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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-64c23d047c","isDeveloperGuidesPage":false,"quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["Getting Started with Access Controls for RAGs (Cortex 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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003ERetrieval-Augmented Generation (RAG) promises to unlock unprecedented value from enterprise data. The vast majority (80-90%) of corporate knowledge is trapped in unstructured documents, emails, and messages, often containing sensitive PII, IP, and financial data with inconsistent access controls. Deploying AI models without robust governance exposes this sensitive data, creating severe risks of data breaches, regulatory non-compliance, and loss of intellectual property.\u003C/p\u003E\n","\u003Cp\u003EFor businesses to adopt AI safely, applications must be "permissions-aware." This is not an optional feature but a foundational requirement. AI systems must respect and enforce the existing security and access permissions of every user at the moment of retrieval. An employee must only be allowed to "see" and surface data that the user querying it is already authorized to access.\u003C/p\u003E\n","\u003Cp\u003EIn this guide, you will build a secure RAG pipeline (using Cortex Search) that enforces document-level access control. Specifically, we will create a RAG that utilises user attributes (such as their role) and pass this on as a filter condition to the cortex search service on the backend of a client-facing application.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to an account that can create External Access Integrations (trial accounts are not able to).\u003C/li\u003E\u003Cli\u003EAccess to an elevated role (such as ACCOUNTADMIN) to create External Access Integrations, Roles and Users.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to build a RAG chatbot in Streamlit in Snowflake (SiS) with access controls.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with the ability to create Compute Pools (SPCS)\u003C/li\u003E\u003Cli\u003EAbility to create new users and roles in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA RAG chatbot built with Streamlit in Snowflake (SiS) with access controls, powered by Cortex Search.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse Case\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EIn this guide, we are going to play the role of a chain of sporting goods stores. We have product information on ski equipment, and bicycles. We want to help our customer service teams be able to query information on our products that currently exists in unstructured documents (PDFs).\u003C/p\u003E\n","\u003Cp\u003EMost importantly, we only want the Ski department to query information on source documents that are related to ski equipment. Similarly we want the Bicycle department to only query information from the PDF documents related to bicycles.\u003C/p\u003E\n","\u003Cp\u003ETo do this, we are going to create a RAG pipeline utilizing Snowflake features (including \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search Service\u003C/a\u003E), and utilize Streamlit in Snowflake (SiS) running on Snowpark Container Services (SPCS) to expose this functionality to users from both the Ski and Bicycle department.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Ch3\u003ESnowflake Setup\u003C/h3\u003E\n","\u003Cp\u003EOpen a SQL Worksheet and run through the following code to set up the necessary objects and permissions in Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE ACCOUNTADMIN;\nCREATE ROLE IF NOT EXISTS RAG_OWNER;\nCREATE ROLE IF NOT EXISTS SKI;\nCREATE ROLE IF NOT EXISTS BICYCLE;\n\n-- Create the users to test access controls. It is strongly recommended that MFA is enabled for these users\nCREATE OR REPLACE USER bicycle_user\n    PASSWORD             = '<enter initial password>'\n    LOGIN_NAME           = 'bicycle_user'\n    FIRST_NAME           = 'Bicycle'\n    LAST_NAME            = 'User'\n    EMAIL                = '<enter your email>'\n    DEFAULT_ROLE         = BICYCLE  \n    MUST_CHANGE_PASSWORD = TRUE;\n\nGRANT ROLE BICYCLE TO USER bicycle_user;\n\nCREATE OR REPLACE USER ski_user\n    PASSWORD             = '<enter initial password>'\n    LOGIN_NAME           = 'ski_user'\n    FIRST_NAME           = 'Ski'\n    LAST_NAME            = 'User'\n    EMAIL                = '<enter your email>'\n    DEFAULT_ROLE         = SKI \n    MUST_CHANGE_PASSWORD = TRUE;\n\nGRANT ROLE SKI TO USER ski_user;\n\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE RAG_OWNER;\nGRANT CREATE COMPUTE POOL ON ACCOUNT TO ROLE RAG_OWNER;\nGRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE RAG_OWNER;\n\nSET USERNAME = (SELECT CURRENT_USER());\nSELECT $USERNAME;\nGRANT ROLE RAG_OWNER to USER identifier($USERNAME);\n\n-- Create compute\n\nUSE ROLE RAG_OWNER;\n\nCREATE COMPUTE POOL IF NOT EXISTS RAG_STREAMLIT\n    MIN_NODES = 1\n    MAX_NODES = 3\n    INSTANCE_FAMILY = CPU_X64_XS;\n\nCREATE WAREHOUSE IF NOT EXISTS RAG_WH\n  WAREHOUSE_TYPE = STANDARD\n  WAREHOUSE_SIZE = XSMALL;\n\n--- Create database and schema\n\nCREATE DATABASE IF NOT EXISTS RAG_DB;\nUSE DATABASE RAG_DB;\nCREATE SCHEMA IF NOT EXISTS RAG_DB.RAG_SCHEMA;\nUSE SCHEMA RAG_SCHEMA;\n\n--- Create network rule and apply it in External Access Integration\n\nCREATE OR REPLACE NETWORK RULE pypi_network_rule\n MODE = EGRESS\n TYPE = HOST_PORT\n VALUE_LIST = ('pypi.org', 'pypi.python.org', 'pythonhosted.org',  'files.pythonhosted.org');\n\nUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION pypi_access_integration\n ALLOWED_NETWORK_RULES = (pypi_network_rule)\n ENABLED = true;\n\n-- Grant necessary privileges\n\nGRANT USAGE ON INTEGRATION pypi_access_integration TO ROLE RAG_OWNER;\n\nGRANT CREATE STREAMLIT ON SCHEMA RAG_DB.RAG_SCHEMA TO ROLE RAG_OWNER;\n\nGRANT USAGE ON WAREHOUSE RAG_WH TO ROLE BICYCLE;\nGRANT USAGE ON WAREHOUSE RAG_WH TO ROLE SKI;\n\nGRANT USAGE ON COMPUTE POOL RAG_STREAMLIT TO ROLE BICYCLE;\nGRANT USAGE ON COMPUTE POOL RAG_STREAMLIT TO ROLE SKI;\n\nGRANT USAGE ON DATABASE RAG_DB TO ROLE BICYCLE;\nGRANT USAGE ON SCHEMA RAG_DB.RAG_SCHEMA TO ROLE BICYCLE;\n\nGRANT USAGE ON DATABASE RAG_DB TO ROLE SKI;\nGRANT USAGE ON SCHEMA RAG_DB.RAG_SCHEMA TO ROLE SKI;\n\n-- Create stages for streamlit source code and source documentation\n\nUSE ROLE RAG_OWNER;\n\nCREATE OR REPLACE STAGE STREAMLIT_UI DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE='SNOWFLAKE_SSE');\nCREATE OR REPLACE STAGE SOURCE_DOCUMENTS DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE='SNOWFLAKE_SSE');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUpload Documents\u003C/h3\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EUpload the documents in the unzipped Documents.zip folder \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-access-controls-for-cortex-search/assets/Documents.zip\"\u003Ehere\u003C/a\u003E to the SOURCE_DOCUMENTS stage we just created. You can do this through the Snowsight UI. The instructions on how to do this can be found in our documentation \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-local-file-system-stage-ui#upload-files-onto-a-named-internal-stage\"\u003Ehere\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EBe sure to select the RAG_DB database, the RAG_SCHEMA schema and the SOURCE_DOCUMENTS stage. These source documents are the PDFs on both the Bicycle and Ski equipment products in one directory. See the screenshot below\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/load_file_menu.png\" alt=\"Upload Files\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce you have uploaded them, run the following SQL from your SQL Worksheet to check if the files have been uploaded successfully.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ELS @SOURCE_DOCUMENTS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the 4 files loaded in the stage.\u003C/p\u003E\n","\u003Ch2\u003EPrepare Data for RAG\u003C/h2\u003E\n","\u003Cp\u003EDuration: 20\u003C/p\u003E\n","\u003Cp\u003EIn this section, we use Snowflake's helper functions to prepare the unstructured documents and set up the Cortex Search Service.\u003C/p\u003E\n","\u003Cp\u003EThe functions below simplify the process of creating a RAG into only a few steps. Specifically, we are using the following unique Snowflake features:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECortex AI Functions (AI_PARSE_DOCUMENT):\u003C/li\u003E\u003Cli\u003ESPLIT_TEXT_RECURSIVE_CHARACTER:\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENote that we are adding an attribute column that we will filter on later to make sure that users only see the correct documents. We are using the file names, but you could also use \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_classify\"\u003EAI_CLASSIFY\u003C/a\u003E as part of your pipeline.\u003C/p\u003E\n","\u003Cp\u003ERun the following SQL in a SQL worksheet\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE SCHEMA RAG_DB.RAG_SCHEMA;\n\nCREATE OR REPLACE TABLE documents_table AS\n  (SELECT TO_FILE('@source_documents', RELATIVE_PATH) AS docs, \n    RELATIVE_PATH as RELATIVE_PATH\n    FROM DIRECTORY(@source_documents));\n\n-- Use Cortex AI function to parse PDF. Create identifier column so we can filter based on user attribute\nCREATE OR REPLACE TABLE EXTRACTED_TEXT_TABLE AS (\n    SELECT  RELATIVE_PATH, \n            AI_PARSE_DOCUMENT(docs, {'mode': 'OCR'}):content::VARCHAR AS EXTRACTED_TEXT,\n            CASE\n                WHEN RELATIVE_PATH ILIKE '%ski%' THEN 'SKI'\n                WHEN RELATIVE_PATH ILIKE '%bike%' OR RELATIVE_PATH ILIKE '%bicycle%' THEN 'BICYCLE'\n                ELSE 'Other'\n            END AS product_department\n            FROM documents_table\n            );\n\n-- Chunk parsed document for RAG\nCREATE OR REPLACE TABLE CHUNKED_TABLE AS (\n        SELECT\n            e.*,\n            c.value::VARCHAR AS chunk\n        FROM EXTRACTED_TEXT_TABLE e,\n        LATERAL FLATTEN(\n            INPUT => snowflake.cortex.split_text_recursive_character(\n                EXTRACTED_TEXT,\n                'none',\n                2000,\n                300\n            )\n        ) c\n    );\n\n-- Test chunked table\nSELECT * FROM CHUNKED_TABLE;\n\n-- Create Cortex Search Service\nCREATE OR REPLACE CORTEX SEARCH SERVICE rag_cortex_search_service\n  ON chunk\n  ATTRIBUTES product_department\n  WAREHOUSE = RAG_WH\n  TARGET_LAG = '1 hour'\n  INITIALIZE = ON_CREATE\nAS SELECT * FROM RAG_DB.RAG_SCHEMA.CHUNKED_TABLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe Cortex Search Service may take a few minutes to set up. Once it has completed,  we can test the cortex search service by running the following SQL. Check if the serving_state column is ACTIVE.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EDESC CORTEX SEARCH SERVICE rag_cortex_search_service;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, we will alter it to return a "scoring profile" which allows us to provide thresholds for the relevancy of returned documents\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EALTER CORTEX SEARCH SERVICE RAG_DB.RAG_SCHEMA.rag_cortex_search_service\n  ADD SCORING PROFILE IF NOT EXISTS default_with_components\n  '{\n    "component_scores": true\n  }';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can execute the following SQL to test if the Cortex Search Service is working.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT PARSE_JSON(\n  SNOWFLAKE.CORTEX.SEARCH_PREVIEW(\n      'rag_cortex_search_service',\n      '{\n         "query": "Tell me about the Ski Bootz",\n         "filter": {"@eq": {"product_department": "SKI"} },\n         "limit":10\n      }'\n  )\n)['results'] as results;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EPrepare Streamlit App\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003EThe next step is to create a UI that can use these objects and present them to our business users in a safe and secure way. We are going to use Streamlit in Snowflake on a Container Runtime to make this simple. Since this is an internal application, we can leverage Snowflake's authentication and authorization mechanisms to ensure the app is "aware" of who is trying to access the data, and only return information from the documents they have access to.\u003C/p\u003E\n","\u003Cp\u003EUpload the streamlit python file and corresponding assets by downloading and unzipping the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-access-controls-for-cortex-search/assets/streamlit_assets.zip\"\u003Estreamlit_assets.zip\u003C/a\u003E and uploading to the STREAMLIT_UI stage. Follow similar steps to how you uploaded the source documents. The instructions on how to do this can be found in our documentation \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-local-file-system-stage-ui#upload-files-onto-a-named-internal-stage\"\u003Ehere\u003C/a\u003E. Be sure to select the RAG_DB database, the RAG_SCHEMA schema and the STREAMLIT_UI stage. See the screenshot below:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/upload_streamlit_files.png\" alt=\"Upload Streamlit Files\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, run the following SQL to create the Streamlit app.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE OR REPLACE STREAMLIT rag_access_control_app\n  FROM '@rag_db.rag_schema.streamlit_ui/'\n  MAIN_FILE = 'rag_access_control_streamlit_ui.py'\n  RUNTIME_NAME = 'SYSTEM$ST_CONTAINER_RUNTIME_PY3_11'\n  COMPUTE_POOL = RAG_STREAMLIT\n  QUERY_WAREHOUSE = RAG_WH\n  EXTERNAL_ACCESS_INTEGRATIONS = (pypi_access_integration);\n\nALTER STREAMLIT rag_access_control_app ADD LIVE VERSION FROM LAST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EOpen App as Owner\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003ENavigate to Projects > Streamlit in the left navigation menu. You should now see RAG_ACCESS_CONTROL_APP. Click to open the app. Make sure you have assumed the role RAG_OWNER (you can check this by clicking over your initials in the bottom left of the screen).\u003C/p\u003E\n","\u003Cp\u003EYou should now see the streamlit UI. See screenshot below:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/streamlit_home_page.png\" alt=\"Streamlit Home Page\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you type a question, it will not respond with a helpful answer since the filters applied do not allow the owner role to see the underlying data via the app. Since the owner role owns the Cortex Search Service, it could however query the service directly outside of the app. If this were to be implemented, this role would not be granted to users that needed access controls applied to them.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/no_documents.png\" alt=\"No Documents\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EShare Streamlit App\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EWe now want to test this functionality as different users with different permissions. First we need to grant access to other roles to this app. In the top right of the page, click the "Share" button. In the modal, grant access to "SKI" and "BICYCLE" roles as "View Only".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/grant_streamlit_access.png\" alt=\"Grant Streamlit Access\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect the 'Copy Link' button. Then Sign Out as the current user. You can find this button in the bottom left of the screen.\u003C/p\u003E\n","\u003Cp\u003ENext sign out as the current user. You can find this by clicking your initials icon in the bottom left of the UI.\u003C/p\u003E\n","\u003Ch2\u003ETest App as Different Users\u003C/h2\u003E\n","\u003Cp\u003EDuration: 15\u003C/p\u003E\n","\u003Cp\u003EPaste the copied link from the previous section into the browser. It should then prompt you to login. We are going to test the app experience as the 'Ski' user. Type in ski_user in the Username field, and the initial password you created as part of the initial set up. N.B. if you have a MFA enforcement policy set up (highly recommended), you will be prompted to complete those steps upon initial sign-in.\u003C/p\u003E\n","\u003Cp\u003EYou should see the streamlit app load in the UI.\u003C/p\u003E\n","\u003Cp\u003EFirst, lets see if we can see any information on bicycles. We should not, since the filter on the backend should only return chunks from documents related to skis.\u003C/p\u003E\n","\u003Cp\u003EAsk "Tell me about the Mondracer bike"\u003C/p\u003E\n","\u003Cp\u003EYou should see that the app responded that it did not have any relevant information. Similarly the "Context Documents" in the left navigation menu (with Debug Mode on) should be empty.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/no_documents_2.png\" alt=\"No Documents with Debug\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow lets ask a question which should return results.\u003C/p\u003E\n","\u003Cp\u003EAsk "Tell me about the TD Ski Bootz"\u003C/p\u003E\n","\u003Cp\u003EYou should see a response and also relevant chunks being returned to answer the question.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/ski_response.png\" alt=\"Ski Response\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can now test this for "bicycle_user" by logging out and logging back in as bicycle_user.\u003C/p\u003E\n","\u003Cp\u003EWhen logged in as bicycle_user, ask:\u003C/p\u003E\n","\u003Cp\u003E"Tell me about the Mondracer bike"\u003C/p\u003E\n","\u003Cp\u003EYou should see a response.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/bicycle_response.png\" alt=\"Bicycle Response\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext ask:\u003C/p\u003E\n","\u003Cp\u003E"Tell me about the TD Ski Bootz".\u003C/p\u003E\n","\u003Cp\u003EYou should not see a response that references source documents.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/bicycle_no_response.png\" alt=\"Bicycle No Response\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUnderstanding the Streamlit Code\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003EThe Streamlit code contains the logic that allows us to safely use the Cortex Search Service and return only the relevant information to the authenticated user.\u003C/p\u003E\n","\u003Cp\u003ECortex Search Services runs with Owner's Rights by design. This is to ensure that it runs with the same security model as other Snowflake objects that run with Owner's Rights and keeps the service performant for real world use cases. More information on this model can be found in our documentation \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/query-cortex-search-service#querying-with-owner-s-rights\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EIn this app, we are following recommended best practice of using explicit filters on the client-side query to filter for access controls. These filters are based on user attributes that are obtained from the authentication flow from logging in to Snowflake.\u003C/p\u003E\n","\u003Cp\u003EOnly the RAG_OWNER Role, which owns both the streamlit app and the Cortex Search Service, has the ability to use the Cortex Search Service, and modify any of the code within the streamlit app. Both the SKI and BICYCLE roles only have access to viewing and using the Streamlit app.\u003C/p\u003E\n","\u003Cp\u003EHere is a walkthrough of some of the key functionality in the streamlit python code:\u003C/p\u003E\n","\u003Cp\u003EThe following block reads the credentials of the owner of the container the app is running on. This is true whether this is Streamlit in Snowflake (SiS) running on containers, or if you were to push your own app to SPCS. Snowflake will provide this OAuth token at the location (/snowflake/session/token). More information can be found in our documentation \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/additional-considerations-services-jobs#connecting-with-a-snowflake-provided-oauth-token\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef get_system_token():\n    """\n    Reads the container's system token.\n    """\n    try:\n        with open("/snowflake/session/token", "r") as f:\n            return f.read().strip()\n    except Exception:\n        return None\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe code below creates two sessions. One is the owner session to utilise Cortex Search (which is not exposed to the user). The other creates the visitor session to obtain the attributes of the user logging in.\u003C/p\u003E\n","\u003Cp\u003EWe do this by obtaining the ingress_user_token from the HTTP headers.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eheaders = st.context.headers or {}\ningress_user_token = headers.get("Sf-Context-Current-User-Token")\ningress_user = headers.get("Sf-Context-Current-User")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce we have obtained the header information, we can initialize both the sessions in the initialize_sessions function.\u003C/p\u003E\n","\u003Cp\u003EThe next key part of the functionality is how we apply the filters to the Cortex Search service. As previously mentioned, the Cortex Search Service runs with Owner's Rights, so we need to utilise the Owner's session in backend code (so it is not exposed to the visiting user). Similarly, we need to provide a filter on the Cortex Search service that cannot be tampered with. We use the Snowflake generated headers and calling the Context Function CURRENT_ROLE() to do this.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ecurrent_role = get_current_role_from_session(current_session)\n\nfilter_condition = {"@eq": {"product_department": current_role}}\ncontext_documents = cortex_search_service.search(\n    query,\n    columns=["CHUNK"],\n    filter=filter_condition,\n    scoring_profile=scoring_profile,\n)\nfilter_applied = f"product_department @eq '{current_role}' (server-validated)"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis way we are able to expose the RAG functionality to the authenticated user, without providing access to all indexed source documents.\u003C/p\u003E\n","\u003Cp\u003EThis same design could be utilized for application architectures other than streamlit. You would similarly pass an identifier along with the \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowflake-rest-api/reference/cortex-search-service#post--api-v2-databases-database-schemas-schema-cortex-search-services-service_name-query\"\u003ECortex Search API request\u003C/a\u003E from the backend.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003ELog in as the RLS Owner user, and run the following SQL to clean up.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE ACCOUNTADMIN;\nDROP ROLE RAG_OWNER;\nDROP ROLE SKI;\nDROP ROLE BICYCLE;\n\nDROP USER bicycle_user;\nDROP USER ski_user;\n\nALTER COMPUTE POOL RAG_STREAMLIT STOP ALL;\nDROP COMPUTE POOL RAG_STREAMLIT;\nDROP WAREHOUSE RAG_WH;\nDROP DATABASE RAG_DB CASCADE;\nDROP EXTERNAL ACCESS INTEGRATION pypi_access_integration;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You have successfully built and deployed a secure, permissions-aware Retrieval-Augmented Generation (RAG) application entirely within Snowflake.\u003C/p\u003E\n","\u003Cp\u003EBy combining \u003Cstrong\u003ECortex Search\u003C/strong\u003E with \u003Cstrong\u003EStreamlit in Snowflake (on Container Runtime)\u003C/strong\u003E, you demonstrated how to move beyond generic chatbots to enterprise-ready solutions that respect existing data governance policies. You utilized Snowflake's native access control (RBAC) to dynamically filter search results, ensuring that users (like our Ski and Bicycle representatives) only interact with data they are authorized to see.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EData Preparation for RAG:\u003C/strong\u003E How to use \u003Ccode\u003EAI_PARSE_DOCUMENT\u003C/code\u003E to extract text from unstructured files (PDFs) and \u003Ccode\u003ESPLIT_TEXT_RECURSIVE_CHARACTER\u003C/code\u003E to chunk data for indexing.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search Configuration:\u003C/strong\u003E How to configure a Cortex Search Service with specific \u003Ccode\u003EATTRIBUTES\u003C/code\u003E to enable metadata filtering.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStreamlit on SPCS:\u003C/strong\u003E How to deploy a Streamlit app using the Container Runtime to leverage specific python versions and libraries.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESecure Filtering Pattern:\u003C/strong\u003E How to implement a pattern that captures the authenticated user's role and applies a server-validated filter to the Cortex Search query, effectively implementing Row-Level Security for your RAG pipeline.\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/cortex-search/cortex-search-overview\"\u003EDocumentation: Cortex Search Service\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EDocumentation: Streamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003EDocumentation: Snowpark Container Services\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-integration/openflow/connectors/sharepoint/setup#query-the-cortex-search-service\"\u003EDocumentation: Openflow Connectors with Access Controls\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguides\"\u003EGuide Source Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build secure RAG chatbot applications with Cortex Search access controls for role-based document retrieval and compliance.","title":"Getting Started with Access Controls for RAGs (Cortex Search)","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nRetrieval-Augmented Generation (RAG) promises to unlock unprecedented value from enterprise data. The vast majority (80-90%) of corporate knowledge is trapped in unstructured documents, emails, and messages, often containing sensitive PII, IP, and financial data with inconsistent access controls. Deploying AI models without robust governance exposes this sensitive data, creating severe risks of data breaches, regulatory non-compliance, and loss of intellectual property.\n\nFor businesses to adopt AI safely, applications must be \"permissions-aware.\" This is not an optional feature but a foundational requirement. AI systems must respect and enforce the existing security and access permissions of every user at the moment of retrieval. An employee must only be allowed to \"see\" and surface data that the user querying it is already authorized to access.\n\nIn this guide, you will build a secure RAG pipeline (using Cortex Search) that enforces document-level access control. Specifically, we will create a RAG that utilises user attributes (such as their role) and pass this on as a filter condition to the cortex search service on the backend of a client-facing application.\n\n\n### Prerequisites\n- Access to an account that can create External Access Integrations (trial accounts are not able to).\n- Access to an elevated role (such as ACCOUNTADMIN) to create External Access Integrations, Roles and Users.\n\n### What You’ll Learn \n- How to build a RAG chatbot in Streamlit in Snowflake (SiS) with access controls.\n\n### What You’ll Need \n- A Snowflake account with the ability to create Compute Pools (SPCS)\n- Ability to create new users and roles in Snowflake\n\n### What You’ll Build \n- A RAG chatbot built with Streamlit in Snowflake (SiS) with access controls, powered by Cortex Search.\n\n\u003C!-- ------------------------ --\u003E\n## Use Case \nDuration: 5\n\nIn this guide, we are going to play the role of a chain of sporting goods stores. We have product information on ski equipment, and bicycles. We want to help our customer service teams be able to query information on our products that currently exists in unstructured documents (PDFs).\n\nMost importantly, we only want the Ski department to query information on source documents that are related to ski equipment. Similarly we want the Bicycle department to only query information from the PDF documents related to bicycles.\n\nTo do this, we are going to create a RAG pipeline utilizing Snowflake features (including [Cortex Search Service](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)), and utilize Streamlit in Snowflake (SiS) running on Snowpark Container Services (SPCS) to expose this functionality to users from both the Ski and Bicycle department.\n\n\u003C!-- ------------------------ --\u003E\n## Setup\nDuration: 10\n\n### Snowflake Setup\n\nOpen a SQL Worksheet and run through the following code to set up the necessary objects and permissions in Snowflake.\n\n```SQL\nUSE ROLE ACCOUNTADMIN;\nCREATE ROLE IF NOT EXISTS RAG_OWNER;\nCREATE ROLE IF NOT EXISTS SKI;\nCREATE ROLE IF NOT EXISTS BICYCLE;\n\n-- Create the users to test access controls. It is strongly recommended that MFA is enabled for these users\nCREATE OR REPLACE USER bicycle_user\n    PASSWORD             = '\u003Center initial password\u003E'\n    LOGIN_NAME           = 'bicycle_user'\n    FIRST_NAME           = 'Bicycle'\n    LAST_NAME            = 'User'\n    EMAIL                = '\u003Center your email\u003E'\n    DEFAULT_ROLE         = BICYCLE  \n    MUST_CHANGE_PASSWORD = TRUE;\n\nGRANT ROLE BICYCLE TO USER bicycle_user;\n\nCREATE OR REPLACE USER ski_user\n    PASSWORD             = '\u003Center initial password\u003E'\n    LOGIN_NAME           = 'ski_user'\n    FIRST_NAME           = 'Ski'\n    LAST_NAME            = 'User'\n    EMAIL                = '\u003Center your email\u003E'\n    DEFAULT_ROLE         = SKI \n    MUST_CHANGE_PASSWORD = TRUE;\n\nGRANT ROLE SKI TO USER ski_user;\n\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE RAG_OWNER;\nGRANT CREATE COMPUTE POOL ON ACCOUNT TO ROLE RAG_OWNER;\nGRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE RAG_OWNER;\n\nSET USERNAME = (SELECT CURRENT_USER());\nSELECT $USERNAME;\nGRANT ROLE RAG_OWNER to USER identifier($USERNAME);\n\n-- Create compute\n\nUSE ROLE RAG_OWNER;\n\nCREATE COMPUTE POOL IF NOT EXISTS RAG_STREAMLIT\n    MIN_NODES = 1\n    MAX_NODES = 3\n    INSTANCE_FAMILY = CPU_X64_XS;\n\nCREATE WAREHOUSE IF NOT EXISTS RAG_WH\n  WAREHOUSE_TYPE = STANDARD\n  WAREHOUSE_SIZE = XSMALL;\n\n--- Create database and schema\n\nCREATE DATABASE IF NOT EXISTS RAG_DB;\nUSE DATABASE RAG_DB;\nCREATE SCHEMA IF NOT EXISTS RAG_DB.RAG_SCHEMA;\nUSE SCHEMA RAG_SCHEMA;\n\n--- Create network rule and apply it in External Access Integration\n\nCREATE OR REPLACE NETWORK RULE pypi_network_rule\n MODE = EGRESS\n TYPE = HOST_PORT\n VALUE_LIST = ('pypi.org', 'pypi.python.org', 'pythonhosted.org',  'files.pythonhosted.org');\n\nUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION pypi_access_integration\n ALLOWED_NETWORK_RULES = (pypi_network_rule)\n ENABLED = true;\n\n-- Grant necessary privileges\n\nGRANT USAGE ON INTEGRATION pypi_access_integration TO ROLE RAG_OWNER;\n\nGRANT CREATE STREAMLIT ON SCHEMA RAG_DB.RAG_SCHEMA TO ROLE RAG_OWNER;\n\nGRANT USAGE ON WAREHOUSE RAG_WH TO ROLE BICYCLE;\nGRANT USAGE ON WAREHOUSE RAG_WH TO ROLE SKI;\n\nGRANT USAGE ON COMPUTE POOL RAG_STREAMLIT TO ROLE BICYCLE;\nGRANT USAGE ON COMPUTE POOL RAG_STREAMLIT TO ROLE SKI;\n\nGRANT USAGE ON DATABASE RAG_DB TO ROLE BICYCLE;\nGRANT USAGE ON SCHEMA RAG_DB.RAG_SCHEMA TO ROLE BICYCLE;\n\nGRANT USAGE ON DATABASE RAG_DB TO ROLE SKI;\nGRANT USAGE ON SCHEMA RAG_DB.RAG_SCHEMA TO ROLE SKI;\n\n-- Create stages for streamlit source code and source documentation\n\nUSE ROLE RAG_OWNER;\n\nCREATE OR REPLACE STAGE STREAMLIT_UI DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE='SNOWFLAKE_SSE');\nCREATE OR REPLACE STAGE SOURCE_DOCUMENTS DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE='SNOWFLAKE_SSE');\n```\n\n### Upload Documents\nDuration: 5\n\nUpload the documents in the unzipped Documents.zip folder [here](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-access-controls-for-cortex-search/assets/Documents.zip) to the SOURCE_DOCUMENTS stage we just created. You can do this through the Snowsight UI. The instructions on how to do this can be found in our documentation [here](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-stage-ui#upload-files-onto-a-named-internal-stage) \n\nBe sure to select the RAG_DB database, the RAG_SCHEMA schema and the SOURCE_DOCUMENTS stage. These source documents are the PDFs on both the Bicycle and Ski equipment products in one directory. See the screenshot below\n\n![Upload Files](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/load_file_menu.png)\n\nOnce you have uploaded them, run the following SQL from your SQL Worksheet to check if the files have been uploaded successfully.\n\n```SQL\nLS @SOURCE_DOCUMENTS;\n```\n\nYou should see the 4 files loaded in the stage.\n\n## Prepare Data for RAG\nDuration: 20\n\nIn this section, we use Snowflake's helper functions to prepare the unstructured documents and set up the Cortex Search Service. \n\nThe functions below simplify the process of creating a RAG into only a few steps. Specifically, we are using the following unique Snowflake features:\n- Cortex AI Functions (AI_PARSE_DOCUMENT):\n- SPLIT_TEXT_RECURSIVE_CHARACTER:\n\nNote that we are adding an attribute column that we will filter on later to make sure that users only see the correct documents. We are using the file names, but you could also use [AI_CLASSIFY](https://docs.snowflake.com/en/sql-reference/functions/ai_classify) as part of your pipeline.\n\nRun the following SQL in a SQL worksheet\n\n```SQL\nUSE SCHEMA RAG_DB.RAG_SCHEMA;\n\nCREATE OR REPLACE TABLE documents_table AS\n  (SELECT TO_FILE('@source_documents', RELATIVE_PATH) AS docs, \n    RELATIVE_PATH as RELATIVE_PATH\n    FROM DIRECTORY(@source_documents));\n\n-- Use Cortex AI function to parse PDF. Create identifier column so we can filter based on user attribute\nCREATE OR REPLACE TABLE EXTRACTED_TEXT_TABLE AS (\n    SELECT  RELATIVE_PATH, \n            AI_PARSE_DOCUMENT(docs, {'mode': 'OCR'}):content::VARCHAR AS EXTRACTED_TEXT,\n            CASE\n                WHEN RELATIVE_PATH ILIKE '%ski%' THEN 'SKI'\n                WHEN RELATIVE_PATH ILIKE '%bike%' OR RELATIVE_PATH ILIKE '%bicycle%' THEN 'BICYCLE'\n                ELSE 'Other'\n            END AS product_department\n            FROM documents_table\n            );\n\n-- Chunk parsed document for RAG\nCREATE OR REPLACE TABLE CHUNKED_TABLE AS (\n        SELECT\n            e.*,\n            c.value::VARCHAR AS chunk\n        FROM EXTRACTED_TEXT_TABLE e,\n        LATERAL FLATTEN(\n            INPUT =\u003E snowflake.cortex.split_text_recursive_character(\n                EXTRACTED_TEXT,\n                'none',\n                2000,\n                300\n            )\n        ) c\n    );\n\n-- Test chunked table\nSELECT * FROM CHUNKED_TABLE;\n\n-- Create Cortex Search Service\nCREATE OR REPLACE CORTEX SEARCH SERVICE rag_cortex_search_service\n  ON chunk\n  ATTRIBUTES product_department\n  WAREHOUSE = RAG_WH\n  TARGET_LAG = '1 hour'\n  INITIALIZE = ON_CREATE\nAS SELECT * FROM RAG_DB.RAG_SCHEMA.CHUNKED_TABLE;\n```\n\nThe Cortex Search Service may take a few minutes to set up. Once it has completed,  we can test the cortex search service by running the following SQL. Check if the serving_state column is ACTIVE.\n\n```SQL\nDESC CORTEX SEARCH SERVICE rag_cortex_search_service;\n```\n\nNext, we will alter it to return a \"scoring profile\" which allows us to provide thresholds for the relevancy of returned documents\n\n```SQL\nALTER CORTEX SEARCH SERVICE RAG_DB.RAG_SCHEMA.rag_cortex_search_service\n  ADD SCORING PROFILE IF NOT EXISTS default_with_components\n  '{\n    \"component_scores\": true\n  }';\n```\n\nWe can execute the following SQL to test if the Cortex Search Service is working.\n\n```SQL\nSELECT PARSE_JSON(\n  SNOWFLAKE.CORTEX.SEARCH_PREVIEW(\n      'rag_cortex_search_service',\n      '{\n         \"query\": \"Tell me about the Ski Bootz\",\n         \"filter\": {\"@eq\": {\"product_department\": \"SKI\"} },\n         \"limit\":10\n      }'\n  )\n)['results'] as results;\n```\n\n## Prepare Streamlit App\nDuration: 10\n\nThe next step is to create a UI that can use these objects and present them to our business users in a safe and secure way. We are going to use Streamlit in Snowflake on a Container Runtime to make this simple. Since this is an internal application, we can leverage Snowflake's authentication and authorization mechanisms to ensure the app is \"aware\" of who is trying to access the data, and only return information from the documents they have access to.\n\nUpload the streamlit python file and corresponding assets by downloading and unzipping the [streamlit_assets.zip](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-access-controls-for-cortex-search/assets/streamlit_assets.zip) and uploading to the STREAMLIT_UI stage. Follow similar steps to how you uploaded the source documents. The instructions on how to do this can be found in our documentation [here](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-stage-ui#upload-files-onto-a-named-internal-stage). Be sure to select the RAG_DB database, the RAG_SCHEMA schema and the STREAMLIT_UI stage. See the screenshot below:\n\n![Upload Streamlit Files](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/upload_streamlit_files.png)\n\nNext, run the following SQL to create the Streamlit app.\n\n```SQL\nCREATE OR REPLACE STREAMLIT rag_access_control_app\n  FROM '@rag_db.rag_schema.streamlit_ui/'\n  MAIN_FILE = 'rag_access_control_streamlit_ui.py'\n  RUNTIME_NAME = 'SYSTEM$ST_CONTAINER_RUNTIME_PY3_11'\n  COMPUTE_POOL = RAG_STREAMLIT\n  QUERY_WAREHOUSE = RAG_WH\n  EXTERNAL_ACCESS_INTEGRATIONS = (pypi_access_integration);\n\nALTER STREAMLIT rag_access_control_app ADD LIVE VERSION FROM LAST;\n```\n\n## Open App as Owner\nDuration: 5\n\nNavigate to Projects \u003E Streamlit in the left navigation menu. You should now see RAG_ACCESS_CONTROL_APP. Click to open the app. Make sure you have assumed the role RAG_OWNER (you can check this by clicking over your initials in the bottom left of the screen).\n\nYou should now see the streamlit UI. See screenshot below:\n\n![Streamlit Home Page](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/streamlit_home_page.png)\n\nIf you type a question, it will not respond with a helpful answer since the filters applied do not allow the owner role to see the underlying data via the app. Since the owner role owns the Cortex Search Service, it could however query the service directly outside of the app. If this were to be implemented, this role would not be granted to users that needed access controls applied to them.\n\n![No Documents](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/no_documents.png)\n\n## Share Streamlit App\nDuration: 5\n\nWe now want to test this functionality as different users with different permissions. First we need to grant access to other roles to this app. In the top right of the page, click the \"Share\" button. In the modal, grant access to \"SKI\" and \"BICYCLE\" roles as \"View Only\".\n\n![Grant Streamlit Access](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/grant_streamlit_access.png)\n\nSelect the 'Copy Link' button. Then Sign Out as the current user. You can find this button in the bottom left of the screen.\n\nNext sign out as the current user. You can find this by clicking your initials icon in the bottom left of the UI.\n\n## Test App as Different Users\nDuration: 15\n\nPaste the copied link from the previous section into the browser. It should then prompt you to login. We are going to test the app experience as the 'Ski' user. Type in ski_user in the Username field, and the initial password you created as part of the initial set up. N.B. if you have a MFA enforcement policy set up (highly recommended), you will be prompted to complete those steps upon initial sign-in.\n\nYou should see the streamlit app load in the UI.\n\nFirst, lets see if we can see any information on bicycles. We should not, since the filter on the backend should only return chunks from documents related to skis.\n\nAsk \"Tell me about the Mondracer bike\"\n\nYou should see that the app responded that it did not have any relevant information. Similarly the \"Context Documents\" in the left navigation menu (with Debug Mode on) should be empty.\n\n![No Documents with Debug](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/no_documents_2.png)\n\nNow lets ask a question which should return results.\n\nAsk \"Tell me about the TD Ski Bootz\"\n\nYou should see a response and also relevant chunks being returned to answer the question.\n\n![Ski Response](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/ski_response.png)\n\nYou can now test this for \"bicycle_user\" by logging out and logging back in as bicycle_user.\n\nWhen logged in as bicycle_user, ask:\n\n\"Tell me about the Mondracer bike\"\n\nYou should see a response. \n\n![Bicycle Response](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/bicycle_response.png)\n\nNext ask:\n\n\"Tell me about the TD Ski Bootz\".\n\nYou should not see a response that references source documents.\n\n![Bicycle No Response](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-access-controls-for-cortex-search/bicycle_no_response.png)\n\n\u003C!-- ------------------------ --\u003E\n## Understanding the Streamlit Code\nDuration: 10\n\nThe Streamlit code contains the logic that allows us to safely use the Cortex Search Service and return only the relevant information to the authenticated user.\n\nCortex Search Services runs with Owner's Rights by design. This is to ensure that it runs with the same security model as other Snowflake objects that run with Owner's Rights and keeps the service performant for real world use cases. More information on this model can be found in our documentation [here](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/query-cortex-search-service#querying-with-owner-s-rights).\n\nIn this app, we are following recommended best practice of using explicit filters on the client-side query to filter for access controls. These filters are based on user attributes that are obtained from the authentication flow from logging in to Snowflake.\n\nOnly the RAG_OWNER Role, which owns both the streamlit app and the Cortex Search Service, has the ability to use the Cortex Search Service, and modify any of the code within the streamlit app. Both the SKI and BICYCLE roles only have access to viewing and using the Streamlit app.\n\nHere is a walkthrough of some of the key functionality in the streamlit python code:\n\nThe following block reads the credentials of the owner of the container the app is running on. This is true whether this is Streamlit in Snowflake (SiS) running on containers, or if you were to push your own app to SPCS. Snowflake will provide this OAuth token at the location (/snowflake/session/token). More information can be found in our documentation [here](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/additional-considerations-services-jobs#connecting-with-a-snowflake-provided-oauth-token).\n\n```python\ndef get_system_token():\n    \"\"\"\n    Reads the container's system token.\n    \"\"\"\n    try:\n        with open(\"/snowflake/session/token\", \"r\") as f:\n            return f.read().strip()\n    except Exception:\n        return None\n```\n\nThe code below creates two sessions. One is the owner session to utilise Cortex Search (which is not exposed to the user). The other creates the visitor session to obtain the attributes of the user logging in.\n\nWe do this by obtaining the ingress_user_token from the HTTP headers.\n\n```python\nheaders = st.context.headers or {}\ningress_user_token = headers.get(\"Sf-Context-Current-User-Token\")\ningress_user = headers.get(\"Sf-Context-Current-User\")\n```\n\nOnce we have obtained the header information, we can initialize both the sessions in the initialize_sessions function.\n\nThe next key part of the functionality is how we apply the filters to the Cortex Search service. As previously mentioned, the Cortex Search Service runs with Owner's Rights, so we need to utilise the Owner's session in backend code (so it is not exposed to the visiting user). Similarly, we need to provide a filter on the Cortex Search service that cannot be tampered with. We use the Snowflake generated headers and calling the Context Function CURRENT_ROLE() to do this. \n\n```python\ncurrent_role = get_current_role_from_session(current_session)\n\nfilter_condition = {\"@eq\": {\"product_department\": current_role}}\ncontext_documents = cortex_search_service.search(\n    query,\n    columns=[\"CHUNK\"],\n    filter=filter_condition,\n    scoring_profile=scoring_profile,\n)\nfilter_applied = f\"product_department @eq '{current_role}' (server-validated)\"\n```\n\nThis way we are able to expose the RAG functionality to the authenticated user, without providing access to all indexed source documents.\n\nThis same design could be utilized for application architectures other than streamlit. You would similarly pass an identifier along with the [Cortex Search API request](https://docs.snowflake.com/developer-guide/snowflake-rest-api/reference/cortex-search-service#post--api-v2-databases-database-schemas-schema-cortex-search-services-service_name-query) from the backend.\n\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\nDuration: 5\n\nLog in as the RLS Owner user, and run the following SQL to clean up.\n\n```SQL\nUSE ROLE ACCOUNTADMIN;\nDROP ROLE RAG_OWNER;\nDROP ROLE SKI;\nDROP ROLE BICYCLE;\n\nDROP USER bicycle_user;\nDROP USER ski_user;\n\nALTER COMPUTE POOL RAG_STREAMLIT STOP ALL;\nDROP COMPUTE POOL RAG_STREAMLIT;\nDROP WAREHOUSE RAG_WH;\nDROP DATABASE RAG_DB CASCADE;\nDROP EXTERNAL ACCESS INTEGRATION pypi_access_integration;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\nDuration: 5\n\nCongratulations! You have successfully built and deployed a secure, permissions-aware Retrieval-Augmented Generation (RAG) application entirely within Snowflake.\n\nBy combining **Cortex Search** with **Streamlit in Snowflake (on Container Runtime)**, you demonstrated how to move beyond generic chatbots to enterprise-ready solutions that respect existing data governance policies. You utilized Snowflake's native access control (RBAC) to dynamically filter search results, ensuring that users (like our Ski and Bicycle representatives) only interact with data they are authorized to see.\n\n### What You Learned\n- **Data Preparation for RAG:** How to use `AI_PARSE_DOCUMENT` to extract text from unstructured files (PDFs) and `SPLIT_TEXT_RECURSIVE_CHARACTER` to chunk data for indexing.\n- **Cortex Search Configuration:** How to configure a Cortex Search Service with specific `ATTRIBUTES` to enable metadata filtering.\n- **Streamlit on SPCS:** How to deploy a Streamlit app using the Container Runtime to leverage specific python versions and libraries.\n- **Secure Filtering Pattern:** How to implement a pattern that captures the authenticated user's role and applies a server-validated filter to the Cortex Search query, effectively implementing Row-Level Security for your RAG pipeline.\n\n### Related Resources\n- [Documentation: Cortex Search Service](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)\n- [Documentation: Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n- [Documentation: Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview)\n- [Documentation: Openflow Connectors with Access Controls](https://docs.snowflake.com/en/user-guide/data-integration/openflow/connectors/sharepoint/setup#query-the-cortex-search-service)\n- [Guide Source Code on GitHub](https://github.com/Snowflake-Labs/sfguides)\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",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-28a61e21fe","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-79742f8181",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-f5a7f0810b","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-64b8bd58a7","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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