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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}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"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-5e05b8ec44","isDeveloperGuidesPage":false,"quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["Build an AI-Powered Analytics App with Natural 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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EA \u003Cstrong\u003Esemantic view\u003C/strong\u003E is a Snowflake object that describes the business meaning of your data. Think of it as a business glossary layered on top of your database -- it tells AI tools what your columns mean, how your tables relate to each other, and what metrics to calculate. Once defined, tools like Cortex Analyst can translate plain English questions into accurate SQL automatically, without the user needing to know any SQL.\u003C/p\u003E\n","\u003Cp\u003EWithout a semantic view, an analyst would need to know that \u003Ccode\u003ESS_QUANTITY\u003C/code\u003E means "store sales quantity" or that \u003Ccode\u003ESTORE_SALES\u003C/code\u003E joins to \u003Ccode\u003ECUSTOMER\u003C/code\u003E on \u003Ccode\u003ESS_CUSTOMER_SK\u003C/code\u003E. With a semantic view, cryptic column names become readable business terms (e.g., \u003Ccode\u003ESS_QUANTITY\u003C/code\u003E → \u003Ccode\u003Estore_sales_quantity\u003C/code\u003E, \u003Ccode\u003ESS_NET_PROFIT\u003C/code\u003E → \u003Ccode\u003Enet_profit\u003C/code\u003E), table relationships are defined once, and every downstream tool (Cortex Analyst, Streamlit chatbots, Snowflake CoWork) benefits from both the clearer names and the business logic.\u003C/p\u003E\n","\u003Cp\u003EIn this guide, you will use \u003Cstrong\u003Enatural language prompts\u003C/strong\u003E in \u003Cstrong\u003ESnowflake CoCo\u003C/strong\u003E (Snowflake's AI coding assistant) to build the entire solution. Instead of writing SQL and Python by hand, you paste a prompt, and Snowflake CoCo generates and runs the code for you.\u003C/p\u003E\n<!-- https://excalidraw.com/#json=O3elexOP5-tpVfE7ckwCH,7jqIJl1q-Ub_WFaqLcBRGw  -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-powered-analytics-app-with-nl/ai-analytics-app-diagram.png?v=e0b0bbaa\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use Snowflake CoCo to build a complete analytics solution using natural language prompts\u003C/li\u003E\u003Cli\u003EHow semantic views model your data with tables, relationships, dimensions, facts, and metrics\u003C/li\u003E\u003Cli\u003EHow to query semantic views using the \u003Ccode\u003ESEMANTIC_VIEW()\u003C/code\u003E SQL function\u003C/li\u003E\u003Cli\u003EHow to use Cortex Analyst for natural language-to-SQL over a semantic view\u003C/li\u003E\u003Cli\u003EHow to build a Streamlit in Snowflake chatbot powered by Cortex Analyst\u003C/li\u003E\u003Cli\u003EHow the semantic view connects to the broader Snowflake CoWork stack\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA conversational analytics chatbot deployed as a Streamlit in Snowflake app. Users type natural language questions like "What are the top-selling brands in Texas?" and the app calls Cortex Analyst, which reads your semantic view, generates the correct SQL, runs it, and displays results with interactive charts.\u003C/p\u003E\n","\u003Ch3\u003EHow to Use These Prompts\u003C/h3\u003E\n","\u003Cp\u003EThis guide is organized as a series of \u003Cstrong\u003E5 natural language prompts\u003C/strong\u003E (plus an optional cleanup). For each step:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen the appropriate Snowflake editor (Workspace Notebook or Streamlit in Snowflake)\u003C/li\u003E\u003Cli\u003EPaste the prompt into \u003Cstrong\u003ESnowflake CoCo\u003C/strong\u003E (the AI assistant)\u003C/li\u003E\u003Cli\u003ESnowflake CoCo generates and runs the SQL or Python code for you\u003C/li\u003E\u003Cli\u003EReview the output to confirm it matches the expected results\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EPrompts 1 through 4 and the optional Cleanup prompt run in a \u003Cstrong\u003EWorkspace Notebook\u003C/strong\u003E. The Streamlit Chatbot prompt (Prompt 5) runs in \u003Cstrong\u003EStreamlit in Snowflake (SiS)\u003C/strong\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Because Snowflake CoCo uses LLMs to generate code from natural language, the exact code it produces may differ from the examples shown in this guide. The logic and results should be equivalent, but variable names, formatting, and SQL style may vary between runs.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrefer code?\u003C/strong\u003E If you'd like to see the exact SQL and Python instead of natural language prompts, check out the \u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/build-an-ai-powered-analytics-app/build-an-ai-powered-analytics-app.ipynb\"\u003Ecompanion notebook\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EACCOUNTADMIN role access (required for creating semantic views)\u003C/li\u003E\u003Cli\u003EAccess to the \u003Ccode\u003ESNOWFLAKE_SAMPLE_DATA\u003C/code\u003E shared database (available by default on all Snowflake accounts)\u003C/li\u003E\u003Cli\u003EA running warehouse (e.g., \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EIn this section, you will set up a database, schema, and views over Snowflake's built-in TPC-DS sample data. These views give you retail data (customers, items, stores, sales) to model without uploading anything.\u003C/p\u003E\n","\u003Ch3\u003EWhere to Run\u003C/h3\u003E\n","\u003Cp\u003EOpen a new Workspace Notebook in Snowsight by clicking on Projects > Workspaces from the sidebar. Next, click on "+ Add new" > Notebook.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: You may be prompted to create a new service on which the notebook is run, if this is your first time running the notebook.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPrompt 1\u003C/h3\u003E\n","\u003Cp\u003EPaste this into Snowflake CoCo:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESet up an environment for a semantic view tutorial. Use ACCOUNTADMIN role and COMPUTE_WH warehouse. Create a SAMPLE_DATA database with a TPCDS_SF10TCL schema, then create 6 views (CUSTOMER, CUSTOMER_DEMOGRAPHICS, DATE_DIM, ITEM, STORE, STORE_SALES) that each select all columns from the matching table in SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL. Verify with SHOW VIEWS, then preview 5 rows from STORE_SALES.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to expect\u003C/strong\u003E: 6 views created, a SHOW VIEWS result listing them, and a preview of raw STORE_SALES data with cryptic column names like \u003Ccode\u003ESS_SOLD_DATE_SK\u003C/code\u003E. These cryptic names are exactly why you need a semantic view -- the next step gives them business-friendly names.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate a Semantic View\u003C/h2\u003E\n","\u003Cp\u003EA semantic view has five sections:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Etables\u003C/strong\u003E: Which physical tables to include, with aliases and primary keys. Primary keys uniquely identify each row in a table, which the semantic view needs to correctly join data.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Erelationships\u003C/strong\u003E: How tables join via foreign keys. A foreign key is a column in one table that points to a row in another table. For example, each sale links to a customer through a foreign key.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Efacts\u003C/strong\u003E: Raw numeric values that can be measured (cost, price, quantity). These are unaggregated row-level numbers.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Edimensions\u003C/strong\u003E: Categorical attributes for grouping and filtering (brand, state, year). These answer "by what?" and "where?"\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Emetrics\u003C/strong\u003E: Aggregated calculations like \u003Ccode\u003ESUM(SS_QUANTITY)\u003C/code\u003E. The \u003Ccode\u003EWITH SYNONYMS\u003C/code\u003E clause helps Cortex Analyst recognize alternate phrasings.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrompt 2\u003C/h3\u003E\n","\u003Cp\u003EPaste this into Snowflake CoCo:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate a semantic view called TPCDS_SEMANTIC_VIEW_SM over the 6 views in SAMPLE_DATA.TPCDS_SF10TCL. Use SQL syntax (CREATE SEMANTIC VIEW, not YAML).\n\nInclude all 6 tables with aliases DATE for DATE_DIM, DEMO for CUSTOMER_DEMOGRAPHICS, and STORESALES for STORE_SALES. Define primary keys for each table and foreign key relationships from STORESALES to the other 5 tables.\n\nFor facts, include item cost, item price, store tax rate, and sales quantity. For dimensions, include customer attributes (birth year, country), date attributes (date, month, week, year), demographic attributes (credit rating, marital status), item attributes (brand, category, class), store attributes (market, square footage, state, country), and the surrogate key columns. For metrics, define total cost as SUM of item cost, total sales price as SUM of SS_SALES_PRICE, and total sales quantity as SUM of SS_QUANTITY with synonyms 'total sales quantity' and 'total sales amount'.\n\nAfter creating it, verify with SHOW SEMANTIC VIEWS and use DESC SEMANTIC VIEW with the flow operator (->>) to list all metrics and dimensions with their parent tables.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to expect\u003C/strong\u003E: The semantic view is created successfully. \u003Ccode\u003ESHOW SEMANTIC VIEWS\u003C/code\u003E lists it, and the \u003Ccode\u003EDESC\u003C/code\u003E output shows each metric and dimension with its parent table. You now have a complete business model on top of your retail data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery Semantic Views\u003C/h2\u003E\n","\u003Cp\u003EThe \u003Ccode\u003ESEMANTIC_VIEW()\u003C/code\u003E function lets you query a semantic view with SQL. You specify which dimensions and metrics you want, and the semantic view handles all the joins automatically.\u003C/p\u003E\n","\u003Ch3\u003EPrompt 3\u003C/h3\u003E\n","\u003Cp\u003EPaste this into Snowflake CoCo:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWrite 3 SEMANTIC_VIEW() queries against TPCDS_SEMANTIC_VIEW_SM in separate cells:\n\n1. What are the top 10 book brands by sales quantity in Texas in December 2002?\n2. Which item categories generated the most revenue in December 2002? Show quantity, sales price, and cost side by side (top 10).\n3. What are the purchasing patterns across item categories and customer credit ratings in July 2002? Show the top 20 by sales quantity.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to expect\u003C/strong\u003E: Three result sets showing retail analytics. The semantic view handles all joins automatically, with no JOIN clauses needed.\u003C/p\u003E\n","\u003Ch3\u003EQuery 1: Top Brands in Texas\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected output:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E#\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBRAND\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECATEGORY\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EYEAR\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMONTH\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESTATE\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETOTALSALESQUANTITY\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Emaximaxi #1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,295,870\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eamalgunivamalg #1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,212,167\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eimportounivamalg #8\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,190,557\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Escholarmaxi #8\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,142,716\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eedu packunivamalg #7\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,115,186\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E6\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Escholarunivamalg #7\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,115,046\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E7\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Escholarunivamalg #8\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,109,582\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eexportiunivamalg #4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,050,496\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E9\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eedu packunivamalg #1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,044,801\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Emaximaxi #2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E12\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,031,232\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EQuery 2: Revenue by Category\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected output:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E#\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECATEGORY\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETOTALSALESQUANTITY\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETOTALSALESPRICE\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETOTALCOST\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJewelry\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,621,648,147\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,464,601,821.81\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,621,542,749.27\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EElectronics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,610,399,852\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,458,991,769.62\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,611,085,628.19\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EShoes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,609,138,575\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,456,238,423.66\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,608,459,675.77\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMusic\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,600,476,410\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,449,655,506.66\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,600,476,085.26\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChildren\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,599,410,238\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,448,763,810.14\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,600,276,652.09\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E6\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESports\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,593,506,426\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,443,422,975.07\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,592,666,550.02\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E7\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHome\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,568,720,116\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,423,952,324.14\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,567,662,115.11\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMen\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,533,955,269\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,399,863,845.91\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,534,921,556.42\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E9\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWomen\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,527,154,871\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,394,914,055.14\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,527,355,370.41\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,506,010,090\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3,380,120,928.25\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4,507,832,214.86\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EQuery 3: Purchasing Patterns by Credit Rating\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected output (first 10 of 20 rows):\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E#\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECATEGORY\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECREDIT_RATING\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETOTALSALESQUANTITY\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJewelry\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigh Risk\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E331,469,969\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EElectronics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnknown\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E331,326,959\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJewelry\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELow Risk\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E331,302,893\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJewelry\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnknown\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E331,288,124\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJewelry\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGood\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E330,967,806\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E6\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EElectronics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGood\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E330,779,045\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E7\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EShoes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnknown\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E330,608,471\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EShoes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigh Risk\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E330,416,355\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E9\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EElectronics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigh Risk\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E330,365,401\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EElectronics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELow Risk\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E330,262,865\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECortex Analyst\u003C/h2\u003E\n","\u003Cp\u003ECortex Analyst is Snowflake's \u003Cstrong\u003Etext-to-SQL\u003C/strong\u003E engine -- it translates plain English questions into SQL queries that a database can execute. It reads your semantic view definition and generates accurate \u003Ccode\u003ESEMANTIC_VIEW()\u003C/code\u003E SQL queries, so users can ask questions about data without writing any SQL themselves.\u003C/p\u003E\n","\u003Ch3\u003EHow It Works\u003C/h3\u003E\n","\u003Cp\u003EWhen you ask a question like "What are the top selling brands in Texas?", Cortex Analyst:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EReads the semantic view definition (dimensions, metrics, relationships, synonyms)\u003C/li\u003E\u003Cli\u003EGenerates the correct \u003Ccode\u003ESEMANTIC_VIEW()\u003C/code\u003E SQL query\u003C/li\u003E\u003Cli\u003EReturns the results\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe semantic view you created is the knowledge base that makes Cortex Analyst accurate.\u003C/p\u003E\n","\u003Ch3\u003EPrompt 4\u003C/h3\u003E\n","\u003Cp\u003EPaste this into Snowflake CoCo:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGenerate a clickable Snowsight URL that opens Cortex Analyst. Use CURRENT_ORGANIZATION_NAME() and CURRENT_ACCOUNT_NAME() to build the URL dynamically.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to expect\u003C/strong\u003E: A clickable URL that opens Cortex Analyst directly on your semantic view.\u003C/p\u003E\n","\u003Ch3\u003ETry Natural Language Queries\u003C/h3\u003E\n","\u003Cp\u003EOpen the resulting URL in your browser to access the Cortex Analyst interface for your semantic view. Try these questions:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"Show me the top selling brands in Texas in 2002"\u003C/li\u003E\u003Cli\u003E"What is the total sales quantity by state?"\u003C/li\u003E\u003Cli\u003E"Which item categories have the highest total cost?"\u003C/li\u003E\u003Cli\u003E"Compare sales across credit ratings for the Books category"\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECortex Analyst translates each question into a \u003Ccode\u003ESEMANTIC_VIEW()\u003C/code\u003E SQL query and returns results. Notice how synonyms (like "total sales amount" for TotalSalesQuantity) help it understand alternate phrasings.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStreamlit Chatbot\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStreamlit in Snowflake (SiS)\u003C/strong\u003E lets you build interactive Python web apps that run entirely inside Snowflake, no external servers, no deployment pipelines. You write Python with the Streamlit library, and Snowflake hosts and runs it for you.\u003C/p\u003E\n","\u003Cp\u003EIn this section, you will build a chatbot app that takes a natural language question from the user, sends it to the Cortex Analyst REST API along with your semantic view, and displays the answer with data tables and interactive charts. The result is a "talk to your data" experience that anyone in your organization can use.\u003C/p\u003E\n","\u003Ch3\u003EArchitecture\u003C/h3\u003E\n","\u003Cp\u003EThe app flow is:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EUser types a natural language question\u003C/li\u003E\u003Cli\u003EThe app sends it to the Cortex Analyst REST API with your semantic view\u003C/li\u003E\u003Cli\u003ECortex Analyst generates SQL and returns results\u003C/li\u003E\u003Cli\u003EThe app displays the answer with data tables and interactive charts\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhere to Run\u003C/h3\u003E\n","\u003Cp\u003EIn Snowsight, navigate to \u003Cstrong\u003EProjects > Streamlit > + Streamlit App\u003C/strong\u003E and configure:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EApp name\u003C/strong\u003E: \u003Ccode\u003ESemantic_View_Chatbot\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse\u003C/strong\u003E: Your warehouse (e.g., \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase/Schema\u003C/strong\u003E: \u003Ccode\u003ESAMPLE_DATA\u003C/code\u003E / \u003Ccode\u003ETPCDS_SF10TCL\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENext, use Snowflake CoCo in the SiS editor to generate the app.\u003C/p\u003E\n","\u003Ch3\u003EPrompt 5\u003C/h3\u003E\n","\u003Cp\u003EPaste this into Snowflake CoCo:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EBuild a Streamlit chatbot that queries retail sales data via Snowflake Cortex Analyst using semantic view `SAMPLE_DATA.TPCDS_SF10TCL.TPCDS_SEMANTIC_VIEW_SM`.\n\n**Connection** (`@st.cache_resource`): Try `get_active_session()` first, fall back to `snowflake.connector.connect(connection_name=os.getenv("SNOWFLAKE_CONNECTION_NAME", "devrel"))` wrapped in `Session.builder.configs({"connection": conn}).create()`.\n\n**API call**: POST `/api/v2/cortex/analyst/message`, body (dict, not JSON string): `{"messages": [...], "semantic_view": "..."}`. User messages: `{"role": "user", "content": [{"type": "text", "text": question}]}`.\n- **SiS**: `_snowflake.send_snow_api_request("POST", endpoint, {}, {}, body, {}, 30000)`, which returns `{"status": int, "content": str}`, so parse: `json.loads(resp["content"])` after checking `resp["status"] < 400`.\n- **Local**: `requests.post(url, json=body, headers={"Authorization": f'Snowflake Token="{conn._rest.token}"'}, timeout=60)` where `conn = session.connection`, `host = conn.host.replace("_", "-").lower()`.\n\n**Response**: `response["message"]["content"]` is a list of blocks with `type` = `"text"`, `"sql"` (has `"statement"`), or `"suggestions"` (has `"suggestions"` list). Render text as markdown, SQL in an expander with a "Run SQL" button + dataframe + bar chart, suggestions as clickable buttons.\n\n**Key patterns**: Use `on_click` callbacks (not button return values). Deterministic widget keys via `f"msg_{idx}_{bidx}"` (same prefix for new and replayed messages). Use `role="assistant"` in `st.chat_message`. Keep separate display (`role="assistant"`) and API (`role="analyst"`) message lists. Don't pass `key=` to `st.expander()`.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to expect\u003C/strong\u003E: A chatbot app generated by Snowflake CoCo. The app displays a chat interface where users type questions, Cortex Analyst generates SQL, and results appear with data tables and interactive charts.\u003C/p\u003E\n","\u003Ch3\u003ETest the Chatbot\u003C/h3\u003E\n","\u003Cp\u003ETry these questions in your chatbot:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"What are the top 10 brands by total sales quantity?"\u003C/li\u003E\u003Cli\u003E"Show me sales by state for 2002"\u003C/li\u003E\u003Cli\u003E"Which item categories generate the most revenue?"\u003C/li\u003E\u003Cli\u003E"Compare married vs single customer spending"\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EMulti-Turn Conversations\u003C/h3\u003E\n","\u003Cp\u003EThe app sends the full message history to Cortex Analyst, so follow-up questions work:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFirst: "Show me total sales by state"\u003C/li\u003E\u003Cli\u003EFollow-up: "Now filter to just Texas"\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECortex Analyst remembers the context from prior messages in the conversation.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup (Optional)\u003C/h2\u003E\n","\u003Cp\u003EThis section removes all objects created in this guide. Only run this if you want to clean up your environment. The underlying \u003Ccode\u003ESNOWFLAKE_SAMPLE_DATA\u003C/code\u003E shared database is untouched.\u003C/p\u003E\n","\u003Ch3\u003EPrompt 6\u003C/h3\u003E\n","\u003Cp\u003EPaste this into Snowflake CoCo (in your Workspace Notebook):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EClean up the following objects created in this tutorial. Use IF EXISTS on everything and run each DROP in a separate cell:\n\n1. Semantic view: SAMPLE_DATA.TPCDS_SF10TCL.TPCDS_SEMANTIC_VIEW_SM\n2. Views in SAMPLE_DATA.TPCDS_SF10TCL: CUSTOMER, CUSTOMER_DEMOGRAPHICS, DATE_DIM, ITEM, STORE, STORE_SALES\n3. Schema: SAMPLE_DATA.TPCDS_SF10TCL\n4. Database: SAMPLE_DATA\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to expect\u003C/strong\u003E: All tutorial objects removed. The Streamlit app can be deleted separately from Snowsight under \u003Cstrong\u003EProjects > Streamlit\u003C/strong\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've built a conversational analytics app using natural language prompts with the help of Snowflake CoCo. We went from raw data to a working chatbot in 5 prompts.\u003C/p\u003E\n","\u003Ch3\u003EKey Takeaways\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe \u003Cstrong\u003Esemantic view\u003C/strong\u003E is the foundation. Define your data well once, and every downstream tool benefits.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E reads the semantic view and translates plain English into accurate SQL. It powers both the Streamlit chatbot and the built-in Snowsight UI.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake CoCo\u003C/strong\u003E lets data analysts build the entire stack (views, semantic models, queries, apps) without writing SQL or Python by hand. You describe what you want, and Snowflake CoCo generates the code.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhy This Matters for Data Analysts\u003C/h3\u003E\n","\u003Cp\u003EThis workflow removes the biggest bottleneck in analytics: translating business questions into code. Instead of writing complex multi-table JOINs, you define the business model once in a semantic view and let AI handle the rest. Non-technical users can then ask questions in plain English through the chatbot, reducing the back-and-forth between analysts and stakeholders.\u003C/p\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n","\u003Cp\u003EThe semantic view you built here also works with \u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E. By adding a Cortex Agent on top of Cortex Analyst, you can create an organization-wide AI assistant that answers data questions across multiple semantic views and data sources.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use Snowflake CoCo's natural language prompts to build a complete analytics stack\u003C/li\u003E\u003Cli\u003EHow semantic views model your data with tables, relationships, dimensions, facts, and metrics\u003C/li\u003E\u003Cli\u003EHow to query semantic views using the \u003Ccode\u003ESEMANTIC_VIEW()\u003C/code\u003E function\u003C/li\u003E\u003Cli\u003EHow Cortex Analyst translates natural language into SQL using the semantic view as its knowledge base\u003C/li\u003E\u003Cli\u003EHow to build a Streamlit in Snowflake chatbot that calls the Cortex Analyst REST API\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EDocumentation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view\"\u003ECREATE SEMANTIC VIEW\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/semantic_view\"\u003ESEMANTIC_VIEW Function\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Build an AI-Powered Analytics App with Natural Language","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nA **semantic view** is a Snowflake object that describes the business meaning of your data. Think of it as a business glossary layered on top of your database -- it tells AI tools what your columns mean, how your tables relate to each other, and what metrics to calculate. Once defined, tools like Cortex Analyst can translate plain English questions into accurate SQL automatically, without the user needing to know any SQL.\n\nWithout a semantic view, an analyst would need to know that `SS_QUANTITY` means \"store sales quantity\" or that `STORE_SALES` joins to `CUSTOMER` on `SS_CUSTOMER_SK`. With a semantic view, cryptic column names become readable business terms (e.g., `SS_QUANTITY` → `store_sales_quantity`, `SS_NET_PROFIT` → `net_profit`), table relationships are defined once, and every downstream tool (Cortex Analyst, Streamlit chatbots, Snowflake CoWork) benefits from both the clearer names and the business logic.\n\nIn this guide, you will use **natural language prompts** in **Snowflake CoCo** (Snowflake's AI coding assistant) to build the entire solution. Instead of writing SQL and Python by hand, you paste a prompt, and Snowflake CoCo generates and runs the code for you.\n\n\u003C!-- https://excalidraw.com/#json=O3elexOP5-tpVfE7ckwCH,7jqIJl1q-Ub_WFaqLcBRGw  --\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-powered-analytics-app-with-nl/ai-analytics-app-diagram.png?v=e0b0bbaa)\n\n### What You'll Learn\n\n- How to use Snowflake CoCo to build a complete analytics solution using natural language prompts\n- How semantic views model your data with tables, relationships, dimensions, facts, and metrics\n- How to query semantic views using the `SEMANTIC_VIEW()` SQL function\n- How to use Cortex Analyst for natural language-to-SQL over a semantic view\n- How to build a Streamlit in Snowflake chatbot powered by Cortex Analyst\n- How the semantic view connects to the broader Snowflake CoWork stack\n\n### What You'll Build\n\nA conversational analytics chatbot deployed as a Streamlit in Snowflake app. Users type natural language questions like \"What are the top-selling brands in Texas?\" and the app calls Cortex Analyst, which reads your semantic view, generates the correct SQL, runs it, and displays results with interactive charts.\n\n### How to Use These Prompts\n\nThis guide is organized as a series of **5 natural language prompts** (plus an optional cleanup). For each step:\n\n1. Open the appropriate Snowflake editor (Workspace Notebook or Streamlit in Snowflake)\n2. Paste the prompt into **Snowflake CoCo** (the AI assistant)\n3. Snowflake CoCo generates and runs the SQL or Python code for you\n4. Review the output to confirm it matches the expected results\n\nPrompts 1 through 4 and the optional Cleanup prompt run in a **Workspace Notebook**. The Streamlit Chatbot prompt (Prompt 5) runs in **Streamlit in Snowflake (SiS)**.\n\n \u003E **Note:** Because Snowflake CoCo uses LLMs to generate code from natural language, the exact code it produces may differ from the examples shown in this guide. The logic and results should be equivalent, but variable names, formatting, and SQL style may vary between runs.\n\n\u003E **Prefer code?** If you'd like to see the exact SQL and Python instead of natural language prompts, check out the [companion notebook](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/build-an-ai-powered-analytics-app/build-an-ai-powered-analytics-app.ipynb).\n\n### Prerequisites\n\n- Access to a [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)\n- ACCOUNTADMIN role access (required for creating semantic views)\n- Access to the `SNOWFLAKE_SAMPLE_DATA` shared database (available by default on all Snowflake accounts)\n- A running warehouse (e.g., `COMPUTE_WH`)\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\nIn this section, you will set up a database, schema, and views over Snowflake's built-in TPC-DS sample data. These views give you retail data (customers, items, stores, sales) to model without uploading anything.\n\n### Where to Run\n\nOpen a new Workspace Notebook in Snowsight by clicking on Projects \u003E Workspaces from the sidebar. Next, click on \"+ Add new\" \u003E Notebook.\n\n\u003E Note: You may be prompted to create a new service on which the notebook is run, if this is your first time running the notebook.\n\n### Prompt 1\n\nPaste this into Snowflake CoCo:\n\n```\nSet up an environment for a semantic view tutorial. Use ACCOUNTADMIN role and COMPUTE_WH warehouse. Create a SAMPLE_DATA database with a TPCDS_SF10TCL schema, then create 6 views (CUSTOMER, CUSTOMER_DEMOGRAPHICS, DATE_DIM, ITEM, STORE, STORE_SALES) that each select all columns from the matching table in SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL. Verify with SHOW VIEWS, then preview 5 rows from STORE_SALES.\n```\n\n**What to expect**: 6 views created, a SHOW VIEWS result listing them, and a preview of raw STORE_SALES data with cryptic column names like `SS_SOLD_DATE_SK`. These cryptic names are exactly why you need a semantic view -- the next step gives them business-friendly names.\n\n\u003C!-- ------------------------ --\u003E\n## Create a Semantic View\n\nA semantic view has five sections:\n\n- **tables**: Which physical tables to include, with aliases and primary keys. Primary keys uniquely identify each row in a table, which the semantic view needs to correctly join data.\n- **relationships**: How tables join via foreign keys. A foreign key is a column in one table that points to a row in another table. For example, each sale links to a customer through a foreign key.\n- **facts**: Raw numeric values that can be measured (cost, price, quantity). These are unaggregated row-level numbers.\n- **dimensions**: Categorical attributes for grouping and filtering (brand, state, year). These answer \"by what?\" and \"where?\"\n- **metrics**: Aggregated calculations like `SUM(SS_QUANTITY)`. The `WITH SYNONYMS` clause helps Cortex Analyst recognize alternate phrasings.\n\n### Prompt 2\n\nPaste this into Snowflake CoCo:\n\n```\nCreate a semantic view called TPCDS_SEMANTIC_VIEW_SM over the 6 views in SAMPLE_DATA.TPCDS_SF10TCL. Use SQL syntax (CREATE SEMANTIC VIEW, not YAML).\n\nInclude all 6 tables with aliases DATE for DATE_DIM, DEMO for CUSTOMER_DEMOGRAPHICS, and STORESALES for STORE_SALES. Define primary keys for each table and foreign key relationships from STORESALES to the other 5 tables.\n\nFor facts, include item cost, item price, store tax rate, and sales quantity. For dimensions, include customer attributes (birth year, country), date attributes (date, month, week, year), demographic attributes (credit rating, marital status), item attributes (brand, category, class), store attributes (market, square footage, state, country), and the surrogate key columns. For metrics, define total cost as SUM of item cost, total sales price as SUM of SS_SALES_PRICE, and total sales quantity as SUM of SS_QUANTITY with synonyms 'total sales quantity' and 'total sales amount'.\n\nAfter creating it, verify with SHOW SEMANTIC VIEWS and use DESC SEMANTIC VIEW with the flow operator (-\u003E\u003E) to list all metrics and dimensions with their parent tables.\n```\n\n**What to expect**: The semantic view is created successfully. `SHOW SEMANTIC VIEWS` lists it, and the `DESC` output shows each metric and dimension with its parent table. You now have a complete business model on top of your retail data.\n\n\u003C!-- ------------------------ --\u003E\n## Query Semantic Views\n\nThe `SEMANTIC_VIEW()` function lets you query a semantic view with SQL. You specify which dimensions and metrics you want, and the semantic view handles all the joins automatically.\n\n### Prompt 3\n\nPaste this into Snowflake CoCo:\n\n```\nWrite 3 SEMANTIC_VIEW() queries against TPCDS_SEMANTIC_VIEW_SM in separate cells:\n\n1. What are the top 10 book brands by sales quantity in Texas in December 2002?\n2. Which item categories generated the most revenue in December 2002? Show quantity, sales price, and cost side by side (top 10).\n3. What are the purchasing patterns across item categories and customer credit ratings in July 2002? Show the top 20 by sales quantity.\n```\n\n**What to expect**: Three result sets showing retail analytics. The semantic view handles all joins automatically, with no JOIN clauses needed.\n\n### Query 1: Top Brands in Texas\n\n**Expected output:**\n\n| # | BRAND | CATEGORY | YEAR | MONTH | STATE | TOTALSALESQUANTITY |\n|---|-------|----------|------|-------|-------|--------------------|\n| 1 | maximaxi #1 | Books | 2002 | 12 | TX | 4,295,870 |\n| 2 | amalgunivamalg #1 | Books | 2002 | 12 | TX | 4,212,167 |\n| 3 | importounivamalg #8 | Books | 2002 | 12 | TX | 4,190,557 |\n| 4 | scholarmaxi #8 | Books | 2002 | 12 | TX | 4,142,716 |\n| 5 | edu packunivamalg #7 | Books | 2002 | 12 | TX | 4,115,186 |\n| 6 | scholarunivamalg #7 | Books | 2002 | 12 | TX | 4,115,046 |\n| 7 | scholarunivamalg #8 | Books | 2002 | 12 | TX | 4,109,582 |\n| 8 | exportiunivamalg #4 | Books | 2002 | 12 | TX | 4,050,496 |\n| 9 | edu packunivamalg #1 | Books | 2002 | 12 | TX | 4,044,801 |\n| 10 | maximaxi #2 | Books | 2002 | 12 | TX | 4,031,232 |\n\n### Query 2: Revenue by Category\n\n**Expected output:**\n\n| # | CATEGORY | TOTALSALESQUANTITY | TOTALSALESPRICE | TOTALCOST |\n|---|----------|--------------------|-----------------|-----------|\n| 1 | Jewelry | 4,621,648,147 | 3,464,601,821.81 | 4,621,542,749.27 |\n| 2 | Electronics | 4,610,399,852 | 3,458,991,769.62 | 4,611,085,628.19 |\n| 3 | Shoes | 4,609,138,575 | 3,456,238,423.66 | 4,608,459,675.77 |\n| 4 | Music | 4,600,476,410 | 3,449,655,506.66 | 4,600,476,085.26 |\n| 5 | Children | 4,599,410,238 | 3,448,763,810.14 | 4,600,276,652.09 |\n| 6 | Sports | 4,593,506,426 | 3,443,422,975.07 | 4,592,666,550.02 |\n| 7 | Home | 4,568,720,116 | 3,423,952,324.14 | 4,567,662,115.11 |\n| 8 | Men | 4,533,955,269 | 3,399,863,845.91 | 4,534,921,556.42 |\n| 9 | Women | 4,527,154,871 | 3,394,914,055.14 | 4,527,355,370.41 |\n| 10 | Books | 4,506,010,090 | 3,380,120,928.25 | 4,507,832,214.86 |\n\n### Query 3: Purchasing Patterns by Credit Rating\n\n**Expected output (first 10 of 20 rows):**\n\n| # | CATEGORY | CREDIT_RATING | TOTALSALESQUANTITY |\n|---|----------|---------------|--------------------|\n| 1 | Jewelry | High Risk | 331,469,969 |\n| 2 | Electronics | Unknown | 331,326,959 |\n| 3 | Jewelry | Low Risk | 331,302,893 |\n| 4 | Jewelry | Unknown | 331,288,124 |\n| 5 | Jewelry | Good | 330,967,806 |\n| 6 | Electronics | Good | 330,779,045 |\n| 7 | Shoes | Unknown | 330,608,471 |\n| 8 | Shoes | High Risk | 330,416,355 |\n| 9 | Electronics | High Risk | 330,365,401 |\n| 10 | Electronics | Low Risk | 330,262,865 |\n\n\u003C!-- ------------------------ --\u003E\n## Cortex Analyst\n\nCortex Analyst is Snowflake's **text-to-SQL** engine -- it translates plain English questions into SQL queries that a database can execute. It reads your semantic view definition and generates accurate `SEMANTIC_VIEW()` SQL queries, so users can ask questions about data without writing any SQL themselves.\n\n### How It Works\n\nWhen you ask a question like \"What are the top selling brands in Texas?\", Cortex Analyst:\n\n1. Reads the semantic view definition (dimensions, metrics, relationships, synonyms)\n2. Generates the correct `SEMANTIC_VIEW()` SQL query\n3. Returns the results\n\nThe semantic view you created is the knowledge base that makes Cortex Analyst accurate.\n\n### Prompt 4\n\nPaste this into Snowflake CoCo:\n\n```\nGenerate a clickable Snowsight URL that opens Cortex Analyst. Use CURRENT_ORGANIZATION_NAME() and CURRENT_ACCOUNT_NAME() to build the URL dynamically.\n```\n\n**What to expect**: A clickable URL that opens Cortex Analyst directly on your semantic view.\n\n### Try Natural Language Queries\n\nOpen the resulting URL in your browser to access the Cortex Analyst interface for your semantic view. Try these questions:\n\n- \"Show me the top selling brands in Texas in 2002\"\n- \"What is the total sales quantity by state?\"\n- \"Which item categories have the highest total cost?\"\n- \"Compare sales across credit ratings for the Books category\"\n\nCortex Analyst translates each question into a `SEMANTIC_VIEW()` SQL query and returns results. Notice how synonyms (like \"total sales amount\" for TotalSalesQuantity) help it understand alternate phrasings.\n\n\u003C!-- ------------------------ --\u003E\n## Streamlit Chatbot\n\n**Streamlit in Snowflake (SiS)** lets you build interactive Python web apps that run entirely inside Snowflake, no external servers, no deployment pipelines. You write Python with the Streamlit library, and Snowflake hosts and runs it for you.\n\nIn this section, you will build a chatbot app that takes a natural language question from the user, sends it to the Cortex Analyst REST API along with your semantic view, and displays the answer with data tables and interactive charts. The result is a \"talk to your data\" experience that anyone in your organization can use.\n\n### Architecture\n\nThe app flow is:\n\n1. User types a natural language question\n2. The app sends it to the Cortex Analyst REST API with your semantic view\n3. Cortex Analyst generates SQL and returns results\n4. The app displays the answer with data tables and interactive charts\n\n### Where to Run\n\nIn Snowsight, navigate to **Projects \u003E Streamlit \u003E + Streamlit App** and configure:\n\n- **App name**: `Semantic_View_Chatbot`\n- **Warehouse**: Your warehouse (e.g., `COMPUTE_WH`)\n- **Database/Schema**: `SAMPLE_DATA` / `TPCDS_SF10TCL`\n\nNext, use Snowflake CoCo in the SiS editor to generate the app.\n\n### Prompt 5\n\nPaste this into Snowflake CoCo:\n\n```\nBuild a Streamlit chatbot that queries retail sales data via Snowflake Cortex Analyst using semantic view `SAMPLE_DATA.TPCDS_SF10TCL.TPCDS_SEMANTIC_VIEW_SM`.\n\n**Connection** (`@st.cache_resource`): Try `get_active_session()` first, fall back to `snowflake.connector.connect(connection_name=os.getenv(\"SNOWFLAKE_CONNECTION_NAME\", \"devrel\"))` wrapped in `Session.builder.configs({\"connection\": conn}).create()`.\n\n**API call**: POST `/api/v2/cortex/analyst/message`, body (dict, not JSON string): `{\"messages\": [...], \"semantic_view\": \"...\"}`. User messages: `{\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": question}]}`.\n- **SiS**: `_snowflake.send_snow_api_request(\"POST\", endpoint, {}, {}, body, {}, 30000)`, which returns `{\"status\": int, \"content\": str}`, so parse: `json.loads(resp[\"content\"])` after checking `resp[\"status\"] \u003C 400`.\n- **Local**: `requests.post(url, json=body, headers={\"Authorization\": f'Snowflake Token=\"{conn._rest.token}\"'}, timeout=60)` where `conn = session.connection`, `host = conn.host.replace(\"_\", \"-\").lower()`.\n\n**Response**: `response[\"message\"][\"content\"]` is a list of blocks with `type` = `\"text\"`, `\"sql\"` (has `\"statement\"`), or `\"suggestions\"` (has `\"suggestions\"` list). Render text as markdown, SQL in an expander with a \"Run SQL\" button + dataframe + bar chart, suggestions as clickable buttons.\n\n**Key patterns**: Use `on_click` callbacks (not button return values). Deterministic widget keys via `f\"msg_{idx}_{bidx}\"` (same prefix for new and replayed messages). Use `role=\"assistant\"` in `st.chat_message`. Keep separate display (`role=\"assistant\"`) and API (`role=\"analyst\"`) message lists. Don't pass `key=` to `st.expander()`.\n```\n\n**What to expect**: A chatbot app generated by Snowflake CoCo. The app displays a chat interface where users type questions, Cortex Analyst generates SQL, and results appear with data tables and interactive charts.\n\n### Test the Chatbot\n\nTry these questions in your chatbot:\n\n- \"What are the top 10 brands by total sales quantity?\"\n- \"Show me sales by state for 2002\"\n- \"Which item categories generate the most revenue?\"\n- \"Compare married vs single customer spending\"\n\n### Multi-Turn Conversations\n\nThe app sends the full message history to Cortex Analyst, so follow-up questions work:\n\n- First: \"Show me total sales by state\"\n- Follow-up: \"Now filter to just Texas\"\n\nCortex Analyst remembers the context from prior messages in the conversation.\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup (Optional)\n\nThis section removes all objects created in this guide. Only run this if you want to clean up your environment. The underlying `SNOWFLAKE_SAMPLE_DATA` shared database is untouched.\n\n### Prompt 6\n\nPaste this into Snowflake CoCo (in your Workspace Notebook):\n\n```\nClean up the following objects created in this tutorial. Use IF EXISTS on everything and run each DROP in a separate cell:\n\n1. Semantic view: SAMPLE_DATA.TPCDS_SF10TCL.TPCDS_SEMANTIC_VIEW_SM\n2. Views in SAMPLE_DATA.TPCDS_SF10TCL: CUSTOMER, CUSTOMER_DEMOGRAPHICS, DATE_DIM, ITEM, STORE, STORE_SALES\n3. Schema: SAMPLE_DATA.TPCDS_SF10TCL\n4. Database: SAMPLE_DATA\n```\n\n**What to expect**: All tutorial objects removed. The Streamlit app can be deleted separately from Snowsight under **Projects \u003E Streamlit**.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You've built a conversational analytics app using natural language prompts with the help of Snowflake CoCo. We went from raw data to a working chatbot in 5 prompts.\n\n### Key Takeaways\n\n- The **semantic view** is the foundation. Define your data well once, and every downstream tool benefits.\n- **Cortex Analyst** reads the semantic view and translates plain English into accurate SQL. It powers both the Streamlit chatbot and the built-in Snowsight UI.\n- **Snowflake CoCo** lets data analysts build the entire stack (views, semantic models, queries, apps) without writing SQL or Python by hand. You describe what you want, and Snowflake CoCo generates the code.\n\n### Why This Matters for Data Analysts\n\nThis workflow removes the biggest bottleneck in analytics: translating business questions into code. Instead of writing complex multi-table JOINs, you define the business model once in a semantic view and let AI handle the rest. Non-technical users can then ask questions in plain English through the chatbot, reducing the back-and-forth between analysts and stakeholders.\n\n### Next Steps\n\nThe semantic view you built here also works with **Snowflake CoWork**. By adding a Cortex Agent on top of Cortex Analyst, you can create an organization-wide AI assistant that answers data questions across multiple semantic views and data sources.\n\n### What You Learned\n\n- How to use Snowflake CoCo's natural language prompts to build a complete analytics stack\n- How semantic views model your data with tables, relationships, dimensions, facts, and metrics\n- How to query semantic views using the `SEMANTIC_VIEW()` function\n- How Cortex Analyst translates natural language into SQL using the semantic view as its knowledge base\n- How to build a Streamlit in Snowflake chatbot that calls the Cortex Analyst REST API\n\n### Related Resources\n\nDocumentation:\n- [CREATE SEMANTIC VIEW](https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view)\n- [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)\n- [Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n- [SEMANTIC_VIEW Function](https://docs.snowflake.com/en/sql-reference/functions/semantic_view)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-f410c60c8e","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-095470864f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-d49db40045","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-02",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-b6ddfbd91b","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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