notebooks/model-upgrades/upgrading-index-to-use-elser.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Upgrade an index to use ELSER model\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/model-upgrades/upgrading-index-to-use-elser.ipynb)\n",
    "\n",
    "In this notebook we will see example on how to upgrade your index to ELSER model `.elser_model_2` using [Reindex API](https://elasticsearch-py.readthedocs.io/en/stable/api.html#elasticsearch.Elasticsearch.reindex). \n",
    "\n",
    "**Note:** Alternatively, you could also [Update by query](https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-update-by-query.html) to update index in place to use ELSER. In this notebook, we will see examples on using Reindex API. \n",
    "\n",
    "\n",
    "Scenerios that we will see in this notebook:\n",
    "\n",
    "1. Migrating a index which hasn't  generated [`text_expansion`](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-text-expansion-query.html) field to  ELSER model `.elser_model_2` \n",
    "2. Upgrade an existing index with `.elser_model_1` to use `.elser_model_2` model\n",
    "3. Upgrade a index which use different model to use ELSER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Install and Connect\n",
    "\n",
    "To get started, we'll need to connect to our Elastic deployment using the Python client.\n",
    "Because we're using an Elastic Cloud deployment, we'll use the **Cloud ID** to identify our deployment.\n",
    "First we need to `pip` install the following packages:\n",
    "\n",
    "- `elasticsearch`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install elasticsearch -qU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will import all the modules that we need. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch, helpers\n",
    "from urllib.request import urlopen\n",
    "import getpass\n",
    "import json\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will instantiate the Python Elasticsearch client. First we prompt for  password and Cloud ID.\n",
    "\n",
    "Then we create a `client` object that instantiates an instance of the `Elasticsearch` class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Found in the 'Manage Deployment' page\n",
    "CLOUD_ID = getpass.getpass('Elastic Cloud ID:  ')\n",
    "\n",
    "# Password for the 'elastic' user generated by Elasticsearch\n",
    "ELASTIC_PASSWORD = getpass.getpass('Elastic password:  ')\n",
    "\n",
    "# Create the client instance\n",
    "client = Elasticsearch(\n",
    "    cloud_id=CLOUD_ID,\n",
    "    basic_auth=(\"elastic\", ELASTIC_PASSWORD)\n",
    ")\n",
    "\n",
    "print(client.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download and Deploy ELSER v2 Model\n",
    "\n",
    "Before we begin, we have to download and deploy ELSER model `.elser_model_2`. \n",
    "\n",
    "Follow the instructions under the section [Download and Deploy ELSER Model](../search/03-ELSER.ipynb#download-and-deploy-elser-model)  from the [ELSER](../search/03-ELSER.ipynb) notebook \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Case 1: Migrate an index with no `text_expansion` field\n",
    "\n",
    "In this case we will see how to upgrade an index which has a [ingestion pipeline](https://www.elastic.co/guide/en/elasticsearch/reference/current/ingest.html) configured, to use ELSER model `elser_model_2` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Ingestion pipeline with lowercase\n",
    "\n",
    "We will create a simple pipeline to convert title field values to lowercase and use this ingestion pipeline on our index. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "client.ingest.put_pipeline(\n",
    "    id=\"ingest-pipeline-lowercase\", \n",
    "    description=\"Ingest pipeline to change title to lowercase\",\n",
    "    processors=[\n",
    "    {\n",
    "      \"lowercase\": {\n",
    "        \"field\": \"title\"\n",
    "      }\n",
    "    }\n",
    "  ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create index - `movies` with mappings\n",
    "\n",
    "Next, we will create a index with pipeline `ingest-pipeline-lowercase` that we created in previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.indices.delete(index=\"movies\",ignore_unavailable=True)\n",
    "client.indices.create(\n",
    "  index=\"movies\",\n",
    "  settings={\n",
    "      \"index\": {\n",
    "          \"number_of_shards\": 1,\n",
    "          \"number_of_replicas\": 1,\n",
    "          \"default_pipeline\": \"ingest-pipeline-lowercase\"\n",
    "      }\n",
    "  },\n",
    "  mappings={\n",
    "    \"properties\": {\n",
    "      \"plot\": {\n",
    "        \"type\": \"text\",\n",
    "        \"fields\": {\n",
    "          \"keyword\": {\n",
    "            \"type\": \"keyword\",\n",
    "            \"ignore_above\": 256\n",
    "          }\n",
    "        }\n",
    "      },\n",
    "    }\n",
    "  }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insert Documents\n",
    "we are now ready to insert sample dataset of 12 movies to our index `movies`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done indexing documents into `movies` index!\n"
     ]
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/elastic/elasticsearch-labs/main/notebooks/search/movies.json\"\n",
    "response = urlopen(url)\n",
    "\n",
    "# Load the response data into a JSON object\n",
    "data_json = json.loads(response.read())\n",
    "\n",
    "# Prepare the documents to be indexed\n",
    "documents = []\n",
    "for doc in data_json:\n",
    "    documents.append({\n",
    "        \"_index\": \"movies\",\n",
    "        \"_source\": doc,\n",
    "    })\n",
    "\n",
    "# Use helpers.bulk to index\n",
    "helpers.bulk(client, documents)\n",
    "\n",
    "print(\"Done indexing documents into `movies` index!\")\n",
    "time.sleep(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Upgrade index `movies` to use ELSER model\n",
    "\n",
    "we are ready to re-index  `movies` to a new index with the ELSER model `.elser_model_2`. As a first step, we have to create new ingestion pipeline and index to use ELSER model. \n",
    "\n",
    "# Create a new pipeline with ELSER \n",
    "Let's create a new ingestion pipeline with ELSER model `.elser_model_2`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.ingest.put_pipeline(\n",
    "    id=\"elser-ingest-pipeline\", \n",
    "    description=\"Ingest pipeline for ELSER\",\n",
    "    processors=[\n",
    "    {\n",
    "      \"inference\": {\n",
    "        \"model_id\": \".elser_model_2\",\n",
    "        \"input_output\": [\n",
    "            {\n",
    "              \"input_field\": \"plot\",\n",
    "              \"output_field\": \"plot_embedding\"\n",
    "            }\n",
    "          ]\n",
    "      }\n",
    "    }\n",
    "  ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a index with mappings\n",
    "\n",
    "Next, create an index with required mappings for ELSER.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.indices.delete(index=\"elser-movies\",ignore_unavailable=True)\n",
    "client.indices.create(\n",
    "  index=\"elser-movies\",\n",
    "  mappings={\n",
    "    \"properties\": {\n",
    "      \"plot\": {\n",
    "        \"type\": \"text\",\n",
    "        \"fields\": {\n",
    "          \"keyword\": {\n",
    "            \"type\": \"keyword\",\n",
    "            \"ignore_above\": 256\n",
    "          }\n",
    "        }\n",
    "      },\n",
    "      \"plot_embedding\": { \n",
    "        \"type\": \"sparse_vector\" \n",
    "      }\n",
    "    }\n",
    "  }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**\n",
    "- `plot_embedding` is the name of the field that contains generated token with the type [`sparse_vector`](https://www.elastic.co/guide/en/elasticsearch/reference/master/sparse-vector.html) \n",
    "- `plot` is the name of the field from which the [`sparse_vector`](https://www.elastic.co/guide/en/elasticsearch/reference/master/sparse-vector.html)  are created. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reindex with updated pipeline \n",
    "\n",
    "With the help of [Reindex API](https://elasticsearch-py.readthedocs.io/en/stable/api.html#elasticsearch.Elasticsearch.reindex), we can copy data from old index `movies` and to new index `elser-movies` with  ingestion pipeline set to `elser-ingest-pipeline` .  On success, the index `elser-movies` creates tokens on the `text_expansion` terms that you targeted for ELSER inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.reindex(source={\n",
    "    \"index\": \"movies\"\n",
    "  }, dest={\n",
    "    \"index\": \"elser-movies\",\n",
    "    \"pipeline\":  \"elser-ingest-pipeline\"\n",
    "  })\n",
    "time.sleep(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once reindex is complete, inspect any document in the index `elser-movies` and notice that the document has a additional field `plot_embedding` with terms that we will be using in `text_expansion` query. \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Querying documents with ELSER \n",
    "\n",
    "Let's try a semantic search on our index with ELSER model `.elser_model_2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 6.403741\n",
      "Title: se7en\n",
      "Plot: Two detectives, a rookie and a veteran, hunt a serial killer who uses the seven deadly sins as his motives.\n",
      "\n",
      "Score: 3.6703415\n",
      "Title: the departed\n",
      "Plot: An undercover cop and a mole in the police attempt to identify each other while infiltrating an Irish gang in South Boston.\n",
      "\n",
      "Score: 2.9359162\n",
      "Title: the usual suspects\n",
      "Plot: A sole survivor tells of the twisty events leading up to a horrific gun battle on a boat, which began when five criminals met at a seemingly random police lineup.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "response = client.search(\n",
    "    index='elser-movies', \n",
    "    size=3,\n",
    "    query={\n",
    "        \"text_expansion\": {\n",
    "            \"plot_embedding\": {\n",
    "                \"model_id\":\".elser_model_2\",\n",
    "                \"model_text\":\"investigation\"\n",
    "            }\n",
    "        }\n",
    "    }\n",
    ")\n",
    "\n",
    "for hit in response['hits']['hits']:\n",
    "    doc_id = hit['_id']\n",
    "    score = hit['_score']\n",
    "    title = hit['_source']['title']\n",
    "    plot = hit['_source']['plot']\n",
    "    print(f\"Score: {score}\\nTitle: {title}\\nPlot: {plot}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case 2: Upgrade index with ELSER model to `.elser_model_2`\n",
    "\n",
    "If you already have a index with ELSER model `.elser_model_1` and would like to upgrade to `.elser_model_2`, you can use the Reindex API with ingestion pipeline to use ELSER `.elser_model_2` model.\n",
    "\n",
    "**`Note:`** Before we begin, ensure that you are on Elasticsearch 8.11 version and ELSER model `.elser_model_2` is deployed. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a new ingestion pipeline\n",
    "\n",
    "We will create a pipeline with `.elser_model_2` to enable us with reindexing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.ingest.put_pipeline(\n",
    "    id=\"elser-pipeline-upgrade-demo\", \n",
    "    description=\"Ingest pipeline for ELSER upgrade demo\",\n",
    "    processors=[\n",
    "    {\n",
    "      \"inference\": {\n",
    "        \"model_id\": \".elser_model_2\",\n",
    "        \"input_output\": [\n",
    "            {\n",
    "              \"input_field\": \"plot\",\n",
    "              \"output_field\": \"plot_embedding\"\n",
    "            }\n",
    "          ]\n",
    "      }\n",
    "    }\n",
    "  ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a new index with mappings\n",
    "We will create  a new index with required mappings supporting ELSER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.indices.delete(index=\"elser-upgrade-index-demo\", ignore_unavailable=True)\n",
    "client.indices.create(\n",
    "  index=\"elser-upgrade-index-demo\",\n",
    "  mappings={\n",
    "    \"properties\": {\n",
    "      \"plot\": {\n",
    "        \"type\": \"text\",\n",
    "        \"fields\": {\n",
    "          \"keyword\": {\n",
    "            \"type\": \"keyword\",\n",
    "            \"ignore_above\": 256\n",
    "          }\n",
    "        }\n",
    "      },\n",
    "      \"plot_embedding\": {\n",
    "        \"type\": \"sparse_vector\"\n",
    "      },\n",
    "    }\n",
    "  }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Reindex API\n",
    "we will use [Reindex API](https://elasticsearch-py.readthedocs.io/en/stable/api.html#elasticsearch.Elasticsearch.reindex) to move data from old index to new index `elser-upgrade-index-demo`. We will be excluding target field from old index and instead generate new tokens in the field `plot_embedding` with `.elser_model_2` while reindexing. \n",
    "\n",
    "**`Note:`** Make sure to replace `my-index` with your index name that you intend to upgrade and the field `my-tokens-field` with the field name that you have generated tokens previously.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.reindex(source={\n",
    "    \"index\": \"my-index\", # replace with your index name\n",
    "    \"_source\": {\n",
    "      \"excludes\": [\"my-tokens-field\"]  # replace with the field-name from your index, that has previously generated tokens\n",
    "    }}, \n",
    "    dest={\n",
    "    \"index\": \"elser-upgrade-index-demo\",\n",
    "    \"pipeline\":  \"elser-pipeline-upgrade-demo\"\n",
    "  })\n",
    "time.sleep(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Querying your data\n",
    "\n",
    "Once reindexing is complete, you are ready to query on your data and perform semantic search "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 3.3168378\n",
      "Title: Fight Club\n",
      "Plot: An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.\n",
      "\n",
      "Score: 1.5777297\n",
      "Title: The Godfather\n",
      "Plot: An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.\n",
      "\n",
      "Score: 1.1162646\n",
      "Title: The Matrix\n",
      "Plot: A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "response = client.search(\n",
    "    index='elser-upgrade-index-demo', \n",
    "    size=3,\n",
    "    query={\n",
    "        \"text_expansion\": {\n",
    "            \"plot_embedding\": {\n",
    "                \"model_id\":\".elser_model_2\",\n",
    "                \"model_text\":\"child toy\"\n",
    "            }\n",
    "        }\n",
    "    }\n",
    ")\n",
    "\n",
    "for hit in response['hits']['hits']:\n",
    "    doc_id = hit['_id']\n",
    "    score = hit['_score']\n",
    "    title = hit['_source']['title']\n",
    "    plot = hit['_source']['plot']\n",
    "    print(f\"Score: {score}\\nTitle: {title}\\nPlot: {plot}\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case 3: Upgrade a index with different model to ELSER\n",
    "\n",
    "Now we will see how to move your index which already has generated `embedding` using a different model. \n",
    "\n",
    "Lets consider the index - `blogs` and has generated `text_embedding` using the NLP model `sentence-transformers__all-minilm-l6-v2`. In case you would like know about more how to load a NLP model to an index, follow the steps from our notebook [loading-model-from-hugging-face.ipynb](../integrations/hugging-face/loading-model-from-hugging-face.ipynb)\n",
    "\n",
    "Follow similiar proceedure that we did in previously: \n",
    "1. Create a ingestion pipeline with ELSER model `.elser_model_2`\n",
    "2. Create a index with mappings, with the pipeline we created in the previous step. \n",
    "3. Reindex, excluding the field that has embedding from the `blogs` index\n",
    "\n",
    "Before we begin, lets take a look at our index `blogs` and see the mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'blogs': {'aliases': {}, 'mappings': {'properties': {'text_embedding': {'properties': {'is_truncated': {'type': 'boolean'}, 'model_id': {'type': 'text', 'fields': {'keyword': {'type': 'keyword', 'ignore_above': 256}}}, 'predicted_value': {'type': 'dense_vector', 'dims': 384, 'index': True, 'similarity': 'l2_norm'}}}, 'title': {'type': 'text', 'fields': {'keyword': {'type': 'keyword', 'ignore_above': 256}}}}}, 'settings': {'index': {'routing': {'allocation': {'include': {'_tier_preference': 'data_content'}}}, 'number_of_shards': '1', 'blocks': {'read_only_allow_delete': 'false'}, 'provided_name': 'blogs', 'default_pipeline': 'vectorize_blogs', 'creation_date': '1697651466693', 'number_of_replicas': '1', 'uuid': 'JWkPyTphQ2GV0sLadHWjjw', 'version': {'created': '8500003'}}}}})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.indices.get(index=\"blogs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the field `text_embedding`, We will exclude this field in our new index and generate new mapping against the field `title` from the `blogs` index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create ingestion pipeline\n",
    "\n",
    "Next, we will create a pipeline using ELSER model `.elser_model_2`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.ingest.put_pipeline(\n",
    "    id=\"elser-pipeline-blogs\", \n",
    "    description=\"Ingest pipeline for ELSER upgrade\",\n",
    "    processors=[\n",
    "    {\n",
    "      \"inference\": {\n",
    "        \"model_id\": \".elser_model_2\",\n",
    "        \"input_output\": [\n",
    "          {\n",
    "            \"input_field\": \"title\",\n",
    "            \"output_field\": \"title_embedding\"\n",
    "          }\n",
    "        ]\n",
    "      }\n",
    "    }\n",
    "  ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create index with mappings\n",
    "\n",
    "Lets create a index `elser-blogs` with mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.indices.delete(index=\"elser-blogs\", ignore_unavailable=True)\n",
    "client.indices.create(\n",
    "  index=\"elser-blogs\",\n",
    "  mappings={\n",
    "    \"properties\": {\n",
    "      \"title\": {\n",
    "        \"type\": \"text\",\n",
    "        \"fields\": {\n",
    "          \"keyword\": {\n",
    "            \"type\": \"keyword\",\n",
    "            \"ignore_above\": 256\n",
    "          }\n",
    "        }\n",
    "      },\n",
    "      \"title_embedding\": {\n",
    "        \"type\": \"sparse_vector\"\n",
    "      },\n",
    "    }\n",
    "  }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reindex API\n",
    "\n",
    "we will use the [Reindex API](https://elasticsearch-py.readthedocs.io/en/stable/api.html#elasticsearch.Elasticsearch.reindex) to copy data and generate `text_expansion` embedding to our new index `elser-blogs`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.reindex(source={\n",
    "    \"index\": \"blogs\",\n",
    "    \"_source\": {\n",
    "      \"excludes\": [\"text_embedding\"]\n",
    "    }\n",
    "  }, dest={\n",
    "    \"index\": \"elser-blogs\",\n",
    "    \"pipeline\":  \"elser-pipeline-blogs\"\n",
    "  })\n",
    "time.sleep(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Querying your data\n",
    "Success! Now we can query data on the index `elser-blogs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 27.618645\n",
      "Title: Brewing in Beats: Track network connections\n",
      "Score: 3.8143802\n",
      "Title: Machine Learning for Nginx Logs - Identifying Operational Issues with Your Website\n",
      "Score: 3.3623078\n",
      "Title: Data Visualization For Machine Learning\n"
     ]
    }
   ],
   "source": [
    "response = client.search(\n",
    "    index='elser-blogs', \n",
    "    size=3,\n",
    "    query={\n",
    "        \"text_expansion\": {\n",
    "            \"title_embedding\": {\n",
    "                \"model_id\":\".elser_model_2\",\n",
    "                \"model_text\":\"Track network connections\"\n",
    "            }\n",
    "        }\n",
    "    }\n",
    ")\n",
    "\n",
    "for hit in response['hits']['hits']:\n",
    "    doc_id = hit['_id']\n",
    "    score = hit['_score']\n",
    "    title = hit['_source']['title']\n",
    "    print(f\"Score: {score}\\nTitle: {title}\")\n"
   ]
  }
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