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Added Python Cognitive API tutorial example + updated readme file
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‎README.md‎

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@@ -15,4 +15,8 @@ This repository contains Python sample code used in Azure Search quickstarts, tu
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## Quickstart-Jupyter-Notebook
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This sample is a .ipynb file containing a Python3 notebook used in [Quickstart: Create and query an Azure Search index using a Jupyter Python notebook](https://docs.microsoft.com/azure/search/search-get-started-python). There are two placeholder values for an Azure Search service and admin API key. Replace them with valid values to create, load, and query an index on your own service.
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This sample is a .ipynb file containing a Python3 notebook used in [Quickstart: Create and query an Azure Search index using a Jupyter Python notebook](https://docs.microsoft.com/azure/search/search-get-started-python). There are two placeholder values for an Azure Search service and admin API key. Replace them with valid values to create, load, and query an index on your own service.
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## Tutorial-AI-Enrichment-Jupyter-Notebook
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This sample is a .ipynb file containing a Python3 notebook used in [Tutorial: Python Tutorial: Call Cognitive Services APIs in an Azure Search indexing pipeline](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-blob-python). There are three placeholder values to insert: an Azure Search service, an admin API key, and a connection string to a blob storage resource that you will create in the tutorial. Replace them with valid values to create an indexing pipeline that searches for and extracts text and text representations of images and scanned documents. This sample leverages cognitive skills from the Azure Cognitive Services API, such as entity recognition and language detection.

‎Tutorial-AI-Enrichment-Jupyter-Notebook/PythonTutorial-AzureSearch-AIEnrichment.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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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 json\n",
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"import requests\n",
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"from pprint import pprint"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Define the names for the data source, skillset, index and indexer\n",
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"datasource_name=\"cogsrch-py-datasource\"\n",
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"skillset_name=\"cogsrch-py-skillset\"\n",
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"index_name=\"cogsrch-py-index\"\n",
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"indexer_name=\"cogsrch-py-indexer\"\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Setup the endpoint\n",
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"endpoint = 'https://<YOUR-SERVICE-NAME>.search.windows.net/'\n",
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"headers = {'Content-Type': 'application/json',\n",
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" 'api-key': '<YOUR-ADMIN-API-KEY>' }\n",
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"params = {\n",
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" 'api-version': '2019-05-06'\n",
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Create a data source\n",
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"datasourceConnectionString = \"<YOUR-BLOB-RESOURCE-CONNECTION-STRING>\"\n",
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"datasource_payload = {\n",
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" \"name\": datasource_name,\n",
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" \"description\": \"Demo files to demonstrate cognitive search capabilities.\",\n",
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" \"type\": \"azureblob\",\n",
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" \"credentials\": {\n",
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" \"connectionString\": datasourceConnectionString\n",
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" },\n",
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" \"container\": {\n",
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" \"name\": \"basic-demo-data-pr\"\n",
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" }\n",
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"}\n",
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"r = requests.put( endpoint + \"/datasources/\" + datasource_name, data=json.dumps(datasource_payload), headers=headers, params=params )\n",
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"pprint (r.json())"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Create a skillset\n",
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"skillset_payload = {\n",
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" \"name\": skillset_name,\n",
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" \"description\":\n",
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" \"Extract entities, detect language and extract key-phrases\",\n",
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" \"skills\":\n",
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" [\n",
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" {\n",
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" \"@odata.type\": \"#Microsoft.Skills.Text.EntityRecognitionSkill\",\n",
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" \"categories\": [ \"Organization\" ],\n",
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" \"defaultLanguageCode\": \"en\",\n",
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" \"inputs\": [\n",
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" {\n",
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" \"name\": \"text\", \"source\": \"/document/content\"\n",
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" }\n",
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" ],\n",
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" \"outputs\": [\n",
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" {\n",
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" \"name\": \"organizations\", \"targetName\": \"organizations\"\n",
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" }\n",
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" ]\n",
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" },\n",
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" {\n",
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" \"@odata.type\": \"#Microsoft.Skills.Text.LanguageDetectionSkill\",\n",
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" \"inputs\": [\n",
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" {\n",
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" \"name\": \"text\", \"source\": \"/document/content\"\n",
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" }\n",
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" ],\n",
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" \"outputs\": [\n",
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" {\n",
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" \"name\": \"languageCode\",\n",
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" \"targetName\": \"languageCode\"\n",
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" }\n",
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" ]\n",
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" },\n",
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" {\n",
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" \"@odata.type\": \"#Microsoft.Skills.Text.SplitSkill\",\n",
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" \"textSplitMode\" : \"pages\",\n",
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" \"maximumPageLength\": 4000,\n",
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" \"inputs\": [\n",
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" {\n",
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" \"name\": \"text\",\n",
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" \"source\": \"/document/content\"\n",
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" },\n",
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" {\n",
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" \"name\": \"languageCode\",\n",
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" \"source\": \"/document/languageCode\"\n",
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" }\n",
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" ],\n",
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" \"outputs\": [\n",
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" {\n",
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" \"name\": \"textItems\",\n",
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" \"targetName\": \"pages\"\n",
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" }\n",
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" ]\n",
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" },\n",
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" {\n",
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" \"@odata.type\": \"#Microsoft.Skills.Text.KeyPhraseExtractionSkill\",\n",
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" \"context\": \"/document/pages/*\",\n",
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" \"inputs\": [\n",
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" {\n",
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" \"name\": \"text\", \"source\": \"/document/pages/*\"\n",
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" },\n",
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" {\n",
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" \"name\":\"languageCode\", \"source\": \"/document/languageCode\"\n",
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" }\n",
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" ],\n",
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" \"outputs\": [\n",
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" {\n",
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" \"name\": \"keyPhrases\",\n",
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" \"targetName\": \"keyPhrases\"\n",
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" }\n",
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" ]\n",
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" }\n",
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" ]\n",
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"}\n",
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"\n",
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"r = requests.put(endpoint + \"/skillsets/\" + skillset_name, data=json.dumps(skillset_payload), headers=headers, params=params)\n",
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"pprint(r.json())"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Create an index\n",
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"index_payload = {\n",
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" \"name\": index_name,\n",
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" \"fields\": [\n",
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" {\n",
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" \"name\": \"id\",\n",
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" \"type\": \"Edm.String\",\n",
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" \"key\": \"true\",\n",
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" \"searchable\": \"true\",\n",
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" \"filterable\": \"false\",\n",
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" \"facetable\": \"false\",\n",
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" \"sortable\": \"true\"\n",
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" },\n",
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" {\n",
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" \"name\": \"content\",\n",
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" \"type\": \"Edm.String\",\n",
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" \"sortable\": \"false\",\n",
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" \"searchable\": \"true\",\n",
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" \"filterable\": \"false\",\n",
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" \"facetable\": \"false\"\n",
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" },\n",
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" {\n",
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" \"name\": \"languageCode\",\n",
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" \"type\": \"Edm.String\",\n",
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" \"searchable\": \"true\",\n",
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" \"filterable\": \"false\",\n",
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" \"facetable\": \"false\"\n",
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" },\n",
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" {\n",
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" \"name\": \"keyPhrases\",\n",
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" \"type\": \"Collection(Edm.String)\",\n",
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" \"searchable\": \"true\",\n",
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" \"filterable\": \"false\",\n",
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" \"facetable\": \"false\"\n",
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" },\n",
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" {\n",
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" \"name\": \"organizations\",\n",
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" \"type\": \"Collection(Edm.String)\",\n",
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" \"searchable\": \"true\",\n",
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" \"sortable\": \"false\",\n",
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" \"filterable\": \"false\",\n",
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" \"facetable\": \"false\"\n",
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" }\n",
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" ]\n",
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"}\n",
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"\n",
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"r = requests.put(endpoint + \"/indexes/\" + index_name, data=json.dumps(index_payload), headers=headers, params=params)\n",
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"pprint(r.json())"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create an indexer\n",
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"indexer_payload = {\n",
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" \"name\": indexer_name,\n",
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" \"dataSourceName\": datasource_name,\n",
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" \"targetIndexName\": index_name,\n",
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" \"skillsetName\": skillset_name,\n",
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" \"fieldMappings\" : [\n",
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" {\n",
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" \"sourceFieldName\" : \"metadata_storage_path\",\n",
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" \"targetFieldName\" : \"id\",\n",
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" \"mappingFunction\" :\n",
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" { \"name\" : \"base64Encode\" }\n",
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" },\n",
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" {\n",
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" \"sourceFieldName\" : \"content\",\n",
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" \"targetFieldName\" : \"content\"\n",
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" }\n",
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" ],\n",
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" \"outputFieldMappings\" :\n",
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" [\n",
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" {\n",
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" \"sourceFieldName\" : \"/document/organizations\",\n",
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" \"targetFieldName\" : \"organizations\"\n",
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" },\n",
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" {\n",
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" \"sourceFieldName\" : \"/document/pages/*/keyPhrases/*\",\n",
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" \"targetFieldName\" : \"keyPhrases\"\n",
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" },\n",
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" {\n",
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" \"sourceFieldName\": \"/document/languageCode\",\n",
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" \"targetFieldName\": \"languageCode\"\n",
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" }\n",
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" ],\n",
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" \"parameters\":\n",
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" {\n",
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" \"maxFailedItems\":-1,\n",
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" \"maxFailedItemsPerBatch\":-1,\n",
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" \"configuration\":\n",
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" {\n",
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" \"dataToExtract\": \"contentAndMetadata\",\n",
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" \"imageAction\": \"generateNormalizedImages\"\n",
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" }\n",
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" }\n",
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"}\n",
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"\n",
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"r = requests.put(endpoint + \"/indexers/\" + indexer_name, data=json.dumps(indexer_payload), headers=headers, params=params)\n",
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"pprint(r.json())\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Query the index for all fields\n",
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"r = requests.get(endpoint + \"/indexes/\" + index_name, headers=headers,params=params)\n",
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"print(json.dumps(r.json(), indent=1))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Query the index to return the contents of organizations\n",
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"#Note: Index creation may take time. If this step returns no data, wait a few minutes\n",
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"# and then try again\n",
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"r = requests.get(endpoint + \"/indexes/\" + index_name + \"/docs?&search=*&$select=organizations\", headers=headers, params=params)\n",
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"pprint(r.json())"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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