From dc4153b0fc5ee539a3ba98173275352728f976cc Mon Sep 17 00:00:00 2001 From: zhuwenxing Date: Wed, 11 Dec 2024 20:17:22 +0800 Subject: [PATCH] add qps measure Signed-off-by: zhuwenxing --- example/milvus_beir_example.py | 114 +-- pdm.lock | 883 +++++++++++++++++- pyproject.toml | 1 + src/milvus_beir/cli/search_cli.py | 7 +- .../retrieval/search/dense/dense_search.py | 55 +- .../search/hybrid/bm25_hybrid_search.py | 100 +- .../search/hybrid/sparse_hybrid_search.py | 73 +- .../retrieval/search/lexical/bm25_search.py | 61 +- .../search/lexical/multi_match_search.py | 55 +- src/milvus_beir/retrieval/search/milvus.py | 15 +- .../retrieval/search/sparse/sparse_search.py | 58 +- src/milvus_beir/utils.py | 112 +++ tests/test_search.py | 67 +- 13 files changed, 1472 insertions(+), 129 deletions(-) create mode 100644 src/milvus_beir/utils.py diff --git a/example/milvus_beir_example.py b/example/milvus_beir_example.py index b91b054..da385da 100644 --- a/example/milvus_beir_example.py +++ b/example/milvus_beir_example.py @@ -1,8 +1,6 @@ from beir import util from beir.datasets.data_loader import GenericDataLoader from beir.retrieval.evaluation import EvaluateRetrieval -from pymilvus import MilvusClient -from ranx import Qrels, Run, compare from milvus_beir.retrieval.search.dense.dense_search import MilvusDenseSearch from milvus_beir.retrieval.search.hybrid.bm25_hybrid_search import MilvusBM25DenseHybridSearch @@ -11,95 +9,35 @@ from milvus_beir.retrieval.search.lexical.multi_match_search import MilvusMultiMatchSearch from milvus_beir.retrieval.search.sparse.sparse_search import MilvusSparseSearch -dataset = "nfcorpus" +dataset = "scifact" url = f"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{dataset}.zip" -data_path = util.download_and_unzip(url, "/tmp/datasets") +data_path = util.download_and_unzip(url, "datasets") corpus, queries, qrels = GenericDataLoader(data_folder=data_path).load(split="test") - print("Corpus:", len(corpus)) print("Queries:", len(queries)) -milvus_client = MilvusClient(uri="http://10.104.20.192:19530") -model = MilvusDenseSearch(milvus_client, collection_name="milvus_beir_demo", nq=100, nb=1000) -retriever = EvaluateRetrieval(model) -dense_results = retriever.retrieve(corpus, queries) -ndcg, _map, recall, precision = retriever.evaluate(qrels, dense_results, retriever.k_values) -print("NDCG:", ndcg) -print("MAP:", _map) -print("Recall:", recall) -print("Precision:", precision) - -model = MilvusSparseSearch(milvus_client, collection_name="milvus_beir_demo", nq=100, nb=1000) -retriever = EvaluateRetrieval(model) -sparse_results = retriever.retrieve(corpus, queries) -ndcg, _map, recall, precision = retriever.evaluate(qrels, sparse_results, retriever.k_values) -print("NDCG:", ndcg) -print("MAP:", _map) -print("Recall:", recall) -print("Precision:", precision) - -model = MilvusBM25DenseHybridSearch( - milvus_client, collection_name="milvus_beir_demo", nq=100, nb=1000 -) -retriever = EvaluateRetrieval(model) -bm25_dense_hybrid_results = retriever.retrieve(corpus, queries) -ndcg, _map, recall, precision = retriever.evaluate( - qrels, bm25_dense_hybrid_results, retriever.k_values -) -print("NDCG:", ndcg) -print("MAP:", _map) -print("Recall:", recall) -print("Precision:", precision) - -model = MilvusSparseDenseHybridSearch( - milvus_client, collection_name="milvus_beir_demo", nq=100, nb=1000 -) -retriever = EvaluateRetrieval(model) -sparse_dense_results = retriever.retrieve(corpus, queries) -ndcg, _map, recall, precision = retriever.evaluate(qrels, sparse_dense_results, retriever.k_values) -print("NDCG:", ndcg) -print("MAP:", _map) -print("Recall:", recall) -print("Precision:", precision) - -model = MilvusMultiMatchSearch(milvus_client, collection_name="milvus_beir_demo", nq=100, nb=1000) -retriever = EvaluateRetrieval(model) -multi_match_results = retriever.retrieve(corpus, queries) -ndcg, _map, recall, precision = retriever.evaluate(qrels, multi_match_results, retriever.k_values) -print("NDCG:", ndcg) -print("MAP:", _map) -print("Recall:", recall) -print("Precision:", precision) - -model = MilvusBM25Search(milvus_client, collection_name="milvus_beir_demo", nq=100, nb=1000) -retriever = EvaluateRetrieval(model) -bm25_results = retriever.retrieve(corpus, queries) -ndcg, _map, recall, precision = retriever.evaluate(qrels, bm25_results, retriever.k_values) -print("NDCG:", ndcg) -print("MAP:", _map) -print("Recall:", recall) -print("Precision:", precision) - - -qrels = Qrels(qrels) -run_dense = Run(dense_results, name="dense") -run_sparse = Run(sparse_results, name="sparse") -run_bm25 = Run(bm25_results, name="bm25") -run_multi_match = Run(multi_match_results, name="multi_match") -run_bm25_dense_hybrid = Run(bm25_dense_hybrid_results, name="bm25_dense_hybrid") -run_sparse_dense = Run(sparse_dense_results, name="sparse_dense") - -report = compare( - qrels=qrels, - runs=[ - run_dense, - run_sparse, - run_bm25, - run_multi_match, - run_bm25_dense_hybrid, - run_sparse_dense, - ], - metrics=["ndcg@10", "map@10", "recall@10", "precision@10"], -) -print(report) +uri = "http://10.104.26.252:19530" +token = None + +models = [ + MilvusDenseSearch(uri, token, collection_name="milvus_beir_demo", nq=100, nb=1000), + MilvusSparseSearch(uri, token, collection_name="milvus_beir_demo", nq=100, nb=1000), + MilvusBM25DenseHybridSearch(uri, token, collection_name="milvus_beir_demo", nq=100, nb=1000), + MilvusSparseDenseHybridSearch(uri, token, collection_name="milvus_beir_demo", nq=100, nb=1000), + MilvusMultiMatchSearch(uri, token, collection_name="milvus_beir_demo", nq=100, nb=1000), + MilvusBM25Search(uri, token, collection_name="milvus_beir_demo", nq=100, nb=1000), +] + +for model in models: + retriever = EvaluateRetrieval(model) + results = retriever.retrieve(corpus, queries) + ndcg, _map, recall, precision = retriever.evaluate(qrels, results, retriever.k_values) + print("NDCG:", ndcg) + print("MAP:", _map) + print("Recall:", recall) + print("Precision:", precision) + qps = model.measure_search_qps( + corpus, queries, top_k=1000, concurrency_levels=[1, 2], test_duration=60 + ) + print("QPS:", qps) diff --git a/pdm.lock b/pdm.lock index fdf96d2..de6c3db 100644 --- a/pdm.lock +++ b/pdm.lock @@ -5,7 +5,7 @@ groups = ["default", "lint", "test"] strategy = [] lock_version = "4.5.0" -content_hash = "sha256:b1d5eb410dbd877eeb7ac0f8730c729bd0d1ef95ef601be91cbf967f6049af4a" +content_hash = "sha256:e5f38914e43f97492d22681cb5ac638749bfe07ebc71a15989ccbb2eab60f43c" [[metadata.targets]] requires_python = ">=3.10" @@ -127,6 +127,18 @@ files = [ {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"}, ] +[[package]] +name = "beautifulsoup4" +version = "4.12.3" +summary = "" +dependencies = [ + "soupsieve", +] +files = [ + {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"}, + {file = 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EvaluateRetrieval -from pymilvus import MilvusClient from milvus_beir.retrieval.search.dense.dense_search import MilvusDenseSearch from milvus_beir.retrieval.search.hybrid.bm25_hybrid_search import MilvusBM25DenseHybridSearch @@ -86,11 +85,11 @@ def evaluate(dataset, uri, token, search_method, collection_name, nq, nb, split) click.echo(f"Corpus size: {len(corpus)}") click.echo(f"Number of queries: {len(queries)}") - # Initialize Milvus client and search model - milvus_client = MilvusClient(uri=uri, token=token) + # Initialize search model search_class = SEARCH_METHODS[search_method] model = search_class( - milvus_client, + uri, + token, collection_name=collection_name or f"beir_{dataset}_{search_method}", nq=nq, nb=nb, diff --git a/src/milvus_beir/retrieval/search/dense/dense_search.py b/src/milvus_beir/retrieval/search/dense/dense_search.py index 65e48df..f81e2a3 100644 --- a/src/milvus_beir/retrieval/search/dense/dense_search.py +++ b/src/milvus_beir/retrieval/search/dense/dense_search.py @@ -1,12 +1,15 @@ import logging +import random +import time from typing import Dict, Optional from milvus_model.base import BaseEmbeddingFunction from milvus_model.dense import SentenceTransformerEmbeddingFunction -from pymilvus import DataType, MilvusClient +from pymilvus import DataType from tqdm.autonotebook import tqdm from milvus_beir.retrieval.search.milvus import MilvusBaseSearch +from milvus_beir.utils import measure_search_qps_decorator logger = logging.getLogger(__name__) @@ -18,7 +21,8 @@ def get_default_model() -> BaseEmbeddingFunction: class MilvusDenseSearch(MilvusBaseSearch): def __init__( self, - milvus_client: MilvusClient, + uri: str, + token: str | None, collection_name: str, nq: int = 100, nb: int = 1000, @@ -28,13 +32,17 @@ def __init__( dense_vector_field: str = "dense_embedding", metric_type: str = "COSINE", search_params: Optional[Dict] = None, + sleep_time: int = 5, ): self.model = model if model is not None else get_default_model() self.dense_vector_field = dense_vector_field self.metric_type = metric_type self.search_params = search_params + self.sleep_time = sleep_time + self.query_embeddings = [] super().__init__( - milvus_client=milvus_client, + uri=uri, + token=token, collection_name=collection_name, nq=nq, nb=nb, @@ -104,8 +112,8 @@ def search( result = self.milvus_client.search( collection_name=self.collection_name, data=embeddings, - anns_field="dense_embedding", - search_params={}, + anns_field=self.dense_vector_field, + search_params=self.search_params, limit=top_k, output_fields=["id"], ) @@ -118,3 +126,40 @@ def search( data[hit["id"]] = hit["distance"] result_dict[query_ids[i]] = data return result_dict + + def measure_search_qps( + self, corpus, queries, top_k=1000, concurrency_levels=None, test_duration=60 + ): + if concurrency_levels is None: + concurrency_levels = [1, 2, 4, 8, 16, 32] + + @measure_search_qps_decorator( + concurrency_levels, test_duration=test_duration, max_threads=None + ) + def _single_search(top_k): + """ """ + embedding = random.choice(self.query_embeddings) + try: + client = self._get_thread_client() + result = client.search( + collection_name=self.collection_name, + data=[embedding], + anns_field=self.dense_vector_field, + search_params=self.search_params, + limit=top_k, + output_fields=["id"], + ) + return result + except Exception as e: + logger.error(f"Search error: {e!s}") + return None + + if not self.index_completed: + self._index(corpus) + time.sleep(self.sleep_time) + query_ids = list(queries.keys()) + query_texts = [queries[qid] for qid in query_ids] + query_embeddings = self.model.encode_queries(query_texts) + self.query_embeddings = query_embeddings + res = _single_search(top_k) + return res diff --git a/src/milvus_beir/retrieval/search/hybrid/bm25_hybrid_search.py b/src/milvus_beir/retrieval/search/hybrid/bm25_hybrid_search.py index 523d03a..d9a4638 100644 --- a/src/milvus_beir/retrieval/search/hybrid/bm25_hybrid_search.py +++ b/src/milvus_beir/retrieval/search/hybrid/bm25_hybrid_search.py @@ -1,4 +1,6 @@ import logging +import random +import time from typing import Any, Dict, Optional from milvus_model.base import BaseEmbeddingFunction @@ -8,12 +10,12 @@ DataType, Function, FunctionType, - MilvusClient, RRFRanker, ) from tqdm.autonotebook import tqdm from milvus_beir.retrieval.search.milvus import MilvusBaseSearch +from milvus_beir.utils import measure_search_qps_decorator logger = logging.getLogger(__name__) @@ -29,7 +31,8 @@ def get_default_ranker(): class MilvusBM25DenseHybridSearch(MilvusBaseSearch): def __init__( self, - milvus_client: MilvusClient, + uri: str, + token: str | None, collection_name: str, nq: int = 100, nb: int = 1000, @@ -43,6 +46,7 @@ def __init__( dense_search_params: Optional[Dict] = None, bm25_search_params: Optional[Dict] = None, ranker: Any = None, + sleep_time: int = 5, ): self.model = model if model is not None else get_default_model() self.dense_vector_field = dense_vector_field @@ -52,9 +56,13 @@ def __init__( self.dense_search_params = dense_search_params if dense_search_params is not None else {} self.bm25_search_params = bm25_search_params if bm25_search_params is not None else {} self.ranker = ranker if ranker is not None else get_default_ranker() + self.sleep_time = sleep_time + self.query_embeddings = [] + self.query_texts = [] super().__init__( - milvus_client=milvus_client, + uri=uri, + token=token, collection_name=collection_name, nq=nq, nb=nb, @@ -130,6 +138,37 @@ def _index(self, corpus): self.index_completed = True logger.info("Indexing Completed!") + def _single_search(self, args): + query_text, top_k = args + query_embeddings = self.model.encode_queries([query_text]) + query_texts = [query_text] + + try: + dense_rqs = AnnSearchRequest( + data=query_embeddings, + anns_field="dense_embedding", + param=self.dense_search_params, + limit=top_k, + ) + bm25_rqs = AnnSearchRequest( + data=query_texts, + anns_field=self.bm25_output_field, + param=self.bm25_search_params, + limit=top_k, + ) + + result = self.milvus_client.hybrid_search( + collection_name=self.collection_name, + reqs=[dense_rqs, bm25_rqs], + ranker=self.ranker, + limit=top_k, + output_fields=["id"], + ) + return result + except Exception as e: + logger.error(f"Search error: {e!s}") + return None + def search( self, corpus: Dict[str, Dict[str, str]], @@ -185,3 +224,58 @@ def search( result_dict[query_ids[i]] = data return result_dict + + def measure_search_qps( + self, corpus, queries, top_k=1000, concurrency_levels=None, test_duration=60 + ): + if concurrency_levels is None: + concurrency_levels = [1, 2, 4, 8, 16, 32] + + @measure_search_qps_decorator( + concurrency_levels, test_duration=test_duration, max_threads=None + ) + def _single_search(top_k): + """ + 执行单个搜索操作的方法 + """ + random_id = random.randint(0, len(queries) - 1) + text = self.query_texts[random_id] + embedding = self.query_embeddings[random_id] + + try: + client = self._get_thread_client() + dense_rqs = AnnSearchRequest( + data=[embedding], + anns_field="dense_embedding", + param=self.dense_search_params, + limit=top_k, + ) + bm25_rqs = AnnSearchRequest( + data=[text], + anns_field=self.bm25_output_field, + param=self.bm25_search_params, + limit=top_k, + ) + + result = client.hybrid_search( + collection_name=self.collection_name, + reqs=[dense_rqs, bm25_rqs], + ranker=self.ranker, + limit=top_k, + output_fields=["id"], + ) + return result + except Exception as e: + logger.error(f"Search error: {e!s}") + return None + + if not self.index_completed: + self._index(corpus) + time.sleep(self.sleep_time) + query_ids = list(queries.keys()) + query_texts = [queries[qid] for qid in query_ids] + query_embeddings = self.model.encode_queries(query_texts) + self.query_embeddings = query_embeddings + self.query_texts = query_texts + res = _single_search(top_k) + return res diff --git a/src/milvus_beir/retrieval/search/hybrid/sparse_hybrid_search.py b/src/milvus_beir/retrieval/search/hybrid/sparse_hybrid_search.py index 06088d1..6d5efdf 100644 --- a/src/milvus_beir/retrieval/search/hybrid/sparse_hybrid_search.py +++ b/src/milvus_beir/retrieval/search/hybrid/sparse_hybrid_search.py @@ -1,4 +1,6 @@ import logging +import random +import time from typing import Any, Dict, Optional from milvus_model.base import BaseEmbeddingFunction @@ -7,12 +9,12 @@ from pymilvus import ( AnnSearchRequest, DataType, - MilvusClient, RRFRanker, ) from tqdm.autonotebook import tqdm from milvus_beir.retrieval.search.milvus import MilvusBaseSearch +from milvus_beir.utils import DEFAULT_CONCURRENCY_LEVELS, measure_search_qps_decorator logger = logging.getLogger(__name__) @@ -32,7 +34,8 @@ def get_default_ranker(): class MilvusSparseDenseHybridSearch(MilvusBaseSearch): def __init__( self, - milvus_client: MilvusClient, + uri: str, + token: str | None, collection_name: str, nq: int = 100, nb: int = 1000, @@ -47,6 +50,7 @@ def __init__( dense_search_params: Optional[Dict] = None, sparse_search_params: Optional[Dict] = None, ranker: Any = None, + sleep_time: int = 5, ): self.dense_model = dense_model if dense_model is not None else get_default_dense_model() self.sparse_model = sparse_model if sparse_model is not None else get_default_sparse_model() @@ -57,9 +61,13 @@ def __init__( self.dense_search_params = dense_search_params if dense_search_params is not None else {} self.sparse_search_params = sparse_search_params if sparse_search_params is not None else {} self.ranker = ranker if ranker is not None else get_default_ranker() + self.sleep_time = sleep_time + self.query_dense_embeddings = [] + self.query_sparse_embeddings = [] super().__init__( - milvus_client=milvus_client, + uri=uri, + token=token, collection_name=collection_name, nq=nq, nb=nb, @@ -93,7 +101,11 @@ def _index(self, corpus): sparse_embeddings = self.sparse_model(texts) ids = corpus_ids[start:end] data = [ - {"id": ids[i], self.dense_vector_field: dense_embeddings[i], self.sparse_vector_field: sparse_embeddings[[i]]} + { + "id": ids[i], + self.dense_vector_field: dense_embeddings[i], + self.sparse_vector_field: sparse_embeddings[[i]], + } for i in range(len(ids)) ] self.milvus_client.insert(collection_name=self.collection_name, data=data) @@ -174,3 +186,56 @@ def search( result_dict[query_ids[i]] = data return result_dict + + def measure_search_qps( + self, corpus, queries, top_k=1000, concurrency_levels=None, test_duration=60 + ): + if concurrency_levels is None: + concurrency_levels = DEFAULT_CONCURRENCY_LEVELS + + @measure_search_qps_decorator( + concurrency_levels, test_duration=test_duration, max_threads=None + ) + def _single_search(top_k): + """ """ + random_id = random.randint(0, len(queries) - 1) + dense_embeddings = [self.query_dense_embeddings[random_id]] + sparse_embeddings = [self.query_sparse_embeddings[random_id]] + try: + client = self._get_thread_client() + dense_rqs = AnnSearchRequest( + data=dense_embeddings, + anns_field=self.dense_vector_field, + param=self.dense_search_params, + limit=top_k, + ) + sparse_rqs = AnnSearchRequest( + data=sparse_embeddings, + anns_field=self.sparse_vector_field, + param=self.sparse_search_params, + limit=top_k, + ) + + result = client.hybrid_search( + collection_name=self.collection_name, + reqs=[dense_rqs, sparse_rqs], + ranker=self.ranker, + limit=top_k, + output_fields=["id"], + ) + return result + except Exception as e: + logger.error(f"Search error: {e!s}") + return None + + if not self.index_completed: + self._index(corpus) + time.sleep(self.sleep_time) + query_ids = list(queries.keys()) + query_texts = [queries[qid] for qid in query_ids] + query_dense_embeddings = self.dense_model.encode_queries(query_texts) + query_sparse_embeddings = self.sparse_model.encode_queries(query_texts) + self.query_dense_embeddings = query_dense_embeddings + self.query_sparse_embeddings = query_sparse_embeddings + res = _single_search(top_k) + return res diff --git a/src/milvus_beir/retrieval/search/lexical/bm25_search.py b/src/milvus_beir/retrieval/search/lexical/bm25_search.py index e4c05c9..00bbf1a 100644 --- a/src/milvus_beir/retrieval/search/lexical/bm25_search.py +++ b/src/milvus_beir/retrieval/search/lexical/bm25_search.py @@ -1,10 +1,13 @@ import logging +import random +import time from typing import Dict, Optional -from pymilvus import DataType, Function, FunctionType, MilvusClient +from pymilvus import DataType, Function, FunctionType from tqdm.autonotebook import tqdm from milvus_beir.retrieval.search.milvus import MilvusBaseSearch +from milvus_beir.utils import DEFAULT_CONCURRENCY_LEVELS, measure_search_qps_decorator logger = logging.getLogger(__name__) @@ -12,7 +15,8 @@ class MilvusBM25Search(MilvusBaseSearch): def __init__( self, - milvus_client: MilvusClient, + uri: str, + token: str | None, collection_name: str, nq: int = 100, nb: int = 1000, @@ -25,13 +29,18 @@ def __init__( search_params: Optional[Dict] = None, sleep_time: int = 5, ): + self.uri = uri + self.token = token self.analyzer = analyzer self.bm25_input_field = bm25_input_field self.bm25_output_field = bm25_output_field self.metric_type = metric_type self.search_params = search_params if search_params is not None else {} + self.sleep_time = sleep_time + self.query_texts = [] super().__init__( - milvus_client=milvus_client, + uri=uri, + token=token, collection_name=collection_name, nq=nq, nb=nb, @@ -105,11 +114,21 @@ def search( *args, **kwargs, ) -> Dict[str, Dict[str, float]]: + """ + Perform BM25 search with optional QPS measurement. + + Args: + corpus: The corpus to search in + queries: The queries to use for testing + top_k: Number of results to return per query + """ + if self.initialize: self._initialize_collection() if not self.index_completed: self._index(corpus) + time.sleep(self.sleep_time) query_ids = list(queries.keys()) query_texts = [queries[qid] for qid in query_ids] @@ -136,3 +155,39 @@ def search( data[hit["id"]] = hit["distance"] result_dict[query_ids[i]] = data return result_dict + + def measure_search_qps( + self, corpus, queries, top_k=1000, concurrency_levels=None, test_duration=60 + ): + if concurrency_levels is None: + concurrency_levels = DEFAULT_CONCURRENCY_LEVELS + + @measure_search_qps_decorator( + concurrency_levels, test_duration=test_duration, max_threads=None + ) + def _single_search(top_k): + """ + 执行单个搜索操作的方法 + """ + query_text = random.choice(self.query_texts) + try: + client = self._get_thread_client() + result = client.search( + collection_name=self.collection_name, + data=[query_text], + anns_field=self.bm25_output_field, + search_params=self.search_params, + limit=top_k, + output_fields=["id"], + ) + return result + except Exception as e: + logger.error(f"Search error: {e!s}") + return None + + if not self.index_completed: + self._index(corpus) + time.sleep(self.sleep_time) + self.query_texts = [queries[qid] for qid in queries.keys()] + res = _single_search(queries, top_k) + return res diff --git a/src/milvus_beir/retrieval/search/lexical/multi_match_search.py b/src/milvus_beir/retrieval/search/lexical/multi_match_search.py index 047bc0a..881dac1 100644 --- a/src/milvus_beir/retrieval/search/lexical/multi_match_search.py +++ b/src/milvus_beir/retrieval/search/lexical/multi_match_search.py @@ -1,10 +1,13 @@ import logging +import random +import time from typing import Dict, Optional -from pymilvus import DataType, Function, FunctionType, MilvusClient +from pymilvus import DataType, Function, FunctionType from tqdm.autonotebook import tqdm from milvus_beir.retrieval.search.milvus import MilvusBaseSearch +from milvus_beir.utils import DEFAULT_CONCURRENCY_LEVELS, measure_search_qps_decorator logger = logging.getLogger(__name__) @@ -12,7 +15,8 @@ class MilvusMultiMatchSearch(MilvusBaseSearch): def __init__( self, - milvus_client: MilvusClient, + uri: str, + token: str | None, collection_name: str, nq: int = 100, nb: int = 1000, @@ -35,8 +39,11 @@ def __init__( self.metric_type = metric_type self.tie_breaker = tie_breaker self.search_params = search_params if search_params is not None else {} + self.sleep_time = sleep_time + self.query_texts = [] super().__init__( - milvus_client=milvus_client, + uri=uri, + token=token, collection_name=collection_name, nq=nq, nb=nb, @@ -213,3 +220,45 @@ def search( # Apply score fusion return self._apply_fusion(combined_results, query_ids) + + def measure_search_qps( + self, corpus, queries, top_k=1000, concurrency_levels=None, test_duration=60 + ): + if concurrency_levels is None: + concurrency_levels = DEFAULT_CONCURRENCY_LEVELS + + @measure_search_qps_decorator( + concurrency_levels, test_duration=test_duration, max_threads=None + ) + def _single_search(top_k): + """ """ + query_text = random.choice(self.query_texts) + try: + client = self._get_thread_client() + multi_result = [] + for bm25_output_field in self.bm25_input_output_mapping.values(): + result = client.search( + collection_name=self.collection_name, + data=[query_text], + anns_field=bm25_output_field, + search_params=self.search_params, + limit=top_k, + output_fields=["id"], + ) + multi_result.append(result) + + # Combine results from all fields + combined_results = self._combine_field_results(multi_result, [query_text]) + + return combined_results + + except Exception as e: + logger.error(f"Search error: {e!s}") + return None + + if not self.index_completed: + self._index(corpus) + time.sleep(self.sleep_time) + self.query_texts = [queries[qid] for qid in queries.keys()] + res = _single_search(queries, top_k) + return res diff --git a/src/milvus_beir/retrieval/search/milvus.py b/src/milvus_beir/retrieval/search/milvus.py index 8fe1814..25f8b93 100644 --- a/src/milvus_beir/retrieval/search/milvus.py +++ b/src/milvus_beir/retrieval/search/milvus.py @@ -1,4 +1,5 @@ import logging +import threading from beir.retrieval.search import BaseSearch from pymilvus import MilvusClient @@ -9,14 +10,17 @@ class MilvusBaseSearch(BaseSearch): def __init__( self, - milvus_client: MilvusClient, + uri: str, + token: str | None, collection_name: str, initialize: bool = True, clean_up: bool = False, nb: int = 2000, nq: int = 100, ): - self.milvus_client = milvus_client + self.uri = uri + self.token = token + self.milvus_client = MilvusClient(uri=uri, token=token) self.collection_name = collection_name self.initialize = initialize self.clean_up = clean_up @@ -24,3 +28,10 @@ def __init__( self.nb = nb self.results = {} self.index_completed = False + self._thread_local = threading.local() + + def _get_thread_client(self): + """ """ + if not hasattr(self._thread_local, "client"): + self._thread_local.client = MilvusClient(uri=self.uri, token=self.token) + return self._thread_local.client diff --git a/src/milvus_beir/retrieval/search/sparse/sparse_search.py b/src/milvus_beir/retrieval/search/sparse/sparse_search.py index aefcf16..36d87bc 100644 --- a/src/milvus_beir/retrieval/search/sparse/sparse_search.py +++ b/src/milvus_beir/retrieval/search/sparse/sparse_search.py @@ -1,12 +1,15 @@ import logging +import random +import time from typing import Dict, Optional from milvus_model.base import BaseEmbeddingFunction from milvus_model.sparse import SpladeEmbeddingFunction -from pymilvus import DataType, MilvusClient +from pymilvus import DataType from tqdm.autonotebook import tqdm from milvus_beir.retrieval.search.milvus import MilvusBaseSearch +from milvus_beir.utils import DEFAULT_CONCURRENCY_LEVELS, measure_search_qps_decorator logger = logging.getLogger(__name__) @@ -18,7 +21,8 @@ def get_default_sparse_model() -> BaseEmbeddingFunction: class MilvusSparseSearch(MilvusBaseSearch): def __init__( self, - milvus_client: MilvusClient, + uri: str, + token: str | None, collection_name: str, nq: int = 100, nb: int = 1000, @@ -28,13 +32,23 @@ def __init__( vector_field: str = "sparse_embedding", metric_type: str = "IP", search_params: Optional[Dict] = None, + sleep_time: int = 5, ): self.model = model if model is not None else get_default_sparse_model() self.vector_field = vector_field self.metric_type = metric_type self.search_params = search_params + self.collection_name = collection_name + self.nq = nq + self.nb = nb + self.initialize = initialize + self.clean_up = clean_up + self.index_completed = False + self.sleep_time = sleep_time + self.query_sparse_embeddings = [] super().__init__( - milvus_client=milvus_client, + uri=uri, + token=token, collection_name=collection_name, nq=nq, nb=nb, @@ -85,6 +99,7 @@ def search( *args, **kwargs, ) -> Dict[str, Dict[str, float]]: + """ """ if self.initialize: self._initialize_collection() @@ -118,3 +133,40 @@ def search( data[hit["id"]] = hit["distance"] result_dict[query_ids[i]] = data return result_dict + + def measure_search_qps( + self, corpus, queries, top_k=1000, concurrency_levels=None, test_duration=60 + ): + if concurrency_levels is None: + concurrency_levels = DEFAULT_CONCURRENCY_LEVELS + + @measure_search_qps_decorator( + concurrency_levels, test_duration=test_duration, max_threads=None + ) + def _single_search(top_k): + """ """ + embedding = random.choice(self.query_sparse_embeddings) + try: + client = self._get_thread_client() + result = client.search( + collection_name=self.collection_name, + data=[embedding], + anns_field=self.vector_field, + search_params=self.search_params, + limit=top_k, + output_fields=["id"], + ) + return result + except Exception as e: + logger.error(f"Search error: {e!s}") + return None + + if not self.index_completed: + self._index(corpus) + time.sleep(self.sleep_time) + query_ids = list(queries.keys()) + query_texts = [queries[qid] for qid in query_ids] + sparse_embeddings = self.model.encode_queries(query_texts) + self.query_sparse_embeddings = sparse_embeddings + res = _single_search(top_k) + return res diff --git a/src/milvus_beir/utils.py b/src/milvus_beir/utils.py new file mode 100644 index 0000000..6fb109c --- /dev/null +++ b/src/milvus_beir/utils.py @@ -0,0 +1,112 @@ +import logging +import time +from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait +from typing import Any, Callable, List, Optional, Tuple + +logger = logging.getLogger(__name__) + + +DEFAULT_MAX_THREADS = 128 +DEFAULT_CONCURRENCY_LEVELS = [1, 2, 4, 8, 16, 32, 64, 128] + + +class QPSMeasurement: + def __init__(self, test_duration: int, concurrency: int): + self.test_duration = test_duration + self.concurrency = concurrency + self.total_queries = 0 + self.successful_queries = 0 + self.error_count = 0 + self.start_time = 0 + + def process_future_result(self, future: Any) -> None: + try: + result = future.result() + if result is not None: + self.successful_queries += 1 + else: + self.error_count += 1 + except Exception as e: + logger.error(f"Error processing result: {e!s}") + self.error_count += 1 + + def process_completed_futures(self, done_futures: set) -> None: + for future in done_futures: + self.process_future_result(future) + + def calculate_qps(self) -> float: + elapsed_time = time.time() - self.start_time + return self.successful_queries / elapsed_time if elapsed_time > 0 else 0 + + def log_results(self, qps: float) -> None: + elapsed_time = time.time() - self.start_time + logger.info( + f"Concurrency {self.concurrency}: {qps:.2f} QPS " + f"(total: {self.total_queries}, successful: {self.successful_queries}, " + f"errors: {self.error_count}, time: {elapsed_time:.2f}s)" + ) + + +def run_concurrency_test( + func: Callable, + args: tuple, + kwargs: dict, + thread_count: int, + concurrency: int, + test_duration: int, +) -> Tuple[int, float]: + measurement = QPSMeasurement(test_duration, concurrency) + + with ThreadPoolExecutor(max_workers=thread_count) as executor: + measurement.start_time = time.time() + futures = [] + + try: + while time.time() - measurement.start_time < test_duration: + while len(futures) < concurrency: + futures.append(executor.submit(func, *args, **kwargs)) + measurement.total_queries += 1 + + done, not_done = wait(futures, timeout=0.1, return_when=FIRST_COMPLETED) + + measurement.process_completed_futures(done) + futures = list(not_done) + + except Exception as e: + logger.error(f"Unexpected error during QPS test: {e!s}") + measurement.process_completed_futures(set(futures)) + + qps = measurement.calculate_qps() + measurement.log_results(qps) + return concurrency, qps + + +def measure_search_qps_decorator( + concurrency_levels: List[int], test_duration: int = 10, max_threads: Optional[int] = None +): + def decorator(func): + def wrapper(*args, **kwargs): + default_max_threads = 128 + max_concurrent_threads = max_threads if max_threads is not None else default_max_threads + logger.info(f"Maximum concurrent threads: {max_concurrent_threads}") + + results = [] + for concurrency in concurrency_levels: + thread_count = min(concurrency, max_concurrent_threads) + logger.info(f"Testing concurrency {concurrency} with {thread_count} threads") + + result = run_concurrency_test( + func=func, + args=args, + kwargs=kwargs, + thread_count=thread_count, + concurrency=concurrency, + test_duration=test_duration, + ) + results.append(result) + + return results + + return wrapper + + return decorator diff --git a/tests/test_search.py b/tests/test_search.py index 66cb7fc..2d8a671 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -6,9 +6,10 @@ from milvus_beir.retrieval.search.sparse.sparse_search import MilvusSparseSearch -def test_dense_search(milvus_client, collection_name, test_corpus, test_queries): +def test_dense_search(milvus_uri, milvus_token, collection_name, test_corpus, test_queries): searcher = MilvusDenseSearch( - milvus_client=milvus_client, + milvus_uri, + milvus_token, collection_name=collection_name, ) @@ -21,10 +22,16 @@ def test_dense_search(milvus_client, collection_name, test_corpus, test_queries) assert qid in results assert isinstance(results[qid], dict) assert len(results[qid]) <= 2 # top_k=2 + res = searcher.measure_search_qps( + test_corpus, test_queries, top_k=2, concurrency_levels=[1, 2], test_duration=10 + ) + assert res is not None + for r in res: + assert isinstance(r, tuple) -def test_sparse_search(milvus_client, collection_name, test_corpus, test_queries): - searcher = MilvusSparseSearch(milvus_client=milvus_client, collection_name=collection_name) +def test_sparse_search(milvus_uri, milvus_token, collection_name, test_corpus, test_queries): + searcher = MilvusSparseSearch(milvus_uri, milvus_token, collection_name=collection_name) results = searcher.search(test_corpus, test_queries, top_k=2) @@ -34,11 +41,18 @@ def test_sparse_search(milvus_client, collection_name, test_corpus, test_queries assert qid in results assert isinstance(results[qid], dict) assert len(results[qid]) <= 2 + res = searcher.measure_search_qps( + test_corpus, test_queries, top_k=2, concurrency_levels=[1, 2], test_duration=10 + ) + assert res is not None + for r in res: + assert isinstance(r, tuple) -def test_sparse_hybrid_search(milvus_client, collection_name, test_corpus, test_queries): +def test_sparse_hybrid_search(milvus_uri, milvus_token, collection_name, test_corpus, test_queries): searcher = MilvusSparseDenseHybridSearch( - milvus_client=milvus_client, + milvus_uri, + milvus_token, collection_name=collection_name, ) @@ -50,11 +64,18 @@ def test_sparse_hybrid_search(milvus_client, collection_name, test_corpus, test_ assert qid in results assert isinstance(results[qid], dict) assert len(results[qid]) <= 2 + res = searcher.measure_search_qps( + test_corpus, test_queries, top_k=2, concurrency_levels=[1, 2], test_duration=10 + ) + assert res is not None + for r in res: + assert isinstance(r, tuple) -def test_bm25_hybrid_search(milvus_client, collection_name, test_corpus, test_queries): +def test_bm25_hybrid_search(milvus_uri, milvus_token, collection_name, test_corpus, test_queries): searcher = MilvusBM25DenseHybridSearch( - milvus_client=milvus_client, + milvus_uri, + milvus_token, collection_name=collection_name, ) @@ -66,23 +87,37 @@ def test_bm25_hybrid_search(milvus_client, collection_name, test_corpus, test_qu assert qid in results assert isinstance(results[qid], dict) assert len(results[qid]) <= 2 + res = searcher.measure_search_qps( + test_corpus, test_queries, top_k=2, concurrency_levels=[1, 2], test_duration=10 + ) + assert res is not None + for r in res: + assert isinstance(r, tuple) -def test_bm25_search(milvus_client, collection_name, test_corpus, test_queries): - searcher = MilvusBM25Search(milvus_client=milvus_client, collection_name=collection_name) +def test_bm25_search(milvus_uri, milvus_token, collection_name, test_corpus, test_queries): + searcher = MilvusBM25Search(milvus_uri, milvus_token, collection_name=collection_name) results = searcher.search(test_corpus, test_queries, top_k=2) - + res = searcher.measure_search_qps( + test_corpus, test_queries, top_k=2, concurrency_levels=[1, 2], test_duration=10 + ) assert isinstance(results, dict) assert len(results) == len(test_queries) for qid in test_queries: assert qid in results assert isinstance(results[qid], dict) assert len(results[qid]) <= 2 + res = searcher.measure_search_qps( + test_corpus, test_queries, top_k=2, concurrency_levels=[1, 2], test_duration=10 + ) + assert res is not None + for r in res: + assert isinstance(r, tuple) -def test_multi_match_search(milvus_client, collection_name, test_corpus, test_queries): - searcher = MilvusMultiMatchSearch(milvus_client=milvus_client, collection_name=collection_name) +def test_multi_match_search(milvus_uri, milvus_token, collection_name, test_corpus, test_queries): + searcher = MilvusMultiMatchSearch(milvus_uri, milvus_token, collection_name=collection_name) results = searcher.search(test_corpus, test_queries, top_k=2) @@ -92,3 +127,9 @@ def test_multi_match_search(milvus_client, collection_name, test_corpus, test_qu assert qid in results assert isinstance(results[qid], dict) assert len(results[qid]) <= 2 + res = searcher.measure_search_qps( + test_corpus, test_queries, top_k=2, concurrency_levels=[1, 2], test_duration=10 + ) + assert res is not None + for r in res: + assert isinstance(r, tuple)