admin/milvus
1
0
Fork 0
mirror of https://gitee.com/milvus-io/milvus.git synced 2025-12-07 17:48:29 +08:00
Code Issues Packages Projects Releases Wiki Activity

test: add field case about reranker and analyzer (#44095)

Browse Source 
related issue: #42126

---------

Signed-off-by: qixuan <673771573@qq.com>
This commit is contained in:
qixuan 2025-09-01 17:27:52 +08:00 committed by GitHub
parent c420e7bd27
commit e126df2330
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 178 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

181
tests/python_client/milvus_client/test_add_field_feature.py
View File

@ -159,8 +159,8 @@ class TestMilvusClientAddFieldFeature(TestMilvusClientV2Base):
"limit": default_limit})
# 5. insert data(old field)
rows_old = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
default_float_field_name: i * 1.0,
default_string_field_name: str(i)} for i in range(default_nb, default_nb * 2)]
default_float_field_name: i * 1.0,
default_string_field_name: str(i)} for i in range(default_nb, default_nb * 2)]
results = self.insert(client, collection_name, rows_old)[0]
assert results['insert_count'] == default_nb
insert_ids_with_old_field = [i for i in range(default_nb, default_nb * 2)]
@ -191,7 +191,6 @@ class TestMilvusClientAddFieldFeature(TestMilvusClientV2Base):
self.release_collection(client, collection_name)
self.drop_collection(client, collection_name)
@pytest.mark.tags(CaseLabel.L1)
def test_milvus_client_upsert_with_added_field(self):
"""
@ -328,6 +327,115 @@ class TestMilvusClientAddFieldFeature(TestMilvusClientV2Base):
self.release_collection(client, collection_name)
self.drop_collection(client, collection_name)
@pytest.mark.tags(CaseLabel.L1)
def test_milvus_client_add_field_with_analyzer(self):
"""
target: test add field with analyzer configuration
method: create collection, add field with standard analyzer,
insert data and verify
expected: successfully add field with analyzer and perform text search
"""
client = self._client()
collection_name = cf.gen_collection_name_by_testcase_name()
dim = 8
# 1. create collection with basic schema
schema = self.create_schema(client, enable_dynamic_field=False)[0]
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_partition_key=True)
index_params = self.prepare_index_params(client)[0]
index_params.add_index(default_vector_field_name, metric_type="COSINE")
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
# 2. insert initial data before adding analyzer field
vectors = cf.gen_vectors(default_nb, dim, vector_data_type=DataType.FLOAT_VECTOR)
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
default_string_field_name: str(i)} for i in range(default_nb)]
results = self.insert(client, collection_name, rows)[0]
assert results['insert_count'] == default_nb
# 3. add new field with standard analyzer
analyzer_params = {
"type": "standard",
"stop_words": ["for", "the", "is", "a"]
}
self.add_collection_field(client, collection_name, field_name="text_content", data_type=DataType.VARCHAR,
nullable=True, max_length=1000, enable_analyzer=True, analyzer_params=analyzer_params)
# 4. insert data with the new analyzer field
text_data = [
"The Milvus vector database is built for scale",
"This is a test document for analyzer",
"Vector search with text analysis capabilities",
"Database performance and scalability features"
]
rows_with_analyzer = []
for i in range(default_nb, default_nb + len(text_data)):
rows_with_analyzer.append({
default_primary_key_field_name: i,
default_vector_field_name: vectors[i - default_nb],
default_string_field_name: str(i),
"text_content": text_data[i - default_nb]
})
results = self.insert(client, collection_name, rows_with_analyzer)[0]
assert results['insert_count'] == len(text_data)
# 5. verify the analyzer field was added correctly
collection_info = self.describe_collection(client, collection_name)[0]
field_names = [field["name"] for field in collection_info["fields"]]
assert "text_content" in field_names
# 6. test text search using the analyzer field
vectors_to_search = [vectors[0]]
# Simple search without filter to verify basic functionality
search_results = self.search(
client, collection_name, vectors_to_search,
check_task=CheckTasks.check_search_results,
check_items={
"enable_milvus_client_api": True,
"nq": len(vectors_to_search),
"limit": 10, # Adjust limit to match actual results
"pk_name": default_primary_key_field_name
}
)
# Verify search returned some results
assert len(search_results[0]) > 0
# 7. test query with analyzer field - use simpler condition
query_results = self.query(
client, collection_name,
filter="text_content is not null",
check_task=CheckTasks.check_query_results,
check_items={
"pk_name": default_primary_key_field_name,
"exp_limit": 4 # We expect 4 documents with text_content
}
)
# Verify that we get results for documents with text_content
assert len(query_results[0]) > 0
# 8. test run_analyzer to verify the analyzer configuration
sample_text = "The Milvus vector database is built for scale"
analyzer_result = client.run_analyzer(sample_text, analyzer_params)
# Verify analyzer produces tokens
# (should remove stop words like "the", "is", "a")
tokens = analyzer_result.tokens
assert len(tokens) > 0
# Handle different token formats - tokens might be strings or dictionaries
if isinstance(tokens[0], str):
token_texts = tokens
else:
token_texts = [token["token"] for token in tokens]
# Check that stop words are filtered out
assert "the" not in token_texts
assert "is" not in token_texts
assert "a" not in token_texts
# 9. cleanup
self.release_collection(client, collection_name)
self.drop_collection(client, collection_name)
class TestMilvusClientAddFieldFeatureInvalid(TestMilvusClientV2Base):
"""Test invalid cases for add field feature"""
@ -515,3 +623,70 @@ class TestMilvusClientAddFieldFeatureInvalid(TestMilvusClientV2Base):
data_type=DataType.VARCHAR, nullable=True, max_length=64)
self.add_collection_field(client, collection_name, field_name=field_name, data_type=DataType.VARCHAR,
nullable=True, max_length=64, check_task=CheckTasks.err_res, check_items=error)
@pytest.mark.tags(CaseLabel.L2)
def test_milvus_client_add_field_with_reranker_unsupported(self):
"""
target: test that add_collection_field and decay ranker combination is not supported
method: create collection without reranker field, add nullable reranker field via add_collection_field,
then try to use it with decay ranker
expected: raise exception because decay ranker requires non-nullable fields but add_collection_field
only supports nullable fields, creating a technical limitation
"""
client = self._client()
collection_name = cf.gen_collection_name_by_testcase_name()
dim = 8
# 1. create collection WITHOUT reranker field initially
schema = self.create_schema(client, enable_dynamic_field=False)[0]
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_partition_key=True)
# Note: NO reranker field here - we'll try to add it later via add_collection_field
index_params = self.prepare_index_params(client)[0]
index_params.add_index(default_vector_field_name, metric_type="COSINE")
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
# 2. insert initial data WITHOUT reranker field
vectors = cf.gen_vectors(default_nb, dim, vector_data_type=DataType.FLOAT_VECTOR)
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
default_string_field_name: str(i)} for i in range(default_nb)]
results = self.insert(client, collection_name, rows)[0]
assert results['insert_count'] == default_nb
# 3. Try to add nullable reranker field via add_collection_field (nullable must be True)
# This will succeed in adding the field, but then we'll test if it can work with decay reranker
# The conflict: add_collection_field only supports nullable fields, but decay reranker needs non-nullable fields
self.add_collection_field(client, collection_name, field_name=ct.default_reranker_field_name,
data_type=DataType.INT64, nullable=True, default_value=0)
# 4. Insert data with the newly added reranker field
# Generate new vectors for the second batch of data
vectors_batch2 = cf.gen_vectors(default_nb, dim, vector_data_type=DataType.FLOAT_VECTOR)
rows_with_reranker = [{default_primary_key_field_name: i, default_vector_field_name: vectors_batch2[i - default_nb],
default_string_field_name: str(i), ct.default_reranker_field_name: i}
for i in range(default_nb, default_nb * 2)]
results = self.insert(client, collection_name, rows_with_reranker)[0]
assert results['insert_count'] == default_nb
# 5. Try to use the nullable reranker field with decay reranker
# This should fail because decay reranker requires non-nullable fields for proper functionality
from pymilvus import Function, FunctionType
my_rerank_fn = Function(
name="my_reranker",
input_field_names=[ct.default_reranker_field_name],
function_type=FunctionType.RERANK,
params={
"reranker": "decay",
"function": "gauss",
"origin": 0,
"offset": 0,
"decay": 0.5,
"scale": 100
}
)
error = {ct.err_code: 65535, ct.err_msg: "Function input field cannot be nullable: field reranker_field"}
self.search(client, collection_name, [vectors[0]], ranker=my_rerank_fn,
check_task=CheckTasks.err_res, check_items=error)