[pull] master from Snowflake-Labs:master

site/sfguides/src/from-zero-to-agents/assets/setup.sql

@@ -0,0 +1,214 @@
+-- Summary of objects created in this script:
+--
+-- Roles:
+--   - snowflake_intelligence_admin
+--
+-- Warehouses:
+--   - dash_wh_si
+--
+-- Databases:
+--   - dash_db_si
+--   - snowflake_intelligence
+--
+-- Schemas:
+--   - dash_db_si.retail
+--   - snowflake_intelligence.agents
+--
+-- File Format:
+--   - swt_csvformat
+--
+-- Stages:
+--   - swt_marketing_data_stage
+--   - swt_products_data_stage
+--   - swt_sales_data_stage
+--   - swt_social_media_data_stage
+--   - swt_support_data_stage
+--   - semantic_models
+--
+-- Git Integration:
+--   - snowflake_labs_si_git_api_integration
+--
+-- Git Repository:
+--   - snowflake_labs_sfguide_si_repo
+--
+-- Tables:
+--   - marketing_campaign_metrics
+--   - products
+--   - sales
+--   - social_media
+--   - support_cases
+--
+-- Notification Integration:
+--   - email_integration
+--
+-- Stored Procedure:
+--   - send_email
+
+
+use role accountadmin;
+
+create or replace role snowflake_intelligence_admin;
+grant create warehouse on account to role snowflake_intelligence_admin;
+grant create database on account to role snowflake_intelligence_admin;
+grant create integration on account to role snowflake_intelligence_admin;
+
+set current_user = (select current_user());   
+grant role snowflake_intelligence_admin to user identifier($current_user);
+alter user set default_role = snowflake_intelligence_admin;
+alter user set default_warehouse = dash_wh_si;
+
+use role snowflake_intelligence_admin;
+create or replace database dash_db_si;
+create or replace schema retail;
+create or replace warehouse dash_wh_si with warehouse_size='large';
+
+create database if not exists snowflake_intelligence;
+create schema if not exists snowflake_intelligence.agents;
+
+grant create agent on schema snowflake_intelligence.agents to role snowflake_intelligence_admin;
+
+use database dash_db_si;
+use schema retail;
+use warehouse dash_wh_si;
+
+create or replace file format swt_csvformat  
+  skip_header = 1  
+  field_optionally_enclosed_by = '"'  
+  type = 'csv';  
+
+-- create table marketing_campaign_metrics and load data from s3 bucket
+create or replace stage swt_marketing_data_stage  
+  file_format = swt_csvformat  
+  url = 's3://sfquickstarts/sfguide_getting_started_with_snowflake_intelligence/marketing/';  
+
+create or replace table marketing_campaign_metrics (
+  date date,
+  category varchar(16777216),
+  campaign_name varchar(16777216),
+  impressions number(38,0),
+  clicks number(38,0)
+);
+
+copy into marketing_campaign_metrics  
+  from @swt_marketing_data_stage;
+
+-- create table products and load data from s3 bucket
+create or replace stage swt_products_data_stage  
+  file_format = swt_csvformat  
+  url = 's3://sfquickstarts/sfguide_getting_started_with_snowflake_intelligence/product/';  
+
+create or replace table products (
+  product_id number(38,0),
+  product_name varchar(16777216),
+  category varchar(16777216)
+);
+
+copy into products  
+  from @swt_products_data_stage;
+
+-- create table sales and load data from s3 bucket
+create or replace stage swt_sales_data_stage  
+  file_format = swt_csvformat  
+  url = 's3://sfquickstarts/sfguide_getting_started_with_snowflake_intelligence/sales/';  
+
+create or replace table sales (
+  date date,
+  region varchar(16777216),
+  product_id number(38,0),
+  units_sold number(38,0),
+  sales_amount number(38,2)
+);
+
+copy into sales  
+  from @swt_sales_data_stage;
+
+-- create table social_media and load data from s3 bucket
+create or replace stage swt_social_media_data_stage  
+  file_format = swt_csvformat  
+  url = 's3://sfquickstarts/sfguide_getting_started_with_snowflake_intelligence/social_media/';  
+
+create or replace table social_media (
+  date date,
+  category varchar(16777216),
+  platform varchar(16777216),
+  influencer varchar(16777216),
+  mentions number(38,0)
+);
+
+copy into social_media  
+  from @swt_social_media_data_stage;
+
+-- create table support_cases and load data from s3 bucket
+create or replace stage swt_support_data_stage  
+  file_format = swt_csvformat  
+  url = 's3://sfquickstarts/sfguide_getting_started_with_snowflake_intelligence/support/';  
+
+create or replace table support_cases (
+  id varchar(16777216),
+  title varchar(16777216),
+  product varchar(16777216),
+  transcript varchar(16777216),
+  date date
+);
+
+copy into support_cases  
+  from @swt_support_data_stage;
+
+create or replace stage semantic_models encryption = (type = 'snowflake_sse') directory = ( enable = true );
+
+-- create git api integration and repository
+create or replace api integration snowflake_labs_si_git_api_integration
+  api_provider = git_https_api
+  api_allowed_prefixes = ('https://github.com/Snowflake-Labs')
+  enabled = true;
+
+create or replace git repository snowflake_labs_sfguide_si_repo
+  api_integration = snowflake_labs_si_git_api_integration
+  origin = 'https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-intelligence';
+
+-- copy semantic model from git repo to stage
+copy files into @semantic_models
+  from @snowflake_labs_sfguide_si_repo/branches/main/
+  files = ('marketing_campaigns.yaml');
+
+create or replace notification integration email_integration
+  type=email
+  enabled=true
+  default_subject = 'snowflake intelligence';
+
+create or replace procedure send_email(
+    recipient_email varchar,
+    subject varchar,
+    body varchar
+)
+returns varchar
+language python
+runtime_version = '3.12'
+packages = ('snowflake-snowpark-python')
+handler = 'send_email'
+as
+$$
+def send_email(session, recipient_email, subject, body):
+    try:
+        # Escape single quotes in the body
+        escaped_body = body.replace("'", "''")
+
+        # Execute the system procedure call
+        session.sql(f"""
+            CALL SYSTEM$SEND_EMAIL(
+                'email_integration',
+                '{recipient_email}',
+                '{subject}',
+                '{escaped_body}',
+                'text/html'
+            )
+        """).collect()
+
+        return "Email sent successfully"
+    except Exception as e:
+        return f"Error sending email: {str(e)}"
+$$;
+
+ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'AWS_US';
+
+select 'Congratulations! Snowflake Intelligence setup has completed successfully!' as status;

site/sfguides/src/from-zero-to-agents/from-zero-to-agents.md

@@ -36,11 +36,24 @@ This hands-on lab guides you through transforming raw data into actionable insig
 
 ## Environment and Data Loading
 
-Establish the secure foundation using the provided `setup.sql` script. This script automates the creation of roles, warehouses, and the ingestion of marketing data from public S3 buckets.
+Imagine you are on a marketing team at a fictional retail company selling sports and outdoor gear. The company has plenty of data from campaign performance from paid ads to customer feedback. 
+
+The problem the team faces:
+1. Campaign performance data is siloed from customer feedback
+2. Customer feedback is unstructured and unanalyzed
+3. Analysts can’t self-serve insights from these data
+
+What you’ll build to solve these issues:
+1. Cortex AI turns raw transcripts into structured sentiment scores
+2. Dynamic Tables automate so that data stays current as new feedback arrives
+3. Semantic View bridges campaign metrics and customer feedback into one model
+4. Snowflake Intelligence that allows analysts to ask natural language questions 
+
 
 ### Setup Instructions
+Establish the secure foundation using the provided `setup.sql` script. This script automates the creation of roles, warehouses, and the ingestion of marketing data from public S3 buckets.
 
-Run [setup.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-intelligence/blob/main/setup.sql) in a Snowsight SQL Worksheet.
+Run [setup.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/from-zero-to-agents/assets/setup.sql) in a Snowsight SQL Worksheet.
 
 ### What Gets Provisioned
 
@@ -91,6 +104,8 @@ AI Enrichment is the "Preprocessing" step. By enriching your data first:
 Transform "dark data" (raw transcripts) into analytical trends using Cortex AI Functions. Run sentiment analysis on customer feedback to create measurable features:
 
 ```sql
+USE DATABASE DASH_DB_SI;
+USE SCHEMA RETAIL;
 UPDATE support_cases SET product = 'Fitness Wear' WHERE product = 'ThermoJacket Pro';
 SELECT 
     title,
@@ -107,7 +122,7 @@ Confirm that the role is set to `SNOWFLAKE_INTELLIGENCE_ADMIN` by clicking on yo
 2. Navigate to `Catalog` > `Database Explorer`
 3. Open `DASH_DB_SI.RETAIL.Tables.MARKETING_CAMPAIGN_METRICS`. If you do not see the database, refresh the data.
 4. Click on `Load Data` in the top right hand corner
-5. Upload the marketing_data.csv and click `next` then load
+5. Upload the marketing_data.csv and click `next` then `load`
 6. Click `View table detail` to see the new data uploaded
 
 ![Updating marketing data](assets/updatingmarketing.png)
@@ -128,6 +143,8 @@ Instead of running a one-time enrichment, you define a **Dynamic Table**. This t
 Create a Dynamic Table that automatically joins campaign metrics with AI-generated sentiment scores:
 
 ```sql
+USE DATABASE DASH_DB_SI;
+USE SCHEMA RETAIL;
 CREATE OR REPLACE DYNAMIC TABLE enriched_marketing_intelligence
 TARGET_LAG = '1 hours'
 WAREHOUSE = dash_wh_si
@@ -189,7 +206,7 @@ This tool allows the agent to search and retrieve information from unstructured
    - **Select Data:** `marketing_campaign_metrics`
    - **Select search column:** `campaign name`
    - **Select attribute columns:** `Select all`
-   - **Warehouse for indexing:** `DASH_DB_SI`
+   - **Warehouse for indexing:** `DASH_WH_SI`
 5. Click `create` and click the refresh icon in the top right corner. `Serving` will update from `INITALIZING` to `ACTIVE`
 
 ![Active Cortex Search](assets/servingactive.png)
@@ -321,11 +338,37 @@ For lab participants, the Trace is the most critical UI interaction. It allows y
 
 Apply **Dynamic Data Masking** to specific metrics to ensure the agent respects **RBAC** (Role-Based Access Control).
 
-### Create the Marketing Role
+### Create the Marketing Role and Granting Access
 
-If you ran the `setup.sql` exactly as provided in the GitHub repo, it created `snowflake_intelligence_admin`. Let's create the `marketing_intelligence_role` to represent your "Restricted Team" and give it the necessary access.
+If you ran the `setup.sql` exactly as provided in the GitHub repo, it created `snowflake_intelligence_admin`. Let's create the `marketing_intelligence_role` to represent your "Restricted Team" and give it the necessary access. Be sure to have your role set to `snowflake_intelligence_admin` and warehouse to `DASH_WH_SI`.
 
 ```sql
+USE DATABASE DASH_DB_SI;
+USE SCHEMA RETAIL;
+
+-- Create the Semantic View
+CREATE OR REPLACE SECURE VIEW marketing_intelligence_view AS
+SELECT 
+    campaign_name AS "Ad Campaign",
+    category AS "Product Category",
+    clicks AS "Engagement Clicks",
+    -- avg_sentiment will come from the Dynamic Table
+    0 AS "Customer Sentiment Score" 
+FROM marketing_campaign_metrics;
+
+-- Check which roles have access to your database and schema
+SHOW GRANTS ON DATABASE dash_db_si;
+SHOW GRANTS ON SCHEMA dash_db_si.retail;
+
+-- Check specifically for your Semantic View
+SHOW GRANTS ON VIEW marketing_intelligence_view;
+
+USE ROLE snowflake_intelligence_admin;
+USE WAREHOUSE dash_wh_si;
+
+-- You should see the actual budget/click numbers
+SELECT * FROM marketing_intelligence_view LIMIT 5;
+
 USE ROLE ACCOUNTADMIN;
 
 -- Create the role
@@ -341,6 +384,7 @@ GRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE marketing_intelligence_role;
 GRANT SELECT ON VIEW dash_db_si.retail.marketing_intelligence_view TO ROLE marketing_intelligence_role;
 
 -- Assign to yourself for testing
+SET current_user = CURRENT_USER();
 GRANT ROLE marketing_intelligence_role TO USER IDENTIFIER($CURRENT_USER);
 ```
 
@@ -383,26 +427,25 @@ SELECT
 FROM marketing_campaign_metrics;
 
 USE ROLE snowflake_intelligence_admin;
+USE SCHEMA dash_db_si.retail;
 -- You should see numbers like 11103
 SELECT "Ad Campaign", "Engagement Clicks" FROM marketing_intelligence_view;
 ```
+![Admin Role](assets/engagementclicks.png)
+
 
 ### Verify as Marketing Role
 
 ```sql
 USE ROLE marketing_intelligence_role;
+USE SCHEMA dash_db_si.retail;
 -- You should see 0 for all clicks
 SELECT "Ad Campaign", "Engagement Clicks" FROM marketing_intelligence_view;
 ```
+![Marketing Role](assets/zeroclicks.png)
 
-### Agent Interaction Verification
-
-Now that the data is masked at the source, the **Agent** will automatically respect it.
+By applying Dynamic Data Masking alongside RBAC, you ensured that the data serves different teams with appropriate access — admins see real click data while the marketing role sees masked values.
 
-1. Switch your UI role to `marketing_intelligence_role`
-2. Ask the Agent: *"How many clicks did the Summer Fitness campaign get?"*
-3. The Agent will query the view, receive `0` from the database engine, and reply: *"The Summer Fitness campaign received 0 clicks."*
-4. **Show Reasoning:** Open the trace to prove the SQL was correct, but the **Data Engine** protected the values
 
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