From 4f4a1bc89c01aae0db06f6762035f3279a9f2384 Mon Sep 17 00:00:00 2001 From: Kalib Brayer Date: Thu, 6 Feb 2025 09:06:31 -0800 Subject: [PATCH 1/4] (perf) Filter construction improvements - Make It Smarter (#131) * Feat make smarter (#91) * fix - pass in the parameters to the big query generate model * make it smater * merge * prevent duplicate triggering of generation * move logic into helper utils * fix state * fix the callbacks * use params instead of a string * add back sorting * fix link generation * remove some consoles * check the filters twice, make sure that we're returning a simpler data structure * fix race condition for page load * separate call for filter and visualization generation * fix typescript errors * add jest tests * update for edge cases when custom urls, and custom dates are in examples need to be handled. * Fix package json * more fixes post merge * fix the store * fix sidepanel * fix the link to explore * improve speed by parallelizing calls to gemini * fix summarization * scroll into view * add filter helper and tests * add tests * run on every commit * only use node 16 and 18 * use a filter validator * remove un-used console logs * post-merge fixes * fix tests * improve scrolling --------- Co-authored-by: Jawad Laraqui Co-authored-by: Flavio Di Berardino * Update useSendVertexMessage.ts * Fixed issue with parsing cleaned url Added case statement to handle both cleaned an uncleaned urls * Origin/feature generate examples (#1) * Added generate examples script and trusted dashboard table * Error handling * terraform bug fix * Handle url queryPrompt parameter * generate_exmples bug fix * Added number filter documentation * work in progress - append examples * working and tested new concat function. * tested * Update looker_filter_doc.md adding more context to filter doc on dates * Add files via upload Adding context file on timeframes and intervals * Update useSendVertexMessage.ts adding import and reference for context * Update ExploreFilterHelper.ts updating file * mostly working with new configs * working settings! * refactoring to use lookml queries * working with cloud function and new lookml model * Update useSendVertexMessage.ts reverted/removed inline date and number filter documentation in useSendVertexMessage * made settings admin-only and hide them for regular users. * committing timeframe filter logic * secure fetchProxy added * working with Bigquery! * Fixed problem with variability * remove indeterminacy, fix filter bug * more context with restored filters call * bug fix * bug fix * add back in filter mods * handle NOT NULL better * merge fixes * Update looker_filter_doc.md to help last x timeframe filters Adding more context to distinguish between last x days and x days ago type of filters. * rm trusted dashboards * work in progress on be installer * testing cloud shell script * testing cloud console run * testing * readme updated * test * testing * test * test * test * test * test * test * test * test * readme edits * updated readme * readme update * readme * adding changes * Fixed READMEs * restore 'query object format' text. * testing * testing * security setup * updates for security testing * error handling fixes and example script updates * readme updates * Revert "Merge branch 'marketplace_deploy' into make-it-smarter-bytecode" This reverts commit 954b73bc59e89380c31b45072950b79d2520e5c9, reversing changes made to cd7ee7eef828a0ba633d98b64778754efd64ca0e. * Remove 'check the response'. --------- Co-authored-by: Luka Fontanilla <51471651+LukaFontanilla@users.noreply.github.com> Co-authored-by: Jawad Laraqui Co-authored-by: Flavio Di Berardino Co-authored-by: dbarrbc <89422790+dbarrbc@users.noreply.github.com> Co-authored-by: Colin Roy-Ehri <39524338+colin-roy-ehri@users.noreply.github.com> Co-authored-by: colin-roy-ehri Co-authored-by: carolbethperkins <34356551+carolbethperkins@users.noreply.github.com> --- .DS_Store | Bin 0 -> 8196 bytes .github/workflows/extension-tests.yml | 29 + explore-assistant-examples/.env | 6 +- .../convert_examples-checkpoint.ipynb | 56 + .../convert_examples.ipynb | 56 + explore-assistant-examples/load_examples.sh | 2 +- explore-assistant-examples/nabc_examples.json | 1022 ++ explore-assistant-examples/samples.json | 12 +- .../update_refinements.sh | 0 explore-assistant-examples/update_samples.sh | 0 explore-assistant-extension/.DS_Store | Bin 0 -> 8196 bytes .../jest-raw-loader.js | 10 + explore-assistant-extension/jest.config.js | 10 + explore-assistant-extension/jest.setup.js | 4 + explore-assistant-extension/package-lock.json | 11922 +++++++++++----- explore-assistant-extension/package.json | 14 +- explore-assistant-extension/src/.DS_Store | Bin 0 -> 8196 bytes explore-assistant-extension/src/Globals.d.ts | 5 + .../src/components/Chat/ExploreMessage.tsx | 14 +- .../src/components/Chat/SummaryMessage.tsx | 26 +- .../src/components/ExploreEmbed.tsx | 47 +- .../src/documents/looker_filter_doc.md | 225 + .../documents/looker_filters_interval_tf.md | 135 + .../looker_pivots_url_parameters_doc.md | 88 + .../src/documents/looker_visualization_doc.md | 243 + .../src/hooks/useSendVertexMessage.ts | 627 +- .../src/pages/AgentPage/MessageThread.tsx | 25 +- .../src/pages/AgentPage/Sidebar.tsx | 4 +- .../src/pages/AgentPage/index.tsx | 73 +- .../src/slices/assistantSlice.ts | 40 +- explore-assistant-extension/src/store.ts | 17 +- .../src/utils/BigQueryHelper.ts | 38 + .../src/utils/ExploreFilterHelper.ts | 284 + .../src/utils/ExploreHelper.ts | 46 + .../src/utils/VertexHelper.ts | 7 + .../tests/generateFilterParams.test.tsx | 107 + .../unit_tests/ExploreFilterHelper.test.ts | 435 + explore-assistant-extension/tsconfig.json | 3 +- explore-assistant-extension/webpack.config.js | 4 + 39 files changed, 11799 insertions(+), 3837 deletions(-) create mode 100644 .DS_Store create mode 100644 .github/workflows/extension-tests.yml create mode 100644 explore-assistant-examples/.ipynb_checkpoints/convert_examples-checkpoint.ipynb create mode 100644 explore-assistant-examples/convert_examples.ipynb mode change 100644 => 100755 explore-assistant-examples/load_examples.sh create mode 100644 explore-assistant-examples/nabc_examples.json mode change 100644 => 100755 explore-assistant-examples/update_refinements.sh mode change 100644 => 100755 explore-assistant-examples/update_samples.sh create mode 100644 explore-assistant-extension/.DS_Store create mode 100644 explore-assistant-extension/jest-raw-loader.js create mode 100644 explore-assistant-extension/jest.config.js create mode 100644 explore-assistant-extension/jest.setup.js create mode 100644 explore-assistant-extension/src/.DS_Store create mode 100644 explore-assistant-extension/src/documents/looker_filter_doc.md create mode 100644 explore-assistant-extension/src/documents/looker_filters_interval_tf.md create mode 100644 explore-assistant-extension/src/documents/looker_pivots_url_parameters_doc.md create mode 100644 explore-assistant-extension/src/documents/looker_visualization_doc.md create mode 100644 explore-assistant-extension/src/utils/BigQueryHelper.ts create mode 100644 explore-assistant-extension/src/utils/ExploreFilterHelper.ts create mode 100644 explore-assistant-extension/src/utils/ExploreHelper.ts create mode 100644 explore-assistant-extension/src/utils/VertexHelper.ts create mode 100644 explore-assistant-extension/tests/generateFilterParams.test.tsx create mode 100644 explore-assistant-extension/tests/unit_tests/ExploreFilterHelper.test.ts diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..28ad8ea38ac8824383e0e7387b0be6bcea2cd10f GIT binary patch literal 8196 zcmeHM-A)rh6g~q8+n|9MO%P+UiMIvYQV`=!DXmJPkkG0ZYU*xVal^7p+O0KdNP6wh zEBFfD8Xv@W@k+llGmvddO#=oc%#fLHcIKSZ&Np+;aJEDw@~v8$XpV?1RL0q9G{+R4 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matrix: + node-version: [16.x, 18.x] + + steps: + - uses: actions/checkout@v3 + - name: Use Node.js ${{ matrix.node-version }} + uses: actions/setup-node@v3 + with: + node-version: ${{ matrix.node-version }} + - name: Change to project directory + run: cd explore-assistant-extension + - name: Install dependencies + working-directory: ./explore-assistant-extension + run: npm ci + - name: Run Unit tests + working-directory: ./explore-assistant-extension + run: npm test tests/unit_tests \ No newline at end of file diff --git a/explore-assistant-examples/.env b/explore-assistant-examples/.env index 1a5759fd7..db2f1a27f 100644 --- a/explore-assistant-examples/.env +++ b/explore-assistant-examples/.env @@ -1,5 +1,5 @@ ##Update the variables in this environment file to automate the bash scripts for loading & updating the examples -PROJECT_ID="PROJECT_ID" ##Required. The Google Cloud project ID where your BigQuery dataset resides. -DATASET_ID="DATASET_ID" ##The ID of the BigQuery dataset. Defaults to explore_assistant. -EXPLORE_ID="MODEL:EXPLORE_ID" ##Required. A unique identifier for the dataset rows related to a specific use case or query (used in deletion and insertion). +PROJECT_ID=seraphic-ripsaw-360618 +DATASET_ID=explore_assistant +EXPLORE_ID=nabc:spins_nlp diff --git a/explore-assistant-examples/.ipynb_checkpoints/convert_examples-checkpoint.ipynb b/explore-assistant-examples/.ipynb_checkpoints/convert_examples-checkpoint.ipynb new file mode 100644 index 000000000..50ca25483 --- /dev/null +++ b/explore-assistant-examples/.ipynb_checkpoints/convert_examples-checkpoint.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "#Convert CSV to JSON\n", + "import csv\n", + "import json\n", + "\n", + "\n", + "def csv_to_json(csv_file, json_file):\n", + " \"\"\"Converts a CSV file to a JSON file.\n", + "\n", + "\n", + " Args:\n", + " csv_file: The path to the CSV file.\n", + " json_file: The path to the output JSON file.\n", + " \"\"\"\n", + "\n", + "\n", + " data = []\n", + " with open(csv_file, 'r') as csvfile:\n", + " csvreader = csv.DictReader(csvfile)\n", + " for row in csvreader:\n", + " data.append(dict(row))\n", + "\n", + "\n", + " with open(json_file, 'w') as jsonfile:\n", + " json.dump(data, jsonfile, indent=4)\n", + "\n", + "\n", + "\n", + "\n", + "# Example usage\n", + "csv_file = 'DMi EA Prompts - Explore Assistant Order Details - Cleansed.csv'\n", + "json_file = 'dmi_examples.json'\n", + "csv_to_json(csv_file, json_file)\n", + "print(f\"CSV converted to JSON: {json_file}\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/explore-assistant-examples/convert_examples.ipynb b/explore-assistant-examples/convert_examples.ipynb new file mode 100644 index 000000000..37da9ea21 --- /dev/null +++ b/explore-assistant-examples/convert_examples.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "#Convert CSV to JSON\n", + "import csv\n", + "import json\n", + "\n", + "\n", + "def csv_to_json(csv_file, json_file):\n", + " \"\"\"Converts a CSV file to a JSON file.\n", + "\n", + "\n", + " Args:\n", + " csv_file: The path to the CSV file.\n", + " json_file: The path to the output JSON file.\n", + " \"\"\"\n", + "\n", + "\n", + " data = []\n", + " with open(csv_file, 'r') as csvfile:\n", + " csvreader = csv.DictReader(csvfile)\n", + " for row in csvreader:\n", + " data.append(dict(row))\n", + "\n", + "\n", + " with open(json_file, 'w') as jsonfile:\n", + " json.dump(data, jsonfile, indent=4)\n", + "\n", + "\n", + "\n", + "\n", + "# Example usage\n", + "csv_file = '/Users/kalib/Downloads/NABC Examples - examples_cleansed.csv'\n", + "json_file = 'nabc_examples.json'\n", + "csv_to_json(csv_file, json_file)\n", + "print(f\"CSV converted to JSON: {json_file}\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/explore-assistant-examples/load_examples.sh b/explore-assistant-examples/load_examples.sh old mode 100644 new mode 100755 index ddccc6527..c3fdd68b9 --- a/explore-assistant-examples/load_examples.sh +++ b/explore-assistant-examples/load_examples.sh @@ -2,7 +2,7 @@ source .env TABLE_ID="explore_assistant_examples" ##The ID of the BigQuery table where the data will be inserted. Set to explore_assistant_examples. -JSON_FILE="examples.json" ##The path to the JSON file containing the data to be loaded. Set to examples.json. +JSON_FILE="nabc_examples.json" ##The path to the JSON file containing the data to be loaded. Set to examples.json. python load_examples.py \ --project_id $PROJECT_ID \ diff --git a/explore-assistant-examples/nabc_examples.json b/explore-assistant-examples/nabc_examples.json new file mode 100644 index 000000000..ad233fee5 --- /dev/null +++ b/explore-assistant-examples/nabc_examples.json @@ -0,0 +1,1022 @@ +[ + { + "input": "How many pounds of frozen blueberries were sold on promotion over this winter versus last winter?", + "output": "fields=spins_nlp.rma_units_incremental_promo_any_sum,spins_nlp.product_type,spins_nlp.period_ending_year&pivots=spins_nlp.period_ending_year&fill_fields=spins_nlp.period_ending_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.period_ending_month_name]=December%2CJanuary%2CFebruary&sorts=spins_nlp.period_ending_year,spins_nlp.rma_units_incremental_promo_any_sum+desc+0" + }, + { + "input": "How many dollars of frozen blueberries were sold on promotion over this fall versus last fall?", + "output": "fields=spins_nlp.rma_units_incremental_promo_any_sum,spins_nlp.product_type,spins_nlp.period_ending_year&pivots=spins_nlp.period_ending_year&fill_fields=spins_nlp.period_ending_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.period_ending_month_name]=September%2COctober%2CNovember&sorts=spins_nlp.period_ending_year,spins_nlp.rma_units_incremental_promo_any_sum+desc+0" + }, + { + "input": "What parent companies sell the most Frozen Blueberries by pounds?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%25&f[spins_nlp.total_units]=&sorts=spins_nlp.total_lbs+desc" + }, + { + "input": "Show all the sales by units at Albertson stores", + "output": "fields=spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=%25ALBERTSON%25&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=&sorts=spins_nlp.total_units+desc+0" + }, + { + "input": "How many pounds of blueberries were sold last month compared to the year prior?", + "output": "fields=spins_nlp.geography_filter,spins_nlp.date_in_period_month,spins_nlp.total_lbs,spins_nlp.total_lbs_prior&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.date_in_period_month+desc" + }, + { + "input": "Is total US blueberry sales up this year versus last year for January? ", + "output": "fields=spins_nlp.total_dollars,spins_nlp.period_ending_year,spins_nlp.period_ending_month_name&pivots=spins_nlp.period_ending_year&fill_fields=spins_nlp.period_ending_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_month]=after+2023%2F01%2F01&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25%2CFZ+FRUIT+-+BLUEBERRIES%25&f[spins_nlp.period_ending_month_name]=January&sorts=spins_nlp.period_ending_year,spins_nlp.total_dollars+desc+0" + }, + { + "input": "What parent companies sell the most Fresh Blueberries by units?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25&f[spins_nlp.total_units]=&sorts=spins_nlp.total_units+desc" + }, + { + "input": "What parent companies sell no Frozen Blueberries?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%25&f[spins_nlp.total_units]=0&sorts=spins_nlp.total_units+desc" + }, + { + "input": "What parent companies sell no Fresh Blueberries?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25&f[spins_nlp.total_units]=0&sorts=spins_nlp.total_units+desc" + }, + { + "input": "How many blueberries were sold in January 2024 in the US?", + "output": "fields=spins_nlp.total_units,spins_nlp.geography_filter,spins_nlp.period_ending_month_name&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_month]=2024%2F01%2F01+to+2024%2F02%2F01" + }, + { + "input": "What are the top 5 regions for Fresh and Frozen Blueberry sales in the last year by pounds sold?", + "output": "fields=spins_nlp.total_lbs,spins_nlp.geography_filter&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=Market&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.total_lbs+desc+0" + }, + { + "input": "What is the sales volume comparison between organic and conventional blueberries?", + "output": "fields=spins_nlp.total_dollars,spins_nlp.geography_filter&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_year]=2024%2F01%2F01+to+2025%2F01%2F01&sorts=spins_nlp.total_dollars+desc+0" + }, + { + "input": "What is the most popular product description by pounds sold?", + "output": "fields=spins_nlp.total_lbs,spins_nlp.product_description&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=&sorts=spins_nlp.total_lbs+desc" + }, + { + "input": "What is the rate of change in blueberry sales volume?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How has the quantity of blueberries sold changed in the past year?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Do blueberry sales volumes peak during specific seasons?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have there been seasonal fluctuations in blueberry sales volume?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any notable trends in the volume of blueberries sold?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have economic factors played a role in blueberry sales volume trends?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What has been the overall volume trend for blueberry sales?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "When are blueberries typically sold in the largest quantities?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have changes in consumer preferences affected the volume of blueberries sold?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the general pattern of blueberry sales volume over the last 12 months?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have weather conditions impacted blueberry production and sales volume?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are blueberry sales volumes increasing, decreasing, or remaining stable?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have there been significant changes in blueberry sales volume?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have seasonal sales volume patterns for blueberries changed in the past year?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How has the quantity of blueberries sold evolved over the past year?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any regional variations in blueberry sales volume seasonality?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How has the quantity of blueberries sold per unit of time evolved?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "2. Volume Trends: What is the trend in sales volume for blueberries over the last year?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What factors have influenced blueberry sales volume trends?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "By how much have blueberry sales volumes increased or decreased?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any regional-specific factors driving blueberry sales volume patterns?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_lbs_sum&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which regions have shown the highest sales growth for blueberries?", + "output": "fields=spins_nlp.total_dollars,spins_nlp.geography_filter,spins_nlp.total_dollars_prior&f[spins_nlp.geography_type]=Region&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.current_date_range]=1+years&sorts=spins_nlp.total_dollars+desc+0" + }, + { + "input": "How many organic blueberries were sold in 2023?", + "output": "fields=spins_nlp.total_units,spins_nlp.geography_filter,spins_nlp.period_ending_year&f[spins_nlp.period_ending_year]=2023%2F01%2F01+to+2024%2F01%2F01&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography]=TOTAL+US+-+NATURAL+CHANNEL" + }, + { + "input": "Do price differences between different blueberry packaging types impact sales?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which blueberry packaging format is the most widely available?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Is the demand for any particular blueberry packaging type growing or declining?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How has the popularity of different blueberry packaging formats changed over time?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which packaging type for blueberries has a larger market share?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any innovative packaging solutions for blueberries gaining traction?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the relative demand for different blueberry packaging types?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any regional variations in the popularity of different blueberry packaging formats?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which packaging types for blueberries are most in demand?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which blueberry packaging types have the highest sales volume?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What factors influence the popularity of different blueberry packaging types?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the most popular way to package blueberries for sale?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do the sales of bulk blueberries compare to pre-packaged blueberries?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What are the preferred packaging options for blueberries among consumers?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there significant differences in popularity between bulk and pre-packaged blueberries?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What are the future prospects for different blueberry packaging types?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do consumer preferences affect the choice of blueberry packaging?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do supply chain factors influence the availability and popularity of different blueberry packaging types?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any emerging trends in blueberry packaging preferences?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which blueberry packaging formats are most successful in the market?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "9. Packaging Preferences: What packaging types (e.g., bulk, pre-packaged) are most popular based on sales?", + "output": "fields=spins_nlp.base_size,spins_nlp.total_dollars,spins_nlp.total_lbs&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have total blueberry sales trended by quarter over the past year across different regions?", + "output": "fields=spins_nlp.geography_filter,spins_nlp.total_dollars,spins_nlp.period_ending_quarter&f[spins_nlp.geography_type]=Region&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.current_date_range]=1+years&sorts=spins_nlp.period_ending_quarter" + }, + { + "input": "How did blueberries stack up against other fresh fruit products in 2023 in the conventional grow method?", + "output": "fields=spins_nlp.rma_units_sum,spins_nlp.product_type&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_month]=after+2023%2F01%2F01&f[spins_nlp.product_type]=FRESH+FRUIT%25&sorts=spins_nlp.rma_units_sum+desc" + }, + { + "input": "How do supply and demand dynamics influence the sales volume differences between different blueberry formats?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "By how much do the sales volumes of different blueberry formats differ?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do consumer preferences affect the sales volumes of fresh, frozen, and dried blueberries?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the ratio of sales volume among fresh, frozen, and dried blueberries?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do the sales volumes of different blueberry formats compare?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do the sales volumes of different blueberry formats stack up against each other?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which blueberry format has the highest sales volume?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How has the sales volume gap between different blueberry formats evolved?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the percentage difference in sales volume between fresh, frozen, and dried blueberries?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there significant differences in sales volume between different blueberry formats?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any trends in the market share of fresh, frozen, and dried blueberries?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the sales volume distribution among fresh, frozen, and dried blueberries?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which blueberry format has the largest market share?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any regional variations in the sales volume comparison among different blueberry formats?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Do price differences between different blueberry formats impact sales volumes?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Is the sales volume of any blueberry format increasing or decreasing relative to the others?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Is the demand for any blueberry format growing faster or slower than the others?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What factors influence the sales volume differences between different blueberry formats?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "6. Product Formats: How do sales volumes compare for different blueberry formats (fresh, frozen, dried)?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have the sales volumes of different blueberry formats changed over time?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the relative popularity of fresh, frozen, and dried blueberries in terms of sales?", + "output": "fields=spins_nlp.rma_lbs_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What market saw the lowest retail sales in fresh blueberries in the last 12 weeks?", + "output": "fields=spins_nlp.geography_filter,spins_nlp.rma_dollars_sum&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.period_ending_week]=12+weeks&f[spins_nlp.geography_type]=Market&sorts=spins_nlp.rma_dollars_sum" + }, + { + "input": "What parent companies sell the most Fresh Blueberries by pounds?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_month]=after+2023%2F01%2F01&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25&sorts=spins_nlp.total_lbs+desc" + }, + { + "input": "What is the trend in sales volume for blueberries over the last year?", + "output": "fields=spins_nlp.total_dollars,spins_nlp.period_ending_quarter&fill_fields=spins_nlp.period_ending_quarter&f[spins_nlp.geography_type]=Region&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.current_date_range]=1+years&sorts=spins_nlp.period_ending_quarter" + }, + { + "input": "how many pounds of blueberries were sold in March of 2023 by week in the total US?", + "output": "fields=spins_nlp.geography_filter,spins_nlp.period_ending_date,spins_nlp.total_lbs&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_month]=2023%2F03%2F01+to+2023%2F04%2F01&sorts=spins_nlp.period_ending_date" + }, + { + "input": "What retail market saw the most growth in fresh blueberry sales by dollars in the last 12 weeks?", + "output": "fields=spins_nlp.geography_filter,spins_nlp.rma_dollars_sum&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.period_ending_week]=12+weeks&f[spins_nlp.geography_type]=Market&sorts=spins_nlp.rma_dollars_sum+desc" + }, + { + "input": "Which Blueberry manufacturer sells the most units of Blueberries in NYC?", + "output": "fields=spins_nlp.total_units,spins_nlp.parent_company&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=%25NEW+YORK%5E%2C+NY%25&sorts=spins_nlp.total_units+desc+0" + }, + { + "input": "What are the bottom 5 markets for Blueberry sales in total pounds?", + "output": "fields=spins_nlp.geography_abbreviated,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=&f[spins_nlp.geography_type]=Market&sorts=spins_nlp.total_lbs" + }, + { + "input": "What is the most popular brand of blueberries?", + "output": "fields=spins_nlp.total_dollars,spins_nlp.geography_filter,spins_nlp.parent_company&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_year]=2024%2F01%2F01+to+2025%2F01%2F01&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.total_dollars+desc+0&limit=50" + }, + { + "input": "What are the total sales in Florida in the last two years by units sold?", + "output": "fields=spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_week]=2+year+ago+for+2+year&f[spins_nlp.geography_abbreviated]=%25FL%25&f[spins_nlp.geography_type]=Market" + }, + { + "input": "What package size is the most popular for organic fresh blueberries in 2023 by total units sold?", + "output": "fields=spins_nlp.total_units,spins_nlp.base_size_filter&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%2CFRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_year]=2023&sorts=spins_nlp.total_units+desc+0" + }, + { + "input": "Are there any regional-specific factors driving blueberry price patterns?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any notable trends in blueberry price fluctuations?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the rate of change in blueberry prices?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry prices fluctuated over the years?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the general pattern of blueberry price changes?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry prices changed per unit of time?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have there been significant changes in blueberry prices?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have economic factors played a role in blueberry price trends?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "By how much have blueberry prices increased or decreased?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "3. Price Trends: How have average prices for blueberries changed over time?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Do blueberry prices peak during specific seasons?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are blueberry prices increasing, decreasing, or remaining stable?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have weather conditions impacted blueberry production and prices?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry prices evolved over time?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have there been seasonal fluctuations in blueberry prices?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have seasonal price patterns for blueberries changed over time?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any regional variations in blueberry price seasonality?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What has been the overall trend in blueberry prices?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have changes in consumer preferences affected blueberry prices?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What factors have influenced blueberry price trends?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "When are blueberry prices typically highest?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.total_dollars&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Can you show me the trend of blueberry sales over the last 3 years by month?", + "output": "fields=spins_nlp.total_dollars,spins_nlp.period_ending_month&fill_fields=spins_nlp.period_ending_month&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_year]=2022%2F01%2F01+to+2025%2F01%2F01&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.period_ending_month" + }, + { + "input": "What parent company sells the most Fresh and Frozen Blueberries in the Florida market by Units sold?", + "output": "fields=spins_nlp.total_units,spins_nlp.parent_company&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=%25FL%25&f[spins_nlp.geography_type]=Market&sorts=spins_nlp.total_units+desc+0" + }, + { + "input": "What are the worst 5 regions for Fresh and Frozen Blueberry sales in the last year by pounds sold?", + "output": "fields=spins_nlp.total_lbs,spins_nlp.geography_filter&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.period_ending_date]=1+year+ago+for+1+year&f[spins_nlp.geography_type]=Market&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.total_lbs" + }, + { + "input": "What is the most popular product size in Florida by total units?", + "output": "fields=spins_nlp.total_units,spins_nlp.base_size_filter&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=%25FL%25&f[spins_nlp.geography_type]=Market&sorts=spins_nlp.total_units+desc+0" + }, + { + "input": "How do the sales volumes of organic and conventional blueberries compare?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which type of blueberry has higher sales volume, organic or conventional?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What factors influence the sales volume difference between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the difference in sales volume between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the ratio of sales volume between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the percentage difference in sales volume between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "By how much do the sales volumes of organic and conventional blueberries differ?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do supply and demand dynamics influence the sales volume difference between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How does the sales volume of organic blueberries compare to that of conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Is the demand for organic blueberries growing faster or slower than that for conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How has the sales volume gap between organic and conventional blueberries evolved?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the relative sales volume of organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Do price differences between organic and conventional blueberries impact sales volumes?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there significant differences in sales volume between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any regional variations in the sales volume comparison between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "5. Organic vs. Conventional: What is the sales volume comparison between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which type of blueberry has a larger market share, organic or conventional?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do consumer preferences affect the sales volumes of organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Is the sales volume of organic blueberries increasing or decreasing relative to conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have the sales volumes of organic and conventional blueberries changed over time?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any trends in the market share of organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.total_lbs&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=-TOTAL+US+-+FOOD&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What are the bottom 5 regions for Fresh and Frozen Blueberry sales in the last complete year by units sold?", + "output": "fields=spins_nlp.geography_filter,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.period_ending_date]=1+year+ago+for+1+year&f[spins_nlp.geography_type]=Market&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.total_units" + }, + { + "input": "How do sales volumes compare for different blueberry formats (fresh, frozen, dried)?", + "output": "fields=spins_nlp.total_dollars,spins_nlp.geography_filter,spins_nlp.product_type&f[spins_nlp.geography_type]=US+Total&f[spins_nlp.period_ending_year]=2024%2F01%2F01+to+2025%2F01%2F01&f[spins_nlp.product_type]=DRIED+FRUIT+-+BLUEBERRY%2CFRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.total_dollars+desc+0" + }, + { + "input": "How have seasonal sales patterns for blueberries changed in the past year?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Where have blueberry sales declined the most in the past year?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Do blueberry sales peak during specific seasons in different parts of the country?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Which regions have experienced the largest growth in blueberry sales?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry sales been by dollars over the past year across different regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What factors have influenced blueberry sales trends in different regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have there been seasonal fluctuations in blueberry sales across different regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have weather conditions impacted blueberry production and sales?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have economic factors played a role in blueberry sales trends?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry sales trended over the past year across different regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have changes in consumer preferences affected blueberry sales?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry sales by dollars over the past year across different regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the general sales trend for blueberries on a regional basis?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there significant regional differences in blueberry sales trends?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry sales been over the past year across different regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry sales been by dollars over the past year across different regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any regional-specific factors driving blueberry sales patterns?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What has been the overall sales trajectory for blueberries in the past year?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "When are blueberries typically most popular in various regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any regional variations in blueberry sales seasonality?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry sales dollars over the past year across different regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How have blueberry sales evolved across various regions in the last 12 months?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do blueberry sales in the northern regions compare to those in the southern regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any notable regional disparities in blueberry sales patterns?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the overall pattern of blueberry sales across the country?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Have blueberry sales increased, decreased, or remained stable in different regions?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.geography,spins_nlp.rma_dollars_sum&pivots=spins_nlp.geography&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_week]=52+weeks&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What are the bottom 5 regions for Fresh and Frozen Blueberry sales in the last complete year by dollars sold?", + "output": "fields=spins_nlp.geography_filter,spins_nlp.total_dollars&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.period_ending_date]=1+year+ago+for+1+year&f[spins_nlp.geography_type]=Market&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.total_dollars" + }, + { + "input": "What are the top 5 regions for Fresh and Frozen Blueberry sales in the last complete year by total units sold?", + "output": "fields=spins_nlp.geography_filter,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.period_ending_date]=1+year+ago+for+1+year&f[spins_nlp.geography_type]=Market&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.total_units+desc" + }, + { + "input": "What are the least popular package sizes when looking at units sold?", + "output": "fields=spins_nlp.total_lbs,spins_nlp.base_size_filter&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=&f[spins_nlp.geography_type]=&f[spins_nlp.base_size_filter]=-NULL&sorts=spins_nlp.total_lbs" + }, + { + "input": "What are the top 5 regions for Fresh and Frozen Blueberry sales in the last complete year by dollars sold?", + "output": "fields=spins_nlp.geography_filter,spins_nlp.total_dollars&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.period_ending_date]=1+year+ago+for+1+year&f[spins_nlp.geography_type]=Market&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.total_dollars+desc+0" + }, + { + "input": "What retailer sold the lowest amount of Blueberry units in the last two complete years?", + "output": "fields=spins_nlp.geography_abbreviated,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.geography_type]=Total+Retailer&sorts=spins_nlp.total_units" + }, + { + "input": "What retailer sells the most Blueberries on promotion?", + "output": "fields=spins_nlp.rma_units_incremental_promo_any_sum,spins_nlp.geography_abbreviated&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_type]=Total+Retailer&sorts=spins_nlp.rma_units_incremental_promo_any_sum+desc+0" + }, + { + "input": "Which Target Division sells the most Blueberries?", + "output": "fields=spins_nlp.geography_abbreviated,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=%25TARGET+CORP%25%2C%25TARGET+CORP+TTL%25%2C%25TARGET+SUPER+TARGET%25&sorts=spins_nlp.total_units+desc+0" + }, + { + "input": "What market sold the highest number of pounds of 16 oz Blueberry containers?", + "output": "fields=spins_nlp.geography_abbreviated,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=&f[spins_nlp.geography_type]=Market&f[spins_nlp.base_size_filter]=16+OUNCE&sorts=spins_nlp.total_lbs+desc+0" + }, + { + "input": "How do sales figures compare between organic and conventional blueberries? Is there a growing preference for organic options? - Need to create a % of total market measure", + "output": "fields=spins_nlp.geography_filter,spins_nlp.rma_tdp_sum&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_type]=Market&f[spins_nlp.comparison_period]=Period&f[spins_nlp.current_date_range]=365+days&sorts=spins_nlp.rma_tdp_sum+desc" + }, + { + "input": "What product descriptions sold zero pounds in the last two years?", + "output": "fields=spins_nlp.total_lbs,spins_nlp.product_description&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_week]=2+year+ago+for+2+year&f[spins_nlp.geography_abbreviated]=&f[spins_nlp.total_lbs]=0&sorts=spins_nlp.total_lbs+desc" + }, + { + "input": "How does the last 12 months of Blueberry sales compare to other fresh berries by pounds sold at Meijer?", + "output": "fields=spins_nlp.product_type,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=%25MEIJER%25&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFRESH+FRUIT+-+BLACKBERRY%2CFRESH+FRUIT+-+RASPBERRY%2CFRESH+FRUIT+-+STRAWBERRY&f[spins_nlp.period_ending_date]=1+year+ago+for+1+year&sorts=spins_nlp.total_lbs+desc+0" + }, + { + "input": "How does the last 12 months of Blueberry sales compare to other fresh berries by pounds sold at Publix?", + "output": "fields=spins_nlp.product_type,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=%25PUBLIX%25&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFRESH+FRUIT+-+BLACKBERRY%2CFRESH+FRUIT+-+RASPBERRY%2CFRESH+FRUIT+-+STRAWBERRY&f[spins_nlp.period_ending_date]=12+month+ago+for+12+month&sorts=spins_nlp.total_lbs+desc+0" + }, + { + "input": "How does the last 12 months of Blueberry sales compare to other fresh berries by pounds sold at Albertsons?", + "output": "fields=spins_nlp.product_type,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=%25ALBERTSON%25&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFRESH+FRUIT+-+BLACKBERRY%2CFRESH+FRUIT+-+RASPBERRY%2CFRESH+FRUIT+-+STRAWBERRY&f[spins_nlp.period_ending_date]=12+month+ago+for+12+month&sorts=spins_nlp.total_lbs+desc+0" + }, + { + "input": "Do the ratios of frozen unit sales to fresh blueberry unit sales differ from different markets?", + "output": "fields=spins_nlp.rma_units_sum,spins_nlp.product_type,spins_nlp.geography_filter&pivots=spins_nlp.product_type&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=Region&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25%2CFZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.product_type,percent_of_total_sales+desc+1" + }, + { + "input": "What are the sales of Fresh Blueberries vs Frozen Blueberries in Florida by Units sold?", + "output": "fields=spins_nlp.total_units,spins_nlp.product_type&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_week]=2+year+ago+for+2+year&f[spins_nlp.geography_abbreviated]=%25FL%25&f[spins_nlp.geography_type]=Market&sorts=spins_nlp.product_type" + }, + { + "input": "What are the top 5 markets for Blueberry sales in total pounds?", + "output": "fields=spins_nlp.geography_abbreviated,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_week]=2+year+ago+for+2+year&f[spins_nlp.geography_abbreviated]=&f[spins_nlp.geography_type]=Market&sorts=spins_nlp.total_lbs+desc+0" + }, + { + "input": "What geography buys the most pounds of Blueberries?", + "output": "fields=spins_nlp.geography_abbreviated,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_week]=2+year+ago+for+2+year&f[spins_nlp.geography_abbreviated]=&f[spins_nlp.geography_type]=Market&sorts=spins_nlp.total_lbs+desc+0" + }, + { + "input": "When were the most retail units sold on promotion for fresh blueberries in the last 2 years?", + "output": "fields=spins_nlp.period_ending_week,spins_nlp.rma_units_incremental_promo_any_sum&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.rma_units_incremental_promo_any_sum]=NOT+NULL&sorts=spins_nlp.rma_units_incremental_promo_any_sum+desc" + }, + { + "input": "What are the forcasted total distribution points for the next 3 months?", + "output": "fields=spins_nlp.rma_tdp_sum,spins_nlp.period_ending_month&fill_fields=spins_nlp.period_ending_month&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_type]=Market&f[spins_nlp.period_ending_month]=after+2023%2F01%2F01&sorts=spins_nlp.period_ending_month+desc" + }, + { + "input": "What market has the highest dollar amount of bagged Blueberry sales?", + "output": "fields=spins_nlp.geography_abbreviated,spins_nlp.total_dollars&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_abbreviated]=&f[spins_nlp.geography_type]=Market&f[spins_nlp.base_size_filter]=&f[spins_nlp.packaging_type_primary]=BAG&sorts=spins_nlp.total_dollars+desc+0" + }, + { + "input": "How does the last 12 months of Blueberry sales in dollars compare to other fresh berries when on promotion?", + "output": "fields=spins_nlp.rma_units_incremental_promo_any_sum,spins_nlp.product_type&pivots=spins_nlp.product_type&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFRESH+FRUIT+-+BLACKBERRY%2CFRESH+FRUIT+-+RASPBERRY%2CFRESH+FRUIT+-+STRAWBERRY&f[spins_nlp.period_ending_date]=12+month+ago+for+12+month&sorts=spins_nlp.product_type" + }, + { + "input": "What yearly quarter has the lowest blueberry sales by dollars?", + "output": "fields=spins_nlp.period_ending_quarter_of_year,spins_nlp.total_dollars&fill_fields=spins_nlp.period_ending_quarter_of_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_month]=8+quarter+ago+for+8+quarter&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25%2CFZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.total_dollars" + }, + { + "input": "What parent company sells the most Fresh Blueberries by total pounds?", + "output": "fields=spins_nlp.product_type,spins_nlp.parent_company,spins_nlp.total_lbs&pivots=spins_nlp.product_type&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_month]=after+2023%2F01%2F01&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25%2CFZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.product_type,spins_nlp.total_lbs+desc+0" + }, + { + "input": "What yearly quarter has the highest blueberry sales by dollars over the last two years?", + "output": "fields=spins_nlp.period_ending_quarter_of_year,spins_nlp.total_dollars&fill_fields=spins_nlp.period_ending_quarter_of_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_month]=8+quarter+ago+for+8+quarter&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25%2CFZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.total_dollars+desc+0" + }, + { + "input": "What yearly quarter has the highest blueberry sales by RMA dollars?", + "output": "fields=spins_nlp.period_ending_quarter_of_year,spins_nlp.rma_dollars_sum&fill_fields=spins_nlp.period_ending_quarter_of_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_month]=8+quarter+ago+for+8+quarter&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25%2CFZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.rma_dollars_sum+desc" + }, + { + "input": "What yearly quarter has the highest blueberry sales by RMA dollars?", + "output": "fields=spins_nlp.period_ending_quarter_of_year,spins_nlp.rma_dollars_sum&fill_fields=spins_nlp.period_ending_quarter_of_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.period_ending_month]=8+quarter+ago+for+8+quarter&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25%2CFZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.rma_dollars_sum+desc+0" + }, + { + "input": "What retailers sell no Fresh or Frozen Blueberries on promotion?", + "output": "fields=spins_nlp.rma_units_incremental_promo_any_sum,spins_nlp.geography_abbreviated&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.geography_type]=Total+Retailer&f[spins_nlp.rma_units_incremental_promo_any_sum]=0&sorts=spins_nlp.rma_units_incremental_promo_any_sum" + }, + { + "input": "What is the percent change year over year in pounds sold?", + "output": "fields=spins_nlp.total_lbs_current,spins_nlp.total_lbs_prior,spins_nlp.total_lbs_perc_change&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%2CFRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.comparison_period]=Period&f[spins_nlp.current_date_range]=365+days&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_year]=2023&sorts=spins_nlp.total_lbs_perc_change+desc" + }, + { + "input": "How do blueberry sales vary throughout the year, and are there any specific weeks or months that see a significant increase or decrease?", + "output": "fields=spins_nlp.period_ending_week_of_year,spins_nlp.total_lbs_current,spins_nlp.total_lbs_prior&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_type]=Market&f[spins_nlp.comparison_period]=Period&f[spins_nlp.current_date_range]=365+days&f[spins_nlp.total_lbs_current]=NOT+NULL&sorts=spins_nlp.total_lbs_current+desc+0" + }, + { + "input": "Compare sales of Fresh and Frozen Blueberries in the last two complete years by total pounds sold", + "output": "fields=spins_nlp.product_type,spins_nlp.period_ending_year,spins_nlp.total_lbs&pivots=spins_nlp.period_ending_year&fill_fields=spins_nlp.period_ending_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%2CFRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.period_ending_month_name]=&sorts=spins_nlp.period_ending_year,spins_nlp.total_lbs+desc+0" + }, + { + "input": "What was the largest region in blueberry sales?", + "output": "fields=spins_nlp.total_dollars,spins_nlp.geography_filter&f[spins_nlp.geography_type]=Region&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&sorts=spins_nlp.total_dollars+desc+0" + }, + { + "input": "Which region realized the greatest growth percentage in blueberry pounds sold in 2023?", + "output": "fields=spins_nlp.total_lbs_current,spins_nlp.total_lbs_prior,spins_nlp.geography_filter,spins_nlp.total_lbs_perc_change&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%2CFRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.comparison_period]=Period&f[spins_nlp.current_date_range]=365+days&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_year]=2023&sorts=spins_nlp.total_lbs_perc_change+desc" + }, + { + "input": "Compare sales of Fresh and Frozen Blueberries in the last two complete years by units sold", + "output": "fields=spins_nlp.product_type,spins_nlp.period_ending_year,spins_nlp.total_units&pivots=spins_nlp.period_ending_year&fill_fields=spins_nlp.period_ending_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%2CFRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.period_ending_month_name]=&sorts=spins_nlp.period_ending_year,spins_nlp.total_units+desc+0" + }, + { + "input": "Compare sales of Fresh and Frozen Blueberries in the last two complete years by total dollars sold", + "output": "fields=spins_nlp.product_type,spins_nlp.period_ending_year,spins_nlp.total_dollars&pivots=spins_nlp.period_ending_year&fill_fields=spins_nlp.period_ending_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%2CFRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.period_ending_month_name]=&sorts=spins_nlp.period_ending_year,spins_nlp.total_dollars+desc+0" + }, + { + "input": "What is the forcasted total distribution points for the next 3 months by region?", + "output": "fields=spins_nlp.rma_tdp_sum,spins_nlp.period_ending_month,spins_nlp.geography_filter&pivots=spins_nlp.geography_filter&fill_fields=spins_nlp.period_ending_month&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_type]=Region&f[spins_nlp.period_ending_month]=after+2023%2F01%2F01&sorts=spins_nlp.geography_filter,spins_nlp.period_ending_month+desc" + }, + { + "input": "How have seasonal blueberry sales trends changed over time?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do seasonal sales fluctuations for blueberries compare to other fruits?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "When are blueberries typically most popular in terms of sales?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "When do blueberry sales reach their peak?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "When do blueberry sales experience their lowest points?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any pronounced seasonal highs and lows in blueberry sales?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any regional variations in seasonal blueberry sales patterns?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the difference in sales volume between peak and off-peak seasons?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there significant seasonal differences in blueberry sales?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the seasonal sales cycle for blueberries?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What factors influence the seasonal fluctuations in blueberry sales?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Do weather conditions impact seasonal blueberry sales?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any seasonal variations in blueberry production that affect sales?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "What is the seasonal sales pattern for blueberries?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do consumer preferences affect seasonal blueberry demand?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "7. Seasonal Trends: How do sales volumes fluctuate seasonally for blueberries?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do blueberry sales fluctuate between peak and off-peak periods?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Are there any emerging patterns in seasonal blueberry sales?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "Is the seasonality of blueberry sales becoming more or less pronounced?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do blueberry sales vary throughout the year?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How do seasonal pricing strategies influence blueberry sales fluctuations?", + "output": "fields=spins_nlp.period_ending_month_name&f[spins_nlp.geography]=&f[spins_nlp.category]=PRODUCE&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.geography_type]=Region&f[spins_nlp.rma_dollars_sum]=NOT+NULL" + }, + { + "input": "How are total unit sales the week of the Christmas compared to other weeks? (has custom chart config settings)", + "output": "fields=spins_nlp.total_units,spins_nlp.period_ending_week,spins_nlp.is_week_before_christmas&pivots=spins_nlp.is_week_before_christmas&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_week]=2+year+ago+for+2+year&f[spins_nlp.geography_abbreviated]=&f[spins_nlp.total_units]=NOT+NULL&sorts=spins_nlp.is_week_before_christmas,spins_nlp.period_ending_week+desc" + }, + { + "input": "What are the top 5 regions for Fresh and Frozen Blueberry sales in the last year by units sold?", + "output": "fields=spins_nlp.total_units,spins_nlp.geography_filter,spins_nlp.period_ending_year&f[spins_nlp.period_ending_year]=1+years&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.geography_type]=Region" + }, + { + "input": "What parent company sells the most Frozen Blueberries by weight?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_lbs&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.total_lbs+desc" + }, + { + "input": "What parent companies sell the most Frozen Blueberries by units?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.total_units+desc" + }, + { + "input": "What parent company sells the most Frozen Blueberries by unit?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.total_units+desc" + }, + { + "input": "How are sales the week before the Fourth of July compared to other weeks? (has custom chart config settings)", + "output": "fields=spins_nlp.total_units,spins_nlp.period_ending_week,spins_nlp.is_week_before_fourth_of_july&pivots=spins_nlp.is_week_before_fourth_of_july&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%2CFZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_week]=2+year+ago+for+2+year&f[spins_nlp.geography_abbreviated]=&f[spins_nlp.total_units]=NOT+NULL&sorts=spins_nlp.is_week_before_fourth_of_july,spins_nlp.period_ending_week+desc" + }, + { + "input": "What parent company sells the most Fresh Blueberries by unit?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_units&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25&sorts=spins_nlp.total_units+desc" + }, + { + "input": "What parent company sells the most Fresh Blueberries by dollars in the conventional grow method?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_dollars&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY&sorts=spins_nlp.total_dollars+desc" + }, + { + "input": "Are there significant price differences between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "Is the price premium for organic blueberries sustainable?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "How do consumer preferences affect the price that consumers are willing to pay for organic blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "17. Organic Premium: What is the price premium for organic blueberries compared to conventional?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "How much more expensive are organic blueberries than conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "Are there any trends in the price premium for organic blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "Is the price gap between organic and conventional blueberries widening or narrowing?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "How has the price difference between organic and conventional blueberries evolved?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "How much more do organic blueberries cost compared to conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "What is the price difference between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "How do supply and demand dynamics influence the price premium for organic blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "How have the price premiums for organic blueberries changed over time?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "By how much do organic blueberries cost more than conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "What is the price markup for organic blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "What is the percentage price difference between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "What is the price ratio between organic and conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "How much of a premium do consumers pay for organic blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "What is the price premium for organic blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "What factors influence the price premium for organic blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "Do production costs for organic blueberries differ significantly from those for conventional blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "Are there any regional variations in the price premium for organic blueberries?", + "output": "fields=spins_nlp.labeled_organic,spins_nlp.price_usd_lb&pivots=spins_nlp.labeled_organic&f[spins_nlp.geography]=TOTAL+US+-+MULO%2CTOTAL+US+-+NATURAL+CHANNEL&f[spins_nlp.commodity]=BLUEBERRIES%2CBLUEBERRY&f[spins_nlp.period_ending_week]=52+weeks" + }, + { + "input": "What parent companies sell the most Frozen Blueberries by dollars?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_dollars&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.total_dollars+desc" + }, + { + "input": "What parent company sells the most Frozen Blueberries by dollars in the conventional grow method?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_dollars&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%25&sorts=spins_nlp.total_dollars+desc" + }, + { + "input": "What parent companies sell the most Fresh Blueberries by dollars?", + "output": "fields=spins_nlp.parent_company,spins_nlp.total_dollars&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.geography_type]=&f[spins_nlp.product_type]=FRESH+FRUIT+-+BLUEBERRY%25&sorts=spins_nlp.total_dollars+desc" + }, + { + "input": "What markets saw a decrease in Blueberry sales Year over Year? (None)", + "output": "fields=spins_nlp.total_lbs_current,spins_nlp.total_lbs_prior,spins_nlp.total_lbs_perc_change,spins_nlp.geography_abbreviated&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES%2CFRESH+FRUIT+-+BLUEBERRY&f[spins_nlp.comparison_period]=Period&f[spins_nlp.current_date_range]=365+days&f[spins_nlp.geography_type]=Market&f[spins_nlp.period_ending_year]=2023&f[spins_nlp.total_lbs_perc_change]=%3C0&sorts=spins_nlp.total_lbs_perc_change" + }, + { + "input": "How many pounds of frozen blueberries were sold on promotion over this spring versus last spring?", + "output": "fields=spins_nlp.rma_units_incremental_promo_any_sum,spins_nlp.product_type,spins_nlp.period_ending_year&pivots=spins_nlp.period_ending_year&fill_fields=spins_nlp.period_ending_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.period_ending_month_name]=March%2CApril%2CMay&sorts=spins_nlp.period_ending_year,spins_nlp.rma_units_incremental_promo_any_sum+desc+0" + }, + { + "input": "How many dollars of frozen blueberries were sold on promotion over this summer versus last summer?", + "output": "fields=spins_nlp.rma_units_incremental_promo_any_sum,spins_nlp.product_type,spins_nlp.period_ending_year&pivots=spins_nlp.period_ending_year&fill_fields=spins_nlp.period_ending_year&f[spins_nlp.labeled_organic]=CONVENTIONAL&f[spins_nlp.geography_filter]=&f[spins_nlp.product_type]=FZ+FRUIT+-+BLUEBERRIES&f[spins_nlp.period_ending_date]=2+year+ago+for+2+year&f[spins_nlp.period_ending_month_name]=June%2CJuly%2CAugust&sorts=spins_nlp.period_ending_year,spins_nlp.rma_units_incremental_promo_any_sum+desc+0" + } +] \ No newline at end of file diff --git a/explore-assistant-examples/samples.json b/explore-assistant-examples/samples.json index 084278bb7..18c31009f 100644 --- a/explore-assistant-examples/samples.json +++ b/explore-assistant-examples/samples.json @@ -1,14 +1,14 @@ [ { - "category": "Cohorting", - "prompt": "Count of Users by first purchase date" + "category": "Performance", + "prompt": "What parent companies sell the most Fresh Blueberries by units?" }, { - "category": "Audience Building", - "prompt":"Users who have purchased more than 100 dollars worth of Calvin Klein products and have purchased in the last 30 days" + "category": "Volume Trends", + "prompt":"What is the trend in sales volume for blueberries over the last year?" }, { - "category": "Period Comparison", - "prompt": "Total revenue by category this year compared to last year in a line chart with year pivoted" + "category": "Seasonality", + "prompt": "Do blueberry sales volumes peak during specific seasons?" } ] \ No newline at end of file diff --git a/explore-assistant-examples/update_refinements.sh b/explore-assistant-examples/update_refinements.sh old mode 100644 new mode 100755 diff --git a/explore-assistant-examples/update_samples.sh b/explore-assistant-examples/update_samples.sh old mode 100644 new mode 100755 diff --git a/explore-assistant-extension/.DS_Store b/explore-assistant-extension/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..d4dba18da7ce914efaa93612b37f1254b10baff8 GIT binary patch literal 8196 zcmeHM%Wl&^6ur|2b<}r7MK?&6*oM%i1zNEQpRQSUihL>ok8AhTSVM=>Gsd(OEu z6}PNI3gC%+s!)qU3g{@Ots680ngPv#W}9Vlc706BwZnNUX@ATeevYq12`8Gka^!m^0>$ca4*<-{@N1 zUR1X_Z}{nD82a|8ZrQIqx8Gd3{VWW+mKV4k3Gf^jDsNwVffM%YVJ~nZiEXGCM#(5O zSMCf3+k218=I+CbvN_nRZkElR?MD|EC1d^GgC~d0Q~xXoKgxxXh|d~YRL|q`6KdmS zpEo*z9|p&0WBoNy<}w|0pE{tm59#Ad!~#t&S|(5`?gF}$pmb^6$Diwzg6$BWGuXuh zHo}Nq>QQ^*s6TVaGt4r;W*)^Ws9@%&bVh!9Yk9sY6IcK$l&L#(-@~it+bZpa)MUENxomZlWYtIJG<4gxmDfyiLyefQ;X$LNwgqc|3v_T7{R|IlV5NB{g<1eiyOnhZ+o!EG5`Po literal 0 HcmV?d00001 diff --git a/explore-assistant-extension/jest-raw-loader.js b/explore-assistant-extension/jest-raw-loader.js new file mode 100644 index 000000000..070711252 --- /dev/null +++ b/explore-assistant-extension/jest-raw-loader.js @@ -0,0 +1,10 @@ +const fs = require('fs'); + +module.exports = { + process(src, filename) { + const content = fs.readFileSync(filename, 'utf8'); + return { + code: `module.exports = ${JSON.stringify(content)}`, + }; + }, +}; diff --git a/explore-assistant-extension/jest.config.js b/explore-assistant-extension/jest.config.js new file mode 100644 index 000000000..02a5d1cd2 --- /dev/null +++ b/explore-assistant-extension/jest.config.js @@ -0,0 +1,10 @@ +module.exports = { + preset: 'ts-jest', + testEnvironment: 'jsdom', + setupFiles: ['dotenv/config', '/jest.setup.js'], // Add this line + transform: { + '^.+\\.ts?$': 'ts-jest', + '^.+\\.md$': '/jest-raw-loader.js', // Add this line to use the custom transformer for .md files + }, + transformIgnorePatterns: ['/node_modules/'], + }; \ No newline at end of file diff --git a/explore-assistant-extension/jest.setup.js b/explore-assistant-extension/jest.setup.js new file mode 100644 index 000000000..323e397a0 --- /dev/null +++ b/explore-assistant-extension/jest.setup.js @@ -0,0 +1,4 @@ +require('regenerator-runtime/runtime'); +const fetch = require('node-fetch'); + +global.fetch = fetch; diff --git a/explore-assistant-extension/package-lock.json b/explore-assistant-extension/package-lock.json index d6371c0ed..6a279dbbc 100644 --- a/explore-assistant-extension/package-lock.json +++ b/explore-assistant-extension/package-lock.json @@ -24,7 +24,6 @@ "@mui/material": "^5.16.5", "@reduxjs/toolkit": "^2.2.2", "@types/crypto-js": "^4.2.2", - 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"@types/estree": "^1.0.5", @@ -13479,7 +18147,6 @@ "version": "3.2.3", "resolved": "https://registry.npmjs.org/webpack-sources/-/webpack-sources-3.2.3.tgz", "integrity": "sha512-/DyMEOrDgLKKIG0fmvtz+4dUX/3Ghozwgm6iPp8KRhvn+eQf9+Q7GWxVNMk3+uCPWfdXYC4ExGBckIXdFEfH1w==", - "devOptional": true, "engines": { "node": ">=10.13.0" } @@ -13488,7 +18155,6 @@ "version": "5.1.1", "resolved": "https://registry.npmjs.org/eslint-scope/-/eslint-scope-5.1.1.tgz", "integrity": "sha512-2NxwbF/hZ0KpepYN0cNbo+FN6XoK7GaHlQhgx/hIZl6Va0bF45RQOOwhLIy8lQDbuCiadSLCBnH2CFYquit5bw==", - "devOptional": true, "dependencies": { "esrecurse": "^4.3.0", "estraverse": "^4.1.1" @@ -13501,7 +18167,6 @@ "version": "4.3.0", "resolved": "https://registry.npmjs.org/estraverse/-/estraverse-4.3.0.tgz", "integrity": "sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw==", - "devOptional": true, "engines": { "node": ">=4.0" } @@ -13510,7 +18175,6 @@ "version": "3.3.0", "resolved": 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"integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==", "dev": true }, + "node_modules/write-file-atomic": { + "version": "4.0.2", + "resolved": "https://registry.npmjs.org/write-file-atomic/-/write-file-atomic-4.0.2.tgz", + "integrity": "sha512-7KxauUdBmSdWnmpaGFg+ppNjKF8uNLry8LyzjauQDOVONfFLNKrKvQOxZ/VuTIcS/gge/YNahf5RIIQWTSarlg==", + "dev": true, + "license": "ISC", + "dependencies": { + "imurmurhash": "^0.1.4", + "signal-exit": "^3.0.7" + }, + "engines": { + "node": "^12.13.0 || ^14.15.0 || >=16.0.0" + } + }, "node_modules/ws": { "version": "7.5.10", "resolved": "https://registry.npmjs.org/ws/-/ws-7.5.10.tgz", @@ -13730,6 +18458,33 @@ } } }, + "node_modules/xml-name-validator": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/xml-name-validator/-/xml-name-validator-4.0.0.tgz", + "integrity": "sha512-ICP2e+jsHvAj2E2lIHxa5tjXRlKDJo4IdvPvCXbXQGdzSfmSpNVyIKMvoZHjDY9DP0zV17iI85o90vRFXNccRw==", + "dev": true, + "license": "Apache-2.0", + "engines": { + "node": ">=12" + } + }, + "node_modules/xmlchars": { + "version": "2.2.0", + "resolved": "https://registry.npmjs.org/xmlchars/-/xmlchars-2.2.0.tgz", + "integrity": "sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw==", + "dev": true, + "license": "MIT" + }, + "node_modules/y18n": { + "version": "5.0.8", + "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz", + "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==", + "dev": true, + "license": "ISC", + "engines": { + "node": ">=10" + } + }, "node_modules/yallist": { "version": "3.1.1", "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz", @@ -13747,6 +18502,57 @@ "node": ">= 14" } }, + "node_modules/yargs": { + "version": "17.7.2", + "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.2.tgz", + "integrity": "sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==", + "dev": true, + "license": "MIT", + "dependencies": { + "cliui": "^8.0.1", + "escalade": "^3.1.1", + "get-caller-file": "^2.0.5", + "require-directory": "^2.1.1", + "string-width": "^4.2.3", + "y18n": "^5.0.5", + "yargs-parser": "^21.1.1" + }, + "engines": { + "node": ">=12" + } + }, + "node_modules/yargs-parser": { + "version": "21.1.1", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz", + "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==", + "dev": true, + "license": "ISC", + "engines": { + "node": ">=12" + } + }, + "node_modules/yargs/node_modules/emoji-regex": { + "version": "8.0.0", + "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", + "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==", + "dev": true, + "license": "MIT" + }, + "node_modules/yargs/node_modules/string-width": { + "version": "4.2.3", + "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", + "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", + "dev": true, + "license": "MIT", + "dependencies": { + "emoji-regex": "^8.0.0", + "is-fullwidth-code-point": "^3.0.0", + "strip-ansi": "^6.0.1" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/yocto-queue": { "version": "0.1.0", "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", diff --git a/explore-assistant-extension/package.json b/explore-assistant-extension/package.json index 257a44676..dac7a3abd 100644 --- a/explore-assistant-extension/package.json +++ b/explore-assistant-extension/package.json @@ -9,7 +9,8 @@ "build": "export BABEL_ENV=build && webpack --config webpack.prod.js", "clean": "rm -rf dist", "start": "webpack serve --hot --server-type https --port 8080 --config webpack.develop.js", - "prebuild": "npm run clean" + "prebuild": "npm run clean", + "test": "jest" }, "author": "Google LLC", "license": "Apache-2.0", @@ -32,7 +33,6 @@ "@mui/material": "^5.16.5", "@reduxjs/toolkit": "^2.2.2", "@types/crypto-js": "^4.2.2", - "@types/react": "^17.0.80", "@types/uuid": "^10.0.0", "clsx": "^2.1.1", "crypto-js": "^4.2.0", @@ -42,6 +42,7 @@ "marked": "^12.0.1", "marked-highlight": "^2.1.1", "npm": "^10.8.2", + "raw-loader": "^4.0.2", "react": "^17.0.2", "react-dom": "^17.0.2", "react-error-boundary": "^4.0.13", @@ -66,10 +67,14 @@ "@babel/preset-react": "^7.16.0", "@babel/preset-typescript": "^7.16.0", "@babel/runtime": "^7.12.5", + "@testing-library/react-hooks": "^8.0.1", + "@types/jest": "^29.5.12", "@types/node": "^14.14.12", + "@types/react": "^17.0.0", "@types/react-dom": "^16.9.19", "@types/react-router-dom": "^5.1.5", "@types/readable-stream": "^2.3.5", + "@types/redux-mock-store": "^1.0.6", "@types/styled-components": "^5.1.28", "@types/styled-system": "^5.1.18", "@typescript-eslint/eslint-plugin": "^6.7.5", @@ -80,11 +85,16 @@ "dotenv": "^8.6.0", "eslint": "^8.13.0", "file-loader": "^6.2.0", + "jest": "^29.7.0", + "jest-environment-jsdom": "^29.7.0", + "node-fetch": "^2.7.0", "postcss": "^8.4.40", "postcss-loader": "^8.1.1", "prettier": "^2.2.1", "react-hot-loader": "^4.12.20", + "redux-mock-store": "^1.5.4", "style-loader": "^3.3.3", + "ts-jest": "^29.2.3", "typescript": "^4.5.2", "webpack": "^5.67.0", "webpack-bundle-analyzer": "^4.5.0", diff --git a/explore-assistant-extension/src/.DS_Store b/explore-assistant-extension/src/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..cf7b9e18179635b4d1ef29384c3be2e56c8110fc GIT binary patch literal 8196 zcmeHM-EI;=82zS&vRE~kXhKX(Ht`k_D76xADApKn#ps0^WLeT~y6g~k2{nX-YkywB zSMb*OAij%NdcK)0Sr^a?)7r!tGIM5U&Y78h-VgYgd)`J(AkPkqN&l8#sTBNvI88u=V+gX_>`0JyL8aQH`6|H`G-oV5nZAl zNg9AJDWE!?dG*2zTsdjU 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f438e81ab..6b171a09d 100644 --- a/explore-assistant-extension/src/components/Chat/ExploreMessage.tsx +++ b/explore-assistant-extension/src/components/Chat/ExploreMessage.tsx @@ -4,9 +4,12 @@ import Message from './Message' import { useContext } from 'react' import { ExtensionContext } from '@looker/extension-sdk-react' import { useDispatch } from 'react-redux' + +import { ExploreParams, setSidePanelExploreParams } from '../../slices/assistantSlice' +import { ExploreHelper } from '../../utils/ExploreHelper' + import { openSidePanel, - setSidePanelExploreUrl, } from '../../slices/assistantSlice' import { OpenInNew } from '@material-ui/icons' @@ -14,19 +17,20 @@ interface ExploreMessageProps { exploreId: string modelName: string prompt: string - queryArgs: string + exploreParams: ExploreParams } -const ExploreMessage = ({ modelName, exploreId, prompt, queryArgs }: ExploreMessageProps) => { +const ExploreMessage = ({ modelName, exploreId, prompt, exploreParams }: ExploreMessageProps) => { const dispatch = useDispatch() const { extensionSDK } = useContext(ExtensionContext) - const exploreHref = `/explore/${modelName}/${exploreId}?${queryArgs}` + + const exploreHref = `/explore/${modelName}/${exploreId}?${ExploreHelper.exploreQueryArgumentString(exploreParams)}&toggle=vis,data` const openExplore = () => { extensionSDK.openBrowserWindow(exploreHref, '_blank') } const openSidePanelExplore = () => { - dispatch(setSidePanelExploreUrl(queryArgs)) + dispatch(setSidePanelExploreParams(exploreParams)) dispatch(openSidePanel()) } diff --git a/explore-assistant-extension/src/components/Chat/SummaryMessage.tsx b/explore-assistant-extension/src/components/Chat/SummaryMessage.tsx index 38f6e089e..d5dbc8029 100644 --- a/explore-assistant-extension/src/components/Chat/SummaryMessage.tsx +++ b/explore-assistant-extension/src/components/Chat/SummaryMessage.tsx @@ -3,20 +3,15 @@ import React, { useEffect } from 'react' import Message from './Message' import useSendVertexMessage from '../../hooks/useSendVertexMessage' import MarkdownText from './MarkdownText' +import { ExploreParams, SummarizeMesage, updateLastHistoryEntry, updateSummaryMessage } from '../../slices/assistantSlice' import { useDispatch } from 'react-redux' -import { - SummarizeMesage, - updateLastHistoryEntry, - updateSummaryMessage, -} from '../../slices/assistantSlice' interface SummaryMessageProps { message: SummarizeMesage + onSummaryComplete: () => void } -const SummaryMessage = ({ message }: SummaryMessageProps) => { - const queryArgs = message.exploreUrl - +const SummaryMessage = ({ message, onSummaryComplete }: SummaryMessageProps) => { const dispatch = useDispatch() const [loading, setLoading] = React.useState(true) const [summary, setSummary] = React.useState('') @@ -24,6 +19,8 @@ const SummaryMessage = ({ message }: SummaryMessageProps) => { const { summarizeExplore } = useSendVertexMessage() useEffect(() => { + let isMounted = true + if (message.summary) { setSummary(message.summary) setLoading(false) @@ -31,7 +28,7 @@ const SummaryMessage = ({ message }: SummaryMessageProps) => { } const fetchSummary = async () => { - const response = await summarizeExplore(queryArgs) + const response = await summarizeExplore(message.exploreParams) if (!response) { setSummary('There was an error summarizing the data') } else { @@ -46,10 +43,19 @@ const SummaryMessage = ({ message }: SummaryMessageProps) => { dispatch(updateLastHistoryEntry()) } - setLoading(false) + // call the parent component to scroll to the bottom + onSummaryComplete() + + if (isMounted) { + setLoading(false) + } } fetchSummary() + + return () => { + isMounted = false + } }, [message]) return ( diff --git a/explore-assistant-extension/src/components/ExploreEmbed.tsx b/explore-assistant-extension/src/components/ExploreEmbed.tsx index 3a2b30854..964c5fb50 100644 --- a/explore-assistant-extension/src/components/ExploreEmbed.tsx +++ b/explore-assistant-extension/src/components/ExploreEmbed.tsx @@ -28,21 +28,29 @@ import React, { useContext, useRef, useEffect } from 'react' import styled from 'styled-components' import { LookerEmbedSDK } from '@looker/embed-sdk' import { ExtensionContext } from '@looker/extension-sdk-react' +import { useSelector } from 'react-redux' +import { RootState } from '../store' +import { ExploreHelper } from '../utils/ExploreHelper' +import { ExploreParams } from '../slices/assistantSlice' export interface ExploreEmbedProps { modelName: string | null | undefined exploreId: string | null | undefined - exploreUrl: string | null | undefined + exploreParams: ExploreParams } -export const ExploreEmbed = ({ modelName, exploreId, exploreUrl }: ExploreEmbedProps) => { - - if(!modelName || !exploreId || !exploreUrl) { +export const ExploreEmbed = ({ + modelName, + exploreId, + exploreParams, +}: ExploreEmbedProps) => { + if (!modelName || !exploreId || !exploreParams) { return <> } const { extensionSDK } = useContext(ExtensionContext) const [exploreRunStart, setExploreRunStart] = React.useState(false) + const { settings } = useSelector((state: RootState) => state.assistant) const canceller = (event: any) => { return { cancel: !event.modal } @@ -63,7 +71,7 @@ export const ExploreEmbed = ({ modelName, exploreId, exploreUrl }: ExploreEmbedP useEffect(() => { const hostUrl = extensionSDK?.lookerHostData?.hostUrl const el = ref.current - if (el && hostUrl && exploreUrl) { + if (el && hostUrl && exploreParams) { const paramsObj: any = { // For Looker Original use window.origin for Looker Core use hostUrl embed_domain: hostUrl, //window.origin, //hostUrl, @@ -72,17 +80,20 @@ export const ExploreEmbed = ({ modelName, exploreId, exploreUrl }: ExploreEmbedP key_color: '#174ea6', background_color: '#f4f6fa', }), + toggle: 'pik,vis,dat', + } + + if (settings['show_explore_data'].value) { + paramsObj['toggle'] = 'pik,vis' + } + + const encodedParams = ExploreHelper.encodeExploreParams(exploreParams) + for (const key in encodedParams) { + paramsObj[key] = encodedParams[key] } - exploreUrl.split('&').map((param) => { - const [key, ...rest] = param.split('=') - // paramsObj[key] = rest.join('=') - if (key === 'filter_expression' || key === 'dynamic_fields') { - // console.log('rest', rest) - paramsObj[key] = rest.join('=') - } else { - paramsObj[key] = param.split('=')[1] - } - }) + + console.log('Explore Embed - Params', paramsObj) + el.innerHTML = '' LookerEmbedSDK.init(hostUrl) LookerEmbedSDK.createExploreWithId(modelName + '/' + exploreId) @@ -107,7 +118,11 @@ export const ExploreEmbed = ({ modelName, exploreId, exploreUrl }: ExploreEmbedP }) } // eslint-disable-next-line react-hooks/exhaustive-deps - }, [exploreUrl]) + }, [exploreParams]) + + if (!exploreParams || Object.keys(exploreParams).length === 0) { + return <> + } return ( <> diff --git a/explore-assistant-extension/src/documents/looker_filter_doc.md b/explore-assistant-extension/src/documents/looker_filter_doc.md new file mode 100644 index 000000000..e542b001c --- /dev/null +++ b/explore-assistant-extension/src/documents/looker_filter_doc.md @@ -0,0 +1,225 @@ +# Looker Filter Expressions + +Filter expressions are an advanced way to filter Looker queries, and this page describes how to write them. In the Explore section of Looker you can use them by adding a filter and choosing the matches (advanced) option. They are also used in LookML for elements that take a filter parameter. + +Please note the type of filter expression you can use depends on the type (ie. string, date and time, boolean, number) of field you are filtering on. Please ensure you are checking the field type when applying filter expressions. +Filters are not always necessary to get results, do not apply filters if they are not needed. + +## String + +Matches in string filters depend on the case_sensitive setting in your model file, and on whether your dialect supports case-sensitivity. For example, if case_sensitive is enabled in your model, the expression FOO% will not match the word "food". If case_sensitive isn't enabled, or if your dialect doesn't support case-sensitivity, the expression FOO% will match the word "food". + +| Example | Description | +|-----------|-------------------------------------------------------------------------------| +| FOO | is equal to "FOO", exactly | +| FOO,BAR | is equal to either "FOO" or "BAR", exactly | +| %FOO% | contains "FOO", matches "buffoon" and "fast | +| FOO% | starts with "FOO", matches "foolish" and "food" but not "buffoon" or "fast food" | +| %FOO | ends with "FOO", matches "buffoo" and "fast foo" but not "buffoon" or "fast food" | +| F%OD | starts with an "F" and ends with "OD", matches "fast food" | +| EMPTY | string is empty (has zero characters) or is null (no value) | +| NULL | value is null (when it is used as part of a LookML filter expression, place NULL in quotes, as shown on the filters documentation page) | +| -FOO | is not equal to "FOO" (is any value except "FOO"), matches "pizza", "trash", "fun" but not "foo" | +| -FOO,-BAR | is not equal to either "FOO" or "BAR", matches any value except "FOO" and "BAR" | +| -%FOO% | doesn't contain "FOO", does not match "buffoon" or "fast food" | +| -FOO% | doesn't start with "FOO", does not match "foolish" or "food" | +| -%FOO | doesn't end with "FOO", does not match "buffoo" or "fast foo" | +| -EMPTY | string is not empty (has at least one character) | +| -NULL | value of column is not null (when it is used as part of a LookML filter expression, place -NULL in quotes, as shown on the filters documentation page) | +| FOO%,BAR | starts with "FOO" or is "BAR" exactly, matches "food" and matches "bar" but not "barfood" | +| FOO%,-FOOD| starts with "FOO" but is not "FOOD" | +| _UF | has any single character followed by "UF", matches "buffoon" | + +## Date and Time + +Looker date filtering allows for English phrases to be used instead of SQL date functions. Use this if the field type starts with date_. It might be date_date, date_time, date_week, date_month, date_quarter, or date_year. + +For the following examples: + +* {n} is an integer. +* {interval} is a time increment such as hours, days, weeks, or months. The phrasing you use determines whether the {interval} will include partial time periods or only complete time periods. For example, the expression 3 days includes the current, partial day as well as the prior two days. The expression 3 days ago for 3 days includes the previous three complete days and excludes the current, partial day. See the Relative Dates section for more information. +* {time} can specify a time formatted as either YYYY-MM-DD HH:MM:SS or YYYY/MM/DD HH:MM:SS, or a date formatted as either YYYY-MM-DD or YYYY/MM/DD. When using the form YYYY-MM-DD, be sure to include both digits for the month and day, for example, 2016-01. Truncating a month or day to a single digit is interpreted as an offset, not a date. For example, 2016-1 is interpreted as 2016 minus one year, or 2015. + +These are all the possible combinations of date filters: + +| Combination | Example | Notes | +|-----------------------------------------------------|----------------------------------------------------|--------------------------------------------------------------| +| this {interval} | this month | You can use this week, this month, this quarter, or this year. Note that this day isn't supported. If you want to get data from the current day, you can use today. | +| {n} {interval} | 3 days | You can use a number combined with days, weeks, months, quarters or years. This will return the last number of days, weeks, months, quarters or years you specify in the combination. For example the last 6 months or last 30 days. This is a very common filter. | +| {n} {interval} ago | 3 days ago | You can use a number combined with days, weeks, months, quarters or years ago. This will return the day you specify in the combination. 3 days ago will return the date 3 days prior to the current date. For example 3 days ago will not include the days between 3 days ago and today. Do not confuse this with the {n} interval filter. | +| {n} {interval} ago for {n} {interval} | 3 months ago for 2 days | You can use a number combined with days, weeks, months, quarters or years ago for the duration of a number you and interval you specify. This will return date range you specify in the combination. 3 months ago for 2 days will return the date for a range of 2 days 3 months prior to the current date. | +| before {n} {interval} ago | before 3 days ago | You can use this combination to specify a number combined with days, weeks, months, quarters or years in the desired range before ending the date range. Before 3 days ago will return all dates before 3 days of the current date. | +| before {time} | before 2018-01-01 12:00:00 | Before is not inclusive of the time you specify. The expression before 2018-01-01 will return data from all dates before 2018-01-01, but it won't return data from 2018-01-01. | +| after {time} | after 2018-10-05 | After is inclusive of the time you specify. So, the expression after 2018-10-05 will return data from 2018-10-05 and all dates later than 2018-10-05. | +| {time} to {time} | 2018-05-18 12:00:00 to 2018-05-18 14:00:00 | The initial time value is inclusive but the latter time value is not. So the expression 2018-05-18 12:00:00 to 2018-05-18 14:00:00 will return data with the time "2018-05-18 12:00:00" through "2018-05-18 13:59:59". | +| this {interval} to {interval} | this year to second | The beginning of each interval is used. For example, the expression this year to second returns data from the beginning of the year the query is run through to the beginning of the second the query is run. this week to day returns data from the beginning of the week the query is run through to the beginning of the day the query is run. | +| {time} for {n} {interval} | 2018-01-01 12:00:00 for 3 days | The initial time value is inclusive but the latter number and interval values are not. So the expression 2018-05-18 12:00:00 for 3 days will return data with the time "2018-01-01 12:00:00" through "2018-01-04 12:00:00". | +| today | today | Returns todays date | +| yesterday | yesterday | Returns yesterdays date | +| tomorrow | tomorrow | Returns tomorrows date | +| {day of week} | Monday | Specifying a day of week with a Dimension Group Date field returns the most recent date that matches the specified day of week. For example, the expression Dimension Group Date matches (advanced) Monday returns the most recent Monday. You can also use {day of week} with the before and after keywords in this context. For example, the expression Dimension Group Date matches (advanced) after Monday returns the most recent Monday and everything after the most recent Monday. The expression Dimension Group Date matches (advanced) before Monday returns every day before the most recent Monday, but it doesn't return the most recent Monday. Specifying a day of the week with a Dimension Group Day of Week field returns every day that matches the specified day of week. So the expression Dimension Group Day of Week matches (advanced) Monday returns every Monday. | +| next {week, month, quarter, fiscal quarter, year, fiscal year} | next week | The next keyword is unique in that it requires one of the intervals listed previously and won't work with other intervals. | +| {n} {interval} from now | 3 days from now | You can use a number combined with days, weeks, months, quarters or years from now. This will return a future date the number of days, weeks, months, quarters or years you specify in the combination. | +| {n} {interval} from now for {n} {interval} | 3 days from now for 2 weeks | You can use a number combined with days, weeks, months, quarters or years from now for the duration of a number you and interval you specify. This will return a date range in the future you specify in the combination. 3 days ago for 2 weeks will return the dates for a range of 3 days 2 weeks ahead of the current date. | + + +Date filters can also be combined together: +* To get OR logic: Type multiple conditions into the same filter, separated by commas. For example, today, 7 days ago means "today or 7 days ago". +* To get AND logic: Type your conditions, one by one, into multiple date or time filters. For example, you could put after 2014-01-01 into a Created Date filter, then put before 2 days ago into a Created Time filter. This would mean "January 1st, 2014 and after, and before 2 days ago". + +### Absolute Dates + +Absolute date filters use the specific date values to generate query results. These are useful when creating queries for specific date ranges. + +| Example | Description | +|---------------------------|------------------------------------------------------------------------------------------------------------------------------| +| 2018/05/29 | sometime on 2018/05/29 | +| 2018/05/10 for 3 days | from 2018/05/10 00:00:00 through 2018/05/12 23:59:59 | +| after 2018/05/10 | 2018/05/10 00:00:00 and after | +| before 2018/05/10 | before 2018/05/10 00:00:00 | +| 2018/05 | within the entire month of 2018/05 | +| 2018/05 for 2 months | within the entire months of 2018/05 and 2018/06 | +| 2018/05/10 05:00 for 5 hours | from 2018/05/10 05:00:00 through 2018/05/10 09:59:59 | +| 2018/05/10 for 5 months | from 2018/05/10 00:00:00 through 2018/10/09 23:59:59 | +| 2018 | entire year of 2018 (2018/01/01 00:00:00 through 2018/12/31 23:59:59) | +| FY2018 | entire fiscal year starting in 2018 (if your Looker developers have specified that your fiscal year starts in April then this is 2018/04/01 00:00 through 2019/03/31 23:59) | +| FY2018-Q1 | first quarter of the fiscal year starting in 2018 (if your Looker developers have specified that your fiscal year starts in April then this is 2018/04/01 00:00:00 through 2018/06/30 23:59:59) | + + +### Relative Dates + +Relative date filters allow you to create queries with rolling date values relative to the current date. These are useful when creating queries that update each time you run the query. + +For all of the following examples, assume today is Friday, 2018/05/18 18:30:02. In Looker, weeks start on Monday unless you change that setting with week_start_day. + +Relative date filters allow you to create queries with rolling date values relative to the current date. These are useful when creating queries that update each time you run the query. + +For all of the following examples, assume today is Friday, 2018/05/18 18:30:02. In Looker, weeks start on Monday unless you change that setting with `week_start_day`. + +#### Seconds +| Example | Description | +|------------------------------|----------------------------------------------------------| +| 1 second | the current second (2018/05/18 18:30:02) | +| 60 seconds | 60 seconds ago for 60 seconds (2018/05/18 18:29:02 through 2018/05/18 18:30:01) | +| 60 seconds ago for 1 second | 60 seconds ago for 1 second (2018/05/18 18:29:02) | + +#### Minutes +| Example | Description | +|------------------------------|----------------------------------------------------------| +| 1 minute | the current minute (2018/05/18 18:30:00 through 18:30:59) | +| 60 minutes | 60 minutes ago for 60 minutes (2018/05/18 17:31:00 through 2018/05/18 18:30:59) | +| 60 minutes ago for 1 minute | 60 minutes ago for 1 minute (2018/05/18 17:30:00 through 2018/05/18 17:30:59) | + +#### Hours +| Example | Description | +|------------------------------|----------------------------------------------------------| +| 1 hour | the current hour (2018/05/18 18:00 through 2018/05/18 18:59) | +| 24 hours | the same hour of day that was 24 hours ago for 24 hours (2018/05/17 19:00 through 2018/05/18 18:59) | +| 24 hours ago for 1 hour | the same hour of day that was 24 hours ago for 1 hour (2018/05/17 18:00 until 2018/05/17 18:59) | + +#### Days +| Example | Description | +|------------------------------|----------------------------------------------------------| +| today | the current day (2018/05/18 00:00 through 2018/05/18 23:59) | +| 2 days | all of yesterday and today (2018/05/17 00:00 through 2018/05/18 23:59) | +| 1 day ago | just yesterday (2018/05/17 00:00 until 2018/05/17 23:59) | +| 7 days ago for 7 days | the last complete 7 days (2018/05/11 00:00 until 2018/05/17 23:59) | +| today for 7 days | the current day, starting at midnight, for 7 days into the future (2018/05/18 00:00 until 2018/05/24 23:59) | +| last 3 days | 2 days ago through the end of the current day (2018/05/16 00:00 until 2018/05/18 23:59) | +| 7 days from now | 7 days in the future (2018/05/18 00:00 until 2018/05/25 23:59) | + +#### Weeks +| Example | Description | +|------------------------------|----------------------------------------------------------| +| 1 week | top of the current week going forward (2018/05/14 00:00 through 2018/05/20 23:59) | +| this week | top of the current week going forward (2018/05/14 00:00 through 2018/05/20 23:59) | +| before this week | anytime until the top of this week (before 2018/05/14 00:00) | +| after this week | anytime after the top of this week (2018/05/14 00:00 and later) | +| next week | the following Monday going forward 1 week (2018/05/21 00:00 through 2018/05/27 23:59) | +| 2 weeks | a week ago Monday going forward (2018/05/07 00:00 through 2018/05/20 23:59) | +| last week | synonym for "1 week ago" | +| 1 week ago | a week ago Monday going forward 1 week (2018/05/07 00:00 through 2018/05/13 23:59) | + +#### Months +| Example | Description | +|------------------------------|----------------------------------------------------------| +| 1 month | the current month (2018/05/01 00:00 through 2018/05/31 23:59) | +| this month | synonym for "0 months ago" (2018/05/01 00:00 through 2018/05/31 23:59) | +| 2 months | the past two months (2018/04/01 00:00 through 2018/05/31 23:59) | +| last month | all of 2018/04 | +| 2 months ago | all of 2018/03 | +| before 2 months ago | all time before 2018/03/01 | +| next month | all of 2018/06 | +| 2 months from now | all of 2018/07 | +| 6 months from now for 3 months | 2018/11 through 2019/01 | + +* note for relative dates, the last 3 days is much more common than 3 days ago. The last 2 years is much more common that 2 years ago. + +#### Years + +| Example | Description | +|---------------------|------------------------------------------------------------------------------------------------------| +| 1 year | all of the current year (2018/01/01 00:00 through 2018/12/31 23:59) | +| this year | all of the current year (2018/01/01 00:00 through 2018/12/31 23:59) | +| next year | all of the following year (2019/01/01 00:00 through 2019/12/31 23:59) | +| 2 years | the past two years (2017/01/01 00:00 through 2018/12/31 23:59) | +| last year | all of 2017 | +| 2 years ago | all of 2016 | +| before 2 years ago | all time before 2016/01/01 (does not include any days between 2016/01/01 and 2016/05/18) | + +## Boolean + +Filtering on true or false type values in Looker requires you to know what type of true or false value you're interacting with. Use this if the field type is a yesno or boolean. + +### Examples + +| Example | Description | +|--------------|-----------------------------------------------------------------------------------------------------------------------| +| yes or Yes | field evaluates to true | +| | **Looker developers:** for `type: yesno` dimensions use lowercase, for filters parameters (like those used in a measure or used in an `always_filter`) use uppercase | +| no or No | field evaluates to false | +| | **Looker developers:** for `type: yesno` dimensions use lowercase, for filters parameters (like those used in a measure or used in an `always_filter`) use uppercase | +| TRUE | field contains true (for fields that contain Boolean database values) | +| FALSE | field contains false (for fields that contain Boolean database values) | + +## Numbers + +Filters on numbers support both natural language expressions (for example, `3 to 10`) and relational operators (for example, `>20`). Looker supports the `OR` operator to express multiple filter ranges (for example, `3 to 10 OR 30 to 100`). The `AND` operator can be used to express numeric ranges with relational operators (for example, `>=3 AND <=10`) to specify a range. Filters on numbers can also use algebraic interval notation to filter numeric fields. + +**Note:** The syntax for numeric filter expressions using `NOT` may not be intuitive. If the first filter condition contains a `NOT`, and no other filter conditions contain a `NOT`, then all of the filter conditions will be negated. See the following examples for more information. + +#### Examples + +| Example | Description | +|------------------------------|----------------------------------------------------------------------------------------------------------| +| 5 | is exactly 5 | +| NOT 5 | is any value but exactly 5 | +| <>5 | is any value but exactly 5 | +| !=5 | is any value but exactly 5 | +| 1, 3, 5, 7 | is one of the values 1, 3, 5, or 7, exactly | +| NOT 66, 99, 4 | is not one of the values 66, 99, or 4, exactly | +| >1 AND <100, NOT 2 | is greater than 1 and less than 100, is not 2 | +| NOT >1, 2, <100 | is less than or equal to 1, is not 2, and is greater than or equal to 100 (Looker recognizes that this is an impossible condition, and will instead write the SQL `IS NULL`) | +| 5, NOT 6, NOT 7 | is 5, is not 6 or 7 | +| 5.5 to 10 | is 5.5 or greater but also 10 or less | +| >=5.5 AND <=10 | is 5.5 or greater but also 10 or less | +| NOT 3 to 80.44 | is less than 3 or greater than 80.44 | +| <3 OR >80.44 | is less than 3 or greater than 80.44 | +| 1 to | is 1 or greater | +| >=1 | is 1 or greater | +| to 10 | is 10 or less | +| <=10 | is 10 or less | +| >10 AND <=20 OR 90 | is greater than 10 and less than or equal to 20, or is 90 exactly | +| >=50 AND <=100 OR >=500 AND <=1000 | is between 50 and 100, inclusive, or between 500 and 1000, inclusive | +| NULL | has no data in it (when it is used as part of a LookML filter expression, place `NULL` in quotes, as shown on the filters documentation page) | +| NOT NULL | has some data in it (when it is used as part of a LookML filter expression, place `NOT NULL` in quotes, as shown on the filters documentation page) | +| (1, 7) | interpreted as 1 < x < 7 where the endpoints aren't included. While this notation resembles an ordered pair, in this context it refers to the interval upon which you are working. | +| [5, 90] | interpreted as 5 <= x <= 90 where the endpoints are included | +| (12, 20] | interpreted as 12 < x <= 20 where 12 is not included, but 20 is included | +| [12, 20) | interpreted as 12 <= x < 20 where 12 is included, but 20 is not included | +| (500, inf) | interpreted as x > 500 where 500 is not included and infinity is always expressed as being "open" (not included). `inf` may be omitted and (500, inf) may be written as (500,) | +| (-inf, 10] | interpreted as x <= 10 where 10 is included and infinity is always expressed as being "open" (not included). `inf` may be omitted and (-inf, 10] may be written as (,10] | +| [0,9],[20,29] | the numbers between 0 and 9 inclusive or 20 to 29 inclusive | +| [0,10],20 | 0 to 10 inclusive or 20 | +| NOT (3,12) | interpreted as x < 3 and x > 12 + | diff --git a/explore-assistant-extension/src/documents/looker_filters_interval_tf.md b/explore-assistant-extension/src/documents/looker_filters_interval_tf.md new file mode 100644 index 000000000..954a7fa27 --- /dev/null +++ b/explore-assistant-extension/src/documents/looker_filters_interval_tf.md @@ -0,0 +1,135 @@ +## Intervals + +Interval options +The intervals parameter tells the dimension group which interval units it should use to measure the time difference between the sql_start time and the sql_end time. The intervals parameter is supported only for dimension groups of type: duration. + +If intervals is not included, the dimension group will include all possible intervals. + +The options for the intervals parameter are: + +Interval | Description | Example Output +|--------|-------------------------------------------------------------|--------------------| +day | Calculates a time difference in days. | 9 days +hour | Calculates a time difference in hours. | 171 hours +minute | Calculates a time difference in minutes. | 10305 minutes +month | Calculates a time difference in months. | 3 months +quarter | Calculates a time difference in quarters of the year. | 2 quarters +second | Calculates a time difference in seconds. | 606770 seconds +week | Calculates a time difference in weeks. | 6 weeks +year | Calculates a time difference in years. | 2 years + +## Timeframes + +Timeframe options +The timeframes parameter is supported only for dimension groups of type: time. For dimension groups of type: duration, use the intervals parameter instead. + +The timeframes parameter tells the dimension group which dimensions it should produce and includes the following options: + +Special timeframes +Time timeframes +Date timeframes +Week timeframes +Month timeframes +Quarter timeframes +Year timeframes +hourX timeframes +minuteX timeframes +millisecondX timeframes + +Special timeframes +Timeframe | Description | Example Output +|---------|-------------|-------------------------------------------------------------------| +raw | The raw value from your database, without casting or time zone conversion. raw is accessible only within LookML and won't show up on the Explore page. The raw timeframe returns a timestamp, unlike most other timeframes that return a formatted string. It is primarily used for performing date operations on a field. | 2014-09-03 17:15:00 +0000 +yesno | A yesno dimension, returning "Yes" if the datetime has a value, otherwise "No". Unlike other timeframes, when you refer to a yesno timeframe dimension from another field, don't include the timeframe in the reference. For example, to refer to a yesno timeframe in the dimension_group: created, use the syntax $ {created}, not $ {created_yesno}. | Yes + +Time timeframes +Timeframe | Description | Example Output +|---------|-------------|-------------------------------------------------------------------| +time | Datetime of the underlying field (some SQL dialects show as much precision as your database contains, while others show only to the second) | 2014-09-03 17:15:00 +time_of_day | Time of day | 17:15 +hour | Datetime truncated to the nearest hour | 2014-09-03 17 +hour_of_day | Integer hour of day of the underlying field | 17 +hourX | Splits each day into intervals with the specified number of hours. | See Using hourX. +minute | Datetime truncated to the nearest minute | 2014-09-03 17:15 +minuteX | Splits each hour into intervals with the specified number of minutes. | See Using minuteX. +second | Datetime truncated to the nearest second | 2014-09-03 17:15:00 +millisecond | Datetime truncated to the nearest millisecond (see the Dialect support for milliseconds and microseconds section on this page for information on dialect support). | 2014-09-03 17:15:00.000 +millisecondX | Splits each second into intervals with the specified number of milliseconds (see the Dialect support for milliseconds and microseconds section on this page for information on dialect support). | See Using millisecondX. +microsecond | Datetime truncated to the nearest microsecond (see the Dialect support for milliseconds and microseconds section on this page for information on dialect support). | 2014-09-03 17:15:00.000000 + +Date timeframes +Timeframe | Description | Example Output +|---------|-------------|-------------------------------------------------------------------| +date | Date of the underlying field | 2017-09-03 + +Week timeframes +Timeframe | Description | Example Output +|---------|-------------|-------------------------------------------------------------------| +week | Date of the week starting on a Monday of the underlying datetime | 2017-09-01 +day_of_week | Day of week alone | Wednesday +day_of_week_index | Day of week index (0 = Monday, 6 = Sunday) | 2 + +Month timeframes +Timeframe | Description | Example Output +|---------|-------------|-------------------------------------------------------------------| +month | Year and month of the underlying datetime | 2014-09 +month_num | Integer number of the month of the underlying datetime | 9 +fiscal_month_num | Integer number of the fiscal month of the underlying datetime | 6 +month_name | Name of the month | September +day_of_month | Day of month | 3 +To use the fiscal_month_num timeframes, the fiscal_month_offset parameter must be set in the model. + +Quarter timeframes +Timeframe | Description | Example Output +|---------|-------------|-------------------------------------------------------------------| +quarter | Year and quarter of the underlying datetime | 2017-Q3 +fiscal_quarter | Fiscal year and quarter of the underlying datetime | 2017-Q3 +quarter_of_year | Quarter of the year preceded by a "Q" | Q3 +fiscal_quarter_of_year | Fiscal quarter of the year preceded by a "Q" | Q3 +To use the fiscal_quarter and fiscal_quarter_of_year timeframes, the fiscal_month_offset parameter must be set in the model. + +Year timeframes +Timeframe | Description | Example Output +|---------|-------------|-------------------------------------------------------------------| +year | Integer year of the underlying datetime | 2017 +fiscal_year | Integer fiscal year of the underlying datetime | FY2017 +day_of_year | Day of year | 143 +week_of_year | Week of the year as a number | 17 +To use the fiscal_year timeframe, the fiscal_month_offset parameter must be set in the model. + + +Using hourX +In hourX the X is replaced with 2, 3, 4, 6, 8, or 12. + +This will split up each day into intervals with the specified number of hours. For example, hour6 will split each day into 6 hour segments, which will appear as follows: + +2014-09-01 00:00:00 +2014-09-01 06:00:00 +2014-09-01 12:00:00 +2014-09-01 18:00:00 +To give an example, a row with a time of 2014-09-01 08:03:17 would have a hour6 of 2014-09-01 06:00:00. + + +Using minuteX +In minuteX the X is replaced with 2, 3, 4, 5, 6, 10, 12, 15, 20, or 30. + +This will split up each hour into intervals with the specified number of minutes. For example, minute15 will split each hour into 15 minute segments, which will appear as follows: + +2014-09-01 01:00:00 +2014-09-01 01:15:00 +2014-09-01 01:30:00 +2014-09-01 01:45:00 +To give an example, a row with a time of 2014-09-01 01:17:35 would have a minute15 of 2014-09-01 01:15:00. + + +Using millisecondX +In millisecondX the X is replaced with 2, 4, 5, 8, 10, 20, 25, 40, 50, 100, 125, 200, 250, or 500. + +This will split up each second into intervals with the specified number of milliseconds. For example, millisecond250 will split each second into 250 millisecond segments, which will appear as follows: + +2014-09-01 01:00:00.000 +2014-09-01 01:00:00.250 +2014-09-01 01:00:00.500 +2014-09-01 01:00:00.750 +To give an example, a row with a time of 2014-09-01 01:00:00.333 would have a millisecond250 of 2014-09-01 01:00:00.250. + | \ No newline at end of file diff --git a/explore-assistant-extension/src/documents/looker_pivots_url_parameters_doc.md b/explore-assistant-extension/src/documents/looker_pivots_url_parameters_doc.md new file mode 100644 index 000000000..18ec0e898 --- /dev/null +++ b/explore-assistant-extension/src/documents/looker_pivots_url_parameters_doc.md @@ -0,0 +1,88 @@ +# Expanded URL generation + +JSON Payload Fields: + +fields: fields=view.field_1,view.field_2,view.count + This parameter specifies the list of fields to be included in the results. In this example, the explore will return data for view.field_1, view.field_2, and the count of rows (view.count). + +f[]: &f[view.filter_1_dimension]={{ value }} & &f[view.filter_2_on_date]=last+60+days + This parameter defines filters for the explore. The f[] syntax is used to declare a filter on a specific dimension (view.filter_1_dimension and view.filter_2_on_date in this case). The {{ value }} placeholder indicates a dynamic value that can be passed through the URL. The second filter uses a Looker expression (last+60+days) to filter data for the past 60 days. + +pivots: pivots=view.field_2 This parameter defines the dimension to pivot on. In this example, view.field_2 will be used to create a pivot table. + +limit: limit=50 This parameter sets the maximum number of rows to be returned by the explore. The default limit is 5000, but here it's explicitly set to 50. + +column_limit: column_limit=20 This parameter sets the maximum number of columns to be displayed in the pivot table. This parameter only has an effect when a pivot dimension is specified (as seen with pivots). The column_limit can be between 1 and 200. Dimensions, dimension table calculations, row total columns, and measure table calculations outside of pivots are not counted toward the column limit. Pivoted groups each count as one column toward the column limit. + +total: total=true This parameter controls whether to display column totals in the explore results. Here, true indicates that column totals will be shown. + +row_total: row_total=right This parameter controls whether to display row totals in the explore results. Here, right specifies that the row totals will be displayed on the right side. Only use row totals if the chart contains pivots. + +sorts: sorts=view.field_1,view.count+desc This parameter defines the order in which the results should be sorted. The first field (view.field_1) is sorted by default in ascending order. The second sort (view.count+desc) sorts the results by view.count in descending order. The +desc syntax specifies descending order. + +filter_config: The filter_config parameter contains detailed JSON objects that control the filtering aspects of the query. The filter_config represents the state of the filter UI on the explore page for a given query. When running a query via the Looker UI, this parameter takes precedence over "filters". + +Vis: The vis parameter contains detailed JSON objects that control the visualization properties of the query. These properties are typically opaque and differ based on the type of visualization used. There is no specified set of allowed keys. The values can be any type supported by JSON. A "type" key with a string value is often present, and is used by Looker to determine which visualization to present. Visualizations ignore unknown vis_config properties. + +Query_timezone: User's timezone, string value. + +Subtotals: When using a table visualization and your data table contains at least two dimensions, you can apply subtotals. Subtotals are not available when you filter on a measure or when the Explore uses the sql_always_having parameter. List of fields to run the subtotals. The leftmost subtotal is always sorted. When you sort by multiple columns, subtotal columns are given precedence. Fields on which to run subtotals. + +# Pivot table reference + +In Looker, pivots allow you to turn a selected dimension into several columns, which creates a matrix of your data similar to a pivot table in spreadsheet software. This is very useful for analyzing metrics by different groupings of your data, such as getting counts for category or label in your dataset. + +When you pivot on a dimension, each unique possible value of that dimension becomes its own column header. Any measures are then repeated under each column header. + +Pivots make it much easier to compare a measure accross dimensions. It also shows you gaps in your data, where you don’t have any numeric values for a particular dimension field. In summary, pivots allow you to create and display a matrix of your data, similar to a pivot table in spreadsheet software. Specifically, pivots turn a selected dimension into several columns and are applied only to the visual display of your results. + +With pivots, Looker allows you to regroup your data, so that you can easily compare results by different groupings and identify potential gaps, all while leaving your underlying data unaffected. + +Whenever you have a question involving one dimension “by” another dimension, that’s a clue that a pivot might come in handy. + +When two time dimensions are in a report and a pivot is required, always pivot by the least granular time dimension. + +|Example | Pivoted Dimension | +|--------------------------------------------------------------------------|----------------------------------| +| What were the hourly total sales by day in the past 3 days? | Day | +| What were the daily total sales by week in the past 3 weeks? | Week | +| What were the total sales by day each week in the past 2 weeks? | Week | +| What were the total sales by day of week each week in the past 2 weeks? | Week | +| What were the weekly total sales by month in the past 2 months? | Month | +| What were the monthly total sales by quarter in the past 2 quarters? | Quarter | +| What were the monthly total sales by quarter in the past 2 years? | Year | +| What were the total sales by week of year each year in the past 2 years? | Year | +| What were the monthly total sales by year in the past 2 years? | Year | +| What were the weekly total sales by quarter in the past 3 years? | Quarter | + +# Looker API JSON Fields + +|application/JSON | Datatype | Description +|--------------------------|------------------------|------------------------------------------------------------------------------------------ +| can | Hash[boolean] | Operations the current user is able to perform on this object +| id | string | Unique Id +| model | string | Model +| view | string | Explore Name +| fields | string[] | Fields +| pivots | string[] | Pivots +| fill_fields | string[] | Fill Fields +| filters | Hash[string] | Filters will contain data pertaining to complex filters that do not contain "or" conditions. When "or" conditions are present, filter data will be found on the filter_expression property. +| filter_expression | string | Filter Expression +| sorts | string[] | Sorting for the query results. Use the format ["view.field", ...] to sort on fields in ascending order. Use the format ["view.field desc", ...] to sort on fields in descending order. Use ["__UNSORTED__"] (2 underscores before and after) to disable sorting entirely. Empty sorts [] will trigger a default sort. +| limit | string | Row limit. To download unlimited results, set the limit to -1 (negative one). +| column_limit | string | Column Limit +| total | boolean | Total +| row_total | string | Raw Total +| subtotals | string[] | Fields on which to run subtotals +| vis_config | Hash[any] | Visualization configuration properties. These properties are typically opaque and differ based on the type of visualization used. There is no specified set of allowed keys. The values can be any type supported by JSON. A "type" key with a string value is often present, and is used by Looker to determine which visualization to present. Visualizations ignore unknown vis_config properties. +| filter_config | Hash[any] | The filter_config represents the state of the filter UI on the explore page for a given query. When running a query via the Looker UI, this parameter takes precedence over "filters". When creating a query or modifying an existing query, "filter_config" should be set to null. Setting it to any other value could cause unexpected filtering behavior. The format should be considered opaque. +| visible_ui_sections | string | Visible UI Sections +| slug | string | Slug +| dynamic_fields | string | Dynamic Fields +| client_id | string | Client Id: used to generate shortened explore URLs. If set by client, must be a unique 22 character alphanumeric string. Otherwise one will be generated. +| share_url | string | Share Url +| expanded_share_url | string | Expanded Share Url +| url | string | Expanded Url +| query_timezone | string | Query Timezone +| has_table_calculations | boolean | Has Table Calculations + | \ No newline at end of file diff --git a/explore-assistant-extension/src/documents/looker_visualization_doc.md b/explore-assistant-extension/src/documents/looker_visualization_doc.md new file mode 100644 index 000000000..2579438b2 --- /dev/null +++ b/explore-assistant-extension/src/documents/looker_visualization_doc.md @@ -0,0 +1,243 @@ + +# Looker Visualization Config Documentation + + +## Customizing Visualizations Using the Chart Config Editor + +You can use the Chart Config Editor to customize formatting options on Looker visualizations that use the HighCharts API. This includes most Cartesian charts, such as the column chart, bar chart, and line chart, among others. + +### Prerequisites + +To access the Chart Config Editor, you must have the `can_override_vis_config` permission. + +### Customizing a Visualization + +To customize a visualization with the Chart Config Editor, follow these steps: + +1. **View or Edit a Visualization**: + - View a visualization in an Explore, or edit a visualization in a Look or dashboard. + +2. **Open the Chart Config Editor**: + - Open the Edit menu in the visualization. + - Click the **Edit Chart Config** button in the Plot tab. Looker displays the Edit Chart Config dialog. + +3. **Modify the JSON**: + - The **Chart Config (Source)** pane contains the original JSON of your visualization and cannot be edited. + - The **Chart Config (Override)** pane contains the JSON that should override the source JSON. When you first open the Edit Chart Config dialog, Looker populates the pane with some default JSON. You can start with this JSON or delete it and enter any valid HighCharts JSON. + - Select the Chart Config (Override) section and enter valid HighCharts JSON. The new values will override any values in the Chart Config (Source) section. + +4. **Format and Apply Changes**: + - Click `<>` (Format code) to allow Looker to properly format your JSON. + - Click **Preview** to test your changes. + - Click **Apply** to apply your changes. The visualization will be displayed using the custom JSON values. + +5. **Save the Visualization**: + - Once you've customized your visualization, save it. If you viewed the visualization in an Explore, save the Explore. If you edited a Look or a dashboard, click Save. + +### Caution + +Do not edit the default visualization options after making changes in the Chart Config Editor. Editing the default visualization options may cause unexpected behavior, including blank visualizations. If you'd like to edit the default visualization options, first remove any changes you've made in the Chart Config Editor, then replace them later. Specifically, follow these steps: + +1. Click the **Edit Chart Config** button in the Plot tab. Looker displays the Edit Chart Config dialog. +2. Copy the text in the Chart Config (Override) pane. +3. Click the **Clear Chart Overrides** button to delete all changes. +4. Click **Apply**. +5. Edit your visualization using the default visualization options. +6. Click the **Edit Chart Config** button in the Plot tab. Looker displays the Edit Chart Config dialog. +7. Enter some valid HighCharts JSON in the Chart Config (Override) pane. You can use the text that you copied in step 2 as a template, but be sure to test your changes using the Preview button to ensure there are no conflicts. +8. Click **Apply**. + +### Conditional Formatting with Series Formatters + +The Chart Config Editor accepts most valid HighCharts JSON. It also accepts the `series.formatters` attribute, which only exists in Looker. Each series can have multiple formatters to combine different style rules. + +The `series.formatters` attribute accepts two attributes: `select` and `style`. + +- Enter a logical expression in the `select` attribute to indicate which data values will be formatted. +- Enter some JSON into the `style` attribute to indicate how to format the data values. + +For example, the following JSON will color each data value orange if it is greater than or equal to 380: + +```json +{ + "series": [{ + "formatters": [{ + "select": "value >= 380", + "style": { + "color": "orange" + } + }] + }] +} +``` + +#### The `select` Attribute + +You can use the following values in a `select` expression: + +- `value`: This variable returns the value of the series. For example, you could use `select: value > 0` to target all positive values, or `value = 100` to only match series with a value of 100. +- `max`: Use `select: max` to target the series value that has the maximum value. +- `min`: Use `select: min` to target the series value that has the minimum value. +- `percent_rank`: This variable targets the series value with a specified percentile. For example, you could use `select: percent_rank >= 0.9` to target series values in the ninetieth percentile. +- `name`: This variable returns the dimension value of the series. For example, if you had a report showing Sold, Canceled, and Returned orders, you could use `select: name = Sold` to target the series where the dimension value is Sold. +- `AND/OR`: Use `AND` and `OR` to combine multiple logical expressions. + +#### The `style` Attribute + +The `style` attribute can be used to apply styles that HighCharts supports. For example, you can color series values using `style.color`, color series borders using `style.borderColor`, and set series border width using `style.borderWidth`. For a more complete list of style options, see the Highcharts options for `series.column.data`. + +For line visualizations, use `style.marker.fillColor` and `style.marker.lineColor` instead of `style.color`. For a more complete list of line style options, see the Highcharts options for `series.line.data.marker`. + +### Examples + +The following sections provide examples of some common use cases for the Chart Config Editor. For a complete list of the attributes that you can edit, see the [HighCharts API documentation](https://api.highcharts.com/highcharts/). + +#### Change the Background Color and Axis Text Color + +To change the background color of a visualization, use the `chart.backgroundColor` attribute. + +Similarly, to change the text color of the axes in a visualization, use the following attributes: + +- `xAxis.labels.style.color` +- `xAxis.title.style.color` +- `yAxis.labels.style.color` +- `yAxis.title.style.color` + +The following HighCharts JSON changes the background color of the visualization to purple, and the text of the axis titles and labels to white. + +```json +{ + "chart": { + "backgroundColor": "purple" + }, + "xAxis": { + "labels": { + "style": { + "color": "white" + } + }, + "title": { + "style": { + "color": "white" + } + } + }, + "yAxis": { + "labels": { + "style": { + "color": "white" + } + }, + "title": { + "style": { + "color": "white" + } + } + } +} +``` + +#### Customize Tooltip Color + +To customize the color of the tooltip, use the following attributes: + +- `tooltip.backgroundColor` +- `tooltip.style.color` + +The following HighCharts JSON changes the background color of the tooltip to cyan, and changes the color of the tooltip text to black. + +```json +{ + "tooltip": { + "backgroundColor": "cyan", + "style": { + "color": "black" + } + } +} +``` + +#### Customize Tooltip Content and Styles + +To customize the content of the tooltip, use the following attributes: + +- `tooltip.format` +- `tooltip.shared` + +The following HighCharts JSON changes the tooltip format such that the x-axis value appears at the top of the tooltip in larger font, followed by a list of all series values at that point. + +This example uses the following HighCharts functions and variables: + +- `{key}` is a variable that returns the x-axis value of the selected point. (in this example, the month and year). +- `{#each points}{/each}` is a function that repeats the enclosed code for each series in the chart. +- `{series.name}` is a variable that returns the name of the series. +- `{y:.2f}` is a variable that returns the y-axis value of the selected point, rounded to two decimal places. +- `{y}` is a variable that returns the y-axis value of the selected point. +- `{variable:.2f}` rounds `variable` to two decimal places. See the [Highcharts templating documentation](https://api.highcharts.com/highcharts/tooltip.formatter) for more examples of value formatting. + +```json +{ + "tooltip": { + "format": "{key}
{#each points}\\u25CF {series.name}: {y:.2f}
{/each}", + "shared": true + } +} +``` + +#### Add Chart Annotations and Captions + +To add an annotation, use the `annotations` attribute. To add a caption to the chart, use the `caption` attribute. + +To get the coordinates for a point, click **Inspect Point Metadata** at the top of the Edit Chart Config dialog. Then, hold the pointer over the data point that you'd like to annotate. Looker displays a point ID, which you can use in the `annotations.labels.point` attribute. + +The following HighCharts JSON adds two annotations to the chart to explain a decrease in inventory items after certain periods of time. It also adds a caption to the bottom of the chart to explain the annotations in more detail. + +```json +{ + "caption": { + "text": "Items go on clearance after 60 days, and are thrown away after 80 days. Thus we see large drops in inventory after these events." + }, + "annotations": [{ + "labels": [{ + "point": "inventory_items.count-60- + +79", + "text": "Clearance sale" + }, + { + "point": "inventory_items.count-80+", + "text": "Thrown away" + } + ] + }] +} +``` + +#### Add Vertical Reference Bands + +To add a vertical reference band, use the `xAxis.plotBands` attribute. + +The following HighCharts JSON adds a vertical reference band between November 24, 2022 and November 29, 2022 to denote a sale period. It also adds a caption to the bottom of the chart to explain the significance of the band. + +Note that the `to` and `from` attributes of `xAxis.plotBands` must correspond to data values in the chart. In this example, since the data is time-based, the attributes accept Unix timestamp values (1669680000000 for November 29, 2022 and 1669248000000 for November 24, 2022). String-based date formats like MM/DD/YYYY and DD-MM-YY are not supported in the `to` and `from` HighCharts attributes. + +```json +{ + "caption": { + "text": "This chart uses the HighCharts plotBands attribute to display a band around the Black Friday Cyber Monday sale period." + }, + "xAxis": { + "plotBands": [{ + "to": 1669680000000, + "from": 1669248000000, + "label": { + "text": "BFCM Sale Period" + } + }] + } +} +``` + +#### Color the Maximum, Minimum, and Percentile Values + +See the [Getting the most out of Looker visualizations cookbook: Conditional formatting customization in Cartesian charts](https://cloud.google.com/looker/docs/visualizations-custom-chart-editor) page for an in-depth example about coloring the maximum, minimum, and percentile values of a Cartesian visualization. \ No newline at end of file diff --git a/explore-assistant-extension/src/hooks/useSendVertexMessage.ts b/explore-assistant-extension/src/hooks/useSendVertexMessage.ts index 32a586167..7cda2f441 100644 --- a/explore-assistant-extension/src/hooks/useSendVertexMessage.ts +++ b/explore-assistant-extension/src/hooks/useSendVertexMessage.ts @@ -1,76 +1,61 @@ import { ExtensionContext } from '@looker/extension-sdk-react' import { useCallback, useContext } from 'react' import { useSelector } from 'react-redux' -import { UtilsHelper } from '../utils/Helper' import CryptoJS from 'crypto-js' import { RootState } from '../store' import process from 'process' import { useErrorBoundary } from 'react-error-boundary' import { AssistantState } from '../slices/assistantSlice' -const unquoteResponse = (response: string | null | undefined) => { - if(!response) { - return '' - } - return response - .substring(response.indexOf('fields=')) - .replace(/^`+|`+$/g, '') - .trim() -} +import looker_filter_doc from '../documents/looker_filter_doc.md' +import looker_visualization_doc from '../documents/looker_visualization_doc.md' +import looker_filters_interval_tf from '../documents/looker_filters_interval_tf.md' +import looker_pivots_url_parameters_doc from '../documents/looker_pivots_url_parameters_doc.md' -interface ModelParameters { - max_output_tokens?: number -} +import { ModelParameters } from '../utils/VertexHelper' +import { BigQueryHelper } from '../utils/BigQueryHelper' +import { ExploreParams } from '../slices/assistantSlice' +import { ExploreFilterValidator, FieldType } from '../utils/ExploreFilterHelper' -const generateSQL = ( - model_id: string, - prompt: string, - parameters: ModelParameters, -) => { - const escapedPrompt = UtilsHelper.escapeQueryAll(prompt) - const subselect = `SELECT '` + escapedPrompt + `' AS prompt` - return ` - - SELECT ml_generate_text_llm_result AS generated_content - FROM - ML.GENERATE_TEXT( - MODEL \`${model_id}\`, - ( - ${subselect} - ), - STRUCT( - 0.05 AS temperature, - 1024 AS max_output_tokens, - 0.98 AS top_p, - TRUE AS flatten_json_output, - 1 AS top_k) - ) - - ` +const parseJSONResponse = (jsonString: string | null | undefined) => { + if (typeof jsonString !== 'string') { + return {} + } + + if (jsonString.startsWith('```json') && jsonString.endsWith('```')) { + jsonString = jsonString.slice(7, -3).trim() + } + + try { + const parsed = JSON.parse(jsonString) + return typeof parsed === 'object' ? parsed : {} + } catch (error) { + return {} + } } -function formatContent(field: { +function formatRow(field: { name?: string type?: string label?: string description?: string tags?: string[] }) { - let result = '' - if (field.name) result += 'name: ' + field.name - if (field.type) result += (result ? ', ' : '') + 'type: ' + field.type - if (field.label) result += (result ? ', ' : '') + 'label: ' + field.label - if (field.description) - result += (result ? ', ' : '') + 'description: ' + field.description - if (field.tags && field.tags.length) - result += (result ? ', ' : '') + 'tags: ' + field.tags.join(', ') - - return result + // Initialize properties with default values if not provided + const name = field.name || '' + const type = field.type || '' + const label = field.label || '' + const description = field.description || '' + const tags = field.tags ? field.tags.join(', ') : '' + + // Return a markdown row + return `| ${name} | ${type} | ${label} | ${description} | ${tags} |` } const useSendVertexMessage = () => { const { showBoundary } = useErrorBoundary() + // cloud function const VERTEX_AI_ENDPOINT = process.env.VERTEX_AI_ENDPOINT || '' const VERTEX_CF_AUTH_TOKEN = process.env.VERTEX_CF_AUTH_TOKEN || '' @@ -81,25 +66,31 @@ const useSendVertexMessage = () => { const VERTEX_BIGQUERY_MODEL_ID = process.env.VERTEX_BIGQUERY_MODEL_ID || '' const { core40SDK } = useContext(ExtensionContext) - const { settings, examples, currentExplore} = - useSelector((state: RootState) => state.assistant as AssistantState) + const { settings, examples, currentExplore } = useSelector( + (state: RootState) => state.assistant as AssistantState, + ) const currentExploreKey = currentExplore.exploreKey - const exploreRefinementExamples = examples.exploreRefinementExamples[currentExploreKey] + const exploreRefinementExamples = + examples.exploreRefinementExamples[currentExploreKey] - const vertextBigQuery = async ( + const vertexBigQuery = async ( contents: string, parameters: ModelParameters, ) => { const createSQLQuery = await core40SDK.ok( core40SDK.create_sql_query({ connection_name: VERTEX_BIGQUERY_LOOKER_CONNECTION_NAME, - sql: generateSQL(VERTEX_BIGQUERY_MODEL_ID, contents, parameters), + sql: BigQueryHelper.generateSQL( + VERTEX_BIGQUERY_MODEL_ID, + contents, + parameters, + ), }), ) if (createSQLQuery.slug) { - const runSQLQuery = await core40SDK.ok( + const runSQLQuery: any = await core40SDK.ok( core40SDK.run_sql_query(createSQLQuery.slug, 'json'), ) const exploreData = await runSQLQuery[0]['generated_content'] @@ -114,7 +105,7 @@ const useSendVertexMessage = () => { } } - const vertextCloudFunction = async ( + const vertexCloudFunction = async ( contents: string, parameters: ModelParameters, ) => { @@ -148,12 +139,14 @@ const useSendVertexMessage = () => { Here are some example prompts the user has asked so far and how to summarize them: -${exploreRefinementExamples && exploreRefinementExamples - .map((item) => { - const inputText = '"' + item.input.join('", "') + '"' - return `- The sequence of prompts from the user: ${inputText}. The summarized prompts: "${item.output}"` - }) - .join('\n')} +${exploreRefinementExamples && + exploreRefinementExamples + .map((item) => { + const inputText = '"' + item.input.join('", "') + '"' + return `- The sequence of prompts from the user: ${inputText}. The summarized prompts: "${item.output}"` + }) + .join('\n') + } Conversation so far ---------- @@ -173,6 +166,87 @@ ${exploreRefinementExamples && exploreRefinementExamples [exploreRefinementExamples], ) + const promptWrapper = (prompt: string) => { + // wrap the prompt with the current date + const currentDate = new Date().toLocaleString() + return `The current date is ${currentDate} + + + ${prompt} + ` + } + + const generateSharedContext = (dimensions: any[], measures: any[], exploreGenerationExamples: any[]) => { + if (!dimensions.length || !measures.length) { + showBoundary(new Error('Dimensions or measures are not defined')) + return + } + let exampleText = '' + if (exploreGenerationExamples && exploreGenerationExamples.length > 0) { + console.log("Line",exploreGenerationExamples) + exampleText = exploreGenerationExamples.map((item) => `input: "${item.input}" ; output: ${JSON.stringify(parseLookerURL(item.output))}`).join('\n') + } + return ` + # Documentation + Here is general documentation about filters: + ${looker_filter_doc} + Here is general documentation on how intervals and timeframes are applied in Looker + ${looker_filters_interval_tf} + Here is general documentation on visualizations: + ${looker_visualization_doc} + Here is general documentation on Looker JSON fields and pivots + ${looker_pivots_url_parameters_doc} + + ## Format of query object + + | Field | Type | Description | + |--------------------|--------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | model | string | Model | + | view | string | Explore Name | + | fields | string[] | Fields | + | pivots | string[] | Pivots | + | fill_fields | string[] | Fill Fields | + | filters | object | Filters | + | filter_expression | string | Filter Expression | + | sorts | string[] | Sorts | + | limit | string | Limit | + | column_limit | string | Column Limit | + | total | boolean | Total | + | row_total | string | Raw Total | + | subtotals | string[] | Subtotals | + | vis_config | object | Visualization configuration properties. These properties are typically opaque and differ based on the type of visualization used. There is no specified set of allowed keys. The values can be any type supported by JSON. A "type" key with a string value is often present, and is used by Looker to determine which visualization to present. Visualizations ignore unknown vis_config properties. | + | filter_config | object | The filter_config represents the state of the filter UI on the explore page for a given query. When running a query via the Looker UI, this parameter takes precedence over "filters". When creating a query or modifying an existing query, "filter_config" should be set to null. Setting it to any other value could cause unexpected filtering behavior. The format should be considered opaque. | + + # End Documentation + + + # Metadata + This information is particular to the current Looker instance and data model. The fields below can be used in the response. + Model: ${currentExplore.modelName} + Explore: ${currentExplore.exploreId} + + Dimensions Used to group by information (follow the instructions in tags when using a specific field; if map used include a location or lat long dimension;): + + | Field Id | Field Type | LookML Type | Label | Description | Tags | + |------------|------------|-------------|-------|-------------|------| + ${dimensions.map(formatRow).join('\n')} + + Measures are used to perform calculations (if top, bottom, total, sum, etc. are used include a measure): + + | Field Id | Field Type | LookML Type | Label | Description | Tags | + |------------|------------|-------------|-------|-------------|------| + ${measures.map(formatRow).join('\n')} + # End LookML Metadata + + # Example + Examples Below include the fields, filters and sometimes visualization configs. + They were taken at a different date. ALL DATE RANGES ARE WRONG COMPARING TO CURRENT DATE. + (BE CAREFUL WITH DATES, DO NOT OUTPUT THE Examples 1:1, as changes could happen with timeframes and date ranges) + ${exampleText} + # End Examples + + `} + const isSummarizationPrompt = async (prompt: string) => { const contents = ` Primer @@ -204,39 +278,23 @@ ${exploreRefinementExamples && exploreRefinementExamples } const summarizeExplore = useCallback( - async (exploreQueryArgs: string) => { - const params = new URLSearchParams(exploreQueryArgs) - - // Initialize an object to construct the query - const queryParams: { - fields: string[] - filters: Record - sorts: string[] - limit: string - } = { - fields: [], - filters: {}, - sorts: [], - limit: '', - } - - // Iterate over the parameters to fill the query object - params.forEach((value, key) => { - if (key === 'fields') { - queryParams.fields = value.split(',') - } else if (key.startsWith('f[')) { - const filterKey = key.match(/\[(.*?)\]/)?.[1] - if (filterKey) { - queryParams.filters[filterKey] = value + async (exploreParams: ExploreParams) => { + const filters: Record = {} + if (exploreParams.filters !== undefined) { + const exploreFiltters = exploreParams.filters + Object.keys(exploreFiltters).forEach((key: string) => { + if (!exploreFiltters[key]) { + return } - } else if (key === 'sorts') { - queryParams.sorts = value.split(',') - } else if (key === 'limit') { - queryParams.limit = value - } - }) - - console.log(params) + const filter: string[] | string = exploreFiltters[key] + if (typeof filter === 'string') { + filters[key] = filter + } + if (Array.isArray(filter)) { + filters[key] = filter.join(', ') + } + }) + } // get the contents of the explore query const createQuery = await core40SDK.ok( @@ -244,10 +302,10 @@ ${exploreRefinementExamples && exploreRefinementExamples model: currentExplore.modelName, view: currentExplore.exploreId, - fields: queryParams.fields || [], - filters: queryParams.filters || {}, - sorts: queryParams.sorts || [], - limit: queryParams.limit || '1000', + fields: exploreParams.fields || [], + filters: filters, + sorts: exploreParams.sorts || [], + limit: exploreParams.limit || '1000', }), ) @@ -291,108 +349,345 @@ ${exploreRefinementExamples && exploreRefinementExamples }, [currentExplore], ) + + const parseLookerURL = (url: string): { [key: string]: any } => { + // Split URL and extract model & explore + console.log("Line 331",url) + const urlSplit = url.split("?"); + let model = "" + let explore = "" + let queryString = "" + if (urlSplit.length == 2) { + const rootURL = urlSplit[0] + queryString = urlSplit[1] + const rootURLElements = rootURL.split("/"); + model = rootURLElements[rootURLElements.length - 2]; + explore = rootURLElements[rootURLElements.length - 1]; + } + else if (urlSplit.length == 1) { + model = "tbd" + explore = "tbd" + queryString = urlSplit[0] + } + // Initialize lookerEncoding object + const lookerEncoding: { [key: string]: any } = {}; + lookerEncoding['model'] = "" + lookerEncoding['explore'] = "" + lookerEncoding['fields'] = [] + lookerEncoding['pivots'] = [] + lookerEncoding['fill_fields'] = [] + lookerEncoding['filters'] = {} + lookerEncoding['filter_expression'] = null + lookerEncoding['sorts'] = [] + lookerEncoding['limit'] = 500 + lookerEncoding['column_limit'] = 50 + lookerEncoding['total'] = null + lookerEncoding['row_total'] = null + lookerEncoding['subtotals'] = null + lookerEncoding['vis'] = [] + // Split query string and iterate key-value pairs + const keyValuePairs = queryString.split("&"); + for (const qq of keyValuePairs) { + const [key, value] = qq.split('='); + console.log(qq) + lookerEncoding['model'] = model + lookerEncoding['explore'] = explore + switch (key) { + case "fields": + case "pivots": + case "fill_fields": + case "sorts": + lookerEncoding[key] = value.split(","); + break; + case "filter_expression": + case "total": + case "row_total": + case "subtotals": + lookerEncoding[key] = value; + break; + case "limit": + case "column_limit": + lookerEncoding[key] = parseInt(value); + break; + case "vis": + lookerEncoding[key] = JSON.parse(decodeURIComponent(value)); + break; + default: + if (key.startsWith("f[")) { + const filterKey = key.slice(2, -1); + lookerEncoding.filters[filterKey] = value; + } else if (key.includes(".")) { + const path = key.split("."); + let currentObject = lookerEncoding; + for (let i = 0; i < path.length - 1; i++) { + const segment = path[i]; + if (!currentObject[segment]) { + currentObject[segment] = {}; + } + currentObject = currentObject[segment]; + } + currentObject[path[path.length - 1]] = value; + } + } + } + return lookerEncoding; + }; + const generateFilterParams = useCallback( + async (prompt: string, sharedContext: string, dimensions: any[], measures: any[]) => { + // get the filters + const filterContents = ` + ${sharedContext} + + # Instructions + + The user asked the following question: + + \`\`\` + ${prompt} + \`\`\` + + Your job is to follow the steps below and generate a JSON object. + + * Step 1: Your task is the look at the following data question that the user is asking and determine the filter expression for it. You should return a JSON list of filters to apply. Each element in the list will be a pair of the field id and the filter expression. Your output will look like \`[ { "field_id": "example_view.created_date", "filter_expression": "this year" } ]\` + * Step 2: verify that you're only using valid expressions for the filter values. If you do not know what the valid expressions are, refer to the table above. If you are still unsure, don't use the filter. + * Step 3: verify that the field ids are indeed Field Ids from the table. If they are not, you should return an empty dictionary. There should be a period in the field id. + ` + + const filterResponseInitial = await sendMessage(filterContents, {}) + + // // check the response + // const filterContentsCheck = + // filterContents + + // ` + + // # Output + + // ${filterResponseInitial} + + // # Instructions + + // Verify the output, make changes and return the JSON + + // ` + // const filterResponseCheck = await sendMessage(filterContentsCheck, {}) + const filterResponseCheckJSON = parseJSONResponse(filterResponseInitial) + + // Ensure filterResponseCheckJSON is an array + const filterResponseArray = Array.isArray(filterResponseCheckJSON) ? filterResponseCheckJSON : [] + + // Iterate through each filter + const filterResponseJSON: any = {} + + // Validate each filter + filterResponseArray.forEach(function (filter: { + field_id: string + filter_expression: string + }) { + const field = + dimensions.find((d) => d.name === filter.field_id) || + measures.find((m) => m.name === filter.field_id) + + if (!field) { + console.log(`Invalid field: ${filter.field_id}`) + return + } - const generateExploreUrl = useCallback( - async ( - prompt: string, - dimensions: any[], - measures: any[], - exploreGenerationExamples: any[], - ) => { - try { - const contents = ` - Context - ---------- + console.log(field) - You are a developer who would transalate questions to a structured Looker URL query based on the following instructions. + const isValid = ExploreFilterValidator.isFilterValid( + field.type as FieldType, + filter.filter_expression, + ) - Instructions: - - choose only the fields in the below lookml metadata - - prioritize the field description, label, tags, and name for what field(s) to use for a given description - - generate only one answer, no more. - - use the Examples (at the bottom) for guidance on how to structure the Looker url query - - try to avoid adding dynamic_fields, provide them when very similar example is found in the bottom - - never respond with sql, always return an looker explore url as a single string - - response should start with fields= , as in the Examples section at the bottom + if (!isValid) { + console.log( + `Invalid filter expression for field ${filter.field_id}: ${filter.filter_expression}`, + ) + return + } - LookML Metadata - ---------- + // Check if the field_id already exists in the hash + if (!filterResponseJSON[filter.field_id]) { + // If not, create an empty array for this field_id + filterResponseJSON[filter.field_id] = [] + } + // Push the filter_expression into the array + filterResponseJSON[filter.field_id].push(filter.filter_expression) + }) - Dimensions Used to group by information (follow the instructions in tags when using a specific field; if map used include a location or lat long dimension;): + console.log('filterResponseInitial', filterResponseInitial) + console.log('filterResponseCheckJSON', filterResponseCheckJSON) + console.log('filterResponseJSON', filterResponseJSON) - ${dimensions.map(formatContent).join('\n')} + return filterResponseJSON + }, + [], + ) - Measures are used to perform calculations (if top, bottom, total, sum, etc. are used include a measure): + const generateVisualizationParams = async ( + exploreParams: ExploreParams, + prompt: string, + ) => { + const contents = ` - ${measures.map(formatContent).join('\n')} + ${looker_visualization_doc} - Example - ---------- + # User Request - ${exploreGenerationExamples && exploreGenerationExamples - .map((item) => `input: "${item.input}" ; output: ${item.output}`) - .join('\n')} + ## Prompt - Input - ---------- - ${prompt} + The user asked the following question: - Output - ---------- - ` - const parameters = { - max_output_tokens: 1000, - } - console.log(contents) - const response = await sendMessage(contents, parameters) + \`\`\` + ${prompt} + \`\`\` - const cleanResponse = unquoteResponse(response) - console.log(cleanResponse) + ## Explore Definition - let toggleString = '&toggle=dat,pik,vis' - if (settings['show_explore_data'].value) { - toggleString = '&toggle=pik,vis' - } + The user is asking for the following explore definition: - const newExploreUrl = cleanResponse + toggleString + \`\`\` + ${JSON.stringify(exploreParams)} + \`\`\` - return newExploreUrl - } catch (error) { - console.error( - 'Error waiting for data (lookml fields & training examples) to load:', - error, - ) - showBoundary({ - message: - 'Error waiting for data (lookml fields & training examples) to load:', - error, - }) + ## Determine Visualization JSON + + Based on the question, and on the original question, determine what the visualization config should be. The visualization config should be a JSON object that is compatible with the Looker API run_inline_query function. Only contain values that are different than the defaults. Here is an example: + + \`\`\` + { + "type": "looker_column", + } + \`\`\` + + ` + const parameters = { + max_output_tokens: 1000, + } + const response = await sendMessage(contents, parameters) + return parseJSONResponse(response) + } + + const generateBaseExploreParams = useCallback( + async ( + prompt: string, + sharedContext, + ) => { + const currentDateTime = new Date().toISOString() + + const contents = ` + ${sharedContext} + + Output + ---------- + + Return a JSON that is compatible with the Looker API run_inline_query function as per the spec. Here is an example: + + { + "model":"${currentExplore.modelName}", + "view":"${currentExplore.exploreId}", + "fields":["category.name","inventory_items.days_in_inventory_tier","products.count"], + "filters":{"category.name":"socks"}, + "sorts":["products.count desc 0"], + "limit":"500", + } + + Instructions: + - choose only the fields in the below lookml metadata + - prioritize the field description, label, tags, and name for what field(s) to use for a given description + - generate only one answer, no more. + - use the Examples for guidance on how to structure the body + - try to avoid adding dynamic_fields, provide them when very similar example is found in the bottom + - Always use the provided current date (${currentDateTime}) when generating Looker URL queries that involve TIMEFRAMES. + - only respond with a JSON object + + User Request + ---------- + ${prompt} + + ` + + const parameters = { + max_output_tokens: 1000, + } + console.log(contents) + const response = await sendMessage(contents, parameters) + const responseJSON = parseJSONResponse(response) + + return responseJSON + }, + [currentExplore], + ) + + const generateExploreParams = useCallback( + async ( + prompt: string, + dimensions: any[], + measures: any[], + exploreGenerationExamples: any[], + ) => { + if (!dimensions.length || !measures.length) { + showBoundary(new Error('Dimensions or measures are not defined')) return } + const sharedContext = generateSharedContext(dimensions, measures, exploreGenerationExamples) || '' + const filterResponseJSON = await generateFilterParams(prompt, sharedContext, dimensions, measures) + const responseJSON = await generateBaseExploreParams(prompt, sharedContext) + + responseJSON['filters'] = filterResponseJSON + + // get the visualizations + // const visualizationResponseJSON = await generateVisualizationParams( + // responseJSON, + // prompt, + // ) + + // console.log(visualizationResponseJSON) + + // responseJSON['vis_config'] = visualizationResponseJSON + + return responseJSON }, [settings], ) const sendMessage = async (message: string, parameters: ModelParameters) => { + const wrappedMessage = promptWrapper(message) try { - let response = '' - if (VERTEX_AI_ENDPOINT) { - response = await vertextCloudFunction(message, parameters) + if ( + VERTEX_AI_ENDPOINT && + VERTEX_BIGQUERY_LOOKER_CONNECTION_NAME && + VERTEX_BIGQUERY_MODEL_ID + ) { + throw new Error( + 'Both Vertex AI and BigQuery are enabled. Please only enable one', + ) } - if (VERTEX_BIGQUERY_LOOKER_CONNECTION_NAME && VERTEX_BIGQUERY_MODEL_ID) { - response = await vertextBigQuery(message, parameters) + let response = '' + if (VERTEX_AI_ENDPOINT) { + response = await vertexCloudFunction(wrappedMessage, parameters) + } else if ( + VERTEX_BIGQUERY_LOOKER_CONNECTION_NAME && + VERTEX_BIGQUERY_MODEL_ID + ) { + response = await vertexBigQuery(wrappedMessage, parameters) + } else { + throw new Error('No Vertex AI or BigQuery connection found') } - return response + return typeof response === 'string' ? response : JSON.stringify(response) } catch (error) { showBoundary(error) - return + return '' } } return { - generateExploreUrl, + generateExploreParams, + generateBaseExploreParams, + generateFilterParams, + generateVisualizationParams, sendMessage, summarizePrompts, isSummarizationPrompt, diff --git a/explore-assistant-extension/src/pages/AgentPage/MessageThread.tsx b/explore-assistant-extension/src/pages/AgentPage/MessageThread.tsx index 58e71196e..7eb19fd97 100644 --- a/explore-assistant-extension/src/pages/AgentPage/MessageThread.tsx +++ b/explore-assistant-extension/src/pages/AgentPage/MessageThread.tsx @@ -1,4 +1,4 @@ -import React from 'react' +import React, { useCallback, useEffect } from 'react' import { useSelector } from 'react-redux' import { RootState } from '../../store' import Message from '../../components/Chat/Message' @@ -7,11 +7,27 @@ import SummaryMessage from '../../components/Chat/SummaryMessage' import { CircularProgress } from '@material-ui/core' import { AssistantState, ChatMessage } from '../../slices/assistantSlice' -const MessageThread = () => { +interface MessageThreadProps { + endOfMessageRef: React.RefObject +} + +const MessageThread = ({ endOfMessageRef }: MessageThreadProps) => { const { currentExploreThread, isQuerying } = useSelector( (state: RootState) => state.assistant as AssistantState, ) + const scrollIntoView = useCallback(() => { + endOfMessageRef.current?.scrollIntoView({ behavior: 'smooth' }) + }, [endOfMessageRef]) + + const handleSummaryComplete = () => { + scrollIntoView() + } + + useEffect(() => { + scrollIntoView() + }, [currentExploreThread]) + if(currentExploreThread === null) { return <> } @@ -24,14 +40,14 @@ const MessageThread = () => { return ( ) } else if (message.type === 'summarize') { - return + return } else { return ( { )} +
) } diff --git a/explore-assistant-extension/src/pages/AgentPage/Sidebar.tsx b/explore-assistant-extension/src/pages/AgentPage/Sidebar.tsx index b4b920f41..1fc69500b 100644 --- a/explore-assistant-extension/src/pages/AgentPage/Sidebar.tsx +++ b/explore-assistant-extension/src/pages/AgentPage/Sidebar.tsx @@ -12,7 +12,7 @@ import { resetChat, setCurrentThread, setIsChatMode, - setSidePanelExploreUrl, + setSidePanelExploreParams, AssistantState, } from '../../slices/assistantSlice' import { RootState } from '../../store' @@ -55,7 +55,7 @@ const Sidebar = ({ expanded, toggleDrawer }: SidebarProps) => { dispatch(resetChat()) dispatch(setCurrentThread(thread)) dispatch(setIsChatMode(true)) - dispatch(setSidePanelExploreUrl(thread.exploreUrl)) + dispatch(setSidePanelExploreParams(thread.exploreParams)) dispatch(openSidePanel()) } diff --git a/explore-assistant-extension/src/pages/AgentPage/index.tsx b/explore-assistant-extension/src/pages/AgentPage/index.tsx index b774f79e7..3280998ac 100644 --- a/explore-assistant-extension/src/pages/AgentPage/index.tsx +++ b/explore-assistant-extension/src/pages/AgentPage/index.tsx @@ -17,7 +17,7 @@ import { setCurrenExplore, setIsQuerying, setQuery, - setSidePanelExploreUrl, + setSidePanelExploreParams, updateCurrentThread, updateLastHistoryEntry, } from '../../slices/assistantSlice' @@ -52,12 +52,13 @@ const AgentPage = () => { const endOfMessagesRef = useRef(null) // Ref for the last message const dispatch = useDispatch() const [expanded, setExpanded] = useState(false) - const { generateExploreUrl, isSummarizationPrompt, summarizePrompts } = + const { generateExploreParams, isSummarizationPrompt, summarizePrompts } = useSendVertexMessage() const { isChatMode, query, + isQuerying, currentExploreThread, currentExplore, sidePanel, @@ -76,6 +77,14 @@ const AgentPage = () => { } }) + const scrollIntoView = useCallback(() => { + endOfMessagesRef.current?.scrollIntoView({ behavior: 'smooth' }) + }, [endOfMessagesRef]) + + useEffect(() => { + scrollIntoView() + }, [currentExploreThread, query, isQuerying]) + const submitMessage = useCallback(async () => { if (query === '') { return @@ -95,7 +104,8 @@ const AgentPage = () => { }), ) - const exploreKey = currentExploreThread?.exploreKey || currentExplore.exploreKey + const exploreKey = + currentExploreThread?.exploreKey || currentExplore.exploreKey // set the explore if it is not set if (!currentExploreThread?.modelName || !currentExploreThread?.exploreId) { @@ -136,28 +146,19 @@ const AgentPage = () => { const exploreGenerationExamples = examples.exploreGenerationExamples[exploreKey] - const newExploreUrl = await generateExploreUrl( + const newExploreParams = await generateExploreParams( promptSummary, dimensions, measures, exploreGenerationExamples, ) - console.log('New Explore URL: ', newExploreUrl) + console.log('New Explore URL: ', newExploreParams) dispatch(setIsQuerying(false)) dispatch(setQuery('')) - // If the newExploreUrl is empty, do not update the current thread - if ( - newExploreUrl === '' || - newExploreUrl === null || - newExploreUrl === undefined - ) { - return - } - dispatch( updateCurrentThread({ - exploreUrl: newExploreUrl, + exploreParams: newExploreParams, summarizedPrompt: promptSummary, }), ) @@ -165,8 +166,8 @@ const AgentPage = () => { if (isSummary) { dispatch( addMessage({ + exploreParams: newExploreParams, uuid: uuidv4(), - exploreUrl: newExploreUrl, actor: 'system', createdAt: Date.now(), summary: '', @@ -174,13 +175,13 @@ const AgentPage = () => { }), ) } else { - dispatch(setSidePanelExploreUrl(newExploreUrl)) + dispatch(setSidePanelExploreParams(newExploreParams)) dispatch(openSidePanel()) dispatch( addMessage({ + exploreParams: newExploreParams, uuid: uuidv4(), - exploreUrl: newExploreUrl, summarizedPrompt: promptSummary, actor: 'system', createdAt: Date.now(), @@ -189,6 +190,9 @@ const AgentPage = () => { ) } + // scroll to bottom of message thread + scrollIntoView() + // update the history with the current contents of the thread dispatch(updateLastHistoryEntry()) }, [query, semanticModels, examples, currentExplore, currentExploreThread]) @@ -196,14 +200,12 @@ const AgentPage = () => { const isDataLoaded = isBigQueryMetadataLoaded && isSemanticModelLoaded useEffect(() => { - if (!query || query === '') { + if (!query || query === '' || !isDataLoaded) { return } - if (query !== '' && isDataLoaded) { - submitMessage() - endOfMessagesRef.current?.scrollIntoView({ behavior: 'smooth' }) - } + submitMessage() + scrollIntoView() }, [query, isDataLoaded]) const toggleDrawer = () => { @@ -315,26 +317,8 @@ const AgentPage = () => { )} >
-
- {!isDataLoaded ? ( -
-

- - Hello. - -

-

- Loading the conversation and LookML Metadata...{' '} -

-
- -
-
- ) : ( -
- -
- )} +
+
{
@@ -422,7 +406,6 @@ const AgentPage = () => { )}
-
{/* Ref for the last message */}
) diff --git a/explore-assistant-extension/src/slices/assistantSlice.ts b/explore-assistant-extension/src/slices/assistantSlice.ts index 83170b495..e528f12df 100644 --- a/explore-assistant-extension/src/slices/assistantSlice.ts +++ b/explore-assistant-extension/src/slices/assistantSlice.ts @@ -1,6 +1,16 @@ import { createSlice, PayloadAction } from '@reduxjs/toolkit' import { v4 as uuidv4 } from 'uuid' +export interface ExploreParams { + fields?: string[] + filters?: Record + pivots?: string[] + vis_config?: any + sorts?: string[] + limit?: string + +} + export interface Setting { name: string description: string @@ -52,7 +62,7 @@ export interface Message { export interface ExploreMessage { uuid: string - exploreUrl: string + exploreParams: ExploreParams actor: 'system' createdAt: number type: 'explore' @@ -61,7 +71,7 @@ export interface ExploreMessage { export interface SummarizeMesage { uuid: string - exploreUrl: string + exploreParams: ExploreParams actor: 'system' createdAt: number type: 'summarize' @@ -76,7 +86,7 @@ export type ExploreThread = { modelName: string exploreKey: string messages: ChatMessage[] - exploreUrl: string + exploreParams: ExploreParams summarizedPrompt: string promptList: string[] createdAt: number @@ -102,7 +112,7 @@ export interface AssistantState { } sidePanel: { isSidePanelOpen: boolean - exploreUrl: string + exploreParams: ExploreParams } history: ExploreThread[] semanticModels: { @@ -126,7 +136,7 @@ export const newThreadState = () => { exploreId: '', modelName: '', messages: [], - exploreUrl: '', + exploreParams: {}, summarizedPrompt: '', promptList: [], createdAt: Date.now() @@ -145,7 +155,7 @@ export const initialState: AssistantState = { }, sidePanel: { isSidePanelOpen: false, - exploreUrl: '', + exploreParams: {}, }, history: [], query: '', @@ -201,8 +211,8 @@ export const assistantSlice = createSlice({ closeSidePanel: (state) => { state.sidePanel.isSidePanelOpen = false }, - setSidePanelExploreUrl: (state, action: PayloadAction) => { - state.sidePanel.exploreUrl = action.payload + setSidePanelExploreParams: (state, action: PayloadAction) => { + state.sidePanel.exploreParams = action.payload }, clearHistory : (state) => { state.history = [] @@ -220,18 +230,18 @@ export const assistantSlice = createSlice({ if (currentUuid !== lastHistoryUuid) { state.history.push({ ...state.currentExploreThread }) } else { - state.history[state.history.length - 1] = state.currentExploreThread + state.history[state.history.length - 1] = { ...state.currentExploreThread } } } }, setSemanticModels: (state, action: PayloadAction) => { state.semanticModels = action.payload }, - setExploreUrl: (state, action: PayloadAction) => { + setExploreParams: (state, action: PayloadAction) => { if (state.currentExploreThread === null) { state.currentExploreThread = newThreadState() } - state.currentExploreThread.exploreUrl = action.payload + state.currentExploreThread.exploreParams = action.payload }, updateCurrentThread: ( state, @@ -291,7 +301,9 @@ export const assistantSlice = createSlice({ const message = state.currentExploreThread.messages.find( (message) => message.uuid === uuid, ) as SummarizeMesage - message.summary = summary + if (message) { + message.summary = summary + } }, setExploreSamples( state, @@ -324,7 +336,7 @@ export const { setSemanticModels, setIsSemanticModelLoaded, - setExploreUrl, + setExploreParams, setQuery, resetChat, addMessage, @@ -339,7 +351,7 @@ export const { openSidePanel, closeSidePanel, - setSidePanelExploreUrl, + setSidePanelExploreParams, setSetting, resetSettings, diff --git a/explore-assistant-extension/src/store.ts b/explore-assistant-extension/src/store.ts index 65b44187c..f60a4a56f 100644 --- a/explore-assistant-extension/src/store.ts +++ b/explore-assistant-extension/src/store.ts @@ -1,4 +1,4 @@ -import { configureStore } from '@reduxjs/toolkit' +import { configureStore, Reducer } from '@reduxjs/toolkit' import { persistStore, persistReducer, createTransform } from 'redux-persist' import storage from 'redux-persist/lib/storage' // defaults to localStorage for web import { combineReducers } from 'redux' @@ -38,6 +38,8 @@ const filterTransform = createTransform( } }) + newState.settings = persistedSettings as Settings + return newState } return inboundState @@ -68,9 +70,16 @@ const persistConfig = { transforms: [filterTransform], } -const rootReducer = combineReducers({ - assistant: assistantReducer, -}) +const rootReducer: Reducer<{ + assistant: AssistantState; +}> = (state, action) => { + if (state === undefined) { + return { assistant: initialState } + } + return combineReducers({ + assistant: assistantReducer, + })(state, action) +} const persistedReducer = persistReducer(persistConfig, rootReducer) diff --git a/explore-assistant-extension/src/utils/BigQueryHelper.ts b/explore-assistant-extension/src/utils/BigQueryHelper.ts new file mode 100644 index 000000000..441660f00 --- /dev/null +++ b/explore-assistant-extension/src/utils/BigQueryHelper.ts @@ -0,0 +1,38 @@ +import { UtilsHelper } from "./Helper" +import { ModelParameters } from "./VertexHelper" + +export class BigQueryHelper { + static generateSQL = ( + model_id: string, + prompt: string, + parameters: ModelParameters, + ) => { + const escapedPrompt = UtilsHelper.escapeQueryAll(prompt) + const subselect = `SELECT '` + escapedPrompt + `' AS prompt` + + const { + max_output_tokens = 1024, + temperature = 0.05, + top_p = 0.98, + flatten_json_output = true, + top_k = 1, + } = parameters + + return ` + SELECT ml_generate_text_llm_result AS generated_content + FROM + ML.GENERATE_TEXT( + MODEL \`${model_id}\`, + ( + ${subselect} + ), + STRUCT( + ${temperature} AS temperature, + ${max_output_tokens} AS max_output_tokens, + ${top_p} AS top_p, + ${flatten_json_output} AS flatten_json_output, + ${top_k} AS top_k) + ) + ` + } +} diff --git a/explore-assistant-extension/src/utils/ExploreFilterHelper.ts b/explore-assistant-extension/src/utils/ExploreFilterHelper.ts new file mode 100644 index 000000000..c81838ba9 --- /dev/null +++ b/explore-assistant-extension/src/utils/ExploreFilterHelper.ts @@ -0,0 +1,284 @@ +export type FieldType = + | 'string' + | 'number' + | 'date' + | 'boolean' + | 'date_date' + | 'date_time' + | 'date_hour' + | 'date_hour_of_day' + | 'date_millisecond' + | 'date_minute' + | 'date_month' + | 'date_month_num' + | 'date_quarter' + | 'date_quarter_of_year' + | 'date_second' + | 'date_time_of_day' + | 'date_week' + | 'date_day_of_week' + | 'date_week_of_year' + | 'date_year' + | 'date_day_of_year' + | 'location' + | 'location_latitude' + | 'location_longitude' + | 'sum' + | 'count' +export interface Field { + name: string + type: FieldType +} +export interface FilterExpression { + [key: string]: string[] +} +export class ExploreFilterValidator { + static isValidStringFilter(filter: string): boolean { + const invalidFilters = ["NOT NULL", "NOT EMPTY", "NOT BLANK"]; + // Regex to match "TOP N" where N is a number + const topNRegex = /^TOP \d+$/i; + if (invalidFilters.includes(filter) || topNRegex.test(filter)) { + return false; + } + const rules: ((f: string) => boolean)[] = [ + (f) => /^[^%,]+$/.test(f), // Exact match + (f) => /^[^%,]+(,[^%,]+)+$/.test(f), // Multiple exact matches + (f) => /^%[^%]+%$/.test(f), // Contains + (f) => /^[^%]+%$/.test(f), // Starts with + (f) => /^%[^%]+$/.test(f), // Ends with + (f) => /^[^%]+%[^%]+$/.test(f), // Starts with and ends with + (f) => f === 'EMPTY' || f === 'NULL', // EMPTY or NULL + (f) => /^-[^%,]+$/.test(f), // Not equal to + (f) => /^-(%[^%]+%|[^%]+%|%[^%]+)$/.test(f), // Not contains, not starts with, not ends with + (f) => f === '-EMPTY' || f === '-NULL', // Not EMPTY or not NULL + (f) => /^[^%]+%(,[^%,]+)+$/.test(f), // Starts with or exact match + (f) => /^_[^%]+$/.test(f), // Single character wildcard + ] + return rules.some((rule) => rule(filter)) + } + static isValidNumberFilter(filter: string): boolean { + const singleRules: ((f: string) => boolean)[] = [ + (f) => /^-?\d+(\.\d+)?$/.test(f), // Exact number (including negative) + (f) => /^NOT\s+-?\d+(\.\d+)?$/.test(f), // NOT number + (f) => /^(<>|!=|[<>]=?)-?\d+(\.\d+)?$/.test(f), // Relational operators + (f) => /^(-?\d+(\.\d+)?(,\s*-?\d+(\.\d+)?)+)$/.test(f), // List of numbers + (f) => /^NOT\s+(-?\d+(\.\d+)?(,\s*-?\d+(\.\d+)?)+)$/.test(f), // NOT list of numbers + (f) => /^-?\d+(\.\d+)?\s+to\s+-?\d+(\.\d+)?$/.test(f), // Range + (f) => /^to\s+-?\d+(\.\d+)?$/.test(f), // "to X" (X or less) + (f) => /^-?\d+(\.\d+)?\s+to$/.test(f), // "X to" (X or greater) + (f) => /^NULL$/.test(f), // NULL + (f) => /^NOT\s+NULL$/.test(f), // NOT NULL + (f) => /^[\[(]-?\d+(\.\d+)?,\s*-?\d+(\.\d+)?[\])]$/.test(f), // Single interval notation + (f) => /^NOT\s+[\[(]-?\d+(\.\d+)?,\s*-?\d+(\.\d+)?[\])]$/.test(f), // NOT single interval + (f) => /^[\[(](-inf|-?\d+(\.\d+)?),\s*(inf|-?\d+(\.\d+)?)[\])]$/.test(f), // Interval with infinity + (f) => + /^([\[(]-?\d+(\.\d+)?,\s*-?\d+(\.\d+)?[\])])(,\s*([\[(]-?\d+(\.\d+)?,\s*-?\d+(\.\d+)?[\])])+)$/.test( + f, + ), // Multiple intervals + (f) => + /^([\[(]-?\d+(\.\d+)?,\s*-?\d+(\.\d+)?[\])])\s*,\s*-?\d+(\.\d+)?$/.test( + f, + ), // Interval and exact number + (f) => + /^-?\d+(\.\d+)?\s*,\s*[\[(]-?\d+(\.\d+)?,\s*-?\d+(\.\d+)?[\])]$/.test( + f, + ), // Exact number and interval + (f) => /^NOT\s+(-?\d+(\.\d+)?\s+to\s+-?\d+(\.\d+)?)$/.test(f), // NOT range + (f) => /^((-?\d+(\.\d+)?|NOT\s+-?\d+(\.\d+)?)(,\s*|$))+$/.test(f), // Combination of exact and NOT + ] + const complexRules: ((f: string) => boolean)[] = [ + (f) => /^(>=|>)-?\d+(\.\d+)?\s+AND\s+(<=|<)-?\d+(\.\d+)?$/.test(f), // AND range + (f) => /^(<|<=)-?\d+(\.\d+)?\s+OR\s+(>|>=)-?\d+(\.\d+)?$/.test(f), // OR range + ] + const parts = filter?.trim()?.split(/\s+OR\s+/) || [] + return parts.every( + (part) => + singleRules.some((rule) => rule(part)) || + complexRules.some((rule) => rule(part)), + ) + } + static isValidDateFilter(filter: string): boolean { + const rules: ((f: string) => boolean)[] = [ + // This {interval} + // This {interval} + (f) => /^(this|next|last)\s+(week|month|quarter|year)$/.test(f), + // {n} {interval}, {n} {interval} ago, {n} {interval} ago for {n} {interval}, {n} {interval} from now, {n} {interval} from now for {n} {interval} + (f) => + /^\d+\s+(second|minute|hour|day|week|month|year)s?(\s+(ago|from\s+now))?$/.test( + f, + ), + (f) => + /^\d+\s+(second|minute|hour|day|week|month|year)s?\s+ago\s+for\s+\d+\s+(second|minute|hour|day|week|month|year)s?$/.test( + f, + ), + (f) => + /^\d+\s+(second|minute|hour|day|week|month|year)s?\s+from\s+now\s+for\s+\d+\s+(second|minute|hour|day|week|month|year)s?$/.test( + f, + ), + // before {n} {interval} ago + (f) => + /^before\s+\d+\s+(second|minute|hour|day|week|month|year)s?\s+ago$/.test( + f, + ), + // before {time}, after {time} + (f) => + /^(before|after)\s+\d{4}[-\/]\d{2}[-\/]\d{2}(\s+\d{2}:\d{2}:\d{2})?$/.test(f), + // {time} to {time} + (f) => + /^\d{4}[-\/]\d{2}[-\/]\d{2}(\s+\d{2}:\d{2}:\d{2})?\s+to\s+\d{4}[-\/]\d{2}[-\/]\d{2}(\s+\d{2}:\d{2}:\d{2})?$/.test( + f, + ), + // quarter: like 2023-Q4 + (f) => /^\d{4}-Q[1-4]$/.test(f), + // today, yesterday, tomorrow + (f) => /^(today|yesterday|tomorrow)$/.test(f), + // {day of week} + (f) => + /^(monday|tuesday|wednesday|thursday|friday|saturday|sunday)$/i.test(f), + // next {week, month, quarter, fiscal quarter, year, fiscal year} + (f) => + /^next\s+(week|month|quarter|fiscal\s+quarter|year|fiscal\s+year)$/.test( + f, + ), + // this {interval} to {interval} + (f) => + /^this\s+(year|month|week|quarter|fiscal\s+year)\s+to\s+(second|minute|hour|day|week|month|quarter|fiscal\s+year)$/.test( + f, + ), + // {time} for {n} {interval} + (f) => + /^\d{4}[-\/]\d{2}[-\/]\d{2}(\s+\d{2}:\d{2}:\d{2})?\s+for\s+\d+\s+(second|minute|hour|day|week|month|year)s?$/.test( + f, + ), + // FY{year}, FY{year}-Q{quarter} + (f) => /^FY\d{4}$/.test(f), + (f) => /^FY\d{4}-Q[1-4]$/.test(f), + // Absolute Dates + // year/month/day or year-month-day + (f) => /^\d{4}[-\/](0[1-9]|1[0-2])[-\/](0[1-9]|[12][0-9]|3[01])$/.test(f), + // year/month or year-month + (f) => /^\d{4}[-\/](0[1-9]|1[0-2])$/.test(f), + // year + (f) => /^\d{4}$/.test(f), + // time like 2024-02-03 12:34:56 + (f) => /^\d{4}[-\/]\d{2}[-\/]\d{2}\s+\d{2}:\d{2}:\d{2}$/.test(f), + // {time} for {n} {interval} (using slashes or dashes) + (f) => + /^\d{4}[-\/]\d{2}[-\/]\d{2}(\s+\d{2}:\d{2}:\d{2})?\s+for\s+\d+\s+(second|minute|hour|day|week|month|year)s?$/.test( + f, + ), + // {quarter} to {date} + (f) => + /^\d{4}-Q[1-4]\s+to\s+\d{4}[-\/]\d{2}[-\/]\d{2}$/.test(f), + // {date} to {quarter} + (f) => + /^\d{4}[-\/]\d{2}[-\/]\d{2}\s+to\s+\d{4}-Q[1-4]$/.test(f), + // {fiscal period} to {date} + (f) => + /^FY\d{4}-Q[1-4]\s+to\s+\d{4}[-\/]\d{2}[-\/]\d{2}$/.test(f), + // {date} to {fiscal period} + (f) => + /^\d{4}[-\/]\d{2}[-\/]\d{2}\s+to\s+FY\d{4}-Q[1-4]$/.test(f), + // {fiscal period} to {fiscal period} + (f) => + /^FY\d{4}-Q[1-4]\s+to\s+FY\d{4}-Q[1-4]$/.test(f), + // {quarter} to {quarter} + (f) => + /^\d{4}-Q[1-4]\s+to\s+\d{4}-Q[1-4]$/.test(f), + // {FY} to {FY} + (f) => + /^FY\d{4}\s+to\s+FY\d{4}$/.test(f), + // {year/month/day} to {year/month/day} or {year-month-day} to {year-month-day} + (f) => + /^\d{4}[-\/](0[1-9]|1[0-2])[-\/](0[1-9]|[12][0-9]|3[01])\s+to\s+\d{4}[-\/](0[1-9]|1[0-2])[-\/](0[1-9]|[12][0-9]|3[01])$/.test(f), + // {year/month} to {year/month} or {year-month} to {year-month} + (f) => + /^\d{4}[-\/](0[1-9]|1[0-2])\s+to\s+\d{4}[-\/](0[1-9]|1[0-2])$/.test(f), + // {year} to {year} + (f) => + /^\d{4}\s+to\s+\d{4}$/.test(f), + // {time} to {time} + (f) => + /^\d{4}[-\/]\d{2}[-\/]\d{2}\s+\d{2}:\d{2}:\d{2}\s+to\s+\d{4}[-\/]\d{2}[-\/]\d{2}\s+\d{2}:\d{2}:\d{2}$/.test(f), + // is on {date} + (f) => /^is\s+on\s+\d{4}[-\/]\d{2}[-\/]\d{2}$/.test(f), + // is on {day of week} + (f) => /^is\s+on\s+(monday|tuesday|wednesday|thursday|friday|saturday|sunday)$/i.test(f), + // is on {quarter} + (f) => /^is\s+on\s+\d{4}-Q[1-4]$/.test(f), + // is on {FY} + (f) => /^is\s+on\s+FY\d{4}$/.test(f), + // is on {fiscal period} + (f) => /^is\s+on\s+FY\d{4}-Q[1-4]$/.test(f), + // is on {month} + (f) => /^is\s+on\s+\d{4}[-\/](0[1-9]|1[0-2])$/.test(f), + ] + const parts = filter?.split(/%2C|\s*,\s*/) || [] + return parts.every((part) => rules.some((rule) => rule(part))) + } + static isValidBooleanFilter(filter: string): boolean { + return /^(yes|no|true|false)$/i.test(filter) + } + static isValidLocationFilter(filter: string): boolean { + return /^(latitude|longitude):-?\d+(\.\d+)?$/.test(filter) + } + static isFilterValid(fieldType: FieldType, filter: string): boolean { + switch (fieldType) { + case 'string': + return this.isValidStringFilter(filter) + case 'sum': + return this.isValidNumberFilter(filter) + case 'number': + return this.isValidNumberFilter(filter) + case 'count': + return this.isValidNumberFilter(filter) + case 'date': + case 'date_date': + case 'date_time': + case 'date_hour': + case 'date_hour_of_day': + case 'date_millisecond': + case 'date_minute': + case 'date_month': + case 'date_month_num': + case 'date_quarter': + case 'date_quarter_of_year': + case 'date_second': + case 'date_time_of_day': + case 'date_week': + case 'date_day_of_week': + case 'date_week_of_year': + case 'date_year': + case 'date_day_of_year': + return this.isValidDateFilter(filter) + case 'boolean': + return this.isValidBooleanFilter(filter) + case 'location': + case 'location_latitude': + case 'location_longitude': + return this.isValidLocationFilter(filter) + default: + return false + } + } + static validateFilters( + fields: Field[], + filterExpression: FilterExpression, + ): boolean { + const fieldMap = new Map(fields.map((field) => [field.name, field.type])) + for (const [filterField, filterValues] of Object.entries( + filterExpression, + )) { + if (!fieldMap.has(filterField)) { + return false // Field does not exist in the field list + } + const fieldType = fieldMap.get(filterField) + for (const filter of filterValues) { + if (!this.isFilterValid(fieldType!, filter)) { + return false // Invalid filter expression + } + } + } + return true // All checks passed + } +} diff --git a/explore-assistant-extension/src/utils/ExploreHelper.ts b/explore-assistant-extension/src/utils/ExploreHelper.ts new file mode 100644 index 000000000..440dff34e --- /dev/null +++ b/explore-assistant-extension/src/utils/ExploreHelper.ts @@ -0,0 +1,46 @@ +import { ExploreParams } from '../slices/assistantSlice' + +export class ExploreHelper { + + static exploreQueryArgumentString = (exploreParams: ExploreParams): string => { + const encodedParams = ExploreHelper.encodeExploreParams(exploreParams) + return new URLSearchParams(encodedParams).toString() + } + + static encodeExploreParams = ( + exploreParams: ExploreParams , + ): { [key: string]: string } => { + if (!exploreParams || Object.keys(exploreParams).length === 0) { + return {} + } + + const { fields, filters, sorts, limit, pivots, vis_config } = exploreParams + + const fieldsString = fields?.join(',') || '' + + const sortsString = Array.isArray(sorts) + ? sorts.map((sort) => sort).join(',') + : '' + + const limitString = limit ? limit.toString() : '' + + const pivotsString = pivots?.join(',') || '' + const visString = vis_config ? JSON.stringify(vis_config) : '' + + const encodedParams: { [key: string]: string } = { + fields: fieldsString, + sorts: sortsString, + limit: limitString, + pivots: pivotsString, + vis: visString, + } + + if (filters) { + Object.entries(filters).forEach(([key, value]) => { + encodedParams[`f[${key}]`] = value + }) + } + + return encodedParams + } +} diff --git a/explore-assistant-extension/src/utils/VertexHelper.ts b/explore-assistant-extension/src/utils/VertexHelper.ts new file mode 100644 index 000000000..c49053c8d --- /dev/null +++ b/explore-assistant-extension/src/utils/VertexHelper.ts @@ -0,0 +1,7 @@ +export interface ModelParameters { + max_output_tokens?: number + temperature?: number + top_p?: number + flatten_json_output?: boolean + top_k?: number + } \ No newline at end of file diff --git a/explore-assistant-extension/tests/generateFilterParams.test.tsx b/explore-assistant-extension/tests/generateFilterParams.test.tsx new file mode 100644 index 000000000..c5a150e9b --- /dev/null +++ b/explore-assistant-extension/tests/generateFilterParams.test.tsx @@ -0,0 +1,107 @@ +import React from 'react' +import { renderHook, act } from '@testing-library/react-hooks' +import useSendVertexMessage from '../src/hooks/useSendVertexMessage' +import dotenv from 'dotenv' +import { ExtensionContext } from '@looker/extension-sdk-react' +import { Provider } from 'react-redux' +import { ErrorBoundary } from 'react-error-boundary' +import { setSemanticModels } from '../src/slices/assistantSlice' +import { store } from '../src/store' + +dotenv.config() + +const mockCore40SDK = { + ok: jest.fn(), + create_sql_query: jest.fn(), + run_sql_query: jest.fn(), + create_query: jest.fn(), + run_query: jest.fn(), +} + +const mockExtensionContextValue = { + core40SDK: mockCore40SDK, + route: jest.fn(), + visualizationSDK: jest.fn(), + tileHostData: jest.fn(), + tileSDK: jest.fn(), + extensionSDK: jest.fn(), +} + +// Fallback render function for ErrorBoundary +const FallbackRender = ({ error }: { error: Error }) => ( +
Error: {error.message}
+) + +describe('useSendVertexMessage', () => { + let result: ReturnType + + const dimensions = [ + { + name: 'orders.created_date', + type: 'string', + description: 'desc1', + tags: ['tag1'], + }, + ] + + const measures = [ + { + name: 'orders.sum_revenue', + type: 'number', + description: 'The sum of the revenue', + tags: ['tag1'], + }, + ] + const semanticModels = { + 'ecommerce:orders': { + exploreKey: 'ecommerce:orders', + modelName: 'ecommerce', + exploreId: 'orders', + dimensions, + measures, + } + } + + beforeEach(async () => { + // Set initial dimensions and measures + await act(async () => { + store.dispatch(setSemanticModels(semanticModels)) + }) + + const { result: hookResult } = renderHook(() => useSendVertexMessage(), { + wrapper: ({ children }) => ( + + {/* @ts-ignore */} + + + {children} + + + + ), + }) + result = hookResult.current + }) + + test('generateFilterParams should return correct filter parameters', async () => { + const prompt = 'Show me the sales data for this year' + + await act(async () => { + const filterParams = await result.generateFilterParams( + prompt, + dimensions, + measures, + ) + + expect( + Object.prototype.hasOwnProperty.call( + filterParams, + 'orders.created_date', + ), + ).toBe(true) + expect(filterParams['orders.created_date']).toContain('this year') + }) + }) + + // Additional test cases can be added here +}) diff --git a/explore-assistant-extension/tests/unit_tests/ExploreFilterHelper.test.ts b/explore-assistant-extension/tests/unit_tests/ExploreFilterHelper.test.ts new file mode 100644 index 000000000..772119b50 --- /dev/null +++ b/explore-assistant-extension/tests/unit_tests/ExploreFilterHelper.test.ts @@ -0,0 +1,435 @@ +import { + ExploreFilterValidator, + Field, + FilterExpression, +} from '../../src/utils/ExploreFilterHelper' + +describe('ExploreFilterValidator', () => { + describe('String Filters', () => { + const testCases = [ + { input: 'FOO', expected: true, description: 'Exact match' }, + { + input: 'FOO,BAR', + expected: true, + description: 'Multiple exact matches', + }, + { input: '%FOO%', expected: true, description: 'Contains' }, + { input: 'FOO%', expected: true, description: 'Starts with' }, + { input: '%FOO', expected: true, description: 'Ends with' }, + { + input: 'F%OD', + expected: true, + description: 'Starts with and ends with', + }, + { input: 'EMPTY', expected: true, description: 'EMPTY' }, + { input: 'NULL', expected: true, description: 'NULL' }, + { input: '-FOO', expected: true, description: 'Not equal to' }, + { + input: '-FOO,-BAR', + expected: true, + description: 'Multiple not equal to', + }, + { input: '-%FOO%', expected: true, description: 'Not contains' }, + { input: '-FOO%', expected: true, description: 'Not starts with' }, + { input: '-%FOO', expected: true, description: 'Not ends with' }, + { input: '-EMPTY', expected: true, description: 'Not EMPTY' }, + { input: '-NULL', expected: true, description: 'Not NULL' }, + { + input: 'FOO%,BAR', + expected: true, + description: 'Starts with or exact match', + }, + { + input: 'FOO%,-FOOD', + expected: true, + description: 'Starts with and not equal to', + }, + { + input: '_UF', + expected: true, + description: 'Single character wildcard', + }, + ] + + testCases.forEach(({ input, expected, description }) => { + it(`isValidStringFilter: ${description} - ${input}`, () => { + expect(ExploreFilterValidator.isValidStringFilter(input)).toBe(expected) + }) + }) + }) + + describe('Number Filters', () => { + const testCases = [ + { input: '5', expected: true, description: 'Exact number' }, + { input: 'NOT 5', expected: true, description: 'NOT number' }, + { input: '<>5', expected: true, description: 'Not equal to' }, + { + input: '!=5', + expected: true, + description: 'Not equal to (alternative)', + }, + { input: '1, 3, 5, 7', expected: true, description: 'List of numbers' }, + { + input: 'NOT 66, 99, 4', + expected: true, + description: 'NOT list of numbers', + }, + { input: '>1 AND <100', expected: true, description: 'Range with AND' }, + { + input: '5, NOT 6, NOT 7', + expected: true, + description: 'Combination of exact and NOT', + }, + { input: '5.5 to 10', expected: true, description: 'Range with to' }, + { + input: '>=5.5 AND <=10', + expected: true, + description: 'Range with AND and =', + }, + { input: 'NOT 3 to 80.44', expected: true, description: 'NOT range' }, + { + input: '<3 OR >80.44', + expected: true, + description: 'Less than OR greater than', + }, + { input: '1 to', expected: true, description: '1 or greater' }, + { input: '>=1', expected: true, description: 'Greater than or equal to' }, + { input: 'to 10', expected: true, description: '10 or less' }, + { input: '<=10', expected: true, description: 'Less than or equal to' }, + { + input: '>10 AND <=20 OR 90', + expected: true, + description: 'Complex condition', + }, + { + input: '>=50 AND <=100 OR >=500 AND <=1000', + expected: true, + description: 'Multiple ranges', + }, + { input: 'NULL', expected: true, description: 'NULL' }, + { input: 'NOT NULL', expected: true, description: 'NOT NULL' }, + { input: '(1, 7)', expected: true, description: 'Open interval' }, + { input: '[5, 90]', expected: true, description: 'Closed interval' }, + { input: '(12, 20]', expected: true, description: 'Half-open interval' }, + { input: '[12, 20)', expected: true, description: 'Half-open interval' }, + { input: '(500, inf)', expected: true, description: 'Open upper bound' }, + { input: '(-inf, 10]', expected: true, description: 'Open lower bound' }, + { + input: '[0, 9],[20, 29]', + expected: true, + description: 'Multiple intervals', + }, + { + input: '[0, 10], 20', + expected: true, + description: 'Interval and exact number', + }, + { + input: 'NOT (3, 12)', + expected: true, + description: 'NOT open interval', + }, + { input: 'INVALID', expected: false, description: 'Invalid input' }, + ] + + testCases.forEach(({ input, expected, description }) => { + it(`isValidNumberFilter: ${description} - ${input}`, () => { + expect(ExploreFilterValidator.isValidNumberFilter(input)).toBe(expected) + }) + }) + }) + + describe('Date Filters', () => { + const testCases = [ + // This {interval} + { input: 'this year', expected: true, description: 'This year' }, + { input: 'this month', expected: true, description: 'This month' }, + { input: 'this week', expected: true, description: 'This week' }, + { input: 'this quarter', expected: true, description: 'This quarter' }, + { input: 'this day', expected: false, description: 'Invalid interval' }, + + // Next {interval} + { input: 'next year', expected: true, description: 'Next year' }, + { input: 'next month', expected: true, description: 'Next month' }, + { input: 'next week', expected: true, description: 'Next week' }, + { input: 'next quarter', expected: true, description: 'Next quarter' }, + { input: 'next day', expected: false, description: 'Invalid interval' }, + + // Last {interval} + { input: 'last year', expected: true, description: 'Last year' }, + { input: 'last month', expected: true, description: 'Last month' }, + { input: 'last week', expected: true, description: 'Last week' }, + { input: 'last quarter', expected: true, description: 'Last quarter' }, + { input: 'last day', expected: false, description: 'Invalid interval' }, + + // {n} {interval}, {n} {interval} ago + { input: '3 days', expected: true, description: 'N days' }, + { input: '3 days ago', expected: true, description: 'N days ago' }, + { input: '5 months', expected: true, description: 'N months' }, + { input: '5 months ago', expected: true, description: 'N months ago' }, + { input: '10 years', expected: true, description: 'N years' }, + { input: '10 years ago', expected: true, description: 'N years ago' }, + + // {n} {interval} ago for {n} {interval} + { + input: '3 months ago for 2 days', + expected: true, + description: 'N months ago for N days', + }, + { + input: '2 years ago for 1 month', + expected: true, + description: 'N years ago for N months', + }, + { + input: '1 week ago for 3 hours', + expected: true, + description: 'N weeks ago for N hours', + }, + { + input: '3 days ago for 2 seconds', + expected: true, + description: 'N days ago for N seconds', + }, + + // Before {n} {interval} ago + { + input: 'before 3 days ago', + expected: true, + description: 'Before N days ago', + }, + { + input: 'before 5 months ago', + expected: true, + description: 'Before N months ago', + }, + { + input: 'before 1 year ago', + expected: true, + description: 'Before N years ago', + }, + + // Before {time}, after {time} + { + input: 'before 2018-01-01 12:00:00', + expected: true, + description: 'Before specific datetime', + }, + { + input: 'after 2018-10-05', + expected: true, + description: 'After specific date', + }, + { + input: 'before 2018-01-01', + expected: true, + description: 'Before specific date', + }, + { + input: 'after 2018-10-05 14:30:00', + expected: true, + description: 'After specific datetime', + }, + + // {time} to {time} + { + input: '2018-05-18 12:00:00 to 2018-05-18 14:00:00', + expected: true, + description: 'Datetime range', + }, + { + input: '2020-01-01 to 2020-12-31', + expected: true, + description: 'Date range', + }, + { + input: '2021-06-01 09:00:00 to 2021-06-01 18:00:00', + expected: true, + description: 'Datetime range within a day', + }, + { + input: '2022-03-15 to 2022-03-16', + expected: true, + description: 'Date range over two days', + }, + + // This {interval} to {interval} + { + input: 'this year to second', + expected: true, + description: 'This year to second', + }, + { + input: 'this month to day', + expected: true, + description: 'This month to day', + }, + { + input: 'this week to hour', + expected: true, + description: 'This week to hour', + }, + + // {time} for {n} {interval} + { + input: '2018-01-01 12:00:00 for 3 days', + expected: true, + description: 'Specific datetime for N days', + }, + { + input: '2020-05-10 for 1 month', + expected: true, + description: 'Specific date for N months', + }, + { + input: '2019-12-31 for 2 years', + expected: true, + description: 'Specific date for N years', + }, + + // Today, yesterday, tomorrow + { input: 'today', expected: true, description: 'Today' }, + { input: 'yesterday', expected: true, description: 'Yesterday' }, + { input: 'tomorrow', expected: true, description: 'Tomorrow' }, + + // Day of week + { input: 'Monday', expected: true, description: 'Day of week' }, + { input: 'Tuesday', expected: true, description: 'Day of week' }, + { input: 'Wednesday', expected: true, description: 'Day of week' }, + { input: 'Thursday', expected: true, description: 'Day of week' }, + { input: 'Friday', expected: true, description: 'Day of week' }, + { input: 'Saturday', expected: true, description: 'Day of week' }, + { input: 'Sunday', expected: true, description: 'Day of week' }, + + // Next {week, month, quarter, fiscal quarter, year, fiscal year} + { input: 'next week', expected: true, description: 'Next week' }, + { input: 'next month', expected: true, description: 'Next month' }, + { input: 'next quarter', expected: true, description: 'Next quarter' }, + { input: 'next fiscal quarter', expected: true, description: 'Next fiscal quarter' }, + { input: 'next year', expected: true, description: 'Next year' }, + { input: 'next fiscal year', expected: true, description: 'Next fiscal year' }, + + // {n} {interval} from now + { + input: '3 days from now', + expected: true, + description: 'N days from now', + }, + { + input: '5 months from now', + expected: true, + description: 'N months from now', + }, + { + input: '10 years from now', + expected: true, + description: 'N years from now', + }, + + // {n} {interval} from now for {n} {interval} + { + input: '3 days from now for 2 weeks', + expected: true, + description: 'N days from now for N weeks', + }, + { + input: '1 month from now for 3 days', + expected: true, + description: 'N months from now for N days', + }, + { + input: '1 year from now for 2 months', + expected: true, + description: 'N years from now for N months', + }, + + // FY{year}, FY{year}-Q{quarter} + { input: 'FY2018', expected: true, description: 'Fiscal year' }, + { input: 'FY2019-Q1', expected: true, description: 'Fiscal quarter' }, + { input: 'FY2020-Q4', expected: true, description: 'Fiscal quarter' }, + { input: 'FY2021', expected: true, description: 'Fiscal year' }, + + // Absolute dates + { input: '2018/05/29', expected: true, description: 'Specific date' }, + { input: '2018/05', expected: true, description: 'Specific month' }, + { input: '2018', expected: true, description: 'Specific year' }, + + // Invalid inputs + { input: 'INVALID', expected: false, description: 'Invalid input' }, + { input: '3 days before now', expected: false, description: 'Invalid input' }, + { input: '2018/13', expected: false, description: 'Invalid month' }, + { input: '2018-05-32', expected: false, description: 'Invalid date' }, + { input: 'next decade', expected: false, description: 'Invalid interval' }, + ] + + testCases.forEach(({ input, expected, description }) => { + it(`isValidDateFilter: ${description} - ${input}`, () => { + expect(ExploreFilterValidator.isValidDateFilter(input)).toBe(expected) + }) + }) + }) + + describe('Boolean Filters', () => { + const testCases = [ + { input: 'yes', expected: true, description: 'yes (lowercase)' }, + { input: 'Yes', expected: true, description: 'Yes (capitalized)' }, + { input: 'no', expected: true, description: 'no (lowercase)' }, + { input: 'No', expected: true, description: 'No (capitalized)' }, + { input: 'TRUE', expected: true, description: 'TRUE (uppercase)' }, + { input: 'FALSE', expected: true, description: 'FALSE (uppercase)' }, + { input: 'true', expected: true, description: 'true (lowercase)' }, + { input: 'false', expected: true, description: 'false (lowercase)' }, + { input: 'INVALID', expected: false, description: 'Invalid input' }, + ] + + testCases.forEach(({ input, expected, description }) => { + it(`isValidBooleanFilter: ${description} - ${input}`, () => { + expect(ExploreFilterValidator.isValidBooleanFilter(input)).toBe( + expected, + ) + }) + }) + }) + + describe('validateFilters', () => { + const fields: Field[] = [ + { name: 'string_field', type: 'string' }, + { name: 'number_field', type: 'number' }, + { name: 'date_field', type: 'date' }, + { name: 'boolean_field', type: 'boolean' }, + ] + + it('should return true for valid filters', () => { + const filterExpression: FilterExpression = { + string_field: ['FOO%', 'BAR'], + number_field: ['5', '>10 AND <=20'], + date_field: ['this year', '3 days ago'], + boolean_field: ['yes'], + } + expect( + ExploreFilterValidator.validateFilters(fields, filterExpression), + ).toBe(true) + }) + + it('should return false for invalid filters', () => { + const filterExpression: FilterExpression = { + string_field: ['INVALID%'], + number_field: ['5', 'INVALID'], + date_field: ['this year', 'INVALID'], + boolean_field: ['INVALID'], + } + expect( + ExploreFilterValidator.validateFilters(fields, filterExpression), + ).toBe(false) + }) + + it('should return false for non-existing fields', () => { + const filterExpression: FilterExpression = { + non_existing_field: ['FOO'], + } + expect( + ExploreFilterValidator.validateFilters(fields, filterExpression), + ).toBe(false) + }) + }) +}) diff --git a/explore-assistant-extension/tsconfig.json b/explore-assistant-extension/tsconfig.json index c5f60a441..427057b15 100644 --- a/explore-assistant-extension/tsconfig.json +++ b/explore-assistant-extension/tsconfig.json @@ -54,5 +54,6 @@ SOFTWARE. "exclude": [ "explore-assistant-extension/node_modules", "explore-assistant-extension/dist", - ] + ], + "include": ["src/**/*.ts", "tests/**/*.ts"] } diff --git a/explore-assistant-extension/webpack.config.js b/explore-assistant-extension/webpack.config.js index 891d09c8e..02c0c480d 100644 --- a/explore-assistant-extension/webpack.config.js +++ b/explore-assistant-extension/webpack.config.js @@ -69,6 +69,10 @@ module.exports = { test: /\.(css|scss|sass)$/i, use: ["style-loader", "css-loader", 'postcss-loader', "sass-loader"], }, + { + test: /\.md$/, + use: 'raw-loader', + }, { test: /\.(png|jpe?g|gif)$/i, use: [ From 1700f74058c62552d8413513f0209a1fa34fbb83 Mon Sep 17 00:00:00 2001 From: aamodic <46719541+aamodic@users.noreply.github.com> Date: Thu, 27 Mar 2025 12:26:16 -0400 Subject: [PATCH 2/4] Update to involve the canonical ids --- .../src/components/SamplePrompts.tsx | 4 +++- .../src/hooks/useSendVertexMessage.ts | 17 ++++++++++++++--- .../src/pages/AgentPage/PromptInput.tsx | 19 +++++++++++++++++-- .../src/pages/AgentPage/Sidebar.tsx | 4 ++-- .../src/pages/AgentPage/index.tsx | 2 ++ .../src/slices/assistantSlice.ts | 11 +++++++++-- 6 files changed, 47 insertions(+), 10 deletions(-) diff --git a/explore-assistant-extension/src/components/SamplePrompts.tsx b/explore-assistant-extension/src/components/SamplePrompts.tsx index 4dae6f2a6..692f8167e 100644 --- a/explore-assistant-extension/src/components/SamplePrompts.tsx +++ b/explore-assistant-extension/src/components/SamplePrompts.tsx @@ -14,6 +14,7 @@ const SamplePrompts = () => { const { currentExplore: { modelName, exploreId }, examples: { exploreSamples }, + canonicalIds } = useSelector((state: RootState) => state.assistant as AssistantState) const samples = exploreSamples[`${modelName}:${exploreId}`] @@ -33,7 +34,8 @@ const SamplePrompts = () => { className="flex flex-col w-56 min-h-44 bg-gray-200/50 hover:bg-gray-200 rounded-lg cursor-pointer text-sm p-4 m-2" key={index} onClick={() => { - handleSubmit(item.prompt) + canonicalIds.length > 0? + handleSubmit(item.prompt):'' }} >
{item.prompt}
diff --git a/explore-assistant-extension/src/hooks/useSendVertexMessage.ts b/explore-assistant-extension/src/hooks/useSendVertexMessage.ts index 7c8d7b995..340f44078 100644 --- a/explore-assistant-extension/src/hooks/useSendVertexMessage.ts +++ b/explore-assistant-extension/src/hooks/useSendVertexMessage.ts @@ -1,6 +1,6 @@ import { ExtensionContext } from '@looker/extension-sdk-react' -import { useCallback, useContext } from 'react' -import { useSelector } from 'react-redux' +import { useCallback, useContext, useEffect } from 'react' +import { useDispatch, useSelector } from 'react-redux' import CryptoJS from 'crypto-js' import { RootState } from '../store' import process from 'process' @@ -17,7 +17,6 @@ import { BigQueryHelper } from '../utils/BigQueryHelper' import { ExploreParams } from '../slices/assistantSlice' import { ExploreFilterValidator, FieldType } from '../utils/ExploreFilterHelper' - const parseJSONResponse = (jsonString: string | null | undefined) => { if (typeof jsonString !== 'string') { return {} @@ -125,6 +124,7 @@ const useSendVertexMessage = () => { body: body, }) + console.log("log response", await responseData.text) const response = await responseData.text() return response.trim() } @@ -621,23 +621,34 @@ ${exploreRefinementExamples && [currentExplore], ) + const generateExploreParams = useCallback( async ( prompt: string, dimensions: any[], measures: any[], exploreGenerationExamples: any[], + canonicalIds: any[] ) => { if (!dimensions.length || !measures.length) { showBoundary(new Error('Dimensions or measures are not defined')) return } + const sharedContext = generateSharedContext(dimensions, measures, exploreGenerationExamples) || '' const filterResponseJSON = await generateFilterParams(prompt, sharedContext, dimensions, measures) const responseJSON = await generateBaseExploreParams(prompt, sharedContext) responseJSON['filters'] = filterResponseJSON + if (canonicalIds.length > 0) { + responseJSON['filters']['user_banking_activity_view.reqctx_canonicalid'] = canonicalIds; + } + + if (canonicalIds?.filter((id) => id.toString().toUpperCase() == "ALL").length > 0) { + responseJSON['filters']['user_banking_activity_view.reqctx_canonicalid'] = []; + } + // get the visualizations // const visualizationResponseJSON = await generateVisualizationParams( // responseJSON, diff --git a/explore-assistant-extension/src/pages/AgentPage/PromptInput.tsx b/explore-assistant-extension/src/pages/AgentPage/PromptInput.tsx index aa5b23c03..96c654cba 100644 --- a/explore-assistant-extension/src/pages/AgentPage/PromptInput.tsx +++ b/explore-assistant-extension/src/pages/AgentPage/PromptInput.tsx @@ -2,16 +2,20 @@ import { Send } from '@material-ui/icons' import React, { useState, useRef, useCallback } from 'react' import { useDispatch, useSelector } from 'react-redux' import { RootState } from '../../store' -import { setIsChatMode, setQuery } from '../../slices/assistantSlice' +import { setCanonicalIds, setIsChatMode, setQuery } from '../../slices/assistantSlice' import clsx from 'clsx' +import { Autocomplete, TextField, Typography } from '@mui/material' const PromptInput = () => { const dispatch = useDispatch() const [inputText, setInputText] = useState('') + //const [ids, setIds] = useState([]); const inputRef = useRef(null) const { isQuerying } = useSelector((state: RootState) => state.assistant) + const {canonicalIds} = useSelector((state: RootState) => state.assistant) + const handleInputChange = (e: any) => { setInputText(e.target.value) } @@ -33,8 +37,19 @@ const PromptInput = () => { handleSubmit() } } + + const handleIdChange = (e, v) => { + dispatch(setCanonicalIds(v)); + } return (
+ }> +
{