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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-811bd008c7","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["Holly - Financial Research Assistant with Snowflake CoWork"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Colm Moynihan","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Cortex Analyst","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/cortex-analyst","tagIcon":""},"quickstartHeroForkRepoLink":{"id":"button-9a5c819026","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/holly-financial-research-assistant"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Holly - Financial Research Assistant with Snowflake 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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-hero","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/holly-financial-research-assistant"},"flexible_column_cont":{"id":"flexible-column-container-0ccb1fe0d7","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-12ee7d749c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-8ffb9c4337","description":"","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/holly-financial-research-assistant/holly.png?v=8283a585\" alt=\"Holly\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHolly\u003C/strong\u003E is an AI-powered financial research assistant built on Snowflake CoWork. It enables portfolio managers, analysts, and traders to perform comprehensive stock research using natural language across structured and unstructured financial data.\u003C/p\u003E\n","\u003Ch3\u003EUse Case\u003C/h3\u003E\n","\u003Cp\u003EYou are a financial analyst in a hedge fund looking into AI Native Tech Stocks. You have 4 in mind: \u003Cstrong\u003EGOOGL\u003C/strong\u003E, \u003Cstrong\u003EMSFT\u003C/strong\u003E, \u003Cstrong\u003EAMZN\u003C/strong\u003E, and \u003Cstrong\u003ENVDA\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EBecause you know NVIDIA makes 90% of the GPUs for AI, you reckon this is worth investigating further. But you want to drill down on the \u003Cstrong\u003Eunstructured data\u003C/strong\u003E — 10-K, 8-K, 10-Q filings, investor call transcripts, and annual reports — to get a holistic view of the security based on all the data available, not just the fundamental data which is all structured.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to source financial data from Snowflake Marketplace\u003C/li\u003E\u003Cli\u003EHow to create Cortex Search services for SEC filings and earnings transcripts\u003C/li\u003E\u003Cli\u003EHow to create Semantic Views for Cortex Analyst\u003C/li\u003E\u003Cli\u003EHow to build a Cortex Agent that combines multiple tools\u003C/li\u003E\u003Cli\u003EHow to use Snowflake CoWork for natural language financial research\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n","\u003Cp\u003EA Snowflake CoWork agent called \u003Cstrong\u003EHolly\u003C/strong\u003E with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E over SEC EDGAR filings (10-K, 10-Q, 8-K) and public company transcripts\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E for querying stock price timeseries and S&P 500 company data\u003C/li\u003E\u003Cli\u003EA multi-tool \u003Cstrong\u003ECortex Agent\u003C/strong\u003E that routes queries to the right data source\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EArchitecture\u003C/h3\u003E\n","\u003Cp\u003EHolly is a Cortex Agent running in Snowflake CoWork that routes queries to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E — SEC EDGAR filings and earnings transcripts (unstructured text retrieval)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E — Stock price timeseries and S&P 500 company data (natural language to SQL)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/\"\u003ESnowflake account\u003C/a\u003E with ACCOUNTADMIN access (works with \u003Cstrong\u003ETrial Accounts\u003C/strong\u003E)\u003C/li\u003E\u003Cli\u003ESubscribe to \u003Cstrong\u003ESnowflake Public Data (Paid)\u003C/strong\u003E from Marketplace (free trial available)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESubscribe to Marketplace Data\u003C/h2\u003E\n","\u003Cp\u003EBefore running the installation, you need to subscribe to the data source that powers Holly.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, navigate to \u003Cstrong\u003EData Products > Marketplace\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESearch for \u003Cstrong\u003E"Snowflake Public Data (Paid)"\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EGet\u003C/strong\u003E to subscribe (free trial available)\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/holly-financial-research-assistant/snowflake-paid.png?v=8283a585\" alt=\"Snowflake Public Data Paid\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis provides the \u003Cstrong\u003ESNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA\u003C/strong\u003E database which contains:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStock price timeseries\u003C/strong\u003E — daily OHLC data for all S&P 500 companies\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESEC EDGAR filings\u003C/strong\u003E — 10-K, 10-Q, 8-K filings with full text content\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPublic company transcripts\u003C/strong\u003E — earnings calls, investor conferences\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis listing has a free trial available, making it compatible with Snowflake trial accounts.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Database and Load Data\u003C/h2\u003E\n","\u003Cp\u003EAll of the SQL in this quickstart is contained in the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/9bd233e29db4de3b18ae3af4073dd38c3554c7f1/site/sfguides/src/holly-financial-research-assistant/assets/INSTALL.sql\"\u003EINSTALL.sql\u003C/a\u003E script. You can either run the full script end-to-end, or follow along step-by-step below. Open a new SQL Worksheet in Snowsight and paste the contents of INSTALL.sql.\u003C/p\u003E\n","\u003Ch3\u003ESet Up Context\u003C/h3\u003E\n","\u003Cp\u003EThe install script begins by setting the role, enabling cross-region Cortex AI, and creating the warehouses Holly needs. \u003Cstrong\u003ESMALL_WH\u003C/strong\u003E is used for data loading and indexing, while \u003Cstrong\u003ESMALL_IW\u003C/strong\u003E is a smaller warehouse with query acceleration for interactive Cortex Analyst queries.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\nCREATE WAREHOUSE IF NOT EXISTS SMALL_WH WITH WAREHOUSE_SIZE = '2X-LARGE' AUTO_SUSPEND = 60;\nCREATE WAREHOUSE IF NOT EXISTS SMALL_IW WITH WAREHOUSE_SIZE = 'SMALL' ENABLE_QUERY_ACCELERATION = TRUE AUTO_SUSPEND = 60;\nALTER WAREHOUSE SMALL_IW RESUME IF SUSPENDED;\nUSE WAREHOUSE SMALL_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Database and Schemas\u003C/h3\u003E\n","\u003Cp\u003EThe script creates the \u003Cstrong\u003EHOLLY_DB\u003C/strong\u003E database with three schemas that organize data by type: \u003Cstrong\u003ESTRUCTURED\u003C/strong\u003E for tabular financial data, \u003Cstrong\u003ESEMI_STRUCTURED\u003C/strong\u003E for SEC filings, and \u003Cstrong\u003EUNSTRUCTURED\u003C/strong\u003E for earnings transcripts.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS HOLLY_DB;\nCREATE SCHEMA IF NOT EXISTS HOLLY_DB.STRUCTURED;\nCREATE SCHEMA IF NOT EXISTS HOLLY_DB.SEMI_STRUCTURED;\nCREATE SCHEMA IF NOT EXISTS HOLLY_DB.UNSTRUCTURED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate S&P 500 Companies Table\u003C/h3\u003E\n","\u003Cp\u003EThe script creates a reference table of all 503 S&P 500 constituents (as of March 2026) and populates it with a large INSERT statement. This table drives the filtering for all downstream data — only companies in this list will have their stock prices, filings, and transcripts loaded.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE HOLLY_DB.STRUCTURED.SP500_COMPANIES (\n    SYMBOL VARCHAR,\n    COMPANY_NAME VARCHAR,\n    SECTOR VARCHAR,\n    INDUSTRY VARCHAR,\n    HEADQUARTERS VARCHAR,\n    DATE_ADDED DATE,\n    CIK VARCHAR,\n    FOUNDED VARCHAR\n);\n\n-- Load S&P 500 companies (full INSERT is in INSTALL.sql)\n-- Copy the INSERT INTO statement from INSTALL.sql Step 3\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Stock Price Data\u003C/h3\u003E\n","\u003Cp\u003EThis step pulls historical daily OHLC (Open, High, Low, Close) stock price data from the Marketplace listing, filtered to only S&P 500 companies. Change tracking is enabled so Cortex Search and Semantic Views can detect updates. Clustering by ticker and date optimizes query performance for time-series analysis.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES\n    COMMENT = 'Historical stock price data for Cortex Analyst'\nAS\nSELECT \n    TICKER,\n    ASSET_CLASS,\n    PRIMARY_EXCHANGE_CODE,\n    PRIMARY_EXCHANGE_NAME,\n    VARIABLE,\n    VARIABLE_NAME,\n    DATE,\n    VALUE\nFROM SNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA.STOCK_PRICE_TIMESERIES\nWHERE TICKER IN (SELECT SYMBOL FROM HOLLY_DB.STRUCTURED.SP500_COMPANIES);\n\nALTER TABLE HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES SET CHANGE_TRACKING = TRUE;\nALTER TABLE HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES CLUSTER BY (TICKER, DATE);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate SEC EDGAR Filings Data\u003C/h3\u003E\n","\u003Cp\u003EThe script joins SEC filing attributes with the report index from the Marketplace data, filtering to S&P 500 companies and filings from 2025 onward. This captures 10-K annual reports, 10-Q quarterly reports, and 8-K current event disclosures with their full plaintext content. The CIK (Central Index Key) join uses zero-padding to handle formatting differences between data sources.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n    COMMENT = 'SEC filings for Cortex Search'\nAS\nSELECT \n    r.COMPANY_NAME,\n    r.FORM_TYPE AS ANNOUNCEMENT_TYPE,\n    r.FILED_DATE,\n    r.FISCAL_PERIOD,\n    r.FISCAL_YEAR,\n    a.ITEM_NUMBER,\n    a.ITEM_TITLE,\n    a.PLAINTEXT_CONTENT AS ANNOUNCEMENT_TEXT\nFROM SNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA.SEC_CORPORATE_REPORT_ITEM_ATTRIBUTES a\nINNER JOIN SNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA.SEC_CORPORATE_REPORT_INDEX r\n    ON a.ADSH = r.ADSH \nINNER JOIN HOLLY_DB.STRUCTURED.SP500_COMPANIES s\n    ON LPAD(r.CIK, 10, '0') = LPAD(s.CIK, 10, '0')\nWHERE r.FILED_DATE >= '2025-01-01'\n  AND r.FORM_TYPE IN ('8-K', '10-K', '10-Q');\n\nALTER TABLE HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS SET CHANGE_TRACKING = TRUE;\nALTER TABLE HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS CLUSTER BY (COMPANY_NAME, FILED_DATE);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Public Transcripts Data\u003C/h3\u003E\n","\u003Cp\u003EThis loads earnings call and investor conference transcripts for S&P 500 companies. Each transcript is assigned a unique ID and includes the full transcript JSON content, which will be parsed when creating the Cortex Search service in the next step.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS AS\nSELECT \n    ROW_NUMBER() OVER (ORDER BY t.EVENT_TIMESTAMP DESC) AS TRANSCRIPT_ID,\n    t.COMPANY_ID,\n    t.CIK,\n    t.COMPANY_NAME,\n    t.PRIMARY_TICKER,\n    t.FISCAL_PERIOD,\n    t.FISCAL_YEAR,\n    t.EVENT_TYPE,\n    t.TRANSCRIPT_TYPE,\n    t.TRANSCRIPT,\n    t.EVENT_TIMESTAMP,\n    t.CREATED_AT,\n    t.UPDATED_AT\nFROM SNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA.COMPANY_EVENT_TRANSCRIPT_ATTRIBUTES t\nINNER JOIN HOLLY_DB.STRUCTURED.SP500_COMPANIES s ON t.PRIMARY_TICKER = s.SYMBOL;\n\nALTER TABLE HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS SET CHANGE_TRACKING = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Cortex Search Services\u003C/h2\u003E\n","\u003Cp\u003ECortex Search indexes your unstructured text data so the agent can retrieve relevant content using natural language. We create two search services: one for SEC filings and one for earnings transcripts.\u003C/p\u003E\n","\u003Ch3\u003EScale Up Warehouse\u003C/h3\u003E\n","\u003Cp\u003EThe install script temporarily scales the warehouse to 4X-LARGE for the indexing step. Building Cortex Search indexes requires embedding all text content into vector space, which is compute-intensive. The larger warehouse significantly reduces indexing time.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER WAREHOUSE SMALL_WH SET WAREHOUSE_SIZE = '4X-LARGE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESEC Filings Search\u003C/h3\u003E\n","\u003Cp\u003EThis creates a Cortex Search service over the SEC filings text. The \u003Cstrong\u003EON ANNOUNCEMENT_TEXT\u003C/strong\u003E clause specifies the column to search, while \u003Cstrong\u003EATTRIBUTES\u003C/strong\u003E defines the metadata columns returned with each search result. The service automatically builds a hybrid search index (vector + keyword) and refreshes daily.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CORTEX SEARCH SERVICE HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS_SEARCH\n    ON ANNOUNCEMENT_TEXT\n    ATTRIBUTES COMPANY_NAME, ANNOUNCEMENT_TYPE, FILED_DATE, FISCAL_PERIOD, FISCAL_YEAR, ITEM_NUMBER, ITEM_TITLE\n    WAREHOUSE = SMALL_WH\n    TARGET_LAG = '1 day'\nAS (\n    SELECT COMPANY_NAME, ANNOUNCEMENT_TYPE, FILED_DATE, FISCAL_PERIOD, FISCAL_YEAR, ITEM_NUMBER, ITEM_TITLE, ANNOUNCEMENT_TEXT\n    FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPublic Transcripts Search\u003C/h3\u003E\n","\u003Cp\u003EThe second search service indexes earnings call transcripts. It parses the JSON transcript field \u003Cstrong\u003ETRANSCRIPT:text\u003C/strong\u003E into plain text and uses the \u003Cstrong\u003Esnowflake-arctic-embed-l-v2.0\u003C/strong\u003E multilingual embedding model for higher quality semantic search across financial language.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CORTEX SEARCH SERVICE HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS_SEARCH\n    ON TRANSCRIPT_TEXT\n    ATTRIBUTES COMPANY_NAME, PRIMARY_TICKER, EVENT_TYPE, FISCAL_PERIOD, FISCAL_YEAR\n    WAREHOUSE = 'SMALL_WH'\n    TARGET_LAG = '1 day'\n    EMBEDDING_MODEL = 'snowflake-arctic-embed-l-v2.0'\nAS (\n    SELECT TRANSCRIPT_ID, COMPANY_ID, CIK, COMPANY_NAME, PRIMARY_TICKER, FISCAL_PERIOD, FISCAL_YEAR, EVENT_TYPE, EVENT_TIMESTAMP,\n           TRANSCRIPT:text::VARCHAR AS TRANSCRIPT_TEXT\n    FROM HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS\n    WHERE TRANSCRIPT:text IS NOT NULL\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EScale Down Warehouse\u003C/h3\u003E\n","\u003Cp\u003EAfter indexing completes, the script scales the warehouse back down to SMALL to minimize credit consumption for the remainder of the setup.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER WAREHOUSE SMALL_WH SET WAREHOUSE_SIZE = 'SMALL' AUTO_SUSPEND = 60;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Semantic Views\u003C/h2\u003E\n","\u003Cp\u003ESemantic Views provide the business-level data model that Cortex Analyst uses to convert natural language questions into SQL queries. We create three semantic views for stock prices, company data, and filing analytics.\u003C/p\u003E\n","\u003Ch3\u003EStock Price Timeseries Semantic View\u003C/h3\u003E\n","\u003Cp\u003EThis semantic view exposes the stock price table to Cortex Analyst. It defines \u003Cstrong\u003EVALUE\u003C/strong\u003E as a fact (numeric measure) and the remaining columns as dimensions, allowing the agent to generate SQL for questions like "plot NVDA closing price over the last 6 months."\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE SEMANTIC VIEW HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES_SV\n    TABLES (HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES)\n    FACTS (STOCK_PRICE_TIMESERIES.VALUE AS VALUE)\n    DIMENSIONS (\n        STOCK_PRICE_TIMESERIES.TICKER AS TICKER,\n        STOCK_PRICE_TIMESERIES.ASSET_CLASS AS ASSET_CLASS,\n        STOCK_PRICE_TIMESERIES.PRIMARY_EXCHANGE_CODE AS PRIMARY_EXCHANGE_CODE,\n        STOCK_PRICE_TIMESERIES.PRIMARY_EXCHANGE_NAME AS PRIMARY_EXCHANGE_NAME,\n        STOCK_PRICE_TIMESERIES.VARIABLE AS VARIABLE,\n        STOCK_PRICE_TIMESERIES.VARIABLE_NAME AS VARIABLE_NAME,\n        STOCK_PRICE_TIMESERIES.DATE AS DATE\n    );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ES&P 500 Companies Semantic View\u003C/h3\u003E\n","\u003Cp\u003EThis semantic view gives the agent access to S&P 500 company metadata — sector, industry, headquarters, and founding date. All columns are dimensions since this is a reference table with no numeric measures.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE SEMANTIC VIEW HOLLY_DB.STRUCTURED.SP500\n    TABLES (HOLLY_DB.STRUCTURED.SP500_COMPANIES)\n    DIMENSIONS (\n        SP500_COMPANIES.SYMBOL AS SYMBOL,\n        SP500_COMPANIES.COMPANY_NAME AS COMPANY_NAME,\n        SP500_COMPANIES.SECTOR AS SECTOR,\n        SP500_COMPANIES.INDUSTRY AS INDUSTRY,\n        SP500_COMPANIES.HEADQUARTERS AS HEADQUARTERS,\n        SP500_COMPANIES.DATE_ADDED AS DATE_ADDED,\n        SP500_COMPANIES.CIK AS CIK,\n        SP500_COMPANIES.FOUNDED AS FOUNDED\n    );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESEC EDGAR Filings Semantic View\u003C/h3\u003E\n","\u003Cp\u003EThis semantic view uses the YAML-based creation method, which allows richer metadata including column descriptions, sample values, and verified queries. Verified queries are pre-validated SQL examples that improve Cortex Analyst accuracy for common question patterns like "how many filings by type" or "which companies file the most."\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML(\n  'HOLLY_DB.SEMI_STRUCTURED',\n  'name: EDGAR_FILINGS_SV\ndescription: SEC EDGAR filings for S&P 500 companies including 10-K annual reports, 10-Q quarterly reports, and 8-K current event disclosures.\n\ntables:\n  - name: EDGAR_FILINGS\n    description: SEC filings containing company announcements, financial reports, and regulatory disclosures for S&P 500 companies.\n    base_table:\n      database: HOLLY_DB\n      schema: SEMI_STRUCTURED\n      table: EDGAR_FILINGS\n\n    dimensions:\n      - name: COMPANY_NAME\n        description: Name of the company that filed the SEC report\n        expr: COMPANY_NAME\n        data_type: TEXT\n        sample_values:\n          - Apple Inc.\n          - Microsoft Corporation\n          - Amazon.com, Inc.\n\n      - name: ANNOUNCEMENT_TYPE\n        description: Type of SEC filing - 10-K (annual report), 10-Q (quarterly report), or 8-K (current event disclosure)\n        expr: ANNOUNCEMENT_TYPE\n        data_type: TEXT\n        sample_values:\n          - 10-K\n          - 10-Q\n          - 8-K\n\n      - name: FISCAL_PERIOD\n        description: Fiscal period of the filing (Q1, Q2, Q3, Q4, FY)\n        expr: FISCAL_PERIOD\n        data_type: TEXT\n        sample_values:\n          - Q1\n          - Q2\n          - FY\n\n      - name: FISCAL_YEAR\n        description: Fiscal year of the filing\n        expr: FISCAL_YEAR\n        data_type: NUMBER\n\n      - name: ITEM_NUMBER\n        description: SEC form item number identifying the specific section of the filing\n        expr: ITEM_NUMBER\n        data_type: TEXT\n\n      - name: ITEM_TITLE\n        description: Title of the SEC form item/section\n        expr: ITEM_TITLE\n        data_type: TEXT\n\n      - name: ANNOUNCEMENT_TEXT\n        description: Full text content of the SEC filing section\n        expr: ANNOUNCEMENT_TEXT\n        data_type: TEXT\n\n    time_dimensions:\n      - name: FILED_DATE\n        description: Date the SEC filing was submitted\n        expr: FILED_DATE\n        data_type: DATE\n\nverified_queries:\n  - name: vqr_0\n    question: How many SEC filings are there by filing type?\n    sql: |\n      SELECT \n        ANNOUNCEMENT_TYPE,\n        COUNT(*) AS FILING_COUNT\n      FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n      GROUP BY ANNOUNCEMENT_TYPE\n      ORDER BY FILING_COUNT DESC\n\n  - name: vqr_1\n    question: Which companies have the most SEC filings?\n    sql: |\n      SELECT \n        COMPANY_NAME,\n        COUNT(*) AS FILING_COUNT\n      FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n      GROUP BY COMPANY_NAME\n      ORDER BY FILING_COUNT DESC\n      LIMIT 10\n\n  - name: vqr_2\n    question: How many filings were submitted each month?\n    sql: |\n      SELECT \n        DATE_TRUNC(''MONTH'', FILED_DATE) AS FILING_MONTH,\n        COUNT(*) AS FILING_COUNT\n      FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n      GROUP BY FILING_MONTH\n      ORDER BY FILING_MONTH',\n  FALSE\n);\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate the Holly Agent\u003C/h2\u003E\n","\u003Cp\u003EThe final step of the install script creates the Holly Cortex Agent, which ties together all the Cortex Search services and Semantic Views into a single conversational interface.\u003C/p\u003E\n","\u003Ch3\u003ECreate Agent Database\u003C/h3\u003E\n","\u003Cp\u003EThe script creates a dedicated database and schema for the agent. This follows the Snowflake CoWork convention of placing agents in a \u003Cstrong\u003ESNOWFLAKE_INTELLIGENCE.AGENTS\u003C/strong\u003E schema.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS SNOWFLAKE_INTELLIGENCE;\nCREATE SCHEMA IF NOT EXISTS SNOWFLAKE_INTELLIGENCE.AGENTS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Agent\u003C/h3\u003E\n","\u003Cp\u003EThis is the core of the install script — the \u003Cstrong\u003ECREATE AGENT\u003C/strong\u003E statement defines Holly's orchestration model, routing instructions, tool definitions, and tool resources. The agent uses \u003Cstrong\u003Eclaude-4-sonnet\u003C/strong\u003E for orchestration and is configured with five tools: two Cortex Search services for unstructured retrieval and three Cortex Analyst semantic views for structured SQL generation. The script also grants PUBLIC access and sets the agent avatar.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY\n  COMMENT = 'Financial research assistant for SEC filings, transcripts, stock prices, and company data'\n  FROM SPECIFICATION $$\nmodels:\n  orchestration: claude-4-sonnet\n\ninstructions:\n  orchestration: |\n    You are Holly the FS Financial Agent. When a user first greets you or says hello, respond with: "Good afternoon, I am Holly the FS Financial Agent. How can I help you?"\n    \n    Route each query to the appropriate tool:\n    \n    **TRANSCRIPTS**: For earnings calls, investor conferences, or company event transcripts from S&P 500 companies, use TRANSCRIPTS_SEARCH.\n    \n    **HISTORICAL PRICES**: For historical stock price analysis, OHLC data, or price trends, use STOCK_PRICES.\n    \n    **COMPANY FUNDAMENTALS**: For S&P 500 company data (market cap, revenue growth, EBITDA, sector), use SP500_COMPANIES.\n    \n    **SEC FILINGS SEARCH**: For searching SEC filing content (8-K, 10-K, 10-Q) or regulatory disclosures, use SEC_FILINGS_SEARCH.\n    \n    **SEC FILINGS ANALYTICS**: For counting or aggregating SEC filings by company, type, date, or fiscal period, use SEC_FILINGS_ANALYST.\n    \n    Combine multiple tools for comprehensive research.\n  response: "Provide clear, data-driven responses with source attribution. Use tables for financial data. Specify dates for stock prices. Cite filing type and date for SEC filings. Be accurate with numbers."\n  sample_questions:\n    - question: "Plot the share price of Microsoft, Amazon, Google and Nvidia starting 20th Feb 2025 to 20th Feb 2026"\n    - question: "Are Nvidia, Microsoft, Amazon, Google in the SP500"\n    - question: "What are the latest public transcripts for NVIDIA"\n    - question: "Compare Nvidia's annual growth rate and Microsoft annual growth rate using the latest Annual reports using a table format for all the key metrics"\n    - question: "What is the latest 10-K for Nvidia from the EDGAR Filings"\n    - question: "Would you recommend buying Nvidia Stock at 195"\n\ntools:\n  - tool_spec:\n      type: cortex_search\n      name: TRANSCRIPTS_SEARCH\n      description: "Search public company event transcripts (earnings calls, investor conferences) from S&P 500 companies."\n  - tool_spec:\n      type: cortex_search\n      name: SEC_FILINGS_SEARCH\n      description: "Search SEC EDGAR filings (10-K, 10-Q, 8-K) for company announcements and regulatory disclosures."\n  - tool_spec:\n      type: cortex_analyst_text_to_sql\n      name: STOCK_PRICES\n      description: "Query historical stock price data with daily OHLC values for price trends and analysis."\n  - tool_spec:\n      type: cortex_analyst_text_to_sql\n      name: SP500_COMPANIES\n      description: "Query S&P 500 company fundamentals: market cap, revenue growth, EBITDA, sector, industry."\n  - tool_spec:\n      type: cortex_analyst_text_to_sql\n      name: SEC_FILINGS_ANALYST\n      description: "Query SEC filing metadata and counts by company, filing type, date, or fiscal period."\n\ntool_resources:\n  TRANSCRIPTS_SEARCH:\n    search_service: "HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS_SEARCH"\n    max_results: 10\n    columns:\n      - COMPANY_NAME\n      - PRIMARY_TICKER\n      - EVENT_TYPE\n      - FISCAL_PERIOD\n      - FISCAL_YEAR\n      - EVENT_TIMESTAMP\n      - TRANSCRIPT_TEXT\n  SEC_FILINGS_SEARCH:\n    search_service: "HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS_SEARCH"\n    max_results: 10\n    columns:\n      - COMPANY_NAME\n      - ANNOUNCEMENT_TYPE\n      - FILED_DATE\n      - FISCAL_PERIOD\n      - FISCAL_YEAR\n      - ITEM_NUMBER\n      - ITEM_TITLE\n      - ANNOUNCEMENT_TEXT\n  STOCK_PRICES:\n    semantic_view: "HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES_SV"\n    execution_environment:\n      type: warehouse\n      warehouse: SMALL_IW\n    query_timeout: 120\n  SP500_COMPANIES:\n    semantic_view: "HOLLY_DB.STRUCTURED.SP500"\n    execution_environment:\n      type: warehouse\n      warehouse: SMALL_WH\n    query_timeout: 60\n  SEC_FILINGS_ANALYST:\n    semantic_view: "HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS_SV"\n    execution_environment:\n      type: warehouse\n      warehouse: SMALL_WH\n    query_timeout: 60\n$$;\n\nGRANT USAGE ON AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY TO ROLE PUBLIC;\nALTER AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY SET PROFILE = '{"avatar": "robot"}';\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVerify Installation\u003C/h2\u003E\n","\u003Cp\u003EOnce the install script completes, run the following queries to verify everything was created successfully:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT 'SP500_COMPANIES' AS table_name, COUNT(*) AS row_count FROM HOLLY_DB.STRUCTURED.SP500_COMPANIES\nUNION ALL SELECT 'STOCK_PRICE_TIMESERIES', COUNT(*) FROM HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES\nUNION ALL SELECT 'EDGAR_FILINGS', COUNT(*) FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\nUNION ALL SELECT 'PUBLIC_TRANSCRIPTS', COUNT(*) FROM HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS;\n\nSHOW CORTEX SEARCH SERVICES IN DATABASE HOLLY_DB;\nSHOW SEMANTIC VIEWS IN DATABASE HOLLY_DB;\nDESC AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESP500_COMPANIES\u003C/strong\u003E: ~503 rows\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESTOCK_PRICE_TIMESERIES\u003C/strong\u003E: varies based on date range\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEDGAR_FILINGS\u003C/strong\u003E: varies based on filings available\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPUBLIC_TRANSCRIPTS\u003C/strong\u003E: varies based on available transcripts\u003C/li\u003E\u003Cli\u003ETwo Cortex Search services\u003C/li\u003E\u003Cli\u003EThree Semantic Views\u003C/li\u003E\u003Cli\u003EThe Holly agent description\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETry Holly in Snowflake CoWork\u003C/h2\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EAI & ML > Snowflake CoWork\u003C/strong\u003E in Snowsight and select \u003Cstrong\u003EHolly\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch3\u003EQ1: Plot Share Prices\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Plot the share price of Microsoft, Amazon, Google and Nvidia starting 20th Feb 2025 to 20th Feb 2026"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EHolly uses the \u003Cstrong\u003ESTOCK_PRICES\u003C/strong\u003E tool (Cortex Analyst) to generate SQL and return a chart of historical prices across multiple tickers.\u003C/p\u003E\n","\u003Ch3\u003EQ2: S&P 500 Membership Check\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Are Nvidia, Microsoft, Amazon, Google in the SP500"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EHolly queries the \u003Cstrong\u003ESP500_COMPANIES\u003C/strong\u003E tool to check membership. It automatically generates SQL from natural language.\u003C/p\u003E\n","\u003Ch3\u003EQ3: Latest Earnings Transcripts\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"What are the latest public transcripts for NVIDIA"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EHolly uses \u003Cstrong\u003ETRANSCRIPTS_SEARCH\u003C/strong\u003E (Cortex Search) to retrieve and summarize the most recent earnings call transcripts.\u003C/p\u003E\n","\u003Ch3\u003EQ4: SEC Filing Lookup\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"What is the latest 10-K for Nvidia from the EDGAR Filings"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EHolly searches \u003Cstrong\u003ESEC_FILINGS_SEARCH\u003C/strong\u003E to find and return the most recent annual report filing.\u003C/p\u003E\n","\u003Ch3\u003EQ5: Company Comparison\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Compare Nvidia's annual growth rate and Microsoft annual growth rate using the latest Annual reports using a table format for all the key metrics"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EHolly combines \u003Cstrong\u003ESEC_FILINGS_SEARCH\u003C/strong\u003E across multiple companies to extract and compare key financial metrics in a table.\u003C/p\u003E\n","\u003Ch3\u003EQ6: Investment Research\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Would you recommend buying Nvidia Stock at 195"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EHolly uses \u003Cstrong\u003Emultiple tools\u003C/strong\u003E — pulling current price data, recent filings, and transcripts — to provide a comprehensive research summary. Note that Holly correctly avoids giving direct investment advice.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003ETo remove all Holly components, run the following cleanup statements. These are also included at the end of the install script as comments:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nUSE WAREHOUSE SMALL_WH;\n\nDROP AGENT IF EXISTS SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY;\nDROP CORTEX SEARCH SERVICE IF EXISTS HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS_SEARCH;\nDROP CORTEX SEARCH SERVICE IF EXISTS HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS_SEARCH;\nDROP SEMANTIC VIEW IF EXISTS HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES_SV;\nDROP SEMANTIC VIEW IF EXISTS HOLLY_DB.STRUCTURED.SP500;\nDROP TABLE IF EXISTS HOLLY_DB.STRUCTURED.SP500_COMPANIES;\nDROP TABLE IF EXISTS HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES;\nDROP TABLE IF EXISTS HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS;\nDROP TABLE IF EXISTS HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS;\nDROP SCHEMA IF EXISTS HOLLY_DB.STRUCTURED;\nDROP SCHEMA IF EXISTS HOLLY_DB.SEMI_STRUCTURED;\nDROP SCHEMA IF EXISTS HOLLY_DB.UNSTRUCTURED;\nDROP DATABASE IF EXISTS HOLLY_DB;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You have successfully built \u003Cstrong\u003EHolly\u003C/strong\u003E, an AI-powered financial research assistant using Snowflake CoWork.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to source financial data from Snowflake Marketplace (Cybersyn)\u003C/li\u003E\u003Cli\u003EHow to create Cortex Search services for unstructured text retrieval over SEC filings and earnings transcripts\u003C/li\u003E\u003Cli\u003EHow to create Semantic Views that enable natural language to SQL conversion\u003C/li\u003E\u003Cli\u003EHow to build a multi-tool Cortex Agent that intelligently routes queries\u003C/li\u003E\u003Cli\u003EHow to use Snowflake CoWork for comprehensive financial research\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/snowflake-intelligence\"\u003ESnowflake CoWork Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agent Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/build-an-ai-assistant-for-fsi-with-aisql-and-snowflake-intelligence\"\u003EBuild an AI Assistant for FSI\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Holly - Financial Research Assistant with Snowflake CoWork","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\n![Holly](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/holly-financial-research-assistant/holly.png?v=8283a585)\n\n**Holly** is an AI-powered financial research assistant built on Snowflake CoWork. It enables portfolio managers, analysts, and traders to perform comprehensive stock research using natural language across structured and unstructured financial data.\n\n### Use Case\n\nYou are a financial analyst in a hedge fund looking into AI Native Tech Stocks. You have 4 in mind: **GOOGL**, **MSFT**, **AMZN**, and **NVDA**.\n\nBecause you know NVIDIA makes 90% of the GPUs for AI, you reckon this is worth investigating further. But you want to drill down on the **unstructured data** — 10-K, 8-K, 10-Q filings, investor call transcripts, and annual reports — to get a holistic view of the security based on all the data available, not just the fundamental data which is all structured.\n\n### What You Will Learn\n\n- How to source financial data from Snowflake Marketplace\n- How to create Cortex Search services for SEC filings and earnings transcripts\n- How to create Semantic Views for Cortex Analyst\n- How to build a Cortex Agent that combines multiple tools\n- How to use Snowflake CoWork for natural language financial research\n\n### What You Will Build\n\nA Snowflake CoWork agent called **Holly** with:\n- **Cortex Search** over SEC EDGAR filings (10-K, 10-Q, 8-K) and public company transcripts\n- **Cortex Analyst** for querying stock price timeseries and S&P 500 company data\n- A multi-tool **Cortex Agent** that routes queries to the right data source\n\n### Architecture\n\nHolly is a Cortex Agent running in Snowflake CoWork that routes queries to:\n\n- **Cortex Search** — SEC EDGAR filings and earnings transcripts (unstructured text retrieval)\n- **Cortex Analyst** — Stock price timeseries and S&P 500 company data (natural language to SQL)\n\n### Prerequisites\n\n- A [Snowflake account](https://signup.snowflake.com/) with ACCOUNTADMIN access (works with **Trial Accounts**)\n- Subscribe to **Snowflake Public Data (Paid)** from Marketplace (free trial available)\n\n\u003C!-- ------------------------ --\u003E\n## Subscribe to Marketplace Data\n\nBefore running the installation, you need to subscribe to the data source that powers Holly.\n\n1. In Snowsight, navigate to **Data Products \u003E Marketplace**\n2. Search for **\"Snowflake Public Data (Paid)\"**\n3. Click **Get** to subscribe (free trial available)\n\n![Snowflake Public Data Paid](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/holly-financial-research-assistant/snowflake-paid.png?v=8283a585)\n\nThis provides the **SNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA** database which contains:\n- **Stock price timeseries** — daily OHLC data for all S&P 500 companies\n- **SEC EDGAR filings** — 10-K, 10-Q, 8-K filings with full text content\n- **Public company transcripts** — earnings calls, investor conferences\n\nThis listing has a free trial available, making it compatible with Snowflake trial accounts.\n\n\u003C!-- ------------------------ --\u003E\n## Create Database and Load Data\n\nAll of the SQL in this quickstart is contained in the [INSTALL.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/9bd233e29db4de3b18ae3af4073dd38c3554c7f1/site/sfguides/src/holly-financial-research-assistant/assets/INSTALL.sql) script. You can either run the full script end-to-end, or follow along step-by-step below. Open a new SQL Worksheet in Snowsight and paste the contents of INSTALL.sql.\n\n### Set Up Context\n\nThe install script begins by setting the role, enabling cross-region Cortex AI, and creating the warehouses Holly needs. **SMALL_WH** is used for data loading and indexing, while **SMALL_IW** is a smaller warehouse with query acceleration for interactive Cortex Analyst queries.\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\nCREATE WAREHOUSE IF NOT EXISTS SMALL_WH WITH WAREHOUSE_SIZE = '2X-LARGE' AUTO_SUSPEND = 60;\nCREATE WAREHOUSE IF NOT EXISTS SMALL_IW WITH WAREHOUSE_SIZE = 'SMALL' ENABLE_QUERY_ACCELERATION = TRUE AUTO_SUSPEND = 60;\nALTER WAREHOUSE SMALL_IW RESUME IF SUSPENDED;\nUSE WAREHOUSE SMALL_WH;\n```\n\n### Create Database and Schemas\n\nThe script creates the **HOLLY_DB** database with three schemas that organize data by type: **STRUCTURED** for tabular financial data, **SEMI_STRUCTURED** for SEC filings, and **UNSTRUCTURED** for earnings transcripts.\n\n```sql\nCREATE DATABASE IF NOT EXISTS HOLLY_DB;\nCREATE SCHEMA IF NOT EXISTS HOLLY_DB.STRUCTURED;\nCREATE SCHEMA IF NOT EXISTS HOLLY_DB.SEMI_STRUCTURED;\nCREATE SCHEMA IF NOT EXISTS HOLLY_DB.UNSTRUCTURED;\n```\n\n### Create S&P 500 Companies Table\n\nThe script creates a reference table of all 503 S&P 500 constituents (as of March 2026) and populates it with a large INSERT statement. This table drives the filtering for all downstream data — only companies in this list will have their stock prices, filings, and transcripts loaded.\n\n```sql\nCREATE OR REPLACE TABLE HOLLY_DB.STRUCTURED.SP500_COMPANIES (\n    SYMBOL VARCHAR,\n    COMPANY_NAME VARCHAR,\n    SECTOR VARCHAR,\n    INDUSTRY VARCHAR,\n    HEADQUARTERS VARCHAR,\n    DATE_ADDED DATE,\n    CIK VARCHAR,\n    FOUNDED VARCHAR\n);\n\n-- Load S&P 500 companies (full INSERT is in INSTALL.sql)\n-- Copy the INSERT INTO statement from INSTALL.sql Step 3\n```\n\n### Create Stock Price Data\n\nThis step pulls historical daily OHLC (Open, High, Low, Close) stock price data from the Marketplace listing, filtered to only S&P 500 companies. Change tracking is enabled so Cortex Search and Semantic Views can detect updates. Clustering by ticker and date optimizes query performance for time-series analysis.\n\n```sql\nCREATE OR REPLACE TABLE HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES\n    COMMENT = 'Historical stock price data for Cortex Analyst'\nAS\nSELECT \n    TICKER,\n    ASSET_CLASS,\n    PRIMARY_EXCHANGE_CODE,\n    PRIMARY_EXCHANGE_NAME,\n    VARIABLE,\n    VARIABLE_NAME,\n    DATE,\n    VALUE\nFROM SNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA.STOCK_PRICE_TIMESERIES\nWHERE TICKER IN (SELECT SYMBOL FROM HOLLY_DB.STRUCTURED.SP500_COMPANIES);\n\nALTER TABLE HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES SET CHANGE_TRACKING = TRUE;\nALTER TABLE HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES CLUSTER BY (TICKER, DATE);\n```\n\n### Create SEC EDGAR Filings Data\n\nThe script joins SEC filing attributes with the report index from the Marketplace data, filtering to S&P 500 companies and filings from 2025 onward. This captures 10-K annual reports, 10-Q quarterly reports, and 8-K current event disclosures with their full plaintext content. The CIK (Central Index Key) join uses zero-padding to handle formatting differences between data sources.\n\n```sql\nCREATE OR REPLACE TABLE HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n    COMMENT = 'SEC filings for Cortex Search'\nAS\nSELECT \n    r.COMPANY_NAME,\n    r.FORM_TYPE AS ANNOUNCEMENT_TYPE,\n    r.FILED_DATE,\n    r.FISCAL_PERIOD,\n    r.FISCAL_YEAR,\n    a.ITEM_NUMBER,\n    a.ITEM_TITLE,\n    a.PLAINTEXT_CONTENT AS ANNOUNCEMENT_TEXT\nFROM SNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA.SEC_CORPORATE_REPORT_ITEM_ATTRIBUTES a\nINNER JOIN SNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA.SEC_CORPORATE_REPORT_INDEX r\n    ON a.ADSH = r.ADSH \nINNER JOIN HOLLY_DB.STRUCTURED.SP500_COMPANIES s\n    ON LPAD(r.CIK, 10, '0') = LPAD(s.CIK, 10, '0')\nWHERE r.FILED_DATE \u003E= '2025-01-01'\n  AND r.FORM_TYPE IN ('8-K', '10-K', '10-Q');\n\nALTER TABLE HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS SET CHANGE_TRACKING = TRUE;\nALTER TABLE HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS CLUSTER BY (COMPANY_NAME, FILED_DATE);\n```\n\n### Create Public Transcripts Data\n\nThis loads earnings call and investor conference transcripts for S&P 500 companies. Each transcript is assigned a unique ID and includes the full transcript JSON content, which will be parsed when creating the Cortex Search service in the next step.\n\n```sql\nCREATE OR REPLACE TABLE HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS AS\nSELECT \n    ROW_NUMBER() OVER (ORDER BY t.EVENT_TIMESTAMP DESC) AS TRANSCRIPT_ID,\n    t.COMPANY_ID,\n    t.CIK,\n    t.COMPANY_NAME,\n    t.PRIMARY_TICKER,\n    t.FISCAL_PERIOD,\n    t.FISCAL_YEAR,\n    t.EVENT_TYPE,\n    t.TRANSCRIPT_TYPE,\n    t.TRANSCRIPT,\n    t.EVENT_TIMESTAMP,\n    t.CREATED_AT,\n    t.UPDATED_AT\nFROM SNOWFLAKE_PUBLIC_DATA_PAID.PUBLIC_DATA.COMPANY_EVENT_TRANSCRIPT_ATTRIBUTES t\nINNER JOIN HOLLY_DB.STRUCTURED.SP500_COMPANIES s ON t.PRIMARY_TICKER = s.SYMBOL;\n\nALTER TABLE HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS SET CHANGE_TRACKING = TRUE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Create Cortex Search Services\n\nCortex Search indexes your unstructured text data so the agent can retrieve relevant content using natural language. We create two search services: one for SEC filings and one for earnings transcripts.\n\n### Scale Up Warehouse\n\nThe install script temporarily scales the warehouse to 4X-LARGE for the indexing step. Building Cortex Search indexes requires embedding all text content into vector space, which is compute-intensive. The larger warehouse significantly reduces indexing time.\n\n```sql\nALTER WAREHOUSE SMALL_WH SET WAREHOUSE_SIZE = '4X-LARGE';\n```\n\n### SEC Filings Search\n\nThis creates a Cortex Search service over the SEC filings text. The **ON ANNOUNCEMENT_TEXT** clause specifies the column to search, while **ATTRIBUTES** defines the metadata columns returned with each search result. The service automatically builds a hybrid search index (vector + keyword) and refreshes daily.\n\n```sql\nCREATE OR REPLACE CORTEX SEARCH SERVICE HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS_SEARCH\n    ON ANNOUNCEMENT_TEXT\n    ATTRIBUTES COMPANY_NAME, ANNOUNCEMENT_TYPE, FILED_DATE, FISCAL_PERIOD, FISCAL_YEAR, ITEM_NUMBER, ITEM_TITLE\n    WAREHOUSE = SMALL_WH\n    TARGET_LAG = '1 day'\nAS (\n    SELECT COMPANY_NAME, ANNOUNCEMENT_TYPE, FILED_DATE, FISCAL_PERIOD, FISCAL_YEAR, ITEM_NUMBER, ITEM_TITLE, ANNOUNCEMENT_TEXT\n    FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n);\n```\n\n### Public Transcripts Search\n\nThe second search service indexes earnings call transcripts. It parses the JSON transcript field **TRANSCRIPT:text** into plain text and uses the **snowflake-arctic-embed-l-v2.0** multilingual embedding model for higher quality semantic search across financial language.\n\n```sql\nCREATE OR REPLACE CORTEX SEARCH SERVICE HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS_SEARCH\n    ON TRANSCRIPT_TEXT\n    ATTRIBUTES COMPANY_NAME, PRIMARY_TICKER, EVENT_TYPE, FISCAL_PERIOD, FISCAL_YEAR\n    WAREHOUSE = 'SMALL_WH'\n    TARGET_LAG = '1 day'\n    EMBEDDING_MODEL = 'snowflake-arctic-embed-l-v2.0'\nAS (\n    SELECT TRANSCRIPT_ID, COMPANY_ID, CIK, COMPANY_NAME, PRIMARY_TICKER, FISCAL_PERIOD, FISCAL_YEAR, EVENT_TYPE, EVENT_TIMESTAMP,\n           TRANSCRIPT:text::VARCHAR AS TRANSCRIPT_TEXT\n    FROM HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS\n    WHERE TRANSCRIPT:text IS NOT NULL\n);\n```\n\n### Scale Down Warehouse\n\nAfter indexing completes, the script scales the warehouse back down to SMALL to minimize credit consumption for the remainder of the setup.\n\n```sql\nALTER WAREHOUSE SMALL_WH SET WAREHOUSE_SIZE = 'SMALL' AUTO_SUSPEND = 60;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Create Semantic Views\n\nSemantic Views provide the business-level data model that Cortex Analyst uses to convert natural language questions into SQL queries. We create three semantic views for stock prices, company data, and filing analytics.\n\n### Stock Price Timeseries Semantic View\n\nThis semantic view exposes the stock price table to Cortex Analyst. It defines **VALUE** as a fact (numeric measure) and the remaining columns as dimensions, allowing the agent to generate SQL for questions like \"plot NVDA closing price over the last 6 months.\"\n\n```sql\nCREATE OR REPLACE SEMANTIC VIEW HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES_SV\n    TABLES (HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES)\n    FACTS (STOCK_PRICE_TIMESERIES.VALUE AS VALUE)\n    DIMENSIONS (\n        STOCK_PRICE_TIMESERIES.TICKER AS TICKER,\n        STOCK_PRICE_TIMESERIES.ASSET_CLASS AS ASSET_CLASS,\n        STOCK_PRICE_TIMESERIES.PRIMARY_EXCHANGE_CODE AS PRIMARY_EXCHANGE_CODE,\n        STOCK_PRICE_TIMESERIES.PRIMARY_EXCHANGE_NAME AS PRIMARY_EXCHANGE_NAME,\n        STOCK_PRICE_TIMESERIES.VARIABLE AS VARIABLE,\n        STOCK_PRICE_TIMESERIES.VARIABLE_NAME AS VARIABLE_NAME,\n        STOCK_PRICE_TIMESERIES.DATE AS DATE\n    );\n```\n\n### S&P 500 Companies Semantic View\n\nThis semantic view gives the agent access to S&P 500 company metadata — sector, industry, headquarters, and founding date. All columns are dimensions since this is a reference table with no numeric measures.\n\n```sql\nCREATE OR REPLACE SEMANTIC VIEW HOLLY_DB.STRUCTURED.SP500\n    TABLES (HOLLY_DB.STRUCTURED.SP500_COMPANIES)\n    DIMENSIONS (\n        SP500_COMPANIES.SYMBOL AS SYMBOL,\n        SP500_COMPANIES.COMPANY_NAME AS COMPANY_NAME,\n        SP500_COMPANIES.SECTOR AS SECTOR,\n        SP500_COMPANIES.INDUSTRY AS INDUSTRY,\n        SP500_COMPANIES.HEADQUARTERS AS HEADQUARTERS,\n        SP500_COMPANIES.DATE_ADDED AS DATE_ADDED,\n        SP500_COMPANIES.CIK AS CIK,\n        SP500_COMPANIES.FOUNDED AS FOUNDED\n    );\n```\n\n### SEC EDGAR Filings Semantic View\n\nThis semantic view uses the YAML-based creation method, which allows richer metadata including column descriptions, sample values, and verified queries. Verified queries are pre-validated SQL examples that improve Cortex Analyst accuracy for common question patterns like \"how many filings by type\" or \"which companies file the most.\"\n\n```sql\nCALL SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML(\n  'HOLLY_DB.SEMI_STRUCTURED',\n  'name: EDGAR_FILINGS_SV\ndescription: SEC EDGAR filings for S&P 500 companies including 10-K annual reports, 10-Q quarterly reports, and 8-K current event disclosures.\n\ntables:\n  - name: EDGAR_FILINGS\n    description: SEC filings containing company announcements, financial reports, and regulatory disclosures for S&P 500 companies.\n    base_table:\n      database: HOLLY_DB\n      schema: SEMI_STRUCTURED\n      table: EDGAR_FILINGS\n\n    dimensions:\n      - name: COMPANY_NAME\n        description: Name of the company that filed the SEC report\n        expr: COMPANY_NAME\n        data_type: TEXT\n        sample_values:\n          - Apple Inc.\n          - Microsoft Corporation\n          - Amazon.com, Inc.\n\n      - name: ANNOUNCEMENT_TYPE\n        description: Type of SEC filing - 10-K (annual report), 10-Q (quarterly report), or 8-K (current event disclosure)\n        expr: ANNOUNCEMENT_TYPE\n        data_type: TEXT\n        sample_values:\n          - 10-K\n          - 10-Q\n          - 8-K\n\n      - name: FISCAL_PERIOD\n        description: Fiscal period of the filing (Q1, Q2, Q3, Q4, FY)\n        expr: FISCAL_PERIOD\n        data_type: TEXT\n        sample_values:\n          - Q1\n          - Q2\n          - FY\n\n      - name: FISCAL_YEAR\n        description: Fiscal year of the filing\n        expr: FISCAL_YEAR\n        data_type: NUMBER\n\n      - name: ITEM_NUMBER\n        description: SEC form item number identifying the specific section of the filing\n        expr: ITEM_NUMBER\n        data_type: TEXT\n\n      - name: ITEM_TITLE\n        description: Title of the SEC form item/section\n        expr: ITEM_TITLE\n        data_type: TEXT\n\n      - name: ANNOUNCEMENT_TEXT\n        description: Full text content of the SEC filing section\n        expr: ANNOUNCEMENT_TEXT\n        data_type: TEXT\n\n    time_dimensions:\n      - name: FILED_DATE\n        description: Date the SEC filing was submitted\n        expr: FILED_DATE\n        data_type: DATE\n\nverified_queries:\n  - name: vqr_0\n    question: How many SEC filings are there by filing type?\n    sql: |\n      SELECT \n        ANNOUNCEMENT_TYPE,\n        COUNT(*) AS FILING_COUNT\n      FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n      GROUP BY ANNOUNCEMENT_TYPE\n      ORDER BY FILING_COUNT DESC\n\n  - name: vqr_1\n    question: Which companies have the most SEC filings?\n    sql: |\n      SELECT \n        COMPANY_NAME,\n        COUNT(*) AS FILING_COUNT\n      FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n      GROUP BY COMPANY_NAME\n      ORDER BY FILING_COUNT DESC\n      LIMIT 10\n\n  - name: vqr_2\n    question: How many filings were submitted each month?\n    sql: |\n      SELECT \n        DATE_TRUNC(''MONTH'', FILED_DATE) AS FILING_MONTH,\n        COUNT(*) AS FILING_COUNT\n      FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\n      GROUP BY FILING_MONTH\n      ORDER BY FILING_MONTH',\n  FALSE\n);\n```\n\n\u003C!-- ------------------------ --\u003E\n## Create the Holly Agent\n\nThe final step of the install script creates the Holly Cortex Agent, which ties together all the Cortex Search services and Semantic Views into a single conversational interface.\n\n### Create Agent Database\n\nThe script creates a dedicated database and schema for the agent. This follows the Snowflake CoWork convention of placing agents in a **SNOWFLAKE_INTELLIGENCE.AGENTS** schema.\n\n```sql\nCREATE DATABASE IF NOT EXISTS SNOWFLAKE_INTELLIGENCE;\nCREATE SCHEMA IF NOT EXISTS SNOWFLAKE_INTELLIGENCE.AGENTS;\n```\n\n### Create the Agent\n\nThis is the core of the install script — the **CREATE AGENT** statement defines Holly's orchestration model, routing instructions, tool definitions, and tool resources. The agent uses **claude-4-sonnet** for orchestration and is configured with five tools: two Cortex Search services for unstructured retrieval and three Cortex Analyst semantic views for structured SQL generation. The script also grants PUBLIC access and sets the agent avatar.\n\n```sql\nCREATE OR REPLACE AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY\n  COMMENT = 'Financial research assistant for SEC filings, transcripts, stock prices, and company data'\n  FROM SPECIFICATION $$\nmodels:\n  orchestration: claude-4-sonnet\n\ninstructions:\n  orchestration: |\n    You are Holly the FS Financial Agent. When a user first greets you or says hello, respond with: \"Good afternoon, I am Holly the FS Financial Agent. How can I help you?\"\n    \n    Route each query to the appropriate tool:\n    \n    **TRANSCRIPTS**: For earnings calls, investor conferences, or company event transcripts from S&P 500 companies, use TRANSCRIPTS_SEARCH.\n    \n    **HISTORICAL PRICES**: For historical stock price analysis, OHLC data, or price trends, use STOCK_PRICES.\n    \n    **COMPANY FUNDAMENTALS**: For S&P 500 company data (market cap, revenue growth, EBITDA, sector), use SP500_COMPANIES.\n    \n    **SEC FILINGS SEARCH**: For searching SEC filing content (8-K, 10-K, 10-Q) or regulatory disclosures, use SEC_FILINGS_SEARCH.\n    \n    **SEC FILINGS ANALYTICS**: For counting or aggregating SEC filings by company, type, date, or fiscal period, use SEC_FILINGS_ANALYST.\n    \n    Combine multiple tools for comprehensive research.\n  response: \"Provide clear, data-driven responses with source attribution. Use tables for financial data. Specify dates for stock prices. Cite filing type and date for SEC filings. Be accurate with numbers.\"\n  sample_questions:\n    - question: \"Plot the share price of Microsoft, Amazon, Google and Nvidia starting 20th Feb 2025 to 20th Feb 2026\"\n    - question: \"Are Nvidia, Microsoft, Amazon, Google in the SP500\"\n    - question: \"What are the latest public transcripts for NVIDIA\"\n    - question: \"Compare Nvidia's annual growth rate and Microsoft annual growth rate using the latest Annual reports using a table format for all the key metrics\"\n    - question: \"What is the latest 10-K for Nvidia from the EDGAR Filings\"\n    - question: \"Would you recommend buying Nvidia Stock at 195\"\n\ntools:\n  - tool_spec:\n      type: cortex_search\n      name: TRANSCRIPTS_SEARCH\n      description: \"Search public company event transcripts (earnings calls, investor conferences) from S&P 500 companies.\"\n  - tool_spec:\n      type: cortex_search\n      name: SEC_FILINGS_SEARCH\n      description: \"Search SEC EDGAR filings (10-K, 10-Q, 8-K) for company announcements and regulatory disclosures.\"\n  - tool_spec:\n      type: cortex_analyst_text_to_sql\n      name: STOCK_PRICES\n      description: \"Query historical stock price data with daily OHLC values for price trends and analysis.\"\n  - tool_spec:\n      type: cortex_analyst_text_to_sql\n      name: SP500_COMPANIES\n      description: \"Query S&P 500 company fundamentals: market cap, revenue growth, EBITDA, sector, industry.\"\n  - tool_spec:\n      type: cortex_analyst_text_to_sql\n      name: SEC_FILINGS_ANALYST\n      description: \"Query SEC filing metadata and counts by company, filing type, date, or fiscal period.\"\n\ntool_resources:\n  TRANSCRIPTS_SEARCH:\n    search_service: \"HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS_SEARCH\"\n    max_results: 10\n    columns:\n      - COMPANY_NAME\n      - PRIMARY_TICKER\n      - EVENT_TYPE\n      - FISCAL_PERIOD\n      - FISCAL_YEAR\n      - EVENT_TIMESTAMP\n      - TRANSCRIPT_TEXT\n  SEC_FILINGS_SEARCH:\n    search_service: \"HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS_SEARCH\"\n    max_results: 10\n    columns:\n      - COMPANY_NAME\n      - ANNOUNCEMENT_TYPE\n      - FILED_DATE\n      - FISCAL_PERIOD\n      - FISCAL_YEAR\n      - ITEM_NUMBER\n      - ITEM_TITLE\n      - ANNOUNCEMENT_TEXT\n  STOCK_PRICES:\n    semantic_view: \"HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES_SV\"\n    execution_environment:\n      type: warehouse\n      warehouse: SMALL_IW\n    query_timeout: 120\n  SP500_COMPANIES:\n    semantic_view: \"HOLLY_DB.STRUCTURED.SP500\"\n    execution_environment:\n      type: warehouse\n      warehouse: SMALL_WH\n    query_timeout: 60\n  SEC_FILINGS_ANALYST:\n    semantic_view: \"HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS_SV\"\n    execution_environment:\n      type: warehouse\n      warehouse: SMALL_WH\n    query_timeout: 60\n$$;\n\nGRANT USAGE ON AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY TO ROLE PUBLIC;\nALTER AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY SET PROFILE = '{\"avatar\": \"robot\"}';\n```\n\n\u003C!-- ------------------------ --\u003E\n## Verify Installation\n\nOnce the install script completes, run the following queries to verify everything was created successfully:\n\n```sql\nSELECT 'SP500_COMPANIES' AS table_name, COUNT(*) AS row_count FROM HOLLY_DB.STRUCTURED.SP500_COMPANIES\nUNION ALL SELECT 'STOCK_PRICE_TIMESERIES', COUNT(*) FROM HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES\nUNION ALL SELECT 'EDGAR_FILINGS', COUNT(*) FROM HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS\nUNION ALL SELECT 'PUBLIC_TRANSCRIPTS', COUNT(*) FROM HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS;\n\nSHOW CORTEX SEARCH SERVICES IN DATABASE HOLLY_DB;\nSHOW SEMANTIC VIEWS IN DATABASE HOLLY_DB;\nDESC AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY;\n```\n\nYou should see:\n- **SP500_COMPANIES**: ~503 rows\n- **STOCK_PRICE_TIMESERIES**: varies based on date range\n- **EDGAR_FILINGS**: varies based on filings available\n- **PUBLIC_TRANSCRIPTS**: varies based on available transcripts\n- Two Cortex Search services\n- Three Semantic Views\n- The Holly agent description\n\n\u003C!-- ------------------------ --\u003E\n## Try Holly in Snowflake CoWork\n\nNavigate to **AI & ML \u003E Snowflake CoWork** in Snowsight and select **Holly**.\n\n### Q1: Plot Share Prices\n\n\u003E \"Plot the share price of Microsoft, Amazon, Google and Nvidia starting 20th Feb 2025 to 20th Feb 2026\"\n\nHolly uses the **STOCK_PRICES** tool (Cortex Analyst) to generate SQL and return a chart of historical prices across multiple tickers.\n\n### Q2: S&P 500 Membership Check\n\n\u003E \"Are Nvidia, Microsoft, Amazon, Google in the SP500\"\n\nHolly queries the **SP500_COMPANIES** tool to check membership. It automatically generates SQL from natural language.\n\n### Q3: Latest Earnings Transcripts\n\n\u003E \"What are the latest public transcripts for NVIDIA\"\n\nHolly uses **TRANSCRIPTS_SEARCH** (Cortex Search) to retrieve and summarize the most recent earnings call transcripts.\n\n### Q4: SEC Filing Lookup\n\n\u003E \"What is the latest 10-K for Nvidia from the EDGAR Filings\"\n\nHolly searches **SEC_FILINGS_SEARCH** to find and return the most recent annual report filing.\n\n### Q5: Company Comparison\n\n\u003E \"Compare Nvidia's annual growth rate and Microsoft annual growth rate using the latest Annual reports using a table format for all the key metrics\"\n\nHolly combines **SEC_FILINGS_SEARCH** across multiple companies to extract and compare key financial metrics in a table.\n\n### Q6: Investment Research\n\n\u003E \"Would you recommend buying Nvidia Stock at 195\"\n\nHolly uses **multiple tools** — pulling current price data, recent filings, and transcripts — to provide a comprehensive research summary. Note that Holly correctly avoids giving direct investment advice.\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nTo remove all Holly components, run the following cleanup statements. These are also included at the end of the install script as comments:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nUSE WAREHOUSE SMALL_WH;\n\nDROP AGENT IF EXISTS SNOWFLAKE_INTELLIGENCE.AGENTS.HOLLY;\nDROP CORTEX SEARCH SERVICE IF EXISTS HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS_SEARCH;\nDROP CORTEX SEARCH SERVICE IF EXISTS HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS_SEARCH;\nDROP SEMANTIC VIEW IF EXISTS HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES_SV;\nDROP SEMANTIC VIEW IF EXISTS HOLLY_DB.STRUCTURED.SP500;\nDROP TABLE IF EXISTS HOLLY_DB.STRUCTURED.SP500_COMPANIES;\nDROP TABLE IF EXISTS HOLLY_DB.STRUCTURED.STOCK_PRICE_TIMESERIES;\nDROP TABLE IF EXISTS HOLLY_DB.SEMI_STRUCTURED.EDGAR_FILINGS;\nDROP TABLE IF EXISTS HOLLY_DB.UNSTRUCTURED.PUBLIC_TRANSCRIPTS;\nDROP SCHEMA IF EXISTS HOLLY_DB.STRUCTURED;\nDROP SCHEMA IF EXISTS HOLLY_DB.SEMI_STRUCTURED;\nDROP SCHEMA IF EXISTS HOLLY_DB.UNSTRUCTURED;\nDROP DATABASE IF EXISTS HOLLY_DB;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nCongratulations! You have successfully built **Holly**, an AI-powered financial research assistant using Snowflake CoWork.\n\n### What You Learned\n\n- How to source financial data from Snowflake Marketplace (Cybersyn)\n- How to create Cortex Search services for unstructured text retrieval over SEC filings and earnings transcripts\n- How to create Semantic Views that enable natural language to SQL conversion\n- How to build a multi-tool Cortex Agent that intelligently routes queries\n- How to use Snowflake CoWork for comprehensive financial research\n\n### Related Resources\n\n- [Snowflake CoWork Documentation](https://docs.snowflake.com/user-guide/snowflake-cortex/snowflake-intelligence)\n- [Cortex Agent Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n- [Cortex Analyst Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)\n- [Cortex Search Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)\n- [Build an AI Assistant for FSI](https://www.snowflake.com/en/developers/guides/build-an-ai-assistant-for-fsi-with-aisql-and-snowflake-intelligence)\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-d8f2fd0d00","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-fbf0823757",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-8194793dd8","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-21",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-d4c02c4c72","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 instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-c5427bb2b4",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-bb7523bf4c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-fb7316d8d2","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-f321cd01ad",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/holly-financial-research-assistant","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESubscribe to Marketplace Data\u003C/h2\u003E","\u003Ch2\u003ECreate Database and Load Data\u003C/h2\u003E","\u003Ch2\u003ECreate Cortex Search Services\u003C/h2\u003E","\u003Ch2\u003ECreate Semantic Views\u003C/h2\u003E","\u003Ch2\u003ECreate the Holly Agent\u003C/h2\u003E","\u003Ch2\u003EVerify Installation\u003C/h2\u003E","\u003Ch2\u003ETry Holly in Snowflake CoWork\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion and 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class=\"sf-footer__column-title\"\u003EProduct\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/platform/\"\u003EPlatform\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/data-engineering/\"\u003EData Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/analytics/\"\u003EAnalytics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/ai/\"\u003EAI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/pricing-options/\"\u003EPricing\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"text_copy":{"id":"text-351ee75db7","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ESupport\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/support/\"\u003ESupport\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/addenda/priority-support-services-description/\"\u003EPriority Support\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStatus\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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