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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-45f3240e0d","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-dc7f0232f7",":items":{"contentfragment":{"id":"contentfragment-59bbc6fc41","description":"Build an intelligent supply chain assistant using Snowflake CoWork and Cortex AI capabilities to help operations managers make data-driven inventory management decisions.","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EModern supply chain operations face a critical challenge: efficiently managing raw material inventory across multiple manufacturing facilities. Operations managers must constantly balance inventory levels, deciding whether to transfer materials between plants with excess and shortage, or purchase new materials from suppliers. Making these decisions manually is time-consuming, error-prone, and often results in suboptimal cost outcomes.\u003C/p\u003E\n","\u003Cp\u003EThis quickstart demonstrates how to build an intelligent supply chain assistant using Snowflake CoWork and Cortex AI capabilities. By combining natural language querying with semantic search over both structured and unstructured data, you'll create a complete solution that helps operations managers make data-driven decisions about inventory management.\u003C/p\u003E\n","\u003Ch3\u003EThe Problem\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/problem.png\" alt=\"The Problem\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESupply chain operations managers face daily challenges managing raw material inventory across manufacturing facilities:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EInventory Imbalances\u003C/strong\u003E: Some plants have excess raw materials while others face shortages, creating inefficiency\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EComplex Decision Making\u003C/strong\u003E: Determining whether to transfer materials between plants or purchase from suppliers requires analyzing multiple factors including material costs, transport costs, lead times, and safety stock levels\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EManual Analysis\u003C/strong\u003E: Traditional approaches require running multiple reports, spreadsheet analysis, and manual cost comparisons\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETime Sensitivity\u003C/strong\u003E: Inventory decisions need to be made quickly to avoid production delays or excess carrying costs\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EThe Solution\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/solution.png\" alt=\"The Solution\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis solution leverages Snowflake CoWork and Cortex AI capabilities to create an intelligent assistant that:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EAnswers Ad-Hoc Questions\u003C/strong\u003E: Operations managers can ask natural language questions about inventory levels, orders, shipments, and supplier information - the agent automatically converts questions to SQL and executes them\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProvides Contextual Information\u003C/strong\u003E: The assistant can search and retrieve relevant information from supply chain documentation using semantic search\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIntelligent Routing\u003C/strong\u003E: Automatically determines whether to query structured data (via Cortex Analyst) or search documents (via Cortex Search) based on the nature of the question\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EComplex Analysis\u003C/strong\u003E: Handles sophisticated multi-table queries like identifying plants with low inventory alongside plants with excess inventory of the same materials, and comparing costs between suppliers and inter-plant transfers\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENo-Code Agent Creation\u003C/strong\u003E: Build and deploy the entire solution using Snowflake CoWork's visual interface without writing application code\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with Cortex features enabled\u003C/li\u003E\u003Cli\u003EA Snowflake account login with ACCOUNTADMIN role OR a role that has the ability to create databases, schemas, tables, stages, and Cortex Search services\u003C/li\u003E\u003Cli\u003ECortex Analyst, Cortex Search, and Snowflake CoWork must be available in your Snowflake region\u003C/li\u003E\u003Cli\u003EFamiliarity with Snowflake SQL and Snowsight interface\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\n\u003Cstrong\u003ENote:\u003C/strong\u003E The custom tools for web search and web scraping require external integration access, which is not available on trial accounts. You can still complete this quickstart without these tools by skipping the custom tool setup steps.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to model a multi-tier supply chain in Snowflake with proper relationships\u003C/li\u003E\u003Cli\u003EHow to create semantic models for Cortex Analyst with dimensions, measures, and verified queries\u003C/li\u003E\u003Cli\u003EHow to set up Cortex Search services on unstructured documents\u003C/li\u003E\u003Cli\u003EHow to build comprehensive AI agents using Snowflake CoWork\u003C/li\u003E\u003Cli\u003EHow to combine multiple semantic models in a single agent for cross-domain analysis\u003C/li\u003E\u003Cli\u003EHow to integrate custom tools (functions and stored procedures) into your agent\u003C/li\u003E\u003Cli\u003EHow to enable web search and scraping capabilities within your AI assistant\u003C/li\u003E\u003Cli\u003EHow to write effective tool descriptions and semantic models for accurate AI responses\u003C/li\u003E\u003Cli\u003EHow to handle complex analytics questions that span multiple data sources\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA comprehensive supply chain database with 11 tables and realistic sample data\u003C/li\u003E\u003Cli\u003ETwo semantic models: one for supply chain data and one for weather forecasts\u003C/li\u003E\u003Cli\u003EA Cortex Search service indexed on supply chain documentation\u003C/li\u003E\u003Cli\u003EA Snowflake CoWork agent with 7 tools:\n\u003Cul\u003E\u003Cli\u003E2 Cortex Analyst tools (supply chain and weather data)\u003C/li\u003E\u003Cli\u003E1 Cortex Search tool (documentation)\u003C/li\u003E\u003Cli\u003E4 Custom tools (web search, web scraping, HTML newsletter generation, email sending)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EComplex verified queries for inventory analysis, cost comparison, and rebalancing opportunities\u003C/li\u003E\u003Cli\u003EA production-ready AI assistant that combines structured data, unstructured data, and external web sources\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Cortex and Intelligence\u003C/h2\u003E\n","\u003Ch3\u003EWhat is Snowflake Cortex?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/cortex-image.png\" alt=\"Alt Text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESnowflake Cortex provides fully managed Generative AI capabilities that run securely within your Snowflake environment and governance boundary. Key features include:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E - Enables business users to ask questions about structured data in natural language. It uses a semantic model to understand your data and generates accurate SQL queries automatically.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/building-the-semantic-layer.png\" alt=\"Alt Text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe semantic model definitions you create are automatically translated into SQL code that powers the natural language queries. For example, defining business concepts like "Low Inventory" and "Excess Inventory" in the semantic model results in precise SQL WHERE clauses:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/semantic-view-code.png\" alt=\"Semantic View Code\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E - Provides easy-to-use semantic search over unstructured data. It handles document chunking, embedding generation, and retrieval, making it simple to implement RAG (Retrieval Augmented Generation) patterns.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex Agents\u003C/strong\u003E - Orchestrates multiple AI capabilities (like Analyst and Search) to intelligently route user queries to the appropriate service and synthesize responses.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/what-is-cortex-agent.png\" alt=\"Alt Text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELearn more about \u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\"\u003ESnowflake Cortex\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Snowflake CoWork?\u003C/h3\u003E\n","\u003Cp\u003ESnowflake CoWork is a unified experience for building and deploying AI agents within Snowflake. It provides:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENo-Code Agent Builder\u003C/strong\u003E: Create agents that combine multiple tools (Cortex Analyst, Cortex Search, Custom Tools) without writing code\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIntegrated Tools\u003C/strong\u003E: Easily connect your semantic models and search services as agent capabilities\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConversational Interface\u003C/strong\u003E: Interact with your agent through a chat interface within Snowsight\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEnterprise Ready\u003C/strong\u003E: Built on Snowflake's security and governance foundation\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHere's what Snowflake CoWork looks like in action for supply chain operations. Users can ask natural language questions about their data, and the agent automatically generates SQL queries, executes them, and returns results:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/si-on-supply-chain.png\" alt=\"Snowflake CoWork on Supply Chain\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELearn more about \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence\"\u003ESnowflake CoWork\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Database and Load Data\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E All SQL scripts, semantic models, and PDF files referenced in this quickstart can be accessed by clicking the \u003Cstrong\u003EFork Repo\u003C/strong\u003E button at the top of this page. This will take you to the GitHub repository containing all necessary files for this tutorial.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn this step, you'll create the complete supply chain database infrastructure with all necessary tables, stages, and sample data.\u003C/p\u003E\n","\u003Cp\u003EThe database will model a multi-tier supply chain network with suppliers, manufacturing plants, and customers. The demo considers key operational factors including inventory levels, demand, safety stock, lead times with variability, and costs. The intelligent assistant will help answer critical questions like "When do I order more material vs. transfer from another facility?"\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/supply-chain-network-demo.png\" alt=\"Supply Chain Network Demo\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Ca href=\"https://app.snowflake.com/_deeplink/#/workspaces/?utm_source=quickstart&utm_medium=quickstart&utm_campaign=-us-en-all&utm_content=app-supply-chain-assistant-with-snowflake-intelligence\"\u003E\u003Cstrong\u003EProjects > Workspaces\u003C/strong\u003E\u003C/a\u003E to create a new private workspace in Snowsight\u003C/li\u003E\u003Cli\u003EAdd a new SQL file to your workspace\u003C/li\u003E\u003Cli\u003EImport the \u003Cstrong\u003Escripts/setup.sql\u003C/strong\u003E file from the quickstart repository\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun All\u003C/strong\u003E to execute the entire script\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis script will create:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E: \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB\u003C/code\u003E with schemas \u003Ccode\u003EENTITIES\u003C/code\u003E and \u003Ccode\u003EWEATHER\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse\u003C/strong\u003E: \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESupply Chain Tables\u003C/strong\u003E: Including suppliers, plants, inventory, customers, orders, shipments, and more\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInternal Stages\u003C/strong\u003E: For storing PDFs and semantic model files\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESample Data\u003C/strong\u003E: Realistic data loaded via INSERT statements for all tables\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustom Functions and Procedures\u003C/strong\u003E: Web search, web scraping, email, and HTML newsletter generation capabilities\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe database schema models a complete supply chain network with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EManufacturing plants located in multiple cities\u003C/li\u003E\u003Cli\u003ESuppliers providing raw materials\u003C/li\u003E\u003Cli\u003ECustomers placing orders for finished goods\u003C/li\u003E\u003Cli\u003EInventory tracking for both raw materials and finished goods\u003C/li\u003E\u003Cli\u003EShipment records with tracking information\u003C/li\u003E\u003Cli\u003EWeather forecast data for plant locations\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHere's the complete Entity Relationship Diagram (ERD) showing all tables and their relationships:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/supply-chain-assistant-erd.png\" alt=\"Supply Chain ERD\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUpload Documents and Models\u003C/h2\u003E\n","\u003Cp\u003EWithin the first step, all objects have been created in \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB.ENTITIES\u003C/code\u003E database/schema. Now you'll upload the necessary files to the internal stages.\u003C/p\u003E\n","\u003Ch3\u003EUpload PDF Documents\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EDatabase Explorer\u003C/strong\u003E from the left side menu under \u003Cstrong\u003ECatalog\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ENavigate to the \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB.ENTITIES\u003C/code\u003E database/schema\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003EStages\u003C/strong\u003E to view available stages\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003ESUPPLY_CHAIN_ASSISTANT_PDF_STAGE\u003C/strong\u003E stage\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003E+ Files\u003C/strong\u003E button on the top right\u003C/li\u003E\u003Cli\u003EUpload the \u003Cstrong\u003Epdfs/Supply Chain Network Overview.pdf\u003C/strong\u003E file from the quickstart repository\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EUpload Semantic Model Files\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn the same Stages view, select the \u003Cstrong\u003ESEMANTIC_MODELS_STAGE\u003C/strong\u003E stage\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003E+ Files\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003EUpload both semantic model files:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Escripts/semantic_models/SUPPLY_CHAIN_ASSISTANT_MODEL.yaml\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Escripts/semantic_models/WEATHER_FORECAST.yaml\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThese semantic models define the structure, relationships, and verified queries that Cortex Analyst will use to answer natural language questions about your supply chain and weather data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Cortex Search Service\u003C/h2\u003E\n","\u003Cp\u003EYou have two options to create the Cortex Search service that will enable semantic search over your supply chain documentation.\u003C/p\u003E\n","\u003Ch3\u003EOption A: Using SQL Script\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn your workspace in Snowsight, add a new SQL file\u003C/li\u003E\u003Cli\u003EImport the \u003Cstrong\u003Escripts/configure_search_services.sql\u003C/strong\u003E file from the quickstart repository\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun All\u003C/strong\u003E to execute the script\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis script will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EParse PDFs using Cortex PARSE_DOCUMENT function\u003C/li\u003E\u003Cli\u003EChunk the content into searchable segments using recursive character splitting\u003C/li\u003E\u003Cli\u003ECreate the \u003Ccode\u003EPARSED_PDFS\u003C/code\u003E table with presigned URLs for document access\u003C/li\u003E\u003Cli\u003ECreate the \u003Ccode\u003ESUPPLY_CHAIN_INFO\u003C/code\u003E Cortex Search service\u003C/li\u003E\u003Cli\u003ESet up a task to refresh presigned URLs daily (they expire after 7 days)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe search service will automatically index the parsed and chunked PDF content, making it available for semantic search queries.\u003C/p\u003E\n","\u003Ch3\u003EOption B: Manual Creation via Snowsight UI\u003C/h3\u003E\n","\u003Cp\u003EIf you prefer to create the search service manually through the UI:\u003C/p\u003E\n","\u003Ch4\u003EStep 1: Prepare the Data (SQL Required)\u003C/h4\u003E\n","\u003Cp\u003EEven with the UI approach, you'll need to run SQL to parse and prepare the documents. In your workspace, add a new SQL file and run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES;\nUSE WAREHOUSE SUPPLY_CHAIN_ASSISTANT_WH;\n\n-- Scale up warehouse for PDF parsing\nALTER WAREHOUSE SUPPLY_CHAIN_ASSISTANT_WH SET WAREHOUSE_SIZE = 'X-LARGE';\n\n-- Parse PDFs\nCREATE OR REPLACE TABLE PARSE_PDFS AS \nSELECT RELATIVE_PATH, \n       SNOWFLAKE.CORTEX.PARSE_DOCUMENT(@SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES.SUPPLY_CHAIN_ASSISTANT_PDF_STAGE, \n                                        RELATIVE_PATH, \n                                        {'mode':'LAYOUT'}) AS DATA\nFROM DIRECTORY(@SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES.SUPPLY_CHAIN_ASSISTANT_PDF_STAGE);\n\n-- Chunk and prepare content\nCREATE OR REPLACE TABLE PARSED_PDFS AS (\n    WITH TMP_PARSED AS (\n        SELECT RELATIVE_PATH,\n               SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER(TO_VARIANT(DATA):content, 'MARKDOWN', 1800, 300) AS CHUNKS\n        FROM PARSE_PDFS \n        WHERE TO_VARIANT(DATA):content IS NOT NULL\n    )\n    SELECT TO_VARCHAR(C.value) AS PAGE_CONTENT,\n           REGEXP_REPLACE(RELATIVE_PATH, '\\\\.pdf$', '') AS TITLE,\n           RELATIVE_PATH,\n           GET_PRESIGNED_URL(@SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES.SUPPLY_CHAIN_ASSISTANT_PDF_STAGE, RELATIVE_PATH, 604800) AS PAGE_URL\n    FROM TMP_PARSED P, LATERAL FLATTEN(INPUT => P.CHUNKS) C\n);\n\n-- Scale warehouse back down\nALTER WAREHOUSE SUPPLY_CHAIN_ASSISTANT_WH SET WAREHOUSE_SIZE = 'SMALL';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 2: Create Search Service via UI\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, navigate to \u003Ca href=\"https://app.snowflake.com/_deeplink/#/cortex/search?utm_source=quickstart&utm_medium=quickstart&utm_campaign=-us-en-all&utm_content=app-supply-chain-assistant-with-snowflake-intelligence\"\u003E\u003Cstrong\u003EAI & ML\u003C/strong\u003E > \u003Cstrong\u003ECortex Search\u003C/strong\u003E\u003C/a\u003E in the left navigation\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003EConfigure the search service:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003EENTITIES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_INFO\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESource Table to be Indexed:\u003C/strong\u003E \u003Ccode\u003EPARSED_PDFS\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESearch Column:\u003C/strong\u003E Select \u003Ccode\u003EPAGE_CONTENT\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAttributes\u003C/strong\u003E: Click \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESelect Columns\u003C/strong\u003E: Select \u003Ccode\u003ETITLE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETarget Lag:\u003C/strong\u003E \u003Ccode\u003E1 hour\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_WH\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate Search Service\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe search service will begin indexing your parsed PDF content and will be ready to use once indexing completes.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild Snowflake CoWork Agent\u003C/h2\u003E\n","\u003Cp\u003ENow that you have your semantic models and search service created, you can combine them into an intelligent agent using Snowflake CoWork. The agent will intelligently route user questions to the appropriate tool based on the nature of the query.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Agent\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EEnsure you are using SUPPLY_CHAIN_ASSISTANT_ROLE in Snowsight\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Ca href=\"https://app.snowflake.com/_deeplink/#/agents?utm_source=quickstart&utm_medium=quickstart&utm_campaign=-us-en-all&utm_content=app-supply-chain-assistant-with-snowflake-intelligence\"\u003Eon \u003Cstrong\u003EAgents\u003C/strong\u003E\u003C/a\u003E within the \u003Cstrong\u003EAI & ML\u003C/strong\u003E section on the left-hand navigation bar in Snowsight\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate Agent\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E \u003Ccode\u003ESNOWFLAKE_INTELLIEGENCE\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E \u003Ccode\u003EAGENTS\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAgent object name\u003C/strong\u003E \u003Cstrong\u003ESupply_Chain_Agent\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDisplay name\u003C/strong\u003E Supply Chain Agent\u003C/li\u003E\u003Cli\u003EOnce created, navigate to \u003Cstrong\u003ETools\u003C/strong\u003E tab\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/create-agent.png\" alt=\"Create Agent\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdd First Cortex Analyst Tool - Supply Chain Data\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Add\u003C/strong\u003E next to \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EConfigure the tool:\n\u003Cul\u003E\u003Cli\u003ESelect the \u003Cstrong\u003ESemantic model file\u003C/strong\u003E radio button\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E \u003Ccode\u003EENTITIES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStage\u003C/strong\u003E \u003Ccode\u003ESEMANTIC_MODELS_STAGE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_MODEL.yaml\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_MODEL\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Cem\u003E"Tool for analyzing supply chain data."\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse:\u003C/strong\u003E Select \u003Cstrong\u003ECustom\u003C/strong\u003E radio button then choose \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_WH\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EAdd Second Cortex Analyst Tool - Weather Data\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Add\u003C/strong\u003E next to \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EConfigure the tool:\n\u003Cul\u003E\u003Cli\u003ESelect the \u003Cstrong\u003ESemantic model file\u003C/strong\u003E radio button\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E \u003Ccode\u003EENTITIES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStage\u003C/strong\u003E \u003Ccode\u003ESEMANTIC_MODELS_STAGE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EWEATHER_FORECAST.yaml\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003EWEATHER_FORECAST\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Cem\u003E"Tool for analyzing weather data."\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse:\u003C/strong\u003E Select \u003Cstrong\u003ECustom\u003C/strong\u003E radio button then choose \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_WH\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/add-analyst-tool.png\" alt=\"Add Analyst Tool\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdd Cortex Search Tool\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Add\u003C/strong\u003E next to \u003Cstrong\u003ECortex Search Services\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EConfigure the tool:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E \u003Ccode\u003EENTITIES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESearch Service:\u003C/strong\u003E Select \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB.ENTITIES.SUPPLY_CHAIN_INFO\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_INFO\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Cem\u003E"Tool for searching supply chain unstructured data."\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EID Column:\u003C/strong\u003E \u003Ccode\u003EPAGE_URL\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETitle Column:\u003C/strong\u003E \u003Ccode\u003ETITLE\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/add-cortex-search-tool.png\" alt=\"Add Search Tool\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdd Custom Tools\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\n\u003Cstrong\u003ENote:\u003C/strong\u003E The WEB_SEARCH and WEB_SCRAPE custom tools require external integration access, which is not available on trial accounts. If you're using a trial account, you can skip adding these two tools and still use the other agent capabilities.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EFor each of the following custom tools, click \u003Cstrong\u003E+ Add\u003C/strong\u003E next to \u003Cstrong\u003ECustom Tool\u003C/strong\u003E, then configure:\u003C/p\u003E\n","\u003Ch4\u003E1. CREATE_HTML_NEWSLETTER\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EType:\u003C/strong\u003E procedure\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB.ENTITIES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustom tool identifier:\u003C/strong\u003E \u003Ccode\u003ECREATE_HTML_NEWSLETTER_SP\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003ECREATE_HTML_NEWSLETTER_SP\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Cem\u003E"Create HTML newsletter from responses."\u003C/em\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E2. WEB_SEARCH\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EType:\u003C/strong\u003E function\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB.ENTITIES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustom tool identifier:\u003C/strong\u003E \u003Ccode\u003EWEB_SEARCH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003EWEB_SEARCH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Cem\u003E"Search the web using DuckDuckGo."\u003C/em\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E3. WEB_SCRAPE\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EType:\u003C/strong\u003E function\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB.ENTITIES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustom tool identifier:\u003C/strong\u003E \u003Ccode\u003EWEB_SCRAPE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003EWEB_SCRAPE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Cem\u003E"Web scraping and content extraction."\u003C/em\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E4. SEND_MAIL\u003C/h4\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\n\u003Cstrong\u003ENote:\u003C/strong\u003E This tool requires a verified email address in Snowflake to function properly.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EType:\u003C/strong\u003E procedure\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_DB.ENTITIES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustom tool identifier:\u003C/strong\u003E \u003Ccode\u003ESEND_MAIL\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003ESEND_MAIL\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse:\u003C/strong\u003E \u003Ccode\u003ESUPPLY_CHAIN_ASSISTANT_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Cem\u003E"Send emails to recipients with HTML formatted content."\u003C/em\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/add-custom-tool.png\" alt=\"Add Custom Tool\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESave and Access Your Agent\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E to save your agent configuration\u003C/li\u003E\u003Cli\u003EYou can test the agent directly in the right-hand pane\u003C/li\u003E\u003Cli\u003EAlternatively, navigate to \u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E in the left navigation AI & ML menu\u003C/li\u003E\u003Cli\u003ESelect your \u003Cstrong\u003ESupply_Chain_Agent\u003C/strong\u003E from the dropdown\u003C/li\u003E\u003Cli\u003EStart asking questions!\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/agent-tools.png\" alt=\"Agent Overview\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUser Personas\u003C/h2\u003E\n","\u003Cp\u003EWe are building for three core user archetypes involved in supply chain operations.\u003C/p\u003E\n","\u003Ch3\u003EMaria Chen (The Operator - Supply Chain Operations Manager)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERole\u003C/strong\u003E: Manages day-to-day operations and material flow. Responsible for resolving inventory issues, coordinating logistics, and keeping the supply chain moving.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGoals\u003C/strong\u003E: Maximize asset availability, resolve stockouts before they impact production, and optimize for cost and speed.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENeeds\u003C/strong\u003E: Real-time, actionable answers. A prioritized list of critical issues. Tools to quickly compare options and take action.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFrustrations\u003C/strong\u003E: Constant "fire-fighting." Waiting hours or days for an analyst to run a report. Making decisions with incomplete data (e.g., not knowing about weather disruptions).\u003C/p\u003E\n","\u003Ch3\u003EVictor Rodriguez (The Executive - VP of Operations)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERole\u003C/strong\u003E: Oversees supply chain budget and long-term strategy. Responsible for profitability, efficiency, and risk management.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGoals\u003C/strong\u003E: Ensure operational continuity, meet financial targets, and understand high-level risks and opportunities.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENeeds\u003C/strong\u003E: A high-level, aggregate view of supply chain health. Clear, concise summaries of critical problems, their financial impact, and the recommended solution.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFrustrations\u003C/strong\u003E: Unforeseen budget overruns. Surprises from major disruptions. Reports that are too detailed and don't provide a clear recommendation.\u003C/p\u003E\n","\u003Ch3\u003ELeo Martinez (The Data Steward - Analytics Engineer)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERole\u003C/strong\u003E: The technical and business-logic expert. Responsible for data quality and enabling the AI agent by teaching it the business's unique logic.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGoals\u003C/strong\u003E: Ensure the agent's answers are accurate and trusted. Codify business definitions. Reduce his own ad-hoc reporting backlog.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENeeds\u003C/strong\u003E: A simple UI to define business semantics. The ability to test, validate, and "bless" queries. Tools to debug why an agent gave a specific answer.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFrustrations\u003C/strong\u003E: Vague requests from users. The same simple questions being asked repeatedly. Business users misinterpreting raw data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EA Real-World Scenario\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAs Maria (The Operator)\u003C/strong\u003E, I want to ask, \u003Cem\u003E"Where do I have critical low inventory levels?"\u003C/em\u003E and instantly see the most critical plants, so I can immediately know where to focus.\u003C/p\u003E\n","\u003Cp\u003EWhen Maria discovers the Miami plant is critically low on rare earth materials, she asks, \u003Cem\u003E"For plants with low inventory of a raw material, compare the cost of replenishing from a supplier vs transferring from another plant with excess inventory."\u003C/em\u003E The agent compares transfer from Austin (71% savings) vs. purchasing from a supplier, so she can make the most cost-effective decision.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/hurricane.png\" alt=\"Hurricane Warning\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBefore finalizing the transfer, Maria asks, \u003Cem\u003E"What type of weather events might impact this transfer?"\u003C/em\u003E The agent automatically checks the Weather Forecast tool and warns: \u003Cstrong\u003E"A major hurricane is forecasted; transport may be impossible,"\u003C/strong\u003E so she doesn't make a costly logistical mistake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAs Victor (The Executive)\u003C/strong\u003E, I want to receive a clear summary when Maria asks the agent to \u003Cem\u003E"Draft an executive summary email with this analysis, our options, and a recommendation."\u003C/em\u003E The agent generates an HTML-formatted email with Executive Summary, Financial Comparison, Key Risks, and Final Recommendation, so I can make an informed decision and reply "Approved" in minutes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAs Leo (The Data Steward)\u003C/strong\u003E, I previously defined business logic like "critical low inventory" in the semantic model YAML file and added verified queries for cost comparisons, so the agent understands our business rules and Maria can trust the answers.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/definitions-used.png\" alt=\"Low Inventory Definitions\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETest Agent with Questions\u003C/h2\u003E\n","\u003Cp\u003ENow it's your turn to experience the Supply Chain Assistant! Follow Maria's journey by asking these questions in order, or explore on your own. Notice how the agent automatically determines which tool to use based on your question!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/agent-screenshot.png\" alt=\"Alt text\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EFollowing Maria's Scenario\u003C/h3\u003E\n","\u003Cp\u003EAsk these questions in sequence to replicate the scenario above:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cem\u003E"Where do I have critical low inventory levels?"\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cem\u003E"Where do we have low inventory of rare earth materials?"\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cem\u003E"For plants with low inventory of a raw material, compare the cost of replenishing from a supplier vs transferring from another plant with excess inventory."\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cem\u003E"What type of weather events might impact this transfer?"\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cem\u003E"Draft an executive summary email with this analysis, our options, and a recommendation."\u003C/em\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\n\u003Cstrong\u003ENote:\u003C/strong\u003E Sending emails requires a verified email addresses. The WEB_SEARCH and WEB_SCRAPE tools require external integration access, which is not available on trial accounts.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EAdditional Questions to Explore\u003C/h3\u003E\n","\u003Cp\u003ETry these variations to explore different capabilities:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"Compare the cost of transferring this inventory from a plant with excess inventory versus replenishing from a supplier."\u003C/li\u003E\u003Cli\u003E"Show me all plants with excess inventory that could support transfers."\u003C/li\u003E\u003Cli\u003E"What are the lead times for our primary suppliers?"\u003C/li\u003E\u003Cli\u003E"Search our documentation for transfer policies." (uses Cortex Search)\u003C/li\u003E\u003Cli\u003E"What orders are scheduled for the aerospace business line?"\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've built a comprehensive Supply Chain Assistant powered by Snowflake CoWork that combines multiple AI capabilities into a single, intelligent agent.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up a comprehensive supply chain database with realistic sample data\u003C/li\u003E\u003Cli\u003EHow to create semantic models for Cortex Analyst to enable natural language queries\u003C/li\u003E\u003Cli\u003EHow to parse and chunk PDF documents for Cortex Search\u003C/li\u003E\u003Cli\u003EHow to build Cortex Search services for semantic search over unstructured data\u003C/li\u003E\u003Cli\u003EHow to create a Snowflake CoWork agent that combines multiple tools\u003C/li\u003E\u003Cli\u003EHow to integrate Cortex Analyst, Cortex Search, and custom tools into a single agent\u003C/li\u003E\u003Cli\u003EHow to enable web search and scraping capabilities in your AI assistant\u003C/li\u003E\u003Cli\u003EHow the agent intelligently routes questions to appropriate tools\u003C/li\u003E\u003Cli\u003EHow to test complex queries that span multiple data sources and tool types\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n","\u003Cp\u003EYour Supply Chain Assistant now includes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDual Analytics:\u003C/strong\u003E Query both supply chain operations and weather data using natural language\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESemantic Search:\u003C/strong\u003E Access unstructured supply chain documentation through intelligent retrieval\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWeb Integration:\u003C/strong\u003E Search and scrape external information from the internet\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECommunication:\u003C/strong\u003E Generate HTML newsletters and send emails (with proper integration)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIntelligent Routing:\u003C/strong\u003E Automatic determination of which tool to use based on question context\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EComplex Analysis:\u003C/strong\u003E Handle sophisticated multi-domain queries spanning structured and unstructured data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps and Extensions\u003C/h3\u003E\n","\u003Ch4\u003EScaling to Multi-Agent Architecture\u003C/h4\u003E\n","\u003Cp\u003EAs your organization's needs grow, you can evolve from a single agent with multiple tools to a multi-agent architecture where a primary orchestrator agent coordinates with specialized domain agents. This architecture enables better scalability, domain expertise, and independent development of agents for different business functions:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/multi-agent-architecture.png\" alt=\"Multi-Agent Architecture\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EYou can extend this solution further by:\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAdding More Semantic Models\u003C/strong\u003E: Create additional semantic models for other business domains (finance, HR, sales, etc.)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIntegrating Additional Data Sources\u003C/strong\u003E: Connect to external APIs or databases\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECreating Custom Tools\u003C/strong\u003E: Build specific tools for your unique business processes\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBuilding Streamlit Applications\u003C/strong\u003E: Create custom UIs that interact with your agent programmatically\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdding More Documents\u003C/strong\u003E: Index additional PDF documents, user manuals, or policy documents\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EImplementing Advanced Analytics\u003C/strong\u003E: Add tools for predictive analytics, optimization, or machine learning inference\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESetting Up Email Integration\u003C/strong\u003E: Configure email sending capabilities for automated reporting\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex\"\u003ESnowflake Cortex Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/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-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\"\u003ECortex Search Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst/semantic-model-spec\"\u003ESemantic Model Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/\"\u003ESnowflake Quickstarts\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://community.snowflake.com/\"\u003ESnowflake Community\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EGitHub Repository\u003C/h3\u003E\n","\u003Cp\u003EAccess the complete code, scripts, and files for this quickstart:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-supply-chain-assistant-with-snowflake-intelligence\"\u003ESupply Chain Assistant GitHub Repository\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Supply Chain Assistant for Inventory Optimization with Snowflake CoWork",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nModern supply chain operations face a critical challenge: efficiently managing raw material inventory across multiple manufacturing facilities. Operations managers must constantly balance inventory levels, deciding whether to transfer materials between plants with excess and shortage, or purchase new materials from suppliers. Making these decisions manually is time-consuming, error-prone, and often results in suboptimal cost outcomes.\n\nThis quickstart demonstrates how to build an intelligent supply chain assistant using Snowflake CoWork and Cortex AI capabilities. By combining natural language querying with semantic search over both structured and unstructured data, you'll create a complete solution that helps operations managers make data-driven decisions about inventory management.\n\n### The Problem\n\n![The Problem](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/problem.png)\n\nSupply chain operations managers face daily challenges managing raw material inventory across manufacturing facilities:\n\n* **Inventory Imbalances**: Some plants have excess raw materials while others face shortages, creating inefficiency\n* **Complex Decision Making**: Determining whether to transfer materials between plants or purchase from suppliers requires analyzing multiple factors including material costs, transport costs, lead times, and safety stock levels\n* **Manual Analysis**: Traditional approaches require running multiple reports, spreadsheet analysis, and manual cost comparisons\n* **Time Sensitivity**: Inventory decisions need to be made quickly to avoid production delays or excess carrying costs\n\n### The Solution\n\n![The Solution](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/solution.png)\n\nThis solution leverages Snowflake CoWork and Cortex AI capabilities to create an intelligent assistant that:\n\n1. **Answers Ad-Hoc Questions**: Operations managers can ask natural language questions about inventory levels, orders, shipments, and supplier information - the agent automatically converts questions to SQL and executes them\n2. **Provides Contextual Information**: The assistant can search and retrieve relevant information from supply chain documentation using semantic search\n3. **Intelligent Routing**: Automatically determines whether to query structured data (via Cortex Analyst) or search documents (via Cortex Search) based on the nature of the question\n4. **Complex Analysis**: Handles sophisticated multi-table queries like identifying plants with low inventory alongside plants with excess inventory of the same materials, and comparing costs between suppliers and inter-plant transfers\n5. **No-Code Agent Creation**: Build and deploy the entire solution using Snowflake CoWork's visual interface without writing application code\n\n### Prerequisites\n\n* A Snowflake account with Cortex features enabled\n* A Snowflake account login with ACCOUNTADMIN role OR a role that has the ability to create databases, schemas, tables, stages, and Cortex Search services\n* Cortex Analyst, Cortex Search, and Snowflake CoWork must be available in your Snowflake region\n* Familiarity with Snowflake SQL and Snowsight interface\n\n\u003E aside negative\n\u003E **Note:** The custom tools for web search and web scraping require external integration access, which is not available on trial accounts. You can still complete this quickstart without these tools by skipping the custom tool setup steps.\n\n### What You'll Learn\n\n* How to model a multi-tier supply chain in Snowflake with proper relationships\n* How to create semantic models for Cortex Analyst with dimensions, measures, and verified queries\n* How to set up Cortex Search services on unstructured documents\n* How to build comprehensive AI agents using Snowflake CoWork\n* How to combine multiple semantic models in a single agent for cross-domain analysis\n* How to integrate custom tools (functions and stored procedures) into your agent\n* How to enable web search and scraping capabilities within your AI assistant\n* How to write effective tool descriptions and semantic models for accurate AI responses\n* How to handle complex analytics questions that span multiple data sources\n\n### What You'll Build\n\n* A comprehensive supply chain database with 11 tables and realistic sample data\n* Two semantic models: one for supply chain data and one for weather forecasts\n* A Cortex Search service indexed on supply chain documentation\n* A Snowflake CoWork agent with 7 tools:\n  * 2 Cortex Analyst tools (supply chain and weather data)\n  * 1 Cortex Search tool (documentation)\n  * 4 Custom tools (web search, web scraping, HTML newsletter generation, email sending)\n* Complex verified queries for inventory analysis, cost comparison, and rebalancing opportunities\n* A production-ready AI assistant that combines structured data, unstructured data, and external web sources\n\n\u003C!-- ------------------------ --\u003E\n\n## Snowflake Cortex and Intelligence\n\n### What is Snowflake Cortex?\n\n![Alt Text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/cortex-image.png)\n\nSnowflake Cortex provides fully managed Generative AI capabilities that run securely within your Snowflake environment and governance boundary. Key features include:\n\n**Cortex Analyst** - Enables business users to ask questions about structured data in natural language. It uses a semantic model to understand your data and generates accurate SQL queries automatically.\n\n![Alt Text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/building-the-semantic-layer.png)\n\nThe semantic model definitions you create are automatically translated into SQL code that powers the natural language queries. For example, defining business concepts like \"Low Inventory\" and \"Excess Inventory\" in the semantic model results in precise SQL WHERE clauses:\n\n![Semantic View Code](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/semantic-view-code.png)\n\n**Cortex Search** - Provides easy-to-use semantic search over unstructured data. It handles document chunking, embedding generation, and retrieval, making it simple to implement RAG (Retrieval Augmented Generation) patterns.\n\n**Cortex Agents** - Orchestrates multiple AI capabilities (like Analyst and Search) to intelligently route user queries to the appropriate service and synthesize responses.\n\n![Alt Text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/what-is-cortex-agent.png)\n\nLearn more about [Snowflake Cortex](https://www.snowflake.com/en/product/features/cortex/).\n\n### What is Snowflake CoWork?\n\nSnowflake CoWork is a unified experience for building and deploying AI agents within Snowflake. It provides:\n\n* **No-Code Agent Builder**: Create agents that combine multiple tools (Cortex Analyst, Cortex Search, Custom Tools) without writing code\n* **Integrated Tools**: Easily connect your semantic models and search services as agent capabilities\n* **Conversational Interface**: Interact with your agent through a chat interface within Snowsight\n* **Enterprise Ready**: Built on Snowflake's security and governance foundation\n\nHere's what Snowflake CoWork looks like in action for supply chain operations. Users can ask natural language questions about their data, and the agent automatically generates SQL queries, executes them, and returns results:\n\n![Snowflake CoWork on Supply Chain](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/si-on-supply-chain.png)\n\nLearn more about [Snowflake CoWork](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence).\n\n\u003C!-- ------------------------ --\u003E\n\n## Setup Database and Load Data\n\n\n\u003E **Note:** All SQL scripts, semantic models, and PDF files referenced in this quickstart can be accessed by clicking the **Fork Repo** button at the top of this page. This will take you to the GitHub repository containing all necessary files for this tutorial.\n\nIn this step, you'll create the complete supply chain database infrastructure with all necessary tables, stages, and sample data.\n\nThe database will model a multi-tier supply chain network with suppliers, manufacturing plants, and customers. The demo considers key operational factors including inventory levels, demand, safety stock, lead times with variability, and costs. The intelligent assistant will help answer critical questions like \"When do I order more material vs. transfer from another facility?\"\n\n![Supply Chain Network Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/supply-chain-network-demo.png)\n\n1. Navigate to [**Projects \u003E Workspaces**](https://app.snowflake.com/_deeplink/#/workspaces/?utm_source=quickstart&utm_medium=quickstart&utm_campaign=-us-en-all&utm_content=app-supply-chain-assistant-with-snowflake-intelligence) to create a new private workspace in Snowsight\n2. Add a new SQL file to your workspace\n3. Import the **scripts/setup.sql** file from the quickstart repository\n4. Click **Run All** to execute the entire script\n\nThis script will create:\n\n* **Database**: `SUPPLY_CHAIN_ASSISTANT_DB` with schemas `ENTITIES` and `WEATHER`\n* **Warehouse**: `SUPPLY_CHAIN_ASSISTANT_WH`\n* **Supply Chain Tables**: Including suppliers, plants, inventory, customers, orders, shipments, and more\n* **Internal Stages**: For storing PDFs and semantic model files\n* **Sample Data**: Realistic data loaded via INSERT statements for all tables\n* **Custom Functions and Procedures**: Web search, web scraping, email, and HTML newsletter generation capabilities\n\nThe database schema models a complete supply chain network with:\n\n* Manufacturing plants located in multiple cities\n* Suppliers providing raw materials\n* Customers placing orders for finished goods\n* Inventory tracking for both raw materials and finished goods\n* Shipment records with tracking information\n* Weather forecast data for plant locations\n\nHere's the complete Entity Relationship Diagram (ERD) showing all tables and their relationships:\n\n![Supply Chain ERD](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/supply-chain-assistant-erd.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## Upload Documents and Models\n\nWithin the first step, all objects have been created in `SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES` database/schema. Now you'll upload the necessary files to the internal stages.\n\n### Upload PDF Documents\n\n1. Navigate to **Database Explorer** from the left side menu under **Catalog**\n2. Navigate to the `SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES` database/schema\n3. Click on **Stages** to view available stages\n4. Select the **SUPPLY_CHAIN_ASSISTANT_PDF_STAGE** stage\n5. Click the **+ Files** button on the top right\n6. Upload the **pdfs/Supply Chain Network Overview.pdf** file from the quickstart repository\n\n### Upload Semantic Model Files\n\n1. In the same Stages view, select the **SEMANTIC_MODELS_STAGE** stage\n2. Click the **+ Files** button\n3. Upload both semantic model files:\n   * **scripts/semantic_models/SUPPLY_CHAIN_ASSISTANT_MODEL.yaml**\n   * **scripts/semantic_models/WEATHER_FORECAST.yaml**\n\nThese semantic models define the structure, relationships, and verified queries that Cortex Analyst will use to answer natural language questions about your supply chain and weather data.\n\n\u003C!-- ------------------------ --\u003E\n\n## Create Cortex Search Service\n\nYou have two options to create the Cortex Search service that will enable semantic search over your supply chain documentation.\n\n### Option A: Using SQL Script\n\n1. In your workspace in Snowsight, add a new SQL file\n2. Import the **scripts/configure_search_services.sql** file from the quickstart repository\n3. Click **Run All** to execute the script\n\nThis script will:\n\n* Parse PDFs using Cortex PARSE_DOCUMENT function\n* Chunk the content into searchable segments using recursive character splitting\n* Create the `PARSED_PDFS` table with presigned URLs for document access\n* Create the `SUPPLY_CHAIN_INFO` Cortex Search service\n* Set up a task to refresh presigned URLs daily (they expire after 7 days)\n\nThe search service will automatically index the parsed and chunked PDF content, making it available for semantic search queries.\n\n### Option B: Manual Creation via Snowsight UI\n\nIf you prefer to create the search service manually through the UI:\n\n#### Step 1: Prepare the Data (SQL Required)\n\nEven with the UI approach, you'll need to run SQL to parse and prepare the documents. In your workspace, add a new SQL file and run:\n\n```sql\nUSE SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES;\nUSE WAREHOUSE SUPPLY_CHAIN_ASSISTANT_WH;\n\n-- Scale up warehouse for PDF parsing\nALTER WAREHOUSE SUPPLY_CHAIN_ASSISTANT_WH SET WAREHOUSE_SIZE = 'X-LARGE';\n\n-- Parse PDFs\nCREATE OR REPLACE TABLE PARSE_PDFS AS \nSELECT RELATIVE_PATH, \n       SNOWFLAKE.CORTEX.PARSE_DOCUMENT(@SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES.SUPPLY_CHAIN_ASSISTANT_PDF_STAGE, \n                                        RELATIVE_PATH, \n                                        {'mode':'LAYOUT'}) AS DATA\nFROM DIRECTORY(@SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES.SUPPLY_CHAIN_ASSISTANT_PDF_STAGE);\n\n-- Chunk and prepare content\nCREATE OR REPLACE TABLE PARSED_PDFS AS (\n    WITH TMP_PARSED AS (\n        SELECT RELATIVE_PATH,\n               SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER(TO_VARIANT(DATA):content, 'MARKDOWN', 1800, 300) AS CHUNKS\n        FROM PARSE_PDFS \n        WHERE TO_VARIANT(DATA):content IS NOT NULL\n    )\n    SELECT TO_VARCHAR(C.value) AS PAGE_CONTENT,\n           REGEXP_REPLACE(RELATIVE_PATH, '\\\\.pdf$', '') AS TITLE,\n           RELATIVE_PATH,\n           GET_PRESIGNED_URL(@SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES.SUPPLY_CHAIN_ASSISTANT_PDF_STAGE, RELATIVE_PATH, 604800) AS PAGE_URL\n    FROM TMP_PARSED P, LATERAL FLATTEN(INPUT =\u003E P.CHUNKS) C\n);\n\n-- Scale warehouse back down\nALTER WAREHOUSE SUPPLY_CHAIN_ASSISTANT_WH SET WAREHOUSE_SIZE = 'SMALL';\n```\n\n#### Step 2: Create Search Service via UI\n\n1. In Snowsight, navigate to [**AI & ML** \u003E **Cortex Search**](https://app.snowflake.com/_deeplink/#/cortex/search?utm_source=quickstart&utm_medium=quickstart&utm_campaign=-us-en-all&utm_content=app-supply-chain-assistant-with-snowflake-intelligence) in the left navigation\n2. Click **Create** button\n3. Configure the search service:\n   * **Database:** `SUPPLY_CHAIN_ASSISTANT_DB`\n   * **Schema:** `ENTITIES`\n   * **Name:** `SUPPLY_CHAIN_INFO`\n   * **Source Table to be Indexed:** `PARSED_PDFS`\n   * **Search Column:** Select `PAGE_CONTENT`\n   * **Attributes**: Click **Next**\n   * **Select Columns**: Select `TITLE`\n   * **Target Lag:** `1 hour`\n   * **Warehouse:** `SUPPLY_CHAIN_ASSISTANT_WH`\n4. Click **Create Search Service**\n\nThe search service will begin indexing your parsed PDF content and will be ready to use once indexing completes.\n\n\u003C!-- ------------------------ --\u003E\n\n## Build Snowflake CoWork Agent\n\nNow that you have your semantic models and search service created, you can combine them into an intelligent agent using Snowflake CoWork. The agent will intelligently route user questions to the appropriate tool based on the nature of the query.\n\n### Create the Agent\n\n\u003E Ensure you are using SUPPLY_CHAIN_ASSISTANT_ROLE in Snowsight\n\n1. Click [on **Agents**](https://app.snowflake.com/_deeplink/#/agents?utm_source=quickstart&utm_medium=quickstart&utm_campaign=-us-en-all&utm_content=app-supply-chain-assistant-with-snowflake-intelligence) within the **AI & ML** section on the left-hand navigation bar in Snowsight\n2. Click **Create Agent** button\n3. * **Database** `SNOWFLAKE_INTELLIEGENCE`\n4. * **Schema** `AGENTS`\n5. **Agent object name** **Supply_Chain_Agent**\n6. **Display name** Supply Chain Agent\n7. Once created, navigate to **Tools** tab\n\n![Create Agent](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/create-agent.png)\n\n### Add First Cortex Analyst Tool - Supply Chain Data\n\n1. Click **+ Add** next to **Cortex Analyst**\n2. Configure the tool:\n   * Select the **Semantic model file** radio button\n   * **Database** `SUPPLY_CHAIN_ASSISTANT_DB`\n   * **Schema** `ENTITIES`\n   * **Stage** `SEMANTIC_MODELS_STAGE`\n   * Select `SUPPLY_CHAIN_ASSISTANT_MODEL.yaml`\n   * **Name:** `SUPPLY_CHAIN_ASSISTANT_MODEL`\n   * **Description:** *\"Tool for analyzing supply chain data.\"*\n   * **Warehouse:** Select **Custom** radio button then choose `SUPPLY_CHAIN_ASSISTANT_WH`\n3. Click **Save**\n\n### Add Second Cortex Analyst Tool - Weather Data\n\n1. Click **+ Add** next to **Cortex Analyst**\n2. Configure the tool:\n   * Select the **Semantic model file** radio button\n   * **Database** `SUPPLY_CHAIN_ASSISTANT_DB`\n   * **Schema** `ENTITIES`\n   * **Stage** `SEMANTIC_MODELS_STAGE`\n   * Select `WEATHER_FORECAST.yaml`\n   * **Name:** `WEATHER_FORECAST`\n   * **Description:** *\"Tool for analyzing weather data.\"*\n   * **Warehouse:** Select **Custom** radio button then choose `SUPPLY_CHAIN_ASSISTANT_WH`\n3. Click **Save**\n\n![Add Analyst Tool](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/add-analyst-tool.png)\n\n### Add Cortex Search Tool\n\n1. Click **+ Add** next to **Cortex Search Services**\n2. Configure the tool:\n   * **Database** `SUPPLY_CHAIN_ASSISTANT_DB`\n   * **Schema** `ENTITIES`\n   * **Search Service:** Select `SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES.SUPPLY_CHAIN_INFO`\n   * **Name:** `SUPPLY_CHAIN_INFO`\n   * **Description:** *\"Tool for searching supply chain unstructured data.\"*\n   * **ID Column:** `PAGE_URL`\n   * **Title Column:** `TITLE`\n3. Click **Save**\n\n![Add Search Tool](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/add-cortex-search-tool.png)\n\n### Add Custom Tools\n\n\u003E aside negative\n\u003E **Note:** The WEB_SEARCH and WEB_SCRAPE custom tools require external integration access, which is not available on trial accounts. If you're using a trial account, you can skip adding these two tools and still use the other agent capabilities.\n\nFor each of the following custom tools, click **+ Add** next to **Custom Tool**, then configure:\n\n#### 1. CREATE_HTML_NEWSLETTER\n\n* **Type:** procedure\n* **Schema:** `SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES`\n* **Custom tool identifier:** `CREATE_HTML_NEWSLETTER_SP`\n* **Name:** `CREATE_HTML_NEWSLETTER_SP`\n* **Warehouse:** `SUPPLY_CHAIN_ASSISTANT_WH`\n* **Description:** *\"Create HTML newsletter from responses.\"*\n\n#### 2. WEB_SEARCH\n\n* **Type:** function\n* **Schema:** `SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES`\n* **Custom tool identifier:** `WEB_SEARCH`\n* **Name:** `WEB_SEARCH`\n* **Warehouse:** `SUPPLY_CHAIN_ASSISTANT_WH`\n* **Description:** *\"Search the web using DuckDuckGo.\"*\n\n#### 3. WEB_SCRAPE\n\n* **Type:** function\n* **Schema:** `SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES`\n* **Custom tool identifier:** `WEB_SCRAPE`\n* **Name:** `WEB_SCRAPE`\n* **Warehouse:** `SUPPLY_CHAIN_ASSISTANT_WH`\n* **Description:** *\"Web scraping and content extraction.\"*\n\n#### 4. SEND_MAIL\n\n\u003E aside negative\n\u003E **Note:** This tool requires a verified email address in Snowflake to function properly.\n\n* **Type:** procedure\n* **Schema:** `SUPPLY_CHAIN_ASSISTANT_DB.ENTITIES`\n* **Custom tool identifier:** `SEND_MAIL`\n* **Name:** `SEND_MAIL`\n* **Warehouse:** `SUPPLY_CHAIN_ASSISTANT_WH`\n* **Description:** *\"Send emails to recipients with HTML formatted content.\"*\n\n![Add Custom Tool](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/add-custom-tool.png)\n\n### Save and Access Your Agent\n\n1. Click **Save** to save your agent configuration\n2. You can test the agent directly in the right-hand pane\n3. Alternatively, navigate to **Snowflake CoWork** in the left navigation AI & ML menu\n4. Select your **Supply_Chain_Agent** from the dropdown\n5. Start asking questions!\n\n![Agent Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/agent-tools.png)\n\u003C!-- ------------------------ --\u003E\n\n## User Personas\n\nWe are building for three core user archetypes involved in supply chain operations.\n\n### Maria Chen (The Operator - Supply Chain Operations Manager)\n\n**Role**: Manages day-to-day operations and material flow. Responsible for resolving inventory issues, coordinating logistics, and keeping the supply chain moving.\n\n**Goals**: Maximize asset availability, resolve stockouts before they impact production, and optimize for cost and speed.\n\n**Needs**: Real-time, actionable answers. A prioritized list of critical issues. Tools to quickly compare options and take action.\n\n**Frustrations**: Constant \"fire-fighting.\" Waiting hours or days for an analyst to run a report. Making decisions with incomplete data (e.g., not knowing about weather disruptions).\n\n### Victor Rodriguez (The Executive - VP of Operations)\n\n**Role**: Oversees supply chain budget and long-term strategy. Responsible for profitability, efficiency, and risk management.\n\n**Goals**: Ensure operational continuity, meet financial targets, and understand high-level risks and opportunities.\n\n**Needs**: A high-level, aggregate view of supply chain health. Clear, concise summaries of critical problems, their financial impact, and the recommended solution.\n\n**Frustrations**: Unforeseen budget overruns. Surprises from major disruptions. Reports that are too detailed and don't provide a clear recommendation.\n\n### Leo Martinez (The Data Steward - Analytics Engineer)\n\n**Role**: The technical and business-logic expert. Responsible for data quality and enabling the AI agent by teaching it the business's unique logic.\n\n**Goals**: Ensure the agent's answers are accurate and trusted. Codify business definitions. Reduce his own ad-hoc reporting backlog.\n\n**Needs**: A simple UI to define business semantics. The ability to test, validate, and \"bless\" queries. Tools to debug why an agent gave a specific answer.\n\n**Frustrations**: Vague requests from users. The same simple questions being asked repeatedly. Business users misinterpreting raw data.\n\n\u003C!-- ------------------------ --\u003E\n\n## A Real-World Scenario\n\n**As Maria (The Operator)**, I want to ask, *\"Where do I have critical low inventory levels?\"* and instantly see the most critical plants, so I can immediately know where to focus.\n\nWhen Maria discovers the Miami plant is critically low on rare earth materials, she asks, *\"For plants with low inventory of a raw material, compare the cost of replenishing from a supplier vs transferring from another plant with excess inventory.\"* The agent compares transfer from Austin (71% savings) vs. purchasing from a supplier, so she can make the most cost-effective decision.\n\n![Hurricane Warning](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/hurricane.png)\n\nBefore finalizing the transfer, Maria asks, *\"What type of weather events might impact this transfer?\"* The agent automatically checks the Weather Forecast tool and warns: **\"A major hurricane is forecasted; transport may be impossible,\"** so she doesn't make a costly logistical mistake.\n\n**As Victor (The Executive)**, I want to receive a clear summary when Maria asks the agent to *\"Draft an executive summary email with this analysis, our options, and a recommendation.\"* The agent generates an HTML-formatted email with Executive Summary, Financial Comparison, Key Risks, and Final Recommendation, so I can make an informed decision and reply \"Approved\" in minutes.\n\n**As Leo (The Data Steward)**, I previously defined business logic like \"critical low inventory\" in the semantic model YAML file and added verified queries for cost comparisons, so the agent understands our business rules and Maria can trust the answers.\n\n![Low Inventory Definitions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/definitions-used.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## Test Agent with Questions\n\nNow it's your turn to experience the Supply Chain Assistant! Follow Maria's journey by asking these questions in order, or explore on your own. Notice how the agent automatically determines which tool to use based on your question!\n\n![Alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/agent-screenshot.png)\n\n### Following Maria's Scenario\n\nAsk these questions in sequence to replicate the scenario above:\n\n1. *\"Where do I have critical low inventory levels?\"*\n2. *\"Where do we have low inventory of rare earth materials?\"*\n3. *\"For plants with low inventory of a raw material, compare the cost of replenishing from a supplier vs transferring from another plant with excess inventory.\"*\n4. *\"What type of weather events might impact this transfer?\"*\n5. *\"Draft an executive summary email with this analysis, our options, and a recommendation.\"*\n\n\u003E aside negative\n\u003E **Note:** Sending emails requires a verified email addresses. The WEB_SEARCH and WEB_SCRAPE tools require external integration access, which is not available on trial accounts.\n\n### Additional Questions to Explore\n\nTry these variations to explore different capabilities:\n\n* \"Compare the cost of transferring this inventory from a plant with excess inventory versus replenishing from a supplier.\"\n* \"Show me all plants with excess inventory that could support transfers.\"\n* \"What are the lead times for our primary suppliers?\"\n* \"Search our documentation for transfer policies.\" (uses Cortex Search)\n* \"What orders are scheduled for the aerospace business line?\"\n\n\u003C!-- ------------------------ --\u003E\n\n## Conclusion and Resources\n\nCongratulations! You've built a comprehensive Supply Chain Assistant powered by Snowflake CoWork that combines multiple AI capabilities into a single, intelligent agent.\n\n### What You Learned\n\n* How to set up a comprehensive supply chain database with realistic sample data\n* How to create semantic models for Cortex Analyst to enable natural language queries\n* How to parse and chunk PDF documents for Cortex Search\n* How to build Cortex Search services for semantic search over unstructured data\n* How to create a Snowflake CoWork agent that combines multiple tools\n* How to integrate Cortex Analyst, Cortex Search, and custom tools into a single agent\n* How to enable web search and scraping capabilities in your AI assistant\n* How the agent intelligently routes questions to appropriate tools\n* How to test complex queries that span multiple data sources and tool types\n\n### What You Built\n\nYour Supply Chain Assistant now includes:\n\n* **Dual Analytics:** Query both supply chain operations and weather data using natural language\n* **Semantic Search:** Access unstructured supply chain documentation through intelligent retrieval\n* **Web Integration:** Search and scrape external information from the internet\n* **Communication:** Generate HTML newsletters and send emails (with proper integration)\n* **Intelligent Routing:** Automatic determination of which tool to use based on question context\n* **Complex Analysis:** Handle sophisticated multi-domain queries spanning structured and unstructured data\n\n### Next Steps and Extensions\n\n#### Scaling to Multi-Agent Architecture\n\nAs your organization's needs grow, you can evolve from a single agent with multiple tools to a multi-agent architecture where a primary orchestrator agent coordinates with specialized domain agents. This architecture enables better scalability, domain expertise, and independent development of agents for different business functions:\n\n![Multi-Agent Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/supply-chain-assistant-with-snowflake-intelligence/multi-agent-architecture.png)\n\n#### You can extend this solution further by:\n\n* **Adding More Semantic Models**: Create additional semantic models for other business domains (finance, HR, sales, etc.)\n* **Integrating Additional Data Sources**: Connect to external APIs or databases\n* **Creating Custom Tools**: Build specific tools for your unique business processes\n* **Building Streamlit Applications**: Create custom UIs that interact with your agent programmatically\n* **Adding More Documents**: Index additional PDF documents, user manuals, or policy documents\n* **Implementing Advanced Analytics**: Add tools for predictive analytics, optimization, or machine learning inference\n* **Setting Up Email Integration**: Configure email sending capabilities for automated reporting\n\n### Related Resources\n\n* [Snowflake Cortex Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex)\n* [Snowflake CoWork Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence)\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)\n* [Semantic Model Guide](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst/semantic-model-spec)\n* [Snowflake Quickstarts](https://quickstarts.snowflake.com/)\n* [Snowflake Community](https://community.snowflake.com/)\n\n### GitHub Repository\n\nAccess the complete code, scripts, and files for this quickstart:\n\n* [Supply Chain Assistant GitHub Repository](https://github.com/Snowflake-Labs/sfguide-supply-chain-assistant-with-snowflake-intelligence)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-7496644c36","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-8308d0644e",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3ef2b686a6","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-01-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-7b9f6ab51e","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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