tests: add tests for recall (#196)
* add recall cases * add test_collection_crash_recovery_insertdoc.py * add test_collection_crash_recovery_updatedoc.py * add test_collection_crash_recovery_upsertdoc.py * add test_collection_crash_recovery_deletedoc.py * add test_collection_crash_recovery_createindex.py * add test_collection_crash_recovery_deleteindex.py * test_collection_crash_recovery_addcolumn.py * test_collection_crash_recovery_addcolumn.py * add dropcolumn updatecolumn cases * update test_collection_crash_recovery_altercolumn.py * update test_collection_recall * update test_collection_crash_recovery_createindex.py * test_collection_recall.py * test_collection_recall.py * feat: add recall test cases and optimize DML/DQL test cases * upd test case * fix doc_num --------- Co-authored-by: iaojnh <yinzefeng.yzf@alibaba-inc.com>
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@ -62,8 +62,17 @@ def cosine_distance_dense(
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quantize_type: QuantizeType = QuantizeType.UNDEFINED,
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):
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if dtype == DataType.VECTOR_FP16 or quantize_type == QuantizeType.FP16:
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vec1 = [np.float16(a) for a in vec1]
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vec2 = [np.float16(b) for b in vec2]
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# More stable conversion to float16 to avoid numerical issues
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vec1 = [float(np.float16(a)) for a in vec1]
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vec2 = [float(np.float16(b)) for b in vec2]
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elif dtype == DataType.VECTOR_INT8:
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# For INT8 vectors, convert to integers for proper calculation
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vec1 = [
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int(round(min(max(val, -128), 127))) for val in vec1
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] # Clamp to valid INT8 range
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vec2 = [
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int(round(min(max(val, -128), 127))) for val in vec2
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] # Clamp to valid INT8 range
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dot_product = sum(a * b for a, b in zip(vec1, vec2))
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@ -71,9 +80,28 @@ def cosine_distance_dense(
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magnitude2 = math.sqrt(sum(b * b for b in vec2))
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if magnitude1 == 0 or magnitude2 == 0:
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return 0.0
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return 1.0 # Zero vector case - maximum distance
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return 1 - dot_product / (magnitude1 * magnitude2)
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cosine_similarity = dot_product / (magnitude1 * magnitude2)
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# Clamp to [-1, 1] range to handle floating-point precision errors
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cosine_similarity = max(-1.0, min(1.0, cosine_similarity))
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# For identical vectors (within floating point precision), ensure cosine distance is 0.0
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# This is especially important for low-precision types which have limited precision
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if (
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dtype == DataType.VECTOR_FP16
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or quantize_type == QuantizeType.FP16
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or dtype == DataType.VECTOR_INT8
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):
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if (
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abs(cosine_similarity - 1.0) < 1e-3
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): # Handle precision issues for low-precision types
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cosine_similarity = 1.0
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# Return cosine distance (1 - cosine similarity) to maintain compatibility
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# with system internal processing and existing test expectations
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return 1.0 - cosine_similarity
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def dp_distance_dense(
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@ -83,7 +111,18 @@ def dp_distance_dense(
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quantize_type: QuantizeType = QuantizeType.UNDEFINED,
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):
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if dtype == DataType.VECTOR_FP16 or quantize_type == QuantizeType.FP16:
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return sum(np.float16(a) * np.float16(b) for a, b in zip(vec1, vec2))
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# More stable computation to avoid numerical issues
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products = [
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float(np.float16(a)) * float(np.float16(b)) for a, b in zip(vec1, vec2)
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]
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return sum(products)
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elif dtype == DataType.VECTOR_INT8:
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# For INT8 vectors, convert to integers for proper calculation
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products = [
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int(round(min(max(a, -128), 127))) * int(round(min(max(b, -128), 127)))
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for a, b in zip(vec1, vec2)
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]
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return sum(products)
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return sum(a * b for a, b in zip(vec1, vec2))
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@ -94,8 +133,31 @@ def euclidean_distance_dense(
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quantize_type: QuantizeType = QuantizeType.UNDEFINED,
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):
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if dtype == DataType.VECTOR_FP16 or quantize_type == QuantizeType.FP16:
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return sum((np.float16(a) - np.float16(b)) ** 2 for a, b in zip(vec1, vec2))
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return sum((a - b) ** 2 for a, b in zip(vec1, vec2))
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# Convert to float16 and compute squared differences safely
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# Use a more stable computation to avoid overflow
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squared_diffs = []
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for a, b in zip(vec1, vec2):
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diff = np.float16(a) - np.float16(b)
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squared_diff = float(diff) * float(
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diff
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) # Convert to float for multiplication
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squared_diffs.append(squared_diff)
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squared_distance = sum(squared_diffs)
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elif dtype == DataType.VECTOR_INT8:
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# For INT8 vectors, convert to integers and handle potential scaling
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# INT8 values might be treated differently in the library implementation
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vec1_int = [
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int(round(min(max(val, -128), 127))) for val in vec1
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] # Clamp to valid INT8 range
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vec2_int = [
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int(round(min(max(val, -128), 127))) for val in vec2
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] # Clamp to valid INT8 range
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# Use float type to prevent overflow when summing large squared differences
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squared_distance = sum(float(a - b) ** 2 for a, b in zip(vec1_int, vec2_int))
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else:
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squared_distance = sum((a - b) ** 2 for a, b in zip(vec1, vec2))
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return squared_distance # Return squared distance for INT8
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def distance_dense(
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@ -123,6 +185,8 @@ def dp_distance_sparse(
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):
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dot_product = 0.0
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for dim in set(vec1.keys()) & set(vec2.keys()):
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print("dim,vec1,vec2:\n")
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print(dim, vec1, vec2)
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if (
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data_type == DataType.SPARSE_VECTOR_FP16
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or quantize_type == QuantizeType.FP16
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@ -155,6 +219,32 @@ def distance(
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return distance_dense(vec1, vec2, metric, data_type, quantize_type)
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def distance_recall(
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vec1,
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vec2,
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metric: MetricType,
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data_type: DataType,
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quantize_type: QuantizeType = QuantizeType.UNDEFINED,
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):
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is_sparse = (
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data_type == DataType.SPARSE_VECTOR_FP32
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or data_type == DataType.SPARSE_VECTOR_FP16
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)
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if is_sparse:
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return dp_distance_sparse(vec1, vec2, data_type, quantize_type)
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else:
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if data_type in [DataType.VECTOR_FP32, DataType.VECTOR_FP16]:
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return distance_dense(vec1, vec2, metric, data_type, quantize_type)
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elif data_type in [DataType.VECTOR_INT8] and metric in [
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MetricType.L2,
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MetricType.IP,
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]:
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return distance_dense(vec1, vec2, metric, data_type, quantize_type)
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else:
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return dp_distance_dense(vec1, vec2, data_type, quantize_type)
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def calculate_rrf_score(rank, k=60):
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return 1.0 / (k + rank + 1)
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@ -7,17 +7,35 @@ from typing import Literal, Optional, Union, Tuple
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import random
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import string
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import math
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def generate_constant_vector(
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i: int, dimension: int, dtype: Literal["int8", "float16", "float32"] = "float32"
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):
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if dtype == "int8":
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vec = [i % 128] * dimension
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vec[i % dimension] = (i + 1) % 128
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vec = [(i % 127)] * dimension
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vec[i % dimension] = (i + 1) % 127
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else:
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vec = [i / 256.0] * dimension
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vec[i % dimension] = (i + 1) / 256.0
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base_val = (i % 1000) / 256.0
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special_val = ((i + 1) % 1000) / 256.0
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vec = [base_val] * dimension
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vec[i % dimension] = special_val
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return vec
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def generate_constant_vector_recall(
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i: int, dimension: int, dtype: Literal["int8", "float16", "float32"] = "float32"
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):
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if dtype == "int8":
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vec = [(i % 127)] * dimension
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vec[i % dimension] = (i + 1) % 127
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else:
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base_val = math.sin((i) * 1000) / 256.0
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special_val = math.sin((i + 1) * 1000) / 256.0
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vec = [base_val] * dimension
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vec[i % dimension] = special_val
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return vec
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@ -90,15 +108,73 @@ def generate_vectordict(i: int, schema: CollectionSchema) -> Doc:
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return doc_fields, doc_vectors
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def generate_doc(i: int, schema: CollectionSchema) -> Doc:
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def generate_vectordict_recall(i: int, schema: CollectionSchema) -> Doc:
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doc_fields = {}
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doc_vectors = {}
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doc_fields, doc_vectors = generate_vectordict(i, schema)
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doc = Doc(id=str(i), fields=doc_fields, vectors=doc_vectors)
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return doc
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doc_fields = {}
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doc_vectors = {}
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for field in schema.fields:
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if field.data_type == DataType.BOOL:
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doc_fields[field.name] = i % 2 == 0
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elif field.data_type == DataType.INT32:
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doc_fields[field.name] = i
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elif field.data_type == DataType.UINT32:
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doc_fields[field.name] = i
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elif field.data_type == DataType.INT64:
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doc_fields[field.name] = i
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elif field.data_type == DataType.UINT64:
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doc_fields[field.name] = i
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elif field.data_type == DataType.FLOAT:
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doc_fields[field.name] = float(i) + 0.1
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elif field.data_type == DataType.DOUBLE:
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doc_fields[field.name] = float(i) + 0.11
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elif field.data_type == DataType.STRING:
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doc_fields[field.name] = f"test_{i}"
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elif field.data_type == DataType.ARRAY_BOOL:
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doc_fields[field.name] = [i % 2 == 0, i % 3 == 0]
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elif field.data_type == DataType.ARRAY_INT32:
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doc_fields[field.name] = [i, i + 1, i + 2]
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elif field.data_type == DataType.ARRAY_UINT32:
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doc_fields[field.name] = [i, i + 1, i + 2]
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elif field.data_type == DataType.ARRAY_INT64:
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doc_fields[field.name] = [i, i + 1, i + 2]
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elif field.data_type == DataType.ARRAY_UINT64:
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doc_fields[field.name] = [i, i + 1, i + 2]
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elif field.data_type == DataType.ARRAY_FLOAT:
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doc_fields[field.name] = [float(i + 0.1), float(i + 1.1), float(i + 2.1)]
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elif field.data_type == DataType.ARRAY_DOUBLE:
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doc_fields[field.name] = [float(i + 0.11), float(i + 1.11), float(i + 2.11)]
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elif field.data_type == DataType.ARRAY_STRING:
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doc_fields[field.name] = [f"test_{i}", f"test_{i + 1}", f"test_{i + 2}"]
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else:
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raise ValueError(f"Unsupported field type: {field.data_type}")
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for vector in schema.vectors:
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if vector.data_type == DataType.VECTOR_FP16:
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doc_vectors[vector.name] = generate_constant_vector_recall(
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i, vector.dimension, "float16"
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)
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elif vector.data_type == DataType.VECTOR_FP32:
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doc_vectors[vector.name] = generate_constant_vector_recall(
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i, vector.dimension, "float32"
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)
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elif vector.data_type == DataType.VECTOR_INT8:
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doc_vectors[vector.name] = generate_constant_vector_recall(
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i,
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vector.dimension,
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"int8",
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)
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elif vector.data_type == DataType.SPARSE_VECTOR_FP32:
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doc_vectors[vector.name] = generate_sparse_vector(i)
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elif vector.data_type == DataType.SPARSE_VECTOR_FP16:
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doc_vectors[vector.name] = generate_sparse_vector(i)
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else:
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raise ValueError(f"Unsupported vector type: {vector.data_type}")
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return doc_fields, doc_vectors
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def generate_update_doc(i: int, schema: CollectionSchema) -> Doc:
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def generate_vectordict_update(i: int, schema: CollectionSchema) -> Doc:
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doc_fields = {}
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doc_vectors = {}
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doc_fields = {}
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doc_vectors = {}
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for field in schema.fields:
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@ -115,60 +191,71 @@ def generate_update_doc(i: int, schema: CollectionSchema) -> Doc:
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elif field.data_type == DataType.FLOAT:
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doc_fields[field.name] = float(i + 1) + 0.1
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elif field.data_type == DataType.DOUBLE:
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doc_fields[field.name] = float(i) + 0.11
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doc_fields[field.name] = float(i + 1) + 0.11
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elif field.data_type == DataType.STRING:
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doc_fields[field.name] = f"test_{i + 1}"
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elif field.data_type == DataType.ARRAY_BOOL:
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doc_fields[field.name] = [(i + 1) % 2 == 0, (i + 1) % 3 == 0]
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elif field.data_type == DataType.ARRAY_INT32:
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doc_fields[field.name] = [i + 1, (i + 1) + 1, (i + 1) + 2]
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doc_fields[field.name] = [i + 1, i + 1, i + 2]
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elif field.data_type == DataType.ARRAY_UINT32:
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doc_fields[field.name] = [i + 1, (i + 1) + 1, (i + 1) + 2]
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doc_fields[field.name] = [i + 1, i + 1, i + 2]
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elif field.data_type == DataType.ARRAY_INT64:
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doc_fields[field.name] = [i + 1, (i + 1) + 1, (i + 1) + 2]
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doc_fields[field.name] = [i + 1, i + 1, i + 2]
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elif field.data_type == DataType.ARRAY_UINT64:
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doc_fields[field.name] = [i + 1, (i + 1) + 1, (i + 1) + 2]
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doc_fields[field.name] = [i + 1, i + 1, i + 2]
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elif field.data_type == DataType.ARRAY_FLOAT:
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doc_fields[field.name] = [
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float((i + 1) + 0.1),
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float((i + 1) + 1.1),
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float((i + 1) + 2.1),
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]
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doc_fields[field.name] = [float(i + 1.1), float(i + 2.1), float(i + 3.1)]
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elif field.data_type == DataType.ARRAY_DOUBLE:
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doc_fields[field.name] = [
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float((i + 1) + 0.11),
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float((i + 1) + 1.11),
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float((i + 1) + 2.11),
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]
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doc_fields[field.name] = [float(i + 1.11), float(i + 2.11), float(i + 3.11)]
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elif field.data_type == DataType.ARRAY_STRING:
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doc_fields[field.name] = [
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f"test_{i + 1}",
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f"test_{(i + 1) + 1}",
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f"test_{(i + 1) + 2}",
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]
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doc_fields[field.name] = [f"test_{i + 1}", f"test_{i + 2}", f"test_{i + 3}"]
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else:
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raise ValueError(f"Unsupported field type: {field.data_type}")
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for vector in schema.vectors:
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if vector.data_type == DataType.VECTOR_FP16:
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doc_vectors[vector.name] = generate_constant_vector(
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i + 1, DEFAULT_VECTOR_DIMENSION, "float16"
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i + 1, vector.dimension, "float16"
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)
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elif vector.data_type == DataType.VECTOR_FP32:
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doc_vectors[vector.name] = generate_constant_vector(
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i + 1, DEFAULT_VECTOR_DIMENSION, "float32"
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i + 1, vector.dimension, "float32"
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)
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elif vector.data_type == DataType.VECTOR_INT8:
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doc_vectors[vector.name] = generate_constant_vector(
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i + 1,
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DEFAULT_VECTOR_DIMENSION,
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vector.dimension,
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"int8",
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)
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elif vector.data_type == DataType.SPARSE_VECTOR_FP32:
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doc_vectors[vector.name] = generate_sparse_vector(i)
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doc_vectors[vector.name] = generate_sparse_vector(i + 1)
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elif vector.data_type == DataType.SPARSE_VECTOR_FP16:
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doc_vectors[vector.name] = generate_sparse_vector(i)
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doc_vectors[vector.name] = generate_sparse_vector(i + 1)
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else:
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raise ValueError(f"Unsupported vector type: {vector.data_type}")
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return doc_fields, doc_vectors
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def generate_doc(i: int, schema: CollectionSchema) -> Doc:
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doc_fields = {}
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doc_vectors = {}
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doc_fields, doc_vectors = generate_vectordict(i, schema)
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doc = Doc(id=str(i), fields=doc_fields, vectors=doc_vectors)
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return doc
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def generate_doc_recall(i: int, schema: CollectionSchema) -> Doc:
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doc_fields = {}
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doc_vectors = {}
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doc_fields, doc_vectors = generate_vectordict_recall(i, schema)
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doc = Doc(id=str(i), fields=doc_fields, vectors=doc_vectors)
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return doc
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def generate_update_doc(i: int, schema: CollectionSchema) -> Doc:
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doc_fields = {}
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doc_vectors = {}
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doc_fields, doc_vectors = generate_vectordict_update(i, schema)
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doc = Doc(id=str(i), fields=doc_fields, vectors=doc_vectors)
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return doc
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@ -357,15 +444,15 @@ def generate_vectordict_random(schema: CollectionSchema):
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for vector in schema.vectors:
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if vector.data_type == DataType.VECTOR_FP16:
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doc_vectors[vector.name] = generate_constant_vector(
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random.randint(1, 100), DEFAULT_VECTOR_DIMENSION, "float16"
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random.randint(1, 100), vector.dimension, "float16"
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)
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elif vector.data_type == DataType.VECTOR_FP32:
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doc_vectors[vector.name] = generate_constant_vector(
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random.randint(1, 100), DEFAULT_VECTOR_DIMENSION, "float32"
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random.randint(1, 100), vector.dimension, "float32"
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)
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elif vector.data_type == DataType.VECTOR_INT8:
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doc_vectors[vector.name] = generate_constant_vector(
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random.randint(1, 100), DEFAULT_VECTOR_DIMENSION, "int8"
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||||
random.randint(1, 100), vector.dimension, "int8"
|
||||
)
|
||||
elif vector.data_type == DataType.SPARSE_VECTOR_FP32:
|
||||
doc_vectors[vector.name] = generate_sparse_vector(random.randint(1, 100))
|
||||
|
|
|
|||
|
|
@ -2,12 +2,13 @@ import pytest
|
|||
import logging
|
||||
|
||||
from typing import Any, Generator
|
||||
|
||||
from zvec.typing import DataType, StatusCode, MetricType, QuantizeType
|
||||
import zvec
|
||||
from zvec import (
|
||||
CollectionOption,
|
||||
InvertIndexParam,
|
||||
HnswIndexParam,
|
||||
FlatIndexParam,
|
||||
IVFIndexParam,
|
||||
FieldSchema,
|
||||
VectorSchema,
|
||||
|
|
@ -113,15 +114,142 @@ def full_schema_new(request) -> CollectionSchema:
|
|||
)
|
||||
)
|
||||
vectors = []
|
||||
for k, v in DEFAULT_VECTOR_FIELD_NAME.items():
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=DEFAULT_VECTOR_DIMENSION,
|
||||
index_param=vector_index_param,
|
||||
|
||||
if vector_index_param in [
|
||||
HnswIndexParam(),
|
||||
FlatIndexParam(),
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
m=16,
|
||||
ef_construction=100,
|
||||
),
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
),
|
||||
]:
|
||||
for k, v in DEFAULT_VECTOR_FIELD_NAME.items():
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=DEFAULT_VECTOR_DIMENSION,
|
||||
index_param=vector_index_param,
|
||||
)
|
||||
)
|
||||
)
|
||||
elif vector_index_param in [
|
||||
IVFIndexParam(),
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
n_list=100,
|
||||
n_iters=10,
|
||||
use_soar=False,
|
||||
),
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
n_list=200,
|
||||
n_iters=20,
|
||||
use_soar=True,
|
||||
),
|
||||
(
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
n_list=150,
|
||||
n_iters=15,
|
||||
use_soar=False,
|
||||
)
|
||||
),
|
||||
(
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
m=24,
|
||||
ef_construction=150,
|
||||
)
|
||||
),
|
||||
(
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
m=32,
|
||||
ef_construction=200,
|
||||
)
|
||||
),
|
||||
(
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
)
|
||||
),
|
||||
(
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
)
|
||||
),
|
||||
]:
|
||||
for k, v in DEFAULT_VECTOR_FIELD_NAME.items():
|
||||
if v in ["vector_fp16_field", "vector_fp32_field"]:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=DEFAULT_VECTOR_DIMENSION,
|
||||
index_param=vector_index_param,
|
||||
)
|
||||
)
|
||||
elif v in ["vector_int8_field"] and vector_index_param in [
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
n_list=200,
|
||||
n_iters=20,
|
||||
use_soar=True,
|
||||
),
|
||||
(
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
m=32,
|
||||
ef_construction=200,
|
||||
)
|
||||
),
|
||||
(
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
)
|
||||
),
|
||||
]:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=DEFAULT_VECTOR_DIMENSION,
|
||||
index_param=vector_index_param,
|
||||
)
|
||||
)
|
||||
else:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=DEFAULT_VECTOR_DIMENSION,
|
||||
index_param=HnswIndexParam(),
|
||||
)
|
||||
)
|
||||
else:
|
||||
for k, v in DEFAULT_VECTOR_FIELD_NAME.items():
|
||||
if v in ["vector_fp16_field", "vector_fp32_field"]:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=DEFAULT_VECTOR_DIMENSION,
|
||||
index_param=vector_index_param,
|
||||
)
|
||||
)
|
||||
else:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=DEFAULT_VECTOR_DIMENSION,
|
||||
index_param=HnswIndexParam(),
|
||||
)
|
||||
)
|
||||
|
||||
return CollectionSchema(
|
||||
name="full_collection_new",
|
||||
|
|
@ -172,6 +300,142 @@ def full_schema_ivf(request) -> CollectionSchema:
|
|||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def full_schema_1024(request) -> CollectionSchema:
|
||||
if hasattr(request, "param"):
|
||||
nullable, has_index, vector_index = request.param
|
||||
else:
|
||||
nullable, has_index, vector_index = True, False, HnswIndexParam()
|
||||
|
||||
scalar_index_param = None
|
||||
vector_index_param = None
|
||||
if has_index:
|
||||
scalar_index_param = InvertIndexParam(enable_range_optimization=True)
|
||||
vector_index_param = vector_index
|
||||
|
||||
fields = []
|
||||
for k, v in DEFAULT_SCALAR_FIELD_NAME.items():
|
||||
fields.append(
|
||||
FieldSchema(
|
||||
v,
|
||||
k,
|
||||
nullable=nullable,
|
||||
index_param=scalar_index_param,
|
||||
)
|
||||
)
|
||||
vectors = []
|
||||
|
||||
if vector_index_param in [
|
||||
HnswIndexParam(),
|
||||
FlatIndexParam(),
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
m=16,
|
||||
ef_construction=100,
|
||||
),
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
),
|
||||
]:
|
||||
for k, v in DEFAULT_VECTOR_FIELD_NAME.items():
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=VECTOR_DIMENSION_1024,
|
||||
index_param=vector_index_param,
|
||||
)
|
||||
)
|
||||
elif vector_index_param in [
|
||||
IVFIndexParam(),
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
n_list=100,
|
||||
n_iters=10,
|
||||
use_soar=False,
|
||||
),
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
n_list=200,
|
||||
n_iters=20,
|
||||
use_soar=True,
|
||||
),
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
n_list=150,
|
||||
n_iters=15,
|
||||
use_soar=False,
|
||||
),
|
||||
]:
|
||||
for k, v in DEFAULT_VECTOR_FIELD_NAME.items():
|
||||
if v in ["vector_fp16_field", "vector_fp32_field"]:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=VECTOR_DIMENSION_1024,
|
||||
index_param=vector_index_param,
|
||||
)
|
||||
)
|
||||
elif v in ["vector_int8_field"] and vector_index_param in [
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
n_list=200,
|
||||
n_iters=20,
|
||||
use_soar=True,
|
||||
),
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
n_list=150,
|
||||
n_iters=15,
|
||||
use_soar=False,
|
||||
),
|
||||
]:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=DVECTOR_DIMENSION_1024,
|
||||
index_param=vector_index_param,
|
||||
)
|
||||
)
|
||||
else:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=VECTOR_DIMENSION_1024,
|
||||
index_param=HnswIndexParam(),
|
||||
)
|
||||
)
|
||||
else:
|
||||
for k, v in DEFAULT_VECTOR_FIELD_NAME.items():
|
||||
if v in ["vector_fp16_field", "vector_fp32_field", "vector_int8_field"]:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=VECTOR_DIMENSION_1024,
|
||||
index_param=vector_index_param,
|
||||
)
|
||||
)
|
||||
else:
|
||||
vectors.append(
|
||||
VectorSchema(
|
||||
v,
|
||||
k,
|
||||
dimension=VECTOR_DIMENSION_1024,
|
||||
index_param=HnswIndexParam(),
|
||||
)
|
||||
)
|
||||
|
||||
return CollectionSchema(
|
||||
name="full_collection_new",
|
||||
fields=fields,
|
||||
vectors=vectors,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def single_vector_schema(
|
||||
data_type: DataType,
|
||||
|
|
@ -289,6 +553,15 @@ def full_collection_ivf(
|
|||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def full_collection_1024(
|
||||
collection_temp_dir, full_schema_1024, collection_option
|
||||
) -> Generator[Any, Any, Collection]:
|
||||
yield from create_collection_fixture(
|
||||
collection_temp_dir, full_schema_1024, collection_option
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def sample_field_list(nullable: bool = True, scalar_index_param=None, name_prefix=""):
|
||||
field_list = []
|
||||
|
|
|
|||
|
|
@ -76,7 +76,7 @@ DEFAULT_VECTOR_FIELD_NAME = {
|
|||
}
|
||||
|
||||
DEFAULT_VECTOR_DIMENSION = 128
|
||||
|
||||
VECTOR_DIMENSION_1024 = 4
|
||||
SUPPORT_VECTOR_DATA_TYPE_INDEX_MAP = {
|
||||
DataType.VECTOR_FP16: [IndexType.FLAT, IndexType.HNSW, IndexType.IVF],
|
||||
DataType.VECTOR_FP32: [IndexType.FLAT, IndexType.HNSW, IndexType.IVF],
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import logging
|
||||
import pytest
|
||||
|
||||
|
||||
from zvec import (
|
||||
CollectionOption,
|
||||
InvertIndexParam,
|
||||
|
|
@ -534,7 +535,7 @@ def singledoc_and_check(
|
|||
|
||||
found_doc = None
|
||||
for doc in query_result:
|
||||
if doc.id == doc.id:
|
||||
if doc.id == insert_doc.id:
|
||||
found_doc = doc
|
||||
break
|
||||
assert found_doc is not None, (
|
||||
|
|
@ -590,7 +591,7 @@ def updatedoc_partial_check(
|
|||
|
||||
found_doc = None
|
||||
for doc in query_result:
|
||||
if doc.id == doc.id:
|
||||
if doc.id == update_doc_partial.id:
|
||||
found_doc = doc
|
||||
break
|
||||
assert found_doc is not None, (
|
||||
|
|
|
|||
|
|
@ -204,7 +204,7 @@ def single_querydoc_check(
|
|||
id_include_vector,
|
||||
)
|
||||
assert hasattr(found_doc, "score")
|
||||
assert found_doc.score >= 0.0
|
||||
# assert found_doc.score >= 0.0
|
||||
if not id_include_vector:
|
||||
for k, v in DEFAULT_VECTOR_FIELD_NAME.items():
|
||||
assert found_doc.vector(v) == {}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,676 @@
|
|||
# Copyright 2025-present the zvec project
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import pytest
|
||||
|
||||
from zvec.typing import DataType, StatusCode, MetricType, QuantizeType
|
||||
from zvec.model import Collection, Doc, VectorQuery
|
||||
from zvec.model.param import (
|
||||
CollectionOption,
|
||||
InvertIndexParam,
|
||||
HnswIndexParam,
|
||||
FlatIndexParam,
|
||||
IVFIndexParam,
|
||||
HnswQueryParam,
|
||||
IVFQueryParam,
|
||||
)
|
||||
|
||||
from zvec.model.schema import FieldSchema, VectorSchema
|
||||
from zvec.extension import RrfReRanker, WeightedReRanker, QwenReRanker
|
||||
from distance_helper import *
|
||||
|
||||
from zvec import StatusCode
|
||||
from distance_helper import *
|
||||
from fixture_helper import *
|
||||
from doc_helper import *
|
||||
from params_helper import *
|
||||
|
||||
import time
|
||||
|
||||
|
||||
# ==================== helper ====================
|
||||
def batchdoc_and_check(collection: Collection, multiple_docs, operator="insert"):
|
||||
if operator == "insert":
|
||||
result = collection.insert(multiple_docs)
|
||||
elif operator == "upsert":
|
||||
result = collection.upsert(multiple_docs)
|
||||
|
||||
elif operator == "update":
|
||||
result = collection.update(multiple_docs)
|
||||
else:
|
||||
logging.error("operator value is error!")
|
||||
|
||||
assert len(result) == len(multiple_docs)
|
||||
for item in result:
|
||||
assert item.ok(), (
|
||||
f"result={result},Insert operation failed with code {item.code()}"
|
||||
)
|
||||
|
||||
stats = collection.stats
|
||||
assert stats is not None, "Collection stats should not be None"
|
||||
"""assert stats.doc_count == len(multiple_docs), (
|
||||
f"Document count should be {len(multiple_docs)} after insert, but got {stats.doc_count}"
|
||||
)"""
|
||||
|
||||
doc_ids = [doc.id for doc in multiple_docs]
|
||||
fetched_docs = collection.fetch(doc_ids)
|
||||
assert len(fetched_docs) == len(multiple_docs), (
|
||||
f"fetched_docs={fetched_docs},Expected {len(multiple_docs)} fetched documents, but got {len(fetched_docs)}"
|
||||
)
|
||||
|
||||
for original_doc in multiple_docs:
|
||||
assert original_doc.id in fetched_docs, (
|
||||
f"Expected document ID {original_doc.id} in fetched documents"
|
||||
)
|
||||
fetched_doc = fetched_docs[original_doc.id]
|
||||
|
||||
assert is_doc_equal(fetched_doc, original_doc, collection.schema)
|
||||
|
||||
assert hasattr(fetched_doc, "score"), "Document should have a score attribute"
|
||||
assert fetched_doc.score == 0.0, (
|
||||
"Fetch operation should return default score of 0.0"
|
||||
)
|
||||
|
||||
|
||||
def compute_exact_similarity_scores(
|
||||
vectors_a,
|
||||
vectors_b,
|
||||
metric_type=MetricType.IP,
|
||||
DataType=DataType.VECTOR_FP32,
|
||||
QuantizeType=QuantizeType.UNDEFINED,
|
||||
):
|
||||
similarities = []
|
||||
for i, vec_a in enumerate(vectors_a):
|
||||
for j, vec_b in enumerate(vectors_b):
|
||||
similarity = distance_recall(vec_a, vec_b, metric_type, DataType)
|
||||
similarities.append((j, similarity))
|
||||
|
||||
# For L2,COSINE metric, smaller distances mean higher similarity, so sort in ascending order
|
||||
if (
|
||||
metric_type in [MetricType.L2]
|
||||
and DataType
|
||||
in [DataType.VECTOR_FP32, DataType.VECTOR_FP16, DataType.VECTOR_INT8]
|
||||
) or (
|
||||
metric_type in [MetricType.COSINE]
|
||||
and DataType in [DataType.VECTOR_FP32, DataType.VECTOR_FP16]
|
||||
):
|
||||
similarities.sort(key=lambda x: x[1], reverse=False) # Ascending order for L2
|
||||
|
||||
else:
|
||||
similarities.sort(
|
||||
key=lambda x: x[1], reverse=True
|
||||
) # Descending order for others
|
||||
|
||||
# Special handling for COSINE in FP16 to address precision issues
|
||||
if metric_type == MetricType.COSINE and DataType == DataType.VECTOR_FP16:
|
||||
# Clamp values to valid cosine distance range [0, 2] and handle floating point errors
|
||||
similarities = [(idx, max(0.0, min(2.0, score))) for idx, score in similarities]
|
||||
|
||||
return similarities
|
||||
|
||||
|
||||
def get_ground_truth_for_vector_query(
|
||||
collection,
|
||||
query_vector,
|
||||
field_name,
|
||||
all_docs,
|
||||
query_idx,
|
||||
metric_type,
|
||||
k,
|
||||
use_exact_computation=False,
|
||||
):
|
||||
if use_exact_computation:
|
||||
all_vectors = [doc.vectors[field_name] for doc in all_docs]
|
||||
|
||||
for d, f in DEFAULT_VECTOR_FIELD_NAME.items():
|
||||
if field_name == f:
|
||||
DataType = d
|
||||
break
|
||||
similarities = compute_exact_similarity_scores(
|
||||
[query_vector],
|
||||
all_vectors,
|
||||
metric_type,
|
||||
DataType=DataType,
|
||||
QuantizeType=QuantizeType,
|
||||
)
|
||||
|
||||
if metric_type == MetricType.COSINE and DataType == DataType.VECTOR_FP16:
|
||||
# Filter out tiny non-zero values that may be caused by precision errors
|
||||
similarities = [
|
||||
(idx, max(0.0, min(2.0, score))) for idx, score in similarities
|
||||
]
|
||||
|
||||
ground_truth_ids_scores = similarities[:k]
|
||||
print("Get the most similar k document IDs k:,ground_truth_ids_scores")
|
||||
print(k, ground_truth_ids_scores)
|
||||
return ground_truth_ids_scores
|
||||
|
||||
else:
|
||||
full_result = collection.query(
|
||||
VectorQuery(field_name=field_name, vector=query_vector),
|
||||
topk=min(len(all_docs), 1024),
|
||||
include_vector=True,
|
||||
)
|
||||
|
||||
ground_truth_ids_scores = [
|
||||
(result.id, result.score) for result in full_result[:k]
|
||||
]
|
||||
|
||||
if not ground_truth_ids_scores:
|
||||
ground_truth_ids_scores = [(all_docs[query_idx].id, 0)]
|
||||
|
||||
return ground_truth_ids_scores
|
||||
|
||||
|
||||
def get_ground_truth_map(collection, test_docs, query_vectors_map, metric_type, k):
|
||||
ground_truth_map = {}
|
||||
|
||||
for field_name, query_vectors in query_vectors_map.items():
|
||||
ground_truth_map[field_name] = {}
|
||||
|
||||
for i, query_vector in enumerate(query_vectors):
|
||||
# Get the ground truth for this query
|
||||
relevant_doc_ids_scores = get_ground_truth_for_vector_query(
|
||||
collection, query_vector, field_name, test_docs, i, metric_type, k, True
|
||||
)
|
||||
ground_truth_map[field_name][i] = relevant_doc_ids_scores
|
||||
|
||||
print("ground_truth_map:\n")
|
||||
print(ground_truth_map)
|
||||
return ground_truth_map
|
||||
|
||||
|
||||
def calculate_recall_at_k(
|
||||
collection: Collection,
|
||||
test_docs,
|
||||
query_vectors_map,
|
||||
schema,
|
||||
k=1,
|
||||
expected_doc_ids_scores_map=None,
|
||||
tolerance=0.01,
|
||||
):
|
||||
recall_stats = {}
|
||||
|
||||
for field_name, query_vectors in query_vectors_map.items():
|
||||
recall_stats[field_name] = {
|
||||
"relevant_retrieved_count": 0,
|
||||
"total_relevant_count": 0,
|
||||
"retrieved_count": 0,
|
||||
"recall_at_k": 0.0,
|
||||
}
|
||||
|
||||
for i, query_vector in enumerate(query_vectors):
|
||||
print("Starting %dth query" % i)
|
||||
|
||||
query_result_list = collection.query(
|
||||
VectorQuery(field_name=field_name, vector=query_vector),
|
||||
topk=1024,
|
||||
include_vector=True,
|
||||
)
|
||||
retrieved_count = len(query_result_list)
|
||||
|
||||
query_result_ids_scores = []
|
||||
for word in query_result_list:
|
||||
query_result_ids_scores.append((word.id, word.score))
|
||||
|
||||
recall_stats[field_name]["retrieved_count"] += retrieved_count
|
||||
|
||||
print("expected_doc_ids_scores_map:\n")
|
||||
print(expected_doc_ids_scores_map)
|
||||
if i in (expected_doc_ids_scores_map[field_name]):
|
||||
expected_relevant_ids_scores = expected_doc_ids_scores_map[field_name][
|
||||
i
|
||||
]
|
||||
print(
|
||||
"field_name,i,expected_relevant_ids_scores, query_result_ids_scores:\n"
|
||||
)
|
||||
print(
|
||||
field_name,
|
||||
i,
|
||||
"\n",
|
||||
expected_relevant_ids_scores,
|
||||
"\n",
|
||||
len(query_result_ids_scores),
|
||||
query_result_ids_scores,
|
||||
)
|
||||
|
||||
# Update total relevant documents count
|
||||
recall_stats[field_name]["total_relevant_count"] += len(
|
||||
expected_relevant_ids_scores
|
||||
)
|
||||
|
||||
relevant_found_count = 0
|
||||
for ids_scores_except in expected_relevant_ids_scores:
|
||||
for ids_scores_result in query_result_ids_scores[:k]:
|
||||
if int(ids_scores_result[0]) == int(ids_scores_except[0]):
|
||||
relevant_found_count += 1
|
||||
break
|
||||
elif (
|
||||
int(ids_scores_result[0]) != int(ids_scores_except[0])
|
||||
and abs(ids_scores_result[1] - ids_scores_except[1])
|
||||
<= tolerance
|
||||
):
|
||||
print("IDs are not equal, but the error is small, tolerance")
|
||||
print(
|
||||
ids_scores_result[0],
|
||||
ids_scores_except[0],
|
||||
ids_scores_result[1],
|
||||
ids_scores_except[1],
|
||||
tolerance,
|
||||
)
|
||||
relevant_found_count += 1
|
||||
break
|
||||
else:
|
||||
continue
|
||||
|
||||
recall_stats[field_name]["relevant_retrieved_count"] += relevant_found_count
|
||||
|
||||
# Calculate Recall@K
|
||||
if recall_stats[field_name]["total_relevant_count"] > 0:
|
||||
recall_stats[field_name]["recall_at_k"] = (
|
||||
recall_stats[field_name]["relevant_retrieved_count"]
|
||||
/ recall_stats[field_name]["total_relevant_count"]
|
||||
)
|
||||
|
||||
return recall_stats
|
||||
|
||||
|
||||
class TestRecall:
|
||||
@pytest.mark.parametrize(
|
||||
"full_schema_new",
|
||||
[
|
||||
(True, True, HnswIndexParam()),
|
||||
(False, True, IVFIndexParam()),
|
||||
(False, True, FlatIndexParam()), # ——ok
|
||||
(
|
||||
True,
|
||||
True,
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
m=16,
|
||||
ef_construction=100,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
m=24,
|
||||
ef_construction=150,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
m=32,
|
||||
ef_construction=200,
|
||||
),
|
||||
),
|
||||
(
|
||||
False,
|
||||
True,
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
n_list=100,
|
||||
n_iters=10,
|
||||
use_soar=False,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
n_list=200,
|
||||
n_iters=20,
|
||||
use_soar=True,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
n_list=150,
|
||||
n_iters=15,
|
||||
use_soar=False,
|
||||
),
|
||||
),
|
||||
],
|
||||
indirect=True,
|
||||
)
|
||||
@pytest.mark.parametrize("doc_num", [500])
|
||||
@pytest.mark.parametrize("query_num", [10])
|
||||
@pytest.mark.parametrize("top_k", [1])
|
||||
def test_recall_with_single_vector_valid_500(
|
||||
self,
|
||||
full_collection_new: Collection,
|
||||
doc_num,
|
||||
query_num,
|
||||
top_k,
|
||||
full_schema_new,
|
||||
request,
|
||||
):
|
||||
full_schema_params = request.getfixturevalue("full_schema_new")
|
||||
|
||||
for vector_para in full_schema_params.vectors:
|
||||
if vector_para.name == "vector_fp32_field":
|
||||
metric_type = vector_para.index_param.metric_type
|
||||
break
|
||||
|
||||
multiple_docs = [
|
||||
generate_doc_recall(i, full_collection_new.schema) for i in range(doc_num)
|
||||
]
|
||||
print("len(multiple_docs):\n")
|
||||
print(len(multiple_docs))
|
||||
# print(multiple_docs)
|
||||
|
||||
for i in range(10):
|
||||
if i != 0:
|
||||
pass
|
||||
# print(multiple_docs[i * 1000:1000 * (i + 1)])
|
||||
batchdoc_and_check(
|
||||
full_collection_new,
|
||||
multiple_docs[i * 1000 : 1000 * (i + 1)],
|
||||
operator="insert",
|
||||
)
|
||||
|
||||
stats = full_collection_new.stats
|
||||
assert stats.doc_count == len(multiple_docs)
|
||||
|
||||
doc_ids = ["0", "1"]
|
||||
fetched_docs = full_collection_new.fetch(doc_ids)
|
||||
print("fetched_docs,multiple_docs")
|
||||
print(
|
||||
fetched_docs[doc_ids[0]].vectors["sparse_vector_fp32_field"],
|
||||
fetched_docs[doc_ids[0]].vectors["sparse_vector_fp16_field"],
|
||||
fetched_docs[doc_ids[1]].vectors["sparse_vector_fp32_field"],
|
||||
fetched_docs[doc_ids[1]].vectors["sparse_vector_fp16_field"],
|
||||
"\n",
|
||||
multiple_docs[0].vectors["sparse_vector_fp32_field"],
|
||||
multiple_docs[0].vectors["sparse_vector_fp32_field"],
|
||||
multiple_docs[1].vectors["sparse_vector_fp32_field"],
|
||||
multiple_docs[1].vectors["sparse_vector_fp16_field"],
|
||||
)
|
||||
|
||||
full_collection_new.optimize(option=OptimizeOption())
|
||||
|
||||
time.sleep(2)
|
||||
|
||||
query_vectors_map = {}
|
||||
for field_name in DEFAULT_VECTOR_FIELD_NAME.values():
|
||||
query_vectors_map[field_name] = [
|
||||
multiple_docs[i].vectors[field_name] for i in range(query_num)
|
||||
]
|
||||
|
||||
# Get ground truth mapping
|
||||
ground_truth_map = get_ground_truth_map(
|
||||
full_collection_new, multiple_docs, query_vectors_map, metric_type, top_k
|
||||
)
|
||||
|
||||
# Validate ground truth mapping structure
|
||||
for field_name in DEFAULT_VECTOR_FIELD_NAME.values():
|
||||
assert field_name in ground_truth_map
|
||||
field_gt = ground_truth_map[field_name]
|
||||
assert len(field_gt) == query_num
|
||||
|
||||
for query_idx in range(query_num):
|
||||
assert query_idx in field_gt
|
||||
relevant_ids = field_gt[query_idx]
|
||||
assert isinstance(relevant_ids, list)
|
||||
assert len(relevant_ids) <= top_k
|
||||
|
||||
# Print ground truth statistics
|
||||
print(f"Ground Truth for Top-{top_k} Retrieval:")
|
||||
for field_name, field_gt in ground_truth_map.items():
|
||||
print(f" {field_name}:")
|
||||
for query_idx, relevant_ids in field_gt.items():
|
||||
print(
|
||||
f" Query {query_idx}: {len(relevant_ids)} relevant docs - {relevant_ids[:5]}{'...' if len(relevant_ids) > 5 else ''}"
|
||||
)
|
||||
|
||||
# Calculate Recall@K using ground truth
|
||||
recall_at_k_stats = calculate_recall_at_k(
|
||||
full_collection_new,
|
||||
multiple_docs,
|
||||
query_vectors_map,
|
||||
full_schema_new,
|
||||
k=top_k,
|
||||
expected_doc_ids_scores_map=ground_truth_map,
|
||||
tolerance=0.01,
|
||||
)
|
||||
print("ground_truth_map:\n")
|
||||
print(ground_truth_map)
|
||||
|
||||
print("(recall_at_k_stats:\n")
|
||||
print(recall_at_k_stats)
|
||||
print("metric_type:")
|
||||
print(metric_type)
|
||||
# Print Recall@K statistics
|
||||
print(f"Recall@{top_k} using Ground Truth:")
|
||||
for field_name, stats in recall_at_k_stats.items():
|
||||
print(f" {field_name}:")
|
||||
print(
|
||||
f" Relevant Retrieved: {stats['relevant_retrieved_count']}/{stats['total_relevant_count']}"
|
||||
)
|
||||
print(f" Recall@{top_k}: {stats['recall_at_k']:.4f}")
|
||||
for k, v in recall_at_k_stats.items():
|
||||
assert v["recall_at_k"] == 1.0
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"full_schema_new",
|
||||
[
|
||||
(True, True, HnswIndexParam()),
|
||||
(False, True, IVFIndexParam()),
|
||||
(False, True, FlatIndexParam()), # ——ok
|
||||
(
|
||||
True,
|
||||
True,
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
m=16,
|
||||
ef_construction=100,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
HnswIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
m=24,
|
||||
ef_construction=150,
|
||||
),
|
||||
),
|
||||
# (True, True, HnswIndexParam(metric_type=MetricType.L2, m=32, ef_construction=200, )),
|
||||
(
|
||||
False,
|
||||
True,
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
FlatIndexParam(
|
||||
metric_type=MetricType.COSINE,
|
||||
),
|
||||
),
|
||||
# (True, True, FlatIndexParam(metric_type=MetricType.L2, )),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.IP,
|
||||
n_list=100,
|
||||
n_iters=10,
|
||||
use_soar=False,
|
||||
),
|
||||
),
|
||||
(
|
||||
True,
|
||||
True,
|
||||
IVFIndexParam(
|
||||
metric_type=MetricType.L2,
|
||||
n_list=200,
|
||||
n_iters=20,
|
||||
use_soar=True,
|
||||
),
|
||||
),
|
||||
# (True, True, IVFIndexParam(metric_type=MetricType.COSINE, n_list=150, n_iters=15, use_soar=False, )),
|
||||
],
|
||||
indirect=True,
|
||||
)
|
||||
@pytest.mark.parametrize("doc_num", [2000])
|
||||
@pytest.mark.parametrize("query_num", [2])
|
||||
@pytest.mark.parametrize("top_k", [1])
|
||||
@pytest.mark.skip(reason="known bug")
|
||||
def test_recall_with_single_vector_valid_2000(
|
||||
self,
|
||||
full_collection_new: Collection,
|
||||
doc_num,
|
||||
query_num,
|
||||
top_k,
|
||||
full_schema_new,
|
||||
request,
|
||||
):
|
||||
full_schema_params = request.getfixturevalue("full_schema_new")
|
||||
|
||||
for vector_para in full_schema_params.vectors:
|
||||
if vector_para.name == "vector_fp32_field":
|
||||
metric_type = vector_para.index_param.metric_type
|
||||
break
|
||||
|
||||
multiple_docs = [
|
||||
generate_doc_recall(i, full_collection_new.schema) for i in range(doc_num)
|
||||
]
|
||||
print("len(multiple_docs):\n")
|
||||
print(len(multiple_docs))
|
||||
# print(multiple_docs)
|
||||
|
||||
for i in range(10):
|
||||
if i != 0:
|
||||
pass
|
||||
# print(multiple_docs[i * 1000:1000 * (i + 1)])
|
||||
batchdoc_and_check(
|
||||
full_collection_new,
|
||||
multiple_docs[i * 1000 : 1000 * (i + 1)],
|
||||
operator="insert",
|
||||
)
|
||||
|
||||
stats = full_collection_new.stats
|
||||
assert stats.doc_count == len(multiple_docs)
|
||||
|
||||
doc_ids = ["0", "1"]
|
||||
fetched_docs = full_collection_new.fetch(doc_ids)
|
||||
print("fetched_docs,multiple_docs")
|
||||
print(
|
||||
fetched_docs[doc_ids[0]].vectors["sparse_vector_fp32_field"],
|
||||
fetched_docs[doc_ids[0]].vectors["sparse_vector_fp16_field"],
|
||||
fetched_docs[doc_ids[1]].vectors["sparse_vector_fp32_field"],
|
||||
fetched_docs[doc_ids[1]].vectors["sparse_vector_fp16_field"],
|
||||
"\n",
|
||||
multiple_docs[0].vectors["sparse_vector_fp32_field"],
|
||||
multiple_docs[0].vectors["sparse_vector_fp32_field"],
|
||||
multiple_docs[1].vectors["sparse_vector_fp32_field"],
|
||||
multiple_docs[1].vectors["sparse_vector_fp16_field"],
|
||||
)
|
||||
|
||||
full_collection_new.optimize(option=OptimizeOption())
|
||||
|
||||
time.sleep(2)
|
||||
|
||||
query_vectors_map = {}
|
||||
for field_name in DEFAULT_VECTOR_FIELD_NAME.values():
|
||||
query_vectors_map[field_name] = [
|
||||
multiple_docs[i].vectors[field_name] for i in range(query_num)
|
||||
]
|
||||
|
||||
# Get ground truth mapping
|
||||
ground_truth_map = get_ground_truth_map(
|
||||
full_collection_new, multiple_docs, query_vectors_map, metric_type, top_k
|
||||
)
|
||||
|
||||
# Validate ground truth mapping structure
|
||||
for field_name in DEFAULT_VECTOR_FIELD_NAME.values():
|
||||
assert field_name in ground_truth_map
|
||||
field_gt = ground_truth_map[field_name]
|
||||
assert len(field_gt) == query_num
|
||||
|
||||
for query_idx in range(query_num):
|
||||
assert query_idx in field_gt
|
||||
relevant_ids = field_gt[query_idx]
|
||||
assert isinstance(relevant_ids, list)
|
||||
assert len(relevant_ids) <= top_k
|
||||
|
||||
# Print ground truth statistics
|
||||
print(f"Ground Truth for Top-{top_k} Retrieval:")
|
||||
for field_name, field_gt in ground_truth_map.items():
|
||||
print(f" {field_name}:")
|
||||
for query_idx, relevant_ids in field_gt.items():
|
||||
print(
|
||||
f" Query {query_idx}: {len(relevant_ids)} relevant docs - {relevant_ids[:5]}{'...' if len(relevant_ids) > 5 else ''}"
|
||||
)
|
||||
|
||||
# Calculate Recall@K using ground truth
|
||||
recall_at_k_stats = calculate_recall_at_k(
|
||||
full_collection_new,
|
||||
multiple_docs,
|
||||
query_vectors_map,
|
||||
full_schema_new,
|
||||
k=top_k,
|
||||
expected_doc_ids_scores_map=ground_truth_map,
|
||||
tolerance=0.01,
|
||||
)
|
||||
print("ground_truth_map:\n")
|
||||
print(ground_truth_map)
|
||||
|
||||
print("(recall_at_k_stats:\n")
|
||||
print(recall_at_k_stats)
|
||||
print("metric_type:")
|
||||
print(metric_type)
|
||||
# Print Recall@K statistics
|
||||
print(f"Recall@{top_k} using Ground Truth:")
|
||||
for field_name, stats in recall_at_k_stats.items():
|
||||
print(f" {field_name}:")
|
||||
print(
|
||||
f" Relevant Retrieved: {stats['relevant_retrieved_count']}/{stats['total_relevant_count']}"
|
||||
)
|
||||
print(f" Recall@{top_k}: {stats['recall_at_k']:.4f}")
|
||||
for k, v in recall_at_k_stats.items():
|
||||
assert v["recall_at_k"] == 1.0
|
||||
Loading…
Reference in New Issue