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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"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"},"gridClassNames":"aem-Grid aem-Grid--12 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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-e0538db307","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-68e04ae500",":items":{"contentfragment":{"id":"contentfragment-8a91f9e247","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003ECortex Search enables low-latency, high-quality “fuzzy” search over your Snowflake data. Cortex Search powers a broad array of search experiences for Snowflake users including Retrieval Augmented Generation (RAG) applications leveraging Large Language Models (LLMs).\u003C/p\u003E\n","\u003Cp\u003ECortex Search gets you up and running with a hybrid (vector and keyword) search engine on your text data in minutes, without having to worry about embedding, infrastructure maintenance, search quality parameter tuning, or ongoing index refreshes. This means you can spend less time on infrastructure and search quality tuning, and more time developing high-quality chat and search experiences using your data. Check out the Cortex Search tutorials for step-by-step instructions on using Cortex Search to power AI chat and search applications.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/searcharch.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAmazon Q Business makes generative AI securely accessible to everyone in your organization. Leveraging your own company's content, data, and systems, Amazon Q Business makes it easier to get you fast, relevant answers to pressing questions, solve problems, generate content, and take actions on your behalf. Amazon Q Business easily and securely connects to commonly used systems and tools so it can synthesize everything and provide tailored assistance empowering your teams to be more data-driven, creative, and productive.\u003C/p\u003E\n","\u003Cp\u003EAmazon Q Business offers plugins to enhance your application's functionality. Plugins help streamline tasks and boost productivity by integrating external services into the Amazon Q Business chat interface. With custom plugins from Q customers can quickly and securely connect to Cortex services like Search.\u003C/p\u003E\n","\u003Ch3\u003EUse Case\u003C/h3\u003E\n","\u003Cp\u003EIn this use case you will build a Cortex Search service on the free text data contained in the two manuals \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-amazon-q-for-business-and-cortex\"\u003Ehere\u003C/a\u003E for pump and pump head maintenance.\u003C/p\u003E\n","\u003Cp\u003EYou will build a Cortex service that allows to you to us plain text to query these documents to get information. It is important to note that Cortex Search can scale much more than this use case. Please check out the Cortex Search documentation \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/query-cortex-search-service\"\u003Ehere.\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EThe end-to-end workflow will look like this:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/qcortexarch.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIngest .pdf files into Snowflake Stage.\u003C/li\u003E\u003Cli\u003EUsing the PARSE_DOCUMENT() and the SPLIT_TEXT_RECURSIVE_CHARACTER() the raw pdf files are converted to a chunked field in a table.\u003C/li\u003E\u003Cli\u003EThe Cortex Search service is built on the chunked data.\u003C/li\u003E\u003Cli\u003EAmazon Q Business is connected to the Cortex Search service with a plugin using oauth authentication.\u003C/li\u003E\u003Cli\u003EUsers can use Amazon Q Business with Cortex securely and seamlessly.\u003C/li\u003E\u003Cli\u003EAlong with the Cortex Plugin users can access other AWS data and services through Amazon Q Business\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with \u003Ca href=\"/en/developers/guides/getting-started-with-snowflake/\"\u003ESnowflake\u003C/a\u003E and a Snowflake account with Cortex Search.\u003C/li\u003E\u003Cli\u003EFamiliarity with \u003Ca href=\"https://aws.amazon.com/free\"\u003EAWS\u003C/a\u003E and an AWS account.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUsing Cortex Search along with complimentary functions in Snowflake.\u003C/li\u003E\u003Cli\u003EUsing Amazon Q to leverage generative AI to get quick answers from your data.\u003C/li\u003E\u003Cli\u003EConnect Amazon Q to Snowflake Cortex Search with a custom plugin.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA free \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake Account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/free\"\u003EAWS Account\u003C/a\u003E with access to Q\u003C/li\u003E\u003Cli\u003EFor the sake of the lab it is best if both platforms have access to the public internet and are not in a virtual network\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n","\u003Cp\u003EYou will build an end-to-end copilot workflow on unstructured data in Snowflake\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Eto load data to Snowflake via Snowsight\u003C/li\u003E\u003Cli\u003Eto extract unstructured data and create chunks in Snowflake\u003C/li\u003E\u003Cli\u003Eto create a Snowflake Cortex Search Service on unstructured data\u003C/li\u003E\u003Cli\u003Eto create a connection from Amazon Q to Cortex Search with Oauth authentication\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up Snowflake and Cortex\u003C/h2\u003E\n","\u003Cp\u003EDownload both of the files in \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-amazon-q-for-business-and-cortex\"\u003Ethis repo\u003C/a\u003E by clicking on each file and clicking the download button near the top right.\u003C/p\u003E\n","\u003Cp\u003EOnce you have the files downloaded locally you will go to the Snowflake UI and open a new SQL worksheet and run the below code to create a database that we will be working out with. I'm using the ACCOUNTADMIN role here, but you can use whatever role you know gives you access to create objects (databases, tables, services) in Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ACCOUNTADMIN;\nCREATE OR REPLACE WAREHOUSE HOL_WH WITH WAREHOUSE_SIZE='X-SMALL';\nCREATE OR REPLACE DATABASE PUMP_DB;\nUSE DATABASE PUMP_DB;\nCREATE STAGE DOCS \n\tDIRECTORY = ( ENABLE = true ) \n\tENCRYPTION = ( TYPE = 'SNOWFLAKE_SSE' );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/createworksheet.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/runquery.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow that you have the database created you can upload the 2 manuals to a stage in the PUMP_DB database in the DOCS stage by clicking on Data on the left side of the Snowflake UI, clicking through to find the DOCS stage in the PUBLIC schema then clicking "+ files" in the top right to upload the files from your local machine.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/addtostage.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow, you will run the below code that will parse the data out of the documents into a field in a table PUMP_TABLE, then chunk the fields into a new table PUMP_TABLE_CHUNK.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E--Create Table for text data\nCREATE OR REPLACE TABLE PUMP_TABLE AS\nSELECT \n    '1290IF_PumpHeadMaintenance_TN' as doc,\n    SNOWFLAKE.CORTEX.PARSE_DOCUMENT(@PUMP_DB.PUBLIC.DOCS, '1290IF_PumpHeadMaintenance_TN.pdf', {'mode': 'LAYOUT'}) as pump_maint_text;\n\nINSERT INTO PUMP_TABLE (doc, pump_maint_text)\nSELECT 'PumpWorks 610', SNOWFLAKE.CORTEX.PARSE_DOCUMENT(@PUMP_DB.PUBLIC.DOCS, 'PumpWorks 610 PWI pump_Maintenance.pdf', {'mode': 'LAYOUT'});\n\n-- Create table with chunked text\nCREATE OR REPLACE TABLE PUMP_TABLE_CHUNK AS\nSELECT\n   TO_VARCHAR(c.value) as CHUNK_TEXT, DOC\nFROM\n   PUMP_TABLE,\n   LATERAL FLATTEN( input => SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n      TO_VARCHAR(pump_maint_text:content),\n      'none',\n      700,\n      100\n   )) c;\nSELECT * FROM PUMP_TABLE_CHUNK;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can see that we're setting the chunks up to be up to 700 tokens with 100 tokens overlap. After the initial setup you can adjust these settings as you would like. Additionally, we are using'none' for the format rather than 'markdown'. You can see more information using the SPLIT_TEXT_RECURSIVE_CHARACTER() funciton \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/split_text_recursive_character-snowflake-cortex\"\u003Ehere.\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, we will run the code on the CHUNK_TEXT field to create a Cortex Search service.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create Search Service\nCREATE OR REPLACE CORTEX SEARCH SERVICE PUMP_SEARCH_SERVICE\n  ON CHUNK_TEXT\n  ATTRIBUTES DOC\n  WAREHOUSE = HOL_WH\n  TARGET_LAG = '30 day'\n  AS (\n    SELECT CHUNK_TEXT as CHUNK_TEXT, DOC FROM PUMP_TABLE_CHUNK);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENotice how we're setting up the Cortex Search service with a 30 day lag so that incremental updates to the service will be made ever 30 days. Additionally, we're setting up the service so that additional filters can be used on the attribute "DOC".\u003C/p\u003E\n","\u003Ch2\u003ESet Up Q Business\u003C/h2\u003E\n","\u003Cp\u003EHead to you AWS console search "Amazon Q Business" then select the orange button "Create Application" then accept all of the default/recommended settings for creating your application, make sure that you have at least one user created for the app with a Q Business Pro subscription the select Create.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/qapp.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetting up OAuth\u003C/h2\u003E\n","\u003Cp\u003EFrom the Q Business Application Details screen copy the "Deployed URL" and save it to a notebook.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/qappurl.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext we will run the code which creates a security integration in Snowflake that we will use to authenticate with Q Business. The only change you will have to make to this code is replacing the value of the "Deployed URL" from the Q App in the value <Deployed URL> below to complete the OAUTH_REDIRECT_URI value.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E--create custom oauth\nCREATE OR REPLACE SECURITY INTEGRATION Q_AUTH_HOL\n  TYPE = OAUTH\n  ENABLED = TRUE\n  OAUTH_ISSUE_REFRESH_TOKENS = TRUE\n  OAUTH_REFRESH_TOKEN_VALIDITY = 3600\n  OAUTH_CLIENT = CUSTOM\n  OAUTH_CLIENT_TYPE = CONFIDENTIAL\n  OAUTH_REDIRECT_URI = '<Deployed URL>/oauth/callback';\n\nGRANT USAGE on database PUMP_DB to role PUBLIC;\nGRANT USAGE on schema PUBLIC to role PUBLIC;\nGRANT USAGE on CORTEX SEARCH SERVICE PUMP_SEARCH_SERVICE to role PUBLIC;\n\nDESC INTEGRATION Q_AUTH_HOL;\n\nSELECT SYSTEM$SHOW_OAUTH_CLIENT_SECRETS('Q_AUTH_HOL');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter the security integration is created we grant the PUBLIC roles usage on the database, schema and the Search service.\u003C/p\u003E\n","\u003Cp\u003EAdditionally, after the "SELECT SYSTEM$SHOW_OAUTH_CLIENT_SECRETS('Q_AUTH_HOL');" query copy the OAUTH_CLIENT_ID and the OAUTH_CLIENT_SECRET. You can learn more about using Snowflake for Oauth \u003Ca href=\"https://docs.snowflake.com/en/user-guide/oauth-custom\"\u003Ehere.\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EAlso, make note of your Snowflake url. You can do this by going to your alias in the bottom left of the Snowflake UI finding your account then selecting the "link" button that says "copy account url" when you hover over it.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Cortex Plugin\u003C/h2\u003E\n","\u003Cp\u003EHead back to your Q Business application and select "Plugins" under "Actions" on the left side. Select the orange "Add Plugin" button then\n"Create Custom Plugin" in the top right.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/createplugin.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003Ename the plugin "cortex-pump" and provide a description.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder "API Schema" select "Define with in-line OpenAPI schema editor" and make sure that "YAML" is selected and paste in the below openapi spec. Several notes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Eyou will have to copy in your <snowflake url> in 3 spots below where indicated.\u003C/li\u003E\u003Cli\u003Ethe path defined in the openapi schema assumes that you set up you Cortex Search service in the pump_db database and in the int public schema with the the name PUMP_SEARCH_SERVICE. If you did not make any changes to the Snowflake code that you already ran in this quickstart you will be good and not have to change anything.\u003C/li\u003E\u003Cli\u003Ethe 'description' value here is important as you will see Q uses this to know when to route questions to the plugin.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Eopenapi: 3.0.0\ninfo:\n  title: Cortex Search API\n  version: 2.0.0\nservers:\n  - url: <snowflake url>\npaths:\n  /api/v2/databases/pump_db/schemas/public/cortex-search-services/PUMP_SEARCH_SERVICE:query:\n    post:\n      parameters:\n      - in: header\n        description: Customer Snowflake OAuth header\n        name: X-Snowflake-Authorization-Token-Type\n        schema:\n          type: string\n          enum: ["OAUTH"]\n        required: true\n      summary: Query the Cortex Search service\n      description: Submit a query to the Cortex Search service in order to answer questions specifically about Pumps or other mechanical parts or repair or maintenance information\n      requestBody:\n        required: true\n        content:\n          application/json:\n            schema:\n              $ref: '#/components/schemas/QueryRequest'\n      responses:\n        '200':\n          description: Successful response\n          content:\n            application/json:\n              schema:\n                $ref: '#/components/schemas/QueryResponse'\n      security:\n        - oauth2: []\ncomponents:\n  schemas:\n    QueryRequest:\n      type: object\n      required:\n        - query\n      properties:\n        query:\n          type: string\n          description: The search query\n        limit:\n          type: integer\n          description: The maximum number of results to return\n          example: 5\n    QueryResponse:\n      type: object\n      description: Search results.\n      properties:\n        results:\n          type: array\n          description: List of result rows.\n          items:\n            type: object\n            additionalProperties: true\n            description: Map of column names to bytes.\n        request_id:\n          type: string\n          description: ID of the request.\n      required:\n      - results\n      - request_id\n  securitySchemes:\n    oauth2:\n      type: oauth2\n      flows:\n        authorizationCode:\n          authorizationUrl: <snowflake url>/oauth/authorize\n          tokenUrl: <snowflake url>/oauth/token-request\n          scopes:\n            refresh_token: Refresh the OAuth token\n            session:role:PUBLIC: The Snowflake role for the integration\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/pluginspec.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUnder "Authentication" select "Authentication required".\u003C/li\u003E\u003Cli\u003EUnder "AWS Secrets Manager" select the dropdown menu and select "create an aws Secrets Manager secret".\n\u003Cul\u003E\u003Cli\u003EGive the secret a name like "...-cortex-pump" then populate the "Client Secret" and the "Client ID" with the values from the last Snowflake query in the previous step.\u003C/li\u003E\u003Cli\u003EThe "OAuth callback url" will be the same as the value for the OAUTH_REDIRECT_URI parameter that was used when creating the security integration object in Snowflake.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick "Create" when done.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/secrets.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELast, select "Create and use a new service role" to quickly create and use a role that has permissions to use Q Business.\u003C/p\u003E\n","\u003Cp\u003EIF you elect to use an existing role you will have to add the below permission to that role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n    "Version": "2012-10-17",\n    "Statement": [\n        {\n            "Sid": "AllowQBusinessToGetSecretValue",\n            "Effect": "Allow",\n            "Action": [\n                "secretsmanager:GetSecretValue"\n            ],\n            "Resource": [\n                "<secrets ARN>"\n            ]\n        }\n    ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally, select "Add Plugin" and it will take 1-2 minutes to get the plugin created.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConnect Q and Cortex\u003C/h2\u003E\n","\u003Cp\u003ENavigate into the "cortex-pump" plugin and select "Preview web experience" in the top right, a new window will open to edit the web experience, leave it as-is and select "View web experience" in the top right:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/viewwebexp.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the chat select "plugin" at the bottom and select the plugin we created for cortex-pump. When you select the plugin you are signaling to Q Business that you want to specifically invoke that action as opposed to using a different source. Ask the below question:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWhat is the part description for part number G4204-68741?\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EQ Business will likely attempt to create query,  retype (or re-paste) the question in the query window and select "Submit".\u003C/p\u003E\n","\u003Cp\u003EYou will be asked to authorize and use the browser to authorize one time.\u003C/p\u003E\n","\u003Cp\u003EOnce complete Q will send the request to the plugin and you will get an answer!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/qanswer.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can check the answer against the manuals, it should match! Test out other prompts including the examples below:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWhat are the pump head assembly parts?\u003C/li\u003E\u003Cli\u003EShould I use a refurbished pump head for maintenance and repair OR disassemble a pump head?\u003C/li\u003E\u003Cli\u003EWhat are the high level steps for Replacing the Heat Exchanger?\u003C/li\u003E\u003Cli\u003EWhat are best ways to prevent the pump from damage?\u003C/li\u003E\u003Cli\u003EWhat are spare parts that are included with this pump?\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EThis quickstart is just that, a quick way to get you started with using Amazon Q with Snowflake Cortex, though with this start you are now enabled to extend the quickstart in the below ways:\n- Scale the workflow to a use case with many documents and use a more robust Cortex Search Service.\n- Use a Cortex Q plugin alongside Quicksight to get next level answers on your data that's represented in your dashboards.\n- Use multiple plugins to Cortex from Q along with AWS service to create a robust web app for getting answers from your data with plain text.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ELoading data to Snowflake via Snowsight\u003C/li\u003E\u003Cli\u003EExtracting unstructured data and creating chunks in Snowflake\u003C/li\u003E\u003Cli\u003ECreating a Snowflake Cortex Search Service on unstructured data\u003C/li\u003E\u003Cli\u003ECreating a connection from Amazon Q to Cortex Search with Oauth authentication using Q plugins\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n","\u003Cp\u003EThere are some great blogs on Medium regarding Snowflake Cortex and Amazon Services work together:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"/en/data-cloud/cortex/\"\u003ESnowflake Cortex\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://aws.amazon.com/q/?trk=c570e8a2-ec3c-4968-baa4-f8537e37dd1d&sc_channel=ps&s_kwcid=AL!4422!10!71949557907688!71950102400240&ef_id=07cc246a6d4218358de8430ee23fc18e:G:s&msclkid=07cc246a6d4218358de8430ee23fc18e\"\u003EAmazon Q\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://catalog.us-east-1.prod.workshops.aws/workshops/2d4e5ea4-78c8-496f-8246-50d8971414c9/en-US/01-overview\"\u003EAmazon Bedrock and Snowflake Cortex\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-bedrock-streamlit-and-snowflake/\"\u003EUsing Snowflake, Streamlit and External Access with amazon Bedrcok\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"description":"Integrate Amazon Q Business with Snowflake Cortex for enterprise AI assistants that query data across multiple cloud platforms.","title":"Getting Started with Amazon Q Business and Cortex",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nCortex Search enables low-latency, high-quality “fuzzy” search over your Snowflake data. Cortex Search powers a broad array of search experiences for Snowflake users including Retrieval Augmented Generation (RAG) applications leveraging Large Language Models (LLMs).\n\nCortex Search gets you up and running with a hybrid (vector and keyword) search engine on your text data in minutes, without having to worry about embedding, infrastructure maintenance, search quality parameter tuning, or ongoing index refreshes. This means you can spend less time on infrastructure and search quality tuning, and more time developing high-quality chat and search experiences using your data. Check out the Cortex Search tutorials for step-by-step instructions on using Cortex Search to power AI chat and search applications.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/searcharch.png)\n\nAmazon Q Business makes generative AI securely accessible to everyone in your organization. Leveraging your own company's content, data, and systems, Amazon Q Business makes it easier to get you fast, relevant answers to pressing questions, solve problems, generate content, and take actions on your behalf. Amazon Q Business easily and securely connects to commonly used systems and tools so it can synthesize everything and provide tailored assistance empowering your teams to be more data-driven, creative, and productive.\n\nAmazon Q Business offers plugins to enhance your application's functionality. Plugins help streamline tasks and boost productivity by integrating external services into the Amazon Q Business chat interface. With custom plugins from Q customers can quickly and securely connect to Cortex services like Search.\n\n### Use Case\n\nIn this use case you will build a Cortex Search service on the free text data contained in the two manuals [here](https://github.com/Snowflake-Labs/sfguide-getting-started-with-amazon-q-for-business-and-cortex) for pump and pump head maintenance.\n\nYou will build a Cortex service that allows to you to us plain text to query these documents to get information. It is important to note that Cortex Search can scale much more than this use case. Please check out the Cortex Search documentation [here.](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/query-cortex-search-service)\n\nThe end-to-end workflow will look like this:\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/qcortexarch.png)\n1. Ingest .pdf files into Snowflake Stage.\n2. Using the PARSE_DOCUMENT() and the SPLIT_TEXT_RECURSIVE_CHARACTER() the raw pdf files are converted to a chunked field in a table.\n3. The Cortex Search service is built on the chunked data.\n4. Amazon Q Business is connected to the Cortex Search service with a plugin using oauth authentication.\n5. Users can use Amazon Q Business with Cortex securely and seamlessly.\n6. Along with the Cortex Plugin users can access other AWS data and services through Amazon Q Business\n\n### Prerequisites\n- Familiarity with [Snowflake](/en/developers/guides/getting-started-with-snowflake/) and a Snowflake account with Cortex Search.\n- Familiarity with [AWS](https://aws.amazon.com/free) and an AWS account.\n\n### What You'll Learn\n- Using Cortex Search along with complimentary functions in Snowflake.\n- Using Amazon Q to leverage generative AI to get quick answers from your data.\n- Connect Amazon Q to Snowflake Cortex Search with a custom plugin.\n\n### What You’ll Need \n- A free [Snowflake Account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)\n- [AWS Account](https://aws.amazon.com/free) with access to Q\n- For the sake of the lab it is best if both platforms have access to the public internet and are not in a virtual network\n\n\n### What You’ll Build \nYou will build an end-to-end copilot workflow on unstructured data in Snowflake\n- to load data to Snowflake via Snowsight\n- to extract unstructured data and create chunks in Snowflake\n- to create a Snowflake Cortex Search Service on unstructured data\n- to create a connection from Amazon Q to Cortex Search with Oauth authentication\n\n\u003C!-- ------------------------ --\u003E\n## Set Up Snowflake and Cortex\n\nDownload both of the files in [this repo](https://github.com/Snowflake-Labs/sfguide-getting-started-with-amazon-q-for-business-and-cortex) by clicking on each file and clicking the download button near the top right.\n\nOnce you have the files downloaded locally you will go to the Snowflake UI and open a new SQL worksheet and run the below code to create a database that we will be working out with. I'm using the ACCOUNTADMIN role here, but you can use whatever role you know gives you access to create objects (databases, tables, services) in Snowflake.\n\n```sql\nUSE ACCOUNTADMIN;\nCREATE OR REPLACE WAREHOUSE HOL_WH WITH WAREHOUSE_SIZE='X-SMALL';\nCREATE OR REPLACE DATABASE PUMP_DB;\nUSE DATABASE PUMP_DB;\nCREATE STAGE DOCS \n\tDIRECTORY = ( ENABLE = true ) \n\tENCRYPTION = ( TYPE = 'SNOWFLAKE_SSE' );\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/createworksheet.png)\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/runquery.png)\n\nNow that you have the database created you can upload the 2 manuals to a stage in the PUMP_DB database in the DOCS stage by clicking on Data on the left side of the Snowflake UI, clicking through to find the DOCS stage in the PUBLIC schema then clicking \"+ files\" in the top right to upload the files from your local machine.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/addtostage.png)\n\n\nNow, you will run the below code that will parse the data out of the documents into a field in a table PUMP_TABLE, then chunk the fields into a new table PUMP_TABLE_CHUNK. \n\n```sql\n--Create Table for text data\nCREATE OR REPLACE TABLE PUMP_TABLE AS\nSELECT \n    '1290IF_PumpHeadMaintenance_TN' as doc,\n    SNOWFLAKE.CORTEX.PARSE_DOCUMENT(@PUMP_DB.PUBLIC.DOCS, '1290IF_PumpHeadMaintenance_TN.pdf', {'mode': 'LAYOUT'}) as pump_maint_text;\n\nINSERT INTO PUMP_TABLE (doc, pump_maint_text)\nSELECT 'PumpWorks 610', SNOWFLAKE.CORTEX.PARSE_DOCUMENT(@PUMP_DB.PUBLIC.DOCS, 'PumpWorks 610 PWI pump_Maintenance.pdf', {'mode': 'LAYOUT'});\n\n-- Create table with chunked text\nCREATE OR REPLACE TABLE PUMP_TABLE_CHUNK AS\nSELECT\n   TO_VARCHAR(c.value) as CHUNK_TEXT, DOC\nFROM\n   PUMP_TABLE,\n   LATERAL FLATTEN( input =\u003E SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n      TO_VARCHAR(pump_maint_text:content),\n      'none',\n      700,\n      100\n   )) c;\nSELECT * FROM PUMP_TABLE_CHUNK;\n```\n\nYou can see that we're setting the chunks up to be up to 700 tokens with 100 tokens overlap. After the initial setup you can adjust these settings as you would like. Additionally, we are using'none' for the format rather than 'markdown'. You can see more information using the SPLIT_TEXT_RECURSIVE_CHARACTER() funciton [here.](https://docs.snowflake.com/en/sql-reference/functions/split_text_recursive_character-snowflake-cortex)\n\nNext, we will run the code on the CHUNK_TEXT field to create a Cortex Search service.\n\n```sql\n-- Create Search Service\nCREATE OR REPLACE CORTEX SEARCH SERVICE PUMP_SEARCH_SERVICE\n  ON CHUNK_TEXT\n  ATTRIBUTES DOC\n  WAREHOUSE = HOL_WH\n  TARGET_LAG = '30 day'\n  AS (\n    SELECT CHUNK_TEXT as CHUNK_TEXT, DOC FROM PUMP_TABLE_CHUNK);\n```\n\nNotice how we're setting up the Cortex Search service with a 30 day lag so that incremental updates to the service will be made ever 30 days. Additionally, we're setting up the service so that additional filters can be used on the attribute \"DOC\".\n\n## Set Up Q Business\n\nHead to you AWS console search \"Amazon Q Business\" then select the orange button \"Create Application\" then accept all of the default/recommended settings for creating your application, make sure that you have at least one user created for the app with a Q Business Pro subscription the select Create.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/qapp.png)\n\n\u003C!-- ------------------------ --\u003E\n## Setting up OAuth\n\nFrom the Q Business Application Details screen copy the \"Deployed URL\" and save it to a notebook.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/qappurl.png)\n\nNext we will run the code which creates a security integration in Snowflake that we will use to authenticate with Q Business. The only change you will have to make to this code is replacing the value of the \"Deployed URL\" from the Q App in the value \u003CDeployed URL\u003E below to complete the OAUTH_REDIRECT_URI value.\n\n```sql\n--create custom oauth\nCREATE OR REPLACE SECURITY INTEGRATION Q_AUTH_HOL\n  TYPE = OAUTH\n  ENABLED = TRUE\n  OAUTH_ISSUE_REFRESH_TOKENS = TRUE\n  OAUTH_REFRESH_TOKEN_VALIDITY = 3600\n  OAUTH_CLIENT = CUSTOM\n  OAUTH_CLIENT_TYPE = CONFIDENTIAL\n  OAUTH_REDIRECT_URI = '\u003CDeployed URL\u003E/oauth/callback';\n\nGRANT USAGE on database PUMP_DB to role PUBLIC;\nGRANT USAGE on schema PUBLIC to role PUBLIC;\nGRANT USAGE on CORTEX SEARCH SERVICE PUMP_SEARCH_SERVICE to role PUBLIC;\n\nDESC INTEGRATION Q_AUTH_HOL;\n\nSELECT SYSTEM$SHOW_OAUTH_CLIENT_SECRETS('Q_AUTH_HOL');\n```\n\nAfter the security integration is created we grant the PUBLIC roles usage on the database, schema and the Search service. \n\nAdditionally, after the \"SELECT SYSTEM$SHOW_OAUTH_CLIENT_SECRETS('Q_AUTH_HOL');\" query copy the OAUTH_CLIENT_ID and the OAUTH_CLIENT_SECRET. You can learn more about using Snowflake for Oauth [here.](https://docs.snowflake.com/en/user-guide/oauth-custom)\n\nAlso, make note of your Snowflake url. You can do this by going to your alias in the bottom left of the Snowflake UI finding your account then selecting the \"link\" button that says \"copy account url\" when you hover over it.\n\n\u003C!-- ------------------------ --\u003E\n## Create Cortex Plugin\n\nHead back to your Q Business application and select \"Plugins\" under \"Actions\" on the left side. Select the orange \"Add Plugin\" button then \n\"Create Custom Plugin\" in the top right.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/createplugin.png)\n\n1. name the plugin \"cortex-pump\" and provide a description. \n\n2. Under \"API Schema\" select \"Define with in-line OpenAPI schema editor\" and make sure that \"YAML\" is selected and paste in the below openapi spec. Several notes:\n    - you will have to copy in your \u003Csnowflake url\u003E in 3 spots below where indicated.\n    - the path defined in the openapi schema assumes that you set up you Cortex Search service in the pump_db database and in the int public schema with the the name PUMP_SEARCH_SERVICE. If you did not make any changes to the Snowflake code that you already ran in this quickstart you will be good and not have to change anything.\n    - the 'description' value here is important as you will see Q uses this to know when to route questions to the plugin.\n\n```yaml\nopenapi: 3.0.0\ninfo:\n  title: Cortex Search API\n  version: 2.0.0\nservers:\n  - url: \u003Csnowflake url\u003E\npaths:\n  /api/v2/databases/pump_db/schemas/public/cortex-search-services/PUMP_SEARCH_SERVICE:query:\n    post:\n      parameters:\n      - in: header\n        description: Customer Snowflake OAuth header\n        name: X-Snowflake-Authorization-Token-Type\n        schema:\n          type: string\n          enum: [\"OAUTH\"]\n        required: true\n      summary: Query the Cortex Search service\n      description: Submit a query to the Cortex Search service in order to answer questions specifically about Pumps or other mechanical parts or repair or maintenance information\n      requestBody:\n        required: true\n        content:\n          application/json:\n            schema:\n              $ref: '#/components/schemas/QueryRequest'\n      responses:\n        '200':\n          description: Successful response\n          content:\n            application/json:\n              schema:\n                $ref: '#/components/schemas/QueryResponse'\n      security:\n        - oauth2: []\ncomponents:\n  schemas:\n    QueryRequest:\n      type: object\n      required:\n        - query\n      properties:\n        query:\n          type: string\n          description: The search query\n        limit:\n          type: integer\n          description: The maximum number of results to return\n          example: 5\n    QueryResponse:\n      type: object\n      description: Search results.\n      properties:\n        results:\n          type: array\n          description: List of result rows.\n          items:\n            type: object\n            additionalProperties: true\n            description: Map of column names to bytes.\n        request_id:\n          type: string\n          description: ID of the request.\n      required:\n      - results\n      - request_id\n  securitySchemes:\n    oauth2:\n      type: oauth2\n      flows:\n        authorizationCode:\n          authorizationUrl: \u003Csnowflake url\u003E/oauth/authorize\n          tokenUrl: \u003Csnowflake url\u003E/oauth/token-request\n          scopes:\n            refresh_token: Refresh the OAuth token\n            session:role:PUBLIC: The Snowflake role for the integration\n\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/pluginspec.png)\n\n- Under \"Authentication\" select \"Authentication required\".\n- Under \"AWS Secrets Manager\" select the dropdown menu and select \"create an aws Secrets Manager secret\".\n    - Give the secret a name like \"...-cortex-pump\" then populate the \"Client Secret\" and the \"Client ID\" with the values from the last Snowflake query in the previous step. \n    - The \"OAuth callback url\" will be the same as the value for the OAUTH_REDIRECT_URI parameter that was used when creating the security integration object in Snowflake.\n- Click \"Create\" when done.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/secrets.png)\n\nLast, select \"Create and use a new service role\" to quickly create and use a role that has permissions to use Q Business.\n\nIF you elect to use an existing role you will have to add the below permission to that role.\n\n```json\n{\n    \"Version\": \"2012-10-17\",\n    \"Statement\": [\n        {\n            \"Sid\": \"AllowQBusinessToGetSecretValue\",\n            \"Effect\": \"Allow\",\n            \"Action\": [\n                \"secretsmanager:GetSecretValue\"\n            ],\n            \"Resource\": [\n                \"\u003Csecrets ARN\u003E\"\n            ]\n        }\n    ]\n}\n```\n\nFinally, select \"Add Plugin\" and it will take 1-2 minutes to get the plugin created.\n\n\u003C!-- ------------------------ --\u003E\n## Connect Q and Cortex\n\nNavigate into the \"cortex-pump\" plugin and select \"Preview web experience\" in the top right, a new window will open to edit the web experience, leave it as-is and select \"View web experience\" in the top right:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/viewwebexp.png)\n\nIn the chat select \"plugin\" at the bottom and select the plugin we created for cortex-pump. When you select the plugin you are signaling to Q Business that you want to specifically invoke that action as opposed to using a different source. Ask the below question:\n\n- What is the part description for part number G4204-68741?\n\nQ Business will likely attempt to create query,  retype (or re-paste) the question in the query window and select \"Submit\".\n\nYou will be asked to authorize and use the browser to authorize one time.\n\nOnce complete Q will send the request to the plugin and you will get an answer!\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-amazon-q-business-and-cortex/qanswer.png)\n\nYou can check the answer against the manuals, it should match! Test out other prompts including the examples below:\n- What are the pump head assembly parts?\n- Should I use a refurbished pump head for maintenance and repair OR disassemble a pump head?\n- What are the high level steps for Replacing the Heat Exchanger?\n- What are best ways to prevent the pump from damage?\n- What are spare parts that are included with this pump?\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nThis quickstart is just that, a quick way to get you started with using Amazon Q with Snowflake Cortex, though with this start you are now enabled to extend the quickstart in the below ways:\n    - Scale the workflow to a use case with many documents and use a more robust Cortex Search Service.\n    - Use a Cortex Q plugin alongside Quicksight to get next level answers on your data that's represented in your dashboards.\n    - Use multiple plugins to Cortex from Q along with AWS service to create a robust web app for getting answers from your data with plain text.\n\n### What You Learned\n- Loading data to Snowflake via Snowsight\n- Extracting unstructured data and creating chunks in Snowflake\n- Creating a Snowflake Cortex Search Service on unstructured data\n- Creating a connection from Amazon Q to Cortex Search with Oauth authentication using Q plugins\n\n### Resources\nThere are some great blogs on Medium regarding Snowflake Cortex and Amazon Services work together:\n\n- [Snowflake Cortex](/en/data-cloud/cortex/)\n\n- [Amazon Q](https://aws.amazon.com/q/?trk=c570e8a2-ec3c-4968-baa4-f8537e37dd1d&sc_channel=ps&s_kwcid=AL!4422!10!71949557907688!71950102400240&ef_id=07cc246a6d4218358de8430ee23fc18e:G:s&msclkid=07cc246a6d4218358de8430ee23fc18e)\n\n- [Amazon Bedrock and Snowflake Cortex](https://catalog.us-east-1.prod.workshops.aws/workshops/2d4e5ea4-78c8-496f-8246-50d8971414c9/en-US/01-overview)\n\n- [Using Snowflake, Streamlit and External Access with amazon Bedrcok](/en/developers/guides/getting-started-with-bedrock-streamlit-and-snowflake/)\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-370e5ff857","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-cde085ed42",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-edd0bfb062","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-e7a8257b15","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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