sync: automatic content update from github

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
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-*.xz filter=lfs diff=lfs merge=lfs -text
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AD_LAYOUT_PUBLISHED.json

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+[
+  {
+    "DATABASE_NAME": "ANALYTICS",
+    "TABLE_SCHEMA": "ADTHRIVE",
+    "TABLE_NAME": "AD_LAYOUT_PUBLISHED",
+    "columns": [
+      {
+        "COLUMN_NAME": "SITE_ID",
+        "DATA_TYPE": "VARCHAR"
+      },
+      {
+        "COLUMN_NAME": "LAYOUT",
+        "DATA_TYPE": "VARCHAR"
+      },
+      {
+        "COLUMN_NAME": "CREATED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      },
+      {
+        "COLUMN_NAME": "UPDATED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      }
+    ],
+    "USAGE_COUNT": 86692.0,
+    "Notes": "Includes site Ad Layout published information found within the layout columns"
+  },
+  {
+    "DATABASE_NAME": "ANALYTICS",
+    "TABLE_SCHEMA": "ADTHRIVE",
+    "TABLE_NAME": "ANNOTATION",
+    "columns": [
+      {
+        "COLUMN_NAME": "ID",
+        "DATA_TYPE": "NUMBER"
+      },
+      {
+        "COLUMN_NAME": "DESCRIPTION",
+        "DATA_TYPE": "TEXT"
+      },
+      {
+        "COLUMN_NAME": "DISPLAY_TO_PUB",
+        "DATA_TYPE": "BOOLEAN"
+      },
+      {
+        "COLUMN_NAME": "ENABLED",
+        "DATA_TYPE": "BOOLEAN"
+      },
+      {
+        "COLUMN_NAME": "ANNOTATION_DATE",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      },
+      {
+        "COLUMN_NAME": "CREATED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      },
+      {
+        "COLUMN_NAME": "UPDATED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      },
+      {
+        "COLUMN_NAME": "DELETED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      },
+      {
+        "COLUMN_NAME": "USER_CREATED_ID",
+        "DATA_TYPE": "TEXT"
+      },
+      {
+        "COLUMN_NAME": "ANNOTATION_TYPE_ID",
+        "DATA_TYPE": "NUMBER"
+      },
+      {
+        "COLUMN_NAME": "ANNOTATION_DISTRIBUTION_TYPE_ID",
+        "DATA_TYPE": "NUMBER"
+      },
+      {
+        "COLUMN_NAME": "SOURCE_ID",
+        "DATA_TYPE": "NUMBER"
+      },
+      {
+        "COLUMN_NAME": "NOTE",
+        "DATA_TYPE": "TEXT"
+      },
+      {
+        "COLUMN_NAME": "AIRBYTE_EMITTED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      }
+    ],
+    "USAGE_COUNT": 40950.0,
+    "Notes": "Includes data related site information annotated by internal users, also zendesk ticket urls."
+  },
+  {
+    "DATABASE_NAME": "ANALYTICS",
+    "TABLE_SCHEMA": "ADTHRIVE",
+    "TABLE_NAME": "ANNOTATION_INCREMENTAL",
+    "columns": [
+      {
+        "COLUMN_NAME": "ANNOTATION_DATE",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      },
+      {
+        "COLUMN_NAME": "ANNOTATION_DISTRIBUTION_TYPE_ID",
+        "DATA_TYPE": "NUMBER"
+      },
+      {
+        "COLUMN_NAME": "ANNOTATION_TYPE_ID",
+        "DATA_TYPE": "NUMBER"
+      },
+      {
+        "COLUMN_NAME": "CREATED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      },
+      {
+        "COLUMN_NAME": "CSV_KEY",
+        "DATA_TYPE": "TEXT"
+      },
+      {
+        "COLUMN_NAME": "DELETED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      },
+      {
+        "COLUMN_NAME": "DESCRIPTION",
+        "DATA_TYPE": "TEXT"
+      },
+      {
+        "COLUMN_NAME": "DISPLAY_TO_PUB",
+        "DATA_TYPE": "BOOLEAN"
+      },
+      {
+        "COLUMN_NAME": "ENABLED",
+        "DATA_TYPE": "BOOLEAN"
+      },
+      {
+        "COLUMN_NAME": "ID",
+        "DATA_TYPE": "NUMBER"
+      },
+      {
+        "COLUMN_NAME": "NOTE",
+        "DATA_TYPE": "TEXT"
+      },
+      {
+        "COLUMN_NAME": "SOURCE_ID",
+        "DATA_TYPE": "NUMBER"
+      },
+      {
+        "COLUMN_NAME": "UPDATED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      },
+      {
+        "COLUMN_NAME": "USER_CREATED_ID",
+        "DATA_TYPE": "TEXT"
+      },
+      {
+        "COLUMN_NAME": "SOURCE_NAME",
+        "DATA_TYPE": "TEXT"
+      },
+      {
+        "COLUMN_NAME": "_AIRBYTE_EMITTED_AT",
+        "DATA_TYPE": "TIMESTAMP_NTZ"
+      }
+    ],
+    "USAGE_COUNT": 954608.0,
+    "Notes": "Includes data related site information annotated by internal users, also zendesk ticket urls. This includes further additional information."
+  }
+]

AVI_SITE_DATASET_snowflake_settings.txt

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+## Overview
+This file provides instructions for extracting data from the SITE_EXTENDED_Snowflake table which should use 'ANALYTICS.SIGMA_SCRATCH.VIEW_AVI_SITE_DATASET_FROM_SQL_4D44494CEF294AF093BA92B8C008977A'. 
+For 'like' operator, be sure to use 'like' as opposed to 'Ilike'. 
+If a user is looking for 'Enterprise' or 'Creator' site groups, please use the column 'NEW SERVICE CONDENSED'. Please use 'NEW SERVICE CONDENSED' instead of 'SERVICE' OR 'SERVICE CONDENSED', unless specifically requested by the user.
+If a user is looking for specific URL's, please use the 'like' operator along with '%' on both ends to help locate the correct URL.
+Do not apply a date filter unless specified by the user which date filter they would like to use.
+
+IMPORTANT: All filters on TIMESTAMP columns (e.g., "Install Date (SITE_EXTENDED)") must use string date literals. 
+Do NOT output any numeric literals such as 20250401. The correct format is 'YYYY-MM-DD' (e.g., '2025-04-01').
+
+Correct Usage:
+    AND "Install Date (SITE_EXTENDED)" > '2025-04-01'
+
+Incorrect Usage:
+    AND "Install Date (SITE_EXTENDED)" > 20250401
+
+Rule: Under no circumstances should a TIMESTAMP column filter be written with a numeric literal.
+
+
+## Example Query:
+SELECT
+    "SITE ID",
+    "STATUS",
+    "TIER",
+    ...
+FROM "ANALYTICS"."SIGMA_SCRATCH"."VIEW_AVI_SITE_DATASET_FROM_SQL_4D44494CEF294AF093BA92B8C008977A"
+WHERE
+    "STATUS" = 'Active'
+    -- Make sure to wrap column names in quotes
+    AND "Install Date (SITE_EXTENDED)" >= '2025-04-01'
+
+
+*********************************************
+DATE FILTERING RULES
+*********************************************
+- The "Install Date (SITE_EXTENDED)" column is defined as TIMESTAMP_LTZ(9).
+- **Always** use date or timestamp string literals when filtering this column.
+- Do **not** use numeric values (e.g., 20250401). This causes type conversion errors.
+- Correct Format: Use the format 'YYYY-MM-DD' (or 'YYYY-MM-DD HH24:MI:SS' if a time component is needed).
+*********************************************
+
+
+
+
+## IMPORTANT
+All column names should be wrapper with parenthesis. For example, "TIER".
+
+
+## Example outputs for most common columns used in this table to use as a reference.
+
+{
+    "title": "Example outputs for each column",
+    "column_names": [
+        "TIER",
+        "STATUS",
+        "SERVICE",
+        "SERVICE CONDENSED",
+        "NEW SERVICE CONDENSED",
+        "SERVICE LEVEL",
+        "PRIMARY VERTICAL"
+    ],
+    "columns": {
+        "TIER": [
+            "AdThrive 1",
+            "AdThrive 2",
+            "AdThrive 3",
+            "AdThrive 4",
+            "AdThrive 5",
+            "AdThrive 6",
+            "AdThrive 7",
+            "AdThrive 8"
+        ],
+        "STATUS": [
+            "Active",
+            "Checkup",
+            "Dropped",
+            "Install",
+            "Pending",
+            "Pending Manager Recommendation",
+            "Setup"
+        ],
+        "SERVICE": [
+            "AdThrive Edge",
+            "AdThrive Premium",
+            "Prime"
+        ],
+        "SERVICE CONDENSED": [
+            "AdThrive",
+            "Prime"
+        ],
+        "NEW SERVICE CONDENSED": [
+            "Creator",
+            "Enterprise"
+        ],
+        "SERVICE LEVEL": [
+            "Enterprise",
+            "Insider",
+            "Luminary",
+            "Platinum",
+            "Platinum Elite",
+            "Rise"
+        ],
+        "PRIMARY VERTICAL": [
+            "Arts & Creativity",
+            "Auto",
+            "Baby",
+            "Beauty",
+            "Business",
+            "Careers",
+            "Clean Eating",
+            "Crafts",
+            "Deals",
+            "Education",
+            "Entertainment",
+            "Family and Parenting",
+            "Fitness",
+            "Food",
+            "Gaming",
+            "Gardening",
+            "Green Living",
+            "Health and Wellness",
+            "History & Culture",
+            "Hobbies & Interests",
+            "Home Decor and Design",
+            "Law, Gov't & Politics",
+            "Lifestyle",
+            "Mens Style and Grooming",
+            "Natural Parenting",
+            "News",
+            "Other",
+            "Personal Finance",
+            "Pets",
+            "Pregnancy",
+            "Professional Finance",
+            "Real Estate",
+            "Religion & Spirituality",
+            "Science",
+            "Shopping",
+            "Sports",
+            "Tech",
+            "Toddler",
+            "Travel",
+            "Vegetarian",
+            "Wedding",
+            "Womens Style"
+        ]
+    }
+}

Annotation.json

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+{"database":"ANALYTICS","schema":"ADTHRIVE","table":"ANNOTATION","metadata":[{"column_name":"UPDATED_AT","data_type":"TIMESTAMP_NTZ"},{"column_name":"USER_CREATED_ID","data_type":"TEXT"},{"column_name":"ID","data_type":"NUMBER"},{"column_name":"CREATED_AT","data_type":"TIMESTAMP_NTZ"},{"column_name":"ANNOTATION_DATE","data_type":"TIMESTAMP_NTZ"},{"column_name":"ANNOTATION_DISTRIBUTION_TYPE_ID","data_type":"NUMBER"},{"column_name":"NOTE","data_type":"TEXT"},{"column_name":"ANNOTATION_TYPE_ID","data_type":"NUMBER"},{"column_name":"DELETED_AT","data_type":"TIMESTAMP_NTZ"},{"column_name":"ENABLED","data_type":"BOOLEAN"},{"column_name":"AIRBYTE_EMITTED_AT","data_type":"TIMESTAMP_NTZ"},{"column_name":"DESCRIPTION","data_type":"TEXT"},{"column_name":"SOURCE_ID","data_type":"NUMBER"},{"column_name":"DISPLAY_TO_PUB","data_type":"BOOLEAN"}],"Sample Output Date":[{"AIRBYTE_EMITTED_AT":"2025-03-28 04:03:34.440","ANNOTATION_DATE":"2025-03-22 14:03:17.194","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2025-03-22 14:03:17.250","DESCRIPTION":"Ad Layout Adjustment","DISPLAY_TO_PUB":false,"ENABLED":true,"ID":29060,"NOTE":"Adjusted Content ad selectors to ensure ads appear on all posts: https://raptive.zendesk.com/agent/tickets/393402","SOURCE_ID":4,"UPDATED_AT":"2025-03-22 14:03:17.250","USER_CREATED_ID":"000000000000000000011451"},{"AIRBYTE_EMITTED_AT":"2025-03-28 04:03:34.440","ANNOTATION_DATE":"2025-03-22 17:42:22.060","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2025-03-22 17:42:22.334","DESCRIPTION":"Set Ad In View Time - 2","DISPLAY_TO_PUB":false,"ENABLED":true,"ID":29066,"NOTE":"iOS refresh - step 1","SOURCE_ID":4,"UPDATED_AT":"2025-03-22 17:42:22.334","USER_CREATED_ID":"000000000000000000007867"},{"AIRBYTE_EMITTED_AT":"2025-03-28 04:03:34.440","ANNOTATION_DATE":"2025-03-22 14:38:20.007","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2025-03-22 14:38:20.082","DESCRIPTION":"Ad Layout Adjustment","DISPLAY_TO_PUB":false,"ENABLED":true,"ID":29061,"NOTE":"Added selector to Content ads for ads to populate after paragraphs within full width columns: https://raptive.zendesk.com/agent/tickets/393402","SOURCE_ID":4,"UPDATED_AT":"2025-03-22 14:38:20.082","USER_CREATED_ID":"000000000000000000011451"},{"AIRBYTE_EMITTED_AT":"2025-03-28 04:03:34.440","ANNOTATION_DATE":"2025-03-22 18:28:49.519","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2025-03-22 18:28:49.517","DESCRIPTION":"Ad Preference updated by FPL Focal","DISPLAY_TO_PUB":true,"ENABLED":true,"ID":29067,"NOTE":"sponsorTileDesktop changed to on; \nsponsorTileMobile changed to on; \nwakeLock changed to on; \nremove_footerCloseButtonDesktop changed to on; \nremove_footerCloseButtonMobile changed to on; \ncustomCreativeEnabled changed to on","SOURCE_ID":2,"UPDATED_AT":"2025-03-22 18:28:49.517","USER_CREATED_ID":"000000000000000000012579"},{"AIRBYTE_EMITTED_AT":"2025-03-28 04:03:34.440","ANNOTATION_DATE":"2025-03-22 18:28:58.798","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2025-03-22 18:28:58.796","DESCRIPTION":"Ad Preference updated by FPL Focal","DISPLAY_TO_PUB":true,"ENABLED":true,"ID":29068,"NOTE":"wakeLock changed to on; \nremoveVideoTitleWrapper changed to on; \noptimizeVideoPlayersForEarnings changed to on; \nadvancedPlaylist.relatedPlayer changed to on; \nstickyOutstream.desktop changed to on; \nstickyOutstream.mobile changed to on","SOURCE_ID":2,"UPDATED_AT":"2025-03-22 18:28:58.796","USER_CREATED_ID":"000000000000000000012579"}]}

Annotation_incremental.json

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+{"database":"ANALYTICS","schema":"ADTHRIVE","table":"ANNOTATION_INCREMENTAL","metadata":[{"column_name":"DELETED_AT","data_type":"TIMESTAMP_NTZ"},{"column_name":"DISPLAY_TO_PUB","data_type":"BOOLEAN"},{"column_name":"UPDATED_AT","data_type":"TIMESTAMP_NTZ"},{"column_name":"SOURCE_ID","data_type":"NUMBER"},{"column_name":"_AIRBYTE_EMITTED_AT","data_type":"TIMESTAMP_NTZ"},{"column_name":"ANNOTATION_DISTRIBUTION_TYPE_ID","data_type":"NUMBER"},{"column_name":"ANNOTATION_TYPE_ID","data_type":"NUMBER"},{"column_name":"ANNOTATION_DATE","data_type":"TIMESTAMP_NTZ"},{"column_name":"DESCRIPTION","data_type":"TEXT"},{"column_name":"CREATED_AT","data_type":"TIMESTAMP_NTZ"},{"column_name":"USER_CREATED_ID","data_type":"TEXT"},{"column_name":"ENABLED","data_type":"BOOLEAN"},{"column_name":"CSV_KEY","data_type":"TEXT"},{"column_name":"SOURCE_NAME","data_type":"TEXT"},{"column_name":"NOTE","data_type":"TEXT"},{"column_name":"ID","data_type":"NUMBER"}],"Sample Output Date":[{"ANNOTATION_DATE":"2024-07-10 18:04:13.655","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2024-07-10 18:04:13.982","DESCRIPTION":"Follow Up Check","DISPLAY_TO_PUB":false,"ENABLED":true,"ID":14894,"NOTE":"Adjusted the video player element selectors to exclude social share elements and appearing higher in posts before their \"Save This Recipe\" container: https://raptive.zendesk.com/agent/tickets/346106 ","SOURCE_ID":4,"SOURCE_NAME":"Creator Request","UPDATED_AT":"2024-07-10 18:04:13.982","USER_CREATED_ID":"000000000000000000008245","_AIRBYTE_EMITTED_AT":"2024-07-10 19:00:35.374"},{"ANNOTATION_DATE":"2024-07-10 18:13:50.023","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2024-07-10 18:13:50.512","DESCRIPTION":"Follow Up Check","DISPLAY_TO_PUB":false,"ENABLED":true,"ID":14895,"NOTE":"Changed ad density on desktop from .28 to .24, on mobile from .28 to .26 | Reduced BP ad max from 99 to 10 on all devices","SOURCE_ID":3,"SOURCE_NAME":"Ad Strategy Review","UPDATED_AT":"2024-07-10 18:13:50.512","USER_CREATED_ID":"000000000000000000006623","_AIRBYTE_EMITTED_AT":"2024-07-10 19:00:35.374"},{"ANNOTATION_DATE":"2024-07-10 18:14:22.994","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2024-07-10 18:14:23.301","DESCRIPTION":"Follow Up Check","DISPLAY_TO_PUB":false,"ENABLED":true,"ID":14896,"NOTE":"Enabled outstream videon on the homepage","SOURCE_ID":3,"SOURCE_NAME":"Ad Strategy Review","UPDATED_AT":"2024-07-10 18:14:23.301","USER_CREATED_ID":"000000000000000000006623","_AIRBYTE_EMITTED_AT":"2024-07-10 19:00:35.374"},{"ANNOTATION_DATE":"2024-07-10 18:29:12.515","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2024-07-10 18:29:12.695","DESCRIPTION":"Follow-up check","DISPLAY_TO_PUB":false,"ENABLED":true,"ID":14897,"NOTE":"Dropped BP max from 99 to 10; adjusted CSS on SB9 so that SB ads load closer to top of screen, but low enough that Slickstream does not cover when scrolling up on page","SOURCE_ID":3,"SOURCE_NAME":"Ad Strategy Review","UPDATED_AT":"2024-07-10 18:29:12.695","USER_CREATED_ID":"000000000000000000011175","_AIRBYTE_EMITTED_AT":"2024-07-10 19:00:35.374"},{"ANNOTATION_DATE":"2024-07-10 18:32:34.781","ANNOTATION_DISTRIBUTION_TYPE_ID":4,"ANNOTATION_TYPE_ID":1,"CREATED_AT":"2024-07-10 18:32:35.015","DESCRIPTION":"Set up a test for higher content ad spacing on article pages","DISPLAY_TO_PUB":false,"ENABLED":true,"ID":14898,"NOTE":"","SOURCE_ID":4,"SOURCE_NAME":"Creator Request","UPDATED_AT":"2024-07-10 18:32:35.015","USER_CREATED_ID":"5e00f363d9dfce0459be7d4a","_AIRBYTE_EMITTED_AT":"2024-07-10 19:00:35.374"}]}

Earnings_and_Analytics_by_Day.json

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+{"database":"ANALYTICS","schema":"SIGMA_SCRATCH","table":"VIEW_EARNINGS_AND_ANALYTICS_BY_DAY_D19312C8622E487D891B72420987C00B_MAT","metadata":[{"column_name":"Site Name (SITE_EXTENDED)","data_type":"TEXT"},{"column_name":"Dropped Reason Id (SITE_EXTENDED)","data_type":"NUMBER"},{"column_name":"SESSIONS","data_type":"NUMBER"},{"column_name":"GROSS EARNINGS","data_type":"FLOAT"},{"column_name":"PUB NET RPS","data_type":"FLOAT"},{"column_name":"Vertical Rollup (SITE_EXTENDED)","data_type":"TEXT"},{"column_name":"PUB NET CPM","data_type":"FLOAT"},{"column_name":"Platinum Level Name Rollup","data_type":"TEXT"},{"column_name":"isLeapYear","data_type":"NUMBER"},{"column_name":"IMP/PV","data_type":"FLOAT"},{"column_name":"AT NET EARNINGS","data_type":"FLOAT"},{"column_name":"Is Ga 4","data_type":"BOOLEAN"},{"column_name":"GROSS RPM","data_type":"FLOAT"},{"column_name":"SITE ID","data_type":"TEXT"},{"column_name":"CLICKS","data_type":"FLOAT"},{"column_name":"ADJ TIME ON PAGE","data_type":"NUMBER"},{"column_name":"PUB NET RPM","data_type":"FLOAT"},{"column_name":"SESSION DURATION TOTAL","data_type":"NUMBER"},{"column_name":"Is Strategic Account?","data_type":"BOOLEAN"},{"column_name":"PAGEVIEWS","data_type":"NUMBER"},{"column_name":"ADJ TOP","data_type":"TIMESTAMP_LTZ"},{"column_name":"Start Date (SITE_EXTENDED)","data_type":"TIMESTAMP_LTZ"},{"column_name":"Organization Id","data_type":"TEXT"},{"column_name":"Latest Date","data_type":"TIMESTAMP_LTZ"},{"column_name":"TIME ON PAGE TOTAL","data_type":"NUMBER"},{"column_name":"USERS","data_type":"NUMBER"},{"column_name":"Rev Share (SITE_EXTENDED)","data_type":"NUMBER"},{"column_name":"Service Level","data_type":"TEXT"},{"column_name":"Primary Vertical (SITE_EXTENDED)","data_type":"TEXT"},{"column_name":"DATE","data_type":"TIMESTAMP_LTZ"},{"column_name":"GROSS CPM","data_type":"FLOAT"},{"column_name":"CAM CALC PUB NET EARNINGS","data_type":"FLOAT"},{"column_name":"Days 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Helpful for viewing pageview data by device and general site performance metrics.","sample_data":[{"DATE":"2025-03-27","SITE ID":"12345","DEVICE":"Desktop","GROSS EARNINGS":1000.5,"PUB NET EARNINGS":800.4,"AT NET EARNINGS":750.3,"CAM CALC PUB NET EARNINGS":790.2,"CAM Net Earnings":760.1,"IMPRESSIONS":100000,"VIEWABLE IMPRESSIONS":95000,"MEASURABLE IMPRESSIONS":92000,"UNFILLED IMPRESSIONS":5000,"IMPRESSION OPPORTUNITY":105000,"PAGEVIEWS":85000,"PUB NET CPM":8.5,"GROSS CPM":10.5,"GROSS ECPM":9,"PUB NET RPM":7.5,"PUB NET RPS":0.75,"UNIQUE PAGEVIEWS":80000,"GROSS RPM":8,"GROSS RPS":0.8,"IMP/PV":"1.18","IMP OPP/PV":"1.24","EXITS":300,"SESSIONS":5000,"SESSION DURATION TOTAL":20000,"SESSION_DURATION_SECONDS":"240","BOUNCES":1500,"TIME ON PAGE TOTAL":18000,"TIME ON PAGE RAW":17500,"TIME ON PAGE":200,"TOP":"Yes","PPS":4.5,"VIEWABILITY":95,"USERS":4500,"Pageviews Projected":90000,"Impressions Projected":110000,"Impressions Opp Projected":115000,"Gross Earnings Projected":1050.75,"AT Net Earnings Projected":780.65,"isLeapYear":"No","Site Name (SITE_EXTENDED)":"Example Site","Url (SITE_EXTENDED)":"https://www.example.com","Start Date (SITE_EXTENDED)":20250101,"Install Date (SITE_EXTENDED)":20250115,"Rev Share (SITE_EXTENDED)":75,"Rev Share Video (SITE_EXTENDED)":70,"Rev Share in Image (SITE_EXTENDED)":65,"Status (SITE_EXTENDED)":"Active","Video Embed (SITE_EXTENDED)":"Embedded","Tier (SITE_EXTENDED)":"AdThrive 1","Video Default Player Type (SITE_EXTENDED)":"static","Dropped Reason Id (SITE_EXTENDED)":null,"Company Name (SITE_EXTENDED)":"Example Company","Primary Vertical (SITE_EXTENDED)":"Business","Verticals (SITE_EXTENDED)":"Business","Vertical Rollup (SITE_EXTENDED)":"Business","Site Service (SITE_EXTENDED)":"AdThrive Edge","Creator Partnerships Manager (SITE_ACCOUNT_MANAGER)":"Manager Name","Latest Date":20250327,"Days in month":31,"Days elapsed":15,"JW":true,"Layout (AD_LAYOUT_PUBLISHED)":{"layout":"default"},"JW Collapsible Player ID":"player123","JW PLAYER ID":"jwplayer456"},{"DATE":"2025-03-26","SITE ID":"67890","DEVICE":"Mobile","GROSS EARNINGS":800.75,"PUB NET EARNINGS":640.6,"AT NET EARNINGS":600.55,"CAM CALC PUB NET EARNINGS":650.45,"CAM Net Earnings":630.35,"IMPRESSIONS":85000,"VIEWABLE IMPRESSIONS":80000,"MEASURABLE IMPRESSIONS":78000,"UNFILLED IMPRESSIONS":4000,"IMPRESSION OPPORTUNITY":89000,"PAGEVIEWS":75000,"PUB NET CPM":7.5,"GROSS CPM":9,"GROSS ECPM":8,"PUB NET RPM":6.5,"PUB NET RPS":0.65,"UNIQUE PAGEVIEWS":70000,"GROSS RPM":7,"GROSS RPS":0.7,"IMP/PV":"1.13","IMP OPP/PV":"1.19","EXITS":250,"SESSIONS":4500,"SESSION DURATION TOTAL":18000,"SESSION_DURATION_SECONDS":"220","BOUNCES":1300,"TIME ON PAGE TOTAL":16000,"TIME ON PAGE RAW":15500,"TIME ON PAGE":190,"TOP":"No","PPS":4,"VIEWABILITY":93,"USERS":4000,"Pageviews Projected":80000,"Impressions Projected":95000,"Impressions Opp Projected":99000,"Gross Earnings Projected":850.65,"AT Net Earnings Projected":620.45,"isLeapYear":"No","Site Name (SITE_EXTENDED)":"Another Example Site","Url (SITE_EXTENDED)":"https://www.anotherexample.com","Start Date (SITE_EXTENDED)":20250105,"Install Date (SITE_EXTENDED)":20250120,"Rev Share (SITE_EXTENDED)":70,"Rev Share Video (SITE_EXTENDED)":68,"Rev Share in Image (SITE_EXTENDED)":62,"Status (SITE_EXTENDED)":"Active","Video Embed (SITE_EXTENDED)":"Embedded","Tier (SITE_EXTENDED)":"AdThrive 2","Video Default Player Type (SITE_EXTENDED)":"responsive","Dropped Reason Id (SITE_EXTENDED)":null,"Company Name (SITE_EXTENDED)":"Another Example Company","Primary Vertical (SITE_EXTENDED)":"Entertainment","Verticals (SITE_EXTENDED)":"Entertainment","Vertical Rollup (SITE_EXTENDED)":"Entertainment","Site Service (SITE_EXTENDED)":"AdThrive Premium","Creator Partnerships Manager (SITE_ACCOUNT_MANAGER)":"Another Manager","Latest Date":20250326,"Days in month":30,"Days elapsed":14,"JW":false,"Layout (AD_LAYOUT_PUBLISHED)":{"layout":"compact"},"JW Collapsible Player ID":"player789","JW PLAYER ID":"jwplayer101112"}]}

Earnings_by_Ad_Network.json

@@ -0,0 +1 @@
+{"database":"ANALYTICS","schema":"SIGMA_SCRATCH","table":"VIEW_EARNINGS_BY_ADNETWORK_55601956261D42A49E72EBDDDE0082C5_MAT","metadata":[{"column_name":"pmp","data_type":"BOOLEAN"},{"column_name":"DEVICE","data_type":"TEXT"},{"column_name":"Tier (SITE_EXTENDED)","data_type":"TEXT"},{"column_name":"Primary Vertical (SITE_EXTENDED)","data_type":"TEXT"},{"column_name":"Install Date","data_type":"TEXT"},{"column_name":"IMPRESSION OPPORTUNITIES","data_type":"FLOAT"},{"column_name":"AD NETWORK","data_type":"TEXT"},{"column_name":"Jw (SITE_EXTENDED)","data_type":"BOOLEAN"},{"column_name":"AD NETWORK ID","data_type":"TEXT"},{"column_name":"Rev Share (SITE_EXTENDED)","data_type":"NUMBER"},{"column_name":"JW Collapsible Player ID","data_type":"TEXT"},{"column_name":"ELIGIBLE IMPRESSIONS","data_type":"NUMBER"},{"column_name":"DATE","data_type":"TIMESTAMP_LTZ"},{"column_name":"Site Id (SITE_EXTENDED)","data_type":"TEXT"},{"column_name":"UNFILLED IMPRESSIONS","data_type":"FLOAT"},{"column_name":"Url 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(SITE_EXTENDED)":"http://www.therealfarmhouse.com/","VIEWABLE IMPRESSIONS":1.300000000000000e+02,"pmp":true}]}

GAMLOG.json

@@ -0,0 +1,1018 @@
+{
+  "database": "ANALYTICS",
+  "schema": "GAM360",
+  "table": "GAMLOG",
+  "metadata": [
+    {
+      "column_name": "TIME",
+      "data_type": "TIMESTAMP_NTZ"
+    },
+    {
+      "column_name": "TIMEUSEC2",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "KEYPART",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['ccpaoptout'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['connection']::ARRAY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ctmap['doba']::ARRAY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ctmap['domain'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['fledge'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['gptv'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['hb_rpb'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['hi_au']::ARRAY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ctmap['id_region'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['idho_p']::ARRAY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ctmap['idp']::ARRAY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ctmap['imav'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['marmalade'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['mcmp']::ARRAY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ctmap['osb'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['ri'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['rsi'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['sens']::ARRAY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ctmap['sitename'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['st'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['verticals']::ARRAY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ctmap['voba']::ARRAY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ctmap['amz_bidder'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['amz_pb'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['amznbid'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['atf'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['hb_adid'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['hb_adomain'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['hb_format'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['hb_size'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['hb_source'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['id'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['location'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ctmap['sequence'][0]::VARCHAR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ISFILLEDREQUEST",
+      "data_type": "BOOLEAN"
+    },
+    {
+      "column_name": "REQUESTEDADUNITSIZES",
+      "data_type": "VARIANT"
+    },
+    {
+      "column_name": "MOBILEDEVICE",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "OSVERSION",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "MOBILECAPABILITY",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "MOBILECARRIER",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "SERVINGRESTRICTION",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "PUBLISHERPROVIDEDID",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ISCOMPANION",
+      "data_type": "BOOLEAN"
+    },
+    {
+      "column_name": "VIDEOPOSITION",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "PODPOSITION",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "DEVICECATEGORY",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ISINTERSTITIAL",
+      "data_type": "BOOLEAN"
+    },
+    {
+      "column_name": "USERID",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "REFERERURL",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "REQUESTLANGUAGE",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ADUNITID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "TOPADUNITID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "TOPADUNITNAME",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ADUNITNAME",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "COUNTRY",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "REGION",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "BROWSER",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "OS",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "METRO",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "POSTALCODE",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "BANDWIDTH",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "GFPCONTENTID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "ADVERTISERID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "CREATIVESIZE",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "CREATIVEID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "LINEITEMID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "LINEITEMNAME",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ORDERID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "ORDERNAME",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "CREATIVESIZEDELIVERED",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "CREATIVEVERSION",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "PRODUCT",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "TARGETEDCUSTOMCRITERIA",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "DEALID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "DEALTYPE",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ESTIMATEDBACKFILLREVENUE",
+      "data_type": "FLOAT"
+    },
+    {
+      "column_name": "IMPRESSIONID",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "YIELDGROUPCOMPANYID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "YIELDGROUPNAMES",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ADVERTISER",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "BUYER",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "ADXACCOUNTID",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "VIDEOFALLBACKPOSITION",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "MEASURABLE",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "VIEWABLE",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "CODESERVES",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "REVENUE",
+      "data_type": "FLOAT"
+    },
+    {
+      "column_name": "IMPRESSIONS",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "PVK",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "SESS",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "SITEID",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "BUCKET",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "DATE",
+      "data_type": "DATE"
+    },
+    {
+      "column_name": "HOUR",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "AMP",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "ABGROUP",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "LAZY",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "DEPLOYMENT",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "REFRESH",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "PLUGIN",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "FLAG",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "FLAG_SLOT",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "HBHO",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "VPWXVPH",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "VBHO",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "FPV",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "VPRENUM",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "NREF",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "DOC_REF",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "SITE_CODE",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "BRANCH",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "UTM_CAMPAIGN",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "UTM_MEDIUM",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "UTM_SOURCE",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "AT_CUSTOM_1",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "HB_PB",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "HB_BIDDER",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "UTC_TIMESTAMP",
+      "data_type": "TIMESTAMP_NTZ"
+    },
+    {
+      "column_name": "UTC_DATE_HOUR",
+      "data_type": "TIMESTAMP_NTZ"
+    },
+    {
+      "column_name": "SOURCE_DATE_HOUR",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "CHILDNETWORKCODE",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "SELLERRESERVEPRICE",
+      "data_type": "FLOAT"
+    },
+    {
+      "column_name": "AUDIENCESEGMENTIDS",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "USERIDENTIFIERSTATUS",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "PPIDPRESENCE",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "VASTERROR",
+      "data_type": "BOOLEAN"
+    },
+    {
+      "column_name": "VASTERRORNAMES",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "CLICK",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "BACKFILLKEYPART",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "PROTECTEDAUDIENCEAPIDELIVERY",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "HB_DSP",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "HB_CRID",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "EVENTTIMEUSEC2",
+      "data_type": "NUMBER"
+    },
+    {
+      "column_name": "VERTICALS",
+      "data_type": "ARRAY"
+    },
+    {
+      "column_name": "YIELDCOMPANYNAMES",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "NATIVEFORMAT",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "NATIVESTYLE",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "PROTECTEDAUDIENCESELLER",
+      "data_type": "VARCHAR"
+    },
+    {
+      "column_name": "HB_DSP_NAME",
+      "data_type": "VARCHAR"
+    }
+  ],
+  "Sample Output Date": [
+    {
+        "TIME":"10:31.0",
+        "TIMEUSEC2":1740030000000000.0,
+        "KEYPART":"0RJg4IEjeyKt-JaG1s3-",
+        "CTMAP":"{\n  \"ccpaoptout\": [\n    \"0\"\n  ],\n  \"connection\": [\n    \"niet:4g\",\n    \"dl:10\"\n  ],\n  \"doba\": [\n    \"ix\",\n    \"mn\",\n    \"ox\",\n    \"un\",\n    \"ma\",\n    \"sh\",\n    \"st\",\n    \"sb\",\n    \"tl\",\n    \"mg\",\n    \"xr\",\n    \"ax\",\n    \"ch\",\n    \"cr\",\n    \"gg\",\n    \"kg\",\n    \"td\",\n    \"ym\"\n  ],\n  \"domain\": [\n    \"www.indianhealthyrecipes.com\"\n  ],\n  \"fledge\": [\n    \"true\"\n  ],\n  \"gptv\": [\n    \"202502130101\"\n  ],\n  \"hb_rpb\": [\n    \"00.00_00.04\"\n  ],\n  \"hi_au\": [\n    \"od\",\n    \"om\"\n  ],\n  \"id_region\": [\n    \"o\"\n  ],\n  \"idho_p\": [\n    \"id5id:10\",\n    \"identitylink:1\",\n    \"pairid:1\",\n    \"unifiedid:1\",\n    \"unifiedid2:1\",\n    \"sharedid:1\",\n    \"criteo:5\",\n    \"connectid:5\",\n    \"fabrickid:95\",\n    \"ftrackid:95\",\n    \"33acrossid:5\",\n    \"liveintentid:1\",\n    \"linkedinadsid:10\",\n    \"lotamepanoramaid:5\",\n    \"merkleid:25\",\n    \"merkuryid:5\"\n  ],\n  \"idp\": [\n    \"i:mrk\",\n    \"i:liid\",\n    \"i:liaid\",\n    \"i:mgncrbn\",\n    \"shl\",\n    \"uid1\",\n    \"id5-d\",\n    \"ftr\",\n    \"lpid\"\n  ],\n  \"imav\": [\n    \"3.686.0\"\n  ],\n  \"marmalade\": [\n    \"true\"\n  ],\n  \"mcmp\": [\n    \"ba_krgkw624\",\n    \"ba_dexc924\",\n    \"iabc_216\",\n    \"ba_dexc1024\",\n    \"ba_aplnkwy625\",\n    \"ba_aplnkwy825\",\n    \"iabc_47\",\n    \"iabc_217\",\n    \"ba_krgkwt\",\n    \"iabc_42\",\n    \"ba_wskw20\",\n    \"nr_rcp24\",\n    \"iabc_210\",\n    \"iabc_214\",\n    \"ba_dexc1324\",\n    \"ba_dexc1124\",\n    \"ba_dexc1224\",\n    \"ba_atkw824\",\n    \"ba_krgkw1224\",\n    \"ba_atkw1124\",\n    \"ba_mskw3\",\n    \"iaba_368\",\n    \"iaba_380\",\n    \"iaba_372\",\n    \"iaba_258\",\n    \"iaba_371\"\n  ],\n  \"osb\": [\n    \"wc\"\n  ],\n  \"ri\": [\n    \"28\"\n  ],\n  \"rsi\": [\n    \"21\"\n  ],\n  \"sens\": [\n    \"alc\",\n    \"ast\",\n    \"cbd\",\n    \"cosm\",\n    \"gamc\",\n    \"gamv\",\n    \"pol\",\n    \"rel\",\n    \"ssr\",\n    \"srh\",\n    \"ske\",\n    \"tob\",\n    \"wtl\"\n  ],\n  \"sitename\": [\n    \"swasthis recipes\"\n  ],\n  \"st\": [\n    \"80\"\n  ],\n  \"verticals\": [\n    \"food\"\n  ],\n  \"voba\": [\n    \"ax\",\n    \"ix\",\n    \"mn\",\n    \"ox\",\n    \"pu\",\n    \"un\",\n    \"ma\",\n    \"sx\",\n    \"tl\",\n    \"sh\",\n    \"mg\",\n    \"xr\",\n    \"gg\",\n    \"ot\",\n    \"sv\"\n  ]\n}",
+        "ISFILLEDREQUEST":false,
+        "REQUESTEDADUNITSIZES":"[\n  \"640x360\"\n]",
+        "MOBILEDEVICE":null,
+        "OSVERSION":null,
+        "MOBILECAPABILITY":null,
+        "MOBILECARRIER":null,
+        "SERVINGRESTRICTION":"Personalization disabled",
+        "PUBLISHERPROVIDEDID":null,
+        "ISCOMPANION":false,
+        "VIDEOPOSITION":1,
+        "PODPOSITION":null,
+        "DEVICECATEGORY":"Desktop",
+        "ISINTERSTITIAL":false,
+        "USERID":null,
+        "REFERERURL":"https:\/\/www.indianhealthyrecipes.com\/bhindi-fry\/",
+        "REQUESTLANGUAGE":"en",
+        "ADUNITID":23268655103,
+        "TOPADUNITID":74522216,
+        "TOPADUNITNAME":"AdThrive_Video_Collapse_Autoplay_SoundOff",
+        "ADUNITNAME":"Swasthis Recipes",
+        "COUNTRY":"India",
+        "REGION":"Maharashtra",
+        "BROWSER":"Google Chrome Any.Any",
+        "OS":"Microsoft Windows Unknown Version",
+        "METRO":null,
+        "POSTALCODE":400076.0,
+        "BANDWIDTH":"?",
+        "GFPCONTENTID":0,
+        "ADVERTISERID":null,
+        "CREATIVESIZE":null,
+        "CREATIVEID":null,
+        "LINEITEMID":null,
+        "LINEITEMNAME":null,
+        "ORDERID":null,
+        "ORDERNAME":null,
+        "CREATIVESIZEDELIVERED":null,
+        "CREATIVEVERSION":null,
+        "PRODUCT":null,
+        "TARGETEDCUSTOMCRITERIA":null,
+        "DEALID":null,
+        "DEALTYPE":null,
+        "ESTIMATEDBACKFILLREVENUE":null,
+        "IMPRESSIONID":null,
+        "YIELDGROUPCOMPANYID":null,
+        "YIELDGROUPNAMES":null,
+        "ADVERTISER":null,
+        "BUYER":null,
+        "ADXACCOUNTID":null,
+        "VIDEOFALLBACKPOSITION":null,
+        "MEASURABLE":null,
+        "VIEWABLE":0,
+        "CODESERVES":0,
+        "REVENUE":null,
+        "IMPRESSIONS":0,
+        "PVK":"01jmgzfsfy7q872fmcpmy0vwvb",
+        "SESS":"01jmgzfsgkk0t8nz1zzw6ez76y",
+        "SITEID":"67521a6689008c5887093adb",
+        "BUCKET":"flex-40",
+        "DATE":"2\/20\/2025",
+        "HOUR":1,
+        "AMP":null,
+        "ABGROUP":"[\n  \"echde2:400@20\",\n  \"iisw:0@25\",\n  \"mto:2@25\",\n  \"flrml1:fbrap@98\",\n  \"fto:1.5@25\",\n  \"athmb:none@0\",\n  \"atd2:@99.5\",\n  \"amzfs:1500@20\",\n  \"hbho2:grid@10\",\n  \"hbho2:adform@95\",\n  \"allbho:none@0\",\n  \"hbhorise:on@80\",\n  \"idho:fabrickid\",\n  \"idho:ftrackid\",\n  \"pbs2s:none\",\n  \"adt3:600@33\",\n  \"s2sparam:on@99\",\n  \"multirefaucd3:20sf1@33\",\n  \"multirefaucv2:off@96\",\n  \"criteodr:both@95\",\n  \"krgodr:client@99\",\n  \"shrdr:client@2.5\",\n  \"yahoodr:server@50\",\n  \"mr4:on@90\",\n  \"pbfs:2000@20\",\n  \"forex:off@95\",\n  \"pbrqt:50@10\",\n  \"rrb:off@90\",\n  \"mstm:off@50\",\n  \"bps:off@50\",\n  \"r_dr:4@20\",\n  \"rfsi:1.1@20\",\n  \"rew:off@50\",\n  \"r_infmlt2:off@25\",\n  \"r_shpmlt:nobid@10\",\n  \"msa:2@50\",\n  \"sca:3@20\",\n  \"mfb:2@33\",\n  \"bsa:round@33\",\n  \"tm:on@50\",\n  \"tal:on@50\",\n  \"bfc:1@33\",\n  \"rffd:on@50\",\n  \"itm:off@50\",\n  \"rrpbs50:west@50\",\n  \"apprqb:rubi_ss@99\",\n  \"dfdb:33_1@80\",\n  \"dfdb:af_1@80\",\n  \"dfdb:an_1@80\",\n  \"dfdb:co_1@80\",\n  \"dfdb:cv_1@80\",\n  \"dfdb:cr_1@80\",\n  \"dfdb:fl_1@80\",\n  \"dfdb:gv_1@80\",\n  \"dfdb:gg_1@80\",\n  \"dfdb:id_1@80\",\n  \"dfdb:ix_1@80\",\n  \"dfdb:kg_0.8@10\",\n  \"dfdb:nt_1.2@10\",\n  \"dfdb:ox_1@80\",\n  \"dfdb:oz_1@80\",\n  \"dfdb:pm_1.2@10\",\n  \"dfdb:rd_1@80\",\n  \"dfdb:ri_1@80\",\n  \"dfdb:rh_1.2@10\",\n  \"dfdb:rb_1@80\",\n  \"dfdb:sh_1@80\",\n  \"dfdb:te_1@80\",\n  \"dfdb:tl_1@80\",\n  \"dfdb:td_1@80\",\n  \"dfdb:ut_1@80\",\n  \"dfdb:ur_1@80\",\n  \"dfdb:yh_1@80\",\n  \"dfdb:ym_1@80\",\n  \"ito_df:2200@50\",\n  \"tkho_2:ttdt@1\",\n  \"vbho2:adform@95\",\n  \"vbhorise:on@80\",\n  \"socbde:0s@85\",\n  \"vasttoisv:15s@60\",\n  \"vasttoosv:3s@80\",\n  \"proto:all@55\",\n  \"pbm:on\",\n  \"addur:off@97\",\n  \"adlifecyc:off@90\",\n  \"reqto:20@20\",\n  \"osp:2@97\",\n  \"rpslt:35@20\",\n  \"dofre:off@99\",\n  \"imabr:500@95\",\n  \"rpbbl:50@20\",\n  \"rpmbl:20@20\",\n  \"rpmbs:60@20\",\n  \"rpmmbl:100@20\",\n  \"rpfbt:60@20\",\n  \"mmbr:on@50\",\n  \"rpep:off@50\",\n  \"rplvto:8000@20\",\n  \"icft:2@25\",\n  \"ocft:3@20\",\n  \"jwpv:8.30.0@99\",\n  \"smhd100:on\",\n  \"tckscroll2:5@90\",\n  \"tckscroll2:7@90\",\n  \"tckscroll2:10@90\",\n  \"intcfg:on@90\",\n  \"scre:on\",\n  \"so2:rv@25\",\n  \"bwh:sm1@20\",\n  \"csync_lim:25@25\",\n  \"s2sto:1250@45\",\n  \"jwb:on@95\",\n  \"cfip_ho:on@95\",\n  \"s2store:1500@85\",\n  \"ayp:off@95\",\n  \"owso:on@99\",\n  \"socon:1.5@34\",\n  \"ntvdl:120@20\",\n  \"ntvho:off@99\",\n  \"ntvrtl:23@20\",\n  \"ixnt:ec3@90\",\n  \"ixnr:off@95\",\n  \"ixntr:er3@33\",\n  \"pubbcat:bc@33\",\n  \"mgnr:off@95\",\n  \"opli:on@99\",\n  \"1st_eid2:lpid@20\",\n  \"vpred_1:0.9@96\",\n  \"usynctrig:2@10\",\n  \"crtvmon:off@90\",\n  \"hemsa:on\",\n  \"shtli:on@80\",\n  \"lito:2500@25\",\n  \"lifpi:d@90\",\n  \"idguid3:on@90\",\n  \"idglr:on@98\",\n  \"sspus:on@90\",\n  \"pba2:none@97.9\",\n  \"lrss:off@80\",\n  \"vpsm:off@99\",\n  \"amzn_hold_disp:on@50\",\n  \"addens:off@75\",\n  \"bmetrics:on@99\",\n  \"confiant1:off@97\",\n  \"scae:on\",\n  \"rrc:on\",\n  \"bpflmult2:off@4\",\n  \"lazyflr:0.7@33\",\n  \"refsoflr:1.3@10\",\n  \"sesflrdis:0.7@10\",\n  \"sesflrso:0.4@25\",\n  \"sesflrvid:0.7@10\",\n  \"mgncrbn:on@50\",\n  \"tpcapi:on@95\",\n  \"rebuildslot:on@95\",\n  \"uid1bs:ix@80\",\n  \"uid1bs:ma@80\",\n  \"uid1bs:mn@80\",\n  \"uid1bs:ox@80\",\n  \"uid1bs:pu@80\",\n  \"uid1bs:sh@80\",\n  \"uid1bs:tl@80\",\n  \"upl:off@75\",\n  \"datapriv:on@10\",\n  \"dssc2:5@16\",\n  \"rpspdbt:10@85\",\n  \"riroe:vpr2@95\",\n  \"vadf:on@98\",\n  \"rptd:0@80\",\n  \"aos:off@99\"\n]",
+        "LAZY":null,
+        "DEPLOYMENT":"[\n  \"2025-02-19-01:acd-913:pr6020:cdb4151:4\"\n]",
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+        "TIMEUSEC2":1740030000000000.0,
+        "KEYPART":"LOSEq1Tpbmwht-QzqKmB",
+        "CTMAP":"{\n  \"ccpaoptout\": [\n    \"0\"\n  ],\n  \"doba\": [\n    \"ix\",\n    \"mn\",\n    \"ox\",\n    \"pu\",\n    \"ma\",\n    \"sh\",\n    \"st\",\n    \"sb\",\n    \"tl\",\n    \"mg\",\n    \"xr\",\n    \"ax\",\n    \"ch\",\n    \"cr\",\n    \"gg\",\n    \"kg\",\n    \"td\",\n    \"ym\"\n  ],\n  \"domain\": [\n    \"pipingpotcurry.com\"\n  ],\n  \"gptv\": [\n    \"202502130101\"\n  ],\n  \"hb_adid\": [\n    \"1110a00eb436bbb758\"\n  ],\n  \"hb_adid_opnx_ss\": [\n    \"1110a00eb436bbb758\"\n  ],\n  \"hb_adomain\": [\n    \"c1li7tt5ck.com\"\n  ],\n  \"hb_format\": [\n    \"banner\"\n  ],\n  \"hb_pb_opnx_ss\": [\n    \"0.07\"\n  ],\n  \"hb_rpb\": [\n    \"00.05_03.00\"\n  ],\n  \"hb_size\": [\n    \"320x100\"\n  ],\n  \"hb_source\": [\n    \"sm\"\n  ],\n  \"hi_au\": [\n    \"od\",\n    \"om\"\n  ],\n  \"id\": [\n    \"adthrive_content_12_phone\"\n  ],\n  \"id_region\": [\n    \"o\"\n  ],\n  \"idho_p\": [\n    \"id5id:10\",\n    \"identitylink:1\",\n    \"pairid:1\",\n    \"unifiedid:1\",\n    \"unifiedid2:1\",\n    \"sharedid:1\",\n    \"criteo:5\",\n    \"connectid:5\",\n    \"fabrickid:95\",\n    \"ftrackid:95\",\n    \"33acrossid:5\",\n    \"liveintentid:1\",\n    \"linkedinadsid:10\",\n    \"lotamepanoramaid:5\",\n    \"merkleid:25\",\n    \"merkuryid:5\"\n  ],\n  \"idp\": [\n    \"i:mrk\",\n    \"i:liid\",\n    \"i:mgncrbn\",\n    \"shl\",\n    \"cid\",\n    \"uid1\",\n    \"lpid\"\n  ],\n  \"location\": [\n    \"content\"\n  ],\n  \"marmalade\": [\n    \"true\"\n  ],\n  \"mcmp\": [\n    \"nr_flp_acme\",\n    \"nr_flp_albertsons\",\n    \"ba_fall21\",\n    \"ba_dexc1324\",\n    \"ba_krgkw24\",\n    \"nr_cdsp21\",\n    \"ba_dexc1124\",\n    \"ba_krgkw824\",\n    \"nr_prod21\",\n    \"nr_flp_vons\",\n    \"ba_atkw624\",\n    \"nr_flp_carrs\",\n    \"nr_flp_jewelosco\",\n    \"nr_flp_pavilions\",\n    \"ba_cdsp21\",\n    \"nr_flp_auto-25d63060\",\n    \"ba_atkw824\",\n    \"ba_krgkw1224\",\n    \"ba_aplnkwy1625\",\n    \"nr_flp_haggen\",\n    \"ba_dexc224\",\n    \"ba_hlwn20\",\n    \"ba_krgkw624\",\n    \"nr_flp_andronico\u2019s\",\n    \"nr_flp_safeway\",\n    \"ba_dexc1524\",\n    \"ba_dexc924\",\n    \"iabc_216\",\n    \"nr_flp_marketstreetimt\",\n    \"nr_thkg22\",\n    \"nr_flp_starmarket\",\n    \"ba_dexc1024\",\n    \"ba_aplnkwy625\",\n    \"ba_lcrb19\",\n    \"ba_aplnkwy1025\",\n    \"ba_tytkw2\",\n    \"nr_albt24_3\",\n    \"nr_flp_kings\",\n    \"ba_krgkwt\",\n    \"ba_arfry25\",\n    \"nr_flp_paknsav\",\n    \"ba_thkg19\",\n    \"ba_nsnwl125\",\n    \"ba_wskw20\",\n    \"ba_flvm24\",\n    \"nr_rcp24\",\n    \"iabc_210\",\n    \"engage5\",\n    \"engage7\",\n    \"engage10\"\n  ],\n  \"osb\": [\n    \"is\"\n  ],\n  \"ri\": [\n    \"139\"\n  ],\n  \"rsi\": [\n    \"82\"\n  ],\n  \"sens\": [\n    \"alc\",\n    \"ast\",\n    \"cbd\",\n    \"cosm\",\n    \"dat\",\n    \"gamc\",\n    \"gamv\",\n    \"pol\",\n    \"rel\",\n    \"wtl\"\n  ],\n  \"sequence\": [\n    \"12\"\n  ],\n  \"sitename\": [\n    \"piping pot curry\"\n  ],\n  \"st\": [\n    \"183\"\n  ],\n  \"verticals\": [\n    \"food\"\n  ]\n}",
+        "ISFILLEDREQUEST":true,
+        "REQUESTEDADUNITSIZES":"[\n  \"fluid|1x1|300x50|320x50|320x100|250x250|300x250|300x300|336x280\"\n]",
+        "MOBILEDEVICE":"iPhone_from_Apple",
+        "OSVERSION":"iOS_18_1",
+        "MOBILECAPABILITY":"Phone calls",
+        "MOBILECARRIER":"Wifi",
+        "SERVINGRESTRICTION":null,
+        "PUBLISHERPROVIDEDID":"X56zH1NUCPOpktiFVeoHLw",
+        "ISCOMPANION":false,
+        "VIDEOPOSITION":0,
+        "PODPOSITION":null,
+        "DEVICECATEGORY":"Smartphone",
+        "ISINTERSTITIAL":false,
+        "USERID":null,
+        "REFERERURL":"https:\/\/pipingpotcurry.com\/air-fryer-pumpkin\/",
+        "REQUESTLANGUAGE":"en",
+        "ADUNITID":22012660312,
+        "TOPADUNITID":22012631261,
+        "TOPADUNITNAME":"AdThrive_Content",
+        "ADUNITNAME":"Piping Pot Curry",
+        "COUNTRY":"Australia",
+        "REGION":"New South Wales",
+        "BROWSER":"Safari (iPhone\/iPod) Any.Any",
+        "OS":"Apple iOS",
+        "METRO":null,
+        "POSTALCODE":null,
+        "BANDWIDTH":"wifi",
+        "GFPCONTENTID":0,
+        "ADVERTISERID":413678576.0,
+        "CREATIVESIZE":"1x1",
+        "CREATIVEID":138497000000.0,
+        "LINEITEMID":6051714127.0,
+        "LINEITEMNAME":"OpenX S2S opnx_ss Display 00.07",
+        "ORDERID":3043267432.0,
+        "ORDERNAME":"300027_3043267432_Prebid_OpenX S2S opnx_ss_Display_CONFIANT",
+        "CREATIVESIZEDELIVERED":"1x1",
+        "CREATIVEVERSION":1.0,
+        "PRODUCT":"Ad Server",
+        "TARGETEDCUSTOMCRITERIA":"hb_pb_opnx_ss=0.07;hb_deal_opnx_ss!=*",
+        "DEALID":null,
+        "DEALTYPE":null,
+        "ESTIMATEDBACKFILLREVENUE":null,
+        "IMPRESSIONID":"Jan-00",
+        "YIELDGROUPCOMPANYID":null,
+        "YIELDGROUPNAMES":null,
+        "ADVERTISER":null,
+        "BUYER":null,
+        "ADXACCOUNTID":null,
+        "VIDEOFALLBACKPOSITION":0.0,
+        "MEASURABLE":null,
+        "VIEWABLE":0,
+        "CODESERVES":1,
+        "REVENUE":0.00007,
+        "IMPRESSIONS":0,
+        "PVK":"01jmh0wre5ptgybeqgjx94v2ye",
+        "SESS":"01jmh0wrhed9y5enmz1tnp61nm",
+        "SITEID":"5aeb089fedbe613495a4463b",
+        "BUCKET":"flex-53",
+        "DATE":"2\/20\/2025",
+        "HOUR":1,
+        "AMP":null,
+        "ABGROUP":"[\n  \"amzn:on\",\n  \"echde2:400@20\",\n  \"iisw:500@25\",\n  \"mto:4@25\",\n  \"flrml1:fbrap@98\",\n  \"fto:2@25\",\n  \"athmb:none@0\",\n  \"atd2:@99.5\",\n  \"amzfs:1250@20\",\n  \"hbho2:sharethrough@5\",\n  \"hbho2:adform@95\",\n  \"allbho:none@0\",\n  \"hbhorise:on@80\",\n  \"idho:fabrickid\",\n  \"idho:ftrackid\",\n  \"pbs2s:none\",\n  \"adt3:600@33\",\n  \"s2sparam:on@99\",\n  \"multirefaucd3:20sf1@33\",\n  \"multirefaucv2:off@96\",\n  \"criteodr:both@95\",\n  \"krgodr:client@99\",\n  \"shrdr:both@95\",\n  \"yahoodr:server@50\",\n  \"mr4:on@90\",\n  \"pbfs:1000@20\",\n  \"forex:off@95\",\n  \"pbrqt:200@20\",\n  \"rrb:off@90\",\n  \"mstm:1250@50\",\n  \"bps:on@50\",\n  \"r_dr:4@20\",\n  \"rfsi:1.2@20\",\n  \"rew:on@50\",\n  \"r_infmlt2:off@25\",\n  \"r_shpmlt:off@90\",\n  \"msa:2@50\",\n  \"sca:6@20\",\n  \"mfb:1@33\",\n  \"bsa:round@33\",\n  \"tm:off@50\",\n  \"tal:on@50\",\n  \"bfc:1@33\",\n  \"rffd:on@50\",\n  \"itm:off@50\",\n  \"rrpbs50:west@50\",\n  \"apprqb:rubi_ss@99\",\n  \"dfdb:33_1@80\",\n  \"dfdb:af_1@80\",\n  \"dfdb:an_1@80\",\n  \"dfdb:co_1@80\",\n  \"dfdb:cv_1@80\",\n  \"dfdb:cr_1@80\",\n  \"dfdb:fl_1@80\",\n  \"dfdb:gv_1@80\",\n  \"dfdb:gg_1@80\",\n  \"dfdb:id_0.8@10\",\n  \"dfdb:ix_1@80\",\n  \"dfdb:kg_1@80\",\n  \"dfdb:nt_0.8@10\",\n  \"dfdb:ox_0.8@10\",\n  \"dfdb:oz_1@80\",\n  \"dfdb:pm_1@80\",\n  \"dfdb:rd_1@80\",\n  \"dfdb:ri_1@80\",\n  \"dfdb:rh_1@80\",\n  \"dfdb:rb_1@80\",\n  \"dfdb:sh_0.8@10\",\n  \"dfdb:te_1@80\",\n  \"dfdb:tl_1@80\",\n  \"dfdb:td_1@80\",\n  \"dfdb:ut_0.8@10\",\n  \"dfdb:ur_1@80\",\n  \"dfdb:yh_1@80\",\n  \"dfdb:ym_1@80\",\n  \"ito_mn:2800@50\",\n  \"tkho_2:none@0\",\n  \"vbho2:gridvid@30\",\n  \"vbho2:gumgum@50\",\n  \"vbho2:adform@95\",\n  \"vbhorise:on@80\",\n  \"socbde:0s@85\",\n  \"vasttoisv:15s@60\",\n  \"vasttoosv:3s@80\",\n  \"proto:all@55\",\n  \"pbm:on\",\n  \"addur:off@97\",\n  \"adlifecyc:off@90\",\n  \"reqto:10@20\",\n  \"osp:2@97\",\n  \"rpslt:40@20\",\n  \"dofre:off@99\",\n  \"imabr:500@95\",\n  \"rpbbl:0@20\",\n  \"rpmbl:20@20\",\n  \"rpmbs:35@20\",\n  \"rpmmbl:100@20\",\n  \"rpfbt:10@20\",\n  \"mmbr:off@50\",\n  \"rpep:on@50\",\n  \"rplvto:15000@20\",\n  \"icft:3@20\",\n  \"ocft:4@25\",\n  \"jwpv:8.30.0@99\",\n  \"smhd100:off\",\n  \"parallax:off\",\n  \"mrsf:on\",\n  \"tckscroll2:5@90\",\n  \"tckscroll2:7@90\",\n  \"tckscroll2:10@90\",\n  \"intcfg:on@90\",\n  \"scre:on\",\n  \"so2:rv@25\",\n  \"bwh:bg@20\",\n  \"csync_lim:0@25\",\n  \"s2sto:1500@45\",\n  \"jwb:on@95\",\n  \"cfip_ho:on@95\",\n  \"s2store:1500@85\",\n  \"ayp:off@95\",\n  \"owso:on@99\",\n  \"socon:1.5@34\",\n  \"ntvdl:120@20\",\n  \"ntvho:off@99\",\n  \"ntvrtl:20@20\",\n  \"ixnt:ec3@90\",\n  \"ixnr:off@95\",\n  \"ixntr:er3@33\",\n  \"pubbcat:bc@33\",\n  \"mgnr:off@95\",\n  \"opli:on@99\",\n  \"1st_eid2:lpid@20\",\n  \"vpred_1:0.9@96\",\n  \"usynctrig:1@70\",\n  \"crtvmon:off@90\",\n  \"hemsa:on\",\n  \"shtli:on@80\",\n  \"lito:0@25\",\n  \"lifpi:d@90\",\n  \"idguid3:on@90\",\n  \"idglr:on@98\",\n  \"sspus:on@90\",\n  \"pba2:none@97.9\",\n  \"lrss:off@80\",\n  \"vpsm:off@99\",\n  \"amzn_hold_disp:off@50\",\n  \"addens:off@75\",\n  \"bmetrics:on@99\",\n  \"confiant1:off@97\",\n  \"scae:on\",\n  \"rrc:on\",\n  \"bpflmult2:1.35@49\",\n  \"lazyflr:0.7@33\",\n  \"refsoflr:ml@55\",\n  \"sesflrdis:0.75@10\",\n  \"sesflrso:0.5@25\",\n  \"sesflrvid:1@10\",\n  \"mgncrbn:on@50\",\n  \"rebuildslot:on@95\",\n  \"uid1bs:ix@80\",\n  \"uid1bs:ma@80\",\n  \"uid1bs:ox@80\",\n  \"uid1bs:pu@80\",\n  \"uid1bs:sh@80\",\n  \"uid1bs:tl@80\",\n  \"upl:off@75\",\n  \"datapriv:off@90\",\n  \"dssc2:18@17\",\n  \"rvadv_ab1:on\",\n  \"rpspdbt:10@85\",\n  \"riroe:vpr2@95\",\n  \"vadf:on@98\",\n  \"rptd:0@80\",\n  \"aos:off@99\",\n  \"slkid:off\"\n]",
+        "LAZY":"[\n  \"true\"\n]",
+        "DEPLOYMENT":"[\n  \"2025-02-19-01:rpsc-150:pr5996:52d4e1b:4\"\n]",
+        "REFRESH":"[\n  \"00\"\n]",
+        "PLUGIN":"[\n  \"adthrive-ads-3.6.2\"\n]",
+        "FLAG":"[\n  \"target:es2018\",\n  \"fr:9\",\n  \"tpme:false\",\n  \"adsmnche:499\",\n  \"region:nsw\",\n  \"brwsrv:18.1.1\",\n  \"pmp_elig:1\",\n  \"lf_elig:1\",\n  \"clson:true\",\n  \"clsbranch:5be969b\",\n  \"clsbucket:prod\",\n  \"integration:plugin\",\n  \"lmdv:1.115.0\",\n  \"rf:1\",\n  \"slkappver:2.13.104\",\n  \"slkplgver:2.0.3\",\n  \"ppid:no_id5storageavail_sharedidsent\",\n  \"het_sync:1\"\n]",
+        "FLAG_SLOT":"[\n  \"bpe:1\",\n  \"fmlli:0@0.7\",\n  \"tbc:none\",\n  \"2bc:none\",\n  \"medb:none\",\n  \"bidr:none\",\n  \"tid:f320fe63-27ea-433c-8aae-d293897471d2\",\n  \"mtr:bn\",\n  \"bic:9\",\n  \"oseq:10\",\n  \"pbop:0.101000\",\n  \"pbpd:0.026080\",\n  \"fv:prod_cb_v1.7.5.0.1_0.45\",\n  \"hbf:0.1006\",\n  \"hbb:server\",\n  \"flrti:1739923076\"\n]",
+        "HBHO":null,
+        "VPWXVPH":"[\n  \"414x705\"\n]",
+        "VBHO":null,
+        "FPV":"[\n  \"1\"\n]",
+        "VPRENUM":null,
+        "NREF":"[\n  \"0\"\n]",
+        "DOC_REF":"[\n  \"www.google.com\"\n]",
+        "SITE_CODE":"[\n  \"afoi_2020\",\n  \"aplarpd_24\",\n  \"apltvsl_25\",\n  \"domp_2025\",\n  \"dslkexln_2025\",\n  \"expftr_24\",\n  \"f1844_25\",\n  \"fmlo_2021\",\n  \"mrloi_22\",\n  \"oite_24\",\n  \"rmoi_24\",\n  \"tgnaf_21\",\n  \"wfat_24\"\n]",
+        "BRANCH":"[\n  \"52d4e1b\"\n]",
+        "UTM_CAMPAIGN":null,
+        "UTM_MEDIUM":null,
+        "UTM_SOURCE":null,
+        "AT_CUSTOM_1":null,
+        "HB_PB":"[\n  \"0.07\"\n]",
+        "HB_BIDDER":"[\n  \"opnx_ss\"\n]",
+        "UTC_TIMESTAMP":"32:57.5",
+        "UTC_DATE_HOUR":"00:00.0",
+        "SOURCE_DATE_HOUR":"20250220_11",
+        "CHILDNETWORKCODE":null,
+        "SELLERRESERVEPRICE":null,
+        "AUDIENCESEGMENTIDS":null,
+        "USERIDENTIFIERSTATUS":null,
+        "PPIDPRESENCE":null,
+        "VASTERROR":false,
+        "VASTERRORNAMES":null,
+        "CLICK":0,
+        "BACKFILLKEYPART":"uevQzRwENiOpreBhrlDa",
+        "PROTECTEDAUDIENCEAPIDELIVERY":null,
+        "HB_DSP":null,
+        "HB_CRID":"[\n  \"78787721\"\n]",
+        "EVENTTIMEUSEC2":null,
+        "VERTICALS":"[\n  \"food\"\n]",
+        "YIELDCOMPANYNAMES":null,
+        "NATIVEFORMAT":null,
+        "NATIVESTYLE":null,
+        "PROTECTEDAUDIENCESELLER":null
+    }
+    
+  ]
+}

GAMLOG_ABGROUP.json

@@ -0,0 +1 @@
+{"database":"ANALYTICS","schema":"ADCODE_DA","table":"AB_GRANULAR","metadata":[{"column_name":"FORMAT","data_type":"TEXT"},{"column_name":"DEVICE","data_type":"TEXT"},{"column_name":"REFRESH_NUM","data_type":"TEXT"},{"column_name":"CS","data_type":"NUMBER"},{"column_name":"TOPADUNITNAME","data_type":"TEXT"},{"column_name":"REV","data_type":"FLOAT"},{"column_name":"PERCENTAGE","data_type":"FLOAT"},{"column_name":"AB_FULL","data_type":"TEXT"},{"column_name":"AB_VALUE","data_type":"TEXT"},{"column_name":"IMPS","data_type":"NUMBER"},{"column_name":"DATE","data_type":"DATE"},{"column_name":"MEASURABLE","data_type":"NUMBER"},{"column_name":"LAZY","data_type":"TEXT"},{"column_name":"AGGREGATION_ID","data_type":"TEXT"},{"column_name":"AB_KEY","data_type":"TEXT"},{"column_name":"BROWSER_CLEAN","data_type":"TEXT"},{"column_name":"VIEWABLE","data_type":"NUMBER"}],"Sample Output Date":[{"AB_FULL":"clsins_ss:true","AB_KEY":"clsins_ss","AB_VALUE":"true","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"Edge","CS":4803,"DATE":"2023-11-02","DEVICE":"Mobile","FORMAT":"display","IMPS":4095,"LAZY":"false","MEASURABLE":4094,"REFRESH_NUM":"0","REV":4.830645672999998e+00,"TOPADUNITNAME":"AdThrive_Content_5","VIEWABLE":1157},{"AB_FULL":"ito_dn:2400@10","AB_KEY":"ito_dn","AB_VALUE":"2400@10","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"Chrome","CS":3737,"DATE":"2023-11-02","DEVICE":"Tablet","FORMAT":"display","IMPS":3111,"LAZY":"false","MEASURABLE":3110,"PERCENTAGE":1.000000000000000e-01,"REFRESH_NUM":"1+","REV":4.358110069000000e+00,"TOPADUNITNAME":"AdThrive_Content_2","VIEWABLE":2143},{"AB_FULL":"refsoflr:1.2@5","AB_KEY":"refsoflr","AB_VALUE":"1.2@5","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"Safari","CS":24151,"DATE":"2023-11-02","DEVICE":"Mobile","FORMAT":"display","IMPS":21719,"LAZY":"false","MEASURABLE":21717,"PERCENTAGE":5.000000000000000e-02,"REFRESH_NUM":"0","REV":1.820323191700000e+01,"TOPADUNITNAME":"AdThrive_Below_Post_1","VIEWABLE":5270},{"AB_FULL":"cbt:4s@75","AB_KEY":"cbt","AB_VALUE":"4s@75","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"Chrome","CS":28913,"DATE":"2023-11-02","DEVICE":"Tablet","FORMAT":"display","IMPS":24082,"LAZY":"true","MEASURABLE":24043,"PERCENTAGE":7.500000000000000e-01,"REFRESH_NUM":"1+","REV":5.864454698900001e+01,"TOPADUNITNAME":"AdThrive_Content_5","VIEWABLE":16597},{"AB_FULL":"dsle:continuing without bids@99","AB_KEY":"dsle","AB_VALUE":"continuing without bids@99","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"Firefox","CS":53,"DATE":"2023-11-02","DEVICE":"Desktop","FORMAT":"display","IMPS":34,"LAZY":"true","MEASURABLE":34,"PERCENTAGE":9.900000000000000e-01,"REFRESH_NUM":"null","REV":8.884499999999998e-03,"TOPADUNITNAME":"AdThrive_Content_2","VIEWABLE":6},{"AB_FULL":"loglev:on@1","AB_KEY":"loglev","AB_VALUE":"on@1","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"other","CS":34834,"DATE":"2023-11-02","DEVICE":"Mobile","FORMAT":"display","IMPS":30744,"LAZY":"false","MEASURABLE":30737,"PERCENTAGE":1.000000000000000e-02,"REFRESH_NUM":"0","REV":6.071192854999998e+01,"TOPADUNITNAME":"AdThrive_Recipe_1","VIEWABLE":13664},{"AB_FULL":"pbsflr1:off@95","AB_KEY":"pbsflr1","AB_VALUE":"off@95","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"Firefox","CS":1477,"DATE":"2023-11-02","DEVICE":"Mobile","FORMAT":"display","IMPS":1283,"LAZY":"false","MEASURABLE":1281,"PERCENTAGE":9.500000000000000e-01,"REFRESH_NUM":"0","REV":5.740430989999999e-01,"TOPADUNITNAME":"AdThrive_Below_Post","VIEWABLE":75},{"AB_FULL":"crtvmon:off@90","AB_KEY":"crtvmon","AB_VALUE":"off@90","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"Firefox","CS":1418,"DATE":"2023-11-02","DEVICE":"Mobile","FORMAT":"display","IMPS":1232,"LAZY":"false","MEASURABLE":1230,"PERCENTAGE":9.000000000000000e-01,"REFRESH_NUM":"0","REV":5.475702990000000e-01,"TOPADUNITNAME":"AdThrive_Below_Post","VIEWABLE":74},{"AB_FULL":"ito_mn:2900@15","AB_KEY":"ito_mn","AB_VALUE":"2900@15","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"other","CS":3421,"DATE":"2023-11-02","DEVICE":"Desktop","FORMAT":"display","IMPS":3330,"LAZY":"false","MEASURABLE":3330,"PERCENTAGE":1.500000000000000e-01,"REFRESH_NUM":"0","REV":4.912878659000000e+00,"TOPADUNITNAME":"AdThrive_Content","VIEWABLE":836},{"AB_FULL":"mgncrbn:on@25","AB_KEY":"mgncrbn","AB_VALUE":"on@25","AGGREGATION_ID":"2023-11-03","BROWSER_CLEAN":"Safari","CS":8496,"DATE":"2023-11-02","DEVICE":"Mobile","FORMAT":"display","IMPS":6481,"LAZY":"true","MEASURABLE":6481,"PERCENTAGE":2.500000000000000e-01,"REFRESH_NUM":"1+","REV":1.069285125900000e+01,"TOPADUNITNAME":"AdThrive_Content_1","VIEWABLE":3745}]}

GAMLOG_ABGROUP_Instructions_file.txt

@@ -0,0 +1,143 @@
+
+## A/B Normalization SQL Prompt Guide (For Model Inference)
+
+This guide outlines how to instruct a model to write Snowflake SQL for normalized A/B test comparisons using the ANALYTICS.ADCODE_DA.AB_GRANULAR table.
+
+---
+
+## Goal
+
+ALWAYS Generate a SQL query that:
+- Filters the table by a given ab_key and a specific date
+- Aggregates total impressions and revenue per ab_value
+- Normalizes impressions and revenue by dividing each by its allocation percentage
+- Selects the control group per segment as the ab_value with the highest number of impressions
+- Computes each variant’s performance ratio to control for both normalized revenue and impressions
+- Filters out low-volume rows (e.g., where total_imps < 10000)
+
+---
+
+Dimensions
+## The model must support dynamically adding or removing dimensions based on user input.
+
+Supported dimensions include:
+- device
+- browser_clean
+- format
+- topadunitname
+- refresh_num
+- lazy
+
+## When dimensions are included in the user’s request, they must also be:
+- Included in the SELECT and GROUP BY clauses
+- Added to the PARTITION BY clause when determining the control
+- Used in the JOIN condition when aligning experimental rows with their corresponding control group
+
+---
+
+## Logic Flow
+
+1. Filter the data for the specific AB test key and date.
+2. Group by the selected dimensions and ab_value.
+3. Aggregate total impressions and total revenue.
+4. Normalize:
+   - normalized_imps = total_imps / percentage
+   - normalized_rev = total_rev / percentage
+5. Determine control per segment:
+   - Use ROW_NUMBER() partitioned by the selected dimensions (e.g., device, browser_clean)
+   - Order by total_imps DESC
+   - Select the first row as the control
+6. Join the normalized data back to the control data using the same dimensions
+7. Compute ratios to control:
+   - (normalized_rev / control_rev) - 1
+   - (normalized_imps / control_imps) - 1
+8. Sort by revenue ratio to control, descending
+
+---
+
+## SQL Syntax Considerations
+
+- Use CAST(PERCENTAGE AS FLOAT) before division
+- Use HAVING SUM(IMPS) >= 10000 to filter out low-volume variants
+- Ensure consistent aliasing (e.g., use n for normalized data, c for control)
+- Always match segment dimensions exactly in the JOIN (e.g., n.device = c.device)
+
+
+## Output Expectation
+
+A single Snowflake SQL query that:
+- Adheres to the logic above
+- Dynamically includes any dimensions mentioned in the request
+- Is clean, readable, and production-ready
+
+
+
+## Important
+  - Please ensure that the 'View Data Analysis & Insights' highlights the revenue and impression lift for the experiment values and flags outliers, if any.
+  - **Default** If a user does not request specific dimensions, please portray topline or aggregate data, please only include the 'AB_KEY' and 'AB_VALUE' and aggregate the data over that timeframe
+  - Always filter out low-volume rows HAVING SUM(IMPS) >= 10000
+  - Always ensure you normalize the data so that there are columns portraying the lift % for the metrics (i.e imps, rev, etc.).
+
+
+## Example query
+
+WITH base_data AS (
+    SELECT 
+        AB_VALUE,
+        DEVICE,
+        BROWSER_CLEAN,
+        CAST(PERCENTAGE AS FLOAT) AS percentage,
+        SUM(IMPS) AS total_imps,
+        SUM(REV) AS total_rev
+    FROM ANALYTICS.ADCODE_DA.AB_GRANULAR
+    WHERE AB_KEY = 'cnftp'
+      AND DATE = '2025-04-01'
+    GROUP BY AB_VALUE, DEVICE, BROWSER_CLEAN, PERCENTAGE
+    HAVING SUM(IMPS) >= 10000
+),
+normalized AS (
+    SELECT
+        AB_VALUE,
+        DEVICE,
+        BROWSER_CLEAN,
+        total_imps,
+        total_rev,
+        percentage,
+        (total_imps / percentage) AS norm_imps,
+        (total_rev / percentage) AS norm_rev
+    FROM base_data
+),
+ranked_controls AS (
+    SELECT *,
+        ROW_NUMBER() OVER (
+            PARTITION BY DEVICE, BROWSER_CLEAN
+            ORDER BY total_imps DESC
+        ) AS rn
+    FROM normalized
+),
+control AS (
+    SELECT 
+        DEVICE,
+        BROWSER_CLEAN,
+        AB_VALUE AS control_ab_value,
+        norm_imps AS control_norm_imps,
+        norm_rev AS control_norm_rev
+    FROM ranked_controls
+    WHERE rn = 1
+)
+SELECT 
+    n.AB_VALUE,
+    n.DEVICE,
+    n.BROWSER_CLEAN,
+    n.total_imps,
+    n.total_rev,
+    n.percentage,
+    ROUND(n.norm_imps, 2) AS norm_imps,
+    ROUND(n.norm_rev, 2) AS norm_rev,
+    ROUND(n.norm_imps / c.control_norm_imps, 4) - 1 AS norm_imps_ratio_to_control,
+    ROUND(n.norm_rev / c.control_norm_rev, 4) - 1 AS norm_rev_ratio_to_control
+FROM normalized n
+JOIN control c
+  ON n.DEVICE = c.DEVICE
+ AND n.BROWSER_CLEAN = c.BROWSER_CLEAN
+ORDER BY n.DEVICE, n.BROWSER_CLEAN, norm_rev_ratio_to_control DESC;

GAM_Hourly.json

@@ -0,0 +1 @@
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@@ -0,0 +1 @@
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IMPRESSIONS","data_type":"NUMBER"},{"column_name":"ADBLOCK RECOVERY","data_type":"TEXT"},{"column_name":"Tier (SITE_EXTENDED)","data_type":"TEXT"},{"column_name":"AD UNIT VIDEO GROUP","data_type":"TEXT"},{"column_name":"Verticals","data_type":"TEXT"},{"column_name":"AD UNIT CODE","data_type":"TEXT"},{"column_name":"AD UNIT 1 ID","data_type":"NUMBER"},{"column_name":"Is Platinum","data_type":"BOOLEAN"},{"column_name":"Service Level","data_type":"TEXT"},{"column_name":"Service (SITE_EXTENDED)","data_type":"TEXT"},{"column_name":"Jw Collapsible Player Id","data_type":"TEXT"},{"column_name":"REV SHARE","data_type":"NUMBER"},{"column_name":"IMPRESSIONS","data_type":"NUMBER"},{"column_name":"Primary Vertical","data_type":"TEXT"},{"column_name":"ADTHRIVE MEDIA REV SHARE","data_type":"TEXT"},{"column_name":"ORDER GROUP ROLLUP","data_type":"TEXT"},{"column_name":"Demand Channel Rollup","data_type":"TEXT"},{"column_name":"REVENUE TYPE","data_type":"TEXT"},{"column_name":"GROSS EARNINGS","data_type":"NUMBER"},{"column_name":"PROGRAMMATIC GUARANTEED","data_type":"TEXT"},{"column_name":"CLICKS","data_type":"NUMBER"},{"column_name":"DEVICE","data_type":"TEXT"},{"column_name":"AD UNIT 1","data_type":"TEXT"},{"column_name":"VIEWABLE IMPRESSIONS","data_type":"NUMBER"},{"column_name":"RMA","data_type":"TEXT"},{"column_name":"MEDIA TYPE","data_type":"TEXT"},{"column_name":"EARNINGS","data_type":"NUMBER"},{"column_name":"ADJUSTED GROSS EARNINGS","data_type":"NUMBER"},{"column_name":"ORDER NAME","data_type":"TEXT"},{"column_name":"AD NETWORK ID","data_type":"TEXT"},{"column_name":"ORDER GROUP","data_type":"TEXT"},{"column_name":"AD UNIT 2","data_type":"TEXT"},{"column_name":"ADBLOCK RECOVERY ID","data_type":"NUMBER"}],"Sample Output Date":[{"AD NETWORK ID":"52e72f29208f222c05b99d99","AD NETWORK NAME":"AdThrive Media","AD UNIT 1":"AdThrive_Content_1","AD UNIT 1 ID":32636336,"AD UNIT 2":"Offbeat Bride","AD UNIT 2 ID":22295945837,"AD UNIT CODE":"60350c9c65ff177880a5f56b","AD UNIT GROUP":"Content","AD UNIT VIDEO GROUP":"AdThrive_Content_1","ADBLOCK RECOVERY":"(not applicable)","ADBLOCK RECOVERY ID":9223372036854775807,"ADJUSTED GROSS EARNINGS":0.000905,"ADTHRIVE MEDIA REV SHARE":"Yes","CLICKS":0,"CUSTOM VIDEO PLAYER REV SHARE":"-","DATE":"2022-02-08 00:00:00.000 Z","DEVICE":"Tablet","DEVICE CATEGORY ID":30002,"Demand Channel Rollup":"Amazon","EARNINGS":0.000679,"ELIGIBLE IMPRESSIONS":2,"GROSS EARNINGS":0.000905,"IMPRESSIONS":2,"Install Date":"2021-04-12 00:00:00.000 Z","Is Platinum":false,"Jw Collapsible Player Id":"StIe2oRF","MEASURABLE IMPRESSIONS":2,"MEDIA TYPE":"Display","ORDER GROUP":"Amazon","ORDER GROUP ID":"1532045","ORDER GROUP ROLLUP":"Amazon","ORDER ID":2603581871,"ORDER NAME":"300091_2603581871_Amazon_TAM-AppNexus amznp is 10thu68","PROGRAMMATIC GUARANTEED":"-","Primary Vertical":"Wedding","REV SHARE":75,"REVENUE TYPE":"OpenMarketHigh","RMA":"-","Rev Type Rollup":"OpenMarketHigh","SITE ID":"60350c9c65ff177880a5f56b","Service (SITE_EXTENDED)":"AdThrive Edge","Service Level":"Insider","Site Name (SITE_EXTENDED)":"Offbeat Wed","Status (SITE_EXTENDED)":"Active","Tier (SITE_EXTENDED)":"AdThrive 7","VIEWABLE IMPRESSIONS":2,"Verticals":"Wedding"},{"AD NETWORK ID":"52e72f29208f222c05b99d99","AD NETWORK NAME":"AdThrive Media","AD UNIT 1":"AdThrive_Header_1","AD UNIT 1 ID":32637416,"AD UNIT 2":"Merriam Webster","AD UNIT 2 ID":22615558410,"AD UNIT CODE":"61575e8e934c48ea554b3caa","AD UNIT GROUP":"Header","AD UNIT VIDEO GROUP":"AdThrive_Header_1","ADBLOCK RECOVERY":"(not applicable)","ADBLOCK RECOVERY ID":9223372036854775807,"ADJUSTED GROSS EARNINGS":28.997052,"ADTHRIVE MEDIA REV SHARE":"Yes","CLICKS":0,"CUSTOM VIDEO PLAYER REV SHARE":"-","DATE":"2022-02-08 00:00:00.000 Z","DEVICE":"Mobile","DEVICE CATEGORY ID":30001,"Demand Channel Rollup":"Prebid","EARNINGS":26.097347,"ELIGIBLE IMPRESSIONS":13363,"GROSS EARNINGS":28.997052,"IMPRESSIONS":13363,"Install Date":"2021-11-10 00:00:00.000 Z","Is Platinum":false,"MEASURABLE IMPRESSIONS":13094,"MEDIA TYPE":"Display","ORDER GROUP":"Prebid","ORDER GROUP ID":"1556163","ORDER GROUP ROLLUP":"Prebid","ORDER ID":2391732709,"ORDER NAME":"300049_2391732709_Prebid_Rubicon Client-Side HB -- Mobile CONFIANT","PROGRAMMATIC GUARANTEED":"-","Primary Vertical":"Education","REV SHARE":90,"REVENUE TYPE":"OpenMarketHigh","RMA":"-","Rev Type Rollup":"OpenMarketHigh","SITE ID":"61575e8e934c48ea554b3caa","Service (SITE_EXTENDED)":"Prime","Service Level":"Enterprise","Site Name (SITE_EXTENDED)":"Merriam Webster","Status (SITE_EXTENDED)":"Active","VIEWABLE IMPRESSIONS":6742,"Verticals":"Education|Gaming"},{"AD NETWORK ID":"52e72f29208f222c05b99d99","AD NETWORK NAME":"AdThrive Media","AD UNIT 1":"AdThrive_Content_3","AD UNIT 1 ID":32636576,"AD UNIT 2":"Sew Much Ado","AD UNIT 2 ID":62279096,"AD UNIT CODE":"589a50c4ff85b43ebd55895e","AD UNIT GROUP":"Content","AD UNIT VIDEO GROUP":"AdThrive_Content_3","ADBLOCK RECOVERY":"(not applicable)","ADBLOCK RECOVERY ID":9223372036854775807,"ADJUSTED GROSS EARNINGS":0.000285,"ADTHRIVE MEDIA REV SHARE":"Yes","CLICKS":0,"CUSTOM VIDEO PLAYER REV SHARE":"-","DATE":"2022-02-08 00:00:00.000 Z","DEVICE":"Desktop","DEVICE CATEGORY ID":30000,"Demand Channel Rollup":"Amazon","EARNINGS":0.000214,"ELIGIBLE IMPRESSIONS":1,"GROSS EARNINGS":0.000285,"IMPRESSIONS":1,"Install Date":"2017-03-16 00:00:00.000 Z","Is Platinum":false,"Jw Collapsible Player Id":"EyV1vMYW","MEASURABLE IMPRESSIONS":1,"MEDIA TYPE":"Display","ORDER GROUP":"Amazon","ORDER GROUP ID":"1532045","ORDER GROUP ROLLUP":"Amazon","ORDER ID":2253771258,"ORDER NAME":"300014_2253771258_Amazon_AMZ Proprietary Demand amznp is 1dfpj4","PROGRAMMATIC GUARANTEED":"-","Primary Vertical":"Crafts","REV SHARE":75,"REVENUE TYPE":"OpenMarketHigh","RMA":"-","Rev Type Rollup":"OpenMarketHigh","SITE ID":"589a50c4ff85b43ebd55895e","Service (SITE_EXTENDED)":"AdThrive Edge","Service Level":"Insider","Site Name (SITE_EXTENDED)":"Sew Much Ado","Status (SITE_EXTENDED)":"Active","Tier (SITE_EXTENDED)":"AdThrive 8","VIEWABLE IMPRESSIONS":1,"Verticals":"Crafts"},{"AD NETWORK ID":"52e72f29208f222c05b99d99","AD NETWORK NAME":"AdThrive Media","AD UNIT 1":"AdThrive_Footer_1","AD UNIT 1 ID":32639336,"AD UNIT 2":"Miss Homemade","AD UNIT 2 ID":63091256,"AD UNIT CODE":"581a50493475ed475dc6fed5","AD UNIT GROUP":"Footer","AD UNIT VIDEO GROUP":"AdThrive_Footer_1","ADBLOCK RECOVERY":"(not applicable)","ADBLOCK RECOVERY ID":9223372036854775807,"ADJUSTED GROSS EARNINGS":0.071435,"ADTHRIVE MEDIA REV SHARE":"Yes","CLICKS":0,"CUSTOM VIDEO PLAYER REV SHARE":"-","DATE":"2022-02-08 00:00:00.000 Z","DEVICE":"Mobile","DEVICE CATEGORY ID":30001,"Demand Channel Rollup":"Amazon","EARNINGS":0.053576,"ELIGIBLE IMPRESSIONS":39,"GROSS EARNINGS":0.071435,"IMPRESSIONS":39,"Install Date":"2016-11-17 00:00:00.000 Z","Is Platinum":false,"Jw Collapsible Player Id":"EyBHw3H6","MEASURABLE IMPRESSIONS":39,"MEDIA TYPE":"Display","ORDER GROUP":"Amazon","ORDER GROUP ID":"1532045","ORDER GROUP ROLLUP":"Amazon","ORDER ID":2605002411,"ORDER NAME":"300094_2605002411_Amazon_TAM-GumGum amznp is 16d9pts","PROGRAMMATIC GUARANTEED":"-","Primary Vertical":"Food","REV SHARE":75,"REVENUE TYPE":"OpenMarketHigh","RMA":"-","Rev Type Rollup":"OpenMarketHigh","SITE ID":"581a50493475ed475dc6fed5","Service (SITE_EXTENDED)":"AdThrive Edge","Service Level":"Insider","Site Name (SITE_EXTENDED)":"Miss Homemade","Status (SITE_EXTENDED)":"Active","Tier (SITE_EXTENDED)":"AdThrive 8","VIEWABLE IMPRESSIONS":19,"Verticals":"Food|Clean Eating"},{"AD NETWORK ID":"52e72f29208f222c05b99d99","AD NETWORK NAME":"AdThrive Media","AD UNIT 1":"AdThrive_Content_9","AD UNIT 1 ID":32637296,"AD UNIT 2":"Joy of Motion Crochet","AD UNIT 2 ID":21889817302,"AD UNIT CODE":"5e02820917a8be5a76a36c45","AD UNIT GROUP":"Content","AD UNIT VIDEO GROUP":"AdThrive_Content_9","ADBLOCK RECOVERY":"(not applicable)","ADBLOCK RECOVERY ID":9223372036854775807,"ADJUSTED GROSS EARNINGS":0.00145,"ADTHRIVE MEDIA REV SHARE":"Yes","CLICKS":0,"CUSTOM VIDEO PLAYER REV SHARE":"-","DATE":"2022-02-08 00:00:00.000 Z","DEVICE":"Tablet","DEVICE CATEGORY ID":30002,"Demand Channel Rollup":"Prebid","EARNINGS":0.001088,"ELIGIBLE IMPRESSIONS":2,"GROSS EARNINGS":0.00145,"IMPRESSIONS":2,"Install Date":"2020-02-03 00:00:00.000 Z","Is Platinum":false,"Jw Collapsible Player Id":"2dngTl4X","MEASURABLE IMPRESSIONS":2,"MEDIA TYPE":"Display","ORDER GROUP":"Prebid","ORDER GROUP ID":"1556163","ORDER GROUP ROLLUP":"Prebid","ORDER ID":2732939880,"ORDER NAME":"300084_2732939880_Prebid_TripleLift Native Client-side -- Mobile CONFIANT","PROGRAMMATIC GUARANTEED":"-","Primary Vertical":"Crafts","REV SHARE":75,"REVENUE TYPE":"OpenMarketHigh","RMA":"-","Rev Type Rollup":"OpenMarketHigh","SITE ID":"5e02820917a8be5a76a36c45","Service (SITE_EXTENDED)":"AdThrive Edge","Service Level":"Insider","Site Name (SITE_EXTENDED)":"Joy of Motion Crochet","Status (SITE_EXTENDED)":"Active","Tier (SITE_EXTENDED)":"AdThrive 5","VIEWABLE IMPRESSIONS":0,"Verticals":"Crafts"}]}

Google_Analytics_Instructions_file.txt_

@@ -0,0 +1,178 @@
+## Important
+Table Selection for Non-Device Data:
+If the user does not request Device data, please select the appropriate table based on the requested dimensions and metrics. Choose either:
+
+- VIEW_EARNINGS_AND_ANALYTICS_BY_DAY_D19312C8622E487D891B72420987C00B_MAT, or
+- VIEW_EARNINGS_BY_ADNETWORK_55601956261D42A49E72EBDDDE0082C5_MAT
+
+Use your best judgment to determine which table best aligns with the user's query. 
+'VIEW_EARNINGS_AND_ANALYTICS_BY_DAY_D19312C8622E487D891B72420987C00B_MAT' is typically the main default table to use unless it doesn't contain specific dimensions.
+
+## Calculation Rules
+- CPM = (Total Revenue ÷ Total Impressions) × 1,000
+
+- RPM = (Total Revenue ÷ Total Pageviews) × 1,000
+
+- IPPV (impressions per pageview) = Total Impressions ÷ Total Pageviews
+
+- If a user asks for CPM, RPM, or IPPV, please ensure that your sql query only contains sites with a status of 'Active' The reason is if a site has zero impressions, pageviews or revenue then the resulting calculation will be null or zero And thus we don't want this to be represented in the table.
+- If a user asks for the top 100 sites with the highest CPM, RPM, or IPPV, then default by using impressions or revenue or pageviews to identify the top 100 sites and then calculate.
+
+- **Examples:**
+SELECT 
+  "Site Name (SITE_EXTENDED)",
+  (SUM("GROSS EARNINGS") / NULLIF(SUM("IMPRESSIONS"), 0)) * 1000 AS "Video RPM"
+FROM ANALYTICS.SIGMA_SCRATCH.VIEW_EARNINGS_BY_ADNETWORK_55601956261D42A49E72EBDDDE0082C5_MAT
+WHERE "Media Type" = 'Video'
+  AND "DATE" >= DATEADD(day, -7, CURRENT_DATE())
+    AND "Status (SITE_EXTENDED)" = 'Active'
+GROUP BY "Site Name (SITE_EXTENDED)"
+HAVING SUM("IMPRESSIONS") >= 1
+ORDER BY "Video RPM" DESC
+LIMIT 100;
+
+
+## DATE COLUMN RULES
+- The "Install Date (SITE_EXTENDED)" column represents a date—even if defined as TEXT in the JSON metadata—and must be treated accordingly in SQL queries.
+- Under NO circumstances may a filter on this column use a numeric value.
+- Always express date comparisons using a string literal in the format `'YYYY-MM-DD'` rather than a numeric literal.
+- Any integer date literal (e.g., 20250401) is forbidden.
+
+- **Examples:**
+  - **Correct:**  
+    ```sql
+    AND "Install Date (SITE_EXTENDED)" >= '2025-04-01'
+    ```
+  - **Incorrect:**  
+    ```sql
+    AND "Install Date (SITE_EXTENDED)" >= 20250401
+    ```
+
+## SQL Query Generation Guidelines:
+For every request, generate a Snowflake SQL query using the metadata found in the JSON files.
+Follow these steps:
+
+1. **Verify Data Existence:**  
+   Ensure that the requested data exists within the provided reference information.
+
+2. **Generate the Query:**  
+   Use the dimensions and metrics specified by the user to build the SQL query.
+
+3. **SQL Code Block Requirements:**  
+   - The SQL code block must contain **only valid SQL statements** wrapped in triple backticks with the `sql` language tag (e.g., ```sql ... ```).
+   - Do not include any disclaimers, bullet points, or extra commentary within the code block.
+   - All supplementary explanatory text should be placed outside of the triple backticks.
+
+4. **Explicit Date Handling Rule:**  
+   - For any column that represents dates—especially `"Install Date (SITE_EXTENDED)"`—ensure that **all date literals are provided as string values**.
+   - **Do not output any numeric date literals** (e.g., 20250401).
+   - The SQL must always compare such columns using correctly formatted date string literals (e.g., `'2025-04-01'`).
+
+## Column Naming Guidelines:
+- All column names must be wrapped in double quotes.
+- For example: `"TIER"`, `"STATUS"`, `"Install Date (SITE_EXTENDED)"`.
+
+## Example Outputs for Reference:
+{
+    "title": "Example outputs for each column",
+    "column_names": [
+        "TIER",
+        "STATUS",
+        "SERVICE",
+        "SERVICE CONDENSED",
+        "NEW SERVICE CONDENSED",
+        "SERVICE LEVEL",
+        "PRIMARY VERTICAL"
+    ],
+    "columns": {
+        "TIER": [
+            "AdThrive 1",
+            "AdThrive 2",
+            "AdThrive 3",
+            "AdThrive 4",
+            "AdThrive 5",
+            "AdThrive 6",
+            "AdThrive 7",
+            "AdThrive 8"
+        ],
+        "STATUS": [
+            "Active",
+            "Checkup",
+            "Dropped",
+            "Install",
+            "Pending",
+            "Pending Manager Recommendation",
+            "Setup"
+        ],
+        "SERVICE": [
+            "AdThrive Edge",
+            "AdThrive Premium",
+            "Prime"
+        ],
+        "SERVICE CONDENSED": [
+            "AdThrive",
+            "Prime"
+        ],
+        "NEW SERVICE CONDENSED": [
+            "Creator",
+            "Enterprise"
+        ],
+        "SERVICE LEVEL": [
+            "Enterprise",
+            "Insider",
+            "Luminary",
+            "Platinum",
+            "Platinum Elite",
+            "Rise"
+        ],
+        "PRIMARY VERTICAL": [
+            "Arts & Creativity",
+            "Auto",
+            "Baby",
+            "Beauty",
+            "Business",
+            "Careers",
+            "Clean Eating",
+            "Crafts",
+            "Deals",
+            "Education",
+            "Entertainment",
+            "Family and Parenting",
+            "Fitness",
+            "Food",
+            "Gaming",
+            "Gardening",
+            "Green Living",
+            "Health and Wellness",
+            "History & Culture",
+            "Hobbies & Interests",
+            "Home Decor and Design",
+            "Law, Gov't & Politics",
+            "Lifestyle",
+            "Mens Style and Grooming",
+            "Natural Parenting",
+            "News",
+            "Other",
+            "Personal Finance",
+            "Pets",
+            "Pregnancy",
+            "Professional Finance",
+            "Real Estate",
+            "Religion & Spirituality",
+            "Science",
+            "Shopping",
+            "Sports",
+            "Tech",
+            "Toddler",
+            "Travel",
+            "Vegetarian",
+            "Wedding",
+            "Womens Style"
+        ]
+    }
+}
+
+---
+
+## Final Note:
+Every query generated must strictly conform to these guidelines. If the query involves filtering on a column representing dates—particularly `"Install Date (SITE_EXTENDED)"`—ensure that the date literal is always provided as a correctly formatted string (e.g., `'2025-04-01'`) and never as an integer (e.g., 20250401). This rule applies regardless of whether the metadata data type is defined as TEXT or a native date/timestamp type. Failure to adhere to this rule will result in type conversion errors in Snowflake.

Performance_Troubleshooting_Instructions.txt

@@ -0,0 +1,103 @@
+### Performance Troubleshooting Instructions
+
+**Purpose:**
+Provide clear guidance for ChatGPT to troubleshoot performance issues using the `Earnings_and_Analytics_by_Day.json` data source. When a user asks a performance-related question, follow these instructions to determine which metrics to pull, how to analyze anomalies, and how to present results.
+
+1. **Initial Data Pull & Site Selection:**
+
+   * **Time Window:** Retrieve data for the last **420 days** relative to today.
+   * **Site Dropdown:** Present a dropdown of all unique `Site Name (SITE_EXTENDED)` values. Filter the dataset to the user’s selection.
+   * **Metrics & Query:** Use the following SQL template, substituting `{{site}}` from the dropdown and dynamic dates:
+
+   ```sql
+   SELECT
+     "DATE",
+     SUM("PUB NET EARNINGS") AS "PUB_NET_EARNINGS",
+     SUM("IMPRESSIONS") AS "IMPRESSIONS",
+     SUM("SESSIONS") AS "SESSIONS",
+     SUM("PAGEVIEWS") AS "PAGEVIEWS",
+    CASE WHEN SUM("IMPRESSIONS") = 0 THEN NULL
+         ELSE (SUM("PUB NET EARNINGS") / SUM("IMPRESSIONS")) * 1000 END AS "CPM",
+    CASE WHEN SUM("PAGEVIEWS") = 0 THEN NULL
+         ELSE (SUM("PUB NET EARNINGS") / SUM("PAGEVIEWS")) * 1000 END AS "PAGE_RPM",
+    CASE WHEN SUM("SESSIONS") = 0 THEN NULL
+         ELSE (SUM("PUB NET EARNINGS") / SUM("SESSIONS")) * 1000 END AS "RPM",
+    CASE WHEN SUM("PAGEVIEWS") = 0 THEN NULL
+         ELSE (SUM("PAGEVIEWS") / SUM("SESSIONS")) * 1000 END AS "PPS",
+    CASE WHEN SUM("PAGEVIEWS") = 0 THEN NULL
+         ELSE (SUM("IMPRESSIONS") / SUM("PAGEVIEWS")) * 1000 END AS "IPPV",
+    CASE WHEN SUM("IMPRESSIONS") = 0 THEN NULL
+         ELSE (SUM("IMPRESSIONS") / SUM("SESSIONS")) * 1000 END AS "IMP/SESSION"
+=======
+     CASE WHEN SUM("IMPRESSIONS") = 0 THEN NULL
+          ELSE SUM("PUB NET EARNINGS") / (SUM("IMPRESSIONS") / 1000) END AS "CPM",
+     CASE WHEN SUM("PAGEVIEWS") = 0 THEN NULL
+          ELSE SUM("PUB NET EARNINGS") / (SUM("PAGEVIEWS") / 1000) END AS "PAGE_RPM",
+     CASE WHEN SUM("SESSIONS") = 0 THEN NULL
+          ELSE SUM("PUB NET EARNINGS") / (SUM("SESSIONS") / 1000) END AS "RPM",
+     CASE WHEN SUM("PAGEVIEWS") = 0 THEN NULL
+          ELSE SUM("PAGEVIEWS") / (SUM("SESSIONS") / 1000) END AS "PPS",
+     CASE WHEN SUM("PAGEVIEWS") = 0 THEN NULL
+          ELSE SUM("IMPRESSIONS") / (SUM("PAGEVIEWS") / 1000) END AS "IPPV",
+     CASE WHEN SUM("IMPRESSIONS") = 0 THEN NULL
+          ELSE SUM("IMPRESSIONS") / (SUM("SESSIONS") / 1000) END AS "IMP/SESSION"
+   FROM "ANALYTICS"."SIGMA_SCRATCH"."VIEW_EARNINGS_AND_ANALYTICS_BY_DAY_D19312C8622E487D891B72420987C00B_MAT"
+   WHERE "DATE" BETWEEN DATEADD(day, -420, CURRENT_DATE()) AND CURRENT_DATE()
+     AND "Site Name (SITE_EXTENDED)" = '{{site}}'
+   GROUP BY "DATE"
+   ORDER BY "DATE";
+   ```
+
+2. **Data Preparation & Visualization:**
+
+   * **Line Graphs:** For each metric (`PUB_NET_EARNINGS`, `IMPRESSIONS`, `SESSIONS`, `PAGEVIEWS`, `CPM`, `PAGE_RPM`, `RPM`, `PPS`, `IPPV`, `IMP/SESSION`), generate a separate time-series line chart over the 420-day window.
+   * **Labeling:** Title each chart with the metric name and include axis labels for date (x‑axis) and metric value (y‑axis).
+
+   * **Recent Decline Check:** After plotting, compare the last 7 days of each metric to the preceding 7 days. Flag any metric whose average drops by more than 5%.
+=======
+
+
+3. **Scope:**
+
+   * Analyze daily earnings trends to identify sudden drops or spikes in any of the metrics.
+   * Detect anomalies by comparing current values to historical baselines (e.g., 7-day or 30-day averages).
+   * Highlight days with significant percentage changes or values outside normal thresholds.
+
+4. **Metrics to Highlight in Troubleshooting:**
+
+   * **Total Revenue** (`PUB_NET_EARNINGS`)
+   * **Impressions**, **Sessions**, **Pageviews**
+   * **CPM**, **Page RPM**, **Session RPM**
+   * **PPS** (pageviews per session), **IPPV** (impressions per pageview), **Imp/Session**
+   * **Percent Change** vs. previous day and vs. rolling averages.
+
+5. **Thresholds & Alerts:**
+
+   * Flag days where any RPM metric falls below the 20th percentile of its past 30 days.
+   * Flag days where volume metrics (revenue, impressions, sessions, pageviews) change by more than ±25% compared to a 7-day moving average.
+
+6. **Recommended Analysis Steps:**
+   a. **Load & Filter Data**
+
+   * Execute the parameterized SQL for the last 420 days and the selected site.
+   * Parse `DATE` as a date type.
+     b. **Compute Rolling Baselines**
+   * Calculate 7-day and 30-day rolling averages and percentiles for each metric.
+     c. **Detect Anomalies**
+   * Compare daily values against baselines; mark anomalies per thresholds.
+     d. **Visualization & Summary**
+   * Produce the line charts.
+   * Annotate anomaly points on the graphs.
+   * Generate a summary table with columns: `date`, each metric, `perc_change_*`, `is_anomaly_*`.
+     e. **Insight Generation**
+   * Summarize key anomaly dates and magnitude of deviations.
+   * Recommend investigative actions (e.g., check tag implementation, ad server logs).
+
+7. **Expected Outputs:**
+
+   * **Dropdown UI** for site selection.
+   * **Ten line charts** showing each metric over time, with anomalies highlighted.
+   * **Summary Table** of flagged dates and metrics.
+   * **Bullet-point Insights** describing trends, anomalies, and next steps.
+
+*Follow this template whenever a user requests a performance line-graph analysis for a specific site over the last 420 days.*

README.md

@@ -1,9 +1,11 @@
 ---
 title: Revops Data Wizard
-emoji: 😻
-colorFrom: gray
-colorTo: gray
-sdk: static
+emoji: 🏃
+colorFrom: purple
+colorTo: red
+sdk: streamlit
+sdk_version: 1.42.0
+app_file: app.py
 pinned: false
 ---
 

RPM_by_URL.json

@@ -0,0 +1 @@
+{"database":"ANALYTICS","schema":"SIGMA_SCRATCH","table":"VIEW_RPM_BY__URL_6D18D88A2BDB45C58F8965B7ED869883_MAT","metadata":[{"column_name":"Exits","data_type":"NUMBER"},{"column_name":"Unfilled Impressions","data_type":"NUMBER"},{"column_name":"Tier","data_type":"TEXT"},{"column_name":"Service","data_type":"TEXT"},{"column_name":"Site Name","data_type":"TEXT"},{"column_name":"Is Amp","data_type":"BOOLEAN"},{"column_name":"Has Gamlog Data","data_type":"BOOLEAN"},{"column_name":"Session Duration","data_type":"NUMBER"},{"column_name":"Traffic_Source","data_type":"TEXT"},{"column_name":"Organization Id","data_type":"TEXT"},{"column_name":"Has Ga Data","data_type":"BOOLEAN"},{"column_name":"Pub Net Revenue","data_type":"FLOAT"},{"column_name":"Gross Revenue","data_type":"NUMBER"},{"column_name":"Site Id","data_type":"TEXT"},{"column_name":"Time on Page","data_type":"NUMBER"},{"column_name":"Unique 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SITE_AD_DENSITY.json

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SITE_RPM_GUARANTEE_ENTRY.json

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SITE_RPM_INPUT_AFTER.json

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SITE_RPM_INPUT_BEFORE.json

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(SITE_RPM_INPUT_AFTER)":"67ed9409c877202f77566a20","Service (SITE_EXTENDED)":"AdThrive Edge","Sidebar":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Site Id":"67ed9409c877202f77566a20","Site Name (SITE_EXTENDED)":"I Can Crochet That","Start Date":"2024-03-01 00:00:00.000 Z","Status (SITE_EXTENDED)":"Setup","Sticky Outstream":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Sticky Sidebar":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Tier (SITE_EXTENDED)":"AdThrive 6","UPDATED_AT (SITE_RPM_INPUT_AFTER)":"2025-04-07 14:51:00.264 Z","Updated At":"2025-04-07 13:44:57.395 Z","Use for Performance Reports":false,"Verticals (SITE_EXTENDED)":"Crafts|Arts & Creativity|Hobbies & Interests","Video Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null}},{"AD_LAYOUT_CHANGES (SITE_RPM_INPUT_AFTER)":"No Changes","Analysis Stage":1,"CREATED_AT (SITE_RPM_INPUT_AFTER)":"2025-04-07 14:51:00.264 Z","Content Ad":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Created At":"2025-04-07 13:44:57.395 Z","Desktop Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Display Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"End Date":"2024-04-30 00:00:00.000 Z","Footer":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"ID (SITE_RPM_INPUT_AFTER)":20274,"Id":55188,"Is Platinum (SITE_EXTENDED)":false,"Mobile Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Overall Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":29.79,"rps":38.99,"viewability":null},"Overall RPM":2.979000000000000e+01,"Overall RPS":3.899000000000000e+01,"Primary Vertical (SITE_EXTENDED)":"Crafts","Recipe Ad":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"SITE_ID (SITE_RPM_INPUT_AFTER)":"67ed9409c877202f77566a20","Service (SITE_EXTENDED)":"AdThrive Edge","Sidebar":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Site Id":"67ed9409c877202f77566a20","Site Name (SITE_EXTENDED)":"I Can Crochet That","Start Date":"2024-04-01 00:00:00.000 Z","Status (SITE_EXTENDED)":"Setup","Sticky Outstream":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Sticky Sidebar":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Tier (SITE_EXTENDED)":"AdThrive 6","UPDATED_AT (SITE_RPM_INPUT_AFTER)":"2025-04-07 14:51:00.264 Z","Updated At":"2025-04-07 13:44:57.395 Z","Use for Performance Reports":false,"Verticals (SITE_EXTENDED)":"Crafts|Arts & Creativity|Hobbies & Interests","Video Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null}},{"AD_LAYOUT_CHANGES (SITE_RPM_INPUT_AFTER)":"No Changes","Analysis Stage":1,"CREATED_AT (SITE_RPM_INPUT_AFTER)":"2025-04-07 14:51:00.264 Z","Content Ad":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Created At":"2025-04-07 13:44:57.395 Z","Desktop Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Display Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"End Date":"2024-05-31 00:00:00.000 Z","Footer":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"ID (SITE_RPM_INPUT_AFTER)":20274,"Id":55189,"Is Platinum (SITE_EXTENDED)":false,"Mobile Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Overall Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":29.61,"rps":38.19,"viewability":null},"Overall RPM":2.961000000000000e+01,"Overall RPS":3.819000000000000e+01,"Primary Vertical (SITE_EXTENDED)":"Crafts","Recipe Ad":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"SITE_ID (SITE_RPM_INPUT_AFTER)":"67ed9409c877202f77566a20","Service (SITE_EXTENDED)":"AdThrive Edge","Sidebar":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Site Id":"67ed9409c877202f77566a20","Site Name (SITE_EXTENDED)":"I Can Crochet That","Start Date":"2024-05-01 00:00:00.000 Z","Status (SITE_EXTENDED)":"Setup","Sticky Outstream":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Sticky Sidebar":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Tier (SITE_EXTENDED)":"AdThrive 6","UPDATED_AT (SITE_RPM_INPUT_AFTER)":"2025-04-07 14:51:00.264 Z","Updated At":"2025-04-07 13:44:57.395 Z","Use for Performance Reports":false,"Verticals (SITE_EXTENDED)":"Crafts|Arts & Creativity|Hobbies & Interests","Video Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null}},{"AD_LAYOUT_CHANGES (SITE_RPM_INPUT_AFTER)":"No Changes","Analysis Stage":1,"CREATED_AT (SITE_RPM_INPUT_AFTER)":"2025-04-07 14:51:00.264 Z","Content Ad":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Created At":"2025-04-07 13:44:57.395 Z","Desktop Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Display Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"End Date":"2024-06-30 00:00:00.000 Z","Footer":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"ID (SITE_RPM_INPUT_AFTER)":20274,"Id":55190,"Is Platinum (SITE_EXTENDED)":false,"Mobile Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Overall Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":33.63,"rps":42.45,"viewability":null},"Overall RPM":3.363000000000000e+01,"Overall RPS":4.245000000000000e+01,"Primary Vertical (SITE_EXTENDED)":"Crafts","Recipe Ad":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"SITE_ID (SITE_RPM_INPUT_AFTER)":"67ed9409c877202f77566a20","Service (SITE_EXTENDED)":"AdThrive Edge","Sidebar":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Site Id":"67ed9409c877202f77566a20","Site Name (SITE_EXTENDED)":"I Can Crochet That","Start Date":"2024-06-01 00:00:00.000 Z","Status (SITE_EXTENDED)":"Setup","Sticky Outstream":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Sticky Sidebar":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null},"Tier (SITE_EXTENDED)":"AdThrive 6","UPDATED_AT (SITE_RPM_INPUT_AFTER)":"2025-04-07 14:51:00.264 Z","Updated At":"2025-04-07 13:44:57.395 Z","Use for Performance Reports":false,"Verticals (SITE_EXTENDED)":"Crafts|Arts & Creativity|Hobbies & Interests","Video Json":{"cpm":null,"fillRate":null,"impressionsPerPageView":null,"rpm":null,"rps":null,"viewability":null}}]}

Sigma data model URL Mapping.json

@@ -0,0 +1,70 @@
+[
+    {
+        "Sigma Table":"AD_LAYOUT_PUBLISHED",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=1F6z0H32Qp"
+    },
+    {
+        "Sigma Table":"ANNOTATION",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=Afex8xzhtg"
+    },
+    {
+        "Sigma Table":"ANNOTATION_INCREMENTAL",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=DrFTu9z2WJ"
+    },
+    {
+        "Sigma Table":"CAL_IMPRESSION_EVENT",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=V0dawih4D0"
+    },
+    {
+        "Sigma Table":"Earnings and Analytics by Day",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=yU6LEbkxPG"
+    },
+    {
+        "Sigma Table":"Earnings and Analytics by Device",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=iSaGudtYnZ"
+    },
+    {
+        "Sigma Table":"Earnings by Ad Network",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=WOsYJJ0XW_"
+    },
+    {
+        "Sigma Table":"GAM Earnings",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=Vr0JPKACf9"
+    },
+    {
+        "Sigma Table":"Jounce - VW_CLASSIFICATION",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=aCzWEa_U8A"
+    },
+    {
+        "Sigma Table":"RPM_by _URL",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ\/edit?:nodeId=wfRKiGNM2H"
+    },
+    {
+        "Sigma Table":"SITE_AD_DENSITY",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=vAw_EHoXiS"
+    },
+    {
+        "Sigma Table":"SITE_EXTENDED_Snowflake",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=y3t1N49D68"
+    },
+    {
+        "Sigma Table":"SITE_RPM_GUARANTEE_ENTRY",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=Ft88AlVaa4"
+    },
+    {
+        "Sigma Table":"SITE_RPM_INPUT_AFTER",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=MqhmXUHaxl"
+    },
+    {
+        "Sigma Table":"SITE_RPM_INPUT_BEFORE",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=ZBlupFABSh"
+    },
+    {
+        "Sigma Table":"VW_ADSLOGGER_PARSED",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=bSSKnREqMb"
+    },
+    {
+        "Sigma Table":"VW_HOURLY_EARNING_RAW",
+        "URL Link":"https:\/\/app.sigmacomputing.com\/raptive\/data-model\/Avi-Data-Model-h2uG3Qai4VA4dLO1q0UBJ?:nodeId=JBP1JPk2E_"
+    }
+]

app.py

@@ -0,0 +1,759 @@
+import streamlit as st
+import openai
+import os
+import pandas as pd
+import json
+import time
+import snowflake.connector
+from cryptography.hazmat.primitives import serialization
+import re
+import datetime
+import logging
+
+import altair as alt
+
+
+# Suppress ScriptRunContext warnings when necessary.
+logging.getLogger("streamlit.runtime.scriptrunner_utils").setLevel(logging.ERROR)
+
+# ─────────────────────────────────────────────────────────────
+# 1. Page Configuration
+# ─────────────────────────────────────────────────────────────
+st.set_page_config(page_title="Raptive Data Wizard", layout="wide")
+st.title("Raptive Data Wizard")
+
+# ─────────────────────────────────────────────────────────────
+# 2. Main Tabs: "Ask a Question", "Instructions", "Additional Info"
+# ─────────────────────────────────────────────────────────────
+main_tabs = st.tabs(["Ask a Question", "Instructions", "Additional Info"])
+
+# ─────────────────────────────────────────────────────────────
+# 3. "Ask a Question" Tab (Main Logic)
+# ─────────────────────────────────────────────────────────────
+with main_tabs[0]:
+    st.header("Ask a Question")
+
+    # Sidebar for Data Source Selection
+    st.sidebar.markdown(
+        """
+        <div style="background-color: #fff3cd; padding: 10px; border-radius: 5px; border-left: 5px solid #ffc107;">
+            <h3 style="color: #856404; margin-bottom: 0;">Data Source</h3>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    data_config = {
+        "Ask a Question": {
+            "files": [
+                "GAMLOG.json",
+                "Earnings_and_Analytics_by_Day.json",
+                "Earnings_and_Analytics_by_Device.json",
+                "Earnings_by_Ad_Network.json",
+                "GAM_earnings.json",
+                "GAM_Hourly.json",
+                "RPM_by_URL.json",
+                "AVI_SITE_DATASET_snowflake.json",
+                "Annotation.json",
+                "Annotation_incremental.json",
+                "SITE_RPM_GUARANTEE_ENTRY.json",
+                "SITE_RPM_INPUT_BEFORE.json",
+                "SITE_RPM_INPUT_AFTER.json",
+                "SITE_AD_DENSITY.json",
+            ],
+            "table_references": (
+                "The user selected 'Ask a Question'. Review all JSON files provided and identify the correct data source based on the user's question. "
+                "Clearly instruct the user which data source toggle to select."
+            ),
+        },
+        "AB Group Experiments": {
+            "files": ["GAMLOG_ABGROUP.json", "GAMLOG_ABGROUP_Instructions_file.txt"],
+            "table_references": (
+                "For GAMLOG_ABGROUP data, help the user write an SQL query and always reference the AB_GRANULAR table along with GAMLOG_ABGROUP_Instructions_file.txt to help normalize the data."
+            ),
+        },
+        "GAMLOG": {
+            "files": ["GAMLOG.json", "gamlog_Instructions_file.txt"],
+            "table_references": (
+                "For GAMLOG data, help the user write an SQL query and always reference the GAMLOG table."
+            ),
+        },
+        "Google Analytics Pageviews": {
+            "files": [
+                "Earnings_and_Analytics_by_Day.json",
+                "Earnings_and_Analytics_by_Device.json",
+                "Earnings_by_Ad_Network.json",
+                "Google_Analytics_Instructions_file.txt",
+            ],
+            "table_references": "Generate a SQL query using the metadata if needed.",
+        },
+        "GAM Hourly": {
+            "files": ["GAM_Hourly.json"],
+            "table_references": (
+                "For GAM Hourly, provide guidance on analyzing order-level earnings by hour. "
+                "Generate a SQL query referencing the appropriate table."
+            ),
+        },
+        "GAM Earnings (Order-level Data)": {
+            "files": ["GAM_earnings.json"],
+            "table_references": (
+                "For GAM Earnings (Order-level Data), provide guidance on analyzing order-level earnings. "
+                "Generate a SQL query referencing the appropriate table."
+            ),
+        },
+        "RPM by URL": {
+            "files": ["RPM_by_URL.json"],
+            "table_references": (
+                "For RPM by URL, reference the RPM_by_URL table. Generate a SQL query using the provided metadata."
+                " If the user asks for URL data they are most likely asking for Page Path data, so please use accordingly"
+            ),
+        },
+        "Site Settings": {
+            "files": [
+                "AVI_SITE_DATASET_snowflake.json",
+                "AVI_SITE_DATASET_snowflake_settings.txt",
+            ],
+            "table_references": (
+                "For Site Settings, reference the listed JSON files. Generate a SQL query using the metadata if needed."
+            ),
+        },
+        "Site Annotations": {
+            "files": ["Annotation.json", "Annotation_incremental.json"],
+            "table_references": (
+                "For Site Annotations, reference the JSON files. Generate a SQL query using the metadata if needed."
+            ),
+        },
+        "Site RPM Settings": {
+            "files": [
+                "SITE_RPM_GUARANTEE_ENTRY.json",
+                "SITE_RPM_INPUT_BEFORE.json",
+                "SITE_RPM_INPUT_AFTER.json",
+            ],
+            "table_references": (
+                "For Site RPM Settings, reference the JSON files. Generate a SQL query using the provided metadata."
+            ),
+        },
+        "Site Ad Density": {
+            "files": ["SITE_AD_DENSITY.json"],
+            "table_references": (
+                "For Site Ad Density, reference the SITE_AD_DENSITY JSON file. Generate a SQL query using the provided metadata."
+            ),
+        },
+        "Performance Troubleshooting": {
+            "files": [
+                "Earnings_and_Analytics_by_Day.json",
+                "Performance_Troubleshooting_Instructions.txt",
+            ],
+            "table_references": (
+                "For Performance Troubleshooting, reference the Earnings_and_Analytics_by_Day data and the Performance Troubleshooting instructions. "
+                "Generate a line-chart for each metric over the last 420 days for a selected site, per the instructions."
+            ),
+        },
+    }
+
+    data_choice = st.sidebar.radio(
+        "Which data source do you want to use?", list(data_config.keys())
+    )
+    st.sidebar.markdown(
+        """
+    <div style="background-color: #fff3cd; padding: 10px; border-radius: 5px; margin-top: 20px;">
+        <p style="color: #000;">If anyone has any questions, feedback, or requests, please contact <strong>Avi Sutton</strong>.</p>
+    </div>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    # ─────────────────────────────────────────────────────────────
+    # Loader: returns merged reference info and dynamic schema (if available)
+    @st.cache_data
+    def load_reference_files(files):
+        chunk_texts = []
+        dynamic_schema = None  # Try to extract schema from JSON metadata if available.
+        for fp in files:
+            if fp.lower().endswith(".json"):
+                try:
+                    with open(fp, "r", encoding="utf-8") as f:
+                        data = json.load(f)
+                        # If a schema is defined in this JSON file, capture it.
+                        if (
+                            dynamic_schema is None
+                            and isinstance(data, dict)
+                            and "schema" in data
+                        ):
+                            dynamic_schema = data["schema"]
+                        chunk_texts.append(json.dumps(data, separators=(",", ":")))
+                except Exception as e:
+                    chunk_texts.append(f"[ERROR loading JSON: {fp}] {e}")
+            elif fp.lower().endswith(".txt"):
+                try:
+                    with open(fp, "r", encoding="utf-8") as f:
+                        chunk_texts.append(f.read())
+                except Exception as e:
+                    chunk_texts.append(f"[ERROR loading TXT: {fp}] {e}")
+            else:
+                chunk_texts.append(f"[Unknown file type: {fp}]")
+        return "\n".join(chunk_texts), dynamic_schema
+
+    @st.cache_data
+    def load_performance_query():
+        with open(
+            "Performance_Troubleshooting_Instructions.txt", "r", encoding="utf-8"
+        ) as f:
+            text = f.read()
+        match = re.search(r"```sql\s*(.*?)\s*```", text, re.DOTALL)
+        return match.group(1).strip() if match else ""
+
+    @st.cache_data
+    def get_site_names():
+        private_key = serialization.load_pem_private_key(
+            os.getenv("snowflake_private_key").encode(), password=None
+        )
+        conn = snowflake.connector.connect(
+            user=os.getenv("snowflake_user"),
+            account=os.getenv("snowflake_account_identifier"),
+            private_key=private_key,
+            role=os.getenv("snowflake_role"),
+            warehouse=os.getenv("snowflake_warehouse"),
+            database=os.getenv("snowflake_database"),
+            schema="SIGMA_SCRATCH",
+        )
+        cs = conn.cursor()
+        cs.execute(
+            'SELECT DISTINCT "Site Name (SITE_EXTENDED)" FROM "ANALYTICS"."SIGMA_SCRATCH"."VIEW_EARNINGS_AND_ANALYTICS_BY_DAY_D19312C8622E487D891B72420987C00B_MAT" ORDER BY 1'
+        )
+        sites = [row[0] for row in cs.fetchall()]
+        cs.close()
+        conn.close()
+        return sites
+
+    selected_config = data_config.get(data_choice, {})
+    chunk_file_paths = selected_config.get("files", [])
+    table_references = selected_config.get("table_references", "")
+
+    reference_info, dynamic_schema = load_reference_files(chunk_file_paths)
+    if dynamic_schema is None:
+        dynamic_schema = "GAM360"
+
+    site_selection = None
+    if data_choice == "Performance Troubleshooting":
+        sites = get_site_names()
+        site_selection = st.selectbox("Select Site", options=sites)
+
+    # Extract available dimensions and metrics from JSON files (if available)
+    available_dimensions = []
+    available_metrics = []
+
+    for fp in chunk_file_paths:
+        if fp.lower().endswith(".json"):
+            try:
+                with open(fp, "r", encoding="utf-8") as f:
+                    data = json.load(f)
+                    if isinstance(data, dict) and "metadata" in data:
+                        for col in data["metadata"]:
+                            col_name = col.get("column_name")
+                            data_type = col.get("data_type", "").upper()
+                            if col_name:
+                                # Use numeric data types as metrics, others as dimensions.
+                                if data_type in [
+                                    "NUMBER",
+                                    "FLOAT",
+                                    "INTEGER",
+                                    "DOUBLE",
+                                    "DECIMAL",
+                                ]:
+                                    if col_name not in available_metrics:
+                                        available_metrics.append(col_name)
+                                else:
+                                    if col_name not in available_dimensions:
+                                        available_dimensions.append(col_name)
+            except Exception as e:
+                st.write(f"Error reading {fp}: {e}")
+
+    if data_choice != "Performance Troubleshooting":
+        # Optional expander for user to select dimensions, metrics, and a date range.
+        with st.expander(
+            "Optional: Specify Dimensions, Metrics, and Date Filter", expanded=False
+        ):
+            selected_dimensions = st.multiselect(
+                "Select Dimensions", options=available_dimensions
+            )
+            selected_metrics = st.multiselect(
+                "Select Metrics", options=available_metrics
+            )
+            today = datetime.date.today()
+            default_start = today - datetime.timedelta(days=7)
+            default_end = today - datetime.timedelta(days=1)
+            apply_date_filter = st.checkbox("Apply Date Filter?")
+            if apply_date_filter:
+                date_range = st.date_input(
+                    "Select Date Range", value=(default_start, default_end)
+                )
+            else:
+                date_range = None
+    else:
+        selected_dimensions = []
+        selected_metrics = []
+        date_range = None
+
+    api_key = os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        st.sidebar.error("No API key found in environment.")
+        st.stop()
+    openai.api_key = api_key
+
+    # Build additional context from optional inputs.
+    additional_context = ""
+    if selected_dimensions:
+        additional_context += f"\nDimensions provided: {', '.join(selected_dimensions)}"
+    if selected_metrics:
+        additional_context += f"\nMetrics provided: {', '.join(selected_metrics)}"
+    if date_range and isinstance(date_range, tuple) and len(date_range) == 2:
+        additional_context += f"\nDate Range: {date_range[0]} to {date_range[1]}"
+
+    if data_choice == "Ask a Question":
+        # Define the list of valid data config keys that can be recommended.
+        valid_data_configs = [
+            "Ask a Question",
+            "AB Group Experiments",
+            "GAMLOG",
+            "Google Analytics Pageviews",
+            "GAM Earnings (Order-level Data)",
+            "GAM Hourly",
+            "RPM by URL",
+            "Site Settings",
+            "Site Annotations",
+            "Site RPM Settings",
+            "Site Ad Density",
+            "Performance Troubleshooting",
+        ]
+
+        valid_data_configs_str = ", ".join(
+            [f'"{config}"' for config in valid_data_configs]
+        )
+
+        system_message_content = f"""
+    You are a helpful data assistant. Your job is to determine which one of the following data config keys is most relevant to the user's question:
+    
+    {valid_data_configs_str}
+    
+    After thoroughly analyzing the provided metadata:
+    
+    1. **Recommendation:** Provide a concise recommendation stating exactly which data config toggle the user should select (for example, "GAMLOG" or "Site RPM Settings"). You must choose from the list above and **do not** reference any JSON filename or file-level detail.
+    2. **Justification:** Briefly justify your choice based on matching the user's request with the available dimensions, metrics, or other metadata details.
+    3. **Response Formatting:** Use the exact format below and do not generate an SQL query for this option.
+    
+    The required response format is:
+    
+    **Recommended Data Source: <Data Config Name>**
+    
+    **Reason:**
+    - Brief justification based on the available metadata.
+    
+    Reference Data:
+    {reference_info}
+    {additional_context}
+    """
+
+    else:
+        # existing system message for other data sources
+        system_message_content = (
+            "You are a helpful data assistant. Your job is to answer user questions about data, including guidance on "
+            "where the data can be found and which datasets should be used. Using the Data Source, JSON files, and the "
+            "instruction file as helpful context, create a **concise summary** that details what data source is being "
+            "used, what table is being used (including its database schema as found in the JSON metadata). "
+            "Your summary should be in Markdown format with each bullet point on a new line, and indent sub-bullets properly. "
+            "The format must **only** include:\n"
+            "1. A short introductory sentence.\n"
+            "2. A section called **Key Points** (in bullet points). Do not include metadata information in this response.\n"
+            "For every request, generate a Snowflake SQL query using the metadata found in the JSON files. "
+            "Follow these steps when generating a SQL query:\n"
+            "1. Verify that the requested data exists within the provided reference info.\n"
+            "2. Generate a SQL query using the dimensions and metrics specified by the user.\n"
+            "3. The SQL code block must contain **only** valid SQL statements and nothing else.\n\n"
+            "When generating SQL code blocks, follow these rules:\n"
+            "- Place only valid SQL statements inside ```sql ... ```.\n"
+            "- Do not include disclaimers, bullet points, or additional commentary inside the code block.\n"
+            "- All explanatory text must be outside the triple backticks.\n\n"
+            "**Additional Rule:** For any column representing dates—especially \"Install Date (SITE_EXTENDED)\"—ensure that all date literals are provided as properly formatted strings (e.g., 'YYYY-MM-DD'). If a numeric date literal (e.g., 20250401) is detected, convert it to the correct format (e.g., '2025-04-01'). Under no circumstances should a date filter be written using a numeric literal.\n\n"
+            "**Crucial Formatting Rules:**\n"
+            "- **Dimensions and Metrics:** Dimensions are descriptive attributes (e.g., 'Site ID', 'Page Path'). Metrics are quantitative measurements (e.g., 'Pageviews', 'Impressions').\n"
+            "- **Dimensions and Metrics:** All dimension and metric names must be in UPPERCASE.\n"
+            '- **Quoting:** Always wrap dimension and metric names in double quotes. For example, use `"Dimension Name"` and `"Metric Name"` instead of `Dimension Name` and `Metric Name`.\n'
+            "- If a dimension or metric name does not contain spaces (e.g., 'status'), you still must wrap it in double quotes (e.g., `\"status\"`).\n"
+            "- String values in `WHERE` clauses **must** be enclosed in single quotes (`'...'`). Numeric values should not be enclosed in quotes.\n"
+            "- **Case Sensitivity:** Pay very close attention to case sensitivity. Ensure that all column names and string values in `WHERE` clauses match the exact case found in the appropriate JSON sample data.\n"
+            "- **Data Types:** Ensure that the data types of values used in `WHERE` clauses match the data types of the corresponding columns.\n"
+            "- **Verify all syntax against the appropriate JSON sample data to ensure accuracy.**\n"
+            "- **Null Values and Percentages:** If the user's question involves calculating percentages or SOV (Share of Voice) pecentages, exclude null values from the query results.\n"
+            "- **Division by Zero:** When performing calculations involving division, ensure the denominator is not zero. Use SQL functions such as `NULLIF(denom, 0)` or CASE statements to safely handle division by zero scenarios.\n"
+            '- **Default Date Filter:** Unless explicitly specified by the user, apply a filter to the SQL query using yesterday\'s date aside for when the data source is "Site Ad Density", "Site Settings", "Site RPM Settings", or "Site Annotations".\n'
+            f"Use the following as a guide for additional information regarding the Data Source: {table_references}\n\n"
+            "Reference Data:\n"
+            f"Please use the following as a guide to understand the Data Source selected by the user and which JSON files "
+            f"to use so that you provide a well-informed answer: {reference_info}"
+            f"{additional_context}"
+        )
+
+    system_message = {"role": "system", "content": system_message_content}
+
+    if "conversation_history" not in st.session_state:
+        st.session_state["conversation_history"] = []
+
+    user_input = st.text_input(
+        "Ask a question: Be as specific as possible so I can give you the most accurate answer"
+    )
+    modified_question = user_input
+    if selected_dimensions or selected_metrics:
+        modified_question += (
+            f"\nDimensions: {', '.join(selected_dimensions)}"
+            if selected_dimensions
+            else ""
+        )
+        modified_question += (
+            f"\nMetrics: {', '.join(selected_metrics)}" if selected_metrics else ""
+        )
+    if date_range and isinstance(date_range, tuple) and len(date_range) == 2:
+        modified_question += f"\nDate Range: {date_range[0]} to {date_range[1]}"
+
+    submit_button = st.button("Submit")
+
+    if submit_button:
+        if data_choice == "Performance Troubleshooting" and site_selection:
+            try:
+                sql_query = load_performance_query().replace("{{site}}", site_selection)
+                with st.expander("View SQL Query (Advanced Users)"):
+                    st.code(sql_query, language="sql")
+                private_key = serialization.load_pem_private_key(
+                    os.getenv("snowflake_private_key").encode(), password=None
+                )
+                conn = snowflake.connector.connect(
+                    user=os.getenv("snowflake_user"),
+                    account=os.getenv("snowflake_account_identifier"),
+                    private_key=private_key,
+                    role=os.getenv("snowflake_role"),
+                    warehouse=os.getenv("snowflake_warehouse"),
+                    database=os.getenv("snowflake_database"),
+                    schema="SIGMA_SCRATCH",
+                )
+                cs = conn.cursor()
+                cs.execute("ALTER SESSION SET STATEMENT_TIMEOUT_IN_SECONDS = 1800")
+                st.write("Executing SQL query...")
+                start_time_query = time.time()
+                with st.spinner("Executing SQL query..."):
+                    cs.execute(sql_query)
+                    query_result = cs.fetchall()
+                elapsed_time_query = time.time() - start_time_query
+                minutes = int(elapsed_time_query // 60)
+                seconds = elapsed_time_query % 60
+                st.write(
+                    f"SQL query executed in {minutes} minutes and {seconds:.2f} seconds."
+                )
+                columns = [col[0] for col in cs.description] if cs.description else []
+                cs.close()
+                conn.close()
+                if columns:
+                    result_df = pd.DataFrame(query_result, columns=columns)
+
+                    result_df["DATE"] = pd.to_datetime(result_df["DATE"]).dt.date
+                    metrics = [c for c in columns if c != "DATE"]
+                    result_df[metrics] = (
+                        result_df[metrics]
+                        .apply(pd.to_numeric, errors="coerce")
+                        .round(2)
+                    )
+                    plot_df = result_df.copy()
+                    result_df["DATE"] = result_df["DATE"].astype(str)
+
+                    for metric in metrics:
+                        chart_df = plot_df[["DATE", metric]].dropna()
+                        brush = alt.selection_interval(encodings=["x"])
+                        base = (
+                            alt.Chart(chart_df)
+                            .mark_line()
+                            .encode(
+                                x="DATE:T",
+                                y=alt.Y(
+                                    f"{metric}:Q",
+                                    title=metric,
+                                    axis=alt.Axis(format=".2f"),
+                                ),
+                                tooltip=[
+                                    alt.Tooltip("DATE:T", title="Date"),
+                                    alt.Tooltip(f"{metric}:Q", format=".2f"),
+                                ],
+                            )
+                        )
+                        upper = base.properties(height=200).add_selection(brush)
+                        lower = base.transform_filter(brush).properties(height=200)
+                        st.altair_chart(
+                            alt.vconcat(upper, lower).resolve_scale(y="independent"),
+                            use_container_width=True,
+                        )
+
+                    recent = plot_df.sort_values("DATE").tail(14)
+                    flagged = {}
+                    if len(recent) == 14:
+                        for metric in metrics:
+                            first_avg = recent.iloc[:7][metric].mean()
+                            last_avg = recent.iloc[7:][metric].mean()
+                            if pd.notna(first_avg) and first_avg != 0:
+                                pct_change = (last_avg - first_avg) / first_avg * 100
+                                if pct_change < -5:
+                                    flagged[metric] = pct_change
+
+                    if flagged:
+                        st.subheader("Metrics Showing Recent Decline")
+                        for metric, pct in flagged.items():
+                            st.warning(
+                                f"{metric}: {pct:.2f}% decline compared with the prior week"
+                            )
+
+                    column_config = {
+                        metric: st.column_config.NumberColumn(format="%.2f")
+                        for metric in metrics
+                    }
+                    st.data_editor(
+                        result_df,
+                        use_container_width=True,
+                        hide_index=True,
+                        column_config=column_config,
+                    )
+                    result_df["DATE"] = pd.to_datetime(result_df["DATE"])
+                    metrics = [c for c in columns if c != "DATE"]
+                    for metric in metrics:
+                        st.line_chart(result_df, x="DATE", y=metric)
+                    st.data_editor(result_df, use_container_width=True, hide_index=True)
+                else:
+                    st.write(query_result)
+            except Exception as e:
+                st.error(f"Error executing Performance Troubleshooting query: {e}")
+        elif modified_question:
+            try:
+                messages = (
+                    [system_message]
+                    + st.session_state["conversation_history"]
+                    + [{"role": "user", "content": modified_question}]
+                )
+                with st.spinner("Waiting for model response..."):
+                    start_time = time.time()
+                    response = openai.chat.completions.create(
+                        model="o4-mini",
+                        messages=messages,
+                        reasoning_effort="high",
+                    )
+                    end_time = time.time()
+                elapsed_time = end_time - start_time
+                minutes = int(elapsed_time // 60)
+                seconds = elapsed_time % 60
+                answer = response.choices[0].message.content.strip()
+                st.session_state["conversation_history"].append(
+                    {"role": "user", "content": modified_question}
+                )
+                st.session_state["conversation_history"].append(
+                    {"role": "assistant", "content": answer}
+                )
+                clean_answer = re.sub(
+                    r"```sql\s+(.*?)\s+```", "", answer, flags=re.DOTALL
+                ).strip()
+                st.write(f"**Assistant:** {clean_answer}")
+                # st.write(f"**Assistant:** {answer}")
+                st.write(
+                    f"Response received in {minutes} minutes and {seconds:.2f} seconds."
+                )
+
+                # st.write("### Extracting SQL Query from assistant response...")
+                # Reminder: The SQL query must use string-formatted date literals.
+                sql_match = re.search(r"```sql\s+(.*?)\s+```", answer, re.DOTALL)
+                if sql_match:
+                    sql_query = sql_match.group(1).strip()
+                    # st.write("SQL query extracted from markdown code block successfully.")
+                else:
+                    sql_query = answer.strip()
+                    # st.write("No markdown code block found; using entire answer as SQL query.")
+
+                # Only execute SQL query if the data source is not 'Ask a Question'
+                if data_choice != "Ask a Question" and "select" in sql_query.lower():
+                    with st.expander("View SQL Query (Advanced Users)"):
+                        st.code(sql_query, language="sql")
+                    try:
+                        private_key = serialization.load_pem_private_key(
+                            os.getenv("snowflake_private_key").encode(), password=None
+                        )
+                        conn = snowflake.connector.connect(
+                            user=os.getenv("snowflake_user"),
+                            account=os.getenv("snowflake_account_identifier"),
+                            private_key=private_key,
+                            role=os.getenv("snowflake_role"),
+                            warehouse=os.getenv("snowflake_warehouse"),
+                            database=os.getenv("snowflake_database"),
+                            schema=dynamic_schema,
+                        )
+                        cs = conn.cursor()
+                        cs.execute(
+                            "ALTER SESSION SET STATEMENT_TIMEOUT_IN_SECONDS = 1800"
+                        )
+                        st.write("Executing SQL query...")
+                        start_time_query = time.time()
+                        with st.spinner("Executing SQL query..."):
+                            cs.execute(sql_query)
+                            query_result = cs.fetchall()
+                        elapsed_time_query = time.time() - start_time_query
+                        minutes = int(elapsed_time // 60)
+                        seconds = elapsed_time % 60
+                        st.write(
+                            f"SQL query executed in {minutes} minutes and {seconds:.2f} seconds."
+                        )
+                        columns = (
+                            [col[0] for col in cs.description] if cs.description else []
+                        )
+                        cs.close()
+                        conn.close()
+
+                        st.write("### Query Result")
+                        if columns:
+                            result_df = pd.DataFrame(query_result, columns=columns)
+                            # Reset the index AND drop it from the DataFrame itself
+                            result_df = result_df.reset_index(drop=True)
+                            st.data_editor(
+                                result_df, use_container_width=True, hide_index=True
+                            )
+                            st.write(
+                                f"Total Rows: {len(result_df):,}"
+                            )  # Add total row count.
+                            csv_data = result_df.to_csv(index=False).encode("utf-8")
+                            st.download_button(
+                                label="Download CSV",
+                                data=csv_data,
+                                file_name="query_results.csv",
+                                mime="text/csv",
+                            )
+                            # New Step: Analyze the entire data using OpenAI's model
+                            if not result_df.empty:
+                                full_data_csv = result_df.to_csv(index=False)
+                                analysis_prompt = (
+                                    "You are a helpful data assistant. Below is a CSV dataset. "
+                                    "Your summary should be in Markdown format with each bullet point on a new line and proper indentation for sub-bullets. "
+                                    "Please provide a dynamic, structured analysis that includes:\n\n"
+                                    "1. Format all large numbers with comma separators and round percentages to two decimals.\n"
+                                    "2. A concise overview summarizing the key dimensions, metrics, filters, and date range (1–2 sentences).\n"
+                                    "3. Notable trends or patterns with mathematical reasoning and comparisons (1–3 bullet points). For example, calculate percentage changes or slopes where applicable.\n"
+                                    "4. Identification of any outliers or anomalies (1–2 bullet points).\n"
+                                    "5. A brief conclusion or recommendations for further analysis (1–2 sentences).\n\n"
+                                    "Keep your response under 150 words and avoid unnecessary repetition. Here is the dataset:\n\n"
+                                    f"{full_data_csv}"
+                                )
+
+                                with st.spinner("Analyzing data with OpenAI model..."):
+                                    analysis_response = openai.chat.completions.create(
+                                        model="o3-mini",  # Replace with your desired model
+                                        messages=[
+                                            {"role": "user", "content": analysis_prompt}
+                                        ],
+                                        reasoning_effort="high",
+                                    )
+                                    analysis_text = analysis_response.choices[
+                                        0
+                                    ].message.content.strip()
+
+                                with st.expander("View Data Analysis & Insights"):
+                                    st.write(analysis_text)
+                        else:
+                            st.write(query_result)
+                    except Exception as e:
+                        st.error(f"Error executing SQL query: {e}")
+                else:
+                    st.write("No valid SQL query detected. Returning response as text.")
+            except Exception as e:
+                st.error(f"Error processing the request: {e}")
+
+    if st.button("Clear History"):
+        st.session_state["conversation_history"] = []
+        st.success("History cleared!")
+
+    # Add the "View History" button
+    if st.button("View History"):
+        st.subheader("Conversation History")
+        if st.session_state["conversation_history"]:
+            for message in st.session_state["conversation_history"]:
+                role = message["role"].capitalize()
+                content = message["content"]
+                st.markdown(f"**{role}:** {content}")
+        else:
+            st.info("No conversation history available.")
+# ─────────────────────────────────────────────────────────────
+# 4. "Instructions" Tab (Updated)
+# ─────────────────────────────────────────────────────────────
+with main_tabs[1]:
+    st.header("📘 Instructions")
+    st.markdown(
+        """
+### 🧠 Raptive Data Wizard – User Instructions
+#### Overview
+Raptive Data Wizard is an interactive tool that helps you quickly explore your data sources using JSON metadata. The wizard guides you through selecting the correct data source, formulating your question, and generating SQL queries to retrieve your desired data.
+<div style="background-color: #fff3cd; padding: 10px; border-left: 6px solid #ffc107; border-radius: 5px;">
+  <strong>⚠️ Reminder:</strong> Limit your date range to a maximum of 30 days to ensure fast query performance.
+</div>
+
+#### 📊 Supported Data Sources & Tables
+Select one of the following data sources from the sidebar. Each option is linked to specific tables or metadata files:
+| **Data Source**                     | **Description & Example Uses** |
+|-------------------------------------|---------------------------------|
+| **Ask a Question**                  | Automatically helps determine the appropriate data source based on your question for you to select |
+| **AB Group Experiments**            | Incrementality - Impressions & Revenue lift data by multiple dimensions |
+| **GAMLOG**                          | Pageviews (PVK), Impressions, Key-Values, Dimensions by Browser, Device, **HOUR** etc. |
+| **Google Analytics Pageviews**      | Pageviews and sessions by day, device, or ad network |
+| **GAM Earnings (Order-level)**      | Revenue and impressions by specific GAM orders. Many dimensions to select. |
+| **GAM Hourly**                      | Hourly Revenue and impressions by specific GAM orders. Many dimensions to select. |
+| **RPM by URL**                      | RPM performance metrics by Traffic Source, Device, Country, URLs |
+| **Site Settings**                   | Settings including Ads.txt, Ad Options, Video Ad Options, Ad Preferences, Sensitive Categories, ad configurations |
+| **Site Annotations**                | Manual annotations or incremental notes for sites |
+| **Site RPM Settings**               | RPM guarantees and inputs before/after adjustments |
+| **Site Ad Density**                 | Analysis of ads per page and Site Ad Density thresholds |
+---
+#### 📝 How to Ask Questions
+- **Step 1:** Navigate to the **"Ask a Question"** tab and type your natural language query.
+- **Step 2:** Optionally, expand the **"Optional: Specify Dimensions, Metrics, and Date Filter"** section to refine your query by selecting specific dimensions, metrics, and a date range.
+- **Step 3:** Based on your input and the available JSON metadata, the system will either recommend the best data source or generate a SQL query to fetch your data.
+- **Step 4:** If a valid SQL query is generated (and your selected data source supports it), the query is executed on your Snowflake database and the results are displayed interactively.
+---
+#### 💡 Example Queries:
+- **Ask a Question:**  
+  *"Where can I find data by traffic source?"*
+- **AB Group Experiment:**  
+  *"Provide me with 'cnftp' for the last 7 days in aggregate?"*
+- **GAMLOG:**  
+  *"Show pageviews and impressions by browser, hour, and device type for the last 7 days."*
+- **Google Analytics Pageviews:**  
+  *" Can you provide me with page view data by device for the last 7 days please include primary vertical, tier, site status, and site name. Filter to tier 1 sites"*
+- **GAM Earnings:**  
+  *"Retrieve earnings by order and ad unit over the past 14 days."*
+- **GAM Hourly:**  
+  *"Retrieve Hourly revenue by order and ad unit over the past 14 days."*
+- **RPM by URL:**  
+  *"List URLs with the highest RPM over the last week."*
+- **Site Settings:**  
+  *"Identify sites with autoplay enabled or video sticky behavior."*
+- **Site Ad Density:**  
+  *"Find sites where Site Ad Density equals 30%."*
+---
+#### ⚙️ Advanced Features & Customization
+- **Optional Filters:** Use the filter options to fine-tune your query with specific dimensions, metrics, and date ranges.
+- **SQL Query Extraction:** The wizard extracts the SQL query from the assistant’s response. If a query is generated, it is automatically executed (except when using the "Ask a Question" data source).
+- **Conversation History:** Your interactions are saved in a conversation history, which you can view or clear using the provided buttons.
+- **Exporting Results:** Query results are shown in an interactive table. Use the **Download CSV** button to export the data for further analysis.
+---
+#### Troubleshooting & Best Practices
+- **Performance:** Keeping your date range within a 30-day limit ensures quicker query execution.
+- **Data Source Selection:** If you are unsure which data source to use, start with the "Ask a Question" option to get a recommendation based on available metadata.
+Use these guidelines to navigate the Raptive Data Wizard efficiently and extract the precise data you need.
+""",
+        unsafe_allow_html=True,
+    )
+
+
+# ─────────────────────────────────────────────────────────────
+# 5. "Additional Info" Tab (Placeholder)
+# ─────────────────────────────────────────────────────────────
+with main_tabs[2]:
+    st.header("Additional Info")
+    st.markdown("Additional information and features will be added here in the future.")

changelog.md

@@ -0,0 +1,3 @@
+# Changelog
+
+- 2025-08-07 14:28 UTC: Initialized changelog to track project updates.

gamlog_Instructions_file.txt

@@ -0,0 +1,443 @@
+## Overview
+This file provides instructions for extracting data from the Gamlog table which should use ANALYTICS.GAM360.GAMLOG. 
+For 'like' operator be sure to use 'like' as opposed to 'Ilike'. 
+When implementing the 'GROUP BY', please just use 'GROUP BY ALL' without listing any dimensions.
+When implementing the 'ORDER BY', please just use the column reference number instead of the column name if it's a metric that is being asked to be ordered by.
+Please add a date filter within the where clause to be set as default for the last 7 days, ending yesterday, unless the user specifies a date period.
+Always include impressions and revenue metrics in all your Gamlog SQL queries.
+If the user requests 'site', or 'site name' data, please reference the 'adunitname' column as this is the the dimension name found within ANALYTICS.GAM360.GAMLOG.
+If the user requests 'ad unit', or 'AU' data, please reference the 'topadunitname' column as this is the the dimension name found within ANALYTICS.GAM360.GAMLOG.
+If the user requests for only 'HBT' data then you will need to filter to where product = 'Header Bidding'
+
+## Important Instructions for Gamlog Data Extraction with Syntax Enforcement
+
+When a user requests data from the `ANALYTICS.GAM360.GAMLOG` table and includes dimensions or filters related to the following columns (or any columns present in `ANALYTICS.SIGMA_SCRATCH.VIEW_AVI_SITE_DATASET_FROM_SQL_4D44494CEF294AF093BA92B8C008977A`):
+
+* `primary vertical`
+* `service condensed`
+* `new service condensed`
+* `tier`
+* `status`
+
+You **must** join the `ANALYTICS.GAM360.GAMLOG` table with `ANALYTICS.SIGMA_SCRATCH.VIEW_AVI_SITE_DATASET_FROM_SQL_4D44494CEF294AF093BA92B8C008977A` on the `siteid` column.
+
+**Crucial Syntax Enforcement:**
+
+* **Case Sensitivity:** Pay very close attention to case sensitivity. Ensure that all column names and string values in `WHERE` clauses match the exact case found in the `SITE_EXTENDED_snowflake.json` sample data. For example, if a column is named `Status` and the value is `'Active'`, the SQL query **must** use `Status = 'Active'`, not `status = 'active'` or `status = 'Active'`.
+* **Quoting:** String values in `WHERE` clauses **must** be enclosed in single quotes (`'...'`). Numeric values should not be enclosed in quotes.
+* **Column Names:** Column names must be used exactly as they appear in the data, including any spaces or special characters. If a column is named `"SITE ID"`, use it as `"SITE ID"` in the query.
+* **Data Types:** Ensure that the data types of values used in `WHERE` clauses match the data types of the corresponding columns.
+
+**Example SQL Query:**
+
+```sql
+SELECT
+    a.date,
+    b."TIER",
+    SUM(a.impressions) AS total_impressions
+FROM
+    ANALYTICS.GAM360.GAMLOG a
+JOIN
+    ANALYTICS.SIGMA_SCRATCH.VIEW_AVI_SITE_DATASET_FROM_SQL_4D44494CEF294AF093BA92B8C008977A b ON a.siteid = b."SITE ID"
+WHERE
+    a.date = DATE '2025-03-30' AND b.Status = 'Active'
+    and b."STATUS" = 'Active'
+GROUP BY
+    a.date,
+    b."TIER";
+
+## Prebid Data Extraction
+Target Column & Table: Use the column `ordername` from the `Gamlog` table.
+Filtering for Prebid Data: Filter for order names that include "prebid".
+Example SQL snippet:
+    WHERE ordername LIKE '%prebid%'
+
+## Open Bidding Data Extraction
+Identification: The terms "open bidding", "exchange bidding", or their abbreviations "OB" or "EB" should be interpreted as "open bidding".
+Filtering for Open Bidding: Filter where the column `product` equals "open bidding".
+Example SQL snippet:
+    WHERE product = 'open bidding'
+
+## Default Metrics in the Gamlog Table
+By default, always use the following metrics in your SQL queries:
+Impressions: 
+    sum(impressions) as Impressions
+Revenue: 
+    sum(impressions * revenue) as Revenue
+
+## Optional Additional Metrics
+The following metrics are available if needed for extended analysis:
+Unfilled Impressions: 
+    sum(case when not isfilledrequest then 1 else 0 end) as Unfilled_Impressions
+Pageviews: 
+    count(distinct pvk) as Pageviews
+Sessions: 
+    count(distinct sess) as Sessions
+Viewable Impressions: 
+    sum(viewable) as Viewable_Impressions
+Measurable Impressions: 
+    sum(measurable) as Measurable_Impressions
+
+- CPM = (sum(impressions * revenue) / sum(impressions)) × 1,000
+
+- RPM = (sum(impressions * revenue) / count(distinct pvk)) × 1,000
+
+- IPPV (impressions per pageview) = (sum(impressions) /  count(distinct pvk))
+
+- If a user asks for CPM, RPM, or IPPV, please ensure that your sql query only contains sites with a status of 'Active' The reason is if a site has zero impressions, pageviews or revenue then the resulting calculation will be null or zero And thus we don't want this to be represented in the table.
+- If a user asks for the top 100 sites with the highest CPM, RPM, or IPPV, then default by using impressions or revenue or pageviews to identify the top 100 sites and then calculate.
+
+
+## Abgroup Metric Filter
+To filter on the abgroup metric, use the following expression:
+    filter(abgroup, x -> x LIKE '%dsllv%') AS filtered_abgroup,
+
+## ctmap Field Extraction
+To extract any field from the ctmap based on the user's request, use the following syntax.
+For example, to extract the 'hb_adomain' field:
+    ctmap['hb_adomain'][0]::varchar
+
+## Mapping for Integration. This is not a mapping for Bidder or SSP.
+Mapping for existing columns found within the Gamlog table for integration:
+CASE 
+  WHEN ordername LIKE '%Prebid%' THEN 'Prebid'
+  WHEN ordername LIKE '%TAM%' OR ordername LIKE '%Amazon%' THEN 'TAM'
+  WHEN product LIKE '%Ad Exchange%' THEN 'AdX'
+  WHEN product IN ('Header Bidding') THEN 'Prebid'
+  WHEN product IN ('Open Bidding') THEN 'OB'
+  WHEN orderid = 0 THEN 'OB'
+  ELSE 'other' 
+END AS Integration,
+
+## Mapping for Format
+Mapping for Format:
+CASE 
+  WHEN topadunitname LIKE '%Outstream%' THEN 'Display'
+  WHEN topadunitname LIKE '%Video%' THEN 'Video' 
+  ELSE 'Display' 
+END AS Format,
+
+## Mapping for Device
+Mapping for Device:
+CASE 
+  WHEN devicecategory LIKE '%Desktop%' THEN 'Desktop'
+  WHEN devicecategory LIKE '%Tablet%' THEN 'Mobile'
+  WHEN devicecategory LIKE '%Smartphone%' THEN 'Mobile'
+  ELSE 'other' 
+END AS Device,
+
+## Mapping for Refresh
+Mapping for Refresh:
+CASE 
+  WHEN nref[0] = '0' THEN '0'  
+  WHEN nref[0] = '1' THEN '1'  
+  WHEN nref[0] = '2' THEN '2' 
+  WHEN nref[0] = '3' THEN '3' 
+  WHEN nref[0] = '3' THEN '3'
+  WHEN nref[0] = '4' THEN '4'  
+  WHEN nref[0] = '5' THEN '5' 
+  WHEN nref[0] = '6' THEN '6' 
+  WHEN nref[0] = '7' THEN '7'
+  WHEN nref[0] = '8' THEN '8'  
+  WHEN nref[0] = '9' THEN '9' 
+  WHEN nref[0] = '10' THEN '10' 
+  ELSE 'Other' 
+END AS refresh_group,
+
+## Mapping for Browser
+Mapping for Browser:
+CASE 
+  WHEN browser LIKE '%Safari%' THEN 'Safari'
+  WHEN browser LIKE '%Chrome%' THEN 'Chrome'
+  WHEN browser LIKE '%Firefox%' THEN 'Firefox'
+  WHEN browser LIKE '%Edge%' THEN 'Edge/IE'
+  WHEN browser LIKE '%Internet Explore%' THEN 'Edge/IE'
+  ELSE 'other' 
+END AS Browser,
+
+## Mapping for Country
+Mapping for Country:
+CASE 
+  WHEN country LIKE '%United States%' THEN 'US'
+  WHEN country LIKE '%Canada%' THEN 'Canada'
+  WHEN country LIKE 'United Kingdom' THEN 'UK'
+  ELSE 'ROW' 
+END AS Country,
+
+## Mapping for Ad Unit
+Mapping for Ad Unit:
+CASE 
+  WHEN topadunitname LIKE '%Content%' THEN 'Content'
+  WHEN topadunitname LIKE '%Footer%' THEN 'Footer'
+  WHEN topadunitname LIKE '%Recipe%' THEN 'Recipe'
+  WHEN topadunitname LIKE '%Sidebar%' THEN 'Sidebar'
+  WHEN topadunitname LIKE '%Header%' THEN 'Header'
+  WHEN topadunitname LIKE '%Below_Post%' THEN 'Below_Post'
+  WHEN topadunitname LIKE '%Outstream%' THEN 'Sticky Outstream'
+  WHEN topadunitname LIKE '%Video%' THEN 'Video'
+  ELSE 'Other' 
+END AS ad_unit_group,
+
+## Mapping for Bidder/SSP (hb_bidder) – Use this for bidder reporting. If a user asks for a specific Bidder/SSP, please only extract that mapping portion into a case when statement.
+## if a user asks for bidder data, the query must extract the bidder using the provided bidder mapping (the CASE statement checking ORDERNAME, PRODUCT, ORDERID, and yieldgroupcompanyid) and not the integration mapping.
+## If the users references any of these Bidder/SSP names, please ensure to use this mapping and be sure you also filter on product, ordername, and yieldgroupcompanyid where neccessary.
+Mapping for Bidder/SSP (hb_bidder):
+CASE
+  WHEN lower(product) = 'ad exchange' THEN 'AdX'
+  WHEN ordername LIKE '39_%_%' THEN 'Direct'
+  WHEN ordername LIKE '38_%_%' THEN 'Direct'
+  WHEN ordername LIKE '3_%_%' AND SUBSTRING(ordername, 5, 1) = '_' THEN 'Direct'
+  WHEN ordername LIKE '8_%_%_%' AND ordername LIKE '%IX%' THEN 'Index Exchange'
+  WHEN ordername LIKE '8_%_%_%' THEN 'Ignore'
+  WHEN ordername LIKE '7_%_%_%' THEN 'PG'
+  WHEN ordername LIKE '5_%_%_%' THEN 'PG'
+  WHEN LEFT(ordername, 1) = '4' AND RIGHT(LEFT(ordername, 2), 1) BETWEEN '0' AND '9' AND RIGHT(LEFT(ordername, 3), 1) BETWEEN '0' AND '9' AND RIGHT(LEFT(ordername, 4), 1) BETWEEN '0' AND '9' AND RIGHT(LEFT(ordername, 5), 1) = '_' THEN 'Affiliate'
+  WHEN ordername LIKE '0_%_%_%' THEN 'Ignore'
+  WHEN lower(ordername) LIKE '%amazon_tam-pubmatic%' THEN 'PubMatic'
+  WHEN lower(ordername) LIKE '%amazon_tam-yieldmo%' THEN 'Yieldmo'
+  WHEN lower(ordername) LIKE '%amazon_tam-amazon proprietary%' THEN 'Amazon Proprietary'
+  WHEN lower(ordername) LIKE '%amazon_amz%' THEN 'Amazon Proprietary'
+  WHEN lower(ordername) LIKE '%33across%' THEN '33Across'
+  WHEN lower(ordername) LIKE '%appnexus%' THEN 'Microsoft'
+  WHEN lower(ordername) LIKE '%blockthrough%' THEN 'Blockthrough'
+  WHEN lower(ordername) LIKE '%brightroll%' THEN 'Brightroll'
+  WHEN lower(ordername) LIKE '%colossus%' THEN 'Colossus'
+  WHEN lower(ordername) LIKE '%concert%' THEN 'Concert'
+  WHEN lower(ordername) LIKE '%conversant%' THEN 'Conversant'
+  WHEN lower(ordername) LIKE '%criteo%' THEN 'C-Grid'
+  WHEN lower(ordername) LIKE '%deepintent%' THEN 'DeepIntent'
+  WHEN lower(ordername) LIKE '%districtm-dmx%' THEN 'DistrictM-DMX'
+  WHEN lower(ordername) LIKE '%districtm%' THEN 'DistrictM'
+  WHEN lower(ordername) LIKE '%-dmx%' THEN 'DMX'
+  WHEN lower(ordername) LIKE '%gumgum%' THEN 'GumGum'
+  WHEN lower(ordername) LIKE '%index exchange%' THEN 'Index Exchange'
+  WHEN lower(ordername) LIKE '%interchange%' THEN 'TTD'
+  WHEN lower(ordername) LIKE '%ix-rounde%' THEN 'Roundel'
+  WHEN lower(ordername) LIKE '%ix%' THEN 'Index Exchange'
+  WHEN lower(ordername) LIKE '%kargo%' THEN 'Kargo'
+  WHEN lower(ordername) LIKE '%mediagrid%' THEN 'C-Grid'
+  WHEN lower(ordername) LIKE '%nativo%' THEN 'Nativo'
+  WHEN lower(ordername) LIKE '%onevideo%' THEN 'Yahoo'
+  WHEN lower(ordername) LIKE '%openx%' THEN 'OpenX'
+  WHEN lower(ordername) LIKE '%pubmatic%' THEN 'PubMatic'
+  WHEN lower(ordername) LIKE '%reset digital%' THEN 'Reset Digital'
+  WHEN lower(ordername) LIKE '%roundel%' THEN 'Roundel'
+  WHEN lower(ordername) LIKE '%rubicon-padsquad%' THEN 'Rubicon-Padsquad'
+  WHEN lower(ordername) LIKE '%rubicon%' THEN 'Magnite'
+  WHEN lower(ordername) LIKE '%sharethrough%' THEN 'Sharethrough'
+  WHEN lower(ordername) LIKE '%sovrn%' THEN 'Sovrn'
+  WHEN lower(ordername) LIKE '%spotx%' THEN 'SpotX'
+  WHEN lower(ordername) LIKE '%teads%' THEN 'Teads'
+  WHEN lower(ordername) LIKE '%telaria%' THEN 'Telaria'
+  WHEN lower(ordername) LIKE '%the trade desk%' THEN 'TTD'
+  WHEN lower(ordername) LIKE '%ttd%' THEN 'TTD'
+  WHEN lower(ordername) LIKE '%triplelift%' THEN 'TripleLift'
+  WHEN lower(ordername) LIKE '%trustx%' THEN 'TrustX'
+  WHEN lower(ordername) LIKE '%undertone%' THEN 'Undertone'
+  WHEN lower(ordername) LIKE '%unruly%' THEN 'Unruly'
+  WHEN lower(ordername) LIKE '%verizon%' THEN 'Yahoo'
+  WHEN lower(ordername) LIKE '%yahoossp%' THEN 'Yahoo'
+  WHEN lower(ordername) LIKE '%yahoo s2s%' THEN 'Yahoo'
+  WHEN lower(ordername) LIKE '%yieldmo%' THEN 'Yieldmo'
+  WHEN lower(ordername) LIKE '%rtb house%' THEN 'RTB House'
+  WHEN lower(ordername) LIKE '%improve digital%' THEN 'Improve Digital'
+  WHEN lower(ordername) LIKE '%ogury%' THEN 'Ogury'
+  WHEN lower(ordername) LIKE '%ozone%' THEN 'Ozone'
+  WHEN lower(ordername) LIKE '%adform%' THEN 'Adform'
+  WHEN lower(ordername) LIKE '%aidem%' THEN 'Aidem'
+  WHEN yieldgroupcompanyid IN (4460609902) THEN 'OpenX'
+  WHEN yieldgroupcompanyid IN (4642901988) THEN 'Unruly'
+  WHEN yieldgroupcompanyid IN (4455444655) THEN 'Sovrn'
+  WHEN yieldgroupcompanyid IN (4697115760) THEN 'Yieldmo'
+  WHEN yieldgroupcompanyid IN (5091519355) THEN 'EMX'
+  WHEN yieldgroupcompanyid IN (4867316569) THEN 'Sharethrough'
+  WHEN yieldgroupcompanyid IN (4623743882) THEN 'PubMatic'
+  WHEN yieldgroupcompanyid IN (5004881021) THEN 'Smart'
+  WHEN yieldgroupcompanyid IN (5198265065) THEN 'GumGum'
+  WHEN yieldgroupcompanyid IN (4634020290) THEN 'Media.net'
+  WHEN yieldgroupcompanyid IN (4463579729) THEN 'Index Exchange'
+  WHEN yieldgroupcompanyid IN (4926199169) THEN 'Yahoo'
+  WHEN yieldgroupcompanyid IN (4805985973) THEN 'Sonobi'
+  WHEN yieldgroupcompanyid IN (4550726766) THEN 'Magnite'
+  WHEN yieldgroupcompanyid IN (4466143841) THEN 'Smaato'
+  WHEN yieldgroupcompanyid IN (4623743690) THEN 'TripleLift'
+  WHEN yieldgroupcompanyid IN (5072598885) THEN 'Microsoft'
+  WHEN yieldgroupcompanyid IN (5032793665) THEN 'C-Grid'
+  WHEN yieldgroupcompanyid IN (4642901988) THEN 'Unruly'
+  WHEN yieldgroupcompanyid IN (5073030913) THEN 'Kargo'
+  WHEN yieldgroupcompanyid IN (4807636548) THEN 'SpotX'
+  WHEN yieldgroupcompanyid IN (5220141531) THEN 'C-Grid'
+  WHEN yieldgroupcompanyid IN (4802163255) THEN 'Telaria'
+  WHEN yieldgroupcompanyid IN (4725727878) THEN 'Chocolate'
+  WHEN yieldgroupcompanyid IN (5282730859) THEN 'OneTag'
+  WHEN yieldgroupcompanyid IN (5119659271) THEN 'TTD'
+  ELSE 'unknown'
+END AS Bidder_Mapped,
+
+
+# Standalone columns within the gamlog table to be used when generating a SQL query.
+TIME
+TIMEUSEC2
+KEYPART
+CTMAP
+ISFILLEDREQUEST
+REQUESTEDADUNITSIZES
+MOBILEDEVICE
+OSVERSION
+MOBILECAPABILITY
+MOBILECARRIER
+SERVINGRESTRICTION
+PUBLISHERPROVIDEDID
+ISCOMPANION
+VIDEOPOSITION
+PODPOSITION
+DEVICECATEGORY
+ISINTERSTITIAL
+USERID
+REFERERURL
+REQUESTLANGUAGE
+ADUNITID
+TOPADUNITID
+TOPADUNITNAME
+ADUNITNAME
+COUNTRY
+REGION
+BROWSER
+OS
+METRO
+POSTALCODE
+BANDWIDTH
+GFPCONTENTID
+ADVERTISERID
+CREATIVESIZE
+CREATIVEID
+LINEITEMID
+LINEITEMNAME
+ORDERID
+ORDERNAME
+CREATIVESIZEDELIVERED
+CREATIVEVERSION
+PRODUCT
+TARGETEDCUSTOMCRITERIA
+DEALID
+DEALTYPE
+ESTIMATEDBACKFILLREVENUE
+IMPRESSIONID
+YIELDGROUPCOMPANYID
+YIELDGROUPNAMES
+ADVERTISER
+BUYER
+ADXACCOUNTID
+VIDEOFALLBACKPOSITION
+MEASURABLE
+VIEWABLE
+CODESERVES
+REVENUE
+IMPRESSIONS
+PVK
+SESS
+SITEID
+BUCKET
+DATE
+HOUR
+AMP
+ABGROUP
+LAZY
+DEPLOYMENT
+REFRESH
+PLUGIN
+FLAG
+FLAG_SLOT
+HBHO
+VPWXVPH
+VBHO
+FPV
+VPRENUM
+NREF
+DOC_REF
+SITE_CODE
+BRANCH
+UTM_CAMPAIGN
+UTM_MEDIUM
+UTM_SOURCE
+AT_CUSTOM_1
+HB_PB
+HB_BIDDER
+UTC_TIMESTAMP
+UTC_DATE_HOUR
+SOURCE_DATE_HOUR
+CHILDNETWORKCODE
+SELLERRESERVEPRICE
+AUDIENCESEGMENTIDS
+USERIDENTIFIERSTATUS
+PPIDPRESENCE
+VASTERROR
+VASTERRORNAMES
+CLICK
+BACKFILLKEYPART
+PROTECTEDAUDIENCEAPIDELIVERY
+HB_DSP
+HB_CRID
+EVENTTIMEUSEC2
+VERTICALS
+YIELDCOMPANYNAMES
+NATIVEFORMAT
+NATIVESTYLE
+PROTECTEDAUDIENCESELLER
+HB_DSP_NAME
+
+
+
+# The below is a JSON object to help the model understand the sample output of the gamlog table for specific dimensions. For example if a user asks for content 1 data, then you would know to adjust your query to look for "AdThrive_Content_1".
+  {
+    "TOPADUNITNAME": [
+        "AdThrive_Sidebar_1",
+        "AdThrive_Video_StickyOutstream_1",
+        "AdThrive_Recipe_1",
+        "AdThrive_Header_2",
+        "AdThrive_Video_Coll_SOff_Smartphone",
+        "AdThrive_Sidebar_4",
+        "AdThrive_Content",
+        "AdThrive_Video_In-Post_ClicktoPlay_SoundOn",
+        "AdThrive_Sidebar_3",
+        "AdThrive_Recipe_3",
+        "AdThrive_Recipe_2",
+        "AdThrive_Native_Recipe_1",
+        "CAM_Direct_2",
+        "AdThrive_Below_Post_1",
+        "AdThrive_Recipe_5",
+        "AdThrive_Content_5",
+        "AdThrive_Content_2",
+        "AdThrive_Recipe_4",
+        "AdThrive_Content_3",
+        "AdThrive_Sidebar_2",
+        "ca-pub-5669406840537886",
+        "AdThrive_Below_Post",
+        "AdThrive_Content_4",
+        "AdThrive_Video_Individual_Autoplay_SOff",
+        "AdThrive_Video_Collapse_Autoplay_SoundOff",
+        "AdThrive_Content_6",
+        "AdThrive_WebStories_1",
+        "AdThrive_Sidebar_5",
+        "AdThrive_Header_1",
+        "AdThrive_Footer_1",
+        "AdThrive_Content_8",
+        "AdThrive_Content_7",
+        "AdThrive_Content_1",
+        "AdThrive_Sponsor_Tile",
+        "AdThrive_Content_9",
+        "AdThrive_Interstitial_1",
+        "ca-pub-5669406840537886: AdThrive",
+        "CAM_Direct_1",
+        "AdX_Rewarded_Video_Test_ADOPSG-1897",
+        "AdThrive_Sidebar_9"
+    ],
+    "DEVICECATEGORY": [
+        "Desktop",
+        "Tablet",
+        "Connected TV",
+        "Feature Phone",
+        "Smartphone",
+        "Set Top Box"
+    ]
+}

hf_push_test.txt

@@ -0,0 +1,3 @@
+This is a test file to verify Hugging Face push pipeline.
+
+Second line to trigger Hugging Face sync.

index.html

@@ -1,19 +0,0 @@
-<!doctype html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
-</html>

requirements.txt

@@ -0,0 +1,11 @@
+streamlit
+openai
+pandas
+python-dotenv
+snowflake-connector-python
+
+cryptography
+altair
+
+cryptography
+

style.css

@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}