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f301692900
related issue: https://github.com/milvus-io/milvus/issues/43989 Signed-off-by: yanliang567 <yanliang.qiao@zilliz.com>
261 lines
13 KiB
Python
261 lines
13 KiB
Python
import logging
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import time
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from utils.util_pymilvus import *
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from common.common_type import CaseLabel, CheckTasks
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from common import common_type as ct
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from common import common_func as cf
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from base.client_v2_base import TestMilvusClientV2Base
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import pytest
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from idx_ngram import NGRAM
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index_type = "NGRAM"
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success = "success"
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pk_field_name = 'id'
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vector_field_name = 'vector'
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content_field_name = 'content_ngram'
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json_field_name = 'json_field'
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dim = ct.default_dim
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default_nb = 2000
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default_build_params = {"min_gram": 2, "max_gram": 3}
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class TestNgramBuildParams(TestMilvusClientV2Base):
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("params", NGRAM.build_params)
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def test_ngram_build_params(self, params):
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"""
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Test the build params of NGRAM index
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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schema, _ = self.create_schema(client)
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schema.add_field(pk_field_name, datatype=DataType.INT64, is_primary=True, auto_id=False)
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schema.add_field(vector_field_name, datatype=DataType.FLOAT_VECTOR, dim=dim)
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schema.add_field(content_field_name, datatype=DataType.VARCHAR, max_length=1000)
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# Check if this test case requires JSON field
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build_params = params.get("params", None)
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has_json_params = (build_params is not None and
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("json_path" in build_params or "json_cast_type" in build_params))
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target_field_name = content_field_name # Default to VARCHAR field
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if has_json_params:
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# Add JSON field for JSON-related parameter tests
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schema.add_field(json_field_name, datatype=DataType.JSON)
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target_field_name = json_field_name
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self.create_collection(client, collection_name, schema=schema)
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# Insert test data
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nb = default_nb
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rows = cf.gen_row_data_by_schema(nb=nb, schema=schema, start=0)
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if has_json_params:
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# Generate JSON test data with varied content
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json_keywords = ["stadium", "park", "school", "library", "hospital", "restaurant", "office", "store"]
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for i, row in enumerate(rows):
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keyword_idx = i % len(json_keywords)
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keyword = json_keywords[keyword_idx]
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row[content_field_name] = f"text content {i}" # Still provide VARCHAR data
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row[json_field_name] = {
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"body": f"This is a {keyword} building",
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"title": f"Location {i}",
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"description": f"Description for {keyword} number {i}"
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}
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else:
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# Generate VARCHAR test data with varied content
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varchar_keywords = ["stadium", "park", "school", "library", "hospital", "restaurant", "office", "store"]
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for i, row in enumerate(rows):
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keyword_idx = i % len(varchar_keywords)
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keyword = varchar_keywords[keyword_idx]
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row[content_field_name] = f"The {keyword} is large and beautiful number {i}"
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# Insert data in batches for better performance
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batch_size = 1000
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for i in range(0, nb, batch_size):
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batch_rows = rows[i:i + batch_size]
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self.insert(client, collection_name, batch_rows)
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self.flush(client, collection_name)
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# Create index
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index_params = self.prepare_index_params(client)[0]
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index_name = cf.gen_str_by_length(10, letters_only=True)
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index_params.add_index(field_name=target_field_name,
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index_name=index_name,
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index_type=index_type,
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params=build_params)
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# Build index
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if params.get("expected", None) != success:
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self.create_index(client, collection_name, index_params,
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check_task=CheckTasks.err_res,
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check_items=params.get("expected"))
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else:
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self.create_index(client, collection_name, index_params)
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self.wait_for_index_ready(client, collection_name, index_name=index_name)
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# Create vector index before loading collection
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vector_index_params = self.prepare_index_params(client)[0]
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vector_index_params.add_index(field_name=vector_field_name,
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metric_type=cf.get_default_metric_for_vector_type(
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vector_type=DataType.FLOAT_VECTOR),
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index_type="IVF_FLAT",
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params={"nlist": 128})
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self.create_index(client, collection_name, vector_index_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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# Load collection
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self.load_collection(client, collection_name)
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# Test query based on field type
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if has_json_params:
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filter_expr = f"{json_field_name}['body'] LIKE \"%stadium%\""
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else:
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filter_expr = f'{content_field_name} LIKE "%stadium%"'
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# Calculate expected count: 2000 data points with 8 keywords cycling
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# Each keyword appears 2000/8 = 250 times
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expected_count = default_nb // 8 # 250 matches for "stadium"
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self.query(client, collection_name, filter=filter_expr,
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output_fields=["count(*)"],
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check_task=CheckTasks.check_query_results,
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check_items={"enable_milvus_client_api": True,
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"count(*)": expected_count})
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# Verify the index params are persisted
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idx_info = client.describe_index(collection_name, index_name)
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if build_params is not None:
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for key, value in build_params.items():
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if value is not None and key not in ["json_path", "json_cast_type"]:
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assert key in idx_info.keys()
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assert str(value) in idx_info.values()
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("scalar_field_type", ct.all_scalar_data_types)
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def test_ngram_on_all_scalar_fields(self, scalar_field_type):
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"""
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Test NGRAM index on all scalar field types and verify proper error handling
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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schema, _ = self.create_schema(client)
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schema.add_field(pk_field_name, datatype=DataType.INT64, is_primary=True, auto_id=False)
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schema.add_field(vector_field_name, datatype=DataType.FLOAT_VECTOR, dim=dim)
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# Add the scalar field with appropriate parameters
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if scalar_field_type == DataType.VARCHAR:
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schema.add_field("scalar_field", datatype=scalar_field_type, max_length=1000)
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elif scalar_field_type == DataType.ARRAY:
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schema.add_field("scalar_field", datatype=scalar_field_type,
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element_type=DataType.VARCHAR, max_capacity=10, max_length=100)
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else:
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schema.add_field("scalar_field", datatype=scalar_field_type)
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self.create_collection(client, collection_name, schema=schema)
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# Generate appropriate test data for each field type
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nb = default_nb
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rows = cf.gen_row_data_by_schema(nb=nb, schema=schema, start=0)
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# Update scalar field with appropriate test data
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if scalar_field_type == DataType.VARCHAR:
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# Generate varied VARCHAR data for better testing
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keywords = ["stadium", "park", "school", "library", "hospital", "restaurant", "office", "store"]
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for i, row in enumerate(rows):
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keyword_idx = i % len(keywords)
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keyword = keywords[keyword_idx]
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row["scalar_field"] = f"The {keyword} is a large building number {i}"
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elif scalar_field_type == DataType.JSON:
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# Generate varied JSON data for better testing
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keywords = ["school", "park", "mall", "library", "hospital", "restaurant", "office", "store"]
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for i, row in enumerate(rows):
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keyword_idx = i % len(keywords)
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keyword = keywords[keyword_idx]
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row["scalar_field"] = {
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"body": f"This is a {keyword}",
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"title": f"Location {i}",
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"category": f"Category {keyword_idx}"
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}
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elif scalar_field_type == DataType.ARRAY:
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# Generate varied ARRAY data for better testing
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base_words = ["word", "text", "data", "item", "element"]
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keywords = ["stadium", "park", "school", "library", "hospital"]
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for i, row in enumerate(rows):
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base_idx = i % len(base_words)
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keyword_idx = i % len(keywords)
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row["scalar_field"] = [f"{base_words[base_idx]}1", f"{base_words[base_idx]}2", keywords[keyword_idx]]
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# For other scalar types, keep the auto-generated data
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# Insert data in batches for better performance
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batch_size = 1000
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for i in range(0, nb, batch_size):
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batch_rows = rows[i:i + batch_size]
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self.insert(client, collection_name, batch_rows)
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self.flush(client, collection_name)
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# Create index
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index_name = cf.gen_str_by_length(10, letters_only=True)
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index_params = self.prepare_index_params(client)[0]
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if scalar_field_type == DataType.JSON:
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# JSON field requires json_path and json_cast_type
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index_params.add_index(field_name="scalar_field",
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index_name=index_name,
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index_type=index_type,
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params={
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"min_gram": 2,
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"max_gram": 3,
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"json_path": "scalar_field['body']",
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"json_cast_type": "varchar"
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})
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else:
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index_params.add_index(field_name="scalar_field",
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index_name=index_name,
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index_type=index_type,
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params=default_build_params)
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# Check if the field type is supported for NGRAM index
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if scalar_field_type not in NGRAM.supported_field_types:
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self.create_index(client, collection_name, index_params,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 999,
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"err_msg": "ngram index can only be created on VARCHAR or JSON field"})
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else:
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self.create_index(client, collection_name, index_params)
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self.wait_for_index_ready(client, collection_name, index_name=index_name)
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# Create vector index before loading collection
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vector_index_params = self.prepare_index_params(client)[0]
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vector_index_params.add_index(field_name=vector_field_name,
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metric_type=cf.get_default_metric_for_vector_type(vector_type=DataType.FLOAT_VECTOR),
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index_type="IVF_FLAT",
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params={"nlist": 128})
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self.create_index(client, collection_name, vector_index_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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# Test query for supported types
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if scalar_field_type == DataType.VARCHAR:
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# Calculate expected count: 2000 data points with 8 keywords cycling
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# Each keyword appears 2000/8 = 250 times
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expected_count = default_nb // 8 # 250 matches for "stadium"
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filter_expr = 'scalar_field LIKE "%stadium%"'
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self.query(client, collection_name, filter=filter_expr,
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output_fields=["count(*)"],
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check_task=CheckTasks.check_query_results,
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check_items={"enable_milvus_client_api": True,
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"count(*)": expected_count})
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elif scalar_field_type == DataType.JSON:
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# Calculate expected count: 2000 data points with 8 keywords cycling
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# Each keyword appears 2000/8 = 250 times
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expected_count = default_nb // 8 # 250 matches for "school"
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filter_expr = "scalar_field['body'] LIKE \"%school%\""
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self.query(client, collection_name, filter=filter_expr,
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output_fields=["count(*)"],
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check_task=CheckTasks.check_query_results,
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check_items={"enable_milvus_client_api": True,
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"count(*)": expected_count})
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