examples/fasthtml-demo/deploy_app.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "given-adoption",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<picture>\n",
    "  <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://vespa.ai/assets/vespa-ai-logo-heather.svg\">\n",
    "  <source media=\"(prefers-color-scheme: light)\" srcset=\"https://vespa.ai/assets/vespa-ai-logo-rock.svg\">\n",
    "  <img alt=\"#Vespa\" width=\"200\" src=\"https://vespa.ai/assets/vespa-ai-logo-rock.svg\" style=\"margin-bottom: 25px;\">\n",
    "</picture>\n",
    "\n",
    "# Deploy a sample app to Vespa Cloud\n",
    "\n",
    "This is the same guide as [getting-started-pyvespa](https://pyvespa.readthedocs.io/en/latest/getting-started-pyvespa.html), deploying to Vespa Cloud.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8c1448",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    Refer to <a href=\"https://pyvespa.readthedocs.io/en/latest/troubleshooting.html\">troubleshooting</a>\n",
    "    for any problem when running this guide.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "148d275b",
   "metadata": {},
   "source": [
    "**Pre-requisite**: Create a tenant at [cloud.vespa.ai](https://cloud.vespa.ai/), save the tenant name.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vespa-engine/pyvespa/blob/master/docs/sphinx/source/getting-started-pyvespa-cloud.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366b0d83",
   "metadata": {},
   "source": [
    "## Install\n",
    "\n",
    "Install [pyvespa](https://pyvespa.readthedocs.io/) >= 0.45\n",
    "and the [Vespa CLI](https://docs.vespa.ai/en/vespa-cli.html).\n",
    "The Vespa CLI is used for data and control plane key management ([Vespa Cloud Security Guide](https://cloud.vespa.ai/en/security/guide)).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "136750de",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install pyvespa vespacli datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f706ff",
   "metadata": {},
   "source": [
    "## Configure application\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ca4da83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace with your tenant name from the Vespa Cloud Console\n",
    "tenant_name = \"mytenant\"\n",
    "# Replace with your application name (does not need to exist yet)\n",
    "application = \"fasthtml\"\n",
    "# Token id (from Vespa Cloud Console)\n",
    "token_id = \"fasthtmltoken\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db637322",
   "metadata": {},
   "source": [
    "## Create an application package\n",
    "\n",
    "The [application package](https://pyvespa.readthedocs.io/en/latest/reference-api.html#vespa.package.ApplicationPackage)\n",
    "has all the Vespa configuration files -\n",
    "create one from scratch:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd5c2629",
   "metadata": {},
   "outputs": [],
   "source": [
    "from vespa.package import (\n",
    "    ApplicationPackage,\n",
    "    Field,\n",
    "    Schema,\n",
    "    Document,\n",
    "    HNSW,\n",
    "    RankProfile,\n",
    "    Component,\n",
    "    Parameter,\n",
    "    FieldSet,\n",
    "    GlobalPhaseRanking,\n",
    "    Function,\n",
    "    AuthClient,\n",
    ")\n",
    "\n",
    "package = ApplicationPackage(\n",
    "    name=application,\n",
    "    schema=[\n",
    "        Schema(\n",
    "            name=\"doc\",\n",
    "            document=Document(\n",
    "                fields=[\n",
    "                    Field(name=\"id\", type=\"string\", indexing=[\"summary\"]),\n",
    "                    Field(\n",
    "                        name=\"title\",\n",
    "                        type=\"string\",\n",
    "                        indexing=[\"index\", \"summary\"],\n",
    "                        index=\"enable-bm25\",\n",
    "                    ),\n",
    "                    Field(\n",
    "                        name=\"body\",\n",
    "                        type=\"string\",\n",
    "                        indexing=[\"index\", \"summary\"],\n",
    "                        index=\"enable-bm25\",\n",
    "                        bolding=True,\n",
    "                    ),\n",
    "                    Field(\n",
    "                        name=\"embedding\",\n",
    "                        type=\"tensor<float>(x[384])\",\n",
    "                        indexing=[\n",
    "                            'input title . \" \" . input body',\n",
    "                            \"embed\",\n",
    "                            \"index\",\n",
    "                            \"attribute\",\n",
    "                        ],\n",
    "                        ann=HNSW(distance_metric=\"angular\"),\n",
    "                        is_document_field=False,\n",
    "                    ),\n",
    "                ]\n",
    "            ),\n",
    "            fieldsets=[FieldSet(name=\"default\", fields=[\"title\", \"body\"])],\n",
    "            rank_profiles=[\n",
    "                RankProfile(\n",
    "                    name=\"bm25\",\n",
    "                    inputs=[(\"query(q)\", \"tensor<float>(x[384])\")],\n",
    "                    functions=[\n",
    "                        Function(name=\"bm25sum\", expression=\"bm25(title) + bm25(body)\")\n",
    "                    ],\n",
    "                    first_phase=\"bm25sum\",\n",
    "                ),\n",
    "                RankProfile(\n",
    "                    name=\"semantic\",\n",
    "                    inputs=[(\"query(q)\", \"tensor<float>(x[384])\")],\n",
    "                    first_phase=\"closeness(field, embedding)\",\n",
    "                ),\n",
    "                RankProfile(\n",
    "                    name=\"fusion\",\n",
    "                    inherits=\"bm25\",\n",
    "                    inputs=[(\"query(q)\", \"tensor<float>(x[384])\")],\n",
    "                    first_phase=\"closeness(field, embedding)\",\n",
    "                    global_phase=GlobalPhaseRanking(\n",
    "                        expression=\"reciprocal_rank_fusion(bm25sum, closeness(field, embedding))\",\n",
    "                        rerank_count=1000,\n",
    "                    ),\n",
    "                ),\n",
    "            ],\n",
    "        )\n",
    "    ],\n",
    "    components=[\n",
    "        Component(\n",
    "            id=\"e5\",\n",
    "            type=\"hugging-face-embedder\",\n",
    "            parameters=[\n",
    "                Parameter(\n",
    "                    \"transformer-model\",\n",
    "                    {\n",
    "                        \"url\": \"https://github.com/vespa-engine/sample-apps/raw/master/examples/model-exporting/model/e5-small-v2-int8.onnx\"\n",
    "                    },\n",
    "                ),\n",
    "                Parameter(\n",
    "                    \"tokenizer-model\",\n",
    "                    {\n",
    "                        \"url\": \"https://raw.githubusercontent.com/vespa-engine/sample-apps/master/examples/model-exporting/model/tokenizer.json\"\n",
    "                    },\n",
    "                ),\n",
    "            ],\n",
    "        )\n",
    "    ],\n",
    "    auth_clients=[\n",
    "        AuthClient(\n",
    "            id=\"mtls\",\n",
    "            permissions=[\"read\", \"write\"],\n",
    "            parameters=[Parameter(\"certificate\", {\"file\": \"security/clients.pem\"})],\n",
    "        ),\n",
    "        AuthClient(\n",
    "            id=\"token\",\n",
    "            permissions=[\"read\"], # Token client only needs read permission\n",
    "            parameters=[Parameter(\"token\", {\"id\": token_id})],\n",
    "        ),\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c5e2943",
   "metadata": {},
   "source": [
    "Note that the name cannot have `-` or `_`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "careful-savage",
   "metadata": {},
   "source": [
    "## Deploy to Vespa Cloud\n",
    "\n",
    "The app is now defined and ready to deploy to Vespa Cloud.\n",
    "\n",
    "Deploy `package` to Vespa Cloud, by creating an instance of\n",
    "[VespaCloud](https://pyvespa.readthedocs.io/en/latest/reference-api.html#vespa.deployment.VespaCloud):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "canadian-blood",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting application...\n",
      "Running: vespa config set application scoober.fasthtml\n",
      "Setting target cloud...\n",
      "Running: vespa config set target cloud\n",
      "\n",
      "No api-key found for control plane access. Using access token.\n",
      "Checking for access token in auth.json...\n",
      "Successfully obtained access token for control plane access.\n"
     ]
    }
   ],
   "source": [
    "from vespa.deployment import VespaCloud\n",
    "\n",
    "vespa_cloud = VespaCloud(\n",
    "    tenant=tenant_name,\n",
    "    application=application,\n",
    "    application_package=package,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "197c0a27",
   "metadata": {},
   "source": [
    "The following will upload the application package to Vespa Cloud Dev Zone (`aws-us-east-1c`), read more about [Vespa Zones](https://cloud.vespa.ai/en/reference/zones.html).\n",
    "The Vespa Cloud Dev Zone is considered as a sandbox environment where resources are down-scaled and idle deployments are expired automatically.\n",
    "For information about production deployments, see the following [example](https://pyvespa.readthedocs.io/en/latest/getting-started-pyvespa-cloud.html#Example:-Deploy-the-app-to-the-prod-environment).\n",
    "\n",
    "> Note: Deployments to dev and perf expire after 7 days of inactivity, i.e., 7 days after running deploy. This applies to all plans, not only the Free Trial. Use the Vespa Console to extend the expiry period, or redeploy the application to add 7 more days.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "752166fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deployment started in run 13 of dev-aws-us-east-1c for scoober.fasthtml. This may take a few minutes the first time.\n",
      "INFO    [10:44:08]  Deploying platform version 8.397.20 and application dev build 7 for dev-aws-us-east-1c of default ...\n",
      "INFO    [10:44:08]  Using CA signed certificate version 1\n",
      "INFO    [10:44:08]  Using 1 nodes in container cluster 'fasthtml_container'\n",
      "INFO    [10:44:11]  Validating Onnx models memory usage for container cluster 'fasthtml_container', percentage of available memory too low (10 < 15) to avoid restart, consider a flavor with more memory to avoid this\n",
      "INFO    [10:44:13]  Session 4210 for tenant 'scoober' prepared and activated.\n",
      "INFO    [10:44:13]  ######## Details for all nodes ########\n",
      "INFO    [10:44:13]  h94419b.dev.aws-us-east-1c.vespa-external.aws.oath.cloud: expected to be UP\n",
      "INFO    [10:44:13]  --- platform vespa/cloud-tenant-rhel8:8.397.20\n",
      "INFO    [10:44:13]  --- storagenode on port 19102 has config generation 4210, wanted is 4210\n",
      "INFO    [10:44:13]  --- searchnode on port 19107 has config generation 4210, wanted is 4210\n",
      "INFO    [10:44:13]  --- distributor on port 19111 has config generation 4210, wanted is 4210\n",
      "INFO    [10:44:13]  --- metricsproxy-container on port 19092 has config generation 4210, wanted is 4210\n",
      "INFO    [10:44:13]  h93281d.dev.aws-us-east-1c.vespa-external.aws.oath.cloud: expected to be UP\n",
      "INFO    [10:44:13]  --- platform vespa/cloud-tenant-rhel8:8.397.20\n",
      "INFO    [10:44:13]  --- container-clustercontroller on port 19050 has config generation 4209, wanted is 4210\n",
      "INFO    [10:44:13]  --- metricsproxy-container on port 19092 has config generation 4210, wanted is 4210\n",
      "INFO    [10:44:13]  h93281b.dev.aws-us-east-1c.vespa-external.aws.oath.cloud: expected to be UP\n",
      "INFO    [10:44:13]  --- platform vespa/cloud-tenant-rhel8:8.397.20\n",
      "INFO    [10:44:13]  --- logserver-container on port 4080 has config generation 4209, wanted is 4210\n",
      "INFO    [10:44:13]  --- metricsproxy-container on port 19092 has config generation 4210, wanted is 4210\n",
      "INFO    [10:44:13]  h95982a.dev.aws-us-east-1c.vespa-external.aws.oath.cloud: expected to be UP\n",
      "INFO    [10:44:13]  --- platform vespa/cloud-tenant-rhel8:8.397.20\n",
      "INFO    [10:44:13]  --- container on port 4080 has config generation 4209, wanted is 4210\n",
      "INFO    [10:44:13]  --- metricsproxy-container on port 19092 has config generation 4209, wanted is 4210\n",
      "INFO    [10:44:23]  Found endpoints:\n",
      "INFO    [10:44:23]  - dev.aws-us-east-1c\n",
      "INFO    [10:44:23]   |-- https://d14d3ce0.ba4a39d8.z.vespa-app.cloud/ (cluster 'fasthtml_container')\n",
      "INFO    [10:44:23]  Deployment of new application complete!\n",
      "Found mtls endpoint for fasthtml_container\n",
      "URL: https://d14d3ce0.ba4a39d8.z.vespa-app.cloud/\n",
      "Connecting to https://d14d3ce0.ba4a39d8.z.vespa-app.cloud/\n",
      "Using Mutual TLS with key and cert to connect to Vespa endpoint https://d14d3ce0.ba4a39d8.z.vespa-app.cloud/\n",
      "Application is up!\n",
      "Finished deployment.\n"
     ]
    }
   ],
   "source": [
    "app = vespa_cloud.deploy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaae2f91",
   "metadata": {},
   "source": [
    "If the deployment failed, it is possible you forgot to add the key in the Vespa Cloud Console in the `vespa auth api-key` step above.\n",
    "\n",
    "If you can authenticate, you should see lines like the following\n",
    "\n",
    "```\n",
    " Deployment started in run 1 of dev-aws-us-east-1c for mytenant.hybridsearch.\n",
    "```\n",
    "\n",
    "The deployment takes a few minutes the first time while Vespa Cloud sets up the resources for your Vespa application\n",
    "\n",
    "`app` now holds a reference to a [Vespa](https://pyvespa.readthedocs.io/en/latest/reference-api.html#vespa.application.Vespa) instance. We can access the\n",
    "mTLS protected endpoint name using the control-plane (vespa_cloud) instance. This endpoint we can query and feed to (data plane access) using the\n",
    "mTLS certificate generated in previous steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sealed-mustang",
   "metadata": {},
   "source": [
    "### Feeding documents to Vespa\n",
    "\n",
    "In this example we use the [HF Datasets](https://huggingface.co/docs/datasets/index) library to stream the\n",
    "[BeIR/nfcorpus](https://huggingface.co/datasets/BeIR/nfcorpus) dataset and index in our newly deployed Vespa instance. Read\n",
    "more about the [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/):\n",
    "\n",
    "> NFCorpus is a full-text English retrieval data set for Medical Information Retrieval.\n",
    "\n",
    "The following uses the [stream](https://huggingface.co/docs/datasets/stream) option of datasets to stream the data without\n",
    "downloading all the contents locally. The `map` functionality allows us to convert the\n",
    "dataset fields into the expected feed format for `pyvespa` which expects a dict with the keys `id` and `fields`:\n",
    "\n",
    "`{ \"id\": \"vespa-document-id\", \"fields\": {\"vespa_field\": \"vespa-field-value\"}}`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a49fa8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found token endpoint for fasthtml_container\n",
      "URL: https://d3f601e7.ba4a39d8.z.vespa-app.cloud/\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://d3f601e7.ba4a39d8.z.vespa-app.cloud/'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_endpoint = vespa_cloud.get_token_endpoint()\n",
    "token_endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "126c0c29",
   "metadata": {},
   "source": [
    "Add this endpoint to your `.env.example` file:\n",
    "\n",
    "```bash\n",
    "VESPA_APP_URL=https://d3f601e7.ba4a39d8.z.vespa-app.cloud/\n",
    "```\n",
    "\n",
    "Remember to rename the file to `.env`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775f3dd4",
   "metadata": {},
   "source": [
    "## Feed data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "executed-reservoir",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/thomas/.pyenv/versions/3.9.19/envs/pyvespa-dev/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"BeIR/nfcorpus\", \"corpus\", split=\"corpus\", streaming=True)\n",
    "vespa_feed = dataset.map(\n",
    "    lambda x: {\n",
    "        \"id\": x[\"_id\"],\n",
    "        \"fields\": {\"title\": x[\"title\"], \"body\": x[\"text\"], \"id\": x[\"_id\"]},\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0ca33f",
   "metadata": {},
   "source": [
    "Now we can feed to Vespa using `feed_iterable` which accepts any `Iterable` and an optional callback function where we can\n",
    "check the outcome of each operation. The application is configured to use [embedding](https://docs.vespa.ai/en/embedding.html)\n",
    "functionality, that produce a vector embedding using a concatenation of the title and the body input fields. This step is resource intensive.\n",
    "\n",
    "Read more about embedding inference in Vespa in the [Accelerating Transformer-based Embedding Retrieval with Vespa](https://blog.vespa.ai/accelerating-transformer-based-embedding-retrieval-with-vespa/)\n",
    "blog post.\n",
    "\n",
    "Default node resources in Vespa Cloud have 2 v-cpu for the Dev Zone.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bottom-memorabilia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using mtls_key_cert Authentication against endpoint https://d14d3ce0.ba4a39d8.z.vespa-app.cloud//ApplicationStatus\n"
     ]
    }
   ],
   "source": [
    "from vespa.io import VespaResponse\n",
    "\n",
    "\n",
    "def callback(response: VespaResponse, id: str):\n",
    "    if not response.is_successful():\n",
    "        print(f\"Error when feeding document {id}: {response.get_json()}\")\n",
    "\n",
    "\n",
    "app.feed_iterable(vespa_feed, schema=\"doc\", namespace=\"tutorial\", callback=callback)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "336e339d",
   "metadata": {},
   "source": [
    "### Run a test query\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11faeacf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'root': {'id': 'toplevel',\n",
       "  'relevance': 1.0,\n",
       "  'fields': {'totalCount': 1387},\n",
       "  'coverage': {'coverage': 100,\n",
       "   'documents': 3633,\n",
       "   'full': True,\n",
       "   'nodes': 1,\n",
       "   'results': 1,\n",
       "   'resultsFull': 1},\n",
       "  'children': [{'id': 'id:tutorial:doc::MED-2464',\n",
       "    'relevance': 0.03200204813108039,\n",
       "    'source': 'fasthtml_content',\n",
       "    'fields': {'sddocname': 'doc',\n",
       "     'body': \"BACKGROUND: In recent decades, children's diet quality has changed <hi>and</hi> <hi>asthma</hi> prevalence has increased, although it remains unclear if these events are associated. OBJECTIVE: To examine children's total <hi>and</hi> component diet quality <hi>and</hi> <hi>asthma</hi> <hi>and</hi> airway hyperresponsiveness (AHR), a proxy for <hi>asthma</hi> severity. METHODS: Food frequency questionnaires adapted from the Nurses' Health Study <hi>and</hi> supplemented with foods whose nutrients which have garnered interest of late in relation to <hi>asthma</hi> were administered. From these data, diet quality scores (total <hi>and</hi> component), based on the Youth Healthy Eating Index (YHEI adapted) were developed. <hi>Asthma</hi> assessments were performed by pediatric allergists <hi>and</hi> classified by atopic status: Allergic <hi>asthma</hi> (≥1 positive skin prick test to common allergens >3 mm compared to negative control) versus non-allergic <hi>asthma</hi> (negative skin prick test). AHR was assessed via the Cockcroft technique. Participants included 270 boys (30% with <hi>asthma</hi>) <hi>and</hi> 206 girls (33% with <hi>asthma</hi>) involved in the 1995 Manitoba Prospective Cohort Study nested case-control study. Logistic regression was used to examine associations between diet quality <hi>and</hi> <hi>asthma</hi>, <hi>and</hi> multinomial logistic regression was used to examine associations between diet quality <hi>and</hi> AHR. RESULTS: Four hundred seventy six children (56.7% boys) were seen at 12.6 ± 0.5 years. <hi>Asthma</hi> <hi>and</hi> AHR prevalence were 26.2 <hi>and</hi> 53.8%, respectively. In fully adjusted models, high <hi>vegetable</hi> intake was protective against allergic <hi>asthma</hi> (OR 0.49; 95% CI 0.29-0.84; P < 0.009) <hi>and</hi> moderate/severe AHR (OR 0.58; 0.37-0.91; P < 0.019). CONCLUSIONS: <hi>Vegetable</hi> intake is inversely associated with allergic <hi>asthma</hi> <hi>and</hi> moderate/severe AHR. Copyright © 2012 Wiley Periodicals, Inc.\",\n",
       "     'documentid': 'id:tutorial:doc::MED-2464',\n",
       "     'id': 'MED-2464',\n",
       "     'title': 'Low vegetable intake is associated with allergic asthma and moderate-to-severe airway hyperresponsiveness.'}},\n",
       "   {'id': 'id:tutorial:doc::MED-2450',\n",
       "    'relevance': 0.03177805800756621,\n",
       "    'source': 'fasthtml_content',\n",
       "    'fields': {'sddocname': 'doc',\n",
       "     'body': \"Background Atopy is not uncommon among children living in rural Crete, but wheeze <hi>and</hi> rhinitis are rare. A study was undertaken to examine whether this discrepancy could be attributed to a high consumption of fresh fruit <hi>and</hi> <hi>vegetables</hi> or adherence to a traditional Mediterranean diet. Methods A cross‐sectional survey was performed in 690 children aged 7–18\\u2005years in rural Crete. Parents completed a questionnaire on their child's respiratory <hi>and</hi> allergic symptoms <hi>and</hi> a 58‐item food frequency questionnaire. Adherence to a Mediterranean diet was measured using a scale with 12 dietary items. Children underwent skin prick tests with 10 common aeroallergens. Results 80% of children ate fresh fruit (<hi>and</hi> 68% <hi>vegetables</hi>) at least twice a day. The intake of grapes, oranges, apples, <hi>and</hi> fresh tomatoes—the main local products in Crete—had no association with atopy but was protective for wheezing <hi>and</hi> rhinitis. A high consumption of nuts was found to be inversely associated with wheezing (OR 0.46; 95% CI 0.20 to 0.98), whereas margarine increased the risk of both wheeze (OR 2.19; 95% CI 1.01 to 4.82) <hi>and</hi> allergic rhinitis (OR 2.10; 95% CI 1.31 to 3.37). A high level of adherence to the Mediterranean diet was protective for allergic rhinitis (OR 0.34; 95% CI 0.18 to 0.64) while a more modest protection was observed for wheezing <hi>and</hi> atopy. Conclusion The results of this study suggest a beneficial effect of commonly consumed <hi>fruits</hi>, <hi>vegetables</hi> <hi>and</hi> nuts, <hi>and</hi> of a high adherence to a traditional Mediterranean diet during childhood on symptoms of <hi>asthma</hi> <hi>and</hi> rhinitis. Diet may explain the relative lack of allergic symptoms in this population.\",\n",
       "     'documentid': 'id:tutorial:doc::MED-2450',\n",
       "     'id': 'MED-2450',\n",
       "     'title': 'Protective effect of fruits, vegetables and the Mediterranean diet on asthma and allergies among children in Crete'}},\n",
       "   {'id': 'id:tutorial:doc::MED-2458',\n",
       "    'relevance': 0.030776515151515152,\n",
       "    'source': 'fasthtml_content',\n",
       "    'fields': {'sddocname': 'doc',\n",
       "     'body': 'BACKGROUND: Antioxidant-rich diets are associated with reduced <hi>asthma</hi> prevalence in epidemiologic studies. We previously showed that short-term manipulation of antioxidant defenses leads to changes in <hi>asthma</hi> outcomes. OBJECTIVE: The objective was to investigate the effects of a high-antioxidant diet compared with those of a low-antioxidant diet, with or without lycopene supplementation, in <hi>asthma</hi>. DESIGN: <hi>Asthmatic</hi> adults (n = 137) were randomly assigned to a high-antioxidant diet (5 servings of <hi>vegetables</hi> <hi>and</hi> 2 servings of fruit daily; n = 46) or a low-antioxidant diet (≤2 servings of <hi>vegetables</hi> <hi>and</hi> 1 serving of fruit daily; n = 91) for 14 d <hi>and</hi> then commenced a parallel, randomized, controlled supplementation trial. Subjects who consumed the high-antioxidant diet received placebo. Subjects who consumed the low-antioxidant diet received placebo or tomato extract (45 mg lycopene/d). The intervention continued until week 14 or until an exacerbation occurred. RESULTS: After 14 d, subjects consuming the low-antioxidant diet had a lower percentage predicted forced expiratory volume in 1 s <hi>and</hi> percentage predicted forced vital capacity than did those consuming the high-antioxidant diet. Subjects in the low-antioxidant diet group had increased plasma C-reactive protein at week 14. At the end of the trial, time to exacerbation was greater in the high-antioxidant than in the low-antioxidant diet group, <hi>and</hi> the low-antioxidant diet group was 2.26 (95% CI: 1.04, 4.91; P = 0.039) times as likely to exacerbate. Of the subjects in the low-antioxidant diet group, no difference in airway or systemic inflammation or clinical outcomes was observed between the groups that consumed the tomato extract <hi>and</hi> those who consumed placebo. CONCLUSIONS: Modifying the dietary intake of carotenoids alters clinical <hi>asthma</hi> outcomes. Improvements were evident only after increased fruit <hi>and</hi> <hi>vegetable</hi> intake, which suggests that whole-food interventions are most effective. This trial was registered at http://www.actr.org.au as ACTRN012606000286549.',\n",
       "     'documentid': 'id:tutorial:doc::MED-2458',\n",
       "     'id': 'MED-2458',\n",
       "     'title': 'Manipulating antioxidant intake in asthma: a randomized controlled trial.'}},\n",
       "   {'id': 'id:tutorial:doc::MED-2461',\n",
       "    'relevance': 0.03055037313432836,\n",
       "    'source': 'fasthtml_content',\n",
       "    'fields': {'sddocname': 'doc',\n",
       "     'body': 'This study aimed to evaluate the association of diet with respiratory symptoms <hi>and</hi> <hi>asthma</hi> in schoolchildren in Taipei, Taiwan. An in-class interview survey elicited experiences of <hi>asthma</hi> <hi>and</hi> respiratory symptoms <hi>and</hi> consumption frequencies of the major food categories in 2290 fifth graders. Respiratory symptoms surveyed included persistent cough, chest tightness, wheezing with cold, wheezing without cold, dyspnea-associated wheezing, <hi>and</hi> exercise-induced cough or wheezing. Results showed that the consumption of sweetened beverages had the strongest association with respiratory symptoms <hi>and</hi> was positively associated with six of the seven respiratory symptoms (all p < 0.05). The adjusted odds ratios (aOR) ranged from 1.05 (95% confidence interval (CI = 1.01-1.09) for exercise-induced cough to 1.09 (95% CI = 1.03-1.16) for wheezing without cold. Egg consumption was associated with 5 of the 7 respiratory symptoms. Consumptions of seafood, soy products, <hi>and</hi> <hi>fruits</hi> were each negatively associated with one of the seven respiratory symptoms (all p < 0.05). Consumption of seafood was negatively associated with physician-diagnosed <hi>asthma</hi> <hi>and</hi> consumptions of sweetened beverages <hi>and</hi> eggs were positively associated with suspected <hi>asthma</hi> (p < 0.05). In conclusion, the study suggests that diet is associated with the respiratory symptoms in schoolchildren in Taipei. Consumptions of sweetened beverages <hi>and</hi> eggs are associated with increased risk of respiratory symptoms <hi>and</hi> <hi>asthma</hi> whereas consumptions of soy products <hi>and</hi> <hi>fruits</hi> are associated with reduced risk of respiratory symptoms.',\n",
       "     'documentid': 'id:tutorial:doc::MED-2461',\n",
       "     'id': 'MED-2461',\n",
       "     'title': 'The association of diet with respiratory symptoms and asthma in schoolchildren in Taipei, Taiwan.'}},\n",
       "   {'id': 'id:tutorial:doc::MED-5072',\n",
       "    'relevance': 0.027757078986587184,\n",
       "    'source': 'fasthtml_content',\n",
       "    'fields': {'sddocname': 'doc',\n",
       "     'body': 'Antioxidant-rich diets are associated with reduced <hi>asthma</hi> prevalence. However, direct evidence that altering intake of antioxidant-rich foods affects <hi>asthma</hi> is lacking. The objective was to investigate changes in <hi>asthma</hi> <hi>and</hi> airway inflammation resulting from a low antioxidant diet <hi>and</hi> subsequent use of lycopene-rich treatments. <hi>Asthmatic</hi> adults (n=32) consumed a low antioxidant diet for 10 days, then commenced a randomized, cross-over trial involving 3 x 7 day treatment arms (placebo, tomato extract (45 mg lycopene/day) <hi>and</hi> tomato juice (45 mg lycopene/day)). With consumption of a low antioxidant diet, plasma carotenoid concentrations decreased, <hi>Asthma</hi> Control Score worsened, %FEV(1) <hi>and</hi> %FVC decreased <hi>and</hi> %sputum neutrophils increased. Treatment with both tomato juice <hi>and</hi> extract reduced airway neutrophil influx. Treatment with tomato extract also reduced sputum neutrophil elastase activity. In conclusion, dietary antioxidant consumption modifies clinical <hi>asthma</hi> outcomes. Changing dietary antioxidant intake may be contributing to rising <hi>asthma</hi> prevalence. Lycopene-rich supplements should be further investigated as a therapeutic intervention.',\n",
       "     'documentid': 'id:tutorial:doc::MED-5072',\n",
       "     'id': 'MED-5072',\n",
       "     'title': 'Lycopene-rich treatments modify noneosinophilic airway inflammation in asthma: proof of concept.'}}]}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with app.syncio(connections=1) as session:\n",
    "    query = \"How Fruits and Vegetables Can Treat Asthma?\"\n",
    "    response = session.query(\n",
    "        yql=\"select * from sources * where userQuery() or ({targetHits:1000}nearestNeighbor(embedding,q)) limit 5\",\n",
    "        query=query,\n",
    "        ranking=\"fusion\",\n",
    "        body={\"input.query(q)\": f\"embed({query})\"},\n",
    "    )\n",
    "    assert response.is_successful()\n",
    "response.json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072a12ac",
   "metadata": {},
   "source": [
    "Now, you should be all set to run your frontend against the Vespa Cloud application.\n"
   ]
  }
 ],
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   "language": "python",
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