FIX: Add Support for Declaring Vector Fields in JsonModel with Relevant Unit Tests

aredis_om/model/model.py

@@ -2162,7 +2162,11 @@ def schema_for_type(
             # a proper type, we can pull the type information from the origin of the first argument.
             if not isinstance(typ, type):
                 type_args = typing_get_args(field_info.annotation)
-                typ = type_args[0].__origin__
+                typ = (
+                    getattr(type_args[0], "__origin__", type_args[0])
+                    if type_args
+                    else typ
+                )
 
             # TODO: GEO field
             if is_vector and vector_options:

tests/test_json_model.py

@@ -12,6 +12,7 @@
 
 import pytest
 import pytest_asyncio
+from redis.exceptions import ResponseError
 
 from aredis_om import (
     EmbeddedJsonModel,
@@ -21,6 +22,7 @@
     NotFoundError,
     QueryNotSupportedError,
     RedisModelError,
+    VectorFieldOptions,
 )
 
 # We need to run this check as sync code (during tests) even in async mode
@@ -1173,3 +1175,82 @@ class Game(JsonModel):
     )
     print(q.query)
     assert q.query == "(@player1_username:{username})| (@player2_username:{username})"
+
+
+@py_test_mark_asyncio
+def test_vector_field_definition(redis):
+    """
+    Test the definition and behavior of a vector field in a JsonModel.
+    This test verifies:
+    1. The model schema includes "VECTOR" for a vector field with specified options.
+    2. Instances with vector fields can be saved and retrieved accurately.
+    3. Vector field values remain consistent after persistence.
+
+    Args:
+        redis: Redis connection fixture for testing.
+    """
+
+    class Group(JsonModel):
+        articles: List[str]
+        tender_text: str = Field(index=False)
+        tender_embedding: List[float] = Field(
+            index=True,
+            vector_options=VectorFieldOptions(
+                algorithm=VectorFieldOptions.ALGORITHM.FLAT,
+                type=VectorFieldOptions.TYPE.FLOAT32,
+                dimension=3,
+                distance_metric=VectorFieldOptions.DISTANCE_METRIC.COSINE,
+            ),
+        )
+
+    schema = Group.redisearch_schema()
+    assert "VECTOR" in schema
+
+    group = Group(
+        articles=["article_1", "article_2"],
+        tender_text="Sample text",
+        tender_embedding=[0.1, 0.2, 0.3],
+    )
+    group.save()
+
+    retrieved_group = await Group.get(group.pk)
+    assert retrieved_group.tender_embedding == [0.1, 0.2, 0.3]
+
+    retrieved_group = Group.get(group.pk)
+    assert retrieved_group.tender_embedding == [0.1, 0.2, 0.3]
+
+
+def test_vector_field_schema_debug(redis):
+    """
+    Test and debug the schema definition for a vector field in a JsonModel.
+
+    This test ensures:
+    1. The schema for a vector field is generated correctly.
+    2. No syntax errors occur when saving a model instance.
+    3. The Redis schema syntax is valid and debugged if issues arise.
+
+    Steps:
+    - Define a `TestModel` with a vector field `embedding`.
+    - Attempt to save an instance and print the schema.
+    - Handle and fail gracefully if Redis raises a ResponseError.
+
+    Args:
+        redis: Redis connection fixture for testing.
+    """
+
+    class TestModel(JsonModel):
+        embedding: List[float] = Field(
+            index=True,
+            vector_options=VectorFieldOptions(
+                algorithm=VectorFieldOptions.ALGORITHM.FLAT,
+                type=VectorFieldOptions.TYPE.FLOAT32,
+                dimension=3,
+                distance_metric=VectorFieldOptions.DISTANCE_METRIC.COSINE,
+            ),
+        )
+
+    try:
+        TestModel(embedding=[0.1, 0.2, 0.3]).save()
+        print(TestModel.redisearch_schema())
+    except ResponseError as e:
+        pytest.fail(f"Redis rejected the schema with error: {e}")