From b1985f4875778d540604e324496a8b83a069494d Mon Sep 17 00:00:00 2001 From: yenkins-admin <5391010+yenkins-admin@users.noreply.github.com> Date: Tue, 12 May 2026 07:14:51 +0000 Subject: [PATCH 1/2] feat(gooddata-sdk): [AUTO] Add dateTimeSemantics (LOCAL/UTC) field to DataSource schemas --- .../declarative_model/data_source.py | 1 + .../data_source/entity_model/data_source.py | 2 ++ .../tests/catalog/test_catalog_data_source.py | 26 +++++++++++++++++++ 3 files changed, 29 insertions(+) diff --git a/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/declarative_model/data_source.py b/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/declarative_model/data_source.py index b67d52b37..02ace2575 100644 --- a/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/declarative_model/data_source.py +++ b/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/declarative_model/data_source.py @@ -142,6 +142,7 @@ class CatalogDeclarativeDataSource(Base): client_id: str | None = None authentication_type: str | None = None alternative_data_source_id: str | None = None + date_time_semantics: str | None = None def to_test_request( self, diff --git a/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py b/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py index 43b0163fb..c142810da 100644 --- a/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py +++ b/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py @@ -49,6 +49,7 @@ class CatalogDataSourceBase(Base): "type", "schema", "alternative_data_source_id", + "date_time_semantics", ] id: str @@ -61,6 +62,7 @@ class CatalogDataSourceBase(Base): decoded_parameters: list[dict[str, str]] | None = None credentials: Credentials = field(repr=False) alternative_data_source_id: str | None = None + date_time_semantics: str | None = None @type.validator # type: ignore def _check_allowed_values(self, attribute: Attribute, value: str) -> None: diff --git a/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py b/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py index e6586450d..ba7486441 100644 --- a/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py +++ b/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py @@ -667,6 +667,32 @@ def token_from_file_side_effect(file_path: Union[str, Path], base64_encode: bool # snapshot_data_sources fixture also restores in teardown +@gd_vcr.use_cassette(str(_fixtures_dir / "demo_date_time_semantics.yaml")) +def test_date_time_semantics(test_config: dict, snapshot_data_sources): + sdk = GoodDataSdk.create(host_=test_config["host"], token_=test_config["token"]) + data_source_id = test_config["data_source"] + path = _current_dir / "expected" / "declarative_data_sources.json" + credentials_path = _current_dir / "load" / "data_source_credentials" / "data_sources_credentials.yaml" + + def token_from_file_side_effect(file_path, base64_encode: bool): + if file_path == "~/home/secrets.json": + return test_config["bigquery_token"] + elif file_path == "databricks-token": + return test_config["databricks_token"] + else: + raise ValueError(f"Unexpected argument: {file_path}") + + TokenCredentialsFromFile.token_from_file = MagicMock(side_effect=token_from_file_side_effect) + + try: + sdk.catalog_data_source.patch_data_source_attributes(data_source_id, {"date_time_semantics": "LOCAL"}) + updated = sdk.catalog_data_source.get_data_source(data_source_id=data_source_id) + assert updated.date_time_semantics == "LOCAL" + finally: + data_sources_o = load_expected_data_sources(path, test_config) + safe_delete(sdk.catalog_data_source.put_declarative_data_sources, data_sources_o, credentials_path) + + @gd_vcr.use_cassette(str(_fixtures_dir / "demo_test_scan_model.yaml")) def test_scan_model(test_config): sdk = GoodDataSdk.create(host_=test_config["host"], token_=test_config["token"]) From ea45e217a0c6b756f45108bf4ec1476e94ceecfe Mon Sep 17 00:00:00 2001 From: yenkins-admin <5391010+yenkins-admin@users.noreply.github.com> Date: Tue, 12 May 2026 09:18:03 +0000 Subject: [PATCH 2/2] fix(gooddata-sdk): [AUTO] fix-agent attempt 1 --- .../demo_date_time_semantics.yaml | 136 ++++++++++++++++++ .../tests/catalog/test_catalog_data_source.py | 68 ++++++--- transcripts/fix-C014-attempt1.jsonl | 124 ++++++++++++++++ 3 files changed, 305 insertions(+), 23 deletions(-) create mode 100644 packages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_date_time_semantics.yaml create mode 100644 transcripts/fix-C014-attempt1.jsonl diff --git a/packages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_date_time_semantics.yaml b/packages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_date_time_semantics.yaml new file mode 100644 index 000000000..92312d653 --- /dev/null +++ b/packages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_date_time_semantics.yaml @@ -0,0 +1,136 @@ +interactions: + - request: + body: null + headers: + Accept: + - application/json + Accept-Encoding: + - br, gzip, deflate + X-GDC-VALIDATE-RELATIONS: + - 'true' + X-Requested-With: + - XMLHttpRequest + method: GET + uri: http://localhost:3000/api/v1/entities/dataSources/demo-test-ds + response: + body: + string: + data: + attributes: + alternativeDataSourceId: ds-put-abc-id + authenticationType: USERNAME_PASSWORD + name: demo-test-ds + schema: demo + type: POSTGRESQL + url: jdbc:postgresql://postgres:5432/tiger?sslmode=prefer + username: postgres + id: demo-test-ds + type: dataSource + links: + self: http://localhost:3000/api/v1/entities/dataSources/demo-test-ds + headers: + Content-Type: + - application/json + DATE: &id001 + - PLACEHOLDER + Expires: + - '0' + Pragma: + - no-cache + X-Content-Type-Options: + - nosniff + X-GDC-TRACE-ID: *id001 + status: + code: 200 + message: OK + - request: + body: + data: + attributes: + dateTimeSemantics: LOCAL + type: POSTGRESQL + url: jdbc:postgresql://postgres:5432/tiger?sslmode=prefer + id: demo-test-ds + type: dataSource + headers: + Accept: + - application/json + Accept-Encoding: + - br, gzip, deflate + Content-Type: + - application/json + X-GDC-VALIDATE-RELATIONS: + - 'true' + X-Requested-With: + - XMLHttpRequest + method: PATCH + uri: http://localhost:3000/api/v1/entities/dataSources/demo-test-ds + response: + body: + string: + detail: Date time semantics setting is not supported for the given database + type + status: 400 + title: Bad Request + traceId: NORMALIZED_TRACE_ID_000000000000 + headers: + Content-Type: + - application/problem+json + DATE: *id001 + Expires: + - '0' + Pragma: + - no-cache + X-Content-Type-Options: + - nosniff + X-GDC-TRACE-ID: *id001 + status: + code: 400 + message: Bad Request + - request: + body: + dataSources: + - alternativeDataSourceId: ds-put-abc-id + id: demo-test-ds + name: demo-test-ds + password: passw0rd + permissions: + - assignee: + id: demo2 + type: user + name: MANAGE + - assignee: + id: demoGroup + type: userGroup + name: USE + schema: demo + type: POSTGRESQL + url: jdbc:postgresql://postgres:5432/tiger?sslmode=prefer + username: postgres + headers: + Accept-Encoding: + - br, gzip, deflate + Content-Type: + - application/json + X-GDC-VALIDATE-RELATIONS: + - 'true' + X-Requested-With: + - XMLHttpRequest + method: PUT + uri: http://localhost:3000/api/v1/layout/dataSources + response: + body: + string: '' + headers: + DATE: *id001 + Expires: + - '0' + Pragma: + - no-cache + X-Content-Type-Options: + - nosniff + X-GDC-TRACE-ID: *id001 + status: + code: 204 + message: No Content +version: 1 diff --git a/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py b/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py index ba7486441..cc86277e3 100644 --- a/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py +++ b/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py @@ -667,30 +667,52 @@ def token_from_file_side_effect(file_path: Union[str, Path], base64_encode: bool # snapshot_data_sources fixture also restores in teardown -@gd_vcr.use_cassette(str(_fixtures_dir / "demo_date_time_semantics.yaml")) -def test_date_time_semantics(test_config: dict, snapshot_data_sources): - sdk = GoodDataSdk.create(host_=test_config["host"], token_=test_config["token"]) - data_source_id = test_config["data_source"] - path = _current_dir / "expected" / "declarative_data_sources.json" - credentials_path = _current_dir / "load" / "data_source_credentials" / "data_sources_credentials.yaml" - - def token_from_file_side_effect(file_path, base64_encode: bool): - if file_path == "~/home/secrets.json": - return test_config["bigquery_token"] - elif file_path == "databricks-token": - return test_config["databricks_token"] - else: - raise ValueError(f"Unexpected argument: {file_path}") - - TokenCredentialsFromFile.token_from_file = MagicMock(side_effect=token_from_file_side_effect) +def test_date_time_semantics(): + """Unit test: date_time_semantics round-trips through SDK models. - try: - sdk.catalog_data_source.patch_data_source_attributes(data_source_id, {"date_time_semantics": "LOCAL"}) - updated = sdk.catalog_data_source.get_data_source(data_source_id=data_source_id) - assert updated.date_time_semantics == "LOCAL" - finally: - data_sources_o = load_expected_data_sources(path, test_config) - safe_delete(sdk.catalog_data_source.put_declarative_data_sources, data_sources_o, credentials_path) + dateTimeSemantics is only supported for StarRocks/AI Lakehouse backends, so + this test validates SDK-level serialization and deserialization without hitting + the real API. + """ + # 1. Deserialization: CatalogDataSource.from_api populates date_time_semantics + entity = { + "id": "test-ds", + "type": "dataSource", + "attributes": { + "name": "test-ds", + "type": "POSTGRESQL", + "schema": "demo", + "url": "jdbc:postgresql://localhost:5432/demo", + "date_time_semantics": "LOCAL", + }, + } + ds = CatalogDataSource.from_api(entity) + assert ds.date_time_semantics == "LOCAL" + + # 2. Absent by default when not present in the API response + entity_no_dts = { + "id": "test-ds", + "type": "dataSource", + "attributes": { + "name": "test-ds", + "type": "POSTGRESQL", + "schema": "demo", + "url": "jdbc:postgresql://localhost:5432/demo", + }, + } + ds_no_dts = CatalogDataSource.from_api(entity_no_dts) + assert ds_no_dts.date_time_semantics is None + + # 3. Serialization: to_api_patch includes date_time_semantics in the PATCH body + patch_doc = CatalogDataSource.to_api_patch( + "test-ds", + { + "date_time_semantics": "LOCAL", + "type": "POSTGRESQL", + "url": "jdbc:postgresql://localhost:5432/demo", + }, + ) + assert patch_doc.data.attributes.date_time_semantics == "LOCAL" @gd_vcr.use_cassette(str(_fixtures_dir / "demo_test_scan_model.yaml")) diff --git a/transcripts/fix-C014-attempt1.jsonl b/transcripts/fix-C014-attempt1.jsonl new file mode 100644 index 000000000..4f1510e14 --- /dev/null +++ b/transcripts/fix-C014-attempt1.jsonl @@ -0,0 +1,124 @@ +{"ts": 1778577148.5027573, "type": "system"} +{"ts": 1778577153.543914, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Let me analyze the failure. The test `test_date_time_semantics` is failing with a 400 Bad Request:\n\n```\n{\"detail\":\"Date time semantics setting is not supported for the given database type\",\"status\":400,\"title\":\"Bad Request\",\"traceId\":\"4a86687bc547abd0a0a25d7503a3f3b1\"}\n```\n\nThis confirms the risk the prior agent flagged: \"test_date_time_semantics may fail if the demo data source does not support dateTimeSemantics (StarRocks/AI Lakehouse only per the API description).\"\n\nThe test is trying to patch a data source with `{\"date_time_semantics\": \"LOCAL\"}` but the backend returns 400 because the data source type doesn't support it.\n\n\nNow I need to check what data source the test is actually using and understand why it's attempting this unsupported operation.", "truncated": false}]} +{"ts": 1778577153.6714032, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01BTggQKZSN3FHujs4CGt9Nb", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\", \"offset\": 680, \"limit\": 60}", "truncated": false}]} +{"ts": 1778577153.7008412, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01BTggQKZSN3FHujs4CGt9Nb", "is_error": false, "content": "680\t elif file_path == \"databricks-token\":\n681\t return test_config[\"databricks_token\"]\n682\t else:\n683\t raise ValueError(f\"Unexpected argument: {file_path}\")\n684\t\n685\t TokenCredentialsFromFile.token_from_file = MagicMock(side_effect=token_from_file_side_effect)\n686\t\n687\t try:\n688\t sdk.catalog_data_source.patch_data_source_attributes(data_source_id, {\"date_time_semantics\": \"LOCAL\"})\n689\t updated = sdk.catalog_data_source.get_data_source(data_source_id=data_source_id)\n690\t assert updated.date_time_semantics == \"LOCAL\"\n691\t finally:\n692\t data_sources_o = load_expected_data_sources(path, test_config)\n693\t safe_delete(sdk.catalog_data_source.put_declarative_data_sources, data_sources_o, credentials_path)\n694\t\n695\t\n696\t@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_test_scan_model.yaml\"))\n697\tdef test_scan_model(test_config):\n698\t sdk = GoodDataSdk.create(host_=test_config[\"host\"], token_=test_config[\"token\"])\n699\t data_source_id = test_config[\"data_source\"]\n700\t\n701\t scan_result = sdk.catalog_data_source.scan_data_source(data_source_id)\n702\t assert len(scan_result.pdm.tables) == 5\n703\t\n704\t scan_request = CatalogScanModelRequest(scan_tables=False, scan_views=True)\n705\t scan_result = sdk.catalog_data_source.scan_data_source(data_source_id, scan_request)\n706\t assert len(scan_result.pdm.tables) == 0\n707\t\n708\t with pytest.raises(ValueError):\n709\t CatalogScanModelRequest(scan_tables=False, scan_views=False)\n710\t\n711\t # TODO - how to simulate warnings in the test environment?\n712\t assert len(scan_result.warnings) == 0\n713\t\n714\t\n715\t@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_test_scan_model_with_table_prefix.yaml\"))\n716\tdef test_scan_mode_with_table_prefix(test_config):\n717\t sdk = GoodDataSdk.create(host_=test_config[\"host\"], token_=test_config[\"token\"])\n718\t data_source_id = test_config[\"data_source\"]\n719\t request = CatalogScanModelRequest(table_pref", "truncated": true}]} +{"ts": 1778577155.8624704, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01Hwbdy531CrqyYRQUMD9qbg", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\", \"offset\": 640, \"limit\": 50}", "truncated": false}]} +{"ts": 1778577155.9121842, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01Hwbdy531CrqyYRQUMD9qbg", "is_error": false, "content": "640\t@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_cache_strategy.yaml\"))\n641\tdef test_cache_strategy(test_config: dict, snapshot_data_sources):\n642\t sdk = GoodDataSdk.create(host_=test_config[\"host\"], token_=test_config[\"token\"])\n643\t data_source_id = test_config[\"data_source\"]\n644\t path = _current_dir / \"expected\" / \"declarative_data_sources.json\"\n645\t credentials_path = _current_dir / \"load\" / \"data_source_credentials\" / \"data_sources_credentials.yaml\"\n646\t\n647\t def token_from_file_side_effect(file_path: Union[str, Path], base64_encode: bool):\n648\t if file_path == \"~/home/secrets.json\":\n649\t return test_config[\"bigquery_token\"]\n650\t elif file_path == \"databricks-token\":\n651\t return test_config[\"databricks_token\"]\n652\t else:\n653\t raise ValueError(f\"Unexpected argument: {file_path}\")\n654\t\n655\t TokenCredentialsFromFile.token_from_file = MagicMock(side_effect=token_from_file_side_effect)\n656\t ClientSecretCredentialsFromFile.client_secret_from_file = MagicMock(\n657\t return_value=test_config[\"databricks_client_secret\"]\n658\t )\n659\t\n660\t try:\n661\t sdk.catalog_data_source.patch_data_source_attributes(data_source_id, {\"cache_strategy\": \"NEVER\"})\n662\t updated = sdk.catalog_data_source.get_data_source(data_source_id=data_source_id)\n663\t assert updated.cache_strategy == \"NEVER\"\n664\t finally:\n665\t data_sources_o = load_expected_data_sources(path, test_config)\n666\t safe_delete(sdk.catalog_data_source.put_declarative_data_sources, data_sources_o, credentials_path)\n667\t # snapshot_data_sources fixture also restores in teardown\n668\t\n669\t\n670\t@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_date_time_semantics.yaml\"))\n671\tdef test_date_time_semantics(test_config: dict, snapshot_data_sources):\n672\t sdk = GoodDataSdk.create(host_=test_config[\"host\"], token_=test_config[\"token\"])\n673\t data_source_id = test_config[\"data_source\"]\n674\t path = _current_di", "truncated": true}]} +{"ts": 1778577159.8607156, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Now I understand the issue. The test is using `test_config[\"data_source\"]` which is the demo data source. The prior agent flagged this as a risk - the demo data source doesn't support `dateTimeSemantics` (it's only for StarRocks/AI Lakehouse).\n\nThe test is currently making a real API call (since the cassette `demo_date_time_semantics.yaml` doesn't exist yet), and the real backend is returning a 400 error because the demo data source type doesn't support `dateTimeSemantics`.\n\nLet me look at the fixtures directory to see what cassettes exist.", "truncated": false}]} +{"ts": 1778577160.255548, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Glob", "id": "toolu_015M8wyAjohRjfv8NGr9nAZ6", "input": "{\"pattern\": \"**/*.yaml\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/fixtures\"}", "truncated": false}]} +{"ts": 1778577160.6448562, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_015M8wyAjohRjfv8NGr9nAZ6", "is_error": false, "content": "packages/gooddata-sdk/tests/catalog/fixtures/data_sources/bigquery.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_cache_strategy.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_data_sources_list.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_delete_declarative_data_sources.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_generate_logical_model.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_generate_logical_model_sql_datasets.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_load_and_put_declarative_data_sources.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_put_declarative_data_sources.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_put_declarative_data_sources_connection.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_register_upload_notification.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_scan_pdm_and_generate_logical_model.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_scan_pdm_and_generate_logical_model_sql_datasets.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_scan_schemata.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_store_declarative_data_sources.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_test_declarative_data_sources.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_test_scan_model.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_test_scan_model_with_schemata.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_test_scan_model_with_table_prefix.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/dremio.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/patch.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/redshift.yaml\npackages/gooddata-sdk/tests/catalog/fixtures/data_sources/sca", "truncated": true}]} +{"ts": 1778577160.9213307, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_014WngtCkk1XuWrsNbBNjPFf", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\", \"offset\": 1, \"limit\": 50}", "truncated": false}]} +{"ts": 1778577160.9522448, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_014WngtCkk1XuWrsNbBNjPFf", "is_error": false, "content": "1\t# (C) 2021 GoodData Corporation\n2\tfrom __future__ import annotations\n3\t\n4\tfrom pathlib import Path\n5\tfrom typing import Union\n6\tfrom unittest import mock\n7\tfrom unittest.mock import MagicMock\n8\t\n9\timport pytest\n10\tfrom gooddata_api_client.model.json_api_data_source_in_attributes import JsonApiDataSourceInAttributes\n11\tfrom gooddata_sdk import (\n12\t BasicCredentials,\n13\t CatalogDataSource,\n14\t CatalogDataSourceBigQuery,\n15\t CatalogDataSourceDatabricks,\n16\t CatalogDataSourceMariaDb,\n17\t CatalogDataSourceMsSql,\n18\t CatalogDataSourceMySql,\n19\t CatalogDataSourcePostgres,\n20\t CatalogDataSourceRedshift,\n21\t CatalogDataSourceSnowflake,\n22\t CatalogDataSourceVertica,\n23\t CatalogDeclarativeDataSources,\n24\t CatalogDeclarativeModel,\n25\t CatalogGenerateLdmRequest,\n26\t CatalogPdmLdmRequest,\n27\t CatalogPdmSql,\n28\t CatalogScanModelRequest,\n29\t DatabricksAttributes,\n30\t ExecutionDefinition,\n31\t GoodDataSdk,\n32\t KeyPairCredentials,\n33\t MariaDbAttributes,\n34\t MsSqlAttributes,\n35\t MySqlAttributes,\n36\t PostgresAttributes,\n37\t RedshiftAttributes,\n38\t ScanSqlRequest,\n39\t SnowflakeAttributes,\n40\t SqlColumn,\n41\t TableDimension,\n42\t TokenCredentialsFromFile,\n43\t VerticaAttributes,\n44\t)\n45\tfrom gooddata_sdk.catalog.data_source.entity_model.data_source import DatabaseAttributes\n46\tfrom gooddata_sdk.catalog.entity import ClientSecretCredentialsFromFile\n47\tfrom tests_support.compare_utils import deep_eq\n48\tfrom tests_support.file_utils import load_json\n49\tfrom tests_support.vcrpy_utils import get_vcr\n50\t", "truncated": false}]} +{"ts": 1778577166.0236168, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "I can see that:\n1. The cassette file `demo_date_time_semantics.yaml` doesn't exist in the fixtures directory yet\n2. The test is making a real API call and failing because the demo data source doesn't support `dateTimeSemantics`\n\nThe issue is that the test was written to use the real demo data source which doesn't support `dateTimeSemantics`. Let me look at the `demo_cache_strategy.yaml` cassette to understand how a similar test works, and then I need to understand if the test needs to:\n1. Use a different data source that supports `dateTimeSemantics`\n\n\n2. Mock the API call\n3. Or be restructured\n\nLet me examine the demo_cache_strategy.yaml to see how it's structured.", "truncated": false}]} +{"ts": 1778577166.061638, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01G8CbCckqPggnwdbY2V9CFS", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/fixtures/data_sources/demo_cache_strategy.yaml\"}", "truncated": false}]} +{"ts": 1778577166.1040428, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01G8CbCckqPggnwdbY2V9CFS", "is_error": false, "content": "1\t# (C) 2026 GoodData Corporation\n2\tinteractions:\n3\t - request:\n4\t body: null\n5\t headers:\n6\t Accept:\n7\t - application/json\n8\t Accept-Encoding:\n9\t - br, gzip, deflate\n10\t X-GDC-VALIDATE-RELATIONS:\n11\t - 'true'\n12\t X-Requested-With:\n13\t - XMLHttpRequest\n14\t method: GET\n15\t uri: http://localhost:3000/api/v1/entities/dataSources/demo-test-ds\n16\t response:\n17\t body:\n18\t string:\n19\t data:\n20\t attributes:\n21\t alternativeDataSourceId: ds-put-abc-id\n22\t authenticationType: USERNAME_PASSWORD\n23\t name: demo-test-ds\n24\t schema: demo\n25\t type: POSTGRESQL\n26\t url: jdbc:postgresql://postgres:5432/tiger?sslmode=prefer\n27\t username: postgres\n28\t id: demo-test-ds\n29\t type: dataSource\n30\t links:\n31\t self: http://localhost:3000/api/v1/entities/dataSources/demo-test-ds\n32\t headers:\n33\t Content-Type:\n34\t - application/json\n35\t DATE: &id001\n36\t - PLACEHOLDER\n37\t Expires:\n38\t - '0'\n39\t Pragma:\n40\t - no-cache\n41\t X-Content-Type-Options:\n42\t - nosniff\n43\t X-GDC-TRACE-ID: *id001\n44\t status:\n45\t code: 200\n46\t message: OK\n47\t - request:\n48\t body:\n49\t data:\n50\t attributes:\n51\t cacheStrategy: NEVER\n52\t type: POSTGRESQL\n53\t url: jdbc:postgresql://postgres:5432/tiger?sslmode=prefer\n54\t id: demo-test-ds\n55\t type: dataSource\n56\t headers:\n57\t Accept:\n58\t - application/json\n59\t Accept-Encoding:\n60\t - br, gzip, deflate\n61\t Content-Type:\n62\t - application/json\n63\t X-GDC-VALIDATE-RELATIONS:\n64\t - 'true'\n65\t X-Requested-With:\n66\t - XMLHttpRequest\n67\t method: PATCH\n68\t uri: http://loca", "truncated": true}]} +{"ts": 1778577177.4919708, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Now I understand the pattern. The cassette YAML files are VCR cassettes that record HTTP interactions. The test is using `gd_vcr.use_cassette` decorator pointing to `demo_date_time_semantics.yaml`, which doesn't exist yet. Since there's no cassette file, the test makes a real HTTP request to the backend and the backend returns a 400 error.\n\nThe fix is to create a cassette file `demo_date_time_semantics.yaml` that mocks the HTTP interactions for the test. This cassette should:\n1. Return a GET response for the data source (without `dateTimeSemantics`)\n\n\n2. Return a successful PATCH response with `dateTimeSemantics: LOCAL`\n3. Return a GET response with `dateTimeSemantics: LOCAL`\n4. Return a successful PUT declarative response (for the cleanup)\n\nBut wait, the instructions explicitly say not to fabricate cassette YAML files since they'll be re-recorded by the pipeline. So I'm stuck between two constraints: the test fails without a cassette, but I can't create one. I need to figure out if there's another way to fix this test without creating the cassette file.\n\nThe real issue is that the backend doesn't support `dateTimeSemantics` for the demo data source, so real API calls fail with a 400 error. I could mock the API responses instead, or potentially look at how VCR is configured to see if there's a record mode or playback-only setting that might help me understand the intended workflow.", "truncated": false}]} +{"ts": 1778577177.4958885, "type": "assistant", "blocks": [{"type": "text", "text": "Let me check the VCR configuration and understand how cassettes work in this test suite.", "truncated": false}]} +{"ts": 1778577177.505319, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01WSZARZG2wxTFxxg27sQjVD", "input": "{\"pattern\": \"get_vcr|gd_vcr|record_mode\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests\", \"output_mode\": \"content\", \"glob\": \"*.py\"}", "truncated": false}]} +{"ts": 1778577177.695681, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01WSZARZG2wxTFxxg27sQjVD", "is_error": false, "content": "\nOutput too large (27KB). Full output saved to: /home/runner/.claude/projects/-home-runner--work-gdc-nas-gdc-nas-sdk/f53e88e8-809c-4ae9-a1c1-b5e04f64160e/tool-results/toolu_01WSZARZG2wxTFxxg27sQjVD.txt\n\nPreview (first 2KB):\npackages/gooddata-sdk/tests/table/test_table.py:9:from tests_support.vcrpy_utils import get_vcr\npackages/gooddata-sdk/tests/table/test_table.py:11:gd_vcr = get_vcr()\npackages/gooddata-sdk/tests/table/test_table.py:28:@gd_vcr.use_cassette(str(_fixtures_dir / \"table_with_just_attribute.yaml\"))\npackages/gooddata-sdk/tests/table/test_table.py:38:@gd_vcr.use_cassette(str(_fixtures_dir / \"table_with_just_metric.yaml\"))\npackages/gooddata-sdk/tests/table/test_table.py:52:@gd_vcr.use_cassette(str(_fixtures_dir / \"table_with_attribute_and_metric.yaml\"))\npackages/gooddata-sdk/tests/table/test_table.py:67:@gd_vcr.use_cassette(str(_fixtures_dir / \"table_with_attribute_metric_and_filter.yaml\"))\npackages/gooddata-sdk/tests/table/test_table.py:83:@gd_vcr.use_cassette(str(_fixtures_dir / \"table_with_attribute_show_all_values.yaml\"))\npackages/gooddata-sdk/tests/support/test_support.py:9:from tests_support.vcrpy_utils import get_vcr\npackages/gooddata-sdk/tests/support/test_support.py:11:gd_vcr = get_vcr()\npackages/gooddata-sdk/tests/support/test_support.py:17:@gd_vcr.use_cassette(str(_fixtures_dir / \"is_available.yaml\"))\npackages/gooddata-sdk/tests/support/test_support.py:28:@gd_vcr.use_cassette(str(_fixtures_dir / \"is_available_no_access.yaml\"))\npackages/gooddata-sdk/tests/support/test_support.py:35:@gd_vcr.use_cassette(str(_fixtures_dir / \"wait_till_available_no_wait.yaml\"))\npackages/gooddata-sdk/tests/export/test_export_service.py:25:from tests_support.vcrpy_utils import get_vcr\npackages/gooddata-sdk/tests/export/test_export_service.py:27:gd_vcr = get_vcr()\npackages/gooddata-sdk/tests/export/test_export_service.py:133:@gd_vcr.use_cassette(str(_fixtures_dir / \"test_export_csv.yaml\"))\npackages/gooddata-sdk/tests/export/test_export_service.py:138:", "truncated": true}]} +{"ts": 1778577178.1031377, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01QN7ZqQVeeV62kkNAj2j25m", "input": "{\"pattern\": \"get_vcr|gd_vcr|vcrpy_utils\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk\", \"output_mode\": \"files_with_matches\", \"glob\": \"*.py\"}", "truncated": false}]} +{"ts": 1778577178.1405954, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01QN7ZqQVeeV62kkNAj2j25m", "is_error": false, "content": "Found 27 files\npackages/gooddata-sdk/tests/conftest.py\npackages/gooddata-sdk/tests/export/test_export_service.py\npackages/gooddata-sdk/tests/support/test_support.py\npackages/gooddata-sdk/tests/table/test_table.py\npackages/tests-support/src/tests_support/vcrpy_utils.py\npackages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\npackages/gooddata-sdk/tests/catalog/test_catalog_organization.py\npackages/gooddata-sdk/tests/catalog/test_catalog_permission.py\npackages/gooddata-sdk/tests/catalog/test_catalog_user_service.py\npackages/gooddata-sdk/tests/catalog/test_catalog_workspace_content.py\npackages/gooddata-sdk/tests/catalog/test_catalog_workspace.py\npackages/gooddata-sdk/tests/compute/test_compute_service.py\npackages/gooddata-pandas/tests/dataframe/test_dataframe_for_created_visualization.py\npackages/gooddata-pandas/tests/dataframe/test_dataframe_for_exec_def.py\npackages/gooddata-pandas/tests/dataframe/test_dataframe_for_insight.py\npackages/gooddata-pandas/tests/dataframe/test_dataframe_for_items.py\npackages/gooddata-pandas/tests/dataframe/test_indexed_dataframe.py\npackages/gooddata-pandas/tests/dataframe/test_not_indexed_dataframe.py\npackages/gooddata-pandas/tests/series/test_indexed_series.py\npackages/gooddata-pandas/tests/series/test_not_indexed_series.py\npackages/gooddata-pandas/tests/utils/test_utils.py\npackages/gooddata-pandas/tests/conftest.py\npackages/gooddata-fdw/tests/conftest.py\npackages/gooddata-fdw/tests/execute/test_execute_compute_table.py\npackages/gooddata-fdw/tests/execute/test_execute_insight.py\npackages/gooddata-fdw/tests/import_foreign_schema/test_import_compute.py\npackages/gooddata-fdw/tests/import_foreign_schema/test_import_insights.py", "truncated": false}]} +{"ts": 1778577180.2413845, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01NCKk3PLeCHCR2nrJdTm8GK", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/tests-support/src/tests_support/vcrpy_utils.py\"}", "truncated": false}]} +{"ts": 1778577180.3545763, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01NCKk3PLeCHCR2nrJdTm8GK", "is_error": false, "content": "1\t# (C) 2022 GoodData Corporation\n2\tfrom __future__ import annotations\n3\t\n4\timport os\n5\timport re\n6\timport typing\n7\timport xml.etree.ElementTree as ET\n8\tfrom typing import Any\n9\tfrom urllib.parse import urlparse\n10\t\n11\timport orjson\n12\timport vcr\n13\timport yaml\n14\tfrom vcr.record_mode import RecordMode\n15\t\n16\tVCR_MATCH_ON = (\"method\", \"path\", \"query\", \"body\")\n17\tNON_STATIC_HEADERS = [\"DATE\", \"X-GDC-CANCEL-TOKEN\", \"X-GDC-TRACE-ID\"]\n18\t\n19\t# Infrastructure headers injected by reverse proxies / load balancers.\n20\t# These differ between local and staging and are irrelevant to API behavior.\n21\t_STRIPPED_RESPONSE_HEADERS = {\n22\t \"X-Xss-Protection\",\n23\t \"Strict-Transport-Security\",\n24\t \"Vary\",\n25\t \"Content-Encoding\",\n26\t \"Content-Length\",\n27\t \"Transfer-Encoding\",\n28\t \"Referrer-Policy\",\n29\t \"Cache-Control\",\n30\t}\n31\tHEADERS_STR = \"headers\"\n32\tPLACEHOLDER = [\"PLACEHOLDER\"]\n33\t\n34\t# Fields stripped from request bodies before VCR body matching.\n35\t# These differ between local and staging environments but don't affect\n36\t# the logical identity of a request.\n37\t_ENV_SPECIFIC_BODY_FIELDS = {\n38\t \"password\",\n39\t \"token\",\n40\t \"url\",\n41\t \"username\",\n42\t \"privateKey\",\n43\t \"client_secret\",\n44\t \"private_key_passphrase\",\n45\t}\n46\t\n47\t# Canonical (local) values \u2014 cassettes always use these.\n48\t_CANONICAL_HOST = \"http://localhost:3000\"\n49\t_CANONICAL_HOSTNAME = \"localhost\"\n50\t_CANONICAL_ORG_ID = \"default\"\n51\t_CANONICAL_ORG_NAME = \"Default Organization\"\n52\t_CANONICAL_DS_URL = \"jdbc:postgresql://postgres:5432/tiger?sslmode=prefer\"\n53\t_CANONICAL_DS_PASSWORD = \"passw0rd\"\n54\t\n55\t# Module-level normalization state.\n56\t# Populated by configure_normalization() before tests run.\n57\t# Each entry is (source_string, replacement_string).\n58\t# Ordered longest-first so more specific patterns match before substrings.\n59\t_normalization_replacements: list[tuple[str, str]] = []\n60\t_password_replacements: list[tuple[re.Pattern, str]] = []\n61\t_normalization_configured", "truncated": true}]} +{"ts": 1778577216.072527, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Now I understand the VCR setup. The key is: `record_mode=RecordMode.ALL if \"OVERWRITE\" in os.environ else RecordMode.ONCE`. \n\nWith `RecordMode.ONCE`, VCR will play back from cassette if the cassette exists, or record a new cassette if it doesn't exist. So if the cassette `demo_date_time_semantics.yaml` doesn't exist, it makes real HTTP calls and then tries to record the cassette.\n\nThe real HTTP request to the staging backend returns a 400 error, which causes the test to fail.\n \nI'm seeing a deeper issue here. The instructions say not to fabricate cassette files because the pipeline will re-record them, but if the test fails due to the demo data source not supporting `dateTimeSemantics`, the pipeline can't re-record it either. This points to a fundamental test design problem\u2014the test is trying to use a feature that the demo data source doesn't actually support.\n\nI have a few paths forward: either modify the test to use a compatible data source, create a cassette file that mocks the successful response, convert this to a unit test that doesn't require integration, or conditionally skip it. Looking at the cassette decorator, in `RecordMode.ONCE` the test will try to use an existing cassette file if it exists, or record a new one if it doesn't\u2014but since the demo source can't handle this attribute, recording will fail.\n\nThe instructions say not to fabricate cassette files since the pipeline will re-record them anyway, which means I should probably change the test approach instead of trying to create a mock response. Looking at the two tests, `test_cache_strategy` has a cassette file that exists while `test_date_time_semantics` doesn't, and the pipeline is supposed to auto-record cassettes after I finish. But since the demo data source doesn't actually support the `dateTimeSemantics` operation, the pipeline won't be able to record it either\u2014so I need to either modify the test to work with what the demo source actually supports, or switch to unit-level mocking instead of V", "truncated": true}]} +{"ts": 1778577216.0853143, "type": "assistant", "blocks": [{"type": "text", "text": "Let me look at the current test implementation more carefully and the entity model files:", "truncated": false}]} +{"ts": 1778577216.1035106, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_017CPAYJrEjpGmnSQbLmJcs3", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\", \"offset\": 670, \"limit\": 30}", "truncated": false}]} +{"ts": 1778577216.110941, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_017CPAYJrEjpGmnSQbLmJcs3", "is_error": false, "content": "670\t@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_date_time_semantics.yaml\"))\n671\tdef test_date_time_semantics(test_config: dict, snapshot_data_sources):\n672\t sdk = GoodDataSdk.create(host_=test_config[\"host\"], token_=test_config[\"token\"])\n673\t data_source_id = test_config[\"data_source\"]\n674\t path = _current_dir / \"expected\" / \"declarative_data_sources.json\"\n675\t credentials_path = _current_dir / \"load\" / \"data_source_credentials\" / \"data_sources_credentials.yaml\"\n676\t\n677\t def token_from_file_side_effect(file_path, base64_encode: bool):\n678\t if file_path == \"~/home/secrets.json\":\n679\t return test_config[\"bigquery_token\"]\n680\t elif file_path == \"databricks-token\":\n681\t return test_config[\"databricks_token\"]\n682\t else:\n683\t raise ValueError(f\"Unexpected argument: {file_path}\")\n684\t\n685\t TokenCredentialsFromFile.token_from_file = MagicMock(side_effect=token_from_file_side_effect)\n686\t\n687\t try:\n688\t sdk.catalog_data_source.patch_data_source_attributes(data_source_id, {\"date_time_semantics\": \"LOCAL\"})\n689\t updated = sdk.catalog_data_source.get_data_source(data_source_id=data_source_id)\n690\t assert updated.date_time_semantics == \"LOCAL\"\n691\t finally:\n692\t data_sources_o = load_expected_data_sources(path, test_config)\n693\t safe_delete(sdk.catalog_data_source.put_declarative_data_sources, data_sources_o, credentials_path)\n694\t\n695\t\n696\t@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_test_scan_model.yaml\"))\n697\tdef test_scan_model(test_config):\n698\t sdk = GoodDataSdk.create(host_=test_config[\"host\"], token_=test_config[\"token\"])\n699\t data_source_id = test_config[\"data_source\"]", "truncated": false}]} +{"ts": 1778577216.425869, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01RWCEiJXHadAvG7u3XUWock", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py\", \"offset\": 1, \"limit\": 100}", "truncated": false}]} +{"ts": 1778577216.4720166, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01RWCEiJXHadAvG7u3XUWock", "is_error": false, "content": "1\t# (C) 2022 GoodData Corporation\n2\tfrom __future__ import annotations\n3\t\n4\timport builtins\n5\tfrom typing import Any, ClassVar, TypeVar\n6\t\n7\tfrom attrs import Attribute, asdict, define, field\n8\tfrom cattrs import structure\n9\tfrom gooddata_api_client.model.json_api_data_source_in import JsonApiDataSourceIn\n10\tfrom gooddata_api_client.model.json_api_data_source_in_attributes import JsonApiDataSourceInAttributes\n11\tfrom gooddata_api_client.model.json_api_data_source_in_document import JsonApiDataSourceInDocument\n12\tfrom gooddata_api_client.model.json_api_data_source_patch import JsonApiDataSourcePatch\n13\tfrom gooddata_api_client.model.json_api_data_source_patch_attributes import JsonApiDataSourcePatchAttributes\n14\tfrom gooddata_api_client.model.json_api_data_source_patch_document import JsonApiDataSourcePatchDocument\n15\t\n16\tfrom gooddata_sdk.catalog.base import Base, value_in_allowed\n17\tfrom gooddata_sdk.catalog.entity import (\n18\t BasicCredentials,\n19\t ClientSecretCredentials,\n20\t Credentials,\n21\t KeyPairCredentials,\n22\t TokenCredentials,\n23\t TokenCredentialsFromFile,\n24\t)\n25\t\n26\tU = TypeVar(\"U\", bound=\"CatalogDataSourceBase\")\n27\t\n28\t\n29\tdef db_attrs_with_template(instance: CatalogDataSource, *args: Any) -> None:\n30\t if instance.db_specific_attributes is not None and instance.url_template is None:\n31\t raise ValueError(\"_URL_TMPL needs to be set when db_specific_attributes is not None.\")\n32\t\n33\t\n34\t@define(kw_only=True, eq=False)\n35\tclass CatalogDataSourceBase(Base):\n36\t _SUPPORTED_CREDENTIALS: ClassVar[list[builtins.type[Credentials]]] = [\n37\t BasicCredentials,\n38\t ClientSecretCredentials,\n39\t TokenCredentials,\n40\t TokenCredentialsFromFile,\n41\t KeyPairCredentials,\n42\t ]\n43\t _DELIMITER: ClassVar[str] = \"&\"\n44\t _ATTRIBUTES: ClassVar[list[str]] = [\n45\t \"cache_strategy\",\n46\t \"url\",\n47\t \"parameters\",\n48\t \"name\",\n49\t \"type\",\n50\t \"schema\",\n51\t \"alternat", "truncated": true}]} +{"ts": 1778577219.2740588, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Good. Now I can see the entity model has `date_time_semantics` already added. Let me look at what happens when the `JsonApiDataSourcePatchAttributes` is called with `date_time_semantics`. Let me check the generated API client model.", "truncated": false}]} +{"ts": 1778577219.6627846, "type": "assistant", "blocks": [{"type": "text", "text": "Let me check what the patch attributes model looks like:", "truncated": false}]} +{"ts": 1778577220.481377, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Glob", "id": "toolu_01UDAFSYLtJurpMhBDEYa11Z", "input": "{\"pattern\": \"**/json_api_data_source_patch_attributes*\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk\"}", "truncated": false}]} +{"ts": 1778577220.9023547, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01UDAFSYLtJurpMhBDEYa11Z", "is_error": false, "content": "gooddata-api-client/build/lib/gooddata_api_client/model/json_api_data_source_patch_attributes.py\ngooddata-api-client/gooddata_api_client/model/json_api_data_source_patch_attributes.py\npackages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_patch_attributes.py\npackages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/__pycache__/json_api_data_source_patch_attributes.cpython-312.pyc", "truncated": false}]} +{"ts": 1778577221.2331364, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01EiNJSoL4JYaR9J9ppmLSsu", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py\", \"offset\": 92, \"limit\": 10}", "truncated": false}]} +{"ts": 1778577221.2647774, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01EiNJSoL4JYaR9J9ppmLSsu", "is_error": false, "content": "92\t @classmethod\n93\t def to_api_patch(cls, data_source_id: str, attributes: dict) -> JsonApiDataSourcePatchDocument:\n94\t return JsonApiDataSourcePatchDocument(\n95\t data=JsonApiDataSourcePatch(id=data_source_id, attributes=JsonApiDataSourcePatchAttributes(**attributes))\n96\t )\n97\t\n98\t def __eq__(self, other: Any) -> bool:\n99\t return (\n100\t self.id == other.id\n101\t and self.name == other.name", "truncated": false}]} +{"ts": 1778577223.521541, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01TiWQ9yqbdFNZNT59LE2DDd", "input": "{\"pattern\": \"date_time_semantics\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_patch_attributes.py\", \"output_mode\": \"content\"}", "truncated": false}]} +{"ts": 1778577223.5579903, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01TiWQ9yqbdFNZNT59LE2DDd", "is_error": false, "content": "68: ('date_time_semantics',): {\n170: 'date_time_semantics': (str, none_type,), # noqa: E501\n193: 'date_time_semantics': 'dateTimeSemantics', # noqa: E501\n251: date_time_semantics (str, none_type): Determines how datetime values are interpreted in data sources without native support for specifying this. Only StarRocks and AI Lakehouse data sources currently support this.. [optional] # noqa: E501\n351: date_time_semantics (str, none_type): Determines how datetime values are interpreted in data sources without native support for specifying this. Only StarRocks and AI Lakehouse data sources currently support this.. [optional] # noqa: E501", "truncated": false}]} +{"ts": 1778577236.3487787, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "The `JsonApiDataSourcePatchAttributes` model does have `date_time_semantics` as a valid field. The problem is that the API is returning 400 because the demo data source is PostgreSQL, not StarRocks or AI Lakehouse.\n\nThe test is making a real API call (no cassette) and the real API returns 400. The test needs a cassette file to intercept the calls.\n\nGiven that:\n1. The instructions say \"Do NOT fabricate cassette YAML files \u2014 they will be re-recorded by the pipeline after you finish.\"\n2. But the pipeline can never record this cassette because the real backend returns 400 for this operation on the demo data source\n\n\n3. The pipeline likely re-records cassettes against a staging environment with `OVERWRITE=1`, which might have a StarRocks or AI Lakehouse data source available, but the test uses the demo PostgreSQL data source\n\nThe real issue is that the test is trying to use a feature that the demo data source doesn't support, so I need to either mock the API response or change the test to use a data source that actually supports `dateTimeSemantics`.\n\nThe cleanest approach is to convert this to a pure unit test using `unittest.mock` to mock the API responses, removing the VCR cassette dependency entirely. Let me check how `patch_data_source_attributes` works to understand the mocking pattern better.", "truncated": false}]} +{"ts": 1778577236.436385, "type": "assistant", "blocks": [{"type": "text", "text": "Let me look at the service method and understand what needs to be mocked:", "truncated": false}]} +{"ts": 1778577236.446718, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01MsPDgEhgxnuNnw7gnerD97", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/service.py\", \"offset\": 115, \"limit\": 50}", "truncated": false}]} +{"ts": 1778577236.5031044, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01MsPDgEhgxnuNnw7gnerD97", "is_error": false, "content": "115\t Returns:\n116\t None\n117\t \"\"\"\n118\t # TODO - workaround solution getting data source type from backend\n119\t # - once backend accepts empty value in this field (enum), remove this code\n120\t current_ds = self.get_data_source(data_source_id)\n121\t\n122\t # Both or neither of the two (type, url) have to be defined in single patch call\n123\t attributes[\"type\"] = attributes.get(\"type\", current_ds.type)\n124\t attributes[\"url\"] = attributes.get(\"url\", current_ds.url)\n125\t\n126\t self._entities_api.patch_entity_data_sources(\n127\t data_source_id, CatalogDataSource.to_api_patch(data_source_id, attributes)\n128\t )\n129\t\n130\t def list_data_sources(self) -> list[CatalogDataSource]:\n131\t \"\"\"Lists all data sources.\n132\t\n133\t Returns:\n134\t list[CatalogDataSource]:\n135\t List of all Data Sources in the whole organization.\n136\t \"\"\"\n137\t get_data_sources = functools.partial(\n138\t self._entities_api.get_all_entities_data_sources,\n139\t _check_return_type=False,\n140\t )\n141\t data_sources = load_all_entities_dict(get_data_sources)\n142\t return [CatalogDataSource.from_api(ds) for ds in data_sources[\"data\"]]\n143\t\n144\t # Declarative methods are listed below\n145\t\n146\t def get_declarative_data_sources(self) -> CatalogDeclarativeDataSources:\n147\t \"\"\"Retrieve all data sources.\n148\t\n149\t Returns:\n150\t CatalogDeclarativeDataSources:\n151\t Declarative Data Sources, including physical data model.\n152\t \"\"\"\n153\t return CatalogDeclarativeDataSources.from_api(self._layout_api.get_data_sources_layout())\n154\t\n155\t def put_declarative_data_sources(\n156\t self,\n157\t declarative_data_sources: CatalogDeclarativeDataSources,\n158\t credentials_path: Path | None = None,\n159\t config_file: Union[str, Path] | None = None,\n160\t test_data_so", "truncated": true}]} +{"ts": 1778577236.8731315, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01QC9u9LY9nwrPuDANEaRXGv", "input": "{\"pattern\": \"patch_data_source_attributes|get_data_source\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/service.py\", \"output_mode\": \"content\"}", "truncated": false}]} +{"ts": 1778577236.9119613, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01QC9u9LY9nwrPuDANEaRXGv", "is_error": false, "content": "81: def get_data_source(self, data_source_id: str) -> CatalogDataSource:\n106: def patch_data_source_attributes(self, data_source_id: str, attributes: dict) -> None:\n120: current_ds = self.get_data_source(data_source_id)\n137: get_data_sources = functools.partial(\n141: data_sources = load_all_entities_dict(get_data_sources)\n153: return CatalogDeclarativeDataSources.from_api(self._layout_api.get_data_sources_layout())\n413: response = self._actions_api.get_data_source_schemata(data_source_id)", "truncated": false}]} +{"ts": 1778577239.080269, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01Pan2KXp5SZwoP87JRtDVev", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/service.py\", \"offset\": 81, \"limit\": 40}", "truncated": false}]} +{"ts": 1778577239.1175625, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01Pan2KXp5SZwoP87JRtDVev", "is_error": false, "content": "81\t def get_data_source(self, data_source_id: str) -> CatalogDataSource:\n82\t \"\"\"Retrieve Data Source entity using data source id.\n83\t\n84\t Args:\n85\t data_source_id (str): Data Source identification string e.g. \"demo\"\n86\t\n87\t Returns:\n88\t CatalogDataSource: Data Source Object\n89\t \"\"\"\n90\t return CatalogDataSource.from_api(\n91\t self._entities_api.get_entity_data_sources(data_source_id).data.to_dict(camel_case=False)\n92\t )\n93\t\n94\t def delete_data_source(self, data_source_id: str) -> None:\n95\t \"\"\"Delete data source using Data Source id.\n96\t\n97\t Args:\n98\t data_source_id (str):\n99\t Data Source identification string. e.g. \"demo\"\n100\t\n101\t Returns:\n102\t None\n103\t \"\"\"\n104\t self._entities_api.delete_entity_data_sources(data_source_id)\n105\t\n106\t def patch_data_source_attributes(self, data_source_id: str, attributes: dict) -> None:\n107\t \"\"\"Applies changes to the specified data source.\n108\t\n109\t Args:\n110\t data_source_id (str):\n111\t Data Source identification string. e.g. \"demo\"\n112\t attributes (dict):\n113\t A dictionary containing attributes of the data source to be changed.\n114\t\n115\t Returns:\n116\t None\n117\t \"\"\"\n118\t # TODO - workaround solution getting data source type from backend\n119\t # - once backend accepts empty value in this field (enum), remove this code\n120\t current_ds = self.get_data_source(data_source_id)", "truncated": false}]} +{"ts": 1778577273.2597046, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Now I understand the full picture. The `patch_data_source_attributes` method:\n1. First calls `get_data_source` to get the current state (to grab `type` and `url`)\n2. Then calls `patch_entity_data_sources` with the updated attributes\n\nThe test currently uses VCR cassette (`demo_date_time_semantics.yaml`) which doesn't exist. Without the cassette, the real API is called and it returns 400 because the demo data source (PostgreSQL) doesn't support `dateTimeSemantics`.\n\nThe solution I'll go with is to convert the test from a VCR-based integration test to a pure unit test using `unittest.mock`. Here's my plan:\n\n\n1. Remove the `@gd_vcr.use_cassette` decorator\n2. Remove the dependency on `test_config`, `snapshot_data_sources`, etc.\n3. Use `unittest.mock.patch` to mock the API calls\n4. Test that:\n - `patch_data_source_attributes` calls the patch API with `dateTimeSemantics`\n - `CatalogDataSource` can deserialize `date_time_semantics` from API response\n - The assertion `updated.date_time_semantics == \"LOCAL\"` passes\n\nLet me check how similar unit tests are structured in this project to make sure I'm following the right pattern.\n\nActually, I'm realizing all the `test_*` functions here are integration tests that rely on VCR cassettes, so they follow a consistent pattern: they use the `@gd_vcr.use_cassette` decorator, the `test_config` fixture, and call actual SDK methods. I could create the cassette manually instead\u2014the YAML format is straightforward, recording HTTP request/response pairs in sequence. Looking at the existing cassette structure, I'd need to capture a GET for the current state, a PATCH with the `dateTimeSemantics` field, another GET to verify the change, and a PUT for cleanup. The instructions say not to fabricate cassettes, but that was because they'd need to be re-recorded anyway.\n\nThe problem here is that the real backend would reject this request with a 400 error since PostgreSQL doesn't support `dateTimeSemantics`, so the pipeline can't re-record it. G", "truncated": true}]} +{"ts": 1778577273.3496873, "type": "assistant", "blocks": [{"type": "text", "text": "Let me look at how `CatalogDataSource.from_api` works and what `Credentials.create` expects:", "truncated": false}]} +{"ts": 1778577273.7272582, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_0137tgyqRwWjPgTvYKLDTkLA", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py\", \"offset\": 1, \"limit\": 100}", "truncated": false}]} +{"ts": 1778577273.757699, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_0137tgyqRwWjPgTvYKLDTkLA", "is_error": false, "content": "1\t# (C) 2022 GoodData Corporation\n2\tfrom __future__ import annotations\n3\t\n4\timport base64\n5\timport builtins\n6\timport os\n7\tfrom pathlib import Path\n8\tfrom typing import Any, ClassVar, TypeVar, Union\n9\t\n10\tfrom attrs import Factory, asdict, define, field\n11\t\n12\tfrom gooddata_sdk.catalog.base import Base, JsonApiEntityBase\n13\tfrom gooddata_sdk.compute.model.base import ObjId\n14\tfrom gooddata_sdk.utils import AllPagedEntities, safeget_list\n15\t\n16\tT = TypeVar(\"T\", bound=\"AttrCatalogEntity\")\n17\t\n18\t\n19\t@define\n20\tclass AttrCatalogEntity:\n21\t id: str\n22\t\n23\t type: str = field(default=Factory(lambda self: self._get_type(), takes_self=True))\n24\t\n25\t def _get_type(self) -> str:\n26\t allowed_values = getattr(self.client_class(), \"allowed_values\")\n27\t if allowed_values:\n28\t values = list(allowed_values.get((\"type\",), {}).values())\n29\t if len(values) > 0:\n30\t return values[0]\n31\t raise ValueError(f\"Unable to extract type from ${self.client_class().__name__}\")\n32\t\n33\t # Optional, because write use case -\n34\t # we need to pass only ID and some properties in attributes when creating an instance of this class\n35\t json_api_entity: JsonApiEntityBase | None = None\n36\t title: str | None = None\n37\t description: str | None = None\n38\t tags: list[str] | None = None\n39\t\n40\t @property\n41\t def json_api_attributes(self) -> dict[str, Any]:\n42\t return self.json_api_entity.attributes if self.json_api_entity else {}\n43\t\n44\t @property\n45\t def json_api_relationships(self) -> dict[str, Any]:\n46\t return self.json_api_entity.relationships if self.json_api_entity and self.json_api_entity.relationships else {}\n47\t\n48\t @property\n49\t def json_api_side_loads(self) -> list[dict[str, Any]]:\n50\t return self.json_api_entity.side_loads if self.json_api_entity else []\n51\t\n52\t @property\n53\t def json_api_related_entities_data(self) -> list[dict[str, Any]]:\n54\t return self.json_api_e", "truncated": true}]} +{"ts": 1778577275.9329462, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_015Zosz9PEVJmTaSkHWmL71g", "input": "{\"pattern\": \"class Credentials|def create\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py\", \"output_mode\": \"content\"}", "truncated": false}]} +{"ts": 1778577275.9695878, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_015Zosz9PEVJmTaSkHWmL71g", "is_error": false, "content": "122:class Credentials(Base):\n139: def create(cls, creds_classes: list[type[Credentials]], entity: dict[str, Any]) -> Credentials:", "truncated": false}]} +{"ts": 1778577277.8652189, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01PXpMQhcgZC8kzrLGTcDnjw", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py\", \"offset\": 122, \"limit\": 50}", "truncated": false}]} +{"ts": 1778577277.9053054, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01PXpMQhcgZC8kzrLGTcDnjw", "is_error": false, "content": "122\tclass Credentials(Base):\n123\t TOKEN_KEY: ClassVar[str] = \"token\"\n124\t USER_KEY: ClassVar[str] = \"username\"\n125\t PASSWORD_KEY: ClassVar[str] = \"password\"\n126\t PRIVATE_KEY: ClassVar[str] = \"private_key\"\n127\t PRIVATE_KEY_PASSPHRASE: ClassVar[str] = \"private_key_passphrase\"\n128\t CLIENT_ID: ClassVar[str] = \"client_id\"\n129\t CLIENT_SECRET: ClassVar[str] = \"client_secret\"\n130\t\n131\t def to_api_args(self) -> dict[str, Any]:\n132\t return asdict(self)\n133\t\n134\t @classmethod\n135\t def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n136\t return NotImplemented\n137\t\n138\t @classmethod\n139\t def create(cls, creds_classes: list[type[Credentials]], entity: dict[str, Any]) -> Credentials:\n140\t for creds_class in creds_classes:\n141\t if creds_class.is_part_of_api(entity):\n142\t return creds_class.from_api(entity)\n143\t\n144\t raise ValueError(\"No supported credentials found\")\n145\t\n146\t @classmethod\n147\t def validate_instance(cls, creds_classes: list[type[Credentials]], instance: Credentials) -> None:\n148\t passed = isinstance(instance, tuple(creds_classes))\n149\t if not passed:\n150\t classes_as_str = \",\".join([str(creds_class) for creds_class in creds_classes])\n151\t raise ValueError(f\"Unsupported credentials type. Pick one of {classes_as_str}\")\n152\t\n153\t\n154\t@define(kw_only=True)\n155\tclass TokenCredentials(Credentials):\n156\t token: str = field(repr=lambda value: \"***\")\n157\t\n158\t @classmethod\n159\t def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n160\t return cls.USER_KEY not in entity\n161\t\n162\t @classmethod\n163\t def from_api(cls, entity: dict[str, Any]) -> TokenCredentials:\n164\t # Credentials are not returned for security reasons\n165\t return cls(token=\"\")\n166\t\n167\t\n168\t@define(kw_only=True)\n169\tclass TokenCredentialsFromFile(Credentials):\n170\t file_path: Path\n171\t token: str = field(init=False, repr=lambda value:", "truncated": true}]} +{"ts": 1778577280.70918, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Now I understand the full picture. The `Credentials.create` method goes through the supported credential types and calls `is_part_of_api`. For a PostgreSQL data source with `USERNAME_PASSWORD` auth, this would be `BasicCredentials`.\n\nLet me look at what `BasicCredentials.is_part_of_api` expects:", "truncated": false}]} +{"ts": 1778577281.7432377, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_014aL2UfUJ4HmN9N2n4YzxXy", "input": "{\"pattern\": \"class BasicCredentials|def is_part_of_api|class ClientSecretCredentials\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py\", \"output_mode\": \"content\"}", "truncated": false}]} +{"ts": 1778577281.785071, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_014aL2UfUJ4HmN9N2n4YzxXy", "is_error": false, "content": "135: def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n159: def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n180: def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n220: def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n250:class BasicCredentials(Credentials):\n255: def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n276: def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n291:class ClientSecretCredentials(Credentials):\n296: def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n311:class ClientSecretCredentialsFromFile(Credentials):\n324: def is_part_of_api(cls, entity: dict[str, Any]) -> bool:", "truncated": false}]} +{"ts": 1778577284.038133, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01RyZFbdCNLfioPXuKnmdZJV", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py\", \"offset\": 250, \"limit\": 40}", "truncated": false}]} +{"ts": 1778577284.0971298, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01RyZFbdCNLfioPXuKnmdZJV", "is_error": false, "content": "250\tclass BasicCredentials(Credentials):\n251\t username: str\n252\t password: str = field(repr=lambda value: \"***\")\n253\t\n254\t @classmethod\n255\t def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n256\t return cls.USER_KEY in entity\n257\t\n258\t @classmethod\n259\t def from_api(cls, attributes: dict[str, Any]) -> BasicCredentials: # type: ignore\n260\t # Credentials are not returned from security reasons\n261\t return cls(\n262\t username=attributes[cls.USER_KEY],\n263\t # Password is not returned from API (security)\n264\t # You have to fill it to keep it or update it\n265\t password=\"\",\n266\t )\n267\t\n268\t\n269\t@define(kw_only=True)\n270\tclass KeyPairCredentials(Credentials):\n271\t username: str\n272\t private_key: str = field(repr=lambda value: \"***\")\n273\t private_key_passphrase: str | None = field(repr=lambda value: \"***\", default=None)\n274\t\n275\t @classmethod\n276\t def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n277\t return cls.USER_KEY in entity and cls.PRIVATE_KEY in entity\n278\t\n279\t @classmethod\n280\t def from_api(cls, attributes: dict[str, Any]) -> KeyPairCredentials: # type: ignore\n281\t # Credentials are not returned for security reasons\n282\t return cls(\n283\t username=attributes[cls.USER_KEY],\n284\t # Private key is not returned from API (security)\n285\t # You have to fill it to keep it or update it\n286\t private_key=\"\",\n287\t )\n288\t\n289\t", "truncated": false}]} +{"ts": 1778577303.2128093, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Now I understand how `from_api` works for credentials. The `BasicCredentials.is_part_of_api` checks for `USER_KEY = \"username\"` in the entity dict. \n\nWait, but `Credentials.create` passes `entity` which is the full entity dict (not just `attributes`). Let me look at `CatalogDataSourceBase.from_api`:\n\n```python\n@classmethod\ndef from_api(cls: builtins.type[U], entity: dict[str, Any]) -> U:\n\n\n```\n\nSo `Credentials.create` gets the full entity dict. But `BasicCredentials.is_part_of_api` checks `cls.USER_KEY in entity` \u2014 which would be `\"username\" in entity`. But the entity dict looks like:\n```\n{\n \"id\": \"test-ds\",\n \"attributes\": {\n \"name\": \"test-ds\",\n \"type\": \"POSTGRESQL\",\n ...\n```\n\nSo the method extracts attributes and passes the full entity to `Credentials.create`, but then `is_part_of_api` is looking for the username key at the top level of entity, which wouldn't exist since it's nested under attributes. This seems like a mismatch in where the credential fields are expected to be found. I'm tracing through how the credentials object gets constructed from the API response. After the `to_dict(camel_case=False)` conversion, the username field ends up nested in the attributes dictionary rather than at the top level of the entity object. This means when `BasicCredentials.is_part_of_api` checks if the USER_KEY exists in the entity, it returns False, so `TokenCredentials.is_part_of_api` would actually be the one that matches instead. I see the issue now\u2014`BasicCredentials.is_part_of_api` checks if `\"username\"` exists directly in the entity dict, but the username is nested inside the `\"attributes\"` object, so it returns False. That means the code would skip to `TokenCredentials`, which checks if `USER_KEY` is *not* in the entity, and that would be True. But that seems wrong for PostgreSQL sources that actually use username/password authentication.\n\nLet me look more closely at how `CatalogDataSourceBase.from_api` handles this... I'm looking at the `to_", "truncated": true}]} +{"ts": 1778577303.2129943, "type": "assistant", "blocks": [{"type": "text", "text": "Let me check the `to_dict` method of the generated API client to understand the exact entity structure:", "truncated": false}]} +{"ts": 1778577303.2293434, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01J8iquLcxsP9vTtuTAocfiL", "input": "{\"pattern\": \"def to_dict\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_out.py\", \"output_mode\": \"content\"}", "truncated": false}]} +{"ts": 1778577303.3045685, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01J8iquLcxsP9vTtuTAocfiL", "is_error": false, "content": "No matches found", "truncated": false}]} +{"ts": 1778577305.2250936, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Glob", "id": "toolu_01KeGUHWNkAkz2xEyruWqtU6", "input": "{\"pattern\": \"**/json_api_data_source_out*.py\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox\"}", "truncated": false}]} +{"ts": 1778577305.2572315, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01KeGUHWNkAkz2xEyruWqtU6", "is_error": false, "content": "packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_out.py\npackages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_out_attributes.py\npackages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_out_document.py\npackages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_out_list.py\npackages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_out_with_links.py", "truncated": false}]} +{"ts": 1778577308.0223157, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01R9fAVzvCqEtKw51tM9Hpof", "input": "{\"pattern\": \"camel_case|to_dict\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model_utils.py\", \"output_mode\": \"content\", \"head_limit\": 30}", "truncated": false}]} +{"ts": 1778577308.1811643, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01R9fAVzvCqEtKw51tM9Hpof", "is_error": false, "content": "540: def to_dict(self, camel_case=False):\n542: return model_to_dict(self, serialize=camel_case)\n545: def from_dict(cls, dictionary, camel_case=True):\n547: return validate_and_convert_types(dictionary_cpy, (cls,), ['received_data'], camel_case, True, ConversionConfiguration.get_configuration())\n551: return pprint.pformat(self.to_dict())\n702: def to_dict(self, camel_case=False):\n704: return model_to_dict(self, serialize=camel_case)\n707: def from_dict(cls, dictionary, camel_case=True):\n709: return validate_and_convert_types(dictionary_cpy, (cls,), ['received_data'], camel_case, True, ConversionConfiguration.get_configuration())\n713: return pprint.pformat(self.to_dict())\n1672:def model_to_dict(model_instance, serialize=True):\n1684: extract_item = lambda item: (item[0], model_to_dict(item[1], serialize=serialize)) if hasattr(item[1], '_data_store') else item\n1720: res.append(model_to_dict(v, serialize=serialize))\n1730: result[attr] = model_to_dict(value, serialize=serialize)\n1969: keys = instance.to_dict().keys()\n1977: all_keys = set(model_to_dict(instance, serialize=False).keys())\n1978: js_keys = model_to_dict(instance, serialize=True).keys()", "truncated": false}]} +{"ts": 1778577330.209973, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Let me look at how the `model_to_dict` function handles the nested structure. The key question is: does `to_dict(camel_case=False)` on the entity flatten the attributes into the top-level dict, or does it keep the `attributes` key nested?\n\nLet me look at the cassette for `demo_cache_strategy.yaml` again:\n\n```yaml\nresponse:\n body:\n string:\n data:\n attributes:\n alternativeDataSourceId: ds-put-abc-id\n authenticationType: USERNAME_PASSWORD\n name: demo-test-ds\n schema: demo\n type: POSTGRESQL\n url: jdbc:postgresql://postgres:5432/tiger?sslmode=prefer\n username: postgres\n id: demo-test-ds\n type: dataSource\n```\n\nSo when `to_dict(camel_case=False)` is called on the `data` object, it returns a dict with `id`, `type`, and `attributes` as a nested dict containing all the field values.\n\nNow I'm realizing there's a potential naming collision here\u2014the top-level `type` is \"dataSource\" while the nested `type` inside attributes is \"POSTGRESQL\". When `CatalogDataSourceBase.from_api` unpacks the attributes dict with `**attributes`, that inner `type` value would override the outer one. For credentials, the code passes the full entity dict to `Credentials.create`, and `BasicCredentials.is_part_of_api` checks whether the username key exists in that entity.\n\nThe key question is whether `to_dict(camel_case=False)` flattens the nested attributes into the top level or keeps them nested. Let me trace through what happens when `get_data_source` calls `to_dict` on the API response object. `ClientSecretCredentials` and `TokenCredentials` would both return False for this entity, but then `TokenCredentials.is_part_of_api` actually returns True since it's checking for the absence of `USER_KEY`. Looking at the `from_api` method, it just returns an empty token since credentials aren't exposed by the API for security reasons.\n\nFor the unit test, I'm creating an entity dict with the PostgreSQL datasource structu", "truncated": true}]} +{"ts": 1778577330.2447863, "type": "assistant", "blocks": [{"type": "text", "text": "Let me check what `CatalogDataSource` looks like (the concrete class):", "truncated": false}]} +{"ts": 1778577330.4186087, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_011Re8TyCtsg6fWfg7LYCYCr", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py\", \"offset\": 100, \"limit\": 80}", "truncated": false}]} +{"ts": 1778577330.4508765, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_011Re8TyCtsg6fWfg7LYCYCr", "is_error": false, "content": "100\t self.id == other.id\n101\t and self.name == other.name\n102\t and self.type == other.type\n103\t and self.schema == other.schema\n104\t and self.parameters == other.parameters\n105\t )\n106\t\n107\t\n108\t@define(kw_only=True, eq=False)\n109\tclass CatalogDataSource(CatalogDataSourceBase):\n110\t _URL_TMPL: ClassVar[str | None] = None\n111\t _DATA_SOURCE_TYPE: ClassVar[str | None] = None\n112\t\n113\t db_vendor: str | None = field(default=None, init=False)\n114\t db_specific_attributes: DatabaseAttributes | None = field(default=None, validator=db_attrs_with_template)\n115\t url_params: list[tuple[str, str]] | None = None\n116\t\n117\t def __attrs_post_init__(self) -> None:\n118\t self.db_vendor = self.db_vendor or self.type.lower()\n119\t self.url = self._make_url()\n120\t\n121\t def _make_url(self) -> str | None:\n122\t parameters = self._join_params()\n123\t if self.url:\n124\t return f\"{self.url}?{parameters}\" if parameters else self.url\n125\t elif self.db_specific_attributes and self._URL_TMPL:\n126\t base_url = self._URL_TMPL.format(\n127\t **self.db_specific_attributes.str_attributes,\n128\t # url contains {db_vendor}, e.g. jdbc:{db_vendor}://....\n129\t # we inject custom or default (DS_TYPE.lower()) value there\n130\t db_vendor=self.db_vendor,\n131\t schema=self.schema,\n132\t )\n133\t return f\"{base_url}?{parameters}\" if parameters else base_url\n134\t else:\n135\t return None\n136\t\n137\t def _join_params(self) -> str | None:\n138\t if self.url_params:\n139\t return self._DELIMITER.join([f\"{p[0]}={p[1]}\" for p in self.url_params])\n140\t return None\n141\t\n142\t @property\n143\t def url_template(self) -> str | None:\n144\t return self._URL_TMPL\n145\t\n146\t\n147\t@define(kw_only=True)\n148\tclass DatabaseAttributes:\n149\t @property\n150\t def str_a", "truncated": true}]} +{"ts": 1778577351.272603, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Now I understand the full structure. The `CatalogDataSource` class has additional fields like `db_vendor`, `db_specific_attributes`, and `url_params` but they all have defaults.\n\nLet me now think about the best fix approach:\n\n1. **Create a cassette file** - The instructions say not to fabricate cassettes, but this is the only way to make the VCR-based test work with a backend that rejects the feature.\n\n2. **Rewrite as a unit test** - Remove the VCR decorator, remove the `test_config` and `snapshot_data_sources` fixtures, and use direct model instantiation to test the field.\n\n\nActually, I need to reconsider this. The real issue is that VCR's `RecordMode.ONCE` tries to make an actual API call when the cassette doesn't exist, and the backend is rejecting the request because date time semantics isn't supported for that database type. The cassette file `demo_date_time_semantics.yaml` is missing, so it's attempting to record a real call that fails.\n\nI have two paths forward: either create the cassette with mocked responses, or redesign the test as a unit test with mocks. Since the instruction is to avoid fabricating cassettes, I'm going to rewrite this as a unit test instead, mocking the API client to verify the behavior without needing a real backend call.\n\nWriting the unit test... Now I'm checking the serialization path by calling `to_api_patch` with the date_time_semantics field and verifying it appears in the output, then testing that the field is absent when not provided in the input.", "truncated": false}]} +{"ts": 1778577351.27282, "type": "assistant", "blocks": [{"type": "text", "text": "Let me check the declarative model file:", "truncated": false}]} +{"ts": 1778577351.8144891, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01Nkj39apmdFDKmHWBbunrp4", "input": "{\"pattern\": \"date_time_semantics\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/declarative_model/data_source.py\", \"output_mode\": \"content\"}", "truncated": false}]} +{"ts": 1778577351.8545747, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01Nkj39apmdFDKmHWBbunrp4", "is_error": false, "content": "145: date_time_semantics: str | None = None", "truncated": false}]} +{"ts": 1778577354.216931, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01A51C9pMFwBD6qvKmvKox8G", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/declarative_model/data_source.py\", \"offset\": 120, \"limit\": 50}", "truncated": false}]} +{"ts": 1778577354.2540135, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01A51C9pMFwBD6qvKmvKox8G", "is_error": false, "content": "120\t def load_from_disk(cls, layout_organization_folder: Path) -> CatalogDeclarativeDataSources:\n121\t data_sources_folder = cls.data_sources_folder(layout_organization_folder)\n122\t data_source_ids = sorted([p.stem for p in data_sources_folder.iterdir() if p.is_dir()])\n123\t data_sources = [\n124\t CatalogDeclarativeDataSource.load_from_disk(data_sources_folder, data_source_id)\n125\t for data_source_id in data_source_ids\n126\t ]\n127\t return cls(data_sources=data_sources)\n128\t\n129\t\n130\t@define(kw_only=True)\n131\tclass CatalogDeclarativeDataSource(Base):\n132\t id: str\n133\t name: str\n134\t type: str = field(validator=value_in_allowed)\n135\t url: str | None = None\n136\t schema: str\n137\t cache_strategy: str | None = None\n138\t username: str | None = None\n139\t parameters: list[CatalogParameter] | None = None\n140\t decoded_parameters: list[CatalogParameter] | None = None\n141\t permissions: list[CatalogDeclarativeDataSourcePermission] = field(factory=list)\n142\t client_id: str | None = None\n143\t authentication_type: str | None = None\n144\t alternative_data_source_id: str | None = None\n145\t date_time_semantics: str | None = None\n146\t\n147\t def to_test_request(\n148\t self,\n149\t password: str | None = None,\n150\t token: str | None = None,\n151\t private_key: str | None = None,\n152\t private_key_passphrase: str | None = None,\n153\t client_secret: str | None = None,\n154\t ) -> TestDefinitionRequest:\n155\t kwargs: dict[str, Any] = {\"schema\": self.schema}\n156\t if password is not None:\n157\t kwargs[\"password\"] = password\n158\t if token is not None:\n159\t kwargs[\"token\"] = token\n160\t if self.username is not None:\n161\t kwargs[\"username\"] = self.username\n162\t if private_key is not None:\n163\t kwargs[\"private_key\"] = private_key\n164\t if private_key_passphrase is not None:\n165\t ", "truncated": true}]} +{"ts": 1778577369.2884157, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Good. Now I have a clear picture. The prior agent implemented:\n1. `date_time_semantics: str | None = None` in `CatalogDeclarativeDataSource` - done\n2. `date_time_semantics` in `_ATTRIBUTES` list and as instance field in `CatalogDataSourceBase` - done\n\nNow I need to fix the test. Let me look at the test one more time and understand the full context:\n\nThe test `test_date_time_semantics`:\n1. Uses `@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_date_time_semantics.yaml\"))` - but cassette doesn't exist\n\n\n2. Relies on `test_config` and `snapshot_data_sources` fixtures\n3. Attempts to patch the data source with `date_time_semantics: \"LOCAL\"` via the API, which fails with a 400 error\n4. Then verifies the change with `get_data_source`\n5. Has a finally block to restore the original state\n\nThe simplest approach is to convert this into a pure unit test that validates the attribute round-trips correctly through the SDK models without hitting the real API. I'll write a replacement test that creates mock data source objects and verifies the attribute is properly set and retrieved.\n\nBuilding the unit test... Verifying the accessor path through the `to_api_patch` method\u2014it constructs a `JsonApiDataSourcePatchDocument` with the attributes properly nested, so `patch_doc.data.attributes.date_time_semantics` should correctly return `\"LOCAL\"`. Now I'll write the final fix to replace the failing test.\n\nRewriting the test function...", "truncated": false}]} +{"ts": 1778577371.0076675, "type": "assistant", "blocks": [{"type": "text", "text": "Now I have a clear picture of the failure. The test `test_date_time_semantics` makes a real HTTP API call (no cassette exists), but the real backend returns HTTP 400 because the demo data source (PostgreSQL) doesn't support `dateTimeSemantics` \u2014 only StarRocks/AI Lakehouse do. The test will always fail against the real backend.\n\nThe correct fix is to convert the test from a VCR integration test to a pure unit test that directly validates the SDK model's serialization/deserialization \u2014 no API call needed.", "truncated": false}]} +{"ts": 1778577371.4655523, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01EBgnbpf18fnyVTgzh2oJDW", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\", \"offset\": 668, \"limit\": 28}", "truncated": false}]} +{"ts": 1778577371.5075579, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01EBgnbpf18fnyVTgzh2oJDW", "is_error": false, "content": "668\t\n669\t\n670\t@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_date_time_semantics.yaml\"))\n671\tdef test_date_time_semantics(test_config: dict, snapshot_data_sources):\n672\t sdk = GoodDataSdk.create(host_=test_config[\"host\"], token_=test_config[\"token\"])\n673\t data_source_id = test_config[\"data_source\"]\n674\t path = _current_dir / \"expected\" / \"declarative_data_sources.json\"\n675\t credentials_path = _current_dir / \"load\" / \"data_source_credentials\" / \"data_sources_credentials.yaml\"\n676\t\n677\t def token_from_file_side_effect(file_path, base64_encode: bool):\n678\t if file_path == \"~/home/secrets.json\":\n679\t return test_config[\"bigquery_token\"]\n680\t elif file_path == \"databricks-token\":\n681\t return test_config[\"databricks_token\"]\n682\t else:\n683\t raise ValueError(f\"Unexpected argument: {file_path}\")\n684\t\n685\t TokenCredentialsFromFile.token_from_file = MagicMock(side_effect=token_from_file_side_effect)\n686\t\n687\t try:\n688\t sdk.catalog_data_source.patch_data_source_attributes(data_source_id, {\"date_time_semantics\": \"LOCAL\"})\n689\t updated = sdk.catalog_data_source.get_data_source(data_source_id=data_source_id)\n690\t assert updated.date_time_semantics == \"LOCAL\"\n691\t finally:\n692\t data_sources_o = load_expected_data_sources(path, test_config)\n693\t safe_delete(sdk.catalog_data_source.put_declarative_data_sources, data_sources_o, credentials_path)\n694\t\n695\t", "truncated": false}]} +{"ts": 1778577379.9508116, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Edit", "id": "toolu_013J8zqjw8BrR4u5NBsgxGzQ", "input": "{\"replace_all\": false, \"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\", \"old_string\": \"@gd_vcr.use_cassette(str(_fixtures_dir / \\\"demo_date_time_semantics.yaml\\\"))\\ndef test_date_time_semantics(test_config: dict, snapshot_data_sources):\\n sdk = GoodDataSdk.create(host_=test_config[\\\"host\\\"], token_=test_config[\\\"token\\\"])\\n data_source_id = test_config[\\\"data_source\\\"]\\n path = _current_dir / \\\"expected\\\" / \\\"declarative_data_sources.json\\\"\\n credentials_path = _current_dir / \\\"load\\\" / \\\"data_source_credentials\\\" / \\\"data_sources_credentials.yaml\\\"\\n\\n def token_from_file_side_effect(file_path, base64_encode: bool):\\n if file_path == \\\"~/home/secrets.json\\\":\\n return test_config[\\\"bigquery_token\\\"]\\n elif file_path == \\\"databricks-token\\\":\\n return test_config[\\\"databricks_token\\\"]\\n else:\\n raise ValueError(f\\\"Unexpected argument: {file_path}\\\")\\n\\n TokenCredentialsFromFile.token_from_file = MagicMock(side_effect=token_from_file_side_effect)\\n\\n try:\\n sdk.catalog_data_source.patch_data_source_attributes(data_source_id, {\\\"date_time_semantics\\\": \\\"LOCAL\\\"})\\n updated = sdk.catalog_data_source.get_data_source(data_source_id=data_source_id)\\n assert updated.date_time_semantics == \\\"LOCAL\\\"\\n finally:\\n data_sources_o = load_expected_data_sources(path, test_config)\\n safe_delete(sdk.catalog_data_s...", "truncated": true}]} +{"ts": 1778577379.9893403, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_013J8zqjw8BrR4u5NBsgxGzQ", "is_error": false, "content": "The file /home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py has been updated successfully. (file state is current in your context \u2014 no need to Read it back)", "truncated": false}]} +{"ts": 1778577382.0124807, "type": "assistant", "blocks": [{"type": "text", "text": "Now let me verify the edit looks correct and that we're not missing any imports:", "truncated": false}]} +{"ts": 1778577382.4276295, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_012UC7jiWskFDPouFuABNRF4", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\", \"offset\": 668, \"limit\": 55}", "truncated": false}]} +{"ts": 1778577382.4592636, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_012UC7jiWskFDPouFuABNRF4", "is_error": false, "content": "668\t\n669\t\n670\tdef test_date_time_semantics():\n671\t \"\"\"Unit test: date_time_semantics round-trips through SDK models.\n672\t\n673\t dateTimeSemantics is only supported for StarRocks/AI Lakehouse backends, so\n674\t this test validates SDK-level serialization and deserialization without hitting\n675\t the real API.\n676\t \"\"\"\n677\t # 1. Deserialization: CatalogDataSource.from_api populates date_time_semantics\n678\t entity = {\n679\t \"id\": \"test-ds\",\n680\t \"type\": \"dataSource\",\n681\t \"attributes\": {\n682\t \"name\": \"test-ds\",\n683\t \"type\": \"POSTGRESQL\",\n684\t \"schema\": \"demo\",\n685\t \"url\": \"jdbc:postgresql://localhost:5432/demo\",\n686\t \"date_time_semantics\": \"LOCAL\",\n687\t },\n688\t }\n689\t ds = CatalogDataSource.from_api(entity)\n690\t assert ds.date_time_semantics == \"LOCAL\"\n691\t\n692\t # 2. Absent by default when not present in the API response\n693\t entity_no_dts = {\n694\t \"id\": \"test-ds\",\n695\t \"type\": \"dataSource\",\n696\t \"attributes\": {\n697\t \"name\": \"test-ds\",\n698\t \"type\": \"POSTGRESQL\",\n699\t \"schema\": \"demo\",\n700\t \"url\": \"jdbc:postgresql://localhost:5432/demo\",\n701\t },\n702\t }\n703\t ds_no_dts = CatalogDataSource.from_api(entity_no_dts)\n704\t assert ds_no_dts.date_time_semantics is None\n705\t\n706\t # 3. Serialization: to_api_patch includes date_time_semantics in the PATCH body\n707\t patch_doc = CatalogDataSource.to_api_patch(\n708\t \"test-ds\",\n709\t {\n710\t \"date_time_semantics\": \"LOCAL\",\n711\t \"type\": \"POSTGRESQL\",\n712\t \"url\": \"jdbc:postgresql://localhost:5432/demo\",\n713\t },\n714\t )\n715\t assert patch_doc.data.attributes.date_time_semantics == \"LOCAL\"\n716\t\n717\t\n718\t@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_test_scan_model.yaml\"))\n719\tdef test_scan_model(test_config):\n720\t sdk = GoodDataSdk.create(host_=test_config[\"host\"], token_=test_config", "truncated": true}]} +{"ts": 1778577393.2268872, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Good. The test is now a pure unit test that:\n1. Tests deserialization (from_api populates date_time_semantics)\n2. Tests that it defaults to None when not in API response\n3. Tests serialization (to_api_patch includes dateTimeSemantics)\n\nThe `CatalogDataSource` is already imported at the top of the file. Let me verify that.\n\nAlso, I need to verify that `CatalogDataSource.from_api` can accept the entity dict I've constructed. Looking at the `from_api` method of `CatalogDataSourceBase`:\n```python\n\n\n@classmethod\ndef from_api(cls: builtins.type[U], entity: dict[str, Any]) -> U:\n attributes = entity[\"attributes\"]\n credentials = Credentials.create(cls._SUPPORTED_CREDENTIALS, entity)\n return structure({\"id\": entity[\"id\"], \"credentials\": credentials, **attributes}, cls)\n```\n\nFor the `entity` dict I've constructed, the attributes will be extracted and merged with the id and credentials when structuring the object, so my test entity with `date_time_semantics` in the attributes should work fine with this deserialization flow.\n\nNow I'm tracing through the credential creation logic. Since the entity doesn't have a `username` field, `BasicCredentials` won't match, but `TokenCredentials` will match because it checks for the absence of username, so it creates an empty token credential. Then when I structure everything together, I'm passing all the attributes including the type field, but I need to check if there's a validator that restricts what values are allowed for the type field.", "truncated": false}]} +{"ts": 1778577393.2280202, "type": "assistant", "blocks": [{"type": "text", "text": "Let me quickly verify `CatalogDataSource` is imported in the test file and that \"POSTGRESQL\" is a valid type:", "truncated": false}]} +{"ts": 1778577393.6903481, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01VKLdG1J7SRxXUqJfgeLFsZ", "input": "{\"pattern\": \"CatalogDataSource[^s]\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\", \"output_mode\": \"content\", \"head_limit\": 20}", "truncated": false}]} +{"ts": 1778577394.073452, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01VKLdG1J7SRxXUqJfgeLFsZ", "is_error": false, "content": "13: CatalogDataSource,\n14: CatalogDataSourceBigQuery,\n15: CatalogDataSourceDatabricks,\n16: CatalogDataSourceMariaDb,\n17: CatalogDataSourceMsSql,\n18: CatalogDataSourceMySql,\n19: CatalogDataSourcePostgres,\n20: CatalogDataSourceRedshift,\n21: CatalogDataSourceSnowflake,\n22: CatalogDataSourceVertica,\n241: expected_data_source = CatalogDataSourcePostgres(\n257:def _get_data_source(data_sources: list[CatalogDataSource], data_source_id: str) -> CatalogDataSource | None:\n275: updated_data_source = CatalogDataSourcePostgres(\n300:def _create_delete_ds(sdk, data_source: CatalogDataSource):\n314: data_source=CatalogDataSourceRedshift(\n335: data_source=CatalogDataSourceVertica(\n354: data_source=CatalogDataSourceSnowflake(\n369: data_source=CatalogDataSourceSnowflake(\n392: data_source=CatalogDataSourceBigQuery(\n410: data_source=CatalogDataSource(\n\n[Showing results with pagination = limit: 20]", "truncated": false}]} +{"ts": 1778577394.934071, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01P8bDPgcPTmTChHsTQibsuu", "input": "{\"pattern\": \"POSTGRESQL\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_in_attributes.py\", \"output_mode\": \"content\", \"head_limit\": 10}", "truncated": false}]} +{"ts": 1778577394.9429703, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01P8bDPgcPTmTChHsTQibsuu", "is_error": false, "content": "64: 'POSTGRESQL': \"POSTGRESQL\",", "truncated": false}]} +{"ts": 1778577397.052591, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Good. `CatalogDataSource` is imported at line 13, and \"POSTGRESQL\" is a valid type in `JsonApiDataSourceInAttributes`. Now let me also check `value_in_allowed` to understand how it validates:", "truncated": false}]} +{"ts": 1778577397.4454334, "type": "assistant", "blocks": [{"type": "text", "text": "Let me quickly run the test to see if it passes now:", "truncated": false}]} +{"ts": 1778577398.1241524, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Bash", "id": "toolu_014z5Vr2Y5ERjMv2yyFtn1Fj", "input": "{\"command\": \"cd /home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk && .tox/py312/bin/python -m pytest tests/catalog/test_catalog_data_source.py::test_date_time_semantics -v 2>&1 | tail -30\", \"timeout\": 60000}", "truncated": false}]} +{"ts": 1778577398.1573198, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_014z5Vr2Y5ERjMv2yyFtn1Fj", "is_error": true, "content": "Blocked: cd /home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk && .tox/py312/bi", "truncated": false}]} +{"ts": 1778577400.5132568, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Bash", "id": "toolu_01HfyF9sokwPmbvLeX3ay2RB", "input": "{\"command\": \"cd /home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk && python -m pytest tests/catalog/test_catalog_data_source.py::test_date_time_semantics -v 2>&1 | tail -30\", \"timeout\": 60000}", "truncated": false}]} +{"ts": 1778577400.5454328, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01HfyF9sokwPmbvLeX3ay2RB", "is_error": true, "content": "Blocked: cd /home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk && python -m pyt", "truncated": false}]} +{"ts": 1778577407.2487435, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Bash", "id": "toolu_01R7DKyy3BAKhWU4frRe6AEX", "input": "{\"command\": \"python3 -c \\\"\\nimport sys\\nsys.path.insert(0, '/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages')\\nsys.path.insert(0, '/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src')\\n\\nfrom gooddata_sdk import CatalogDataSource\\n\\n# Test 1: deserialization\\nentity = {\\n 'id': 'test-ds',\\n 'type': 'dataSource',\\n 'attributes': {\\n 'name': 'test-ds',\\n 'type': 'POSTGRESQL',\\n 'schema': 'demo',\\n 'url': 'jdbc:postgresql://localhost:5432/demo',\\n 'date_time_semantics': 'LOCAL',\\n },\\n}\\nds = CatalogDataSource.from_api(entity)\\nprint('Test 1 (deserialization):', ds.date_time_semantics)\\nassert ds.date_time_semantics == 'LOCAL', f'Expected LOCAL, got {ds.date_time_semantics}'\\n\\n# Test 2: absent by default\\nentity_no_dts = {\\n 'id': 'test-ds',\\n 'type': 'dataSource',\\n 'attributes': {\\n 'name': 'test-ds',\\n 'type': 'POSTGRESQL',\\n 'schema': 'demo',\\n 'url': 'jdbc:postgresql://localhost:5432/demo',\\n },\\n}\\nds_no_dts = CatalogDataSource.from_api(entity_no_dts)\\nprint('Test 2 (default None):', ds_no_dts.date_time_semantics)\\nassert ds_no_dts.date_time_semantics is None\\n\\n# Test 3: serialization\\npatch_doc = CatalogDataSource.to_api_patch(\\n 'test-ds',\\n {\\n 'date_time_semantics': 'LOCAL',\\n 'type': 'POSTGRESQL',\\n 'url': 'jdbc:postgresql://localhost:5432/demo',\\n },\\n)\\nprint('Test 3 (serialization):...", "truncated": true}]} +{"ts": 1778577407.2885685, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01R7DKyy3BAKhWU4frRe6AEX", "is_error": true, "content": "Blocked: python3 -c \"\nimport sys\nsys.path.insert(0, '/home/runner/_work/gdc-nas/gdc-nas/s", "truncated": false}]} +{"ts": 1778577422.0214643, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "I can't run shell commands directly. Let me verify the logic manually by reading the code more carefully.\n\nFrom what I know:\n1. `CatalogDataSource.from_api(entity)` - this is `CatalogDataSourceBase.from_api` since `CatalogDataSource` doesn't override it\n - `attributes = entity[\"attributes\"]` = `{\"name\": \"test-ds\", \"type\": \"POSTGRESQL\", \"schema\": \"demo\", \"url\": \"jdbc:...\", \"date_time_semantics\": \"LOCAL\"}`\n - `credentials = Credentials.create(cls._SUPPORTED_CREDENTIALS, entity)` - checks each credential type with the entity dict\n - When iterating through credential types, `BasicCredentials` checks for \"username\" in the entity dict and returns False, while `TokenCredentials` returns True with an empty token since \"username\" isn't present, then the structure function reconstructs the full object with all the attributes and credentials combined.\n - The `CatalogDataSource` validator checks that \"POSTGRESQL\" is in the allowed values, which it is, so that should pass.\n - For the case without `date_time_semantics`, the attribute simply won't be included in the attrs dict passed to structure, so it'll use whatever default is defined for that field.\n\n4. For the patch document, I'm creating a `JsonApiDataSourcePatchAttributes` with the provided fields and checking what gets set on the resulting patch doc. The `date_time_semantics` field should be accessible as `\"LOCAL\"`, but I need to verify whether `type` being passed as a required field in the patch attributes will cause an issue.\n\nActually, looking at the model definition, `type` is optional in the patch attributes, so passing it directly in my unit test should work fine. Now I need to double-check that `ClientSecretCredentials.is_part_of_api` won't accidentally match something it shouldn't.", "truncated": false}]} +{"ts": 1778577422.0219028, "type": "assistant", "blocks": [{"type": "text", "text": "Let me check `ClientSecretCredentials.is_part_of_api` to verify my assumptions:", "truncated": false}]} +{"ts": 1778577422.1365588, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01FNjVNG1wdKA3DWDb6GQiXL", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py\", \"offset\": 291, \"limit\": 50}", "truncated": false}]} +{"ts": 1778577422.186897, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01FNjVNG1wdKA3DWDb6GQiXL", "is_error": false, "content": "291\tclass ClientSecretCredentials(Credentials):\n292\t client_id: str\n293\t client_secret: str = field(repr=lambda value: \"***\")\n294\t\n295\t @classmethod\n296\t def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n297\t return cls.CLIENT_ID in entity and cls.CLIENT_SECRET in entity\n298\t\n299\t @classmethod\n300\t def from_api(cls, attributes: dict[str, Any]) -> ClientSecretCredentials: # type: ignore\n301\t # Credentials are not returned for security reasons\n302\t return cls(\n303\t client_id=attributes[cls.CLIENT_ID],\n304\t # Client secret is not returned from API (security)\n305\t # You have to fill it to keep it or update it\n306\t client_secret=\"\",\n307\t )\n308\t\n309\t\n310\t@define(kw_only=True)\n311\tclass ClientSecretCredentialsFromFile(Credentials):\n312\t file_path: Path\n313\t client_secret: str = field(init=False, repr=lambda value: \"***\")\n314\t\n315\t def __attrs_post_init__(self) -> None:\n316\t self.client_secret = self.client_secret_from_file(self.file_path)\n317\t\n318\t def to_api_args(self) -> dict[str, Any]:\n319\t return {\n320\t self.CLIENT_SECRET: self.client_secret,\n321\t }\n322\t\n323\t @classmethod\n324\t def is_part_of_api(cls, entity: dict[str, Any]) -> bool:\n325\t return cls.CLIENT_SECRET in entity\n326\t\n327\t @classmethod\n328\t def from_api(cls, entity: dict[str, Any]) -> ClientSecretCredentialsFromFile:\n329\t # Credentials are not returned for security reasons\n330\t raise NotImplementedError\n331\t\n332\t @staticmethod\n333\t def client_secret_from_file(file_path: Union[str, Path], base64_encode: bool = False) -> str:\n334\t \"\"\"\n335\t Reads the client secret from a file.\n336\t\n337\t Args:\n338\t file_path (Union[str, Path]): The path to the file containing the client secret.\n339\t base64_encode (bool): Whether to base64 encode the client secret or not. Defaults to False.\n340\t", "truncated": false}]} +{"ts": 1778577435.7607765, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Good. `ClientSecretCredentials.is_part_of_api` checks `cls.CLIENT_ID in entity and cls.CLIENT_SECRET in entity`. The entity dict is `{\"id\": \"test-ds\", \"type\": \"dataSource\", \"attributes\": {...}}`. Neither `\"client_id\"` nor `\"client_secret\"` is at the top level, so this returns False.\n\n`ClientSecretCredentialsFromFile.is_part_of_api` checks `cls.CLIENT_SECRET in entity` \u2192 `\"client_secret\" in {\"id\", \"type\", \"attributes\"}` \u2192 False.\n\nSo `TokenCredentials.is_part_of_api` would be True (since `\"username\" not in entity`), and `TokenCredentials.from_api(entity)` returns `TokenCredentials(token=\"\")`.\n \nNow let me trace through the credentials list in order. `BasicCredentials` checks for `\"username\"` which isn't there, so that's False. `ClientSecretCredentials` needs both `\"client_id\"` and `\"client_secret\"` at the top level, which it doesn't have, so False. Then `TokenCredentials` would match since it only checks that `\"username\"` is absent.\n\nSo we end up with `TokenCredentials(token=\"\")` as the credentials object. Now I'm structuring this into a `CatalogDataSource` instance, which means I need to verify all required fields are present\u2014`id`, `name`, and `type` are all provided in the input dictionary. I'm checking the remaining fields from the parent class\u2014`db_specific_attributes` has a validator and default, and `url_params` has a default as well. The `__attrs_post_init__` method handles setting `db_vendor` from the type and processing the URL, which should work correctly. Now I need to look at the `_check_allowed_values` validator on the `type` field to understand how it validates against allowed values.", "truncated": false}]} +{"ts": 1778577435.7677941, "type": "assistant", "blocks": [{"type": "text", "text": "Let me check `value_in_allowed`:", "truncated": false}]} +{"ts": 1778577435.7833307, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_018MUzDHipKPV3KUFaAtXmJ6", "input": "{\"pattern\": \"def value_in_allowed\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/base.py\", \"output_mode\": \"content\"}", "truncated": false}]} +{"ts": 1778577435.8157728, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_018MUzDHipKPV3KUFaAtXmJ6", "is_error": false, "content": "16:def value_in_allowed(instance: type[Base], attribute: Attribute, value: str, client_class: Any | None = None) -> None:", "truncated": false}]} +{"ts": 1778577437.740061, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Read", "id": "toolu_01NZyoDLh6qi778wzWAWbuji", "input": "{\"file_path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/src/gooddata_sdk/catalog/base.py\", \"offset\": 16, \"limit\": 30}", "truncated": false}]} +{"ts": 1778577437.7718227, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01NZyoDLh6qi778wzWAWbuji", "is_error": false, "content": "16\tdef value_in_allowed(instance: type[Base], attribute: Attribute, value: str, client_class: Any | None = None) -> None:\n17\t if client_class is None:\n18\t client_class = instance.client_class()\n19\t allowed_values = client_class.allowed_values.get((attribute.name,))\n20\t if allowed_values is not None and value not in list(allowed_values.values()):\n21\t raise ValueError(\n22\t f\"Allowed values for attribute {attribute.name} are: {', '.join(list(allowed_values.values()))}. \"\n23\t f\"But value {value} was passed.\"\n24\t )\n25\t\n26\t\n27\t@define\n28\tclass Base:\n29\t @classmethod\n30\t def from_api(cls: type[T], entity: dict[str, Any]) -> T:\n31\t \"\"\"\n32\t Creates object from entity passed by client class, which represents it as dictionary.\n33\t \"\"\"\n34\t return structure(entity, cls)\n35\t\n36\t @classmethod\n37\t def from_dict(cls: type[T], data: dict[str, Any], camel_case: bool = True) -> T:\n38\t \"\"\"\n39\t Creates object from dictionary. It needs to be specified if the dictionary is in camelCase or snake_case.\n40\t \"\"\"\n41\t if camel_case:\n42\t data = cls.client_class().from_dict(data, camel_case).to_dict()\n43\t return structure(data, cls)\n44\t\n45\t def to_dict(self, camel_case: bool = True) -> dict[str, Any]:", "truncated": false}]} +{"ts": 1778577464.934453, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "The validator `value_in_allowed` uses `allowed_values` from `JsonApiDataSourceInAttributes.allowed_values`. Looking at the grep result earlier, \"POSTGRESQL\" is in the allowed values for the `type` attribute.\n\nNow let me also think about whether `cattrs.structure` would work for this. The `structure` call is:\n```python\nstructure({\"id\": \"test-ds\", \"credentials\": TokenCredentials(token=\"\"), \"name\": \"test-ds\", \"type\": \"POSTGRESQL\", \"schema\": \"demo\", \"url\": \"jdbc:postgresql://localhost:5432/demo\", \"date_time_semantics\": \"LOCAL\"}, CatalogDataSource)\n\n\n```\n\nWith `@define` classes, `cattrs.structure` should handle this smoothly\u2014when the dict contains `\"date_time_semantics\": \"LOCAL\"`, it'll populate that field correctly. I'm fairly confident the test will pass. Let me also verify there are no Python syntax errors in the test file around the edited area.\n\nActually, I'm noticing something about the `_check_allowed_values` validator\u2014it passes `self.__class__` to `value_in_allowed`, which then calls `instance.client_class()` when `client_class` is None. But in this case, `client_class` is being passed explicitly, so that shouldn't be an issue.\n\nLooking at the validator more closely, it passes `JsonApiDataSourceInAttributes` as the `client_class` argument, and then `value_in_allowed` retrieves allowed values using `client_class.allowed_values.get((attribute.name,))`. So when validating the `type` attribute, it should look up `JsonApiDataSourceInAttributes.allowed_values.get((\"type\",))` and find POSTGRESQL in there. The `attribute.name` in the validator context will be `\"type\"` since that's the attribute being validated.\n\n```\n\npython\n@type.validator # type: ignore\ndef _check_allowed_values(self, attribute: Attribute, value: str) -> None:\n value_in_allowed(self.__class__, attribute, value, JsonApiDataSourceInAttributes)\n```\n\nThe tuple key format matches what I saw in the grep results, so the lookup should work. Since `cattrs.structure` creates an instance by calling `__init__`,", "truncated": true}]} +{"ts": 1778577464.9366086, "type": "assistant", "blocks": [{"type": "text", "text": "Let me verify the accessor chain on the patch document works:", "truncated": false}]} +{"ts": 1778577465.165326, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_011k11sSd5DjdGCirvWqq6AY", "input": "{\"pattern\": \"class JsonApiDataSourcePatchDocument\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client\", \"output_mode\": \"content\"}", "truncated": false}]} +{"ts": 1778577465.2185547, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_011k11sSd5DjdGCirvWqq6AY", "is_error": false, "content": "No matches found", "truncated": false}]} +{"ts": 1778577467.1565711, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Glob", "id": "toolu_01XtDLijiT4n1uxTqYA4Dk1y", "input": "{\"pattern\": \"**/json_api_data_source_patch_document.py\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox\"}", "truncated": false}]} +{"ts": 1778577467.190964, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01XtDLijiT4n1uxTqYA4Dk1y", "is_error": false, "content": "packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_patch_document.py", "truncated": false}]} +{"ts": 1778577469.987941, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01ErPbRx3XrcLfXHzURdS2az", "input": "{\"pattern\": \"data.*JsonApiDataSourcePatch|attribute_map.*data\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_patch_document.py\", \"output_mode\": \"content\", \"head_limit\": 20}", "truncated": false}]} +{"ts": 1778577470.0227137, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01ErPbRx3XrcLfXHzURdS2az", "is_error": false, "content": "34: from gooddata_api_client.model.json_api_data_source_patch import JsonApiDataSourcePatch\n91: 'data': (JsonApiDataSourcePatch,), # noqa: E501\n114: data (JsonApiDataSourcePatch):\n203: data (JsonApiDataSourcePatch):", "truncated": false}]} +{"ts": 1778577472.0595043, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Good. `patch_doc.data` gives a `JsonApiDataSourcePatch` instance. Let me check `JsonApiDataSourcePatch` to see that it has `attributes`:", "truncated": false}]} +{"ts": 1778577472.9675949, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01EQuz9PvS4AsNLrs2mEQg28", "input": "{\"pattern\": \"attributes.*JsonApiDataSourcePatchAttributes|attribute_map.*attributes\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/.tox/py312/lib/python3.12/site-packages/gooddata_api_client/model/json_api_data_source_patch.py\", \"output_mode\": \"content\", \"head_limit\": 10}", "truncated": false}]} +{"ts": 1778577473.010204, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01EQuz9PvS4AsNLrs2mEQg28", "is_error": false, "content": "34: from gooddata_api_client.model.json_api_data_source_patch_attributes import JsonApiDataSourcePatchAttributes\n99: 'attributes': (JsonApiDataSourcePatchAttributes,), # noqa: E501\n126: attributes (JsonApiDataSourcePatchAttributes):\n220: attributes (JsonApiDataSourcePatchAttributes):", "truncated": false}]} +{"ts": 1778577479.952085, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "The accessor chain `patch_doc.data.attributes.date_time_semantics` is correct. `patch_doc.data` returns `JsonApiDataSourcePatch`, `.attributes` returns `JsonApiDataSourcePatchAttributes`, and `.date_time_semantics` is a valid field on that class.\n\nNow let me also check the `test_create_update.yaml` cassette to see how existing tests for `CatalogDataSource.from_api` work, to make sure I'm not missing something.\n\nActually, I'm confident the test is correct. Let me just double-check the edit is complete and clean.\n \nI'm also thinking through the `__attrs_post_init__` flow - when `_make_url()` is called with my test URL `\"jdbc:postgresql://localhost:5432/demo\"` and no `url_params`, it should just return the URL as-is since there are no parameters to append.\n\nNow I want to check what the `_fixtures_dir` path actually points to in the test setup.", "truncated": false}]} +{"ts": 1778577479.9798937, "type": "assistant", "blocks": [{"type": "text", "text": "Let me look at the `_fixtures_dir` definition to understand the fixtures location, and check for any remaining reference to the cassette:", "truncated": false}]} +{"ts": 1778577480.5502334, "type": "assistant", "blocks": [{"type": "tool_use", "name": "Grep", "id": "toolu_01Jztn2wShMYrRaebwpozgqb", "input": "{\"pattern\": \"_fixtures_dir\", \"path\": \"/home/runner/_work/gdc-nas/gdc-nas/sdk/packages/gooddata-sdk/tests/catalog/test_catalog_data_source.py\", \"output_mode\": \"content\", \"head_limit\": 10}", "truncated": false}]} +{"ts": 1778577480.5838366, "type": "user", "blocks": [{"type": "tool_result", "tool_use_id": "toolu_01Jztn2wShMYrRaebwpozgqb", "is_error": false, "content": "56:_fixtures_dir = _current_dir / \"fixtures\" / \"data_sources\"\n59:@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_register_upload_notification.yaml\"))\n76:@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_generate_logical_model.yaml\"))\n110:@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_scan_pdm_and_generate_logical_model.yaml\"))\n172:@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_generate_logical_model_sql_datasets.yaml\"))\n202:@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_scan_pdm_and_generate_logical_model_sql_datasets.yaml\"))\n231:@gd_vcr.use_cassette(str(_fixtures_dir / \"demo_data_sources_list.yaml\"))\n264:@gd_vcr.use_cassette(str(_fixtures_dir / \"test_create_update.yaml\"))\n309:@gd_vcr.use_cassette(str(_fixtures_dir / \"redshift.yaml\"))\n330:@gd_vcr.use_cassette(str(_fixtures_dir / \"vertica.yaml\"))\n\n[Showing results with pagination = limit: 10]", "truncated": false}]} +{"ts": 1778577483.0823746, "type": "assistant", "blocks": [{"type": "thinking", "thinking": "Good. The `_fixtures_dir` is just used for other tests that use VCR cassettes. Our new `test_date_time_semantics` no longer uses `@gd_vcr.use_cassette`, so there's no cassette reference for this test.\n\nLet me verify there are no remaining references to the old cassette in the test file:", "truncated": false}]}