From 8789ad8f16dfa250fab607946250a2857a25d5ef Mon Sep 17 00:00:00 2001 From: Google Team Member Date: Mon, 5 Jan 2026 12:52:18 -0800 Subject: [PATCH] fix: Include back-ticks around the BQ asset names in the tools examples These changes add extra hint for the LLM in the `execute_tool` SQL examples to always use back-ticks around BQ project, dataset and table names in the generated SQL, to save the SQL parsing error when the name has special characters. PiperOrigin-RevId: 852418943 --- src/google/adk/tools/bigquery/query_tool.py | 80 +++++++++---------- .../bigquery/test_bigquery_query_tool.py | 80 +++++++++---------- 2 files changed, 80 insertions(+), 80 deletions(-) diff --git a/src/google/adk/tools/bigquery/query_tool.py b/src/google/adk/tools/bigquery/query_tool.py index 5bcd734e..1f03835b 100644 --- a/src/google/adk/tools/bigquery/query_tool.py +++ b/src/google/adk/tools/bigquery/query_tool.py @@ -229,7 +229,7 @@ def execute_sql( >>> execute_sql("my_project", ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [ @@ -253,7 +253,7 @@ def execute_sql( >>> execute_sql( ... "my_project", ... "SELECT island FROM " - ... "bigquery-public-data.ml_datasets.penguins", + ... "`bigquery-public-data`.`ml_datasets`.`penguins`", ... dry_run=True ... ) { @@ -269,7 +269,7 @@ def execute_sql( "tableId": "anon..." }, "priority": "INTERACTIVE", - "query": "SELECT island FROM bigquery-public-data.ml_datasets.penguins", + "query": "SELECT island FROM `bigquery-public-data`.`ml_datasets`.`penguins`", "useLegacySql": False, "writeDisposition": "WRITE_TRUNCATE" } @@ -319,7 +319,7 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: >>> execute_sql("my_project", ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [ @@ -343,7 +343,7 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: >>> execute_sql( ... "my_project", ... "SELECT island FROM " - ... "bigquery-public-data.ml_datasets.penguins", + ... "`bigquery-public-data`.`ml_datasets`.`penguins`", ... dry_run=True ... ) { @@ -359,7 +359,7 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: "tableId": "anon..." }, "priority": "INTERACTIVE", - "query": "SELECT island FROM bigquery-public-data.ml_datasets.penguins", + "query": "SELECT island FROM `bigquery-public-data`.`ml_datasets`.`penguins`", "useLegacySql": False, "writeDisposition": "WRITE_TRUNCATE" } @@ -374,7 +374,7 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Create a table with schema prescribed: >>> execute_sql("my_project", - ... "CREATE TABLE my_project.my_dataset.my_table " + ... "CREATE TABLE `my_project`.`my_dataset`.`my_table` " ... "(island STRING, population INT64)") { "status": "SUCCESS", @@ -384,7 +384,7 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Insert data into an existing table: >>> execute_sql("my_project", - ... "INSERT INTO my_project.my_dataset.my_table (island, population) " + ... "INSERT INTO `my_project`.`my_dataset`.`my_table` (island, population) " ... "VALUES ('Dream', 124), ('Biscoe', 168)") { "status": "SUCCESS", @@ -394,9 +394,9 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Create a table from the result of a query: >>> execute_sql("my_project", - ... "CREATE TABLE my_project.my_dataset.my_table AS " + ... "CREATE TABLE `my_project`.`my_dataset`.`my_table` AS " ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [] @@ -405,7 +405,7 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Delete a table: >>> execute_sql("my_project", - ... "DROP TABLE my_project.my_dataset.my_table") + ... "DROP TABLE `my_project`.`my_dataset`.`my_table`") { "status": "SUCCESS", "rows": [] @@ -414,8 +414,8 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Copy a table to another table: >>> execute_sql("my_project", - ... "CREATE TABLE my_project.my_dataset.my_table_clone " - ... "CLONE my_project.my_dataset.my_table") + ... "CREATE TABLE `my_project`.`my_dataset`.`my_table_clone` " + ... "CLONE `my_project`.`my_dataset`.`my_table`") { "status": "SUCCESS", "rows": [] @@ -425,8 +425,8 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: table: >>> execute_sql("my_project", - ... "CREATE SNAPSHOT TABLE my_project.my_dataset.my_table_snapshot " - ... "CLONE my_project.my_dataset.my_table") + ... "CREATE SNAPSHOT TABLE `my_project`.`my_dataset`.`my_table_snapshot` " + ... "CLONE `my_project`.`my_dataset`.`my_table`") { "status": "SUCCESS", "rows": [] @@ -435,9 +435,9 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Create a BigQuery ML linear regression model: >>> execute_sql("my_project", - ... "CREATE MODEL `my_dataset.my_model` " + ... "CREATE MODEL `my_dataset`.`my_model` " ... "OPTIONS (model_type='linear_reg', input_label_cols=['body_mass_g']) AS " - ... "SELECT * FROM `bigquery-public-data.ml_datasets.penguins` " + ... "SELECT * FROM `bigquery-public-data`.`ml_datasets`.`penguins` " ... "WHERE body_mass_g IS NOT NULL") { "status": "SUCCESS", @@ -447,7 +447,7 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Evaluate BigQuery ML model: >>> execute_sql("my_project", - ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`)") + ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset`.`my_model`)") { "status": "SUCCESS", "rows": [{'mean_absolute_error': 227.01223667447218, @@ -461,8 +461,8 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Evaluate BigQuery ML model on custom data: >>> execute_sql("my_project", - ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, " - ... "(SELECT * FROM `my_dataset.my_table`))") + ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset`.`my_model`, " + ... "(SELECT * FROM `my_dataset`.`my_table`))") { "status": "SUCCESS", "rows": [{'mean_absolute_error': 227.01223667447218, @@ -476,8 +476,8 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Predict using BigQuery ML model: >>> execute_sql("my_project", - ... "SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, " - ... "(SELECT * FROM `my_dataset.my_table`))") + ... "SELECT * FROM ML.PREDICT(MODEL `my_dataset`.`my_model`, " + ... "(SELECT * FROM `my_dataset`.`my_table`))") { "status": "SUCCESS", "rows": [ @@ -494,7 +494,7 @@ def _execute_sql_write_mode(*args, **kwargs) -> dict: Delete a BigQuery ML model: - >>> execute_sql("my_project", "DROP MODEL `my_dataset.my_model`") + >>> execute_sql("my_project", "DROP MODEL `my_dataset`.`my_model`") { "status": "SUCCESS", "rows": [] @@ -539,7 +539,7 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: >>> execute_sql("my_project", ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [ @@ -563,7 +563,7 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: >>> execute_sql( ... "my_project", ... "SELECT island FROM " - ... "bigquery-public-data.ml_datasets.penguins", + ... "`bigquery-public-data`.`ml_datasets`.`penguins`", ... dry_run=True ... ) { @@ -579,7 +579,7 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: "tableId": "anon..." }, "priority": "INTERACTIVE", - "query": "SELECT island FROM bigquery-public-data.ml_datasets.penguins", + "query": "SELECT island FROM `bigquery-public-data`.`ml_datasets`.`penguins`", "useLegacySql": False, "writeDisposition": "WRITE_TRUNCATE" } @@ -594,7 +594,7 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Create a temporary table with schema prescribed: >>> execute_sql("my_project", - ... "CREATE TEMP TABLE my_table (island STRING, population INT64)") + ... "CREATE TEMP TABLE `my_table` (island STRING, population INT64)") { "status": "SUCCESS", "rows": [] @@ -603,7 +603,7 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Insert data into an existing temporary table: >>> execute_sql("my_project", - ... "INSERT INTO my_table (island, population) " + ... "INSERT INTO `my_table` (island, population) " ... "VALUES ('Dream', 124), ('Biscoe', 168)") { "status": "SUCCESS", @@ -613,9 +613,9 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Create a temporary table from the result of a query: >>> execute_sql("my_project", - ... "CREATE TEMP TABLE my_table AS " + ... "CREATE TEMP TABLE `my_table` AS " ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [] @@ -623,7 +623,7 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Delete a temporary table: - >>> execute_sql("my_project", "DROP TABLE my_table") + >>> execute_sql("my_project", "DROP TABLE `my_table`") { "status": "SUCCESS", "rows": [] @@ -632,7 +632,7 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Copy a temporary table to another temporary table: >>> execute_sql("my_project", - ... "CREATE TEMP TABLE my_table_clone CLONE my_table") + ... "CREATE TEMP TABLE `my_table_clone` CLONE `my_table`") { "status": "SUCCESS", "rows": [] @@ -641,9 +641,9 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Create a temporary BigQuery ML linear regression model: >>> execute_sql("my_project", - ... "CREATE TEMP MODEL my_model " + ... "CREATE TEMP MODEL `my_model` " ... "OPTIONS (model_type='linear_reg', input_label_cols=['body_mass_g']) AS" - ... "SELECT * FROM `bigquery-public-data.ml_datasets.penguins` " + ... "SELECT * FROM `bigquery-public-data`.`ml_datasets`.`penguins` " ... "WHERE body_mass_g IS NOT NULL") { "status": "SUCCESS", @@ -652,7 +652,7 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Evaluate BigQuery ML model: - >>> execute_sql("my_project", "SELECT * FROM ML.EVALUATE(MODEL my_model)") + >>> execute_sql("my_project", "SELECT * FROM ML.EVALUATE(MODEL `my_model`)") { "status": "SUCCESS", "rows": [{'mean_absolute_error': 227.01223667447218, @@ -666,8 +666,8 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Evaluate BigQuery ML model on custom data: >>> execute_sql("my_project", - ... "SELECT * FROM ML.EVALUATE(MODEL my_model, " - ... "(SELECT * FROM `my_dataset.my_table`))") + ... "SELECT * FROM ML.EVALUATE(MODEL `my_model`, " + ... "(SELECT * FROM `my_dataset`.`my_table`))") { "status": "SUCCESS", "rows": [{'mean_absolute_error': 227.01223667447218, @@ -681,8 +681,8 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Predict using BigQuery ML model: >>> execute_sql("my_project", - ... "SELECT * FROM ML.PREDICT(MODEL my_model, " - ... "(SELECT * FROM `my_dataset.my_table`))") + ... "SELECT * FROM ML.PREDICT(MODEL `my_model`, " + ... "(SELECT * FROM `my_dataset`.`my_table`))") { "status": "SUCCESS", "rows": [ @@ -699,7 +699,7 @@ def _execute_sql_protected_write_mode(*args, **kwargs) -> dict: Delete a BigQuery ML model: - >>> execute_sql("my_project", "DROP MODEL my_model") + >>> execute_sql("my_project", "DROP MODEL `my_model`") { "status": "SUCCESS", "rows": [] diff --git a/tests/unittests/tools/bigquery/test_bigquery_query_tool.py b/tests/unittests/tools/bigquery/test_bigquery_query_tool.py index 1791100e..a0873046 100644 --- a/tests/unittests/tools/bigquery/test_bigquery_query_tool.py +++ b/tests/unittests/tools/bigquery/test_bigquery_query_tool.py @@ -114,7 +114,7 @@ async def test_execute_sql_declaration_read_only(tool_settings): >>> execute_sql("my_project", ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [ @@ -138,7 +138,7 @@ async def test_execute_sql_declaration_read_only(tool_settings): >>> execute_sql( ... "my_project", ... "SELECT island FROM " - ... "bigquery-public-data.ml_datasets.penguins", + ... "`bigquery-public-data`.`ml_datasets`.`penguins`", ... dry_run=True ... ) { @@ -154,7 +154,7 @@ async def test_execute_sql_declaration_read_only(tool_settings): "tableId": "anon..." }, "priority": "INTERACTIVE", - "query": "SELECT island FROM bigquery-public-data.ml_datasets.penguins", + "query": "SELECT island FROM `bigquery-public-data`.`ml_datasets`.`penguins`", "useLegacySql": False, "writeDisposition": "WRITE_TRUNCATE" } @@ -213,7 +213,7 @@ async def test_execute_sql_declaration_write(tool_settings): >>> execute_sql("my_project", ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [ @@ -237,7 +237,7 @@ async def test_execute_sql_declaration_write(tool_settings): >>> execute_sql( ... "my_project", ... "SELECT island FROM " - ... "bigquery-public-data.ml_datasets.penguins", + ... "`bigquery-public-data`.`ml_datasets`.`penguins`", ... dry_run=True ... ) { @@ -253,7 +253,7 @@ async def test_execute_sql_declaration_write(tool_settings): "tableId": "anon..." }, "priority": "INTERACTIVE", - "query": "SELECT island FROM bigquery-public-data.ml_datasets.penguins", + "query": "SELECT island FROM `bigquery-public-data`.`ml_datasets`.`penguins`", "useLegacySql": False, "writeDisposition": "WRITE_TRUNCATE" } @@ -268,7 +268,7 @@ async def test_execute_sql_declaration_write(tool_settings): Create a table with schema prescribed: >>> execute_sql("my_project", - ... "CREATE TABLE my_project.my_dataset.my_table " + ... "CREATE TABLE `my_project`.`my_dataset`.`my_table` " ... "(island STRING, population INT64)") { "status": "SUCCESS", @@ -278,7 +278,7 @@ async def test_execute_sql_declaration_write(tool_settings): Insert data into an existing table: >>> execute_sql("my_project", - ... "INSERT INTO my_project.my_dataset.my_table (island, population) " + ... "INSERT INTO `my_project`.`my_dataset`.`my_table` (island, population) " ... "VALUES ('Dream', 124), ('Biscoe', 168)") { "status": "SUCCESS", @@ -288,9 +288,9 @@ async def test_execute_sql_declaration_write(tool_settings): Create a table from the result of a query: >>> execute_sql("my_project", - ... "CREATE TABLE my_project.my_dataset.my_table AS " + ... "CREATE TABLE `my_project`.`my_dataset`.`my_table` AS " ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [] @@ -299,7 +299,7 @@ async def test_execute_sql_declaration_write(tool_settings): Delete a table: >>> execute_sql("my_project", - ... "DROP TABLE my_project.my_dataset.my_table") + ... "DROP TABLE `my_project`.`my_dataset`.`my_table`") { "status": "SUCCESS", "rows": [] @@ -308,8 +308,8 @@ async def test_execute_sql_declaration_write(tool_settings): Copy a table to another table: >>> execute_sql("my_project", - ... "CREATE TABLE my_project.my_dataset.my_table_clone " - ... "CLONE my_project.my_dataset.my_table") + ... "CREATE TABLE `my_project`.`my_dataset`.`my_table_clone` " + ... "CLONE `my_project`.`my_dataset`.`my_table`") { "status": "SUCCESS", "rows": [] @@ -319,8 +319,8 @@ async def test_execute_sql_declaration_write(tool_settings): table: >>> execute_sql("my_project", - ... "CREATE SNAPSHOT TABLE my_project.my_dataset.my_table_snapshot " - ... "CLONE my_project.my_dataset.my_table") + ... "CREATE SNAPSHOT TABLE `my_project`.`my_dataset`.`my_table_snapshot` " + ... "CLONE `my_project`.`my_dataset`.`my_table`") { "status": "SUCCESS", "rows": [] @@ -329,9 +329,9 @@ async def test_execute_sql_declaration_write(tool_settings): Create a BigQuery ML linear regression model: >>> execute_sql("my_project", - ... "CREATE MODEL `my_dataset.my_model` " + ... "CREATE MODEL `my_dataset`.`my_model` " ... "OPTIONS (model_type='linear_reg', input_label_cols=['body_mass_g']) AS " - ... "SELECT * FROM `bigquery-public-data.ml_datasets.penguins` " + ... "SELECT * FROM `bigquery-public-data`.`ml_datasets`.`penguins` " ... "WHERE body_mass_g IS NOT NULL") { "status": "SUCCESS", @@ -341,7 +341,7 @@ async def test_execute_sql_declaration_write(tool_settings): Evaluate BigQuery ML model: >>> execute_sql("my_project", - ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`)") + ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset`.`my_model`)") { "status": "SUCCESS", "rows": [{'mean_absolute_error': 227.01223667447218, @@ -355,8 +355,8 @@ async def test_execute_sql_declaration_write(tool_settings): Evaluate BigQuery ML model on custom data: >>> execute_sql("my_project", - ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, " - ... "(SELECT * FROM `my_dataset.my_table`))") + ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset`.`my_model`, " + ... "(SELECT * FROM `my_dataset`.`my_table`))") { "status": "SUCCESS", "rows": [{'mean_absolute_error': 227.01223667447218, @@ -370,8 +370,8 @@ async def test_execute_sql_declaration_write(tool_settings): Predict using BigQuery ML model: >>> execute_sql("my_project", - ... "SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, " - ... "(SELECT * FROM `my_dataset.my_table`))") + ... "SELECT * FROM ML.PREDICT(MODEL `my_dataset`.`my_model`, " + ... "(SELECT * FROM `my_dataset`.`my_table`))") { "status": "SUCCESS", "rows": [ @@ -388,7 +388,7 @@ async def test_execute_sql_declaration_write(tool_settings): Delete a BigQuery ML model: - >>> execute_sql("my_project", "DROP MODEL `my_dataset.my_model`") + >>> execute_sql("my_project", "DROP MODEL `my_dataset`.`my_model`") { "status": "SUCCESS", "rows": [] @@ -450,7 +450,7 @@ async def test_execute_sql_declaration_protected_write(tool_settings): >>> execute_sql("my_project", ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [ @@ -474,7 +474,7 @@ async def test_execute_sql_declaration_protected_write(tool_settings): >>> execute_sql( ... "my_project", ... "SELECT island FROM " - ... "bigquery-public-data.ml_datasets.penguins", + ... "`bigquery-public-data`.`ml_datasets`.`penguins`", ... dry_run=True ... ) { @@ -490,7 +490,7 @@ async def test_execute_sql_declaration_protected_write(tool_settings): "tableId": "anon..." }, "priority": "INTERACTIVE", - "query": "SELECT island FROM bigquery-public-data.ml_datasets.penguins", + "query": "SELECT island FROM `bigquery-public-data`.`ml_datasets`.`penguins`", "useLegacySql": False, "writeDisposition": "WRITE_TRUNCATE" } @@ -505,7 +505,7 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Create a temporary table with schema prescribed: >>> execute_sql("my_project", - ... "CREATE TEMP TABLE my_table (island STRING, population INT64)") + ... "CREATE TEMP TABLE `my_table` (island STRING, population INT64)") { "status": "SUCCESS", "rows": [] @@ -514,7 +514,7 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Insert data into an existing temporary table: >>> execute_sql("my_project", - ... "INSERT INTO my_table (island, population) " + ... "INSERT INTO `my_table` (island, population) " ... "VALUES ('Dream', 124), ('Biscoe', 168)") { "status": "SUCCESS", @@ -524,9 +524,9 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Create a temporary table from the result of a query: >>> execute_sql("my_project", - ... "CREATE TEMP TABLE my_table AS " + ... "CREATE TEMP TABLE `my_table` AS " ... "SELECT island, COUNT(*) AS population " - ... "FROM bigquery-public-data.ml_datasets.penguins GROUP BY island") + ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island") { "status": "SUCCESS", "rows": [] @@ -534,7 +534,7 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Delete a temporary table: - >>> execute_sql("my_project", "DROP TABLE my_table") + >>> execute_sql("my_project", "DROP TABLE `my_table`") { "status": "SUCCESS", "rows": [] @@ -543,7 +543,7 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Copy a temporary table to another temporary table: >>> execute_sql("my_project", - ... "CREATE TEMP TABLE my_table_clone CLONE my_table") + ... "CREATE TEMP TABLE `my_table_clone` CLONE `my_table`") { "status": "SUCCESS", "rows": [] @@ -552,9 +552,9 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Create a temporary BigQuery ML linear regression model: >>> execute_sql("my_project", - ... "CREATE TEMP MODEL my_model " + ... "CREATE TEMP MODEL `my_model` " ... "OPTIONS (model_type='linear_reg', input_label_cols=['body_mass_g']) AS" - ... "SELECT * FROM `bigquery-public-data.ml_datasets.penguins` " + ... "SELECT * FROM `bigquery-public-data`.`ml_datasets`.`penguins` " ... "WHERE body_mass_g IS NOT NULL") { "status": "SUCCESS", @@ -563,7 +563,7 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Evaluate BigQuery ML model: - >>> execute_sql("my_project", "SELECT * FROM ML.EVALUATE(MODEL my_model)") + >>> execute_sql("my_project", "SELECT * FROM ML.EVALUATE(MODEL `my_model`)") { "status": "SUCCESS", "rows": [{'mean_absolute_error': 227.01223667447218, @@ -577,8 +577,8 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Evaluate BigQuery ML model on custom data: >>> execute_sql("my_project", - ... "SELECT * FROM ML.EVALUATE(MODEL my_model, " - ... "(SELECT * FROM `my_dataset.my_table`))") + ... "SELECT * FROM ML.EVALUATE(MODEL `my_model`, " + ... "(SELECT * FROM `my_dataset`.`my_table`))") { "status": "SUCCESS", "rows": [{'mean_absolute_error': 227.01223667447218, @@ -592,8 +592,8 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Predict using BigQuery ML model: >>> execute_sql("my_project", - ... "SELECT * FROM ML.PREDICT(MODEL my_model, " - ... "(SELECT * FROM `my_dataset.my_table`))") + ... "SELECT * FROM ML.PREDICT(MODEL `my_model`, " + ... "(SELECT * FROM `my_dataset`.`my_table`))") { "status": "SUCCESS", "rows": [ @@ -610,7 +610,7 @@ async def test_execute_sql_declaration_protected_write(tool_settings): Delete a BigQuery ML model: - >>> execute_sql("my_project", "DROP MODEL my_model") + >>> execute_sql("my_project", "DROP MODEL `my_model`") { "status": "SUCCESS", "rows": []