MDBRetrieve-SM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 32908,
     "status": "ok",
     "timestamp": 1705688253629,
     "user": {
      "displayName": "John Willis",
      "userId": "15271974867993570949"
     },
     "user_tz": 360
    },
    "id": "Sl3lgsKvh47O",
    "outputId": "f6d14390-c367-485c-f809-94b02c996c60"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m802.4/802.4 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m283.9/283.9 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m677.1/677.1 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m228.7/228.7 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m222.3/222.3 kB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "llmx 0.0.15a0 requires cohere, which is not installed.\n",
      "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%pip install --quiet langchain pypdf pymongo langchain-openai tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5948,
     "status": "ok",
     "timestamp": 1705688365699,
     "user": {
      "displayName": "John Willis",
      "userId": "15271974867993570949"
     },
     "user_tz": 360
    },
    "id": "WlkitefoiJ24",
    "outputId": "dd36f4c9-b383-4464-9a53-3208b7a6893d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: langchain\n",
      "Version: 0.1.1\n",
      "Summary: Building applications with LLMs through composability\n",
      "Home-page: https://github.com/langchain-ai/langchain\n",
      "Author: \n",
      "Author-email: \n",
      "License: MIT\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: aiohttp, async-timeout, dataclasses-json, jsonpatch, langchain-community, langchain-core, langsmith, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity\n",
      "Required-by: \n",
      "3.10.12\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "%pip show langchain\n",
    "\n",
    "from platform import python_version\n",
    "print(python_version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 126,
     "status": "ok",
     "timestamp": 1705688369019,
     "user": {
      "displayName": "John Willis",
      "userId": "15271974867993570949"
     },
     "user_tz": 360
    },
    "id": "49aSKLSk8A9u"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
    "\n",
    "MONGODB_ATLAS_CLUSTER_URI = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 627,
     "status": "ok",
     "timestamp": 1705688380351,
     "user": {
      "displayName": "John Willis",
      "userId": "15271974867993570949"
     },
     "user_tz": 360
    },
    "id": "aPqh0syziQ2k"
   },
   "outputs": [],
   "source": [
    "from pymongo import MongoClient\n",
    "\n",
    "# initialize MongoDB python client\n",
    "client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)\n",
    "\n",
    "DB_NAME = \"langchain_db\"\n",
    "COLLECTION_NAME = \"test\"\n",
    "ATLAS_VECTOR_SEARCH_INDEX_NAME = \"index_name\"\n",
    "\n",
    "MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 1060,
     "status": "ok",
     "timestamp": 1705688384009,
     "user": {
      "displayName": "John Willis",
      "userId": "15271974867993570949"
     },
     "user_tz": 360
    },
    "id": "7himXISricVE"
   },
   "outputs": [],
   "source": [
    "from langchain_community.vectorstores import MongoDBAtlasVectorSearch\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "\n",
    "vector_search = MongoDBAtlasVectorSearch.from_connection_string(\n",
    "    MONGODB_ATLAS_CLUSTER_URI,\n",
    "    DB_NAME + \".\" + COLLECTION_NAME,\n",
    "    OpenAIEmbeddings(disallowed_special=()),\n",
    "    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "40NPNogCi69r"
   },
   "source": [
    "#Pre-filtering with Similarity Search\n",
    "Atlas Vector Search supports pre-filtering using MQL Operators for filtering. Below is an example index and query on the same data loaded above that allows you do metadata filtering on the “page” field. You can update your existing index with the filter defined and do pre-filtering with vector search.\n",
    "\n",
    "Use:\n",
    "\n",
    "~~~\n",
    "{\n",
    "  \"name\": \"index_name\",\n",
    "  \"type\": \"vectorSearch\",\n",
    "  \"fields\":[\n",
    "    {\n",
    "      \"type\": \"vector\",\n",
    "      \"path\": \"embedding\",\n",
    "      \"numDimensions\": 1536,\n",
    "      \"similarity\": \"cosine\"\n",
    "    },\n",
    "    {\n",
    "      \"type\": \"filter\",\n",
    "      \"path\": \"page\"\n",
    "    }\n",
    "  ]\n",
    "}\n",
    "~~~\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uPHaftImi3Cj"
   },
   "outputs": [],
   "source": [
    "#Don't run withput adding the filers in in the index JSON\n",
    "query = \"What were the compute requirements for training GPT 4\"\n",
    "\n",
    "results = vector_search.similarity_search_with_score(\n",
    "    query=query, k=5, pre_filter={\"page\": {\"$eq\": 1}}\n",
    ")\n",
    "\n",
    "# Display results\n",
    "for result in results:\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jddBa70YjpAt"
   },
   "source": [
    "#Similarity Search with Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 568,
     "status": "ok",
     "timestamp": 1705688419504,
     "user": {
      "displayName": "John Willis",
      "userId": "15271974867993570949"
     },
     "user_tz": 360
    },
    "id": "5pflRaNwjh8w",
    "outputId": "b24c70d1-171d-4b96-df65-8f49f714f121"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Document(page_content='performance based on models trained with no more than 1/1,000th the compute of\\nGPT-4.\\n1 Introduction\\nThis technical report presents GPT-4, a large multimodal model capable of processing image and\\ntext inputs and producing text outputs. Such models are an important area of study as they have the\\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\\nand machine translation. As such, they have been the subject of substantial interest and progress in\\nrecent years [1–34].\\nOne of the main goals of developing such models is to improve their ability to understand and generate\\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In\\nthese evaluations it performs quite well and often outscores the vast majority of human test takers.', metadata={'_id': ObjectId('65a9969745663e0f276278e7'), 'embedding': [-0.025845477909846506, -0.004418385456763085, -0.003348416013386979, -0.014695633389079482, 0.017535035540777472, 0.017507334283522497, -0.011219098595226984, 0.006876891595915078, -0.0183106763318519, -0.027327506348857927, 0.011897784054586477, 0.030997952737462822, -0.03531938239684971, -0.020776108716640165, -0.008455875832302699, 0.02296452480358858, 0.0167455422584305, -0.007770264127306936, 0.0007275965976458073, -0.01742422864911252, 0.014051574501288706, 0.008532055221076404, -0.018296825703224412, -0.018366078846361846, 0.010187218380576468, 0.011780052779930312, 0.031081056509227743, -0.03706457650507349, -0.014709484017706968, 0.0030869833025674778, 0.01501420064147926, -0.016842498521467963, -0.022036525234966742, -0.006876891595915078, 0.002865371614713265, -0.007396294360397208, 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      "(Document(page_content='performance based on models trained with no more than 1/1,000th the compute of\\nGPT-4.\\n1 Introduction\\nThis technical report presents GPT-4, a large multimodal model capable of processing image and\\ntext inputs and producing text outputs. Such models are an important area of study as they have the\\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\\nand machine translation. As such, they have been the subject of substantial interest and progress in\\nrecent years [1–34].\\nOne of the main goals of developing such models is to improve their ability to understand and generate\\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. 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      "(Document(page_content='performance based on models trained with no more than 1/1,000th the compute of\\nGPT-4.\\n1 Introduction\\nThis technical report presents GPT-4, a large multimodal model capable of processing image and\\ntext inputs and producing text outputs. Such models are an important area of study as they have the\\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\\nand machine translation. As such, they have been the subject of substantial interest and progress in\\nrecent years [1–34].\\nOne of the main goals of developing such models is to improve their ability to understand and generate\\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In\\nthese evaluations it performs quite well and often outscores the vast majority of human test takers.', metadata={'_id': ObjectId('65aabc229ab9ff25804b7874'), 'embedding': [-0.025845477909846506, -0.004418385456763085, -0.003348416013386979, -0.014695633389079482, 0.017535035540777472, 0.017507334283522497, -0.011219098595226984, 0.006876891595915078, -0.0183106763318519, -0.027327506348857927, 0.011897784054586477, 0.030997952737462822, -0.03531938239684971, -0.020776108716640165, -0.008455875832302699, 0.02296452480358858, 0.0167455422584305, -0.007770264127306936, 0.0007275965976458073, -0.01742422864911252, 0.014051574501288706, 0.008532055221076404, -0.018296825703224412, -0.018366078846361846, 0.010187218380576468, 0.011780052779930312, 0.031081056509227743, -0.03706457650507349, -0.014709484017706968, 0.0030869833025674778, 0.01501420064147926, -0.016842498521467963, -0.022036525234966742, -0.006876891595915078, 0.002865371614713265, -0.007396294360397208, 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      "(Document(page_content='performance based on models trained with no more than 1/1,000th the compute of\\nGPT-4.\\n1 Introduction\\nThis technical report presents GPT-4, a large multimodal model capable of processing image and\\ntext inputs and producing text outputs. Such models are an important area of study as they have the\\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\\nand machine translation. As such, they have been the subject of substantial interest and progress in\\nrecent years [1–34].\\nOne of the main goals of developing such models is to improve their ability to understand and generate\\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. 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'https://arxiv.org/pdf/2303.08774.pdf', 'page': 0}), 0.9197463989257812)\n",
      "(Document(page_content='2 GPT-4 Observed Safety Challenges\\nGPT-4 demonstrates increased performance in areas such as reasoning, knowledge retention, and\\ncoding, compared to earlier models such as GPT-2[ 22] and GPT-3.[ 10] Many of these improvements\\nalso present new safety challenges, which we highlight in this section.\\nWe conducted a range of qualitative and quantitative evaluations of GPT-4. These evaluations\\nhelped us gain an understanding of GPT-4’s capabilities, limitations, and risks; prioritize our\\nmitigation eﬀorts; and iteratively test and build safer versions of the model. Some of the speciﬁc\\nrisks we explored are:6\\n•Hallucinations\\n•Harmful content\\n•Harms of representation, allocation, and quality of service\\n•Disinformation and inﬂuence operations\\n•Proliferation of conventional and unconventional weapons\\n•Privacy\\n•Cybersecurity\\n•Potential for risky emergent behaviors\\n•Interactions with other systems\\n•Economic impacts\\n•Acceleration\\n•Overreliance', metadata={'_id': ObjectId('65a93e3045663e0f276276a7'), 'embedding': [0.0003741897057805365, -0.011466288356160054, -0.006069378646086221, -0.034035180798075784, -0.004923436008964041, 0.020640690506753143, -0.018582111789086506, 0.004113727839848054, -0.023193328414683006, -0.02276788907380223, 0.025361698891361563, 0.029670991904965693, -0.046908163959382215, -0.02032504106223908, 0.01705876230866736, 0.03247065940802717, 0.03831702587192694, -0.007946117091195873, -0.007060927553420494, -0.005263101469439357, 0.0033074511288624913, 0.019021275099843773, -0.02474412583485513, 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-0.01884286535409461, 0.011342773558594248, -0.007932393121319383, -0.0012016957990841199, -0.0034566981371160657, 0.004089711358225498, -0.009977248800432133, -0.0025749398246404593, -0.007177580366048055, -0.02614395958638587, -0.006662935686631396, -0.00509498423543626], 'source': 'https://arxiv.org/pdf/2303.08774.pdf', 'page': 43}), 0.915168285369873)\n"
     ]
    }
   ],
   "source": [
    "query = \"What were the compute requirements for training GPT 4\"\n",
    "\n",
    "results = vector_search.similarity_search_with_score(\n",
    "    query=query,\n",
    "    k=5,\n",
    ")\n",
    "\n",
    "# Display results\n",
    "for result in results:\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "43GA0yPQjw6T"
   },
   "source": [
    "#Question Answering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 175,
     "status": "ok",
     "timestamp": 1705688423256,
     "user": {
      "displayName": "John Willis",
      "userId": "15271974867993570949"
     },
     "user_tz": 360
    },
    "id": "aEn15wD-iN5n"
   },
   "outputs": [],
   "source": [
    "qa_retriever = vector_search.as_retriever(\n",
    "    search_type=\"similarity\",\n",
    "    search_kwargs={\"k\": 15},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 111,
     "status": "ok",
     "timestamp": 1705688425950,
     "user": {
      "displayName": "John Willis",
      "userId": "15271974867993570949"
     },
     "user_tz": 360
    },
    "id": "ovtUEpElj8n9"
   },
   "outputs": [],
   "source": [
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
    "\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "\"\"\"\n",
    "PROMPT = PromptTemplate(\n",
    "    template=prompt_template, input_variables=[\"context\", \"question\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3683,
     "status": "ok",
     "timestamp": 1705688433575,
     "user": {
      "displayName": "John Willis",
      "userId": "15271974867993570949"
     },
     "user_tz": 360
    },
    "id": "930wqfZJj_z9",
    "outputId": "5dabcdf1-1dd8-4860-8818-255371690ff9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
      "  warn_deprecated(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "What are the compute requirements for GPT-4?\n",
      "[Document(page_content='2 GPT-4 Observed Safety Challenges\\nGPT-4 demonstrates increased performance in areas such as reasoning, knowledge retention, and\\ncoding, compared to earlier models such as GPT-2[ 22] and GPT-3.[ 10] Many of these improvements\\nalso present new safety challenges, which we highlight in this section.\\nWe conducted a range of qualitative and quantitative evaluations of GPT-4. These evaluations\\nhelped us gain an understanding of GPT-4’s capabilities, limitations, and risks; prioritize our\\nmitigation eﬀorts; and iteratively test and build safer versions of the model. Some of the speciﬁc\\nrisks we explored are:6\\n•Hallucinations\\n•Harmful content\\n•Harms of representation, allocation, and quality of service\\n•Disinformation and inﬂuence operations\\n•Proliferation of conventional and unconventional weapons\\n•Privacy\\n•Cybersecurity\\n•Potential for risky emergent behaviors\\n•Interactions with other systems\\n•Economic impacts\\n•Acceleration\\n•Overreliance', metadata={'_id': ObjectId('65a93e3045663e0f276276a7'), 'embedding': [0.0003741897057805365, -0.011466288356160054, -0.006069378646086221, -0.034035180798075784, -0.004923436008964041, 0.020640690506753143, -0.018582111789086506, 0.004113727839848054, -0.023193328414683006, -0.02276788907380223, 0.025361698891361563, 0.029670991904965693, -0.046908163959382215, -0.02032504106223908, 0.01705876230866736, 0.03247065940802717, 0.03831702587192694, -0.007946117091195873, -0.007060927553420494, -0.005263101469439357, 0.0033074511288624913, 0.019021275099843773, -0.02474412583485513, 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-0.01884286535409461, 0.011342773558594248, -0.007932393121319383, -0.0012016957990841199, -0.0034566981371160657, 0.004089711358225498, -0.009977248800432133, -0.0025749398246404593, -0.007177580366048055, -0.02614395958638587, -0.006662935686631396, -0.00509498423543626], 'source': 'https://arxiv.org/pdf/2303.08774.pdf', 'page': 43}), Document(page_content='2 GPT-4 Observed Safety Challenges\\nGPT-4 demonstrates increased performance in areas such as reasoning, knowledge retention, and\\ncoding, compared to earlier models such as GPT-2[ 22] and GPT-3.[ 10] Many of these improvements\\nalso present new safety challenges, which we highlight in this section.\\nWe conducted a range of qualitative and quantitative evaluations of GPT-4. These evaluations\\nhelped us gain an understanding of GPT-4’s capabilities, limitations, and risks; prioritize our\\nmitigation eﬀorts; and iteratively test and build safer versions of the model. Some of the speciﬁc\\nrisks we explored are:6\\n•Hallucinations\\n•Harmful content\\n•Harms of representation, allocation, and quality of service\\n•Disinformation and inﬂuence operations\\n•Proliferation of conventional and unconventional weapons\\n•Privacy\\n•Cybersecurity\\n•Potential for risky emergent behaviors\\n•Interactions with other systems\\n•Economic impacts\\n•Acceleration\\n•Overreliance', metadata={'_id': ObjectId('65aabc249ab9ff25804b7913'), 'embedding': [0.0003686984077932626, -0.011455373683442247, -0.006183843975978708, -0.033995706127740215, -0.004918265089856345, 0.020743144395234565, -0.018534382554279354, 0.00414314115398255, -0.02313025078152362, -0.022677523708261902, 0.025352731003817176, 0.029687938809762164, -0.04683669965628943, -0.02033157246598788, 0.017121324083850576, 0.032486617125297906, 0.0382760385456867, -0.007991323139352274, -0.007030992673507776, -0.005247521608798875, 0.0033079962729551738, 0.01908314388484485, -0.02462562326527271, 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-0.0188636386075606, 0.011345621044800124, -0.007922728438692867, -0.0011566839955598463, -0.003486343379426064, 0.004081405643992316, -0.009966861321684893, -0.002639194681896996, -0.007058430833168308, -0.02618959138100377, -0.00669830655923066, -0.005052025361501853], 'source': 'https://arxiv.org/pdf/2303.08774.pdf', 'page': 43}), Document(page_content='2 GPT-4 Observed Safety Challenges\\nGPT-4 demonstrates increased performance in areas such as reasoning, knowledge retention, and\\ncoding, compared to earlier models such as GPT-2[ 22] and GPT-3.[ 10] Many of these improvements\\nalso present new safety challenges, which we highlight in this section.\\nWe conducted a range of qualitative and quantitative evaluations of GPT-4. These evaluations\\nhelped us gain an understanding of GPT-4’s capabilities, limitations, and risks; prioritize our\\nmitigation eﬀorts; and iteratively test and build safer versions of the model. Some of the speciﬁc\\nrisks we explored are:6\\n•Hallucinations\\n•Harmful content\\n•Harms of representation, allocation, and quality of service\\n•Disinformation and inﬂuence operations\\n•Proliferation of conventional and unconventional weapons\\n•Privacy\\n•Cybersecurity\\n•Potential for risky emergent behaviors\\n•Interactions with other systems\\n•Economic impacts\\n•Acceleration\\n•Overreliance', metadata={'_id': ObjectId('65a9969a45663e0f27627986'), 'embedding': [0.00039090678811358687, -0.0114042712085632, -0.006110411121810159, -0.03397951266472859, -0.004919893095802245, 0.020736286363939437, -0.018458176357318182, 0.004113634572152329, -0.02315163047687174, -0.022685030417594856, 0.025319952453386096, 0.029642870574345018, -0.04690709698197655, -0.020352026395920153, 0.01708582039304654, 0.03244247279265146, 0.038288708678908004, -0.007959659191104595, -0.007095075660045068, -0.005242396598394469, 0.003341684575080174, 0.019089460324400554, -0.024674944516879075, 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-0.007911627160763471, -0.0012136765259200778, -0.0034634810784336376, 0.004072463595200956, -0.00997016017584836, -0.0025834585474326676, -0.0070882141409940305, -0.02611591846562881, -0.00667650763110931, -0.005077714087572838], 'source': 'https://arxiv.org/pdf/2303.08774.pdf', 'page': 43}), Document(page_content='2 GPT-4 Observed Safety Challenges\\nGPT-4 demonstrates increased performance in areas such as reasoning, knowledge retention, and\\ncoding, compared to earlier models such as GPT-2[ 22] and GPT-3.[ 10] Many of these improvements\\nalso present new safety challenges, which we highlight in this section.\\nWe conducted a range of qualitative and quantitative evaluations of GPT-4. These evaluations\\nhelped us gain an understanding of GPT-4’s capabilities, limitations, and risks; prioritize our\\nmitigation eﬀorts; and iteratively test and build safer versions of the model. Some of the speciﬁc\\nrisks we explored are:6\\n•Hallucinations\\n•Harmful content\\n•Harms of representation, allocation, and quality of service\\n•Disinformation and inﬂuence operations\\n•Proliferation of conventional and unconventional weapons\\n•Privacy\\n•Cybersecurity\\n•Potential for risky emergent behaviors\\n•Interactions with other systems\\n•Economic impacts\\n•Acceleration\\n•Overreliance', metadata={'_id': ObjectId('65a9966045663e0f27627817'), 'embedding': [0.00039090678811358687, -0.0114042712085632, -0.006110411121810159, -0.03397951266472859, -0.004919893095802245, 0.020736286363939437, -0.018458176357318182, 0.004113634572152329, -0.02315163047687174, -0.022685030417594856, 0.025319952453386096, 0.029642870574345018, -0.04690709698197655, -0.020352026395920153, 0.01708582039304654, 0.03244247279265146, 0.038288708678908004, -0.007959659191104595, -0.007095075660045068, -0.005242396598394469, 0.003341684575080174, 0.019089460324400554, -0.024674944516879075, 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Such models are an important area of study as they have the\\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\\nand machine translation. As such, they have been the subject of substantial interest and progress in\\nrecent years [1–34].\\nOne of the main goals of developing such models is to improve their ability to understand and generate\\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In\\nthese evaluations it performs quite well and often outscores the vast majority of human test takers.', metadata={'_id': ObjectId('65a9969745663e0f276278e7'), 'embedding': [-0.025845477909846506, -0.004418385456763085, -0.003348416013386979, -0.014695633389079482, 0.017535035540777472, 0.017507334283522497, -0.011219098595226984, 0.006876891595915078, -0.0183106763318519, -0.027327506348857927, 0.011897784054586477, 0.030997952737462822, -0.03531938239684971, -0.020776108716640165, -0.008455875832302699, 0.02296452480358858, 0.0167455422584305, -0.007770264127306936, 0.0007275965976458073, -0.01742422864911252, 0.014051574501288706, 0.008532055221076404, -0.018296825703224412, -0.018366078846361846, 0.010187218380576468, 0.011780052779930312, 0.031081056509227743, -0.03706457650507349, -0.014709484017706968, 0.0030869833025674778, 0.01501420064147926, -0.016842498521467963, -0.022036525234966742, -0.006876891595915078, 0.002865371614713265, -0.007396294360397208, 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Document(page_content='performance based on models trained with no more than 1/1,000th the compute of\\nGPT-4.\\n1 Introduction\\nThis technical report presents GPT-4, a large multimodal model capable of processing image and\\ntext inputs and producing text outputs. Such models are an important area of study as they have the\\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\\nand machine translation. As such, they have been the subject of substantial interest and progress in\\nrecent years [1–34].\\nOne of the main goals of developing such models is to improve their ability to understand and generate\\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In\\nthese evaluations it performs quite well and often outscores the vast majority of human test takers.', metadata={'_id': ObjectId('65a9965e45663e0f27627778'), 'embedding': [-0.025845477909846506, -0.004418385456763085, -0.003348416013386979, -0.014695633389079482, 0.017535035540777472, 0.017507334283522497, -0.011219098595226984, 0.006876891595915078, -0.0183106763318519, -0.027327506348857927, 0.011897784054586477, 0.030997952737462822, -0.03531938239684971, -0.020776108716640165, -0.008455875832302699, 0.02296452480358858, 0.0167455422584305, -0.007770264127306936, 0.0007275965976458073, -0.01742422864911252, 0.014051574501288706, 0.008532055221076404, -0.018296825703224412, -0.018366078846361846, 0.010187218380576468, 0.011780052779930312, 0.031081056509227743, -0.03706457650507349, -0.014709484017706968, 0.0030869833025674778, 0.01501420064147926, -0.016842498521467963, -0.022036525234966742, -0.006876891595915078, 0.002865371614713265, -0.007396294360397208, 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Document(page_content='performance based on models trained with no more than 1/1,000th the compute of\\nGPT-4.\\n1 Introduction\\nThis technical report presents GPT-4, a large multimodal model capable of processing image and\\ntext inputs and producing text outputs. Such models are an important area of study as they have the\\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\\nand machine translation. As such, they have been the subject of substantial interest and progress in\\nrecent years [1–34].\\nOne of the main goals of developing such models is to improve their ability to understand and generate\\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In\\nthese evaluations it performs quite well and often outscores the vast majority of human test takers.', metadata={'_id': ObjectId('65aabc229ab9ff25804b7874'), 'embedding': [-0.025845477909846506, -0.004418385456763085, -0.003348416013386979, -0.014695633389079482, 0.017535035540777472, 0.017507334283522497, -0.011219098595226984, 0.006876891595915078, -0.0183106763318519, -0.027327506348857927, 0.011897784054586477, 0.030997952737462822, -0.03531938239684971, -0.020776108716640165, -0.008455875832302699, 0.02296452480358858, 0.0167455422584305, -0.007770264127306936, 0.0007275965976458073, -0.01742422864911252, 0.014051574501288706, 0.008532055221076404, -0.018296825703224412, -0.018366078846361846, 0.010187218380576468, 0.011780052779930312, 0.031081056509227743, -0.03706457650507349, -0.014709484017706968, 0.0030869833025674778, 0.01501420064147926, -0.016842498521467963, -0.022036525234966742, -0.006876891595915078, 0.002865371614713265, -0.007396294360397208, 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Document(page_content='performance based on models trained with no more than 1/1,000th the compute of\\nGPT-4.\\n1 Introduction\\nThis technical report presents GPT-4, a large multimodal model capable of processing image and\\ntext inputs and producing text outputs. Such models are an important area of study as they have the\\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\\nand machine translation. As such, they have been the subject of substantial interest and progress in\\nrecent years [1–34].\\nOne of the main goals of developing such models is to improve their ability to understand and generate\\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In\\nthese evaluations it performs quite well and often outscores the vast majority of human test takers.', metadata={'_id': ObjectId('65a93e2d45663e0f27627608'), 'embedding': [-0.025784911582672307, -0.0045352089209715965, -0.003337359753700535, -0.014664995481118208, 0.01755922101086516, 0.01753152347750164, -0.011203004045031786, 0.00685820495572478, -0.01841779425371524, -0.027252797031907135, 0.01186078227818982, 0.030964050435781466, -0.035284616046040544, -0.020758099849836762, -0.008426487444144346, 0.02289068698424793, 0.01675603910945182, -0.007761784827645432, 0.0006971585097258345, -0.017393045123909788, 0.014055685137378609, 0.008509574456299457, -0.0182239226960415, -0.01832085847487837, 0.010171330531885453, 0.01176384649935295, 0.03115792199345521, -0.036974068723667486, -0.014748083424595894, 0.0030777102795787317, 0.015052738596465692, -0.016825277354925172, -0.02205980941211622, -0.006882438900433998, 0.0028890317765800054, -0.007339421658238697, 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'https://arxiv.org/pdf/2303.08774.pdf', 'page': 0}), Document(page_content='wide scoring bins. For example although GPT-4 attains the highest possible score on AP Biology (5/5),\\nthis is only shown in the plot as 85th percentile because 15 percent of test-takers achieve that score.\\nGPT-4 exhibits human-level performance on the majority of these professional and academic exams.\\nNotably, it passes a simulated version of the Uniform Bar Examination with a score in the top 10% of\\ntest takers (Table 1, Figure 4).\\nThe model’s capabilities on exams appear to stem primarily from the pre-training process and are not\\nsignificantly affected by RLHF. On multiple choice questions, both the base GPT-4 model and the\\nRLHF model perform equally well on average across the exams we tested (see Appendix B).\\nWe also evaluated the pre-trained base GPT-4 model on traditional benchmarks designed for evaluating\\nlanguage models. 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Document(page_content='wide scoring bins. For example although GPT-4 attains the highest possible score on AP Biology (5/5),\\nthis is only shown in the plot as 85th percentile because 15 percent of test-takers achieve that score.\\nGPT-4 exhibits human-level performance on the majority of these professional and academic exams.\\nNotably, it passes a simulated version of the Uniform Bar Examination with a score in the top 10% of\\ntest takers (Table 1, Figure 4).\\nThe model’s capabilities on exams appear to stem primarily from the pre-training process and are not\\nsignificantly affected by RLHF. On multiple choice questions, both the base GPT-4 model and the\\nRLHF model perform equally well on average across the exams we tested (see Appendix B).\\nWe also evaluated the pre-trained base GPT-4 model on traditional benchmarks designed for evaluating\\nlanguage models. 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'https://arxiv.org/pdf/2303.08774.pdf', 'page': 5}), Document(page_content='wide scoring bins. For example although GPT-4 attains the highest possible score on AP Biology (5/5),\\nthis is only shown in the plot as 85th percentile because 15 percent of test-takers achieve that score.\\nGPT-4 exhibits human-level performance on the majority of these professional and academic exams.\\nNotably, it passes a simulated version of the Uniform Bar Examination with a score in the top 10% of\\ntest takers (Table 1, Figure 4).\\nThe model’s capabilities on exams appear to stem primarily from the pre-training process and are not\\nsignificantly affected by RLHF. On multiple choice questions, both the base GPT-4 model and the\\nRLHF model perform equally well on average across the exams we tested (see Appendix B).\\nWe also evaluated the pre-trained base GPT-4 model on traditional benchmarks designed for evaluating\\nlanguage models. 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'https://arxiv.org/pdf/2303.08774.pdf', 'page': 5}), Document(page_content='wide scoring bins. For example although GPT-4 attains the highest possible score on AP Biology (5/5),\\nthis is only shown in the plot as 85th percentile because 15 percent of test-takers achieve that score.\\nGPT-4 exhibits human-level performance on the majority of these professional and academic exams.\\nNotably, it passes a simulated version of the Uniform Bar Examination with a score in the top 10% of\\ntest takers (Table 1, Figure 4).\\nThe model’s capabilities on exams appear to stem primarily from the pre-training process and are not\\nsignificantly affected by RLHF. On multiple choice questions, both the base GPT-4 model and the\\nRLHF model perform equally well on average across the exams we tested (see Appendix B).\\nWe also evaluated the pre-trained base GPT-4 model on traditional benchmarks designed for evaluating\\nlanguage models. 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Document(page_content='Observed\\nPrediction\\ngpt-4\\n100p 10n 1µ 100µ 0.01 1\\nCompute1.02.03.04.05.06.0Bits per wordOpenAI codebase next word predictionFigure 1. Performance of GPT-4 and smaller models. The metric is final loss on a dataset derived\\nfrom our internal codebase. This is a convenient, large dataset of code tokens which is not contained in\\nthe training set. We chose to look at loss because it tends to be less noisy than other measures across\\ndifferent amounts of training compute. A power law fit to the smaller models (excluding GPT-4) is\\nshown as the dotted line; this fit accurately predicts GPT-4’s final loss. The x-axis is training compute\\nnormalized so that GPT-4 is 1.\\nObserved\\nPrediction\\ngpt-4\\n1µ 10µ 100µ 0.001 0.01 0.1 1\\nCompute012345– Mean Log Pass RateCapability prediction on 23 coding problems\\nFigure 2. Performance of GPT-4 and smaller models. The metric is mean log pass rate on a subset of\\nthe HumanEval dataset. 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Performance of GPT-4 and smaller models. The metric is final loss on a dataset derived\\nfrom our internal codebase. This is a convenient, large dataset of code tokens which is not contained in\\nthe training set. We chose to look at loss because it tends to be less noisy than other measures across\\ndifferent amounts of training compute. A power law fit to the smaller models (excluding GPT-4) is\\nshown as the dotted line; this fit accurately predicts GPT-4’s final loss. The x-axis is training compute\\nnormalized so that GPT-4 is 1.\\nObserved\\nPrediction\\ngpt-4\\n1µ 10µ 100µ 0.001 0.01 0.1 1\\nCompute012345– Mean Log Pass RateCapability prediction on 23 coding problems\\nFigure 2. Performance of GPT-4 and smaller models. The metric is mean log pass rate on a subset of\\nthe HumanEval dataset. 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Document(page_content='Observed\\nPrediction\\ngpt-4\\n100p 10n 1µ 100µ 0.01 1\\nCompute1.02.03.04.05.06.0Bits per wordOpenAI codebase next word predictionFigure 1. Performance of GPT-4 and smaller models. The metric is final loss on a dataset derived\\nfrom our internal codebase. This is a convenient, large dataset of code tokens which is not contained in\\nthe training set. We chose to look at loss because it tends to be less noisy than other measures across\\ndifferent amounts of training compute. A power law fit to the smaller models (excluding GPT-4) is\\nshown as the dotted line; this fit accurately predicts GPT-4’s final loss. The x-axis is training compute\\nnormalized so that GPT-4 is 1.\\nObserved\\nPrediction\\ngpt-4\\n1µ 10µ 100µ 0.001 0.01 0.1 1\\nCompute012345– Mean Log Pass RateCapability prediction on 23 coding problems\\nFigure 2. Performance of GPT-4 and smaller models. The metric is mean log pass rate on a subset of\\nthe HumanEval dataset. 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'https://arxiv.org/pdf/2303.08774.pdf', 'page': 2})]\n"
     ]
    }
   ],
   "source": [
    "from langchain.chains import RetrievalQA\n",
    "from langchain_openai import OpenAI\n",
    "\n",
    "qa = RetrievalQA.from_chain_type(\n",
    "    llm=OpenAI(),\n",
    "    chain_type=\"stuff\",\n",
    "    retriever=qa_retriever,\n",
    "    return_source_documents=\"True\",\n",
    "    chain_type_kwargs={\"prompt\": PROMPT},\n",
    ")\n",
    "\n",
    "docs = qa({\"query\": \"gpt-4 compute requirements\"})\n",
    "\n",
    "print(docs[\"result\"])\n",
    "print(docs[\"source_documents\"])\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMtf34sUB0HF1YMy1HvcN1b",
   "provenance": [
    {
     "file_id": "1gMGVSHvrHDh8Mf3okBAyK8JMEJJKE4lU",
     "timestamp": 1705687576328
    },
    {
     "file_id": "1tRDG7JrUiqRAJOQShKk_3hDNZvOpqN2T",
     "timestamp": 1705687451345
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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   "name": "python",
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