03_vectorstores_and_embeddings.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "0d647f70",
   "metadata": {},
   "source": [
    "# Vectorstores and Embeddings\n",
    "\n",
    "Recall the overall workflow for retrieval augmented generation (RAG):"
   ]
  },
  {
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AYN/6ET/yqaj/APJNHsZf1/wwfWodmfjH/wAPy/8Ago5/0N1j/wCCmz/+NUf8Py/+Cjn/AEN1j/4KbP8A+NV+zn/Dq/8AYN/6ET/yqaj/APJNH/Dq/wDYN/6ET/yqaj/8k0exl/X/AAwfWodmfjH/AMPy/wDgo5/0N1j/AOCmz/8AjVH/AA/L/wCCjn/Q3WP/AIKbP/41X7Of8Or/ANg3/oRP/KpqP/yTR/w6v/YN/wChE/8AKpqP/wAk0exl/X/DB9ah2Z+OFj/wXV/4KK2k3mT+JdNuV/uS6VbAf+OIp/WvavAf/Bw3+2RoGoK/jnQvDfiCz43xi3mtJv8AgMiSlR+MbV+i+of8EpP2FbyHyrfwdLaN/fi1O9Lf+PzuP0rw7x1/wRZ/Zl17T3XwRqutaBefwOZUu4R/vRuisfwkWj2MhrFQPqD9nz/g4P8A2ZfiNqEOhfHHQr/wBcS7VF2H/tKxDHg7njRJUGe/lEAdSMV+4nw7+Jvw7+LnheDxt8LtcsfEGk3H+ru9PnS4iJwDtLISAwBGVOCO4r+FT4+/8Ekv2lvg9YTeIfBvkeNtLhBZjpqst4qjOSbZsk/SN5D7V8l/s5/tV/tDfscePm8VfBvWrjRbxH8u9sZQWtbgIeY7i3bCtjGMkB1/hINZOLW5tGalqmf6TFFflP8A8E5f+Cp/ws/bp0k+ENYji8NfEGyjMlxpDPmO6jUZaa0ZuXUdWQ/OnfK/Mf1YpFBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRXx7+2P+3F8Cf2IvAH/CZfFy+L310rDTdItSrXt9IvaNCRtQEjfI2FX1JIBAPsB3SNDJIQqqMkngACvzO/aX/wCCuP7Ev7Mks+jaz4l/4SfXIcg6Z4eC3sqsB0kl3LBGeeQ0gYf3eK/lC/bd/wCCr/7S37Zt1ceHJrs+FPBZc+VoemyMokTt9qmGGnPsQsY7JnmvnH4FfsRftJftDCK+8BeHpYdLl5Gp3/8AotpjjlXYZkHP/LNXNVGLk7RQH7mfFX/g5F8RTeZa/BD4aW1tgfu7rXLxp8n3gt1ix+E5r4717/g4B/b31fd/Z6+GtKz0+y6c7Y+nnzS/rXrXw2/4IneF7ZY7n4u+NLm7fgvb6TAsCj282bzC318ta+sdM/4JPfsXWCqLrQ769x1M2oTjP18tk/SumOBqvfQfKz8sW/4Ll/8ABRskkeLbEew0mz/+NUn/AA/L/wCCjn/Q3WP/AIKbP/41X67j/gl9+wwBj/hB8/8AcT1D/wCSaX/h1/8AsMf9CN/5U9Q/+Sav6hU7r8R8rPyH/wCH5f8AwUc/6G6x/wDBTZ//ABqj/h+X/wAFHP8AobrH/wAFNn/8ar9eP+HX/wCwx/0I3/lT1D/5Jo/4df8A7DH/AEI3/lT1D/5Jo+oVO6/EOVn5D/8AD8v/AIKOf9DdY/8Agps//jVH/D8v/go5/wBDdY/+Cmz/APjVfrx/w6//AGGP+hG/8qeof/JNH/Dr/wDYY/6Eb/yp6h/8k0fUKndfiHKz8h/+H5f/AAUc/wChusf/AAU2f/xqj/h+X/wUc/6G6x/8FNn/APGq/Xj/AIdf/sMf9CN/5U9Q/wDkmj/h1/8AsMf9CN/5U9Q/+SaPqFTuvxDlZ+Q//D8v/go5/wBDdY/+Cmz/APjVH/D8v/go5/0N1j/4KbP/AONV+vH/AA6//YY/6Eb/AMqeof8AyTR/w6//AGGP+hG/8qeof/JNH1Cp3X4hys/If/h+X/wUc/6G6x/8FNn/APGqP+H5f/BRz/obrH/wU2f/AMar9eP+HX/7DH/Qjf8AlT1D/wCSaP8Ah1/+wx/0I3/lT1D/AOSaPqFTuvxDlZ+Q/wDw/L/4KOf9DdY/+Cmz/wDjVH/D8v8A4KOf9DdY/wDgps//AI1X68f8Ov8A9hj/AKEb/wAqeof/ACTR/wAOv/2GP+hG/wDKnqH/AMk0fUKndfiHKz8h/wDh+X/wUc/6G6x/8FNn/wDGqP8Ah+X/AMFHP+husf8AwU2f/wAar9eP+HX/AOwx/wBCN/5U9Q/+SaP+HX/7DH/Qjf8AlT1D/wCSaPqFTuvxDlZ+Q/8Aw/L/AOCjn/Q3WP8A4KbP/wCNUf8AD8v/AIKOf9DdY/8Agps//jVfrx/w6/8A2GP+hG/8qeof/JNH/Dr/APYY/wChG/8AKnqH/wAk0fUKndfiHKz8h/8Ah+X/AMFHP+husf8AwU2f/wAao/4fl/8ABRz/AKG6x/8ABTZ//Gq/Xj/h1/8AsMf9CN/5U9Q/+SaP+HX/AOwx/wBCN/5U9Q/+SaPqFTuvxDlZ+Q//AA/L/wCCjn/Q3WP/AIKbP/41R/w/L/4KOf8AQ3WP/gps/wD41X68f8Ov/wBhj/oRv/KnqH/yTR/w6/8A2GP+hG/8qeof/JNH1Cp3X4hys/If/h+X/wAFHP8AobrH/wAFNn/8ao/4fl/8FHP+husf/BTZ/wDxqv14/wCHX/7DH/Qjf+VPUP8A5Jo/4df/ALDH/Qjf+VPUP/kmj6hU7r8Q5WfkP/w/L/4KOf8AQ3WP/gps/wD41R/w/L/4KOf9DdY/+Cmz/wDjVfrx/wAOv/2GP+hG/wDKnqH/AMk0f8Ov/wBhj/oRv/KnqH/yTR9Qqd1+IcrPyH/4fl/8FHP+husf/BTZ/wDxqlX/AILl/wDBRsEE+LbE+x0mz/8AjVfrv/w6/wD2GP8AoRv/ACp6h/8AJNIf+CX37DBGP+EHx/3E9Q/+SaPqFTuvxDlZ+Zmg/wDBwD+3vpG3+0F8Narjr9q051z9fImi/SvsT4Vf8HIviKHy7X43/DS2ucj95daHeNBg+0FwsufxnFej6n/wSe/Yuv1ZbXQ76yz0MOoTkj6eYz/rmvkn4mf8ETvD1wkl18HvGc9s/JS21eFZlPsZodhH/fpqiWCqrbUXKz97v2aP+CuP7Ev7TcsGjaN4l/4RjXJsAaZ4hC2UrMR0jl3NBIeOAshY/wB3mv0xR0kQSRkMrDII5BBr/N4+Of7F37R37PJlu/iH4dm/syM4/tKz/wBJsyOxMifcz6SBT7V9M/sRf8FX/wBpb9jK6t/DkN2fFfgsOPN0PUpGYRp3+yzHLQH2AaM90zzXNKLi7NCP78KK+Pf2OP24vgT+274A/wCEy+Ed8UvrVVGpaRdFVvbGRu0iAnchIOyRcq3qCCB9hVIBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRX5S/t1/8Fcf2d/2LpZvBVnnxl42XcraPYSqqWjAZH2ubDCLJx8gDydyoHNfyj/taf8ABVH9rv8Aa5kuNI8Ua6fD/hqYkLomjFra2KdhM4PmTcdfMYrnkKKAP7Mfj1/wUh/Yr/ZvWa2+JPj3Tm1GEc6dpzG/vN391o7feYyf+mhQe9fkn8Sf+DkH4QaY0sPwk+G+r6yRkJLqt3Dp6/722Jbokd8ZBPtX82fwO/ZA/aI/aIuEPwy8N3NxZMcNqFwPs9mv/baTCtj0Tc3tX6XfD/8A4Ii/EnUBHP8AE/xpp+lqeXi06CS8f6bpDAAffDAe9OxrChUnrFHeeLf+Dir9rzVLt/8AhDvCnhXSbY/dWaG6uph9X+0Rqf8Av2K8V1j/AILy/wDBQrUyTZavo2nZ/wCffS4m/wDRvmV93eHf+CLv7L+mwJ/b+seINTnA+c/aIIYyfZVg3Af8DNet6R/wSe/Yk00AXnhy71DH/PxqNyuf+/UkdHKzdYGp5H5K/wDD8v8A4KOf9DdY/wDgps//AI1R/wAPy/8Ago5/0N1j/wCCmz/+NV+xX/Drf9hP/oRv/KnqH/yTR/w63/YT/wChG/8AKnqH/wAk0+VlfUKndfifjr/w/L/4KOf9DdY/+Cmz/wDjVH/D8v8A4KOf9DdY/wDgps//AI1X7Ff8Ot/2E/8AoRv/ACp6h/8AJNH/AA63/YT/AOhG/wDKnqH/AMk0crD6hU7r8T8df+H5f/BRz/obrH/wU2f/AMao/wCH5f8AwUc/6G6x/wDBTZ//ABqv2K/4db/sJ/8AQjf+VPUP/kmj/h1v+wn/ANCN/wCVPUP/AJJo5WH1Cp3X4n46/wDD8v8A4KOf9DdY/wDgps//AI1R/wAPy/8Ago5/0N1j/wCCmz/+NV+xX/Drf9hP/oRv/KnqH/yTR/w63/YT/wChG/8AKnqH/wAk0crD6hU7r8T8df8Ah+X/AMFHP+husf8AwU2f/wAao/4fl/8ABRz/AKG6x/8ABTZ//Gq/Yr/h1v8AsJ/9CN/5U9Q/+SaP+HW/7Cf/AEI3/lT1D/5Jo5WH1Cp3X4n46/8AD8v/AIKOf9DdY/8Agps//jVH/D8v/go5/wBDdY/+Cmz/APjVfsV/w63/AGE/+hG/8qeof/JNH/Drf9hP/oRv/KnqH/yTRysPqFTuvxPx1/4fl/8ABRz/AKG6x/8ABTZ//GqP+H5f/BRz/obrH/wU2f8A8ar9iv8Ah1v+wn/0I3/lT1D/AOSaP+HW/wCwn/0I3/lT1D/5Jo5WH1Cp3X4n46/8Py/+Cjn/AEN1j/4KbP8A+NUf8Py/+Cjn/Q3WP/gps/8A41X7Ff8ADrf9hP8A6Eb/AMqeof8AyTR/w63/AGE/+hG/8qeof/JNHKw+oVO6/E/HX/h+X/wUc/6G6x/8FNn/APGqP+H5f/BRz/obrH/wU2f/AMar9iv+HW/7Cf8A0I3/AJU9Q/8Akmj/AIdb/sJ/9CN/5U9Q/wDkmjlYfUKndfifjr/w/L/4KOf9DdY/+Cmz/wDjVH/D8v8A4KOf9DdY/wDgps//AI1X7Ff8Ot/2E/8AoRv/ACp6h/8AJNH/AA63/YT/AOhG/wDKnqH/AMk0crD6hU7r8T8df+H5f/BRz/obrH/wU2f/AMao/wCH5f8AwUc/6G6x/wDBTZ//ABqv2K/4db/sJ/8AQjf+VPUP/kmj/h1v+wn/ANCN/wCVPUP/AJJo5WH1Cp3X4n46/wDD8v8A4KOf9DdY/wDgps/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    }
   },
   "cell_type": "markdown",
   "id": "125247c2",
   "metadata": {},
   "source": [
    "![overview.jpeg](attachment:overview.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f8d2266-4a35-4904-ae9d-c89790c5ae61",
   "metadata": {
    "height": 166,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import openai\n",
    "import sys\n",
    "sys.path.append('../..')\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file\n",
    "\n",
    "openai.api_key  = os.environ['OPENAI_API_KEY']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460a54b0",
   "metadata": {},
   "source": [
    "We just discussed `Document Loading` and `Splitting`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2437469e",
   "metadata": {
    "height": 234,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.document_loaders import PyPDFLoader\n",
    "\n",
    "# Load PDF\n",
    "loaders = [\n",
    "    # Duplicate documents on purpose - messy data\n",
    "    PyPDFLoader(\"docs/cs229_lectures/MachineLearning-Lecture01.pdf\"),\n",
    "    PyPDFLoader(\"docs/cs229_lectures/MachineLearning-Lecture01.pdf\"),\n",
    "    PyPDFLoader(\"docs/cs229_lectures/MachineLearning-Lecture02.pdf\"),\n",
    "    PyPDFLoader(\"docs/cs229_lectures/MachineLearning-Lecture03.pdf\")\n",
    "]\n",
    "docs = []\n",
    "for loader in loaders:\n",
    "    docs.extend(loader.load())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb44bf0d",
   "metadata": {
    "height": 115,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Split\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size = 1500,\n",
    "    chunk_overlap = 150\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b71e46cc",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "splits = text_splitter.split_documents(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e061f22d",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "209"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(splits)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "848e26fd",
   "metadata": {},
   "source": [
    "## Embeddings\n",
    "\n",
    "Let's take our splits and embed them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9dca7a8",
   "metadata": {
    "height": 47,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "embedding = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4099521",
   "metadata": {
    "height": 64,
    "tags": []
   },
   "outputs": [],
   "source": [
    "sentence1 = \"i like dogs\"\n",
    "sentence2 = \"i like canines\"\n",
    "sentence3 = \"the weather is ugly outside\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d553549a",
   "metadata": {
    "height": 64,
    "tags": []
   },
   "outputs": [],
   "source": [
    "embedding1 = embedding.embed_query(sentence1)\n",
    "embedding2 = embedding.embed_query(sentence2)\n",
    "embedding3 = embedding.embed_query(sentence3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0cbe9a9e",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "49fc0f8f",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9631903595036999"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(embedding1, embedding2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "32a1fac7",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7709997651294672"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(embedding1, embedding3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9dd18328",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7595923420315172"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(embedding2, embedding3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fc7b24f",
   "metadata": {},
   "source": [
    "## Vectorstores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da2213e6",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ! pip install chromadb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "201e6afa",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.vectorstores import Chroma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "93960ac5",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "persist_directory = 'docs/chroma/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a195e72a",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf ./docs/chroma  # remove old database files if any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "690efd0a",
   "metadata": {
    "height": 98,
    "tags": []
   },
   "outputs": [],
   "source": [
    "vectordb = Chroma.from_documents(\n",
    "    documents=splits,\n",
    "    embedding=embedding,\n",
    "    persist_directory=persist_directory\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f777480c",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "209\n"
     ]
    }
   ],
   "source": [
    "print(vectordb._collection.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efca7589",
   "metadata": {},
   "source": [
    "### Similarity Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3e20837d",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "question = \"is there an email i can ask for help\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f9bde572",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs = vectordb.similarity_search(question,k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41388af1",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "183434f6",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"cs229-qa@cs.stanford.edu. This goes to an acc ount that's read by all the TAs and me. So \\nrather than sending us email individually, if you send email to this account, it will \\nactually let us get back to you maximally quickly with answers to your questions.  \\nIf you're asking questions about homework probl ems, please say in the subject line which \\nassignment and which question the email refers to, since that will also help us to route \\nyour question to the appropriate TA or to me  appropriately and get the response back to \\nyou quickly.  \\nLet's see. Skipping ahead — let's see — for homework, one midterm, one open and term \\nproject. Notice on the honor code. So one thi ng that I think will help you to succeed and \\ndo well in this class and even help you to enjoy this cla ss more is if you form a study \\ngroup.  \\nSo start looking around where you' re sitting now or at the end of class today, mingle a \\nlittle bit and get to know your classmates. I strongly encourage you to form study groups \\nand sort of have a group of people to study with and have a group of your fellow students \\nto talk over these concepts with. You can also  post on the class news group if you want to \\nuse that to try to form a study group.  \\nBut some of the problems sets in this cla ss are reasonably difficult.  People that have \\ntaken the class before may tell you they were very difficult. And just I bet it would be \\nmore fun for you, and you'd probably have a be tter learning experience if you form a\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs[0].page_content"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1edb21d8",
   "metadata": {},
   "source": [
    "Let's save this so we can use it later!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ea657123",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "vectordb.persist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cefe9f6a",
   "metadata": {},
   "source": [
    "## Failure modes\n",
    "\n",
    "This seems great, and basic similarity search will get you 80% of the way there very easily. \n",
    "\n",
    "But there are some failure modes that can creep up. \n",
    "\n",
    "Here are some edge cases that can arise - we'll fix them in the next class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "df0f29f9",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "question = \"what did they say about matlab?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "02be97df",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs = vectordb.similarity_search(question,k=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9f579e",
   "metadata": {},
   "source": [
    "Notice that we're getting duplicate chunks (because of the duplicate `MachineLearning-Lecture01.pdf` in the index).\n",
    "\n",
    "Semantic search fetches all similar documents, but does not enforce diversity.\n",
    "\n",
    "`docs[0]` and `docs[1]` are indentical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d39f6954",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(page_content='those homeworks will be done in either MATLA B or in Octave, which is sort of — I \\nknow some people call it a free ve rsion of MATLAB, which it sort  of is, sort of isn\\'t.  \\nSo I guess for those of you that haven\\'t s een MATLAB before, and I know most of you \\nhave, MATLAB is I guess part of the programming language that makes it very easy to write codes using matrices, to write code for numerical routines, to move data around, to \\nplot data. And it\\'s sort of an extremely easy to  learn tool to use for implementing a lot of \\nlearning algorithms.  \\nAnd in case some of you want to work on your  own home computer or something if you \\ndon\\'t have a MATLAB license, for the purposes of  this class, there\\'s also — [inaudible] \\nwrite that down [inaudible] MATLAB — there\\' s also a software package called Octave \\nthat you can download for free off the Internet. And it has somewhat fewer features than MATLAB, but it\\'s free, and for the purposes of  this class, it will work for just about \\neverything.  \\nSo actually I, well, so yeah, just a side comment for those of you that haven\\'t seen \\nMATLAB before I guess, once a colleague of mine at a different university, not at \\nStanford, actually teaches another machine l earning course. He\\'s taught it for many years. \\nSo one day, he was in his office, and an old student of his from, lik e, ten years ago came \\ninto his office and he said, \"Oh, professo r, professor, thank you so much for your', metadata={'source': 'docs/cs229_lectures/MachineLearning-Lecture01.pdf', 'page': 8})"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e04e3d1b",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(page_content='those homeworks will be done in either MATLA B or in Octave, which is sort of — I \\nknow some people call it a free ve rsion of MATLAB, which it sort  of is, sort of isn\\'t.  \\nSo I guess for those of you that haven\\'t s een MATLAB before, and I know most of you \\nhave, MATLAB is I guess part of the programming language that makes it very easy to write codes using matrices, to write code for numerical routines, to move data around, to \\nplot data. And it\\'s sort of an extremely easy to  learn tool to use for implementing a lot of \\nlearning algorithms.  \\nAnd in case some of you want to work on your  own home computer or something if you \\ndon\\'t have a MATLAB license, for the purposes of  this class, there\\'s also — [inaudible] \\nwrite that down [inaudible] MATLAB — there\\' s also a software package called Octave \\nthat you can download for free off the Internet. And it has somewhat fewer features than MATLAB, but it\\'s free, and for the purposes of  this class, it will work for just about \\neverything.  \\nSo actually I, well, so yeah, just a side comment for those of you that haven\\'t seen \\nMATLAB before I guess, once a colleague of mine at a different university, not at \\nStanford, actually teaches another machine l earning course. He\\'s taught it for many years. \\nSo one day, he was in his office, and an old student of his from, lik e, ten years ago came \\ninto his office and he said, \"Oh, professo r, professor, thank you so much for your', metadata={'source': 'docs/cs229_lectures/MachineLearning-Lecture01.pdf', 'page': 8})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a3a915d",
   "metadata": {},
   "source": [
    "We can see a new failure mode.\n",
    "\n",
    "The question below asks a question about the third lecture, but includes results from other lectures as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b19135e5",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "question = \"what did they say about regression in the third lecture?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d434942c",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs = vectordb.similarity_search(question,k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c2c5df59",
   "metadata": {
    "height": 47,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'source': 'docs/cs229_lectures/MachineLearning-Lecture03.pdf', 'page': 0}\n",
      "{'source': 'docs/cs229_lectures/MachineLearning-Lecture03.pdf', 'page': 14}\n",
      "{'source': 'docs/cs229_lectures/MachineLearning-Lecture02.pdf', 'page': 0}\n",
      "{'source': 'docs/cs229_lectures/MachineLearning-Lecture03.pdf', 'page': 6}\n",
      "{'source': 'docs/cs229_lectures/MachineLearning-Lecture01.pdf', 'page': 8}\n"
     ]
    }
   ],
   "source": [
    "for doc in docs:\n",
    "    print(doc.metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8e6cb50c",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "into his office and he said, \"Oh, professo r, professor, thank you so much for your \n",
      "machine learning class. I learned so much from it. There's this stuff that I learned in your \n",
      "class, and I now use every day. And it's help ed me make lots of money, and here's a \n",
      "picture of my big house.\"  \n",
      "So my friend was very excited. He said, \"W ow. That's great. I'm glad to hear this \n",
      "machine learning stuff was actually useful. So what was it that you learned? Was it \n",
      "logistic regression? Was it the PCA? Was it the data ne tworks? What was it that you \n",
      "learned that was so helpful?\" And the student said, \"Oh, it was the MATLAB.\"  \n",
      "So for those of you that don't know MATLAB yet, I hope you do learn it. It's not hard, \n",
      "and we'll actually have a short MATLAB tutori al in one of the discussion sections for \n",
      "those of you that don't know it.  \n",
      "Okay. The very last piece of logistical th ing is the discussion s ections. So discussion \n",
      "sections will be taught by the TAs, and atte ndance at discussion sections is optional, \n",
      "although they'll also be recorded and televi sed. And we'll use the discussion sections \n",
      "mainly for two things. For the next two or th ree weeks, we'll use the discussion sections \n",
      "to go over the prerequisites to this class or if some of you haven't seen probability or \n",
      "statistics for a while or maybe algebra, we'll go over those in the discussion sections as a \n",
      "refresher for those of you that want one.\n"
     ]
    }
   ],
   "source": [
    "print(docs[4].page_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3dbca56",
   "metadata": {},
   "source": [
    "Approaches discussed in the next lecture can be used to address both!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cdc67ae-59e0-4a84-9fe6-e3a0391114d0",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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