feat(indexes): add source_column field to index responses

Author: hotdata-automation[bot]

CHANGELOG.md

@@ -7,6 +7,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.4.2] - 2026-06-18
+
+### Changed
+
+- feat(indexes): add source_column field to index responses
+
 ## [0.4.1] - 2026-06-17
 
 ### Changed

docs/IndexEntryResponse.md

@@ -11,6 +11,7 @@ Name | Type | Description | Notes
 **index_name** | **str** |  | 
 **index_type** | **str** |  | 
 **metric** | **str** | Distance metric this index was built with. Only present for vector indexes. | [optional] 
+**source_column** | **str** | Source text column for an embedding-backed vector index. A query searches it via `vector_distance(<source_column>, …)`; the indexed `columns` hold the generated embedding column instead. Absent for BM25, sorted, and direct (existing-column) vector indexes. | [optional] 
 **status** | [**IndexStatus**](IndexStatus.md) |  | 
 **updated_at** | **datetime** |  | 
 **connection_id** | **str** |  | [optional] 

docs/IndexInfoResponse.md

@@ -11,6 +11,7 @@ Name | Type | Description | Notes
 **index_name** | **str** |  | 
 **index_type** | **str** |  | 
 **metric** | **str** | Distance metric this index was built with. Only present for vector indexes. | [optional] 
+**source_column** | **str** | Source text column for an embedding-backed vector index. A query searches it via `vector_distance(<source_column>, …)`; the indexed `columns` hold the generated embedding column instead. Absent for BM25, sorted, and direct (existing-column) vector indexes. | [optional] 
 **status** | [**IndexStatus**](IndexStatus.md) |  | 
 **updated_at** | **datetime** |  | 
 

docs/JobResult.md

@@ -24,6 +24,7 @@ Name | Type | Description | Notes
 **index_name** | **str** |  | 
 **index_type** | **str** |  | 
 **metric** | **str** | Distance metric this index was built with. Only present for vector indexes. | [optional] 
+**source_column** | **str** | Source text column for an embedding-backed vector index. A query searches it via `vector_distance(<source_column>, …)`; the indexed `columns` hold the generated embedding column instead. Absent for BM25, sorted, and direct (existing-column) vector indexes. | [optional] 
 **updated_at** | **datetime** |  | 
 
 ## Example

docs/QueryApi.md

@@ -104,7 +104,7 @@ Name | Type | Description  | Notes
 **202** | Query submitted asynchronously |  -  |
 **400** | Invalid request (no database specified, or header/body database_id conflict) |  -  |
 **404** | Database not found |  -  |
-**429** | Too many concurrent queries; retry after the Retry-After delay |  -  |
+**429** | The engine was too busy to run this query right now — too many concurrent queries, or not enough memory available (often because of other queries running at the same time). Retry after the Retry-After delay; if it persists, narrowing the query (add a filter or LIMIT) may help. |  -  |
 **500** | Internal server error |  -  |
 **503** | Result store temporarily unavailable (a truncated result could not be persisted); retry after the Retry-After delay |  -  |
 

hotdata/api_client.py

@@ -91,7 +91,7 @@ def __init__(
             self.default_headers[header_name] = header_value
         self.cookie = cookie
         # Set default User-Agent.
-        self.user_agent = 'OpenAPI-Generator/0.4.1/python'
+        self.user_agent = 'OpenAPI-Generator/0.4.2/python'
         self.client_side_validation = configuration.client_side_validation
 
     def __enter__(self):

hotdata/models/index_entry_response.py

@@ -34,12 +34,13 @@ class IndexEntryResponse(BaseModel):
     index_name: StrictStr
     index_type: StrictStr
     metric: Optional[StrictStr] = Field(default=None, description="Distance metric this index was built with. Only present for vector indexes.")
+    source_column: Optional[StrictStr] = Field(default=None, description="Source text column for an embedding-backed vector index. A query searches it via `vector_distance(<source_column>, …)`; the indexed `columns` hold the generated embedding column instead. Absent for BM25, sorted, and direct (existing-column) vector indexes.")
     status: IndexStatus
     updated_at: datetime
     connection_id: Optional[StrictStr] = None
     schema_name: StrictStr
     table_name: StrictStr
-    __properties: ClassVar[List[str]] = ["columns", "created_at", "index_name", "index_type", "metric", "status", "updated_at", "connection_id", "schema_name", "table_name"]
+    __properties: ClassVar[List[str]] = ["columns", "created_at", "index_name", "index_type", "metric", "source_column", "status", "updated_at", "connection_id", "schema_name", "table_name"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -102,6 +103,7 @@ def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
             "index_name": obj.get("index_name"),
             "index_type": obj.get("index_type"),
             "metric": obj.get("metric"),
+            "source_column": obj.get("source_column"),
             "status": obj.get("status"),
             "updated_at": obj.get("updated_at"),
             "connection_id": obj.get("connection_id"),

hotdata/models/index_info_response.py

@@ -34,9 +34,10 @@ class IndexInfoResponse(BaseModel):
     index_name: StrictStr
     index_type: StrictStr
     metric: Optional[StrictStr] = Field(default=None, description="Distance metric this index was built with. Only present for vector indexes.")
+    source_column: Optional[StrictStr] = Field(default=None, description="Source text column for an embedding-backed vector index. A query searches it via `vector_distance(<source_column>, …)`; the indexed `columns` hold the generated embedding column instead. Absent for BM25, sorted, and direct (existing-column) vector indexes.")
     status: IndexStatus
     updated_at: datetime
-    __properties: ClassVar[List[str]] = ["columns", "created_at", "index_name", "index_type", "metric", "status", "updated_at"]
+    __properties: ClassVar[List[str]] = ["columns", "created_at", "index_name", "index_type", "metric", "source_column", "status", "updated_at"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -82,6 +83,11 @@ def to_dict(self) -> Dict[str, Any]:
         if self.metric is None and "metric" in self.model_fields_set:
             _dict['metric'] = None
 
+        # set to None if source_column (nullable) is None
+        # and model_fields_set contains the field
+        if self.source_column is None and "source_column" in self.model_fields_set:
+            _dict['source_column'] = None
+
         return _dict
 
     @classmethod
@@ -99,6 +105,7 @@ def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
             "index_name": obj.get("index_name"),
             "index_type": obj.get("index_type"),
             "metric": obj.get("metric"),
+            "source_column": obj.get("source_column"),
             "status": obj.get("status"),
             "updated_at": obj.get("updated_at")
         })

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "hotdata"
-version = "0.4.1"
+version = "0.4.2"
 description = "Hotdata API"
 authors = [
   {name = "Hotdata",email = "developers@hotdata.dev"},

test/test_index_entry_response.py

@@ -43,6 +43,7 @@ def make_instance(self, include_optional) -> IndexEntryResponse:
                 index_name = '',
                 index_type = '',
                 metric = '',
+                source_column = '',
                 status = 'ready',
                 updated_at = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'),
                 connection_id = '',