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-->\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\nThe Document AI feature referenced in this guide has been deprecated. Please see \u003Ca href=\"https://www.snowflake.com/en/developers/guides/create-a-document-processing-pipeline-with-ai-extract/\"\u003Ethis guide\u003C/a\u003E to learn more about the successor feature AI_EXTRACT.\u003C/strong\u003E<br>\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/docaiheader.png?v=1abfa748\" alt=\"assets/docaiheader.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWithin this Quickstart, we will walk through how to train and leverage a Document Extraction Model using Document AI. We will use this model to convert unstructured Inspection Reports into structured, easy-to-analyze rows and columns all within the Snowflake AI Data Cloud.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/overview\"\u003EDocument AI Documentation\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"/blog/arctic-tilt-compact-llm-advanced-document-ai/\"\u003ESnowflake's Artic-TILT LLM for Document AI Documentation\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Supported Snowflake \u003Ca href=\"https://docs.snowflake.com/en/user-guide/setup#browser-requirements\"\u003EBrowser\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EA Snowflake Account\n\u003Cul\u003E\u003Cli\u003EIf you do not have a Snowflake Account, please \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003E\u003Cstrong\u003Esign up for a Free 30 Day Trial Account\u003C/strong\u003E\u003C/a\u003E. When signing up, please make sure to select \u003Cstrong\u003EEnterprise\u003C/strong\u003E edition. You can choose any AWS or Azure \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-regions\"\u003ESnowflake Region\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EAfter registering, you will receive an email with an activation link and your Snowflake Account URL.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to Upload Files to a Stage\u003C/li\u003E\u003Cli\u003EHow to Train a Data Extraction Model using Document AI\u003C/li\u003E\u003Cli\u003EHow to Extract Data from Unstructured Documents\u003C/li\u003E\u003Cli\u003EHow to Flatten Semi-Structured Data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EDatabase, Warehouse, Role and Privileges\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EFor this section, please create a new Snowflake SQL Worksheet by Navigating to Projects -> Worksheets and clicking the \u003Cstrong\u003E+\u003C/strong\u003E button.\u003C/p\u003E\n","\u003Cp\u003EOnce your Worksheet is created, rename the Worksheet to \u003Cstrong\u003EDocumentAI Quickstart Setup\u003C/strong\u003E before moving on.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Setting up our Account\u003C/h3\u003E\n","\u003Cp\u003EPlease copy the following SQL and paste it in your Worksheet. Once pasted, run all of the queries at once using \u003Cstrong\u003ERun All\u003C/strong\u003E which will deploy the following within your account:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDatabase: \u003Ccode\u003Etb_doc_ai\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESchema: \u003Ccode\u003Eraw_doc\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EStages: \u003Ccode\u003Edoc_ai\u003C/code\u003E, \u003Ccode\u003Einspection_reports\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EWarehouse: \u003Ccode\u003Edoc_ai\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ERole: \u003Ccode\u003Etb_doc_ai\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EGrants and Privileges\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E-- assume the accountadmin role\nUSE ROLE accountadmin;\n\n-- create the tb_doc_ai database\nCREATE OR REPLACE DATABASE tb_doc_ai;\n\n-- create the raw_doc schema\nCREATE OR REPLACE SCHEMA tb_doc_ai.raw_doc;\n\n-- create the doc_ai stage\nCREATE OR REPLACE STAGE tb_doc_ai.raw_doc.doc_ai\n    DIRECTORY = (ENABLE = TRUE)\n    ENCRYPTION =  (TYPE = 'SNOWFLAKE_SSE');\n\n-- create the inspection_reports stage\nCREATE OR REPLACE STAGE tb_doc_ai.raw_doc.inspection_reports\n    DIRECTORY = (ENABLE = TRUE)\n    ENCRYPTION =  (TYPE = 'SNOWFLAKE_SSE');\n\n-- create the doc_ai warehouse\nCREATE OR REPLACE WAREHOUSE doc_ai\n    WAREHOUSE_SIZE = 'small'\n    WAREHOUSE_TYPE = 'standard'\n    AUTO_SUSPEND = 60\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE\nCOMMENT = 'document ai warehouse';\n\n-- create the tb_doc_ai role\nCREATE OR REPLACE ROLE tb_doc_ai;\n\n-- grant document ai privileges\nGRANT DATABASE ROLE SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR TO ROLE tb_doc_ai;\n\n-- grant doc_ai warehouse privileges\nGRANT USAGE, OPERATE ON WAREHOUSE doc_ai TO ROLE tb_doc_ai;\n\n-- grant tb_doc_ai database privileges\nGRANT ALL ON DATABASE tb_doc_ai TO ROLE tb_doc_ai;\nGRANT ALL ON SCHEMA tb_doc_ai.raw_doc TO ROLE tb_doc_ai;\nGRANT CREATE STAGE ON SCHEMA tb_doc_ai.raw_doc TO ROLE tb_doc_ai;\nGRANT CREATE SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE ON SCHEMA tb_doc_ai.raw_doc TO ROLE tb_doc_ai;\nGRANT ALL ON ALL STAGES IN SCHEMA tb_doc_ai.raw_doc TO ROLE tb_doc_ai;\n\n-- set my_user_var variable to equal the logged-in user\nSET my_user_var = (SELECT  '"' || CURRENT_USER() || '"' );\n\n-- grant the logged in user the doc_ai_role\nGRANT ROLE tb_doc_ai TO USER identifier($my_user_var);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 - Downloading and Unzipping our Inspection Reports\u003C/h3\u003E\n","\u003Cp\u003EFor use in a future step, we will need to download 2 separate .zip files that contain a \u003Cstrong\u003ETrain\u003C/strong\u003E (5 PDFs) and \u003Cstrong\u003EFull\u003C/strong\u003E (11 PDFs) set of Inspection Reports.\u003C/p\u003E\n","\u003Cp\u003EPlease leverage the two buttons below to download these .zip files. Once downloaded please unzip the files into a location that can easily be accessed in our next steps.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/tasty_bytes_extracting_insights_with_docai/assets/inspection_reports_train.zip\"\u003ETraining Set ZIP\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/tasty_bytes_extracting_insights_with_docai/assets/inspection_reports_full.zip\"\u003EFull Set ZIP\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 3 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EUploading our Inspection Reports to our Stage\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Ch3\u003EStep 1 - Navigating to our Stage\u003C/h3\u003E\n","\u003Cp\u003EWithin the Snowsight interface, navigate to Data -> Databases and then search for \u003Ccode\u003ETB_DOC_AI\u003C/code\u003E. From there navigate to the \u003Ccode\u003ERAW_DOC\u003C/code\u003E schema and the \u003Ccode\u003EINSPECTION_REPORTS\u003C/code\u003E stage.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/find_stage.png?v=1abfa748\" alt=\"assets/find_stage.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Uploading our Inspection Reports\u003C/h3\u003E\n","\u003Cp\u003EIn the top-right corner, click the \u003Cstrong\u003E+Files\u003C/strong\u003E button and either drop or browse to the unzipped Inspection Reports Full from Step 1. From there click \u003Cstrong\u003EUpload\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/upload_to_stage.png?v=1abfa748\" alt=\"assets/upload_to_stage.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will kick off our file upload and you will eventually see our Inspection Report PDF's within the Stage.\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003ETraining our Document AI Model\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Ch3\u003EStep 1 - Creating our Document AI Build\u003C/h3\u003E\n","\u003Cp\u003EWithin the Snowsight interface please switch your role to \u003Ccode\u003ETB_DOC_AI\u003C/code\u003E and, navigate to AI & ML -> Document AI. From there click the \u003Cstrong\u003E+ Build\u003C/strong\u003E button.\u003C/p\u003E\n","\u003Cp\u003EWithin the New Build screen, enter the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EBuild Name:\u003C/strong\u003E INSPECTION_REPORT_EXTRACTION\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EChoose Database:\u003C/strong\u003E TB_DOC_AI\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EChoose Schema:\u003C/strong\u003E RAW_DOC\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/new_build.png?v=1abfa748\" alt=\"assets/new_build.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Uploading our Training Documents\u003C/h3\u003E\n","\u003Cp\u003EAfter the build creation is successful, we will land in our Document AI Build Details window, where we can begin our document upload by clicking the \u003Cstrong\u003EUpload documents\u003C/strong\u003E button.\u003C/p\u003E\n","\u003Cp\u003EWithin the Upload documents screen drop or browse to the unzipped Training Inspection Reports from Step 1. From there click \u003Cstrong\u003EUpload\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/upload_to_build.png?v=1abfa748\" alt=\"assets/upload_to_build.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Specifying our Values and Questions\u003C/h3\u003E\n","\u003Cp\u003EOnce the upload process is complete, please click \u003Cstrong\u003EDefine value\u003C/strong\u003E so we can begin to specify value names and questions we want to ask the model.\u003C/p\u003E\n","\u003Cp\u003ETo begin defining our values to extract from our documents, click the \u003Cstrong\u003EAdd value\u003C/strong\u003E button in the right-hand window.\u003C/p\u003E\n","\u003Cp\u003EFrom here please enter the following set of Values and Questions one by one that are documented below. For each pair, please complete the following before clicking \u003Cstrong\u003EAdd value\u003C/strong\u003E to enter the next pair:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDid the Model extract the Value correctly?\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIf \u003Cstrong\u003EYes\u003C/strong\u003E - Click the check-box to indicate this value was extracted correctly.\u003C/li\u003E\u003Cli\u003EIf \u003Cstrong\u003ENo\u003C/strong\u003E - Delete the provided value and enter the correct value.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EValue | Question\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETRUCK_ID:\u003C/strong\u003E What is the Truck Identifier?\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDATE:\u003C/strong\u003E What is the Date?\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPIC_PRESENT:\u003C/strong\u003E Was the Person in charge present (Y or N)?\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFOOD_PROPER_TEMP:\u003C/strong\u003E Was the Food received at the proper temperature (Y or N)?\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVEHICLE_RUNS_WELL:\u003C/strong\u003E Did the Vehicle run and was it in well maintained condition?\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EFor demonstration purposes, we are only extracting 5 values however please feel free to add more.\u003C/p\u003E\n","\u003Cp\u003EPlease see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/optimizing-questions\"\u003EQuestion optimization for extracting information with Document AI\u003C/a\u003E for best practices.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/question_values.png?v=1abfa748\" alt=\"assets/question_values.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Reviewing our Test Documents\u003C/h3\u003E\n","\u003Cp\u003EAfter completing our initial document review in the previous step, we will now review the models' initial extraction results for our remaining test documents.\u003C/p\u003E\n","\u003Cp\u003ETo begin, please move on to the second document by clicking the arrow at the bottom of the screen.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/review_next.png?v=1abfa748\" alt=\"assets/review_next.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce the next document appears, please again conduct the following steps for each Value and Question pair.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDid the Model extract the Value correctly?\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIf \u003Cstrong\u003EYes\u003C/strong\u003E - Click the check-box to indicate this value was extracted correctly.\u003C/li\u003E\u003Cli\u003EIf \u003Cstrong\u003ENo\u003C/strong\u003E - Delete the provided value and enter the correct value.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/docai_review.gif?v=1abfa748\" alt=\"assets/docai_review.gif\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAfter completing review of all documents, please navigate back to the Document AI UI by click the arrow next to \u003Cstrong\u003EDocuments review\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/doc_review_arrow.png?v=1abfa748\" alt=\"assets/doc_review_arrow.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext navigate to the Build Details tab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/build_details.png?v=1abfa748\" alt=\"assets/build_details.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 5 - Training our Model\u003C/h3\u003E\n","\u003Cp\u003EUsing the \u003Cstrong\u003EModel accuracy\u003C/strong\u003E tile, we will now train our model by clicking the \u003Cstrong\u003ETrain model\u003C/strong\u003E button. Within the \u003Cstrong\u003EStart training\u003C/strong\u003E pop-up click \u003Cstrong\u003EStart Training\u003C/strong\u003E which will take around 20 minutes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/start_training.png?v=1abfa748\" alt=\"assets/start_training.png\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFor more on Document AI training time estimation please visit our \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/prepare-model-build#training-time-estimation\"\u003EDocument AI documentation\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhen training is complete, you will see \u003Cstrong\u003ETrained\u003C/strong\u003E next to the model name indicating it is ready to be leveraged.\u003C/p\u003E\n","\u003Ch3\u003EStep 6 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EUsing our Document AI Model against our Inspection Reports\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn the last section, we walked through training our Inspection Report Extraction model in Snowflake. We will now use that model to extract our values from the full set of documents we uploaded to our stage earlier.\u003C/p\u003E\n","\u003Cp\u003EPlease create a new Snowflake SQL Worksheet by Navigating to Projects -> Worksheets and clicking the \u003Cstrong\u003E+\u003C/strong\u003E button.\u003C/p\u003E\n","\u003Cp\u003EOnce your Worksheet is created, rename the Worksheet to \u003Cstrong\u003EDocumentAI Quickstart SQL\u003C/strong\u003E before moving on.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Setting our Context\u003C/h3\u003E\n","\u003Cp\u003EWithin our fresh SQL Worksheet, please execute the following three queries below to set our Role, Warehouse, Database and Schema context. Each of these will result in a \u003Ccode\u003EStatement executed successfully message.\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tb_doc_ai;\nUSE WAREHOUSE doc_ai;\nUSE DATABASE tb_doc_ai;\nUSE SCHEMA raw_doc;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 - Listing our Staged Inspection Reports\u003C/h3\u003E\n","\u003Cp\u003EUsing a \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/list\"\u003ELIST\u003C/a\u003E command, let's first take a look at the files that we staged earlier and want to use our extraction model against.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ELIST @inspection_reports;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/list_files.png?v=1abfa748\" alt=\"assets/list_files.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Extraction for a single Document\u003C/h3\u003E\n","\u003Cp\u003ETo begin our extraction, let's use our model and the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/classes/classification/methods/predict\"\u003EPREDICT\u003C/a\u003E method against one of those staged files by executing the next query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT inspection_report_extraction!PREDICT(GET_PRESIGNED_URL(@inspection_reports, '02.13.2022.5.pdf'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/one_file.png?v=1abfa748\" alt=\"assets/one_file.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Extraction for all Documents\u003C/h3\u003E\n","\u003Cp\u003EOur extracted object looks great, but before we begin to flatten this out let's create a raw table based on extraction from all our staged documents. Please execute the next query which may take around 2 minutes and result in a \u003Ccode\u003ETable IR_RAW successfully created.\u003C/code\u003E message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE TABLE ir_raw\nCOMMENT = '{"origin":"sf_sit-is", "name":"voc", "version":{"major":1, "minor":0}, "attributes":{"is_quickstart":1, "source":"sql", "vignette":"docai"}}'\nAS\nSELECT inspection_report_extraction!PREDICT(GET_PRESIGNED_URL(@inspection_reports, RELATIVE_PATH)) AS ir_object\nFROM DIRECTORY(@inspection_reports);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBefore moving on let's take a look at our raw, extracted results.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT * FROM ir_raw;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/select_all.png?v=1abfa748\" alt=\"assets/select_all.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 5 - Flattening our Extracted Object\u003C/h3\u003E\n","\u003Cp\u003EEverything that was extracted for each document is present in the \u003Ccode\u003Eir_object\u003C/code\u003E column. To make analysis easier, let's complete the Quickstart by using Snowflake's Semi-Structured Data Support to show how we can extract columns from our object.\u003C/p\u003E\n","\u003Cp\u003EIn production, a Data Engineer would typically flatten and promote this data downstream through our Medallion Architecture using objects like Dynamic Tables or Views.\u003C/p\u003E\n","\u003Cp\u003EPlease execute the next query in which we will use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/querying-semistructured#dot-notation\"\u003EDot Notation\u003C/a\u003E to:\n- Extract and normalize Date to a consistent format\n- Extract TRUCK_ID\n- Extract PIC_PRESENT and FOOD_PROPER_TEMP and convert Y to Pass, N to Fail and X to Not Observed\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    TO_DATE(REPLACE(ir_object:"DATE"[0].value::varchar,'-','/')) AS date,\n    ir_object:"TRUCK_ID"[0].value::varchar AS truck_id, \n    CASE\n        WHEN ir_object:"PIC_PRESENT"[0].value::varchar = 'Y' THEN 'Pass' -- convert Y to Pass\n        WHEN ir_object:"PIC_PRESENT"[0].value::varchar = 'N' THEN 'Fail' -- convert N to Fail\n        WHEN ir_object:"PIC_PRESENT"[0].value::varchar = 'X' THEN 'Not Observed' -- convert X to Not Observed\n        ELSE 'Not Observed'\n    END AS person_in_charge_present,\n    CASE\n        WHEN ir_object:"FOOD_PROPER_TEMP"[0].value::varchar = 'Y' THEN 'Pass' -- convert Y to Pass\n        WHEN ir_object:"FOOD_PROPER_TEMP"[0].value::varchar = 'N' THEN 'Fail' -- convert N to Fail\n        WHEN ir_object:"FOOD_PROPER_TEMP"[0].value::varchar = 'X' THEN 'Not Observed' -- convert X to Not Observed\n        ELSE 'Not Observed'\n    END AS food_proper_temp\nFROM ir_raw\nORDER BY truck_id;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/extract.png?v=1abfa748\" alt=\"assets/extract.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAmazing!\u003C/strong\u003E Exactly as desired we have moved from Unstructured PDF to easy to digest tabular results in the matter of minutes all within the Snowflake AI Data Cloud.\u003C/p\u003E\n","\u003Ch3\u003EStep 6 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFantastic work!\u003C/strong\u003E You have successfully completed the Extracting Insights with Document AI Quickstart walking through the process of moving PDF to tabular result.\u003C/p\u003E\n","\u003Ch3\u003EWhat you Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to Upload Files to a Stage\u003C/li\u003E\u003Cli\u003EHow to Train a Data Extraction Model using Document AI\u003C/li\u003E\u003Cli\u003EHow to Extract Data from Unstructured Documents\u003C/li\u003E\u003Cli\u003EHow to Flatten Semi-Structured Data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-tasty-bytes-extract-insights-from-unstructured-data-using-document-ai\"\u003ESource Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/tasty-bytes-introduction/\"\u003EPowered by Tasty Bytes - Quickstarts Table of Contents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/overview\"\u003EDocument AI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"(/blog/arctic-tilt-compact-llm-advanced-document-ai/)\"\u003ESnowflake's Artic-TILT LLM for Document AI Documentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Extracting Insights from Unstructured Data with Document AI","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n\u003E**Note:\n\u003EThe Document AI feature referenced in this guide has been deprecated. Please see [this guide](https://www.snowflake.com/en/developers/guides/create-a-document-processing-pipeline-with-ai-extract/) to learn more about the successor feature AI_EXTRACT.**\u003Cbr\u003E \n\n## Overview\n![assets/docaiheader.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/docaiheader.png?v=1abfa748)\n\n### Overview\nWithin this Quickstart, we will walk through how to train and leverage a Document Extraction Model using Document AI. We will use this model to convert unstructured Inspection Reports into structured, easy-to-analyze rows and columns all within the Snowflake AI Data Cloud.\n\n- [Document AI Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/overview)\n\n- [Snowflake's Artic-TILT LLM for Document AI Documentation](/blog/arctic-tilt-compact-llm-advanced-document-ai/)\n\n### Prerequisites\n- A Supported Snowflake [Browser](https://docs.snowflake.com/en/user-guide/setup#browser-requirements)\n- A Snowflake Account\n    - If you do not have a Snowflake Account, please [**sign up for a Free 30 Day Trial Account**](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides). When signing up, please make sure to select **Enterprise** edition. You can choose any AWS or Azure [Snowflake Region](https://docs.snowflake.com/en/user-guide/intro-regions).\n    - After registering, you will receive an email with an activation link and your Snowflake Account URL.\n    \n### What You Will Learn \n- How to Upload Files to a Stage\n- How to Train a Data Extraction Model using Document AI\n- How to Extract Data from Unstructured Documents\n- How to Flatten Semi-Structured Data\n\n## Database, Warehouse, Role and Privileges\n\n### Overview\nFor this section, please create a new Snowflake SQL Worksheet by Navigating to Projects -\u003E Worksheets and clicking the **+** button. \n\nOnce your Worksheet is created, rename the Worksheet to **DocumentAI Quickstart Setup** before moving on.\n\n### Step 1 - Setting up our Account\nPlease copy the following SQL and paste it in your Worksheet. Once pasted, run all of the queries at once using **Run All** which will deploy the following within your account:\n\n- Database: `tb_doc_ai`\n- Schema: `raw_doc`\n- Stages: `doc_ai`, `inspection_reports`\n- Warehouse: `doc_ai`\n- Role: `tb_doc_ai`\n- Grants and Privileges\n\n\n```\n-- assume the accountadmin role\nUSE ROLE accountadmin;\n\n-- create the tb_doc_ai database\nCREATE OR REPLACE DATABASE tb_doc_ai;\n\n-- create the raw_doc schema\nCREATE OR REPLACE SCHEMA tb_doc_ai.raw_doc;\n\n-- create the doc_ai stage\nCREATE OR REPLACE STAGE tb_doc_ai.raw_doc.doc_ai\n    DIRECTORY = (ENABLE = TRUE)\n    ENCRYPTION =  (TYPE = 'SNOWFLAKE_SSE');\n\n-- create the inspection_reports stage\nCREATE OR REPLACE STAGE tb_doc_ai.raw_doc.inspection_reports\n    DIRECTORY = (ENABLE = TRUE)\n    ENCRYPTION =  (TYPE = 'SNOWFLAKE_SSE');\n\n-- create the doc_ai warehouse\nCREATE OR REPLACE WAREHOUSE doc_ai\n    WAREHOUSE_SIZE = 'small'\n    WAREHOUSE_TYPE = 'standard'\n    AUTO_SUSPEND = 60\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE\nCOMMENT = 'document ai warehouse';\n\n-- create the tb_doc_ai role\nCREATE OR REPLACE ROLE tb_doc_ai;\n\n-- grant document ai privileges\nGRANT DATABASE ROLE SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR TO ROLE tb_doc_ai;\n\n-- grant doc_ai warehouse privileges\nGRANT USAGE, OPERATE ON WAREHOUSE doc_ai TO ROLE tb_doc_ai;\n\n-- grant tb_doc_ai database privileges\nGRANT ALL ON DATABASE tb_doc_ai TO ROLE tb_doc_ai;\nGRANT ALL ON SCHEMA tb_doc_ai.raw_doc TO ROLE tb_doc_ai;\nGRANT CREATE STAGE ON SCHEMA tb_doc_ai.raw_doc TO ROLE tb_doc_ai;\nGRANT CREATE SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE ON SCHEMA tb_doc_ai.raw_doc TO ROLE tb_doc_ai;\nGRANT ALL ON ALL STAGES IN SCHEMA tb_doc_ai.raw_doc TO ROLE tb_doc_ai;\n\n-- set my_user_var variable to equal the logged-in user\nSET my_user_var = (SELECT  '\"' || CURRENT_USER() || '\"' );\n\n-- grant the logged in user the doc_ai_role\nGRANT ROLE tb_doc_ai TO USER identifier($my_user_var);\n```\n\n### Step 2 - Downloading and Unzipping our Inspection Reports\nFor use in a future step, we will need to download 2 separate .zip files that contain a **Train** (5 PDFs) and **Full** (11 PDFs) set of Inspection Reports. \n\nPlease leverage the two buttons below to download these .zip files. Once downloaded please unzip the files into a location that can easily be accessed in our next steps.\n\n- [Training Set ZIP](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/tasty_bytes_extracting_insights_with_docai/assets/inspection_reports_train.zip)\n\n- [Full Set ZIP](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/tasty_bytes_extracting_insights_with_docai/assets/inspection_reports_full.zip)\n\n\n### Step 3 - Click Next --\u003E\n\n## Uploading our Inspection Reports to our Stage\n\n### Overview\n\n### Step 1 - Navigating to our Stage\nWithin the Snowsight interface, navigate to Data -\u003E Databases and then search for `TB_DOC_AI`. From there navigate to the `RAW_DOC` schema and the `INSPECTION_REPORTS` stage.\n\n![assets/find_stage.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/find_stage.png?v=1abfa748)\n\n### Step 2 - Uploading our Inspection Reports\nIn the top-right corner, click the **+Files** button and either drop or browse to the unzipped Inspection Reports Full from Step 1. From there click **Upload**.\n\n![assets/upload_to_stage.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/upload_to_stage.png?v=1abfa748)\n\nThis will kick off our file upload and you will eventually see our Inspection Report PDF's within the Stage.\n\n### Step 3 - Click Next --\u003E\n\n## Training our Document AI Model\n\n### Overview\n\n### Step 1 - Creating our Document AI Build\nWithin the Snowsight interface please switch your role to `TB_DOC_AI` and, navigate to AI & ML -\u003E Document AI. From there click the **+ Build** button.\n\nWithin the New Build screen, enter the following:\n- **Build Name:** INSPECTION_REPORT_EXTRACTION\n- **Choose Database:** TB_DOC_AI\n- **Choose Schema:** RAW_DOC\n\n![assets/new_build.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/new_build.png?v=1abfa748)\n\n### Step 2 - Uploading our Training Documents\nAfter the build creation is successful, we will land in our Document AI Build Details window, where we can begin our document upload by clicking the **Upload documents** button. \n\nWithin the Upload documents screen drop or browse to the unzipped Training Inspection Reports from Step 1. From there click **Upload**.\n\n![assets/upload_to_build.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/upload_to_build.png?v=1abfa748)\n\n\n### Step 3 - Specifying our Values and Questions\nOnce the upload process is complete, please click **Define value** so we can begin to specify value names and questions we want to ask the model.\n\nTo begin defining our values to extract from our documents, click the **Add value** button in the right-hand window. \n\nFrom here please enter the following set of Values and Questions one by one that are documented below. For each pair, please complete the following before clicking **Add value** to enter the next pair:\n\n**Did the Model extract the Value correctly?**\n- If **Yes** - Click the check-box to indicate this value was extracted correctly.\n- If **No** - Delete the provided value and enter the correct value.\n\n**Value | Question**\n- **TRUCK_ID:** What is the Truck Identifier?\n- **DATE:** What is the Date?\n- **PIC_PRESENT:** Was the Person in charge present (Y or N)?\n- **FOOD_PROPER_TEMP:** Was the Food received at the proper temperature (Y or N)?\n- **VEHICLE_RUNS_WELL:** Did the Vehicle run and was it in well maintained condition?\n\n\u003E \n\u003E For demonstration purposes, we are only extracting 5 values however please feel free to add more.\n\u003E \n\u003E Please see [Question optimization for extracting information with Document AI](https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/optimizing-questions) for best practices.\n\u003E\n\n![assets/question_values.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/question_values.png?v=1abfa748)\n\n### Step 4 - Reviewing our Test Documents\nAfter completing our initial document review in the previous step, we will now review the models' initial extraction results for our remaining test documents.\n\nTo begin, please move on to the second document by clicking the arrow at the bottom of the screen.\n\n![assets/review_next.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/review_next.png?v=1abfa748)\n\nOnce the next document appears, please again conduct the following steps for each Value and Question pair.\n\n**Did the Model extract the Value correctly?**\n- If **Yes** - Click the check-box to indicate this value was extracted correctly.\n- If **No** - Delete the provided value and enter the correct value.\n\n![assets/docai_review.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/docai_review.gif?v=1abfa748)\n\nAfter completing review of all documents, please navigate back to the Document AI UI by click the arrow next to **Documents review**\n\n![assets/doc_review_arrow.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/doc_review_arrow.png?v=1abfa748)\n\nNext navigate to the Build Details tab.\n\n![assets/build_details.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/build_details.png?v=1abfa748)\n\n### Step 5 - Training our Model\nUsing the **Model accuracy** tile, we will now train our model by clicking the **Train model** button. Within the **Start training** pop-up click **Start Training** which will take around 20 minutes.\n\n![assets/start_training.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/start_training.png?v=1abfa748)\n\n- For more on Document AI training time estimation please visit our [Document AI documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/prepare-model-build#training-time-estimation).\n\nWhen training is complete, you will see **Trained** next to the model name indicating it is ready to be leveraged.\n\n\n### Step 6 - Click Next --\u003E\n\n## Using our Document AI Model against our Inspection Reports\n\n### Overview\nIn the last section, we walked through training our Inspection Report Extraction model in Snowflake. We will now use that model to extract our values from the full set of documents we uploaded to our stage earlier.\n\nPlease create a new Snowflake SQL Worksheet by Navigating to Projects -\u003E Worksheets and clicking the **+** button.\n\nOnce your Worksheet is created, rename the Worksheet to **DocumentAI Quickstart SQL** before moving on.\n\n### Step 1 - Setting our Context\nWithin our fresh SQL Worksheet, please execute the following three queries below to set our Role, Warehouse, Database and Schema context. Each of these will result in a `Statement executed successfully message.`\n\n```\nUSE ROLE tb_doc_ai;\nUSE WAREHOUSE doc_ai;\nUSE DATABASE tb_doc_ai;\nUSE SCHEMA raw_doc;\n```\n\n### Step 2 - Listing our Staged Inspection Reports\nUsing a [LIST](https://docs.snowflake.com/en/sql-reference/sql/list) command, let's first take a look at the files that we staged earlier and want to use our extraction model against.\n\n```\nLIST @inspection_reports;\n```\n\n![assets/list_files.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/list_files.png?v=1abfa748)\n\n\n### Step 3 - Extraction for a single Document\nTo begin our extraction, let's use our model and the [PREDICT](https://docs.snowflake.com/en/sql-reference/classes/classification/methods/predict) method against one of those staged files by executing the next query.\n\n```\nSELECT inspection_report_extraction!PREDICT(GET_PRESIGNED_URL(@inspection_reports, '02.13.2022.5.pdf'));\n```\n\n![assets/one_file.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/one_file.png?v=1abfa748)\n\n\n### Step 4 - Extraction for all Documents\nOur extracted object looks great, but before we begin to flatten this out let's create a raw table based on extraction from all our staged documents. Please execute the next query which may take around 2 minutes and result in a `Table IR_RAW successfully created.` message.\n\n```\nCREATE OR REPLACE TABLE ir_raw\nCOMMENT = '{\"origin\":\"sf_sit-is\", \"name\":\"voc\", \"version\":{\"major\":1, \"minor\":0}, \"attributes\":{\"is_quickstart\":1, \"source\":\"sql\", \"vignette\":\"docai\"}}'\nAS\nSELECT inspection_report_extraction!PREDICT(GET_PRESIGNED_URL(@inspection_reports, RELATIVE_PATH)) AS ir_object\nFROM DIRECTORY(@inspection_reports);\n```\n\nBefore moving on let's take a look at our raw, extracted results.\n\n```\nSELECT * FROM ir_raw;\n```\n\n![assets/select_all.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/select_all.png?v=1abfa748)\n\n\n### Step 5 - Flattening our Extracted Object\nEverything that was extracted for each document is present in the `ir_object` column. To make analysis easier, let's complete the Quickstart by using Snowflake's Semi-Structured Data Support to show how we can extract columns from our object. \n\nIn production, a Data Engineer would typically flatten and promote this data downstream through our Medallion Architecture using objects like Dynamic Tables or Views.\n\nPlease execute the next query in which we will use [Dot Notation](https://docs.snowflake.com/en/user-guide/querying-semistructured#dot-notation) to:\n    - Extract and normalize Date to a consistent format\n    - Extract TRUCK_ID\n    - Extract PIC_PRESENT and FOOD_PROPER_TEMP and convert Y to Pass, N to Fail and X to Not Observed\n\n```\nSELECT\n    TO_DATE(REPLACE(ir_object:\"DATE\"[0].value::varchar,'-','/')) AS date,\n    ir_object:\"TRUCK_ID\"[0].value::varchar AS truck_id, \n    CASE\n        WHEN ir_object:\"PIC_PRESENT\"[0].value::varchar = 'Y' THEN 'Pass' -- convert Y to Pass\n        WHEN ir_object:\"PIC_PRESENT\"[0].value::varchar = 'N' THEN 'Fail' -- convert N to Fail\n        WHEN ir_object:\"PIC_PRESENT\"[0].value::varchar = 'X' THEN 'Not Observed' -- convert X to Not Observed\n        ELSE 'Not Observed'\n    END AS person_in_charge_present,\n    CASE\n        WHEN ir_object:\"FOOD_PROPER_TEMP\"[0].value::varchar = 'Y' THEN 'Pass' -- convert Y to Pass\n        WHEN ir_object:\"FOOD_PROPER_TEMP\"[0].value::varchar = 'N' THEN 'Fail' -- convert N to Fail\n        WHEN ir_object:\"FOOD_PROPER_TEMP\"[0].value::varchar = 'X' THEN 'Not Observed' -- convert X to Not Observed\n        ELSE 'Not Observed'\n    END AS food_proper_temp\nFROM ir_raw\nORDER BY truck_id;\n```\n\n![assets/extract.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-extracting-insights-with-docai/extract.png?v=1abfa748)\n\n**Amazing!** Exactly as desired we have moved from Unstructured PDF to easy to digest tabular results in the matter of minutes all within the Snowflake AI Data Cloud.\n\n### Step 6 - Click Next --\u003E\n\n## Conclusion and Resources\n\n**Fantastic work!** You have successfully completed the Extracting Insights with Document AI Quickstart walking through the process of moving PDF to tabular result. \n\n### What you Learned\n- How to Upload Files to a Stage\n- How to Train a Data Extraction Model using Document AI\n- How to Extract Data from Unstructured Documents\n- How to Flatten Semi-Structured Data\n\n### Related Resources\n- [Source Code on GitHub](https://github.com/Snowflake-Labs/sfguide-tasty-bytes-extract-insights-from-unstructured-data-using-document-ai)\n- [Powered by Tasty Bytes - Quickstarts Table of Contents](/en/developers/guides/tasty-bytes-introduction/)\n- [Document AI](https://docs.snowflake.com/en/user-guide/snowflake-cortex/document-ai/overview)\n- [Snowflake's Artic-TILT LLM for Document AI Documentation]((/blog/arctic-tilt-compact-llm-advanced-document-ai/))\n\n  \n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-fb666fd743","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-b5fb4d57aa",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c5e9ebaf51","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-03-18",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-107788b31e","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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