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‎.env.sample‎

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AZURE_OPENAI_SERVICE="SERVICE-NAME"
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AZURE_OPENAI_DEPLOYMENT_NAME=DEPLOYMENT-NAME
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AZURE_OPENAI_ADA_DEPLOYMENT=ADA-DEPLOYMENT
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AZURE_SEARCH_SERVICE=SEARCH-SERVICE-NAME
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AZURE_VISION_ENDPOINT=https://YOUR-RESOURCE-NAME.cognitiveservices.azure.com
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AZURE_TENANT_ID=TENANT-ID

‎README.md‎

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1. Run `azd up` on [azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo/) with multimodal feature enabled. This will create the necessary resources for the Azure OpenAI, Azure AI Search services, and the Azure AI Vision service.
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2. Create a .env with these variables, and the values taken from `.azure/ENV-NAME/.env`
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2. Create a .env with these variables, with the values taken from `.azure/ENV-NAME/.env` in the azure-search-openai-demo repository.
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```shell
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AZURE_OPENAI_SERVICE=YOUR-SERVICE-NAME
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AZURE_OPENAI_DEPLOYMENT_NAME=YOUR-OPENAI-DEPLOYMENT-NAME
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AZURE_OPENAI_EMBEDDING_DEPLOYMENT=YOUR-EMBED-DEPLOYMENT-NAME
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AZURE_SEARCH_SERVICE=YOUR-SEARCH-SERVICE-NAME
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AZURE_COMPUTERVISION_SERVICE=YOUR-COMPUTERVISION-SERVICE-NAME
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AZURE_TENANT_ID=YOUR-TENANT-ID
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```
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3. Login to your Azure account using either the Azure CLI or the Azure Developer CLI:
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3. Login to your Azure account using the Azure CLI. Specify `--tenant-id` if you deployed that repo to a non-default tenant.
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```shell
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azd auth login
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```
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4. If you deployed your resource group to a tenant other than your home tenant, set the tenant ID:
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4. Create a Python virtual environment or open the project in a container.
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```shell
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AZURE_TENANT_ID=YOUR-TENANT-ID

‎azure_ai_search.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import dotenv\n",
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"from azure.search.documents.indexes import SearchIndexClient\n",
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"\n",
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"dotenv.load_dotenv()\n",
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"dotenv.load_dotenv(override=True)\n",
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"\n",
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"AZURE_SEARCH_SERVICE = os.getenv(\"AZURE_SEARCH_SERVICE\")\n",
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"AZURE_SEARCH_ENDPOINT = f\"https://{AZURE_SEARCH_SERVICE}.search.windows.net\"\n",
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"\n",
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"azure_credential = azure.identity.DefaultAzureCredential()\n",
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"index_client = SearchIndexClient(endpoint=AZURE_SEARCH_ENDPOINT, credential=azure_credential)"
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"azure_credential = azure.identity.AzureDeveloperCliCredential(tenant_id=os.getenv(\"AZURE_TENANT_ID\"))\n",
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"index_client = SearchIndexClient(endpoint=AZURE_SEARCH_ENDPOINT, credential=azure_credential)\n"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<azure.search.documents.indexes.models._index.SearchIndex at 0x114201090>"
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"<azure.search.documents.indexes.models._index.SearchIndex at 0x138703b90>"
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]
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},
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"execution_count": 5,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}

‎image_search.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"import dotenv\n",
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"from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
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"from azure.identity import AzureDeveloperCliCredential, get_bearer_token_provider\n",
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"from azure.search.documents import SearchClient\n",
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"from azure.search.documents.indexes import SearchIndexClient\n",
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"from azure.search.documents.indexes.models import (\n",
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"\n",
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"AZURE_SEARCH_SERVICE = os.getenv(\"AZURE_SEARCH_SERVICE\")\n",
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"AZURE_SEARCH_ENDPOINT = f\"https://{AZURE_SEARCH_SERVICE}.search.windows.net\"\n",
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"AZURE_SEARCH_IMAGES_INDEX = \"images-index\"\n",
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"azure_credential = DefaultAzureCredential()\n",
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"AZURE_SEARCH_IMAGES_INDEX = \"images-index4\"\n",
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"azure_credential = AzureDeveloperCliCredential(tenant_id=os.getenv(\"AZURE_TENANT_ID\"))\n",
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"search_client = SearchClient(AZURE_SEARCH_ENDPOINT, AZURE_SEARCH_IMAGES_INDEX, credential=azure_credential)"
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<azure.search.documents.indexes.models._index.SearchIndex at 0x120d3efd0>"
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"<azure.search.documents.indexes.models._index.SearchIndex at 0x12c3ba910>"
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]
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},
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"metadata": {},
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}
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Configure Azure Computer Vision multi-modal embeddings API "
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"### Configure Azure AI Vision multi-modal embeddings API "
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]
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},
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{
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"cell_type": "code",
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"source": [
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"from PIL import Image\n",
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"\n",
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"token_provider = get_bearer_token_provider(azure_credential, \"https://cognitiveservices.azure.com/.default\")\n",
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"AZURE_COMPUTERVISION_SERVICE = os.getenv(\"AZURE_COMPUTERVISION_SERVICE\")\n",
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"AZURE_COMPUTER_VISION_URL = f\"https://{AZURE_COMPUTERVISION_SERVICE}.cognitiveservices.azure.com/computervision/retrieval\"\n",
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"AZURE_VISION_ENDPOINT = os.getenv(\"AZURE_VISION_ENDPOINT\")\n",
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"AZURE_AIVISION_URL = f\"{AZURE_VISION_ENDPOINT}/computervision/retrieval\"\n",
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"\n",
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"def get_model_params():\n",
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" return {\"api-version\": \"2023-02-01-preview\", \"modelVersion\": \"latest\"}\n",
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" return {\"api-version\": \"2024-02-01\", \"model-version\": \"2023-04-15\"}\n",
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"\n",
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"def get_auth_headers():\n",
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" return {\"Authorization\": \"Bearer \" + token_provider()}\n",
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"\n",
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"def get_image_embedding(image_file):\n",
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" mimetype = mimetypes.guess_type(image_file)[0]\n",
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" url = f\"{AZURE_COMPUTER_VISION_URL}:vectorizeImage\"\n",
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" url = f\"{AZURE_AIVISION_URL}:vectorizeImage\"\n",
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" headers = get_auth_headers()\n",
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" headers[\"Content-Type\"] = mimetype\n",
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" # add error checking\n",
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" response = requests.post(url, headers=headers, params=get_model_params(), data=open(image_file, \"rb\"))\n",
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" if response.status_code != 200:\n",
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" print(image_file, response.status_code, response.json())\n",
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" return response.json()[\"vector\"]\n",
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"\n",
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"def get_text_embedding(text):\n",
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" url = f\"{AZURE_COMPUTER_VISION_URL}:vectorizeText\"\n",
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" url = f\"{AZURE_AIVISION_URL}:vectorizeText\"\n",
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" return requests.post(url, headers=get_auth_headers(), params=get_model_params(),\n",
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" json={\"text\": text}).json()[\"vector\"]"
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]
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"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x451>"
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"metadata": {},
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"output_type": "execute_result"
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}

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