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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-49a04d8cc1","quickstartHeroTitle":{"lines":["Batch Data Extraction at Scale with Cortex AI Functions"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"James Cha-Earley, Sho Tanaka, Anh Kieu, Emre Oezdamarlar","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Cortex 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------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThe fictitious food truck company, Tasty Bytes, receives thousands of customer reviews, food photos, social media video clips, and voicemail complaints across multiple channels. To improve operations, the company needs to extract structured, queryable data from all of this unstructured content at scale — turning free-text reviews into categorized issues, food truck photos into menu item inventories, video clips into brand mentions, and voicemails into actionable tickets.\u003C/p\u003E\n","\u003Cp\u003EThis guide shows you how to build a batch data extraction pipeline entirely within Snowflake using \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql\"\u003ECortex AI Functions\u003C/a\u003E. You will use AI_EXTRACT for structured field extraction from text and images, AI_COMPLETE with video support (public preview) for multimodal analysis, and AI_TRANSCRIBE for audio processing. Finally, you will use \u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-cortex/cortex-ai-function-studio\"\u003ECortex AI Function Studio\u003C/a\u003E to create a reusable custom AI function, evaluate its accuracy, and optimize it for production use.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with SQL\u003C/li\u003E\u003Cli\u003EFamiliarity with Snowflake\u003C/li\u003E\u003Cli\u003EFamiliarity with Snowflake Notebooks\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account in a cloud region where Cortex AI Functions are \u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/llm-functions#availability\"\u003Esupported\u003C/a\u003E. If you do not have a Snowflake account, you can register for a \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_&_fsi=yYZEVo4S&_fsi=yYZEVo4S\"\u003Efree trial account\u003C/a\u003E.\n\u003Cul\u003E\u003Cli\u003ECortex AI Functions: AI_EXTRACT, AI_COMPLETE, AI_TRANSCRIBE\u003C/li\u003E\u003Cli\u003EModels: gemini-3.1-pro (video/audio), claude-sonnet-4-6 (text/images), arctic-extract (AI_EXTRACT)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003ESnowflake Notebooks enabled in your Snowflake account.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to extract structured fields from free-text reviews using AI_EXTRACT\u003C/li\u003E\u003Cli\u003EHow to extract information from images using AI_EXTRACT with file input\u003C/li\u003E\u003Cli\u003EHow to analyze video content and extract metadata using AI_COMPLETE with structured output\u003C/li\u003E\u003Cli\u003EHow to transcribe audio and extract structured data from transcriptions\u003C/li\u003E\u003Cli\u003EHow to build a unified batch extraction pipeline across all modalities\u003C/li\u003E\u003Cli\u003EHow to create, evaluate, and optimize a custom AI function using AI Function Studio\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA batch data extraction pipeline that processes Tasty Bytes' multimodal customer feedback using \u003Cstrong\u003ESnowflake Cortex AI Functions\u003C/strong\u003E within a \u003Cstrong\u003ESnowflake Notebook\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003EExtract structured fields from text reviews with \u003Cstrong\u003EAI_EXTRACT\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EExtract menu items and branding from food truck photos with \u003Cstrong\u003EAI_EXTRACT\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EExtract metadata from social media video clips with \u003Cstrong\u003EAI_COMPLETE\u003C/strong\u003E (gemini-3.1-pro)\u003C/li\u003E\u003Cli\u003ETranscribe voicemails and extract issue details with \u003Cstrong\u003EAI_TRANSCRIBE\u003C/strong\u003E + \u003Cstrong\u003EAI_COMPLETE\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EA custom AI function created, evaluated, and optimized with \u003Cstrong\u003EAI Function Studio\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Data\u003C/h2\u003E\n","\u003Cp\u003EThis phase focuses on initializing your Snowflake environment. You will use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003E, the Snowflake web interface, to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreate Snowflake objects (warehouse, database, schema, raw tables)\u003C/li\u003E\u003Cli\u003EIngest review data from S3\u003C/li\u003E\u003Cli\u003ECreate stages for images, audio, and video files\u003C/li\u003E\u003Cli\u003EUpload media files\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECreating Objects and Loading Data\u003C/h3\u003E\n","\u003Cp\u003EWe will use the setup.sql file to automate the creation of the required infrastructure and load the sample text data.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EDownload the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/gain-insights-from-unstructured-data/setup.sql\"\u003Esetup.sql\u003C/a\u003E file from the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/gain-insights-from-unstructured-data\"\u003EGitHub repository\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EOpen up a <a href="https://app.snowflake.com/_deeplink/#/workspaces?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=batch-data-extraction&utm_cta=developer-guides-deeplink" class="_deeplink">Workspaces</a> in Snowflake\u003C/li\u003E\u003Cli\u003ECopy and paste the contents of setup.sql or upload and run the file\u003C/li\u003E\u003Cli\u003EThe script will:\n\u003Cul\u003E\u003Cli\u003ECreate Snowflake objects (warehouse, database, schema, raw tables)\u003C/li\u003E\u003Cli\u003EIngest shift and review data from S3\u003C/li\u003E\u003Cli\u003ECreate the review view\u003C/li\u003E\u003Cli\u003ECreate stages for images, audio, and video files\u003C/li\u003E\u003Cli\u003ECreate tables for evaluation data (used in the AI Function Studio section)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EUpload Media Files to Stages\u003C/h3\u003E\n","\u003Cp\u003ENow upload the media files into the dedicated stages created by setup.sql:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/gain-insights-from-unstructured-data/data.zip\"\u003Edata.zip\u003C/a\u003E and extract its contents\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003ECatalog\u003C/strong\u003E >> \u003Cstrong\u003EDatabase Explorer\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUpload Image Files:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect your database: \u003Cstrong\u003ETB_VOC\u003C/strong\u003E >> \u003Cstrong\u003EMEDIA\u003C/strong\u003E >> \u003Cstrong\u003EStages\u003C/strong\u003E >> \u003Cstrong\u003EIMAGES\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Files\u003C/strong\u003E on the top right hand corner\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EBrowse\u003C/strong\u003E and upload the files in the \u003Ccode\u003Eimages/\u003C/code\u003E folder\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUpload Audio Files:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect your database: \u003Cstrong\u003ETB_VOC\u003C/strong\u003E >> \u003Cstrong\u003EMEDIA\u003C/strong\u003E >> \u003Cstrong\u003EStages\u003C/strong\u003E >> \u003Cstrong\u003EAUDIO\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Files\u003C/strong\u003E on the top right hand corner\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EBrowse\u003C/strong\u003E and upload the files in the \u003Ccode\u003Eaudio/\u003C/code\u003E folder\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUpload Video Files:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect your database: \u003Cstrong\u003ETB_VOC\u003C/strong\u003E >> \u003Cstrong\u003EMEDIA\u003C/strong\u003E >> \u003Cstrong\u003EStages\u003C/strong\u003E >> \u003Cstrong\u003EVIDEO\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Files\u003C/strong\u003E on the top right hand corner\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EBrowse\u003C/strong\u003E and upload the files in the \u003Ccode\u003Evideo/\u003C/code\u003E folder\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERun Post-Upload SQL:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAfter uploading all files, open a new SQL worksheet in \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ELoad and execute the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/gain-insights-from-unstructured-data/setup-post-upload.sql\"\u003E\u003Cstrong\u003E\u003Ccode\u003Esetup-post-upload.sql\u003C/code\u003E\u003C/strong\u003E file\u003C/a\u003E to register the uploaded files into the corresponding tables\u003C/li\u003E\u003Cli\u003EThis step is required to make the uploaded audio, video, and image files available for processing in subsequent steps\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYour Snowflake environment now contains the complete set of data across all modalities.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Notebook\u003C/h2\u003E\n","\u003Cp\u003EThis phase prepares your execution environment by importing the primary code into a Snowflake Notebook.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDownload the notebook \u003Cstrong\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/gain-insights-from-unstructured-data/batch_data_extraction.ipynb\"\u003Ebatch_data_extraction.ipynb\u003C/a\u003E\u003C/strong\u003E from the GitHub repository\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EProjects\u003C/strong\u003E >> \u003Cstrong\u003ENotebooks\u003C/strong\u003E in \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003E+ Notebook\u003C/strong\u003E drop-down and select \u003Cstrong\u003EImport .ipynb file\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect the batch_data_extraction.ipynb file\u003C/li\u003E\u003Cli\u003EProvide a name for the notebook and select appropriate \u003Cstrong\u003Edatabase\u003C/strong\u003E \u003Ccode\u003ETB_VOC\u003C/code\u003E and \u003Cstrong\u003Eschema\u003C/strong\u003E \u003Ccode\u003EANALYTICS\u003C/code\u003E for Notebook location\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003ERuntime\u003C/strong\u003E select \u003Ccode\u003ERun on container\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ENow you are ready to run the notebook by clicking the "Run all" button on the top right or running each cell individually\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EText Extraction with AI_EXTRACT\u003C/h2\u003E\n","\u003Cp\u003EIn this phase, you will use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_extract\"\u003EAI_EXTRACT\u003C/a\u003E to pull structured fields from free-text customer reviews. AI_EXTRACT is purpose-built for structured data extraction — you define the fields you want, and it returns a clean JSON object.\u003C/p\u003E\n","\u003Cp\u003ETasty Bytes receives thousands of reviews in unstructured text. Rather than reading each one manually, AI_EXTRACT lets you define a schema and extract specific fields at scale across the entire review dataset.\u003C/p\u003E\n","\u003Ch3\u003EEntity Extraction from Reviews\u003C/h3\u003E\n","\u003Cp\u003EThe following query extracts the truck name, dish mentioned, issue type, and recommendation intent from each review:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT \n    REVIEW,\n    AI_EXTRACT(\n        text => REVIEW,\n        responseFormat => {\n            'truck_name': 'What food truck or brand is mentioned?',\n            'dish': 'What specific dish or menu item is mentioned?',\n            'issue_type': 'What type of issue did the customer experience (food quality, service, wait time, cleanliness, none)?',\n            'would_recommend': 'Would the customer recommend this food truck (yes, no, unclear)?'\n        }\n    ) AS extracted_fields\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExtraction with Scores\u003C/h3\u003E\n","\u003Cp\u003EAI_EXTRACT supports confidence scores (preview) that indicate the model's certainty about each extracted value. Use scores to flag low-confidence extractions for human review:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT \n    REVIEW,\n    AI_EXTRACT(\n        text => REVIEW,\n        responseFormat => {\n            'truck_name': 'What food truck or brand is mentioned?',\n            'dish': 'What specific dish or menu item is mentioned?',\n            'issue_type': 'What type of issue did the customer experience?'\n        },\n        scores => TRUE\n    ) AS extraction_with_scores\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe result includes both the extracted values and a \u003Ccode\u003Escoring\u003C/code\u003E object with confidence scores between 0 and 1 for each field. You can use these scores to build deterministic processing logic — for example, routing low-score extractions to a human review queue.\u003C/p\u003E\n","\u003Ch3\u003EBatch Extraction at Scale\u003C/h3\u003E\n","\u003Cp\u003ETo run extraction across the entire dataset and flatten results into queryable columns:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE TB_VOC.ANALYTICS.EXTRACTED_REVIEWS AS\n(\n    SELECT\n        TRUCK_BRAND_NAME,\n        REVIEW,\n        AI_EXTRACT(\n            text => REVIEW,\n            responseFormat => {\n                'truck_name': 'What food truck or brand is mentioned?',\n                'dish': 'What specific dish or menu item is mentioned?',\n                'issue_type': 'What type of issue did the customer experience (food quality, service, wait time, cleanliness, none)?',\n                'would_recommend': 'Would the customer recommend this food truck (yes, no, unclear)?'\n            }\n        ):response AS extracted\n    FROM\n        TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\n);\n\n-- Query the flattened results\nSELECT \n    TRUCK_BRAND_NAME,\n    extracted:truck_name::VARCHAR AS truck_name,\n    extracted:dish::VARCHAR AS dish,\n    extracted:issue_type::VARCHAR AS issue_type,\n    extracted:would_recommend::VARCHAR AS would_recommend\nFROM\n    TB_VOC.ANALYTICS.EXTRACTED_REVIEWS\nWHERE\n    extracted:issue_type::VARCHAR != 'none'\nLIMIT 20\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhen to Use AI_COMPLETE Instead\u003C/h3\u003E\n","\u003Cp\u003EAI_EXTRACT is optimized for structured field extraction — it handles schema definition, output parsing, and confidence scoring automatically. However, some text extraction tasks require \u003Cstrong\u003Ereasoning\u003C/strong\u003E, \u003Cstrong\u003Emulti-step logic\u003C/strong\u003E, or \u003Cstrong\u003Econtextual interpretation\u003C/strong\u003E that go beyond direct field extraction. In these cases, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_complete\"\u003EAI_COMPLETE\u003C/a\u003E gives you full control over the prompt and model behavior.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAI_COMPLETE enables:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EReasoning over context\u003C/strong\u003E — e.g., inferring root cause from multiple complaint signals\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConditional logic\u003C/strong\u003E — e.g., "If the issue is food-related AND the customer mentions illness, escalate to urgent"\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESummarization with judgment\u003C/strong\u003E — e.g., generating a one-sentence action item for each review\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustom output formats\u003C/strong\u003E — e.g., generating a severity score (1–5) with justification text\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Example: AI_COMPLETE for reasoning-based extraction\nSELECT\n    REVIEW,\n    AI_COMPLETE(\n        'claude-sonnet-4-6',\n        CONCAT(\n            'Analyze this food truck review and determine:\\n',\n            '1. The root cause of any dissatisfaction\\n',\n            '2. A priority score (1-5) based on severity and business impact\\n',\n            '3. A recommended next action for the operations team\\n\\n',\n            'Review: ', REVIEW\n        ),\n        {},\n        {\n            'type': 'json',\n            'schema': {\n                'type': 'object',\n                'properties': {\n                    'root_cause': {'type': 'string'},\n                    'priority_score': {'type': 'integer'},\n                    'recommended_action': {'type': 'string'},\n                    'reasoning': {'type': 'string'}\n                },\n                'required': ['root_cause', 'priority_score', 'recommended_action', 'reasoning']\n            }\n        }\n    ) AS analysis\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDecision Flowchart: AI_EXTRACT vs AI_COMPLETE\u003C/h3\u003E\n","\u003Cp\u003EUse the following flowchart to determine which function to use for your text extraction task:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/ai_extract_vs_ai_complete_flowchart.png?v=583da7ff\" alt=\"AI_EXTRACT vs AI_COMPLETE Decision Flowchart\"\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAspect\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAI_EXTRACT\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAI_COMPLETE\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExtract predefined fields\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReasoning, generation, complex extraction\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInput definition\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESchema via \u003Ccode\u003EresponseFormat\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFree-form prompt instructions\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOutput format\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAutomatically structured JSON\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJSON via \u003Ccode\u003Estructured_output\u003C/code\u003E option\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConfidence scores\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAvailable with \u003Ccode\u003Escores => TRUE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENot built-in (can be prompted manually)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EModel selection\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAutomatic (arctic-extract)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExplicitly specified\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBest use cases\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENames, dates, categories — clear values\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJudgment, reasoning, summarization, action generation\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EImage Extraction with AI_EXTRACT\u003C/h2\u003E\n","\u003Cp\u003EIn this phase, you will use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_extract\"\u003EAI_EXTRACT\u003C/a\u003E with file input to extract structured data from food truck photos. AI_EXTRACT supports images directly via the FILE data type, allowing you to extract menu items, prices, and branding from photos stored in a stage.\u003C/p\u003E\n","\u003Cp\u003ETasty Bytes collects photos from their food truck locations — menu boards, signage, and truck exteriors. Extracting structured data from these images enables inventory tracking and brand compliance monitoring.\u003C/p\u003E\n","\u003Ch3\u003EExtract Menu Information from Images\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    IMAGE_PATH,\n    AI_EXTRACT(\n        file => TO_FILE('@TB_VOC.MEDIA.IMAGES', IMAGE_PATH),\n        responseFormat => {\n            'brand_name': 'What is the food truck or restaurant brand name visible?',\n            'car_color': 'Identify the color of the car.',\n            'menu_items': 'What menu items or dishes are visible?'\n        }\n    ) AS extracted_data\nFROM\n    TB_VOC.MEDIA.IMAGE_TABLE\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBatch Image Extraction Using Directory Table\u003C/h3\u003E\n","\u003Cp\u003EProcess all images in a stage using the directory table pattern:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    RELATIVE_PATH,\n    AI_EXTRACT(\n        file => TO_FILE('@TB_VOC.MEDIA.IMAGES', RELATIVE_PATH),\n        responseFormat => {\n            'brand_name': 'What is the food truck or restaurant brand name visible?',\n            'car_color': 'Identify the color of the car.',\n            'menu_items': 'What menu items or dishes are visible?'\n        }\n    ) AS extracted_data\nFROM\n    DIRECTORY(@TB_VOC.MEDIA.IMAGES);\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVideo Analysis with AI_COMPLETE\u003C/h2\u003E\n","\u003Cp\u003EIn this phase, you will use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_complete\"\u003EAI_COMPLETE\u003C/a\u003E with video file input to extract structured metadata from social media clips. Video processing with AI_COMPLETE is in public preview and uses the \u003Ccode\u003Egemini-3.1-pro\u003C/code\u003E model, which supports up to 10 video files per prompt with a combined payload of up to 100 MB.\u003C/p\u003E\n","\u003Cp\u003ETasty Bytes monitors social media for clips mentioning their food trucks. By analyzing video content, they can track brand mentions, assess sentiment, and identify which products are being featured organically.\u003C/p\u003E\n","\u003Ch3\u003EExtract Metadata from Video Clips\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    VIDEO_PATH,\n    AI_COMPLETE(\n        'gemini-3.1-pro',\n        'Analyze this food truck social media video. Extract structured metadata.',\n        TO_FILE('@TB_VOC.MEDIA.VIDEO', VIDEO_PATH),\n        {},\n        {\n            'type': 'json',\n            'schema': {\n                'type': 'object',\n                'properties': {\n                    'sentiment': {'type': 'string'},\n                    'summary': {'type': 'string'},\n                    'brands_mentioned': {'type': 'array', 'items': {'type': 'string'}},\n                    'dishes_shown': {'type': 'array', 'items': {'type': 'string'}},\n                    'setting': {'type': 'string'},\n                    'audience_type': {'type': 'string'}\n                },\n                'required': ['sentiment', 'summary', 'brands_mentioned', 'dishes_shown', 'setting', 'audience_type']\n            }\n        }\n    ) AS video_metadata\nFROM\n    TB_VOC.MEDIA.VIDEO_TABLE\nLIMIT 3;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStructured Video Extraction at Scale\u003C/h3\u003E\n","\u003Cp\u003ECreate a materialized table of video insights for downstream analytics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE TB_VOC.ANALYTICS.VIDEO_INSIGHTS AS\nSELECT\n    VIDEO_PATH,\n    AI_COMPLETE(\n        'gemini-3.1-pro',\n        'Analyze this food truck social media video clip. Identify the brand, products shown, overall sentiment, and a brief summary of what is happening.',\n        TO_FILE('@TB_VOC.MEDIA.VIDEO', VIDEO_PATH),\n        {},\n        {\n            'type': 'json',\n            'schema': {\n                'type': 'object',\n                'properties': {\n                    'sentiment': {'type': 'string'},\n                    'summary': {'type': 'string'},\n                    'brands_mentioned': {'type': 'array', 'items': {'type': 'string'}},\n                    'dishes_shown': {'type': 'array', 'items': {'type': 'string'}},\n                    'setting': {'type': 'string'},\n                    'audience_type': {'type': 'string'}\n                },\n                'required': ['sentiment', 'summary', 'brands_mentioned', 'dishes_shown', 'setting', 'audience_type']\n            }\n        }\n    ) AS video_metadata\nFROM\n    TB_VOC.MEDIA.VIDEO_TABLE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAudio Extraction\u003C/h2\u003E\n","\u003Cp\u003EIn this phase, you will use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_transcribe\"\u003EAI_TRANSCRIBE\u003C/a\u003E to convert voicemail recordings to text, and then use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_complete\"\u003EAI_COMPLETE\u003C/a\u003E to extract structured fields from the transcriptions.\u003C/p\u003E\n","\u003Cp\u003ETasty Bytes receives voicemail complaints from customers. By transcribing and extracting structured data from these recordings, they can automatically route issues to the right team and prioritize by urgency.\u003C/p\u003E\n","\u003Ch3\u003ETranscribe Audio Files\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    AUDIO_PATH,\n    AI_TRANSCRIBE(\n        TO_FILE('@TB_VOC.MEDIA.AUDIO', AUDIO_PATH)\n    ) AS transcription_result\nFROM\n    TB_VOC.MEDIA.AUDIO_TABLE\nLIMIT 3;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExtract Structured Fields from Transcriptions\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    AUDIO_PATH,\n    AI_COMPLETE(\n        'claude-sonnet-4-6',\n        CONCAT(\n            'Extract structured information from this customer voicemail transcription. ',\n            'Return a JSON object with: caller_issue, truck_name, urgency (low/medium/high), and action_required. ',\n            'Transcription: ',\n            AI_TRANSCRIBE(TO_FILE('@TB_VOC.MEDIA.AUDIO', AUDIO_PATH)):text::VARCHAR\n        ),\n        {},\n        {\n            'type': 'json',\n            'schema': {\n                'type': 'object',\n                'properties': {\n                    'caller_issue': {'type': 'string'},\n                    'truck_name': {'type': 'string'},\n                    'urgency': {'type': 'string'},\n                    'action_required': {'type': 'string'}\n                },\n                'required': ['caller_issue', 'truck_name', 'urgency', 'action_required']\n            }\n        }\n    ) AS extracted_issue\nFROM\n    TB_VOC.MEDIA.AUDIO_TABLE\nLIMIT 3;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUnified Batch Pipeline\u003C/h2\u003E\n","\u003Cp\u003EIn this phase, you will combine all modalities into a single extraction pipeline. The FILE data type in Snowflake allows you to consolidate text, images, video, and audio into one table and process them uniformly.\u003C/p\u003E\n","\u003Ch3\u003ECreate a Unified Extraction Table\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE TB_VOC.ANALYTICS.UNIFIED_EXTRACTIONS AS\n-- Text reviews\nSELECT\n    'text' AS modality,\n    REVIEW AS source_content,\n    NULL AS file_path,\n    AI_EXTRACT(\n        REVIEW,\n        {'truck_name': 'What food truck is mentioned?', 'issue_type': 'What issue did the customer experience?', 'dish': 'What dish is mentioned?'}\n    ):response AS extracted_data\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\n\nUNION ALL\n\n-- Images\nSELECT\n    'image' AS modality,\n    NULL AS source_content,\n    RELATIVE_PATH AS file_path,\n    AI_EXTRACT(\n        TO_FILE('@TB_VOC.MEDIA.IMAGES', RELATIVE_PATH),\n        {'brand_name': 'What is the food truck brand name?', 'car_color': 'Identify the color of the car.', 'menu_items': 'What menu items or dishes are visible?'}\n    ):response AS extracted_data\nFROM\n    DIRECTORY(@TB_VOC.MEDIA.IMAGES)\n\nUNION ALL\n\n-- Video\nSELECT\n    'video' AS modality,\n    NULL AS source_content,\n    VIDEO_PATH AS file_path,\n    PARSE_JSON(\n        REGEXP_REPLACE(\n            AI_COMPLETE(\n                'gemini-3.1-pro',\n                'Extract brand names and dishes shown from this food truck video. Return JSON with keys: brand_name, dishes_shown, sentiment. Return ONLY raw JSON, no markdown.',\n                TO_FILE('@TB_VOC.MEDIA.VIDEO', VIDEO_PATH)\n            ),\n            '```(json)?\\\\n?|```',\n            ''\n        )\n    ) AS extracted_data\nFROM\n    TB_VOC.MEDIA.VIDEO_TABLE\n\nUNION ALL\n\n-- Audio\nSELECT\n    'audio' AS modality,\n    NULL AS source_content,\n    AUDIO_PATH AS file_path,\n    PARSE_JSON(\n        REGEXP_REPLACE(\n            AI_COMPLETE(\n                'claude-sonnet-4-6',\n                CONCAT(\n                    'Extract: caller_issue, truck_name, urgency from this voicemail. Return ONLY raw JSON, no markdown: ',\n                    AI_TRANSCRIBE(TO_FILE('@TB_VOC.MEDIA.AUDIO', AUDIO_PATH)):text::VARCHAR\n                )\n            ),\n            '```(json)?\\\\n?|```',\n            ''\n        )\n    ) AS extracted_data\nFROM\n    TB_VOC.MEDIA.AUDIO_TABLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EQuery Unified Results\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Find all high-urgency issues across modalities\nSELECT\n    modality,\n    file_path,\n    extracted_data:truck_name::VARCHAR AS truck_name,\n    extracted_data:issue_type::VARCHAR AS issue_type,\n    extracted_data:urgency::VARCHAR AS urgency\nFROM\n    TB_VOC.ANALYTICS.UNIFIED_EXTRACTIONS\nWHERE\n    extracted_data:urgency::VARCHAR = 'high'\n    OR extracted_data:issue_type::VARCHAR NOT IN ('none', 'null')\n;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECortex AI Function Studio: Create\u003C/h2\u003E\n","\u003Cp\u003EIn this phase, you will use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-function-studio\"\u003ECortex AI Function Studio\u003C/a\u003E, Public Preview, to create a reusable custom AI function that encapsulates the text extraction logic from earlier. This allows you to call extraction as a simple SQL function across any table without rewriting prompts.\u003C/p\u003E\n","\u003Cp\u003EAI Function Studio provides a managed workflow to create, evaluate, and optimize custom AI functions. The function you create here uses AI_COMPLETE under the hood but is deployed as a standard SQL UDF that can be called from any query.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Cortex AI Function Studio uses stored procedures internally to create, evaluate, and optimize functions. These procedures are not intended to be called directly — their signatures and behavior may change without notice. Use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code\u003C/a\u003E as the primary interface for AI Function Studio workflows.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate the Extraction Function\u003C/h3\u003E\n","\u003Cp\u003EThe easiest way to create a custom AI function is through \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code\u003C/a\u003E, Snowflake Coding Agent. Cortex Code includes a built-in AI Function Studio skill that lets you describe what you want in natural language and handles the function creation automatically.\u003C/p\u003E\n","\u003Cp\u003EIn Cortex Code Snowsight's chat panel, type the following prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E/cortex-ai-function-studio Create a function that takes in the review from tb_voc.analytics.truck_review_v_samples and extract review into the following fields: truck_name, dish, issue_type, would_recommend (yes/no/unclear) by sysadmin role\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EDetect your intent\u003C/strong\u003E — It recognizes this as a CREATE workflow for a custom AI function\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECheck prerequisites\u003C/strong\u003E — It verifies your Snowflake connection, role privileges, and target schema\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInspect the source table\u003C/strong\u003E — It reads the schema of \u003Ccode\u003ETB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\u003C/code\u003E to understand the input column\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConfigure the function\u003C/strong\u003E — It determines the appropriate model, prompt template, input/output schema, and deploys the function using \u003Ccode\u003ESNOWFLAKE.CORTEX.CREATE_AI_FUNCTION\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EOnce complete, Cortex Code will confirm the function was created and show you a sample query to test it.\u003C/p\u003E\n","\u003Cp\u003ECortex Code uses an LLM under the hood, so its output is non-deterministic and may vary depending on your environment. Ideally, the function created should look like the following:\u003C/p\u003E\n","\u003Cp\u003EExample\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SNOWFLAKE.CORTEX.CREATE_AI_FUNCTION(\n  'TB_VOC.ANALYTICS.EXTRACT_REVIEW',\n  'claude-sonnet-4-6',\n  'You are a review extraction assistant. Extract structured information from food truck reviews. Be concise and accurate. If information is not clearly stated, use "unknown" for text fields and "unclear" for would_recommend.',\n  'Extract the following from this review:\\n- truck_name: the name of the food truck\\n- dish: the specific dish mentioned (if multiple, pick the primary one discussed)\\n- issue_type: the type of complaint or issue (use "none" if no issue)\\n- would_recommend: whether the reviewer would recommend (yes/no/unclear)\\n\\nReview: {REVIEW}',\n  [{'name': 'REVIEW', 'type': 'VARCHAR'}],\n  [{'name': 'truck_name', 'type': 'VARCHAR'}, {'name': 'dish', 'type': 'VARCHAR'}, {'name': 'issue_type', 'type': 'VARCHAR'}, {'name': 'would_recommend', 'type': 'VARCHAR'}],\n  NULL,\n  NULL,\n  NULL\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample output from the AI Function Studio CREATE workflow in Cortex Code:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/create_ai_function_studio.jpg?v=583da7ff\" alt=\"AI Function Studio Create Function\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ETest the Function\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE SYSADMIN;\nUSE TB_VOC.ANALYTICS;\n\nSELECT\n    REVIEW,\n    TB_VOC.ANALYTICS.EXTRACT_REVIEW(REVIEW) AS extracted\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAI Function Studio: Evaluate\u003C/h2\u003E\n","\u003Cp\u003ENow that you have a working extraction function, evaluate its accuracy against a labeled dataset. The evaluation measures how well your function's outputs match expected ground-truth values.\u003C/p\u003E\n","\u003Ch3\u003EPrepare Labeled Test Data\u003C/h3\u003E\n","\u003Cp\u003EThe setup.sql script created a labeled evaluation table \u003Ccode\u003ETB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA\u003C/code\u003E with manually verified extractions. This table has columns: \u003Ccode\u003EREVIEW_TEXT\u003C/code\u003E (input) and \u003Ccode\u003EEXPECTED_OUTPUT\u003C/code\u003E (VARIANT with the correct extraction).\u003C/p\u003E\n","\u003Cp\u003EPreview the evaluation data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    *\nFROM\n    TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun the Evaluation\u003C/h3\u003E\n","\u003Cp\u003EIn Cortex Code's chat panel, type the following prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E/cortex-ai-faunction-studio Evaluate TB_VOC.ANALYTICS.EXTRACT_REVIEW against test table TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA with input column REVIEW_TEXT and label column EXPECTED_OUTPUT with llm-judge by sysadmin role\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EDetect your intent\u003C/strong\u003E — It recognizes this as an EVALUATE workflow\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConfigure the evaluation\u003C/strong\u003E — It identifies the function, test table, input/label columns, and selects the appropriate metric\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERun the evaluation\u003C/strong\u003E — It calls \u003Ccode\u003ESNOWFLAKE.CORTEX.EVALUATE_AI_FUNCTION\u003C/code\u003E and returns the accuracy score\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ECortex Code uses an LLM under the hood, so its output is non-deterministic and may vary depending on your environment. Ideally, the evaluation call should look like the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE TB_VOC.ANALYTICS;\n\n\nCALL SNOWFLAKE.CORTEX.EVALUATE_AI_FUNCTION(\n    'TB_VOC.ANALYTICS.EXTRACT_REVIEW',\n    'TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA',\n    ARRAY_CONSTRUCT('REVIEW_TEXT'),\n    'EXPECTED_OUTPUT',\n    'llm_judge',\n    'claude-sonnet-4-6',\n    NULL,\n    NULL,\n    PARSE_JSON('{"task_description": "Extract structured fields (truck_name, dish, issue_type, would_recommend) from food truck reviews"}'),\n    500,\n    NULL,\n    NULL\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample output from the AI Function Studio EVALUATE workflow in Cortex Code:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/evaluate_ai_function_studio.jpg?v=583da7ff\" alt=\"AI Function Studio Evaluate Function\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe evaluation returns an overall score between 0.0 and 1.0. A score above 0.8 indicates good extraction quality. If the score is lower, the optimize step can help improve it.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAI Function Studio: Optimize\u003C/h2\u003E\n","\u003Cp\u003EIn this final phase, you will optimize your extraction function to improve accuracy. The optimizer iteratively modifies the function body — including prompts, model references, and SQL pre/post-processing — to find the best-performing variant.\u003C/p\u003E\n","\u003Ch3\u003ERun Optimization\u003C/h3\u003E\n","\u003Cp\u003EAfter running the evaluation prompt in the previous step, Cortex Code may automatically ask whether you'd like to proceed with optimization. If it does, go ahead and run it — select the models you want to compare and let the optimizer execute.\u003C/p\u003E\n","\u003Cp\u003EIf you're starting a fresh optimization session (or Cortex Code did not offer the follow-up), type the following prompt in Cortex Code's chat panel:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E/cortex-ai-function-studio　Optimize TB_VOC.ANALYTICS.EXTRACT_REVIEW against test table TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA \nwith input column REVIEW_TEXT and label column EXPECTED_OUTPUT for better accuracy using the same model claude-sonnet-4-6 with demo budget by sysadmin role\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENOTE: Snowflake Optimization offers different iteration budget, demo, light, medium and heavy. \u003Ccode\u003Edemo budget\u003C/code\u003E means the lightest iteration.\u003C/p\u003E\n","\u003Cp\u003ECortex Code uses an LLM under the hood, so its output is non-deterministic and may vary depending on your environment. Ideally, the optimization call should look like the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE TB_VOC.ANALYTICS;\n\nCALL SNOWFLAKE.CORTEX.OPTIMIZE_AI_FUNCTION(\n    'TB_VOC.ANALYTICS.EXTRACT_REVIEW',\n    'TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA',\n    'EXPECTED_OUTPUT',\n    ARRAY_CONSTRUCT('REVIEW_TEXT'),\n    'llm_judge',\n    ARRAY_CONSTRUCT('claude-sonnet-4-6'),\n    'claude-sonnet-4-6',\n    NULL,\n    'demo',\n    0.3,\n    0.0,\n    8192,\n    NULL,\n    NULL,\n    NULL,\n    NULL,\n    'body',\n    'ai_func_opt_EXTRACT_REVIEW_sonnet'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe optimizer will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EScore the current function body against training examples\u003C/li\u003E\u003Cli\u003EGenerate variations of the function body (modified prompts, SQL logic)\u003C/li\u003E\u003Cli\u003EKeep Pareto-optimal performers (best quality/cost tradeoffs)\u003C/li\u003E\u003Cli\u003EReturn the best-performing function body\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EView Optimization Results\u003C/h3\u003E\n","\u003Cp\u003EAfter optimization completes, check the experiment for results:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW RUN METRICS IN EXPERIMENT TB_VOC.ANALYTICS.AI_FUNC_OPT_EXTRACT_REVIEW_DEMO;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample output from the optimization in Cortex Code:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/ai_function_studio_optimize_result.jpg?v=583da7ff\" alt=\"AI Function Studio Optimization Results\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ETest the Optimized Function\u003C/h3\u003E\n","\u003Cp\u003EThe optimization automatically updates your function with the best-performing body. Test it again to see improved results:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    REVIEW,\n    EXTRACT_REVIEW_FIELDS(REVIEW):truck_name::VARCHAR AS truck_name,\n    EXTRACT_REVIEW_FIELDS(REVIEW):dish_mentioned::VARCHAR AS dish,\n    EXTRACT_REVIEW_FIELDS(REVIEW):issue_type::VARCHAR AS issue_type,\n    EXTRACT_REVIEW_FIELDS(REVIEW):would_recommend::VARCHAR AS recommendation\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've built a complete batch data extraction pipeline using Snowflake Cortex AI Functions, processing text reviews, images, video clips, and audio recordings — all within Snowflake's secure environment. You then used AI Function Studio to create a production-ready custom AI function, evaluated its accuracy, and optimized it for better performance.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EWith the completion of this quickstart, you have now:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUsed AI_EXTRACT to pull structured fields from text and image data at scale\u003C/li\u003E\u003Cli\u003EUsed AI_COMPLETE with gemini-3.1-pro to analyze video content and return structured JSON\u003C/li\u003E\u003Cli\u003EUsed AI_TRANSCRIBE combined with AI_COMPLETE to extract actionable data from audio\u003C/li\u003E\u003Cli\u003EBuilt a unified batch pipeline across all four modalities using the FILE data type\u003C/li\u003E\u003Cli\u003ECreated, evaluated, and optimized a custom AI function using AI Function Studio\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAppendix 1: Multi-Model Cost Optimization\u003C/h3\u003E\n","\u003Cp\u003EThe optimization in the previous section used a single model (\u003Ccode\u003Eclaude-sonnet-4-6\u003C/code\u003E). In production, you may want to compare your current model against additional cheaper alternatives to find the best cost/quality tradeoff. AI Function Studio supports passing multiple models in a single optimization call — it evaluates them concurrently and returns the Pareto-optimal result.\u003C/p\u003E\n","\u003Cp\u003ETo extend the optimization you already ran, type the following prompt in Cortex Code's chat panel:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EOptimize my function for more models, current model claude-sonnet-4-6 and a cheaper model\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code will add candidate models to the optimization, run them concurrently against your evaluation data, and present a comparison showing quality scores and cost for each model. You can then choose whether to deploy the cheaper variant or keep the original.\u003C/p\u003E\n","\u003Cp\u003EExample output from the multi-model optimization workflow in Cortex Code:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/optimize_cost_ai_function_studio.jpg?v=583da7ff\" alt=\"AI Function Studio Multi-Model Optimization\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n","\u003Cp\u003EWant to learn more about the tools and technologies used in this quickstart? Check out the following resources:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/aisql\"\u003ECortex AI Functions Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_extract\"\u003EAI_EXTRACT Reference\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-audio\"\u003ECortex AI Functions: Multimodal\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-cortex/cortex-ai-function-studio\"\u003EAI Function Studio\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/gain-insights-from-unstructured-data\"\u003EGitHub Repository\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Analyze documents, images, and audio with Snowflake Cortex AI Functions for unstructured data insights and extraction.","title":"Batch Data Extraction at Scale with Cortex AI Functions",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nThe fictitious food truck company, Tasty Bytes, receives thousands of customer reviews, food photos, social media video clips, and voicemail complaints across multiple channels. To improve operations, the company needs to extract structured, queryable data from all of this unstructured content at scale — turning free-text reviews into categorized issues, food truck photos into menu item inventories, video clips into brand mentions, and voicemails into actionable tickets.\n\nThis guide shows you how to build a batch data extraction pipeline entirely within Snowflake using [Cortex AI Functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql). You will use AI_EXTRACT for structured field extraction from text and images, AI_COMPLETE with video support (public preview) for multimodal analysis, and AI_TRANSCRIBE for audio processing. Finally, you will use [Cortex AI Function Studio](https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-cortex/cortex-ai-function-studio) to create a reusable custom AI function, evaluate its accuracy, and optimize it for production use.\n\n### Prerequisites\n* Familiarity with SQL\n* Familiarity with Snowflake\n* Familiarity with Snowflake Notebooks\n\n### What You'll Need\n\n* A Snowflake account in a cloud region where Cortex AI Functions are [supported](https://docs.snowflake.com/user-guide/snowflake-cortex/llm-functions#availability). If you do not have a Snowflake account, you can register for a [free trial account](https://signup.snowflake.com/?utm_cta=quickstarts_&_fsi=yYZEVo4S&_fsi=yYZEVo4S).\n  * Cortex AI Functions: AI_EXTRACT, AI_COMPLETE, AI_TRANSCRIBE\n  * Models: gemini-3.1-pro (video/audio), claude-sonnet-4-6 (text/images), arctic-extract (AI_EXTRACT)\n* Snowflake Notebooks enabled in your Snowflake account.\n\n### What You'll Learn \n\n* How to extract structured fields from free-text reviews using AI_EXTRACT\n* How to extract information from images using AI_EXTRACT with file input\n* How to analyze video content and extract metadata using AI_COMPLETE with structured output\n* How to transcribe audio and extract structured data from transcriptions\n* How to build a unified batch extraction pipeline across all modalities\n* How to create, evaluate, and optimize a custom AI function using AI Function Studio\n\n### What You'll Build \n* A batch data extraction pipeline that processes Tasty Bytes' multimodal customer feedback using **Snowflake Cortex AI Functions** within a **Snowflake Notebook**:\n  * Extract structured fields from text reviews with **AI_EXTRACT**\n  * Extract menu items and branding from food truck photos with **AI_EXTRACT**\n  * Extract metadata from social media video clips with **AI_COMPLETE** (gemini-3.1-pro)\n  * Transcribe voicemails and extract issue details with **AI_TRANSCRIBE** + **AI_COMPLETE**\n  * A custom AI function created, evaluated, and optimized with **AI Function Studio**\n\n\u003C!-- ------------------------ --\u003E\n## Setup Data\n\nThis phase focuses on initializing your Snowflake environment. You will use [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#), the Snowflake web interface, to:\n* Create Snowflake objects (warehouse, database, schema, raw tables)\n* Ingest review data from S3\n* Create stages for images, audio, and video files\n* Upload media files\n\n### Creating Objects and Loading Data\n\nWe will use the setup.sql file to automate the creation of the required infrastructure and load the sample text data.\n\n1. Download the [setup.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/gain-insights-from-unstructured-data/setup.sql) file from the [GitHub repository](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/gain-insights-from-unstructured-data)\n2. Open up a \u003Ca href=\"https://app.snowflake.com/_deeplink/#/workspaces?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=batch-data-extraction&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003EWorkspaces\u003C/a\u003E in Snowflake\n3. Copy and paste the contents of setup.sql or upload and run the file\n4. The script will:\n   - Create Snowflake objects (warehouse, database, schema, raw tables)\n   - Ingest shift and review data from S3\n   - Create the review view\n   - Create stages for images, audio, and video files\n   - Create tables for evaluation data (used in the AI Function Studio section)\n\n### Upload Media Files to Stages\n\nNow upload the media files into the dedicated stages created by setup.sql:\n\n1. Download [data.zip](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/gain-insights-from-unstructured-data/data.zip) and extract its contents\n2. Navigate to **Catalog** \u003E\u003E **Database Explorer**\n3. Upload Image Files:\n   * Select your database: **TB_VOC** \u003E\u003E **MEDIA** \u003E\u003E **Stages** \u003E\u003E **IMAGES**\n   * Click **+ Files** on the top right hand corner\n   * Click **Browse** and upload the files in the `images/` folder\n4. Upload Audio Files:\n   * Select your database: **TB_VOC** \u003E\u003E **MEDIA** \u003E\u003E **Stages** \u003E\u003E **AUDIO**\n   * Click **+ Files** on the top right hand corner\n   * Click **Browse** and upload the files in the `audio/` folder\n5. Upload Video Files:\n   * Select your database: **TB_VOC** \u003E\u003E **MEDIA** \u003E\u003E **Stages** \u003E\u003E **VIDEO**\n   * Click **+ Files** on the top right hand corner\n   * Click **Browse** and upload the files in the `video/` folder\n\n6. Run Post-Upload SQL:\n   * After uploading all files, open a new SQL worksheet in [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#)\n   * Load and execute the [**`setup-post-upload.sql`** file](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/gain-insights-from-unstructured-data/setup-post-upload.sql) to register the uploaded files into the corresponding tables\n   * This step is required to make the uploaded audio, video, and image files available for processing in subsequent steps\n\nYour Snowflake environment now contains the complete set of data across all modalities.\n\n\n\n\u003C!-- ------------------------ --\u003E\n## Setup Notebook\n\nThis phase prepares your execution environment by importing the primary code into a Snowflake Notebook.\n\n* Download the notebook **[batch_data_extraction.ipynb](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/gain-insights-from-unstructured-data/batch_data_extraction.ipynb)** from the GitHub repository\n* Select **Projects** \u003E\u003E **Notebooks** in [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#)\n* Click the **+ Notebook** drop-down and select **Import .ipynb file**\n* Select the batch_data_extraction.ipynb file\n* Provide a name for the notebook and select appropriate **database** `TB_VOC` and **schema** `ANALYTICS` for Notebook location\n* For **Runtime** select `Run on container`\n* Now you are ready to run the notebook by clicking the \"Run all\" button on the top right or running each cell individually\n\n\u003C!-- ------------------------ --\u003E\n## Text Extraction with AI_EXTRACT\n\nIn this phase, you will use [AI_EXTRACT](https://docs.snowflake.com/en/sql-reference/functions/ai_extract) to pull structured fields from free-text customer reviews. AI_EXTRACT is purpose-built for structured data extraction — you define the fields you want, and it returns a clean JSON object.\n\nTasty Bytes receives thousands of reviews in unstructured text. Rather than reading each one manually, AI_EXTRACT lets you define a schema and extract specific fields at scale across the entire review dataset.\n\n### Entity Extraction from Reviews\n\nThe following query extracts the truck name, dish mentioned, issue type, and recommendation intent from each review:\n\n```sql\nSELECT \n    REVIEW,\n    AI_EXTRACT(\n        text =\u003E REVIEW,\n        responseFormat =\u003E {\n            'truck_name': 'What food truck or brand is mentioned?',\n            'dish': 'What specific dish or menu item is mentioned?',\n            'issue_type': 'What type of issue did the customer experience (food quality, service, wait time, cleanliness, none)?',\n            'would_recommend': 'Would the customer recommend this food truck (yes, no, unclear)?'\n        }\n    ) AS extracted_fields\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 10;\n```\n\n### Extraction with Scores\n\nAI_EXTRACT supports confidence scores (preview) that indicate the model's certainty about each extracted value. Use scores to flag low-confidence extractions for human review:\n\n```sql\nSELECT \n    REVIEW,\n    AI_EXTRACT(\n        text =\u003E REVIEW,\n        responseFormat =\u003E {\n            'truck_name': 'What food truck or brand is mentioned?',\n            'dish': 'What specific dish or menu item is mentioned?',\n            'issue_type': 'What type of issue did the customer experience?'\n        },\n        scores =\u003E TRUE\n    ) AS extraction_with_scores\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 5;\n```\n\nThe result includes both the extracted values and a `scoring` object with confidence scores between 0 and 1 for each field. You can use these scores to build deterministic processing logic — for example, routing low-score extractions to a human review queue.\n\n### Batch Extraction at Scale\n\nTo run extraction across the entire dataset and flatten results into queryable columns:\n\n```sql\nCREATE OR REPLACE TABLE TB_VOC.ANALYTICS.EXTRACTED_REVIEWS AS\n(\n    SELECT\n        TRUCK_BRAND_NAME,\n        REVIEW,\n        AI_EXTRACT(\n            text =\u003E REVIEW,\n            responseFormat =\u003E {\n                'truck_name': 'What food truck or brand is mentioned?',\n                'dish': 'What specific dish or menu item is mentioned?',\n                'issue_type': 'What type of issue did the customer experience (food quality, service, wait time, cleanliness, none)?',\n                'would_recommend': 'Would the customer recommend this food truck (yes, no, unclear)?'\n            }\n        ):response AS extracted\n    FROM\n        TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\n);\n\n-- Query the flattened results\nSELECT \n    TRUCK_BRAND_NAME,\n    extracted:truck_name::VARCHAR AS truck_name,\n    extracted:dish::VARCHAR AS dish,\n    extracted:issue_type::VARCHAR AS issue_type,\n    extracted:would_recommend::VARCHAR AS would_recommend\nFROM\n    TB_VOC.ANALYTICS.EXTRACTED_REVIEWS\nWHERE\n    extracted:issue_type::VARCHAR != 'none'\nLIMIT 20\n;\n```\n\n### When to Use AI_COMPLETE Instead\n\nAI_EXTRACT is optimized for structured field extraction — it handles schema definition, output parsing, and confidence scoring automatically. However, some text extraction tasks require **reasoning**, **multi-step logic**, or **contextual interpretation** that go beyond direct field extraction. In these cases, [AI_COMPLETE](https://docs.snowflake.com/en/sql-reference/functions/ai_complete) gives you full control over the prompt and model behavior.\n\n**AI_COMPLETE enables:**\n- **Reasoning over context** — e.g., inferring root cause from multiple complaint signals\n- **Conditional logic** — e.g., \"If the issue is food-related AND the customer mentions illness, escalate to urgent\"\n- **Summarization with judgment** — e.g., generating a one-sentence action item for each review\n- **Custom output formats** — e.g., generating a severity score (1–5) with justification text\n\n```sql\n-- Example: AI_COMPLETE for reasoning-based extraction\nSELECT\n    REVIEW,\n    AI_COMPLETE(\n        'claude-sonnet-4-6',\n        CONCAT(\n            'Analyze this food truck review and determine:\\n',\n            '1. The root cause of any dissatisfaction\\n',\n            '2. A priority score (1-5) based on severity and business impact\\n',\n            '3. A recommended next action for the operations team\\n\\n',\n            'Review: ', REVIEW\n        ),\n        {},\n        {\n            'type': 'json',\n            'schema': {\n                'type': 'object',\n                'properties': {\n                    'root_cause': {'type': 'string'},\n                    'priority_score': {'type': 'integer'},\n                    'recommended_action': {'type': 'string'},\n                    'reasoning': {'type': 'string'}\n                },\n                'required': ['root_cause', 'priority_score', 'recommended_action', 'reasoning']\n            }\n        }\n    ) AS analysis\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 5;\n```\n\n### Decision Flowchart: AI_EXTRACT vs AI_COMPLETE\n\nUse the following flowchart to determine which function to use for your text extraction task:\n\n![AI_EXTRACT vs AI_COMPLETE Decision Flowchart](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/ai_extract_vs_ai_complete_flowchart.png?v=583da7ff)\n\n| Aspect | AI_EXTRACT | AI_COMPLETE |\n|--------|-----------|-------------|\n| Purpose | Extract predefined fields | Reasoning, generation, complex extraction |\n| Input definition | Schema via `responseFormat` | Free-form prompt instructions |\n| Output format | Automatically structured JSON | JSON via `structured_output` option |\n| Confidence scores | Available with `scores =\u003E TRUE` | Not built-in (can be prompted manually) |\n| Model selection | Automatic (arctic-extract) | Explicitly specified |\n| Best use cases | Names, dates, categories — clear values | Judgment, reasoning, summarization, action generation |\n\n\u003C!-- ------------------------ --\u003E\n## Image Extraction with AI_EXTRACT\n\nIn this phase, you will use [AI_EXTRACT](https://docs.snowflake.com/en/sql-reference/functions/ai_extract) with file input to extract structured data from food truck photos. AI_EXTRACT supports images directly via the FILE data type, allowing you to extract menu items, prices, and branding from photos stored in a stage.\n\nTasty Bytes collects photos from their food truck locations — menu boards, signage, and truck exteriors. Extracting structured data from these images enables inventory tracking and brand compliance monitoring.\n\n### Extract Menu Information from Images\n\n```sql\nSELECT\n    IMAGE_PATH,\n    AI_EXTRACT(\n        file =\u003E TO_FILE('@TB_VOC.MEDIA.IMAGES', IMAGE_PATH),\n        responseFormat =\u003E {\n            'brand_name': 'What is the food truck or restaurant brand name visible?',\n            'car_color': 'Identify the color of the car.',\n            'menu_items': 'What menu items or dishes are visible?'\n        }\n    ) AS extracted_data\nFROM\n    TB_VOC.MEDIA.IMAGE_TABLE\nLIMIT 5;\n```\n\n### Batch Image Extraction Using Directory Table\n\nProcess all images in a stage using the directory table pattern:\n\n```sql\nSELECT\n    RELATIVE_PATH,\n    AI_EXTRACT(\n        file =\u003E TO_FILE('@TB_VOC.MEDIA.IMAGES', RELATIVE_PATH),\n        responseFormat =\u003E {\n            'brand_name': 'What is the food truck or restaurant brand name visible?',\n            'car_color': 'Identify the color of the car.',\n            'menu_items': 'What menu items or dishes are visible?'\n        }\n    ) AS extracted_data\nFROM\n    DIRECTORY(@TB_VOC.MEDIA.IMAGES);\n```\n\n\u003C!-- ------------------------ --\u003E\n## Video Analysis with AI_COMPLETE\n\nIn this phase, you will use [AI_COMPLETE](https://docs.snowflake.com/en/sql-reference/functions/ai_complete) with video file input to extract structured metadata from social media clips. Video processing with AI_COMPLETE is in public preview and uses the `gemini-3.1-pro` model, which supports up to 10 video files per prompt with a combined payload of up to 100 MB.\n\nTasty Bytes monitors social media for clips mentioning their food trucks. By analyzing video content, they can track brand mentions, assess sentiment, and identify which products are being featured organically.\n\n### Extract Metadata from Video Clips\n\n```sql\nSELECT\n    VIDEO_PATH,\n    AI_COMPLETE(\n        'gemini-3.1-pro',\n        'Analyze this food truck social media video. Extract structured metadata.',\n        TO_FILE('@TB_VOC.MEDIA.VIDEO', VIDEO_PATH),\n        {},\n        {\n            'type': 'json',\n            'schema': {\n                'type': 'object',\n                'properties': {\n                    'sentiment': {'type': 'string'},\n                    'summary': {'type': 'string'},\n                    'brands_mentioned': {'type': 'array', 'items': {'type': 'string'}},\n                    'dishes_shown': {'type': 'array', 'items': {'type': 'string'}},\n                    'setting': {'type': 'string'},\n                    'audience_type': {'type': 'string'}\n                },\n                'required': ['sentiment', 'summary', 'brands_mentioned', 'dishes_shown', 'setting', 'audience_type']\n            }\n        }\n    ) AS video_metadata\nFROM\n    TB_VOC.MEDIA.VIDEO_TABLE\nLIMIT 3;\n```\n\n### Structured Video Extraction at Scale\n\nCreate a materialized table of video insights for downstream analytics:\n\n```sql\nCREATE OR REPLACE TABLE TB_VOC.ANALYTICS.VIDEO_INSIGHTS AS\nSELECT\n    VIDEO_PATH,\n    AI_COMPLETE(\n        'gemini-3.1-pro',\n        'Analyze this food truck social media video clip. Identify the brand, products shown, overall sentiment, and a brief summary of what is happening.',\n        TO_FILE('@TB_VOC.MEDIA.VIDEO', VIDEO_PATH),\n        {},\n        {\n            'type': 'json',\n            'schema': {\n                'type': 'object',\n                'properties': {\n                    'sentiment': {'type': 'string'},\n                    'summary': {'type': 'string'},\n                    'brands_mentioned': {'type': 'array', 'items': {'type': 'string'}},\n                    'dishes_shown': {'type': 'array', 'items': {'type': 'string'}},\n                    'setting': {'type': 'string'},\n                    'audience_type': {'type': 'string'}\n                },\n                'required': ['sentiment', 'summary', 'brands_mentioned', 'dishes_shown', 'setting', 'audience_type']\n            }\n        }\n    ) AS video_metadata\nFROM\n    TB_VOC.MEDIA.VIDEO_TABLE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Audio Extraction\n\nIn this phase, you will use [AI_TRANSCRIBE](https://docs.snowflake.com/en/sql-reference/functions/ai_transcribe) to convert voicemail recordings to text, and then use [AI_COMPLETE](https://docs.snowflake.com/en/sql-reference/functions/ai_complete) to extract structured fields from the transcriptions.\n\nTasty Bytes receives voicemail complaints from customers. By transcribing and extracting structured data from these recordings, they can automatically route issues to the right team and prioritize by urgency.\n\n### Transcribe Audio Files\n\n```sql\nSELECT\n    AUDIO_PATH,\n    AI_TRANSCRIBE(\n        TO_FILE('@TB_VOC.MEDIA.AUDIO', AUDIO_PATH)\n    ) AS transcription_result\nFROM\n    TB_VOC.MEDIA.AUDIO_TABLE\nLIMIT 3;\n```\n\n### Extract Structured Fields from Transcriptions\n\n```sql\nSELECT\n    AUDIO_PATH,\n    AI_COMPLETE(\n        'claude-sonnet-4-6',\n        CONCAT(\n            'Extract structured information from this customer voicemail transcription. ',\n            'Return a JSON object with: caller_issue, truck_name, urgency (low/medium/high), and action_required. ',\n            'Transcription: ',\n            AI_TRANSCRIBE(TO_FILE('@TB_VOC.MEDIA.AUDIO', AUDIO_PATH)):text::VARCHAR\n        ),\n        {},\n        {\n            'type': 'json',\n            'schema': {\n                'type': 'object',\n                'properties': {\n                    'caller_issue': {'type': 'string'},\n                    'truck_name': {'type': 'string'},\n                    'urgency': {'type': 'string'},\n                    'action_required': {'type': 'string'}\n                },\n                'required': ['caller_issue', 'truck_name', 'urgency', 'action_required']\n            }\n        }\n    ) AS extracted_issue\nFROM\n    TB_VOC.MEDIA.AUDIO_TABLE\nLIMIT 3;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Unified Batch Pipeline\n\nIn this phase, you will combine all modalities into a single extraction pipeline. The FILE data type in Snowflake allows you to consolidate text, images, video, and audio into one table and process them uniformly.\n\n### Create a Unified Extraction Table\n\n```sql\nCREATE OR REPLACE TABLE TB_VOC.ANALYTICS.UNIFIED_EXTRACTIONS AS\n-- Text reviews\nSELECT\n    'text' AS modality,\n    REVIEW AS source_content,\n    NULL AS file_path,\n    AI_EXTRACT(\n        REVIEW,\n        {'truck_name': 'What food truck is mentioned?', 'issue_type': 'What issue did the customer experience?', 'dish': 'What dish is mentioned?'}\n    ):response AS extracted_data\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\n\nUNION ALL\n\n-- Images\nSELECT\n    'image' AS modality,\n    NULL AS source_content,\n    RELATIVE_PATH AS file_path,\n    AI_EXTRACT(\n        TO_FILE('@TB_VOC.MEDIA.IMAGES', RELATIVE_PATH),\n        {'brand_name': 'What is the food truck brand name?', 'car_color': 'Identify the color of the car.', 'menu_items': 'What menu items or dishes are visible?'}\n    ):response AS extracted_data\nFROM\n    DIRECTORY(@TB_VOC.MEDIA.IMAGES)\n\nUNION ALL\n\n-- Video\nSELECT\n    'video' AS modality,\n    NULL AS source_content,\n    VIDEO_PATH AS file_path,\n    PARSE_JSON(\n        REGEXP_REPLACE(\n            AI_COMPLETE(\n                'gemini-3.1-pro',\n                'Extract brand names and dishes shown from this food truck video. Return JSON with keys: brand_name, dishes_shown, sentiment. Return ONLY raw JSON, no markdown.',\n                TO_FILE('@TB_VOC.MEDIA.VIDEO', VIDEO_PATH)\n            ),\n            '```(json)?\\\\n?|```',\n            ''\n        )\n    ) AS extracted_data\nFROM\n    TB_VOC.MEDIA.VIDEO_TABLE\n\nUNION ALL\n\n-- Audio\nSELECT\n    'audio' AS modality,\n    NULL AS source_content,\n    AUDIO_PATH AS file_path,\n    PARSE_JSON(\n        REGEXP_REPLACE(\n            AI_COMPLETE(\n                'claude-sonnet-4-6',\n                CONCAT(\n                    'Extract: caller_issue, truck_name, urgency from this voicemail. Return ONLY raw JSON, no markdown: ',\n                    AI_TRANSCRIBE(TO_FILE('@TB_VOC.MEDIA.AUDIO', AUDIO_PATH)):text::VARCHAR\n                )\n            ),\n            '```(json)?\\\\n?|```',\n            ''\n        )\n    ) AS extracted_data\nFROM\n    TB_VOC.MEDIA.AUDIO_TABLE;\n```\n\n### Query Unified Results\n\n```sql\n-- Find all high-urgency issues across modalities\nSELECT\n    modality,\n    file_path,\n    extracted_data:truck_name::VARCHAR AS truck_name,\n    extracted_data:issue_type::VARCHAR AS issue_type,\n    extracted_data:urgency::VARCHAR AS urgency\nFROM\n    TB_VOC.ANALYTICS.UNIFIED_EXTRACTIONS\nWHERE\n    extracted_data:urgency::VARCHAR = 'high'\n    OR extracted_data:issue_type::VARCHAR NOT IN ('none', 'null')\n;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Cortex AI Function Studio: Create\n\nIn this phase, you will use [Cortex AI Function Studio](https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-function-studio), Public Preview, to create a reusable custom AI function that encapsulates the text extraction logic from earlier. This allows you to call extraction as a simple SQL function across any table without rewriting prompts.\n\nAI Function Studio provides a managed workflow to create, evaluate, and optimize custom AI functions. The function you create here uses AI_COMPLETE under the hood but is deployed as a standard SQL UDF that can be called from any query.\n\n\u003E **Important:** Cortex AI Function Studio uses stored procedures internally to create, evaluate, and optimize functions. These procedures are not intended to be called directly — their signatures and behavior may change without notice. Use [Cortex Code](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) as the primary interface for AI Function Studio workflows.\n\n### Create the Extraction Function\n\nThe easiest way to create a custom AI function is through [Cortex Code](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code), Snowflake Coding Agent. Cortex Code includes a built-in AI Function Studio skill that lets you describe what you want in natural language and handles the function creation automatically.\n\nIn Cortex Code Snowsight's chat panel, type the following prompt:\n\n```\n/cortex-ai-function-studio Create a function that takes in the review from tb_voc.analytics.truck_review_v_samples and extract review into the following fields: truck_name, dish, issue_type, would_recommend (yes/no/unclear) by sysadmin role\n```\n\nCortex Code will:\n1. **Detect your intent** — It recognizes this as a CREATE workflow for a custom AI function\n2. **Check prerequisites** — It verifies your Snowflake connection, role privileges, and target schema\n3. **Inspect the source table** — It reads the schema of `TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE` to understand the input column\n4. **Configure the function** — It determines the appropriate model, prompt template, input/output schema, and deploys the function using `SNOWFLAKE.CORTEX.CREATE_AI_FUNCTION`\n\nOnce complete, Cortex Code will confirm the function was created and show you a sample query to test it.\n\nCortex Code uses an LLM under the hood, so its output is non-deterministic and may vary depending on your environment. Ideally, the function created should look like the following:\n\nExample\n```sql\nCALL SNOWFLAKE.CORTEX.CREATE_AI_FUNCTION(\n  'TB_VOC.ANALYTICS.EXTRACT_REVIEW',\n  'claude-sonnet-4-6',\n  'You are a review extraction assistant. Extract structured information from food truck reviews. Be concise and accurate. If information is not clearly stated, use \"unknown\" for text fields and \"unclear\" for would_recommend.',\n  'Extract the following from this review:\\n- truck_name: the name of the food truck\\n- dish: the specific dish mentioned (if multiple, pick the primary one discussed)\\n- issue_type: the type of complaint or issue (use \"none\" if no issue)\\n- would_recommend: whether the reviewer would recommend (yes/no/unclear)\\n\\nReview: {REVIEW}',\n  [{'name': 'REVIEW', 'type': 'VARCHAR'}],\n  [{'name': 'truck_name', 'type': 'VARCHAR'}, {'name': 'dish', 'type': 'VARCHAR'}, {'name': 'issue_type', 'type': 'VARCHAR'}, {'name': 'would_recommend', 'type': 'VARCHAR'}],\n  NULL,\n  NULL,\n  NULL\n);\n```\n\nExample output from the AI Function Studio CREATE workflow in Cortex Code:\n\n![AI Function Studio Create Function](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/create_ai_function_studio.jpg?v=583da7ff)\n\n### Test the Function\n\n```sql\nUSE ROLE SYSADMIN;\nUSE TB_VOC.ANALYTICS;\n\nSELECT\n    REVIEW,\n    TB_VOC.ANALYTICS.EXTRACT_REVIEW(REVIEW) AS extracted\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 5;\n```\n\n\u003C!-- ------------------------ --\u003E\n## AI Function Studio: Evaluate\n\nNow that you have a working extraction function, evaluate its accuracy against a labeled dataset. The evaluation measures how well your function's outputs match expected ground-truth values.\n\n### Prepare Labeled Test Data\n\nThe setup.sql script created a labeled evaluation table `TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA` with manually verified extractions. This table has columns: `REVIEW_TEXT` (input) and `EXPECTED_OUTPUT` (VARIANT with the correct extraction).\n\n\nPreview the evaluation data:\n\n```sql\nSELECT\n    *\nFROM\n    TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA\nLIMIT 5;\n```\n\n### Run the Evaluation\n\nIn Cortex Code's chat panel, type the following prompt:\n\n```\n/cortex-ai-faunction-studio Evaluate TB_VOC.ANALYTICS.EXTRACT_REVIEW against test table TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA with input column REVIEW_TEXT and label column EXPECTED_OUTPUT with llm-judge by sysadmin role\n```\n\nCortex Code will:\n1. **Detect your intent** — It recognizes this as an EVALUATE workflow\n2. **Configure the evaluation** — It identifies the function, test table, input/label columns, and selects the appropriate metric\n3. **Run the evaluation** — It calls `SNOWFLAKE.CORTEX.EVALUATE_AI_FUNCTION` and returns the accuracy score\n\nCortex Code uses an LLM under the hood, so its output is non-deterministic and may vary depending on your environment. Ideally, the evaluation call should look like the following:\n\n```sql\nUSE TB_VOC.ANALYTICS;\n\n\nCALL SNOWFLAKE.CORTEX.EVALUATE_AI_FUNCTION(\n    'TB_VOC.ANALYTICS.EXTRACT_REVIEW',\n    'TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA',\n    ARRAY_CONSTRUCT('REVIEW_TEXT'),\n    'EXPECTED_OUTPUT',\n    'llm_judge',\n    'claude-sonnet-4-6',\n    NULL,\n    NULL,\n    PARSE_JSON('{\"task_description\": \"Extract structured fields (truck_name, dish, issue_type, would_recommend) from food truck reviews\"}'),\n    500,\n    NULL,\n    NULL\n);\n```\n\nExample output from the AI Function Studio EVALUATE workflow in Cortex Code:\n\n![AI Function Studio Evaluate Function](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/evaluate_ai_function_studio.jpg?v=583da7ff)\n\nThe evaluation returns an overall score between 0.0 and 1.0. A score above 0.8 indicates good extraction quality. If the score is lower, the optimize step can help improve it.\n\n\u003C!-- ------------------------ --\u003E\n## AI Function Studio: Optimize\n\nIn this final phase, you will optimize your extraction function to improve accuracy. The optimizer iteratively modifies the function body — including prompts, model references, and SQL pre/post-processing — to find the best-performing variant.\n\n### Run Optimization\n\nAfter running the evaluation prompt in the previous step, Cortex Code may automatically ask whether you'd like to proceed with optimization. If it does, go ahead and run it — select the models you want to compare and let the optimizer execute.\n\nIf you're starting a fresh optimization session (or Cortex Code did not offer the follow-up), type the following prompt in Cortex Code's chat panel:\n\n```\n/cortex-ai-function-studio　Optimize TB_VOC.ANALYTICS.EXTRACT_REVIEW against test table TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA \nwith input column REVIEW_TEXT and label column EXPECTED_OUTPUT for better accuracy using the same model claude-sonnet-4-6 with demo budget by sysadmin role\n```\n\nNOTE: Snowflake Optimization offers different iteration budget, demo, light, medium and heavy. `demo budget` means the lightest iteration.\n\nCortex Code uses an LLM under the hood, so its output is non-deterministic and may vary depending on your environment. Ideally, the optimization call should look like the following:\n\n\n\n```sql\nUSE TB_VOC.ANALYTICS;\n\nCALL SNOWFLAKE.CORTEX.OPTIMIZE_AI_FUNCTION(\n    'TB_VOC.ANALYTICS.EXTRACT_REVIEW',\n    'TB_VOC.ANALYTICS.EXTRACTION_EVAL_DATA',\n    'EXPECTED_OUTPUT',\n    ARRAY_CONSTRUCT('REVIEW_TEXT'),\n    'llm_judge',\n    ARRAY_CONSTRUCT('claude-sonnet-4-6'),\n    'claude-sonnet-4-6',\n    NULL,\n    'demo',\n    0.3,\n    0.0,\n    8192,\n    NULL,\n    NULL,\n    NULL,\n    NULL,\n    'body',\n    'ai_func_opt_EXTRACT_REVIEW_sonnet'\n);\n```\n\nThe optimizer will:\n1. Score the current function body against training examples\n2. Generate variations of the function body (modified prompts, SQL logic)\n3. Keep Pareto-optimal performers (best quality/cost tradeoffs)\n4. Return the best-performing function body\n\n### View Optimization Results\n\nAfter optimization completes, check the experiment for results:\n\n```sql\nSHOW RUN METRICS IN EXPERIMENT TB_VOC.ANALYTICS.AI_FUNC_OPT_EXTRACT_REVIEW_DEMO;\n```\n\nExample output from the optimization in Cortex Code:\n\n![AI Function Studio Optimization Results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/ai_function_studio_optimize_result.jpg?v=583da7ff)\n\n### Test the Optimized Function\n\nThe optimization automatically updates your function with the best-performing body. Test it again to see improved results:\n\n```sql\nSELECT\n    REVIEW,\n    EXTRACT_REVIEW_FIELDS(REVIEW):truck_name::VARCHAR AS truck_name,\n    EXTRACT_REVIEW_FIELDS(REVIEW):dish_mentioned::VARCHAR AS dish,\n    EXTRACT_REVIEW_FIELDS(REVIEW):issue_type::VARCHAR AS issue_type,\n    EXTRACT_REVIEW_FIELDS(REVIEW):would_recommend::VARCHAR AS recommendation\nFROM\n    TB_VOC.ANALYTICS.TRUCK_REVIEWS_V_SAMPLE\nLIMIT 10;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nCongratulations! You've built a complete batch data extraction pipeline using Snowflake Cortex AI Functions, processing text reviews, images, video clips, and audio recordings — all within Snowflake's secure environment. You then used AI Function Studio to create a production-ready custom AI function, evaluated its accuracy, and optimized it for better performance.\n\n### What You Learned\nWith the completion of this quickstart, you have now: \n  * Used AI_EXTRACT to pull structured fields from text and image data at scale\n  * Used AI_COMPLETE with gemini-3.1-pro to analyze video content and return structured JSON\n  * Used AI_TRANSCRIBE combined with AI_COMPLETE to extract actionable data from audio\n  * Built a unified batch pipeline across all four modalities using the FILE data type\n  * Created, evaluated, and optimized a custom AI function using AI Function Studio\n\n### Appendix 1: Multi-Model Cost Optimization\n\nThe optimization in the previous section used a single model (`claude-sonnet-4-6`). In production, you may want to compare your current model against additional cheaper alternatives to find the best cost/quality tradeoff. AI Function Studio supports passing multiple models in a single optimization call — it evaluates them concurrently and returns the Pareto-optimal result.\n\nTo extend the optimization you already ran, type the following prompt in Cortex Code's chat panel:\n\n```\nOptimize my function for more models, current model claude-sonnet-4-6 and a cheaper model\n```\n\nCortex Code will add candidate models to the optimization, run them concurrently against your evaluation data, and present a comparison showing quality scores and cost for each model. You can then choose whether to deploy the cheaper variant or keep the original.\n\nExample output from the multi-model optimization workflow in Cortex Code:\n\n![AI Function Studio Multi-Model Optimization](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/gain-insights-from-unstructured-data/optimize_cost_ai_function_studio.jpg?v=583da7ff)\n\n### Resources\n\nWant to learn more about the tools and technologies used in this quickstart? Check out the following resources:\n\n* [Cortex AI Functions Documentation](https://docs.snowflake.com/user-guide/snowflake-cortex/aisql)\n* [AI_EXTRACT Reference](https://docs.snowflake.com/en/sql-reference/functions/ai_extract)\n* [Cortex AI Functions: Multimodal](https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-audio)\n* [AI Function Studio](https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-cortex/cortex-ai-function-studio)\n* [GitHub Repository](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/gain-insights-from-unstructured-data)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-4e1ff1584b","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-fed71d0356",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-e80a16bdca","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-15",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-85cf479ddf","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. It may be out of date with current Snowflake 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Data\u003C/h2\u003E","\u003Ch2\u003ESetup Notebook\u003C/h2\u003E","\u003Ch2\u003EText Extraction with AI_EXTRACT\u003C/h2\u003E","\u003Ch2\u003EImage Extraction with AI_EXTRACT\u003C/h2\u003E","\u003Ch2\u003EVideo Analysis with AI_COMPLETE\u003C/h2\u003E","\u003Ch2\u003EAudio Extraction\u003C/h2\u003E","\u003Ch2\u003EUnified Batch Pipeline\u003C/h2\u003E","\u003Ch2\u003ECortex AI Function Studio: Create\u003C/h2\u003E","\u003Ch2\u003EAI Function Studio: Evaluate\u003C/h2\u003E","\u003Ch2\u003EAI Function Studio: Optimize\u003C/h2\u003E","\u003Ch2\u003EConclusion and 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