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------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EDocuments contain information teams need in two forms: selected fields for operational workflows and searchable content for research and review. Snowflake Cortex AI Functions let you process both from SQL.\u003C/p\u003E\n","\u003Cp\u003EThis lab has two independent tracks, each in its own notebook:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPart A, mortgage (~25 minutes):\u003C/strong\u003E Classify four synthetic mortgage-document PDFs with \u003Ccode\u003EAI_CLASSIFY\u003C/code\u003E, extract fields from the supported page types with \u003Ccode\u003EAI_EXTRACT\u003C/code\u003E, and review each result.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPart B, greenhouse report (~20 minutes):\u003C/strong\u003E Parse a greenhouse gas report with \u003Ccode\u003EAI_PARSE_DOCUMENT\u003C/code\u003E in AGENTIC mode, index its text, visual summaries, and chart details with Cortex Search, and answer a question about regional CO₂ emissions with \u003Ccode\u003EAI_COMPLETE\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with SQL, including SELECT statements, joins, and table creation\u003C/li\u003E\u003Cli\u003EBasic understanding of Snowflake stages and JSON field access\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to classify documents and route each supported type to an extraction schema\u003C/li\u003E\u003Cli\u003EHow to extract business fields from PDFs and review blank or placeholder values\u003C/li\u003E\u003Cli\u003EHow to parse reports in AGENTIC mode and index text, visual summaries, and chart details\u003C/li\u003E\u003Cli\u003EHow to answer a question from retrieved passages, with citations to the page and source type\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E with access to \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql\"\u003ECortex AI Functions\u003C/a\u003E, AI_PARSE_DOCUMENT in AGENTIC mode, and Cortex Search; confirm approved \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cross-region-inference\"\u003Ecross-region inference\u003C/a\u003E before starting\u003C/li\u003E\u003Cli\u003EA role with privileges to create a database, schema, internal stages, tables, and a Cortex Search service, plus access to a running warehouse\u003C/li\u003E\u003Cli\u003EAccess to \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-in-workspaces/notebooks-in-workspaces-overview\"\u003ESnowflake Notebooks in Workspaces\u003C/a\u003E, or a SQL worksheet\u003C/li\u003E\u003Cli\u003EThe \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/mortgage_reports.zip\"\u003Emortgage sample PDFs\u003C/a\u003E (\u003Ccode\u003Emortgage_reports.zip\u003C/code\u003E) and \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/greenhouse_gas_report_2024.pdf\"\u003Egreenhouse_gas_report_2024.pdf\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EPart A: one result row per mortgage PDF, with its document type, extracted fields, processing status, and review reason\u003C/li\u003E\u003Cli\u003EPart B: a Cortex Search index over one greenhouse gas report, and a cited answer about annual regional CO₂ emissions between 2020 and 2023\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe two tracks are independent. Parsing is not a prerequisite for direct-file extraction. This guide does not use native extraction citations or make loan-approval or borrower-eligibility decisions.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5 minutes\u003C/p\u003E\n","\u003Ch3\u003EImport the Notebooks\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EDownload \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/vhol_document_ai_mortgage.ipynb\"\u003Evhol_document_ai_mortgage.ipynb\u003C/a\u003E and \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/vhol_document_ai_greenhouse_reports.ipynb\"\u003Evhol_document_ai_greenhouse_reports.ipynb\u003C/a\u003E. For GitHub-hosted notebooks, select \u003Cstrong\u003EDownload raw file\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EIn Snowsight, go to \u003Cstrong\u003EProjects > Workspaces\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Add new > Upload files\u003C/strong\u003E and select each downloaded \u003Ccode\u003E.ipynb\u003C/code\u003E file.\u003C/li\u003E\u003Cli\u003EOpen a notebook and select an available warehouse and a role with the privileges listed above.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ERun the first setup cell in each notebook to set the lab context. Each track is self-contained, so you can run either one first. Cleanup is a separate manual step at the end of this guide.\u003C/p\u003E\n","\u003Ch3\u003ESet the Lab Context\u003C/h3\u003E\n","\u003Cp\u003EEach notebook starts with the same context cell. In a shared event account, use the participant-specific database or schema name assigned by the host in place of these defaults.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS VHOL_DOCUMENT_AI_DB;\nCREATE SCHEMA IF NOT EXISTS VHOL_DOCUMENT_AI_DB.DOCUMENT_AI;\n\nUSE DATABASE VHOL_DOCUMENT_AI_DB;\nUSE SCHEMA DOCUMENT_AI;\n\nSELECT CURRENT_ROLE(), CURRENT_DATABASE(), CURRENT_SCHEMA(), CURRENT_WAREHOUSE();\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClassify Mortgage Documents\u003C/h2\u003E\n","\u003Cp\u003EDuration: 25 minutes\u003C/p\u003E\n","\u003Cp\u003EThis part uses \u003Ccode\u003Evhol_document_ai_mortgage.ipynb\u003C/code\u003E. Each step below matches a cell in that notebook.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Stage\u003C/h3\u003E\n","\u003Cp\u003ECreate an internal stage for the mortgage documents. Directory tables let the workflow discover the uploaded files. Use \u003Ccode\u003ESNOWFLAKE_SSE\u003C/code\u003E encryption for the direct-file AI calls; see \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-stage\"\u003ECREATE STAGE\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_classify#limitations\"\u003EAI_CLASSIFY limitations\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE STAGE IF NOT EXISTS MORTGAGE_DOCS\n    ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')\n    DIRECTORY = (ENABLE = TRUE);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUpload the Sample PDFs\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EDownload \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/mortgage_reports.zip\"\u003Emortgage_reports.zip\u003C/a\u003E and unzip it. Upload the individual PDFs, not the ZIP archive. For GitHub-hosted files, select \u003Cstrong\u003EDownload raw file\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EIn Snowsight, go to \u003Cstrong\u003EIngestion > Add Data > Load files into a Stage\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect those four PDFs and choose \u003Cstrong\u003EVHOL_DOCUMENT_AI_DB > DOCUMENT_AI > MORTGAGE_DOCS\u003C/strong\u003E, then upload them.\u003C/li\u003E\u003C/ol\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFile\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDocument\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExpected route\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E01.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDeclaration of Citizenship / Household Status\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECitizenship declaration\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E02.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAffidavit of Non-Taxpayer / Household Schedule\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENon taxpayer affidavit\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E03.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EResidential Loan Application, pages 1 and 2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAssets and transaction details\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E04.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESettlement Charges / Disbursement Worksheet\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESettlement charges\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EKeep the filenames unchanged. The four PDFs are synthetic test documents with fictional names, numbers, and signatures.\u003C/p\u003E\n","\u003Cp\u003ESee \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-local-file-system-stage-ui\"\u003EStaging files using Snowsight\u003C/a\u003E for the upload workflow. Then refresh the directory table and confirm the files are visible:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER STAGE MORTGAGE_DOCS REFRESH;\n\nLIST @MORTGAGE_DOCS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESelect the PDFs\u003C/h3\u003E\n","\u003Cp\u003EThe query keeps every PDF on the stage. Each file is classified, and files that do not match a route are reported as \u003Ccode\u003EOther\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE MORTGAGE_FILES AS\nSELECT\n    relative_path,\n    SPLIT_PART(relative_path, '/', -1) AS filename,\n    size AS file_size_bytes,\n    last_modified\nFROM DIRECTORY(@MORTGAGE_DOCS)\nWHERE relative_path ILIKE '%.pdf';\n\nSELECT * FROM MORTGAGE_FILES ORDER BY relative_path;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDefine the Routes\u003C/h3\u003E\n","\u003Cp\u003EDefine one route for each supported page type. The \u003Ccode\u003EOther\u003C/code\u003E route has no extraction schema, so those documents go to review without an \u003Ccode\u003EAI_EXTRACT\u003C/code\u003E call. The \u003Ccode\u003ECitizenship declaration\u003C/code\u003E route extracts three fields from the declaration form.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE MORTGAGE_ROUTES (\n    document_type VARCHAR,\n    category_description VARCHAR,\n    response_format VARIANT\n);\n\nINSERT INTO MORTGAGE_ROUTES\nSELECT\n    'Non taxpayer affidavit',\n    'Affidavit of Non-Taxpayer with selected people, roles, years not filed, county, and notarization.',\n    PARSE_JSON($${\n  "county": "What county is entered near the top of the affidavit? Return an empty string if blank.",\n  "affidavit_date": "What date does the notary acknowledgment in Section F give? Return YYYY-MM-DD, or an empty string if blank. Do not use the commission expiration date.",\n  "commission_expiration_date": "What date is written as Commission expiration in the notary section? The date is in MM/DD/YYYY format. Return YYYY-MM-DD, or an empty string if blank."\n}$$)\nUNION ALL\nSELECT\n    'Assets and transaction details',\n    'Residential loan application with Section VII assets and liabilities and Section VIII Details of Transaction, including purchase price, loan proceeds, and cash to close.',\n    PARSE_JSON($${\n  "total_assets": "What amount is written for TOTAL ASSETS in Section VII? Do not use Liquid Assets Subtotal. Return the written amount, or an empty string if blank.",\n  "total_liabilities": "What amount is written for TOTAL LIABILITIES in Section VII, in the Unpaid balance column? Return the written amount, or an empty string if blank.",\n  "purchase_price": "What amount is written for line a, Purchase price, in Section VIII, Details of Transaction? Return the written amount, or an empty string if blank.",\n  "loan_proceeds": "What amount is written for line d, Loan proceeds, in Section VIII? Read the written amount; do not calculate it. Return an empty string if blank.",\n  "cash_to_close": "What amount is written for line g, Cash to close, in Section VIII? Read the written amount; do not calculate it. Return an empty string if blank."\n}$$)\nUNION ALL\nSELECT\n    'Settlement charges',\n    'Settlement charges worksheet listing fees by line number and line 1400 borrower and seller settlement charge totals.',\n    PARSE_JSON($${\n  "borrower_settlement_charges": "What amount is written on line 1400, Total Settlement Charges, in the Borrower paid column? Read the written total; do not calculate it. Return an empty string if blank.",\n  "seller_settlement_charges": "What amount is written on line 1400, Total Settlement Charges, in the Seller paid column? Read the written total; do not calculate it. Return an empty string if blank.",\n  "appraisal_fee": "What appraisal fee is written on line 803? Return the amount shown, or an empty string if blank.",\n  "credit_report_fee": "What credit report fee is written on line 804? Return the amount shown, or an empty string if blank."\n}$$)\nUNION ALL\nSELECT\n    'Citizenship declaration',\n    'Declaration of Citizenship / Household Status form with numbered blocks 1, 2, and 3 in Section II for citizenship status, followed by a signature and date.',\n    PARSE_JSON($${\n  "declarant_name": "What name is printed after I, at the top of the declaration? Return the printed name as written, or an empty string if blank.",\n  "citizenship_status": "Which status block is checked? Return exactly one of: U.S. citizen; Noncitizen with eligible immigration status; Not contending eligible immigration status. Return an empty string if no block is checked.",\n  "signature_date": "What date is written next to the signature? The date on this form is in MM/DD/YYYY format. Return YYYY-MM-DD, or an empty string if blank."\n}$$)\nUNION ALL\nSELECT\n    'Other',\n    'Any other document, including notices and disclosures such as the Right to Financial Privacy Act notice or VA and HUD/FHA borrower notices, government program agreement summaries, and property insurance rating or underwriting forms. Choose Other for notice or disclosure text even when it appears alongside a loan application.',\n    NULL::VARIANT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EClassify Each PDF\u003C/h3\u003E\n","\u003Cp\u003EBuild the category list from the routes and classify each PDF. The final \u003Ccode\u003ETRUE\u003C/code\u003E returns error details in the result instead of failing the query. Compare the assigned types with the document headings. \u003Ccode\u003E01.pdf\u003C/code\u003E should route to \u003Ccode\u003ECitizenship declaration\u003C/code\u003E, \u003Ccode\u003E02.pdf\u003C/code\u003E to \u003Ccode\u003ENon taxpayer affidavit\u003C/code\u003E, \u003Ccode\u003E03.pdf\u003C/code\u003E to \u003Ccode\u003EAssets and transaction details\u003C/code\u003E, and \u003Ccode\u003E04.pdf\u003C/code\u003E to \u003Ccode\u003ESettlement charges\u003C/code\u003E. The filename is not supplied to the classifier as evidence.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE MORTGAGE_CLASSIFIED AS\nWITH categories AS (\n    SELECT ARRAY_AGG(\n        OBJECT_CONSTRUCT('label', document_type, 'description', category_description)\n    ) WITHIN GROUP (ORDER BY document_type) AS categories\n    FROM MORTGAGE_ROUTES\n),\nclassified AS (\n    SELECT\n        f.relative_path,\n        f.filename,\n        AI_CLASSIFY(\n            TO_FILE('@MORTGAGE_DOCS', f.relative_path),\n            c.categories,\n            {\n                'task_description': 'Classify the supplied document page by its visible headings and sections. Distinguish loan application pages by section content. Use Other when none of the defined form types matches. Do not use the filename as evidence.',\n                'output_mode': 'single'\n            },\n            TRUE  -- returns error details instead of failing the query\n        ) AS raw_classification\n    FROM MORTGAGE_FILES AS f\n    CROSS JOIN categories AS c\n)\nSELECT\n    relative_path,\n    filename,\n    NULLIF(raw_classification:value:labels[0]::VARCHAR, 'null') AS document_type,\n    NULLIF(NULLIF(TRIM(raw_classification:error::VARCHAR), ''), 'null') AS classification_error\nFROM classified;\n\nSELECT filename, document_type, classification_error\nFROM MORTGAGE_CLASSIFIED\nORDER BY filename;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/document-intelligence-ai-functions/classification.png?v=9ad97d0a\" alt=\"Classification results showing the four source filenames, assigned document types, and any processing errors\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EExtract Business Fields\u003C/h3\u003E\n","\u003Cp\u003ERoute each classified document to its extraction schema and call \u003Ccode\u003EAI_EXTRACT\u003C/code\u003E on the original PDF. Unsupported types and classification errors are skipped.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE MORTGAGE_EXTRACTED AS\nWITH extraction_raw AS (\n    SELECT\n        c.relative_path,\n        c.filename,\n        c.document_type,\n        AI_EXTRACT(\n            file => TO_FILE('@MORTGAGE_DOCS', c.relative_path),\n            responseFormat => r.response_format\n        ) AS raw_extraction\n    FROM MORTGAGE_CLASSIFIED AS c\n    JOIN MORTGAGE_ROUTES AS r ON c.document_type = r.document_type\n    WHERE c.classification_error IS NULL\n      AND r.response_format IS NOT NULL\n)\nSELECT\n    relative_path,\n    filename,\n    document_type,\n    raw_extraction:response AS extracted_data,\n    NULLIF(NULLIF(TRIM(raw_extraction:error::VARCHAR), ''), 'null') AS extraction_error\nFROM extraction_raw;\n\nSELECT filename, document_type, extracted_data, extraction_error\nFROM MORTGAGE_EXTRACTED\nORDER BY filename;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EReview the Results\u003C/h3\u003E\n","\u003Cp\u003EEach document gets one processing status. A field is flagged for review when it is blank, null, or a placeholder such as \u003Ccode\u003Eunknown\u003C/code\u003E. A citizenship status outside the three allowed options is also flagged. This check does not verify that values are correct.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EEXTRACTED\u003C/code\u003E: the call succeeded, no field is blank, and any citizenship status is one of the three allowed options\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EFIELD_REVIEW\u003C/code\u003E: at least one field is blank, or a citizenship status is outside the three allowed options\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EUNSUPPORTED_DOCUMENT\u003C/code\u003E: no extraction schema exists for this type\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ECLASSIFICATION_ERROR\u003C/code\u003E or \u003Ccode\u003EEXTRACTION_ERROR\u003C/code\u003E: the call returned an error\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE MORTGAGE_RESULTS AS\nWITH blank_fields AS (\n    SELECT\n        e.relative_path,\n        LISTAGG(f.key, ', ') WITHIN GROUP (ORDER BY f.key) AS blank_fields\n    FROM MORTGAGE_EXTRACTED AS e,\n         LATERAL FLATTEN(INPUT => e.extracted_data) AS f\n    WHERE NULLIF(TRIM(f.value::VARCHAR), '') IS NULL\n       OR LOWER(TRIM(f.value::VARCHAR)) IN ('null', 'none', 'unknown', 'n/a')\n    GROUP BY e.relative_path\n)\nSELECT\n    c.filename,\n    c.document_type,\n    CASE\n        WHEN c.classification_error IS NOT NULL THEN 'CLASSIFICATION_ERROR'\n        WHEN r.response_format IS NULL THEN 'UNSUPPORTED_DOCUMENT'\n        WHEN e.extraction_error IS NOT NULL OR e.extracted_data IS NULL THEN 'EXTRACTION_ERROR'\n        WHEN b.blank_fields IS NOT NULL THEN 'FIELD_REVIEW'\n        WHEN c.document_type = 'Citizenship declaration'\n             AND NOT COALESCE(\n                 (e.extracted_data:citizenship_status::VARCHAR) IN (\n                     'U.S. citizen',\n                     'Noncitizen with eligible immigration status',\n                     'Not contending eligible immigration status'\n                 ), FALSE)\n            THEN 'FIELD_REVIEW'\n        ELSE 'EXTRACTED'\n    END AS processing_status,\n    b.blank_fields,\n    e.extracted_data\nFROM MORTGAGE_CLASSIFIED AS c\nLEFT JOIN MORTGAGE_ROUTES AS r ON c.document_type = r.document_type\nLEFT JOIN MORTGAGE_EXTRACTED AS e ON c.relative_path = e.relative_path\nLEFT JOIN blank_fields AS b ON c.relative_path = b.relative_path;\n\nSELECT filename, document_type, processing_status, blank_fields, extracted_data\nFROM MORTGAGE_RESULTS\nORDER BY processing_status, filename;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003EEXTRACTED\u003C/code\u003E means the call succeeded and no field is blank. It does not establish accuracy, completeness, or borrower eligibility. Check sample values against the source pages.\u003C/p\u003E\n","\u003Ch3\u003ECheck Values Against the Source PDFs\u003C/h3\u003E\n","\u003Cp\u003EThe query below returns the extracted values as text. Compare them with the source pages. The values in the table are the ones read from the sample PDFs.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESource\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EField\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003ESource value\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E03.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESection VII total assets\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E355,000.00\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E03.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESection VII total liabilities, unpaid balance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E27,950.00\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E03.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESection VIII line a, purchase price\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E480,000.00\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E03.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESection VIII line d, loan proceeds\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E384,000.00\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E03.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESection VIII line g, cash to close\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E96,963.68\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E04.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELine 1400, borrower paid\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E11,963.68\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E04.pdf\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELine 1400, seller paid\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E33,275.00\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    filename,\n    extracted_data:total_assets::VARCHAR AS total_assets,\n    extracted_data:total_liabilities::VARCHAR AS total_liabilities,\n    extracted_data:purchase_price::VARCHAR AS purchase_price,\n    extracted_data:loan_proceeds::VARCHAR AS loan_proceeds,\n    extracted_data:cash_to_close::VARCHAR AS cash_to_close,\n    extracted_data:borrower_settlement_charges::VARCHAR AS borrower_settlement_charges,\n    extracted_data:seller_settlement_charges::VARCHAR AS seller_settlement_charges\nFROM MORTGAGE_RESULTS\nWHERE filename IN ('03.pdf', '04.pdf');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/document-intelligence-ai-functions/extraction-1.png?v=9ad97d0a\" alt=\"Extraction results for 03.pdf showing total assets of $355,000.00, total liabilities of $27,950.00, and purchase price of $480,000.00\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/document-intelligence-ai-functions/extraction-2.png?v=9ad97d0a\" alt=\"Extraction results for 04.pdf showing borrower settlement charges of $11,963.68 and seller settlement charges of $33,275.00\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry it:\u003C/strong\u003E Refine the description for the assets-and-transaction route, then re-run the classification cell and compare the results. Re-running model calls can incur additional usage.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESearch Greenhouse Reports\u003C/h2\u003E\n","\u003Cp\u003EDuration: 20 minutes\u003C/p\u003E\n","\u003Cp\u003EThis part uses \u003Ccode\u003Evhol_document_ai_greenhouse_reports.ipynb\u003C/code\u003E. Each step below matches a cell in that notebook. The source is a single synthetic report, \u003Ccode\u003Egreenhouse_gas_report_2024.pdf\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Stage\u003C/h3\u003E\n","\u003Cp\u003ECreate an internal stage for the greenhouse report. Directory tables let the workflow discover the uploaded file.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE STAGE IF NOT EXISTS GHG_REPORTS\n    ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')\n    DIRECTORY = (ENABLE = TRUE);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUpload the PDF\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EDownload \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/greenhouse_gas_report_2024.pdf\"\u003Egreenhouse_gas_report_2024.pdf\u003C/a\u003E. For GitHub-hosted files, select \u003Cstrong\u003EDownload raw file\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EIn Snowsight, go to \u003Cstrong\u003EIngestion > Add Data > Load files into a Stage\u003C/strong\u003E and upload the PDF to \u003Ccode\u003EGHG_REPORTS\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ERefresh the directory table and confirm the file is listed:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER STAGE GHG_REPORTS REFRESH;\n\nLIST @GHG_REPORTS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERegister the Report\u003C/h3\u003E\n","\u003Cp\u003EThe report year is read from the filename. The query returns one row for the 2024 report.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE GHG_FILES AS\nSELECT\n    relative_path,\n    SPLIT_PART(relative_path, '/', -1) AS filename,\n    REGEXP_SUBSTR(SPLIT_PART(relative_path, '/', -1), '(20[0-9]{2})', 1, 1, 'e', 1) AS report_year,\n    size AS file_size_bytes,\n    last_modified\nFROM DIRECTORY(@GHG_REPORTS)\nWHERE relative_path ILIKE '%.pdf';\n\nSELECT * FROM GHG_FILES ORDER BY report_year;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EParse the Report with AGENTIC Mode\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003EAI_PARSE_DOCUMENT\u003C/code\u003E in AGENTIC mode returns page text and a visual interpretation for each image. \u003Ccode\u003Epage_split\u003C/code\u003E returns one record per page. The final \u003Ccode\u003ETRUE\u003C/code\u003E returns error details in the result instead of failing the query.\u003C/p\u003E\n","\u003Cp\u003EThis step calls the model and can take a few minutes.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE GHG_PARSED AS\nSELECT\n    relative_path,\n    filename,\n    report_year,\n    AI_PARSE_DOCUMENT(\n        TO_FILE('@GHG_REPORTS', relative_path),\n        {'mode': 'AGENTIC', 'page_split': TRUE},\n        TRUE\n    ) AS raw_parse\nFROM GHG_FILES;\n\nSELECT\n    filename,\n    report_year,\n    ARRAY_SIZE(raw_parse:value:pages) AS returned_pages,\n    raw_parse:error AS call_error,\n    raw_parse:value:error AS document_error\nFROM GHG_PARSED\nORDER BY report_year;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESplit Pages, Visual Summaries, and Chart Details\u003C/h3\u003E\n","\u003Cp\u003EThe parsed output is split into one row per page and one row per image. Images that return an error are skipped. Each image's structured details are kept so the chart series can be indexed.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE GHG_PAGES AS\nSELECT\n    r.relative_path,\n    r.filename,\n    r.report_year,\n    p.value:index::NUMBER + 1 AS pdf_page_number,\n    p.value:content::VARCHAR AS page_content,\n    p.value:images AS images\nFROM GHG_PARSED AS r,\n     LATERAL FLATTEN(INPUT => r.raw_parse:value:pages) AS p\nWHERE NULLIF(r.raw_parse:error::VARCHAR, 'null') IS NULL;\n\nCREATE OR REPLACE TEMP TABLE GHG_VISUALS AS\nSELECT\n    p.relative_path,\n    p.filename,\n    p.report_year,\n    p.pdf_page_number,\n    i.index AS image_index,\n    i.value:results:image_type::VARCHAR AS image_type,\n    i.value:results:summary::VARCHAR AS summary,\n    i.value:results:details AS details\nFROM GHG_PAGES AS p,\n     LATERAL FLATTEN(INPUT => p.images) AS i\nWHERE NULLIF(TRIM(i.value:results:summary::VARCHAR), '') IS NOT NULL\n  AND COALESCE(LOWER(i.value:results:description::VARCHAR), '') <> 'error';\n\nSELECT report_year, COUNT(*) AS pages FROM GHG_PAGES GROUP BY report_year ORDER BY report_year;\n\nSELECT report_year, COUNT(*) AS visuals_with_summary FROM GHG_VISUALS GROUP BY report_year ORDER BY report_year;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EList All Extracted Images\u003C/h3\u003E\n","\u003Cp\u003EThis lists every image the parse returned, with its page, image ID, type, summary, and structured details. Images without a summary or details show NULL in those columns.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    p.pdf_page_number,\n    i.index AS image_index,\n    i.value:id::VARCHAR AS image_id,\n    i.value:results:image_type::VARCHAR AS image_type,\n    i.value:results:summary::VARCHAR AS summary,\n    i.value:results:details AS details\nFROM GHG_PAGES AS p,\n     LATERAL FLATTEN(INPUT => p.images) AS i\nORDER BY p.pdf_page_number, i.index;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECheck the Regional Figure on Page 2\u003C/h3\u003E\n","\u003Cp\u003EPage 2 has the regional CO₂ figure. The query lists each image on that page with its summary and chart details. Values read from the chart are estimates. If the details do not include the values you need, the answer relies on the printed text instead.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    pdf_page_number,\n    image_index,\n    image_type,\n    summary,\n    LEFT(TO_JSON(details), 1200) AS details_preview\nFROM GHG_VISUALS\nWHERE report_year = '2024'\n  AND pdf_page_number = 2\nORDER BY image_index;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBuild the Search Content\u003C/h3\u003E\n","\u003Cp\u003EIndex three kinds of content: the page text, the visual summaries, and the chart details. Each chunk repeats the report year, PDF page, and content type, so a search can be traced back to its page.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE GHG_SEARCH_UNITS AS\nSELECT relative_path, filename, report_year, pdf_page_number,\n       'text'::VARCHAR AS content_type,\n       page_content AS body\nFROM GHG_PAGES\nWHERE NULLIF(TRIM(page_content), '') IS NOT NULL\nUNION ALL\nSELECT relative_path, filename, report_year, pdf_page_number,\n       'visual_summary'::VARCHAR AS content_type,\n       summary AS body\nFROM GHG_VISUALS\nUNION ALL\nSELECT\n    v.relative_path,\n    v.filename,\n    v.report_year,\n    v.pdf_page_number,\n    'visual_details'::VARCHAR AS content_type,\n    LISTAGG(d.path || ': ' || d.value::VARCHAR, '\\n') WITHIN GROUP (ORDER BY d.path) AS body\nFROM GHG_VISUALS AS v,\n     LATERAL FLATTEN(INPUT => v.details, RECURSIVE => TRUE) AS d\nWHERE TYPEOF(d.value) NOT IN ('OBJECT', 'ARRAY', 'NULL_VALUE')\n  AND d.path NOT LIKE '%value_range%'\n  AND d.path NOT LIKE '%value_precision%'\nGROUP BY v.relative_path, v.filename, v.report_year, v.pdf_page_number, v.image_index;\n\nCREATE OR REPLACE TABLE GHG_SEARCH_CHUNKS\n    DATA_RETENTION_TIME_IN_DAYS = 1 AS\nSELECT\n    MD5(u.relative_path || '|' || u.pdf_page_number::VARCHAR || '|' ||\n        u.content_type || '|' || c.index::VARCHAR) AS chunk_id,\n    u.relative_path,\n    u.filename,\n    u.report_year,\n    u.pdf_page_number,\n    u.content_type,\n    'Report: Greenhouse Gas Report ' || u.report_year ||\n    '. PDF page: ' || u.pdf_page_number::VARCHAR ||\n    '. Content: ' || u.content_type || CHR(10) || c.value::VARCHAR AS search_text\nFROM GHG_SEARCH_UNITS AS u,\n     LATERAL FLATTEN(INPUT => SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER(\n         u.body, 'markdown', 1400, 150\n     )) AS c\nWHERE NULLIF(TRIM(c.value::VARCHAR), '') IS NOT NULL;\n\nALTER TABLE GHG_SEARCH_CHUNKS SET CHANGE_TRACKING = TRUE;\n\nSELECT report_year, content_type, COUNT(*) AS chunks\nFROM GHG_SEARCH_CHUNKS\nGROUP BY report_year, content_type\nORDER BY report_year, content_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Cortex Search Service\u003C/h3\u003E\n","\u003Cp\u003EThe service uses the warehouse selected in the notebook and filters on report year, content type, and filename. The index builds after creation, which can take a short time. If the retrieval below returns nothing right away, wait a minute and run the DESCRIBE statement again to check that \u003Ccode\u003Eindexing_state\u003C/code\u003E is \u003Ccode\u003EACTIVE\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Replaces only the Search service created by this notebook.\nDROP CORTEX SEARCH SERVICE IF EXISTS GHG_REPORT_SEARCH;\n\nEXECUTE IMMEDIATE $$\nDECLARE\n    wh VARCHAR DEFAULT CURRENT_WAREHOUSE();\n    ddl VARCHAR;\n    missing_warehouse EXCEPTION (-20001, 'Select a lab warehouse before creating Search.');\nBEGIN\n    IF (wh IS NULL) THEN\n        RAISE missing_warehouse;\n    END IF;\n    ddl := 'CREATE OR REPLACE CORTEX SEARCH SERVICE GHG_REPORT_SEARCH '\n        || 'ON search_text ATTRIBUTES report_year, content_type, filename '\n        || 'WAREHOUSE = "' || REPLACE(wh, '"', '""') || '" '\n        || 'TARGET_LAG = ''1 hour'' INITIALIZE = ON_CREATE '\n        || 'AS SELECT chunk_id, search_text, relative_path, filename, '\n        || 'report_year, pdf_page_number, content_type FROM GHG_SEARCH_CHUNKS';\n    EXECUTE IMMEDIATE :ddl;\n    RETURN 'Created GHG_REPORT_SEARCH.';\nEND;\n$$;\n\nDESCRIBE CORTEX SEARCH SERVICE GHG_REPORT_SEARCH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERetrieve Passages for the Question\u003C/h3\u003E\n","\u003Cp\u003EThe question is searched within the 2024 report. Each passage is shown with the chart details from the same page, so the regional figure values reach the answer step.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TEMP TABLE GHG_RETRIEVED_PASSAGES AS\nWITH hits AS (\n    SELECT\n        r.value:FILENAME::VARCHAR AS filename,\n        r.value:REPORT_YEAR::VARCHAR AS report_year,\n        r.value:PDF_PAGE_NUMBER::NUMBER AS pdf_page_number,\n        r.value:CONTENT_TYPE::VARCHAR AS content_type,\n        r.value:SEARCH_TEXT::VARCHAR AS passage\n    FROM (\n        SELECT PARSE_JSON(SNOWFLAKE.CORTEX.SEARCH_PREVIEW(\n            'GHG_REPORT_SEARCH',\n            '{"query": "Which year had the highest and which had the lowest annual regional total CO2 emissions between 2020 and 2023", "columns": ["SEARCH_TEXT", "FILENAME", "REPORT_YEAR", "PDF_PAGE_NUMBER", "CONTENT_TYPE"], "filter": {"@eq": {"REPORT_YEAR": "2024"}}, "limit": 6}'\n        )) AS response\n    ) AS s,\n    LATERAL FLATTEN(INPUT => s.response:results) AS r\n),\ndetails AS (\n    SELECT report_year, pdf_page_number, LISTAGG(search_text, '\\n') WITHIN GROUP (ORDER BY chunk_id) AS chart_details\n    FROM GHG_SEARCH_CHUNKS\n    WHERE content_type = 'visual_details'\n    GROUP BY report_year, pdf_page_number\n)\nSELECT h.filename, h.report_year, h.pdf_page_number, h.content_type, h.passage, d.chart_details\nFROM hits AS h\nLEFT JOIN details AS d\n  ON d.report_year = h.report_year\n AND d.pdf_page_number = h.pdf_page_number\nORDER BY h.pdf_page_number;\n\nSELECT * FROM GHG_RETRIEVED_PASSAGES ORDER BY pdf_page_number;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAnswer the Question with AI_COMPLETE\u003C/h3\u003E\n","\u003Cp\u003EThe answer step passes the retrieved passages and chart details to \u003Ccode\u003Eclaude-sonnet-5-5\u003C/code\u003E. The model names the year with the highest and lowest annual regional total CO₂ between 2020 and 2023, using values read from the chart where the passages give them. It cites each claim by report year, PDF page, and source type (text, visual summary, or visual details), and says when a value comes from a chart description rather than printed text.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EWITH context AS (\n    SELECT\n        LISTAGG(\n            'Report ' || report_year || ', PDF page ' || pdf_page_number::VARCHAR || ' (' || content_type || '):\\n' || passage ||\n            IFF(chart_details IS NULL, '', '\\nChart details for page ' || pdf_page_number::VARCHAR || ':\\n' || chart_details),\n            '\\n---\\n'\n        ) WITHIN GROUP (ORDER BY pdf_page_number) AS passages\n    FROM GHG_RETRIEVED_PASSAGES\n)\nSELECT AI_COMPLETE(\n    'claude-sonnet-5-5',\n    'You answer questions about a 2024 greenhouse gas report using only the passages below. Question: which year had the highest and which had the lowest annual regional total CO2 emissions between 2020 and 2023? Cite each claim as [Report 2024, PDF page P, source], where source is the content label of the passage you used: text, visual_summary, or visual_details. If the passages do not name a region or the fire series, say so rather than guessing. If the passages do not contain the answer, say what is missing.\\n\\nPassages:\\n' || passages\n) AS answer\nFROM context;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe answer returned by the notebook looks like this:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHighest: 2020. Lowest: 2023.\u003C/strong\u003E I read "between 2020 and 2023" as inclusive of both years.\u003C/p\u003E\n","\u003Cp\u003EAnnual regional total CO₂ (GtC yr⁻¹), from the dark/"Black Bars" series in Figure 1:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E2020: 0.57 [Report 2024, PDF page 2, visual_details]\u003C/li\u003E\u003Cli\u003E2021: 0.36 [Report 2024, PDF page 2, visual_details]\u003C/li\u003E\u003Cli\u003E2022: 0.33 [Report 2024, PDF page 2, visual_details]\u003C/li\u003E\u003Cli\u003E2023: 0.21 [Report 2024, PDF page 2, visual_details]\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe 2023 total of 0.21 is also given in Table 3 (fire 0.47, NBE −0.26) [Report 2024, PDF page 2, text].\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESeries identification:\u003C/strong\u003E The chart legend is illegible in the extracted details, so the series are labeled only by appearance. The Figure 1 caption states that dark bars show total emissions [Report 2024, PDF page 2, text]. I therefore took the "Black Bars" series as the total.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConsistency check:\u003C/strong\u003E For each of these years, fire plus NBE matches the total, as the report's formula requires [Report 2024, PDF page 1, text]. For 2020, grey bars (0.43) plus striped bars (0.14) give 0.57 [Report 2024, PDF page 2, visual_details].\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEstimated values:\u003C/strong\u003E The chart details are flagged \u003Ccode\u003Evalues_estimated: true\u003C/code\u003E [Report 2024, PDF page 2, visual_details]. Only the 2023 figure is confirmed by an exact table value. The ranking is still clear, because the gaps between years are large relative to rounding.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERegion:\u003C/strong\u003E The passages do not name a region. They describe only "one illustrative land area" [Report 2024, PDF page 1, text] and an "Illustrative region" [Report 2024, PDF page 2, text]. The totals exclude fossil-fuel and ocean fluxes, so they are not a global inventory [Report 2024, PDF page 1, text].\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExclusive reading:\u003C/strong\u003E If you meant only the years strictly between 2020 and 2023 (2021 and 2022), the highest would be 2021 (0.36) and the lowest 2022 (0.33).\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EDuration: 1 minute\u003C/p\u003E\n","\u003Cp\u003EBoth notebooks create their objects in \u003Ccode\u003EVHOL_DOCUMENT_AI_DB\u003C/code\u003E. Dropping the database removes everything the lab created: the mortgage and greenhouse stages and their uploaded PDFs, the \u003Ccode\u003EGHG_REPORT_SEARCH\u003C/code\u003E Cortex Search service, and the tables.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP DATABASE IF EXISTS VHOL_DOCUMENT_AI_DB;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDo not run this against a shared event database. In a shared account, ask the host which objects you can remove.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EDuration: 1 minute\u003C/p\u003E\n","\u003Cp\u003EYou classified mortgage pages and extracted fields from the supported types, including the citizenship declarations. You also parsed a greenhouse gas report with AGENTIC mode, indexed its text, visual summaries, and chart details, and answered a question with citations to the report, page, and source type.\u003C/p\u003E\n","\u003Cp\u003EUse \u003Cstrong\u003EAI_CLASSIFY\u003C/strong\u003E to select a processing route, \u003Cstrong\u003EAI_EXTRACT\u003C/strong\u003E for selected business fields, \u003Cstrong\u003EAI_PARSE_DOCUMENT\u003C/strong\u003E for reusable document content and interpretation, and \u003Cstrong\u003ECortex Search\u003C/strong\u003E with \u003Cstrong\u003EAI_COMPLETE\u003C/strong\u003E to answer from the saved content.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to classify document pages and select a type-specific extraction schema\u003C/li\u003E\u003Cli\u003EHow to extract values from the original PDFs, review blank or placeholder values, and flag unexpected statuses\u003C/li\u003E\u003Cli\u003EHow to parse reports in AGENTIC mode and index their text, visual summaries, and chart details\u003C/li\u003E\u003Cli\u003EHow to answer a question from retrieved passages, with citations to the page and source type\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDataset Sources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EMortgage PDFs are synthetic test documents with fictional names, numbers, and signatures. They are not real loan records.\u003C/li\u003E\u003Cli\u003EThe greenhouse gas report \u003Ccode\u003Egreenhouse_gas_report_2024.pdf\u003C/code\u003E is a synthetic document created for this lab.\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/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/vhol_document_ai_mortgage.ipynb\"\u003EMortgage notebook used in Part A\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/vhol_document_ai_greenhouse_reports.ipynb\"\u003EGreenhouse reports notebook used in Part B\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/mortgage_reports.zip\"\u003EMortgage sample PDFs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/greenhouse_gas_report_2024.pdf\"\u003EGreenhouse gas report\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_classify\"\u003EAI_CLASSIFY\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_extract\"\u003EAI_EXTRACT\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_parse_document\"\u003EAI_PARSE_DOCUMENT\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/parse-document\"\u003EParsing documents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql\"\u003ESnowflake Cortex AI Functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/search_preview-snowflake-cortex\"\u003ESEARCH_PREVIEW\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"From PDFs to Pipelines: Document Intelligence with Cortex AI Functions","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nDocuments contain information teams need in two forms: selected fields for operational workflows and searchable content for research and review. Snowflake Cortex AI Functions let you process both from SQL.\n\nThis lab has two independent tracks, each in its own notebook:\n\n- **Part A, mortgage (~25 minutes):** Classify four synthetic mortgage-document PDFs with `AI_CLASSIFY`, extract fields from the supported page types with `AI_EXTRACT`, and review each result.\n- **Part B, greenhouse report (~20 minutes):** Parse a greenhouse gas report with `AI_PARSE_DOCUMENT` in AGENTIC mode, index its text, visual summaries, and chart details with Cortex Search, and answer a question about regional CO₂ emissions with `AI_COMPLETE`.\n\n### Prerequisites\n- Familiarity with SQL, including SELECT statements, joins, and table creation\n- Basic understanding of Snowflake stages and JSON field access\n\n### What You'll Learn\n- How to classify documents and route each supported type to an extraction schema\n- How to extract business fields from PDFs and review blank or placeholder values\n- How to parse reports in AGENTIC mode and index text, visual summaries, and chart details\n- How to answer a question from retrieved passages, with citations to the page and source type\n\n### What You'll Need\n- A [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with access to [Cortex AI Functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql), AI_PARSE_DOCUMENT in AGENTIC mode, and Cortex Search; confirm approved [cross-region inference](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cross-region-inference) before starting\n- A role with privileges to create a database, schema, internal stages, tables, and a Cortex Search service, plus access to a running warehouse\n- Access to [Snowflake Notebooks in Workspaces](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-in-workspaces/notebooks-in-workspaces-overview), or a SQL worksheet\n- The [mortgage sample PDFs](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/mortgage_reports.zip) (`mortgage_reports.zip`) and [greenhouse_gas_report_2024.pdf](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/greenhouse_gas_report_2024.pdf)\n\n### What You'll Build\n- Part A: one result row per mortgage PDF, with its document type, extracted fields, processing status, and review reason\n- Part B: a Cortex Search index over one greenhouse gas report, and a cited answer about annual regional CO₂ emissions between 2020 and 2023\n\nThe two tracks are independent. Parsing is not a prerequisite for direct-file extraction. This guide does not use native extraction citations or make loan-approval or borrower-eligibility decisions.\n\n\u003C!-- ------------------------ --\u003E\n## Setup\nDuration: 5 minutes\n\n### Import the Notebooks\n\n1. Download [vhol_document_ai_mortgage.ipynb](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/vhol_document_ai_mortgage.ipynb) and [vhol_document_ai_greenhouse_reports.ipynb](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/vhol_document_ai_greenhouse_reports.ipynb). For GitHub-hosted notebooks, select **Download raw file**.\n2. In Snowsight, go to **Projects \u003E Workspaces**.\n3. Click **+ Add new \u003E Upload files** and select each downloaded `.ipynb` file.\n4. Open a notebook and select an available warehouse and a role with the privileges listed above.\n\nRun the first setup cell in each notebook to set the lab context. Each track is self-contained, so you can run either one first. Cleanup is a separate manual step at the end of this guide.\n\n### Set the Lab Context\n\nEach notebook starts with the same context cell. In a shared event account, use the participant-specific database or schema name assigned by the host in place of these defaults.\n\n```sql\nCREATE DATABASE IF NOT EXISTS VHOL_DOCUMENT_AI_DB;\nCREATE SCHEMA IF NOT EXISTS VHOL_DOCUMENT_AI_DB.DOCUMENT_AI;\n\nUSE DATABASE VHOL_DOCUMENT_AI_DB;\nUSE SCHEMA DOCUMENT_AI;\n\nSELECT CURRENT_ROLE(), CURRENT_DATABASE(), CURRENT_SCHEMA(), CURRENT_WAREHOUSE();\n```\n\n\u003C!-- ------------------------ --\u003E\n## Classify Mortgage Documents\nDuration: 25 minutes\n\nThis part uses `vhol_document_ai_mortgage.ipynb`. Each step below matches a cell in that notebook.\n\n### Create the Stage\n\nCreate an internal stage for the mortgage documents. Directory tables let the workflow discover the uploaded files. Use `SNOWFLAKE_SSE` encryption for the direct-file AI calls; see [CREATE STAGE](https://docs.snowflake.com/en/sql-reference/sql/create-stage) and [AI_CLASSIFY limitations](https://docs.snowflake.com/en/sql-reference/functions/ai_classify#limitations).\n\n```sql\nCREATE STAGE IF NOT EXISTS MORTGAGE_DOCS\n    ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')\n    DIRECTORY = (ENABLE = TRUE);\n```\n\n### Upload the Sample PDFs\n\n1. Download [mortgage_reports.zip](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/mortgage_reports.zip) and unzip it. Upload the individual PDFs, not the ZIP archive. For GitHub-hosted files, select **Download raw file**.\n2. In Snowsight, go to **Ingestion \u003E Add Data \u003E Load files into a Stage**.\n3. Select those four PDFs and choose **VHOL_DOCUMENT_AI_DB \u003E DOCUMENT_AI \u003E MORTGAGE_DOCS**, then upload them.\n\n| File | Document | Expected route |\n|---|---|---|\n| `01.pdf` | Declaration of Citizenship / Household Status | Citizenship declaration |\n| `02.pdf` | Affidavit of Non-Taxpayer / Household Schedule | Non taxpayer affidavit |\n| `03.pdf` | Residential Loan Application, pages 1 and 2 | Assets and transaction details |\n| `04.pdf` | Settlement Charges / Disbursement Worksheet | Settlement charges |\n\nKeep the filenames unchanged. The four PDFs are synthetic test documents with fictional names, numbers, and signatures.\n\nSee [Staging files using Snowsight](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-stage-ui) for the upload workflow. Then refresh the directory table and confirm the files are visible:\n\n```sql\nALTER STAGE MORTGAGE_DOCS REFRESH;\n\nLIST @MORTGAGE_DOCS;\n```\n\n### Select the PDFs\n\nThe query keeps every PDF on the stage. Each file is classified, and files that do not match a route are reported as `Other`.\n\n```sql\nCREATE OR REPLACE TEMP TABLE MORTGAGE_FILES AS\nSELECT\n    relative_path,\n    SPLIT_PART(relative_path, '/', -1) AS filename,\n    size AS file_size_bytes,\n    last_modified\nFROM DIRECTORY(@MORTGAGE_DOCS)\nWHERE relative_path ILIKE '%.pdf';\n\nSELECT * FROM MORTGAGE_FILES ORDER BY relative_path;\n```\n\n### Define the Routes\n\nDefine one route for each supported page type. The `Other` route has no extraction schema, so those documents go to review without an `AI_EXTRACT` call. The `Citizenship declaration` route extracts three fields from the declaration form.\n\n```sql\nCREATE OR REPLACE TEMP TABLE MORTGAGE_ROUTES (\n    document_type VARCHAR,\n    category_description VARCHAR,\n    response_format VARIANT\n);\n\nINSERT INTO MORTGAGE_ROUTES\nSELECT\n    'Non taxpayer affidavit',\n    'Affidavit of Non-Taxpayer with selected people, roles, years not filed, county, and notarization.',\n    PARSE_JSON($${\n  \"county\": \"What county is entered near the top of the affidavit? Return an empty string if blank.\",\n  \"affidavit_date\": \"What date does the notary acknowledgment in Section F give? Return YYYY-MM-DD, or an empty string if blank. Do not use the commission expiration date.\",\n  \"commission_expiration_date\": \"What date is written as Commission expiration in the notary section? The date is in MM/DD/YYYY format. Return YYYY-MM-DD, or an empty string if blank.\"\n}$$)\nUNION ALL\nSELECT\n    'Assets and transaction details',\n    'Residential loan application with Section VII assets and liabilities and Section VIII Details of Transaction, including purchase price, loan proceeds, and cash to close.',\n    PARSE_JSON($${\n  \"total_assets\": \"What amount is written for TOTAL ASSETS in Section VII? Do not use Liquid Assets Subtotal. Return the written amount, or an empty string if blank.\",\n  \"total_liabilities\": \"What amount is written for TOTAL LIABILITIES in Section VII, in the Unpaid balance column? Return the written amount, or an empty string if blank.\",\n  \"purchase_price\": \"What amount is written for line a, Purchase price, in Section VIII, Details of Transaction? Return the written amount, or an empty string if blank.\",\n  \"loan_proceeds\": \"What amount is written for line d, Loan proceeds, in Section VIII? Read the written amount; do not calculate it. Return an empty string if blank.\",\n  \"cash_to_close\": \"What amount is written for line g, Cash to close, in Section VIII? Read the written amount; do not calculate it. Return an empty string if blank.\"\n}$$)\nUNION ALL\nSELECT\n    'Settlement charges',\n    'Settlement charges worksheet listing fees by line number and line 1400 borrower and seller settlement charge totals.',\n    PARSE_JSON($${\n  \"borrower_settlement_charges\": \"What amount is written on line 1400, Total Settlement Charges, in the Borrower paid column? Read the written total; do not calculate it. Return an empty string if blank.\",\n  \"seller_settlement_charges\": \"What amount is written on line 1400, Total Settlement Charges, in the Seller paid column? Read the written total; do not calculate it. Return an empty string if blank.\",\n  \"appraisal_fee\": \"What appraisal fee is written on line 803? Return the amount shown, or an empty string if blank.\",\n  \"credit_report_fee\": \"What credit report fee is written on line 804? Return the amount shown, or an empty string if blank.\"\n}$$)\nUNION ALL\nSELECT\n    'Citizenship declaration',\n    'Declaration of Citizenship / Household Status form with numbered blocks 1, 2, and 3 in Section II for citizenship status, followed by a signature and date.',\n    PARSE_JSON($${\n  \"declarant_name\": \"What name is printed after I, at the top of the declaration? Return the printed name as written, or an empty string if blank.\",\n  \"citizenship_status\": \"Which status block is checked? Return exactly one of: U.S. citizen; Noncitizen with eligible immigration status; Not contending eligible immigration status. Return an empty string if no block is checked.\",\n  \"signature_date\": \"What date is written next to the signature? The date on this form is in MM/DD/YYYY format. Return YYYY-MM-DD, or an empty string if blank.\"\n}$$)\nUNION ALL\nSELECT\n    'Other',\n    'Any other document, including notices and disclosures such as the Right to Financial Privacy Act notice or VA and HUD/FHA borrower notices, government program agreement summaries, and property insurance rating or underwriting forms. Choose Other for notice or disclosure text even when it appears alongside a loan application.',\n    NULL::VARIANT;\n```\n\n### Classify Each PDF\n\nBuild the category list from the routes and classify each PDF. The final `TRUE` returns error details in the result instead of failing the query. Compare the assigned types with the document headings. `01.pdf` should route to `Citizenship declaration`, `02.pdf` to `Non taxpayer affidavit`, `03.pdf` to `Assets and transaction details`, and `04.pdf` to `Settlement charges`. The filename is not supplied to the classifier as evidence.\n\n```sql\nCREATE OR REPLACE TEMP TABLE MORTGAGE_CLASSIFIED AS\nWITH categories AS (\n    SELECT ARRAY_AGG(\n        OBJECT_CONSTRUCT('label', document_type, 'description', category_description)\n    ) WITHIN GROUP (ORDER BY document_type) AS categories\n    FROM MORTGAGE_ROUTES\n),\nclassified AS (\n    SELECT\n        f.relative_path,\n        f.filename,\n        AI_CLASSIFY(\n            TO_FILE('@MORTGAGE_DOCS', f.relative_path),\n            c.categories,\n            {\n                'task_description': 'Classify the supplied document page by its visible headings and sections. Distinguish loan application pages by section content. Use Other when none of the defined form types matches. Do not use the filename as evidence.',\n                'output_mode': 'single'\n            },\n            TRUE  -- returns error details instead of failing the query\n        ) AS raw_classification\n    FROM MORTGAGE_FILES AS f\n    CROSS JOIN categories AS c\n)\nSELECT\n    relative_path,\n    filename,\n    NULLIF(raw_classification:value:labels[0]::VARCHAR, 'null') AS document_type,\n    NULLIF(NULLIF(TRIM(raw_classification:error::VARCHAR), ''), 'null') AS classification_error\nFROM classified;\n\nSELECT filename, document_type, classification_error\nFROM MORTGAGE_CLASSIFIED\nORDER BY filename;\n```\n\n![Classification results showing the four source filenames, assigned document types, and any processing errors](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/document-intelligence-ai-functions/classification.png?v=9ad97d0a)\n\n### Extract Business Fields\n\nRoute each classified document to its extraction schema and call `AI_EXTRACT` on the original PDF. Unsupported types and classification errors are skipped.\n\n```sql\nCREATE OR REPLACE TEMP TABLE MORTGAGE_EXTRACTED AS\nWITH extraction_raw AS (\n    SELECT\n        c.relative_path,\n        c.filename,\n        c.document_type,\n        AI_EXTRACT(\n            file =\u003E TO_FILE('@MORTGAGE_DOCS', c.relative_path),\n            responseFormat =\u003E r.response_format\n        ) AS raw_extraction\n    FROM MORTGAGE_CLASSIFIED AS c\n    JOIN MORTGAGE_ROUTES AS r ON c.document_type = r.document_type\n    WHERE c.classification_error IS NULL\n      AND r.response_format IS NOT NULL\n)\nSELECT\n    relative_path,\n    filename,\n    document_type,\n    raw_extraction:response AS extracted_data,\n    NULLIF(NULLIF(TRIM(raw_extraction:error::VARCHAR), ''), 'null') AS extraction_error\nFROM extraction_raw;\n\nSELECT filename, document_type, extracted_data, extraction_error\nFROM MORTGAGE_EXTRACTED\nORDER BY filename;\n```\n\n### Review the Results\n\nEach document gets one processing status. A field is flagged for review when it is blank, null, or a placeholder such as `unknown`. A citizenship status outside the three allowed options is also flagged. This check does not verify that values are correct.\n\n- `EXTRACTED`: the call succeeded, no field is blank, and any citizenship status is one of the three allowed options\n- `FIELD_REVIEW`: at least one field is blank, or a citizenship status is outside the three allowed options\n- `UNSUPPORTED_DOCUMENT`: no extraction schema exists for this type\n- `CLASSIFICATION_ERROR` or `EXTRACTION_ERROR`: the call returned an error\n\n```sql\nCREATE OR REPLACE TEMP TABLE MORTGAGE_RESULTS AS\nWITH blank_fields AS (\n    SELECT\n        e.relative_path,\n        LISTAGG(f.key, ', ') WITHIN GROUP (ORDER BY f.key) AS blank_fields\n    FROM MORTGAGE_EXTRACTED AS e,\n         LATERAL FLATTEN(INPUT =\u003E e.extracted_data) AS f\n    WHERE NULLIF(TRIM(f.value::VARCHAR), '') IS NULL\n       OR LOWER(TRIM(f.value::VARCHAR)) IN ('null', 'none', 'unknown', 'n/a')\n    GROUP BY e.relative_path\n)\nSELECT\n    c.filename,\n    c.document_type,\n    CASE\n        WHEN c.classification_error IS NOT NULL THEN 'CLASSIFICATION_ERROR'\n        WHEN r.response_format IS NULL THEN 'UNSUPPORTED_DOCUMENT'\n        WHEN e.extraction_error IS NOT NULL OR e.extracted_data IS NULL THEN 'EXTRACTION_ERROR'\n        WHEN b.blank_fields IS NOT NULL THEN 'FIELD_REVIEW'\n        WHEN c.document_type = 'Citizenship declaration'\n             AND NOT COALESCE(\n                 (e.extracted_data:citizenship_status::VARCHAR) IN (\n                     'U.S. citizen',\n                     'Noncitizen with eligible immigration status',\n                     'Not contending eligible immigration status'\n                 ), FALSE)\n            THEN 'FIELD_REVIEW'\n        ELSE 'EXTRACTED'\n    END AS processing_status,\n    b.blank_fields,\n    e.extracted_data\nFROM MORTGAGE_CLASSIFIED AS c\nLEFT JOIN MORTGAGE_ROUTES AS r ON c.document_type = r.document_type\nLEFT JOIN MORTGAGE_EXTRACTED AS e ON c.relative_path = e.relative_path\nLEFT JOIN blank_fields AS b ON c.relative_path = b.relative_path;\n\nSELECT filename, document_type, processing_status, blank_fields, extracted_data\nFROM MORTGAGE_RESULTS\nORDER BY processing_status, filename;\n```\n\n`EXTRACTED` means the call succeeded and no field is blank. It does not establish accuracy, completeness, or borrower eligibility. Check sample values against the source pages.\n\n### Check Values Against the Source PDFs\n\nThe query below returns the extracted values as text. Compare them with the source pages. The values in the table are the ones read from the sample PDFs.\n\n| Source | Field | Source value |\n|---|---|---:|\n| `03.pdf` | Section VII total assets | 355,000.00 |\n| `03.pdf` | Section VII total liabilities, unpaid balance | 27,950.00 |\n| `03.pdf` | Section VIII line a, purchase price | 480,000.00 |\n| `03.pdf` | Section VIII line d, loan proceeds | 384,000.00 |\n| `03.pdf` | Section VIII line g, cash to close | 96,963.68 |\n| `04.pdf` | Line 1400, borrower paid | 11,963.68 |\n| `04.pdf` | Line 1400, seller paid | 33,275.00 |\n\n```sql\nSELECT\n    filename,\n    extracted_data:total_assets::VARCHAR AS total_assets,\n    extracted_data:total_liabilities::VARCHAR AS total_liabilities,\n    extracted_data:purchase_price::VARCHAR AS purchase_price,\n    extracted_data:loan_proceeds::VARCHAR AS loan_proceeds,\n    extracted_data:cash_to_close::VARCHAR AS cash_to_close,\n    extracted_data:borrower_settlement_charges::VARCHAR AS borrower_settlement_charges,\n    extracted_data:seller_settlement_charges::VARCHAR AS seller_settlement_charges\nFROM MORTGAGE_RESULTS\nWHERE filename IN ('03.pdf', '04.pdf');\n```\n\n![Extraction results for 03.pdf showing total assets of $355,000.00, total liabilities of $27,950.00, and purchase price of $480,000.00](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/document-intelligence-ai-functions/extraction-1.png?v=9ad97d0a)\n\n![Extraction results for 04.pdf showing borrower settlement charges of $11,963.68 and seller settlement charges of $33,275.00](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/document-intelligence-ai-functions/extraction-2.png?v=9ad97d0a)\n\n\u003E **Try it:** Refine the description for the assets-and-transaction route, then re-run the classification cell and compare the results. Re-running model calls can incur additional usage.\n\n\u003C!-- ------------------------ --\u003E\n## Search Greenhouse Reports\nDuration: 20 minutes\n\nThis part uses `vhol_document_ai_greenhouse_reports.ipynb`. Each step below matches a cell in that notebook. The source is a single synthetic report, `greenhouse_gas_report_2024.pdf`.\n\n### Create the Stage\n\nCreate an internal stage for the greenhouse report. Directory tables let the workflow discover the uploaded file.\n\n```sql\nCREATE STAGE IF NOT EXISTS GHG_REPORTS\n    ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')\n    DIRECTORY = (ENABLE = TRUE);\n```\n\n### Upload the PDF\n\n1. Download [greenhouse_gas_report_2024.pdf](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/greenhouse_gas_report_2024.pdf). For GitHub-hosted files, select **Download raw file**.\n2. In Snowsight, go to **Ingestion \u003E Add Data \u003E Load files into a Stage** and upload the PDF to `GHG_REPORTS`.\n3. Refresh the directory table and confirm the file is listed:\n\n```sql\nALTER STAGE GHG_REPORTS REFRESH;\n\nLIST @GHG_REPORTS;\n```\n\n### Register the Report\n\nThe report year is read from the filename. The query returns one row for the 2024 report.\n\n```sql\nCREATE OR REPLACE TEMP TABLE GHG_FILES AS\nSELECT\n    relative_path,\n    SPLIT_PART(relative_path, '/', -1) AS filename,\n    REGEXP_SUBSTR(SPLIT_PART(relative_path, '/', -1), '(20[0-9]{2})', 1, 1, 'e', 1) AS report_year,\n    size AS file_size_bytes,\n    last_modified\nFROM DIRECTORY(@GHG_REPORTS)\nWHERE relative_path ILIKE '%.pdf';\n\nSELECT * FROM GHG_FILES ORDER BY report_year;\n```\n\n### Parse the Report with AGENTIC Mode\n\n`AI_PARSE_DOCUMENT` in AGENTIC mode returns page text and a visual interpretation for each image. `page_split` returns one record per page. The final `TRUE` returns error details in the result instead of failing the query.\n\nThis step calls the model and can take a few minutes.\n\n```sql\nCREATE OR REPLACE TEMP TABLE GHG_PARSED AS\nSELECT\n    relative_path,\n    filename,\n    report_year,\n    AI_PARSE_DOCUMENT(\n        TO_FILE('@GHG_REPORTS', relative_path),\n        {'mode': 'AGENTIC', 'page_split': TRUE},\n        TRUE\n    ) AS raw_parse\nFROM GHG_FILES;\n\nSELECT\n    filename,\n    report_year,\n    ARRAY_SIZE(raw_parse:value:pages) AS returned_pages,\n    raw_parse:error AS call_error,\n    raw_parse:value:error AS document_error\nFROM GHG_PARSED\nORDER BY report_year;\n```\n\n### Split Pages, Visual Summaries, and Chart Details\n\nThe parsed output is split into one row per page and one row per image. Images that return an error are skipped. Each image's structured details are kept so the chart series can be indexed.\n\n```sql\nCREATE OR REPLACE TEMP TABLE GHG_PAGES AS\nSELECT\n    r.relative_path,\n    r.filename,\n    r.report_year,\n    p.value:index::NUMBER + 1 AS pdf_page_number,\n    p.value:content::VARCHAR AS page_content,\n    p.value:images AS images\nFROM GHG_PARSED AS r,\n     LATERAL FLATTEN(INPUT =\u003E r.raw_parse:value:pages) AS p\nWHERE NULLIF(r.raw_parse:error::VARCHAR, 'null') IS NULL;\n\nCREATE OR REPLACE TEMP TABLE GHG_VISUALS AS\nSELECT\n    p.relative_path,\n    p.filename,\n    p.report_year,\n    p.pdf_page_number,\n    i.index AS image_index,\n    i.value:results:image_type::VARCHAR AS image_type,\n    i.value:results:summary::VARCHAR AS summary,\n    i.value:results:details AS details\nFROM GHG_PAGES AS p,\n     LATERAL FLATTEN(INPUT =\u003E p.images) AS i\nWHERE NULLIF(TRIM(i.value:results:summary::VARCHAR), '') IS NOT NULL\n  AND COALESCE(LOWER(i.value:results:description::VARCHAR), '') \u003C\u003E 'error';\n\nSELECT report_year, COUNT(*) AS pages FROM GHG_PAGES GROUP BY report_year ORDER BY report_year;\n\nSELECT report_year, COUNT(*) AS visuals_with_summary FROM GHG_VISUALS GROUP BY report_year ORDER BY report_year;\n```\n\n### List All Extracted Images\n\nThis lists every image the parse returned, with its page, image ID, type, summary, and structured details. Images without a summary or details show NULL in those columns.\n\n```sql\nSELECT\n    p.pdf_page_number,\n    i.index AS image_index,\n    i.value:id::VARCHAR AS image_id,\n    i.value:results:image_type::VARCHAR AS image_type,\n    i.value:results:summary::VARCHAR AS summary,\n    i.value:results:details AS details\nFROM GHG_PAGES AS p,\n     LATERAL FLATTEN(INPUT =\u003E p.images) AS i\nORDER BY p.pdf_page_number, i.index;\n```\n\n### Check the Regional Figure on Page 2\n\nPage 2 has the regional CO₂ figure. The query lists each image on that page with its summary and chart details. Values read from the chart are estimates. If the details do not include the values you need, the answer relies on the printed text instead.\n\n```sql\nSELECT\n    pdf_page_number,\n    image_index,\n    image_type,\n    summary,\n    LEFT(TO_JSON(details), 1200) AS details_preview\nFROM GHG_VISUALS\nWHERE report_year = '2024'\n  AND pdf_page_number = 2\nORDER BY image_index;\n```\n\n### Build the Search Content\n\nIndex three kinds of content: the page text, the visual summaries, and the chart details. Each chunk repeats the report year, PDF page, and content type, so a search can be traced back to its page.\n\n```sql\nCREATE OR REPLACE TEMP TABLE GHG_SEARCH_UNITS AS\nSELECT relative_path, filename, report_year, pdf_page_number,\n       'text'::VARCHAR AS content_type,\n       page_content AS body\nFROM GHG_PAGES\nWHERE NULLIF(TRIM(page_content), '') IS NOT NULL\nUNION ALL\nSELECT relative_path, filename, report_year, pdf_page_number,\n       'visual_summary'::VARCHAR AS content_type,\n       summary AS body\nFROM GHG_VISUALS\nUNION ALL\nSELECT\n    v.relative_path,\n    v.filename,\n    v.report_year,\n    v.pdf_page_number,\n    'visual_details'::VARCHAR AS content_type,\n    LISTAGG(d.path || ': ' || d.value::VARCHAR, '\\n') WITHIN GROUP (ORDER BY d.path) AS body\nFROM GHG_VISUALS AS v,\n     LATERAL FLATTEN(INPUT =\u003E v.details, RECURSIVE =\u003E TRUE) AS d\nWHERE TYPEOF(d.value) NOT IN ('OBJECT', 'ARRAY', 'NULL_VALUE')\n  AND d.path NOT LIKE '%value_range%'\n  AND d.path NOT LIKE '%value_precision%'\nGROUP BY v.relative_path, v.filename, v.report_year, v.pdf_page_number, v.image_index;\n\nCREATE OR REPLACE TABLE GHG_SEARCH_CHUNKS\n    DATA_RETENTION_TIME_IN_DAYS = 1 AS\nSELECT\n    MD5(u.relative_path || '|' || u.pdf_page_number::VARCHAR || '|' ||\n        u.content_type || '|' || c.index::VARCHAR) AS chunk_id,\n    u.relative_path,\n    u.filename,\n    u.report_year,\n    u.pdf_page_number,\n    u.content_type,\n    'Report: Greenhouse Gas Report ' || u.report_year ||\n    '. PDF page: ' || u.pdf_page_number::VARCHAR ||\n    '. Content: ' || u.content_type || CHR(10) || c.value::VARCHAR AS search_text\nFROM GHG_SEARCH_UNITS AS u,\n     LATERAL FLATTEN(INPUT =\u003E SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER(\n         u.body, 'markdown', 1400, 150\n     )) AS c\nWHERE NULLIF(TRIM(c.value::VARCHAR), '') IS NOT NULL;\n\nALTER TABLE GHG_SEARCH_CHUNKS SET CHANGE_TRACKING = TRUE;\n\nSELECT report_year, content_type, COUNT(*) AS chunks\nFROM GHG_SEARCH_CHUNKS\nGROUP BY report_year, content_type\nORDER BY report_year, content_type;\n```\n\n### Create the Cortex Search Service\n\nThe service uses the warehouse selected in the notebook and filters on report year, content type, and filename. The index builds after creation, which can take a short time. If the retrieval below returns nothing right away, wait a minute and run the DESCRIBE statement again to check that `indexing_state` is `ACTIVE`.\n\n```sql\n-- Replaces only the Search service created by this notebook.\nDROP CORTEX SEARCH SERVICE IF EXISTS GHG_REPORT_SEARCH;\n\nEXECUTE IMMEDIATE $$\nDECLARE\n    wh VARCHAR DEFAULT CURRENT_WAREHOUSE();\n    ddl VARCHAR;\n    missing_warehouse EXCEPTION (-20001, 'Select a lab warehouse before creating Search.');\nBEGIN\n    IF (wh IS NULL) THEN\n        RAISE missing_warehouse;\n    END IF;\n    ddl := 'CREATE OR REPLACE CORTEX SEARCH SERVICE GHG_REPORT_SEARCH '\n        || 'ON search_text ATTRIBUTES report_year, content_type, filename '\n        || 'WAREHOUSE = \"' || REPLACE(wh, '\"', '\"\"') || '\" '\n        || 'TARGET_LAG = ''1 hour'' INITIALIZE = ON_CREATE '\n        || 'AS SELECT chunk_id, search_text, relative_path, filename, '\n        || 'report_year, pdf_page_number, content_type FROM GHG_SEARCH_CHUNKS';\n    EXECUTE IMMEDIATE :ddl;\n    RETURN 'Created GHG_REPORT_SEARCH.';\nEND;\n$$;\n\nDESCRIBE CORTEX SEARCH SERVICE GHG_REPORT_SEARCH;\n```\n\n### Retrieve Passages for the Question\n\nThe question is searched within the 2024 report. Each passage is shown with the chart details from the same page, so the regional figure values reach the answer step.\n\n```sql\nCREATE OR REPLACE TEMP TABLE GHG_RETRIEVED_PASSAGES AS\nWITH hits AS (\n    SELECT\n        r.value:FILENAME::VARCHAR AS filename,\n        r.value:REPORT_YEAR::VARCHAR AS report_year,\n        r.value:PDF_PAGE_NUMBER::NUMBER AS pdf_page_number,\n        r.value:CONTENT_TYPE::VARCHAR AS content_type,\n        r.value:SEARCH_TEXT::VARCHAR AS passage\n    FROM (\n        SELECT PARSE_JSON(SNOWFLAKE.CORTEX.SEARCH_PREVIEW(\n            'GHG_REPORT_SEARCH',\n            '{\"query\": \"Which year had the highest and which had the lowest annual regional total CO2 emissions between 2020 and 2023\", \"columns\": [\"SEARCH_TEXT\", \"FILENAME\", \"REPORT_YEAR\", \"PDF_PAGE_NUMBER\", \"CONTENT_TYPE\"], \"filter\": {\"@eq\": {\"REPORT_YEAR\": \"2024\"}}, \"limit\": 6}'\n        )) AS response\n    ) AS s,\n    LATERAL FLATTEN(INPUT =\u003E s.response:results) AS r\n),\ndetails AS (\n    SELECT report_year, pdf_page_number, LISTAGG(search_text, '\\n') WITHIN GROUP (ORDER BY chunk_id) AS chart_details\n    FROM GHG_SEARCH_CHUNKS\n    WHERE content_type = 'visual_details'\n    GROUP BY report_year, pdf_page_number\n)\nSELECT h.filename, h.report_year, h.pdf_page_number, h.content_type, h.passage, d.chart_details\nFROM hits AS h\nLEFT JOIN details AS d\n  ON d.report_year = h.report_year\n AND d.pdf_page_number = h.pdf_page_number\nORDER BY h.pdf_page_number;\n\nSELECT * FROM GHG_RETRIEVED_PASSAGES ORDER BY pdf_page_number;\n```\n\n### Answer the Question with AI_COMPLETE\n\nThe answer step passes the retrieved passages and chart details to `claude-sonnet-5-5`. The model names the year with the highest and lowest annual regional total CO₂ between 2020 and 2023, using values read from the chart where the passages give them. It cites each claim by report year, PDF page, and source type (text, visual summary, or visual details), and says when a value comes from a chart description rather than printed text.\n\n```sql\nWITH context AS (\n    SELECT\n        LISTAGG(\n            'Report ' || report_year || ', PDF page ' || pdf_page_number::VARCHAR || ' (' || content_type || '):\\n' || passage ||\n            IFF(chart_details IS NULL, '', '\\nChart details for page ' || pdf_page_number::VARCHAR || ':\\n' || chart_details),\n            '\\n---\\n'\n        ) WITHIN GROUP (ORDER BY pdf_page_number) AS passages\n    FROM GHG_RETRIEVED_PASSAGES\n)\nSELECT AI_COMPLETE(\n    'claude-sonnet-5-5',\n    'You answer questions about a 2024 greenhouse gas report using only the passages below. Question: which year had the highest and which had the lowest annual regional total CO2 emissions between 2020 and 2023? Cite each claim as [Report 2024, PDF page P, source], where source is the content label of the passage you used: text, visual_summary, or visual_details. If the passages do not name a region or the fire series, say so rather than guessing. If the passages do not contain the answer, say what is missing.\\n\\nPassages:\\n' || passages\n) AS answer\nFROM context;\n```\n\nThe answer returned by the notebook looks like this:\n\n\u003E **Highest: 2020. Lowest: 2023.** I read \"between 2020 and 2023\" as inclusive of both years.\n\u003E\n\u003E Annual regional total CO₂ (GtC yr⁻¹), from the dark/\"Black Bars\" series in Figure 1:\n\u003E - 2020: 0.57 [Report 2024, PDF page 2, visual_details]\n\u003E - 2021: 0.36 [Report 2024, PDF page 2, visual_details]\n\u003E - 2022: 0.33 [Report 2024, PDF page 2, visual_details]\n\u003E - 2023: 0.21 [Report 2024, PDF page 2, visual_details]\n\u003E\n\u003E The 2023 total of 0.21 is also given in Table 3 (fire 0.47, NBE −0.26) [Report 2024, PDF page 2, text].\n\u003E\n\u003E - **Series identification:** The chart legend is illegible in the extracted details, so the series are labeled only by appearance. The Figure 1 caption states that dark bars show total emissions [Report 2024, PDF page 2, text]. I therefore took the \"Black Bars\" series as the total.\n\u003E - **Consistency check:** For each of these years, fire plus NBE matches the total, as the report's formula requires [Report 2024, PDF page 1, text]. For 2020, grey bars (0.43) plus striped bars (0.14) give 0.57 [Report 2024, PDF page 2, visual_details].\n\u003E - **Estimated values:** The chart details are flagged `values_estimated: true` [Report 2024, PDF page 2, visual_details]. Only the 2023 figure is confirmed by an exact table value. The ranking is still clear, because the gaps between years are large relative to rounding.\n\u003E - **Region:** The passages do not name a region. They describe only \"one illustrative land area\" [Report 2024, PDF page 1, text] and an \"Illustrative region\" [Report 2024, PDF page 2, text]. The totals exclude fossil-fuel and ocean fluxes, so they are not a global inventory [Report 2024, PDF page 1, text].\n\u003E - **Exclusive reading:** If you meant only the years strictly between 2020 and 2023 (2021 and 2022), the highest would be 2021 (0.36) and the lowest 2022 (0.33).\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\nDuration: 1 minute\n\nBoth notebooks create their objects in `VHOL_DOCUMENT_AI_DB`. Dropping the database removes everything the lab created: the mortgage and greenhouse stages and their uploaded PDFs, the `GHG_REPORT_SEARCH` Cortex Search service, and the tables.\n\n```sql\nDROP DATABASE IF EXISTS VHOL_DOCUMENT_AI_DB;\n```\n\nDo not run this against a shared event database. In a shared account, ask the host which objects you can remove.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\nDuration: 1 minute\n\nYou classified mortgage pages and extracted fields from the supported types, including the citizenship declarations. You also parsed a greenhouse gas report with AGENTIC mode, indexed its text, visual summaries, and chart details, and answered a question with citations to the report, page, and source type.\n\nUse **AI_CLASSIFY** to select a processing route, **AI_EXTRACT** for selected business fields, **AI_PARSE_DOCUMENT** for reusable document content and interpretation, and **Cortex Search** with **AI_COMPLETE** to answer from the saved content.\n\n### What You Learned\n- How to classify document pages and select a type-specific extraction schema\n- How to extract values from the original PDFs, review blank or placeholder values, and flag unexpected statuses\n- How to parse reports in AGENTIC mode and index their text, visual summaries, and chart details\n- How to answer a question from retrieved passages, with citations to the page and source type\n\n### Dataset Sources\n- Mortgage PDFs are synthetic test documents with fictional names, numbers, and signatures. They are not real loan records.\n- The greenhouse gas report `greenhouse_gas_report_2024.pdf` is a synthetic document created for this lab.\n\n### Related Resources\n- [Mortgage notebook used in Part A](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/vhol_document_ai_mortgage.ipynb)\n- [Greenhouse reports notebook used in Part B](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/vhol_document_ai_greenhouse_reports.ipynb)\n- [Mortgage sample PDFs](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/mortgage_reports.zip)\n- [Greenhouse gas report](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/document-intelligence-ai-functions/assets/data/greenhouse_gas_report_2024.pdf)\n- [AI_CLASSIFY](https://docs.snowflake.com/en/sql-reference/functions/ai_classify)\n- [AI_EXTRACT](https://docs.snowflake.com/en/sql-reference/functions/ai_extract)\n- [AI_PARSE_DOCUMENT](https://docs.snowflake.com/en/sql-reference/functions/ai_parse_document)\n- [Parsing documents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/parse-document)\n- [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)\n- [Snowflake Cortex AI Functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql)\n- [SEARCH_PREVIEW](https://docs.snowflake.com/en/sql-reference/functions/search_preview-snowflake-cortex)\n","title":"Quickstart Article Body","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-b49200ef6a","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-6e00ec4d30",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-4b261687a8","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-11-05",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-07c6a77437","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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