tutorials/6_RAG_CE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RAG-CE with Synthetic Data Manipulation\n",
    "\n",
    "In this notebook, we will introduce the concepts of RAG-CE with synthetic data manipulation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "\n",
    "import sys\n",
    "sys.path.append('..\\\\code')\n",
    "\n",
    "\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "\n",
    "from IPython.display import display, Markdown, HTML\n",
    "from PIL import Image\n",
    "from doc_utils import *\n",
    "\n",
    "\n",
    "def show_img(img_path, width = None):\n",
    "    if width is not None:\n",
    "        display(HTML(f'<img src=\"{img_path}\" width={width}>'))\n",
    "    else:\n",
    "        display(Image.open(img_path))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make sure we have the OpenAI Models information\n",
    "\n",
    "We will need the GPT-4-Turbo and GPT-4-Vision models for this notebook.\n",
    "\n",
    "When running the below cell, the values should reflect the OpenAI reource you have created in the `.env` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_info = {\n",
    "        'AZURE_OPENAI_RESOURCE': os.environ.get('AZURE_OPENAI_RESOURCE'),\n",
    "        'AZURE_OPENAI_KEY': os.environ.get('AZURE_OPENAI_KEY'),\n",
    "        'AZURE_OPENAI_MODEL_VISION': os.environ.get('AZURE_OPENAI_MODEL_VISION'),\n",
    "        'AZURE_OPENAI_MODEL': os.environ.get('AZURE_OPENAI_MODEL'),\n",
    "}\n",
    "\n",
    "oai_client = AzureOpenAI(\n",
    "    azure_endpoint = OPENAI_API_BASE, \n",
    "    api_key= AZURE_OPENAI_KEY,  \n",
    "    api_version= AZURE_OPENAI_API_VERSION,\n",
    ")\n",
    "\n",
    "oai_emb_client = AzureOpenAI(\n",
    "    azure_endpoint = AZURE_OPENAI_EMBEDDING_API_BASE, \n",
    "    api_key= AZURE_OPENAI_EMBEDDING_MODEL_RESOURCE_KEY,  \n",
    "    api_version= AZURE_OPENAI_EMBEDDING_MODEL_API_VERSION,\n",
    ")\n",
    "\n",
    "model_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the Index\n",
    "\n",
    "In this section, we will build the vector index that will be used in the remainder of the notebook.\n",
    "\n",
    "The dataset consists of 76 facts generated by GPT-4 around the Tesla Model S vehicle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has 76 fact items in it\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'The Tesla Model S Plaid version boasts an extraordinary acceleration speed, sprinting from 0 to 60 mph in under 2 seconds precisely at 1.99 seconds, making it one of the fastest accelerating production cars available. Its power comes from a tri-motor setup that produces over 1,020 horsepower, enabling it to complete a quarter mile in just 9.23 seconds. '"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = './sample_data/Tesla_Model_S.txt'\n",
    "\n",
    "text = read_asset_file(data)[0]\n",
    "\n",
    "facts = text.split('\\n\\n')\n",
    "print(f\"This dataset has {len(facts)} fact items in it\")\n",
    "facts[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make sure we have the AI Search information\n",
    "\n",
    "When running the below cell, the values should reflect the AI Search reource you have created in the `.env` file, specifically, the `COG_SEARCH_ENDPOINT` and `COG_SEARCH_ADMIN_KEY` entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ.get('COG_SEARCH_ENDPOINT'), os.environ.get('COG_SEARCH_ADMIN_KEY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantiating the Index Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.cogsearch_rest import *\n",
    "\n",
    "index_name = 'tesla_facts'\n",
    "\n",
    "fields = [\n",
    "            {\"name\": \"id\", \"type\": \"Edm.String\", \"key\": True, \"searchable\": True, \"filterable\": True, \"retrievable\": True, \"sortable\": True},\n",
    "            {\"name\": \"vector\", \"type\": \"Collection(Edm.Single)\", \"searchable\": True,\"retrievable\": True, \"dimensions\": 1536,\"vectorSearchProfile\": \"my-vector-profile\"},\n",
    "            {\"name\": \"tags\", \"type\": \"Edm.String\",\"searchable\": True, \"filterable\": False, \"retrievable\": True, \"sortable\": False, \"facetable\": False},\n",
    "            {\"name\": \"text\", \"type\": \"Edm.String\",\"searchable\": True, \"filterable\": False, \"retrievable\": True, \"sortable\": False, \"facetable\": False},\n",
    "]\n",
    "\n",
    "index = CogSearchRestAPI(index_name, fields=fields)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the AI Search Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment if needed\n",
    "# index.delete()\n",
    "\n",
    "## Creating the AI Search Index\n",
    "if index.get_index() is None:\n",
    "    print(f\"No index {index_name} detected, creating one ... \")\n",
    "    index.create_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Populating the Index\n",
    "\n",
    "In this cell, we do create very simple metadata, and upload it to AI Search vector index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " metadatas = []\n",
    "\n",
    " for fact in facts: \n",
    "    metadata = {\n",
    "        \"text\": fact, \n",
    "        \"vector\": get_embeddings(fact, client=oai_emb_client),\n",
    "        \"tags\": generate_tag_list(fact, client=oai_client),\n",
    "        \"id\": generate_uuid_from_string(fact)\n",
    "    }\n",
    "\n",
    "    metadatas.append(metadata)\n",
    "    \n",
    "    if len(metadatas) % 10 == 0:\n",
    "        print(f\"Processed {len(metadatas)} items\")\n",
    "\n",
    "# Save the data to a pickle file\n",
    "save_to_pickle(metadatas, './sample_data/tesla_facts.pkl')\n",
    "\n",
    "# Upload to the AI Search index\n",
    "upload_output = index.upload_documents(metadatas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Querying the Index\n",
    "\n",
    "Searching the vector index with Hybrid Search enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance Score: \u001b[94m0.03110\u001b[0m\n",
      "Text: \u001b[92mThe Model S is equipped with a high-fidelity sound system, including up to 11 speakers with neodymium magnets and a specially designed acoustic architecture. This setup ensures an immersive listening experience for all passengers, with crystal-clear highs and deep, resonant lows. \u001b[0m\n",
      "\n",
      "\n",
      "Distance Score: \u001b[94m0.03182\u001b[0m\n",
      "Text: \u001b[92mTesla's Model S comes with a premium audio system that includes 11 speakers with neodymium magnets, providing an immersive listening experience. This high-fidelity system is meticulously tuned to the car's interior acoustics, enhancing the enjoyment of music and media.\u001b[0m\n",
      "\n",
      "\n",
      "Distance Score: \u001b[94m0.03200\u001b[0m\n",
      "Text: \u001b[92mThe Tesla Model S features a dedicated pedestrian warning system that uses external speakers to produce noise at low speeds, alerting pedestrians of the vehicle's presence. This safety feature addresses the quiet nature of electric vehicles. \u001b[0m\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "query = \"How many speakers are in the Tesla Model S, and what are their characteristics?\"\n",
    "\n",
    "def query_search(query, top=3):\n",
    "    context = \"\"\n",
    "    results = index.search_documents(query, top=top)\n",
    "    for r in results['value']:\n",
    "        print(f\"Distance Score: {bc.OKBLUE}{r['@search.score']:.5f}{bc.ENDC}\\nText: {bc.OKGREEN}{r['text']}{bc.ENDC}\\n\\n\")\n",
    "        context += r['text'] + '\\n\\n'\n",
    "\n",
    "    return context\n",
    "\n",
    "context = query_search(query)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RAG-CE\n",
    "\n",
    "Use AI Search to retrieve the context necessary for the Assistants API to work with and to produce a final answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Search Context:\n",
      "\n",
      "Distance Score: \u001b[94m0.03280\u001b[0m\n",
      "Text: \u001b[92mThe Tesla Model S Plaid version boasts an extraordinary acceleration speed, sprinting from 0 to 60 mph in under 2 seconds precisely at 1.99 seconds, making it one of the fastest accelerating production cars available. Its power comes from a tri-motor setup that produces over 1,020 horsepower, enabling it to complete a quarter mile in just 9.23 seconds. \u001b[0m\n",
      "\n",
      "\n",
      "Distance Score: \u001b[94m0.01667\u001b[0m\n",
      "Text: \u001b[92mTesla offers a comprehensive warranty for the Model S, including an 8-year or unlimited mile battery and drive unit warranty and a 4-year or 50,000-mile limited warranty covering the rest of the vehicle. This coverage underscores Tesla's confidence in the reliability and longevity of their electric vehicles.\u001b[0m\n",
      "\n",
      "\n",
      "Distance Score: \u001b[94m0.01020\u001b[0m\n",
      "Text: \u001b[92mThe rear seats of the Model S can be folded down, providing a flat loading area that expands the cargo space significantly. This versatility makes the Model S practical for a variety of uses, from daily commuting to hauling larger items. \u001b[0m\n",
      "\n",
      "\n",
      "Assistants API generated 1 files for this answer.\n",
      "\n",
      "The final response from Assistants-API:\n",
      "\u001b[94mHere is a chart showing the time it takes for the Tesla Model S to cover 1/4 mile, 1/2 mile, and 3/4 mile:\n",
      "\n",
      "- 1/4 mile: 9.23 seconds\n",
      "- 1/2 mile: Total time after second quarter-mile (9.23 + 6.53 = 15.76 seconds)\n",
      "- 3/4 mile: Total time after third quarter-mile (9.23 + 6.53 + 4.62 = 20.37 seconds)\n",
      "\n",
      "These times indicate how the vehicle's hypothetical acceleration affects its performance over each quarter-mile stretch.\u001b[0m\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"../downloads\\assistant-co6RJDYzmh4iTVoaXpjoysYc.jpg\" width=700>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_template = \"\"\"\n",
    "\n",
    "## START OF CONTEXT\n",
    "{context}\n",
    "## END OF CONTEXT\n",
    "\n",
    "Based on the Context above, please answer the below question. You **MUST** use the context to answer the question. If the answer is not in the context, please respond with \"I don't know\". If the question requires calculations, make sure to break down your methodology step by step in the final answer, walk the user through your process, and show intermediate calculation results.\n",
    "\n",
    "Question: {question}\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "question = \"How much time will it take for the Tesla Model S to cover three quarters of a mile? Assume the continuing acceleration is twice as much for the next quarter-mile as it is for the initial quarter-mile, and then double that for the last quarter mile. Make a chart of the time it takes to cover 1/4, 1/2, and 3/4 of a mile.\"\n",
    "\n",
    "# Get context for AI Search\n",
    "print(\"The Search Context:\\n\")\n",
    "query = query_template.format(question=question, context=query_search(question))\n",
    "\n",
    "# Use Assistants API\n",
    "assistant, thread = create_assistant(oai_client)    \n",
    "messages = query_assistant(query, assistant, thread, client = oai_client)\n",
    "response, files = process_assistants_api_response(messages, client = oai_client)\n",
    "\n",
    "print(f\"Assistants API generated {len(files)} files for this answer.\\n\")\n",
    "print(f\"The final response from Assistants-API:\\n{bc.OKBLUE}{response}{bc.ENDC}\\n\")\n",
    "\n",
    "for f in files:\n",
    "    if f['type'] == 'assistant_image':\n",
    "        show_img(f['asset'], width=700)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Embeddings\n",
    "\n",
    "Just out of curiosity, the below is just a function to plot the embeddings array in 2-D just to see any semantic clusters for the dataset.\n",
    "\n",
    "In the generated plots below, you can hover on the dots with your mouse to see the tags associated with each entry. You can check that for dots that are closely located, the subject of the text is very semantically close. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## In case these libraries are not installed, uncomment the below lines\n",
    "%pip install scikit-learn matplotlib plotly nbformat hdbscan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## In case metadatas was lost during kernel restart\n",
    "metadatas = load_from_pickle('./sample_data/tesla_facts.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 75 nearest neighbors...\n",
      "[t-SNE] Indexed 76 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 76 samples in 0.003s...\n",
      "[t-SNE] Computed conditional probabilities for sample 76 / 76\n",
      "[t-SNE] Mean sigma: 0.210971\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 47.934624\n",
      "[t-SNE] KL divergence after 300 iterations: 0.349468\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "customdata": [
          [
           "Tags: Tesla Model S Plaid, acceleration, 0 to 60 mph, 1.99 seconds, fastest accelerating production cars, tri-motor setup, 1,020 horsepower, quarter mile, 9.23 seconds, electric vehicle, performance car, lu"
          ],
          [
           "Tags: Tesla Model S, long-range, EPA-estimated driving range, 405 miles, full charge, efficient battery technology, aerodynamic design, long-distance travel, electric vehicle, EV, Tesla battery, zero emissi"
          ],
          [
           "Tags: Tesla Model S, 17-inch touchscreen, car dashboard revolution, software updates, navigation, climate control, new features, existing features enhancement, physical modifications, vehicle technology\n\nel"
          ],
          [
           "Tags: Tesla Model S, aerodynamics, drag coefficient, 0.208, production cars, air resistance, range, speed, efficiency, electric vehicle\n\nelectric car efficiency, low drag coefficient, automotive aerodynamic"
          ],
          [
           "Tags: Tesla Model S, over-the-air software updates, remote improvement, car performance enhancement, new features introduction, bug fixes, vehicle evolution, software update capability"
          ],
          [
           "Tags: Tesla Model S, electric vehicle, 400-mile range, EPA range, 405 miles, single charge, battery technology, energy efficiency, electric vehicles, Tesla's leading position\n\nadditional tags: long-range el"
          ],
          [
           "Tags: Tesla Model S, battery pack, vehicle floor, low center of gravity, handling, safety, rollover risk reduction, conventional vehicles, electric vehicle design, automotive innovation\n\nelectric car safety"
          ],
          [
           "Tags: Tesla Model S, Motor Trend, Car of the Year, 2013, electric vehicle, performance, innovative technology, automotive industry\n\nelectric car, EV award, vehicle innovation, sustainable transportation, el"
          ],
          [
           "Tags: Tesla Model S, electric vehicle, charging cost, home charging, cost-effective alternative, gasoline savings, $15 full charge, substantial savings, electric car recharging, EV home electricity cost, EV"
          ],
          [
           "Tags: Tesla Model S, storage capacity, 28 cubic feet cargo space, rear seats folded, front trunk, electric vehicle, sedan, luggage storage, spacious interior, no traditional engine\n\nelectric sedan, EV cargo"
          ],
          [
           "Tags: Tesla Model S, electric vehicle safety, 5-star safety rating, National Highway Traffic Safety Administration, NHTSA, crash tests, safest cars, electric drivetrain, vehicle safety standards, car safety"
          ],
          [
           "Tags: Tesla Model S, electric vehicle industry, 2012, 300 miles per charge, innovation, sustainability, electric car, vehicle range, Tesla's commitment\n\nelectric vehicle, EV range, zero-emission transportat"
          ],
          [
           "Tags: Model S, regenerative braking system, energy recovery, braking, deceleration, electricity, recharge, battery, vehicle range, brake pad wear, electric vehicle, EV technology, sustainable transportation"
          ],
          [
           "Tags: Tesla Model S, comprehensive warranty, 8-year warranty, unlimited mile battery warranty, drive unit warranty, 4-year warranty, 50,000-mile limited warranty, vehicle reliability, electric vehicles, Tes"
          ],
          [
           "Tags: Model S, autopilot system, semi-autonomous driving, cameras, ultrasonic sensors, radar, steering control, acceleration control, braking control, driver safety, convenience, Tesla, autonomous vehicles,"
          ],
          [
           "Tags: Tesla, Model S, vegan interior, synthetic materials, sustainability, environmentally conscious, carbon footprint, animal product consumption, electric vehicle, eco-friendly transportation, cruelty-fre"
          ],
          [
           "Tags: Model S, glass roof, panoramic view, driving experience, UV protection, infrared protection, cabin comfort, weather conditions, airy feel\n\nsunroof, Tesla, luxury car features, automotive design, vehic"
          ],
          [
           "Tags: Tesla, Supercharger Network, Model S, Electric Vehicle Charging, 200 Miles Range, 15 Minutes Charging, Quick Charge, Long Trips Convenience, EV Infrastructure, Charging Stations, Fast Charging, Tesla "
          ],
          [
           "Tags: Tesla Model S, Plaid, electric cars, performance version, top speed, 200 mph, fastest accelerating, engineering prowess, electric vehicle performance, Tesla engineering, high-speed electric vehicle, E"
          ],
          [
           "Tags: electric powertrain, Model S, efficient, less maintenance, internal combustion engines, fewer moving parts, lower service costs, vehicle life, electric vehicles, EV maintenance, Tesla Model S, EV powe"
          ],
          [
           "Tags: Tesla Model S, premium audio system, 11 speakers, neodymium magnets, immersive listening experience, high-fidelity, interior acoustics, music enjoyment, media enhancement, audio tuning, car audio syst"
          ],
          [
           "Tags: Model S, adaptive lighting technology, headlights, driving conditions, visibility, night, poor weather, vehicle safety\n\nadaptive headlights, smart lighting system, automotive lighting, headlamp techno"
          ],
          [
           "Tags: Tesla Model S, electric vehicles, adoption, traditional automakers, electric technology, sustainable future, industry shift, EV adoption acceleration, automotive industry transformation, electric vehi"
          ],
          [
           "Tags: vehicle exterior door handles, retractable door handles, key fob detection, aerodynamics, Tesla, flush with body, form and function, car design, automotive innovation, smart handles, electric vehicles"
          ],
          [
           "Tags: Tesla Model S, HEPA air filtration system, 99.97% particulate matter filtering, allergens, cleaner air, bioweapons protection, Tesla\n\nair purification, vehicle air quality, advanced filtration technol"
          ],
          [
           "Tags: Tesla Model S, dashboard updates, interior trim, central touchscreen, horizontal touchscreen, continuous improvement, consumer feedback, vehicle interior design, electric vehicles, automotive technolo"
          ],
          [
           "Tags: Tesla Model S, towing capacity, 2,000 pounds, electric sedan, vehicle versatility, practicality, appropriate accessories\n\nelectric vehicle towing, EV towing capabilities, Model S features, Tesla towin"
          ],
          [
           "Tags: Tesla Model S, Dog Mode, pet safety, car feature, cabin temperature, central screen message, passersby awareness, vehicle technology, pet-friendly car, animal welfare, climate control, in-car pet care"
          ],
          [
           "Tags: Tesla, Model S, Ludicrous Mode, performance, acceleration, electric motors, torque, exhilarating performance, instant torque\n\nelectric vehicle performance, Tesla acceleration, Model S features, high-p"
          ],
          [
           "Tags: Model S, aftermarket customization, body kits, performance upgrades, car popularity, owner community, personalization, vehicle customization, Tesla Model S enhancements, electric vehicle modifications"
          ],
          [
           "Tags: Tesla Model S, electric vehicle safety, 5-star safety rating, NHTSA, electric architecture, low center of gravity, vehicle safety standards, crash test ratings, EV safety, Tesla safety features, elect"
          ],
          [
           "Tags: Tesla, Autopilot, Model S, advanced driver-assistance systems, lane keeping, traffic-aware cruise control, self-parking, software updates, full self-driving capabilities, ADAS, autonomous vehicles, ve"
          ],
          [
           "Tags: Tesla Model S, exterior, door handles, flush with body, automatic extension, driver approach, touch activation, aerodynamic drag, sleek design, vehicle design, retractable door handles, electric car f"
          ],
          [
           "Tags: Model S, LED headlights, energy efficiency, longer life, halogen lights, HID lights, illumination, vehicle battery, power consumption, automotive lighting\n\nelectric vehicles, efficient lighting, headl"
          ],
          [
           "Tags: Tesla Model S, glass roof, panoramic view, aesthetic appeal, cabin space, openness, vehicle interior, electric car, luxury sedan, automotive design\n\nsunroof, overhead view, transparent roof, spacious "
          ],
          [
           "Tags: Model S, high-fidelity sound system, 11 speakers, neodymium magnets, acoustic architecture, immersive listening experience, crystal-clear highs, deep resonant lows, audio quality, car audio system, Te"
          ],
          [
           "Tags: Tesla Model S, interior design, minimalistic, dashboard, vehicle controls, touchscreen, spacious cabin, futuristic environment, electric vehicle, automotive design, car interior, user interface, minim"
          ],
          [
           "Tags: Tesla, Model S, HVAC system, HEPA filtration, cabin air quality, pollen, bacteria, viruses, pollution, air purification, clean air technology, automotive innovation, vehicle interior, air filtering\n\na"
          ],
          [
           "Tags: Tesla Model S, battery architecture, scalability, upgradeability, battery options, larger capacities, longer range, improved performance, continuous improvement, Tesla updates"
          ],
          [
           "Tags: Tesla Model S, Supercharger network, rapid charging, 200 miles, 15 minutes, high-speed chargers, long-distance travel, charging times"
          ],
          [
           "Tags: Tesla Model S, electric vehicle, dual motor setup, all-wheel drive, AWD, traction, handling, acceleration, efficiency, single motor setups, electric car performance, EV technology, vehicle dynamics, e"
          ],
          [
           "Tags: regenerative braking system, kinetic energy, battery power, driving range, energy conservation, brake pad wear, maintenance costs, vehicle technology, electric vehicles, brake system efficiency\n\nelect"
          ],
          [
           "Tags: Tesla Model S, electric vehicles, automotive industry, EV development, competitive electric models, market influence, traditional automakers, shift towards EVs, industry shift, electric vehicle market"
          ],
          [
           "Tags: Model S, user interface, customizable, drivers, personalization, regenerative braking, steering feel, acceleration modes, driving experience, Tesla, electric vehicle, car settings, UI customization, v"
          ],
          [
           "Tags: Tesla, Full Self-Driving, FSD package, Model S, Navigate on Autopilot, Auto Lane Change, Autopark, autonomous driving, semi-autonomous features, advanced driver-assistance systems, ADAS, self-driving "
          ],
          [
           "Tags: Model S, smartphone app, remote monitoring, vehicle control, climate control, charging, location tracking, user experience, convenience, electric vehicle, Tesla app, smart car technology, remote acces"
          ],
          [
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   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import DBSCAN\n",
    "import hdbscan\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "\n",
    "embeddings = np.array([np.array(m['vector']) for m in metadatas])\n",
    "\n",
    "# Perform dimensionality reduction using PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca_result = pca.fit_transform(embeddings)\n",
    "\n",
    "# Perform dimensionality reduction using t-SNE\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
    "tsne_result = tsne.fit_transform(embeddings)\n",
    "\n",
    "# Perform HDBSCAN clustering on the PCA reduced data\n",
    "hdbscan_pca = hdbscan.HDBSCAN(min_cluster_size=5, gen_min_span_tree=True)\n",
    "pca_clusters = hdbscan_pca.fit_predict(pca_result)\n",
    "\n",
    "# Perform HDBSCAN clustering on the t-SNE reduced data\n",
    "hdbscan_tsne = hdbscan.HDBSCAN(min_cluster_size=5, gen_min_span_tree=True)\n",
    "tsne_clusters = hdbscan_tsne.fit_predict(tsne_result)\n",
    "\n",
    "# Convert clusters and results to a format suitable for Plotly\n",
    "df_pca = pd.DataFrame(pca_result, columns=['PC1', 'PC2'])\n",
    "df_pca['cluster'] = pca_clusters\n",
    "df_pca['label'] = [f'Tags: {m[\"tags\"][:200]}' for m in metadatas]  # Customize with desired metadata\n",
    "\n",
    "df_tsne = pd.DataFrame(tsne_result, columns=['Dim1', 'Dim2'])\n",
    "df_tsne['cluster'] = tsne_clusters\n",
    "df_tsne['label'] = [f'Tags: {m[\"tags\"][:200]}' for m in metadatas]   # Customize with desired metadata\n",
    "\n",
    "# PCA Plot with Plotly\n",
    "fig_pca = px.scatter(df_pca, x='PC1', y='PC2', color='cluster', hover_data=['label'])\n",
    "fig_pca.update_layout(title='PCA of Embeddings with DBSCAN Clusters', title_x=0.5)\n",
    "fig_pca.show()\n",
    "\n",
    "# t-SNE Plot with Plotly\n",
    "fig_tsne = px.scatter(df_tsne, x='Dim1', y='Dim2', color='cluster', hover_data=['label'])\n",
    "fig_tsne.update_layout(title='t-SNE of Embeddings with DBSCAN Clusters', title_x=0.5)\n",
    "fig_tsne.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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