mirror of
https://gitee.com/milvus-io/milvus.git
synced 2025-12-08 01:58:34 +08:00
233 lines
11 KiB
Python
233 lines
11 KiB
Python
import logging
|
|
import time
|
|
from utils.util_pymilvus import *
|
|
from common.common_type import CaseLabel, CheckTasks
|
|
from common import common_type as ct
|
|
from common import common_func as cf
|
|
from base.client_v2_base import TestMilvusClientV2Base
|
|
import pytest
|
|
from idx_ngram import NGRAM
|
|
|
|
index_type = "NGRAM"
|
|
success = "success"
|
|
pk_field_name = 'id'
|
|
vector_field_name = 'vector'
|
|
content_field_name = 'content_ngram'
|
|
json_field_name = 'json_field'
|
|
dim = ct.default_dim
|
|
default_nb = 2000
|
|
default_build_params = {"min_gram": 2, "max_gram": 3}
|
|
|
|
|
|
class TestNgramBuildParams(TestMilvusClientV2Base):
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("params", NGRAM.build_params)
|
|
def test_ngram_build_params(self, params):
|
|
"""
|
|
Test the build params of NGRAM index
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
schema, _ = self.create_schema(client)
|
|
schema.add_field(pk_field_name, datatype=DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(vector_field_name, datatype=DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(content_field_name, datatype=DataType.VARCHAR, max_length=1000)
|
|
|
|
# Check if this test case requires JSON field
|
|
build_params = params.get("params", None)
|
|
has_json_params = (build_params is not None and
|
|
("json_path" in build_params or "json_cast_type" in build_params))
|
|
|
|
target_field_name = content_field_name # Default to VARCHAR field
|
|
|
|
if has_json_params:
|
|
# Add JSON field for JSON-related parameter tests
|
|
schema.add_field(json_field_name, datatype=DataType.JSON)
|
|
target_field_name = json_field_name
|
|
|
|
self.create_collection(client, collection_name, schema=schema)
|
|
|
|
# Insert test data
|
|
nb = 3
|
|
rows = cf.gen_row_data_by_schema(nb=nb, schema=schema, start=0)
|
|
|
|
if has_json_params:
|
|
# JSON test data
|
|
json_test_data = [
|
|
{"body": "The stadium is large", "title": "Sports"},
|
|
{"body": "The park is beautiful", "title": "Nature"},
|
|
{"body": "The school is nearby", "title": "Education"}
|
|
]
|
|
for i, row in enumerate(rows):
|
|
row[content_field_name] = f"text content {i}" # Still provide VARCHAR data
|
|
row[json_field_name] = json_test_data[i]
|
|
else:
|
|
# VARCHAR test data
|
|
varchar_test_data = ["The stadium is large", "The park is beautiful", "The school is nearby"]
|
|
for i, row in enumerate(rows):
|
|
row[content_field_name] = varchar_test_data[i]
|
|
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
|
|
# Create index
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_name = cf.gen_str_by_length(10, letters_only=True)
|
|
index_params.add_index(field_name=target_field_name,
|
|
index_name=index_name,
|
|
index_type=index_type,
|
|
params=build_params)
|
|
|
|
# Build index
|
|
if params.get("expected", None) != success:
|
|
self.create_index(client, collection_name, index_params,
|
|
check_task=CheckTasks.err_res,
|
|
check_items=params.get("expected"))
|
|
else:
|
|
self.create_index(client, collection_name, index_params)
|
|
self.wait_for_index_ready(client, collection_name, index_name=index_name)
|
|
|
|
# Create vector index before loading collection
|
|
vector_index_params = self.prepare_index_params(client)[0]
|
|
vector_index_params.add_index(field_name=vector_field_name,
|
|
metric_type=cf.get_default_metric_for_vector_type(
|
|
vector_type=DataType.FLOAT_VECTOR),
|
|
index_type="IVF_FLAT",
|
|
params={"nlist": 128})
|
|
self.create_index(client, collection_name, vector_index_params)
|
|
self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
|
|
|
|
# Load collection
|
|
self.load_collection(client, collection_name)
|
|
|
|
# Test query based on field type
|
|
if has_json_params:
|
|
filter_expr = f"{json_field_name}['body'] LIKE \"%stadium%\""
|
|
else:
|
|
filter_expr = f'{content_field_name} LIKE "%stadium%"'
|
|
|
|
self.query(client, collection_name, filter=filter_expr,
|
|
output_fields=["count(*)"],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"count(*)": 1})
|
|
|
|
# Verify the index params are persisted
|
|
idx_info = client.describe_index(collection_name, index_name)
|
|
if build_params is not None:
|
|
for key, value in build_params.items():
|
|
if value is not None and key not in ["json_path", "json_cast_type"]:
|
|
assert key in idx_info.keys()
|
|
assert str(value) in idx_info.values()
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("scalar_field_type", ct.all_scalar_data_types)
|
|
def test_ngram_on_all_scalar_fields(self, scalar_field_type):
|
|
"""
|
|
Test NGRAM index on all scalar field types and verify proper error handling
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
schema, _ = self.create_schema(client)
|
|
schema.add_field(pk_field_name, datatype=DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(vector_field_name, datatype=DataType.FLOAT_VECTOR, dim=dim)
|
|
|
|
# Add the scalar field with appropriate parameters
|
|
if scalar_field_type == DataType.VARCHAR:
|
|
schema.add_field("scalar_field", datatype=scalar_field_type, max_length=1000)
|
|
elif scalar_field_type == DataType.ARRAY:
|
|
schema.add_field("scalar_field", datatype=scalar_field_type,
|
|
element_type=DataType.VARCHAR, max_capacity=10, max_length=100)
|
|
else:
|
|
schema.add_field("scalar_field", datatype=scalar_field_type)
|
|
|
|
self.create_collection(client, collection_name, schema=schema)
|
|
|
|
# Generate appropriate test data for each field type
|
|
nb = 3
|
|
rows = cf.gen_row_data_by_schema(nb=nb, schema=schema, start=0)
|
|
|
|
# Update scalar field with appropriate test data
|
|
if scalar_field_type == DataType.VARCHAR:
|
|
test_data = ["The stadium is large", "The park is beautiful", "The school is nearby"]
|
|
for i, row in enumerate(rows):
|
|
row["scalar_field"] = test_data[i]
|
|
elif scalar_field_type == DataType.JSON:
|
|
test_data = [
|
|
{"body": "This is a school", "title": "Education"},
|
|
{"body": "This is a park", "title": "Recreation"},
|
|
{"body": "This is a mall", "title": "Shopping"}
|
|
]
|
|
for i, row in enumerate(rows):
|
|
row["scalar_field"] = test_data[i]
|
|
elif scalar_field_type == DataType.ARRAY:
|
|
test_data = [
|
|
["word1", "word2", "stadium"],
|
|
["text1", "text2", "park"],
|
|
["data1", "data2", "school"]
|
|
]
|
|
for i, row in enumerate(rows):
|
|
row["scalar_field"] = test_data[i]
|
|
# For other scalar types, keep the auto-generated data
|
|
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
|
|
# Create index
|
|
index_name = cf.gen_str_by_length(10, letters_only=True)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
if scalar_field_type == DataType.JSON:
|
|
# JSON field requires json_path and json_cast_type
|
|
index_params.add_index(field_name="scalar_field",
|
|
index_name=index_name,
|
|
index_type=index_type,
|
|
params={
|
|
"min_gram": 2,
|
|
"max_gram": 3,
|
|
"json_path": "scalar_field['body']",
|
|
"json_cast_type": "varchar"
|
|
})
|
|
else:
|
|
index_params.add_index(field_name="scalar_field",
|
|
index_name=index_name,
|
|
index_type=index_type,
|
|
params=default_build_params)
|
|
|
|
# Check if the field type is supported for NGRAM index
|
|
if scalar_field_type not in NGRAM.supported_field_types:
|
|
self.create_index(client, collection_name, index_params,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 999,
|
|
"err_msg": "ngram index can only be created on VARCHAR or JSON field"})
|
|
else:
|
|
self.create_index(client, collection_name, index_params)
|
|
self.wait_for_index_ready(client, collection_name, index_name=index_name)
|
|
|
|
# Create vector index before loading collection
|
|
vector_index_params = self.prepare_index_params(client)[0]
|
|
vector_index_params.add_index(field_name=vector_field_name,
|
|
metric_type=cf.get_default_metric_for_vector_type(vector_type=DataType.FLOAT_VECTOR),
|
|
index_type="IVF_FLAT",
|
|
params={"nlist": 128})
|
|
self.create_index(client, collection_name, vector_index_params)
|
|
self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
|
|
|
|
self.load_collection(client, collection_name)
|
|
|
|
# Test query for supported types
|
|
if scalar_field_type == DataType.VARCHAR:
|
|
filter_expr = 'scalar_field LIKE "%stadium%"'
|
|
self.query(client, collection_name, filter=filter_expr,
|
|
output_fields=["count(*)"],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"count(*)": 1})
|
|
elif scalar_field_type == DataType.JSON:
|
|
filter_expr = "scalar_field['body'] LIKE \"%school%\""
|
|
self.query(client, collection_name, filter=filter_expr,
|
|
output_fields=["count(*)"],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"count(*)": 1})
|
|
|