vectors-01-create-index.ipynb

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   "source": [
    "#### Create an Azure AI Search Index including Vector Fields\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! pip install azure-search-documents==11.4.0b11 --quiet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "from azure.core.credentials import AzureKeyCredential  \n",
    "from azure.search.documents import SearchClient  \n",
    "from azure.search.documents.indexes import SearchIndexClient  \n",
    "from azure.search.documents.models import (\n",
    "    RawVectorQuery,\n",
    ")\n",
    "from azure.search.documents.indexes.models import (  \n",
    "    CorsOptions,\n",
    "    ExhaustiveKnnParameters,  \n",
    "    ExhaustiveKnnVectorSearchAlgorithmConfiguration,\n",
    "    HnswParameters,  \n",
    "    HnswVectorSearchAlgorithmConfiguration,\n",
    "    SimpleField,\n",
    "    SearchField,  \n",
    "    ComplexField,\n",
    "    SearchFieldDataType,  \n",
    "    SearchIndex,  \n",
    "    VectorSearch,  \n",
    "    VectorSearchAlgorithmKind,  \n",
    "    VectorSearchProfile,  \n",
    ")\n",
    "  \n",
    "\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "# Load environment variables from .env file\n",
    "load_dotenv()\n",
    "\n",
    "# import azure.search.documents\n",
    "# print (azure.search.documents.__version__)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Azure Cognitive Search Index <a id='create-index'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the service endpoint and API key from the environment\n",
    "# Create an SDK client\n",
    "endpoint = os.environ[\"SEARCH_SERVICE_ENDPOINT\"]\n",
    "search_client = SearchClient(\n",
    "    endpoint=endpoint,\n",
    "    index_name=os.environ[\"SEARCH_INDEX_NAME\"],\n",
    "    credential=AzureKeyCredential(os.environ[\"SEARCH_ADMIN_API_KEY\"]),\n",
    ")\n",
    "\n",
    "admin_client = SearchIndexClient(\n",
    "    endpoint=endpoint,\n",
    "    index_name=os.environ[\"SEARCH_INDEX_NAME\"],\n",
    "    credential=AzureKeyCredential(os.environ[\"SEARCH_ADMIN_API_KEY\"]),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete the index if it exists\n",
    "try:\n",
    "    result = admin_client.delete_index(os.environ[\"SEARCH_INDEX_NAME\"])\n",
    "    print(\"Index\", os.environ[\"SEARCH_INDEX_NAME\"], \"deleted\")\n",
    "except Exception as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the index\n",
    "fields = [\n",
    "    SimpleField(\n",
    "        name=\"id\",\n",
    "        type=SearchFieldDataType.String,\n",
    "        filterable=True,\n",
    "        sortable=True,\n",
    "        key=True,\n",
    "    ),\n",
    "    SimpleField(\n",
    "        name=\"doc_id\",\n",
    "        type=SearchFieldDataType.String,\n",
    "        filterable=True,\n",
    "        facetable=True,\n",
    "        sortable=True,\n",
    "        key=False,\n",
    "    ),\n",
    "    SimpleField(\n",
    "        name=\"chunk_id\",\n",
    "        type=SearchFieldDataType.Int32,\n",
    "        filterable=True,\n",
    "        sortable=True,\n",
    "        key=False,\n",
    "    ),\n",
    "    SearchField(\n",
    "        name=\"name\", type=SearchFieldDataType.String, filterable=True, sortable=True, analyzer_name=\"en.microsoft\"\n",
    "    ),\n",
    "    SimpleField(\n",
    "        name=\"created_datetime\",\n",
    "        type=SearchFieldDataType.DateTimeOffset,\n",
    "        facetable=True,\n",
    "        filterable=True,\n",
    "        sortable=True,\n",
    "    ),\n",
    "    SearchField(\n",
    "        name=\"created_by\",\n",
    "        type=SearchFieldDataType.String,\n",
    "        filterable=True,\n",
    "        sortable=True,\n",
    "    ),\n",
    "    SimpleField(\n",
    "        name=\"size\",\n",
    "        type=SearchFieldDataType.Int32,\n",
    "        facetable=True,\n",
    "        filterable=True,\n",
    "        sortable=True,\n",
    "    ),\n",
    "    SimpleField(\n",
    "        name=\"last_modified_datetime\",\n",
    "        type=SearchFieldDataType.DateTimeOffset,\n",
    "        facetable=True,\n",
    "        filterable=True,\n",
    "        sortable=True,\n",
    "    ),\n",
    "    SearchField(\n",
    "        name=\"last_modified_by\",\n",
    "        type=SearchFieldDataType.String,\n",
    "        filterable=True,\n",
    "        sortable=True,\n",
    "    ),\n",
    "    SimpleField(name=\"source\", type=SearchFieldDataType.String),\n",
    "    SearchField(\n",
    "        name=\"content\", type=SearchFieldDataType.String, analyzer_name=\"en.microsoft\"\n",
    "    ),\n",
    "    SearchField(\n",
    "        name=\"contentVector\",  \n",
    "        hidden=False,\n",
    "        type=SearchFieldDataType.Collection(SearchFieldDataType.Single), \n",
    "        searchable=True,\n",
    "        vector_search_dimensions=1536,  \n",
    "        vector_search_profile=\"myHnswProfile\"\n",
    "    ), \n",
    "    ComplexField(\n",
    "            name=\"read_access_entity\",\n",
    "            collection=True,\n",
    "            fields=[SimpleField(name=\"list_item\", type=SearchFieldDataType.String, searchable=True, filterable=True,)],\n",
    "            searchable=True),\n",
    "\n",
    "]\n",
    "cors_options = CorsOptions(allowed_origins=[\"*\"], max_age_in_seconds=60)\n",
    "scoring_profiles = []\n",
    "suggester = [{\"name\": \"sg\", \"source_fields\": [\"name\"]}]\n",
    "\n",
    "\n",
    "# Configure the vector search configuration  \n",
    "vector_search = VectorSearch(  \n",
    "    algorithms=[  \n",
    "        HnswVectorSearchAlgorithmConfiguration(  \n",
    "            name=\"myHnsw\",  \n",
    "            kind=VectorSearchAlgorithmKind.HNSW,  \n",
    "            parameters=HnswParameters(  \n",
    "                m=4,  \n",
    "                ef_construction=400,  \n",
    "                ef_search=1000,  \n",
    "                metric=\"cosine\",  \n",
    "            ),  \n",
    "        )\n",
    "    ],  \n",
    "   profiles=[  \n",
    "        VectorSearchProfile(  \n",
    "            name=\"myHnswProfile\",  \n",
    "            algorithm=\"myHnsw\",  \n",
    "        ),   \n",
    "    ],  \n",
    ") \n",
    "\n",
    "index = SearchIndex(\n",
    "    name=os.environ[\"SEARCH_INDEX_NAME\"],\n",
    "    fields=fields,\n",
    "    scoring_profiles=scoring_profiles,\n",
    "    suggesters=suggester,\n",
    "    cors_options=cors_options,\n",
    "    vector_search=vector_search\n",
    ")\n",
    "\n",
    "try:\n",
    "    result = admin_client.create_index(index)\n",
    "    print(\"Index\", result.name, \"created\")\n",
    "except Exception as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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