client.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "47e978f7-e443-4be6-a1f4-39f0fb52dccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"How do I execute a command in a running docker container?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "0aa388ed-405f-4b9c-8fc7-2a45f2099e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def elasticSearch(query):\n",
    "    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",
    "                \"filter\": {\n",
    "                    \"term\": {\n",
    "                        \"course\": \"data-engineering-zoomcamp\"\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    response = es_client.search(index=index_name, body=search_query)\n",
    "    result_docs = []\n",
    "    for hit in response[\"hits\"][\"hits\"]:\n",
    "        result_docs.append(hit)\n",
    "    return result_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "fd8f4158-230c-4a28-8ae8-5244c9e11d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = elasticSearch(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "58d13ca0-3c08-42cb-b5d3-8ab1c27c970b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'_index': 'course-questions',\n",
       "  '_id': 'IlUBR5AB_8SXgAguQHws',\n",
       "  '_score': 75.54128,\n",
       "  '_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>',\n",
       "   'section': 'Module 1: Docker and Terraform',\n",
       "   'question': 'PGCLI - running in a Docker container',\n",
       "   'course': 'data-engineering-zoomcamp'}},\n",
       " {'_index': 'course-questions',\n",
       "  '_id': 'QVUBR5AB_8SXgAguYX12',\n",
       "  '_score': 43.922554,\n",
       "  '_source': {'text': 'You can check the version of your local spark using spark-submit --version. In the build.sh file of the Python folder, make sure that SPARK_VERSION matches your local version. Similarly, make sure the pyspark you pip installed also matches this version.',\n",
       "   'section': 'Module 6: streaming with kafka',\n",
       "   'question': 'How do I check compatibility of local and container Spark versions?',\n",
       "   'course': 'data-engineering-zoomcamp'}},\n",
       " {'_index': 'course-questions',\n",
       "  '_id': 'vVUBR5AB_8SXgAguNHtk',\n",
       "  '_score': 38.684105,\n",
       "  '_source': {'text': \"There are 3 Zoom Camps in a year, as of 2024. However, they are for separate courses:\\nData-Engineering (Jan - Apr)\\nMLOps (May - Aug)\\nMachine Learning (Sep - Jan)\\nThere's only one Data-Engineering Zoomcamp “live” cohort per year, for the certification. Same as for the other Zoomcamps.\\nThey follow pretty much the same schedule for each cohort per zoomcamp. For Data-Engineering it is (generally) from Jan-Apr of the year. If you’re not interested in the Certificate, you can take any zoom camps at any time, at your own pace, out of sync with any “live” cohort.\",\n",
       "   'section': 'General course-related questions',\n",
       "   'question': 'Course - how many Zoomcamps in a year?',\n",
       "   'course': 'data-engineering-zoomcamp'}},\n",
       " {'_index': 'course-questions',\n",
       "  '_id': '8VUBR5AB_8SXgAguOnt0',\n",
       "  '_score': 38.33403,\n",
       "  '_source': {'text': \"It is recommended by the Docker do\\n[Windows 10 / 11 Home Edition] If you're running a Home Edition, you can still make it work with WSL2 (Windows Subsystem for Linux) by following the tutorial here\\nIf even after making sure your WSL2 (or Hyper-V) is set up accordingly, Docker remains stuck, you can try the option to Reset to Factory Defaults or do a fresh install.\",\n",
       "   'section': 'Module 1: Docker and Terraform',\n",
       "   'question': 'Should I run docker commands from the windows file system or a file system of a Linux distribution in WSL?',\n",
       "   'course': 'data-engineering-zoomcamp'}},\n",
       " {'_index': 'course-questions',\n",
       "  '_id': 'w1UBR5AB_8SXgAguNXsU',\n",
       "  '_score': 35.94081,\n",
       "  '_source': {'text': \"No, you can only get a certificate if you finish the course with a “live” cohort. We don't award certificates for the self-paced mode. The reason is you need to peer-review capstone(s) after submitting a project. You can only peer-review projects at the time the course is running.\",\n",
       "   'section': 'General course-related questions',\n",
       "   'question': 'Certificate - Can I follow the course in a self-paced mode and get a certificate?',\n",
       "   'course': 'data-engineering-zoomcamp'}}]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "9c914e05-c008-4d19-8b43-297082df9fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def elasticSearch(query):\n",
    "    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",
    "                \"filter\": {\n",
    "                    \"term\": {\n",
    "                        \"course\": \"machine-learning-zoomcamp\"\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    response = es_client.search(index=index_name, body=search_query)\n",
    "    result_docs = []\n",
    "    for hit in response[\"hits\"][\"hits\"]:\n",
    "        result_docs.append(hit['_source'])\n",
    "    return result_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "549f7b4b-019c-423d-9f1f-494d8c69850e",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = elasticSearch(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "ea6af255-9417-403d-9aff-d19157a980a7",
   "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'},\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",
       " {'text': 'Solution\\nThis error was because there was another instance of gunicorn running. So I thought of removing this along with the zoomcamp_test image. However, it didn’t let me remove the orphan container. So I did the following\\nRunning the following commands\\ndocker ps -a <to list all docker containers>\\ndocker images <to list images>\\ndocker stop <container ID>\\ndocker rm <container ID>\\ndocker rmi image\\nI rebuilt the Docker image, and ran it once again; this time it worked correctly and I was able to serve the test script to the endpoint.',\n",
       "  'section': '5. Deploying Machine Learning Models',\n",
       "  'question': 'How to fix error after running the Docker run command',\n",
       "  'course': 'machine-learning-zoomcamp'}]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "784887e5-f0a9-4ff2-a16a-9f916a8c8cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'How do I copy files from a different folder into docker container’s working directory?'"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[2]['question']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "d1739520-e7df-4638-83f1-a361fa6e7891",
   "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_template = \"\"\"\n",
    "    Q: {question}\\n\\n\n",
    "    A: {text}\n",
    "    \"\"\".strip()\n",
    "\n",
    "    context = \"\"\n",
    "    for doc in search_results:\n",
    "        context = context_template.format(question=doc['question'], text=doc['text'])\n",
    "    \n",
    "    prompt = prompt_template.format(question=query, context=context).strip()\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "77df8f56-9b6f-4f2e-ac0c-bd411dfdb17c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "client = OpenAI(api_key=os.environ['OPEN_AI_KEY'])\n",
    "def llm(prompt):\n",
    "    response = client.chat.completions.create(\n",
    "        model='gpt-',\n",
    "        messages=[{\"role\": \"user\", \"content\": prompt}]\n",
    "    )\n",
    "    \n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "2bc3aa08-4a16-4ecf-8162-3eef7a098cc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def RAG(query):\n",
    "    results = elasticSearch(query)\n",
    "    prompt = build_prompt(query,results)\n",
    "    answer = llm(prompt)\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "74054d8e-d092-461b-973b-fd4fd0e5d3ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "871\n",
      "188\n"
     ]
    }
   ],
   "source": [
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")\n",
    "results = elasticSearch(query)\n",
    "prompt = build_prompt(query,results)\n",
    "encoded_text = encoding.encode(prompt)\n",
    "num_tokens = len(encoded_text)\n",
    "prompt_length = len(prompt)\n",
    "print(prompt_length)\n",
    "print(num_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d3b7d2b-3da8-4baa-8c02-622ca1e4c00d",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}