run-llama--llama_index/docs/examples/node_postprocessor/OptimizerDemo.ipynb

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   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/node_postprocessor/OptimizerDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7ff2c269",
   "metadata": {},
   "source": [
    "# Sentence Embedding Optimizer\n",
    "\n",
    "This postprocessor optimizes token usage by removing sentences that are not relevant to the query (this is done using embeddings).The percentile cutoff is a measure for using the top percentage of relevant sentences. The threshold cutoff can be specified instead, which uses a raw similarity cutoff for picking which sentences to keep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d8d212e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-readers-wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "839c4a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# My OpenAI Key\n",
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "40cf0773",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1429a4c9",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01f3b5d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa34cd83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import download_loader\n",
    "\n",
    "from llama_index.readers.wikipedia import WikipediaReader\n",
    "\n",
    "loader = WikipediaReader()\n",
    "documents = loader.load_data(pages=[\"Berlin\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f59e6c18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'llama_index.readers.schema.base.Document'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
      "INFO:root:> [build_index_from_documents] Total embedding token usage: 18390 tokens\n"
     ]
    }
   ],
   "source": [
    "from llama_index.core import VectorStoreIndex\n",
    "\n",
    "index = VectorStoreIndex.from_documents(documents)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "827ada33",
   "metadata": {},
   "source": [
    "Compare query with and without optimization for LLM token usage, Embedding Model usage on query, Embedding model usage for optimizer, and total time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a04e4535",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Without optimization\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:> [query] Total LLM token usage: 3545 tokens\n",
      "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time elapsed: 2.8928110599517822\n",
      "Answer: \n",
      "The population of Berlin in 1949 was approximately 2.2 million inhabitants. After the fall of the Berlin Wall in 1989, the population of Berlin increased to approximately 3.7 million inhabitants.\n",
      "\n",
      "With optimization\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:> [optimize] Total embedding token usage: 7 tokens\n",
      "INFO:root:> [query] Total LLM token usage: 1779 tokens\n",
      "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time elapsed: 2.346346139907837\n",
      "Answer: \n",
      "The population of Berlin is around 4.5 million.\n",
      "Alternate optimization cutoff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:> [optimize] Total embedding token usage: 7 tokens\n",
      "INFO:root:> [query] Total LLM token usage: 3215 tokens\n",
      "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time elapsed: 2.101111888885498\n",
      "Answer: \n",
      "The population of Berlin is around 4.5 million.\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from llama_index.core import VectorStoreIndex\n",
    "from llama_index.core.postprocessor import SentenceEmbeddingOptimizer\n",
    "\n",
    "print(\"Without optimization\")\n",
    "start_time = time.time()\n",
    "query_engine = index.as_query_engine()\n",
    "res = query_engine.query(\"What is the population of Berlin?\")\n",
    "end_time = time.time()\n",
    "print(\"Total time elapsed: {}\".format(end_time - start_time))\n",
    "print(\"Answer: {}\".format(res))\n",
    "\n",
    "print(\"With optimization\")\n",
    "start_time = time.time()\n",
    "query_engine = index.as_query_engine(\n",
    "    node_postprocessors=[SentenceEmbeddingOptimizer(percentile_cutoff=0.5)]\n",
    ")\n",
    "res = query_engine.query(\"What is the population of Berlin?\")\n",
    "end_time = time.time()\n",
    "print(\"Total time elapsed: {}\".format(end_time - start_time))\n",
    "print(\"Answer: {}\".format(res))\n",
    "\n",
    "print(\"Alternate optimization cutoff\")\n",
    "start_time = time.time()\n",
    "query_engine = index.as_query_engine(\n",
    "    node_postprocessors=[SentenceEmbeddingOptimizer(threshold_cutoff=0.7)]\n",
    ")\n",
    "res = query_engine.query(\"What is the population of Berlin?\")\n",
    "end_time = time.time()\n",
    "print(\"Total time elapsed: {}\".format(end_time - start_time))\n",
    "print(\"Answer: {}\".format(res))"
   ]
  }
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