From ad4b7d907ef8cfcaaf5228b2a6df8c91196eabc8 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Wed, 21 Jun 2023 11:24:19 +0800 Subject: [PATCH 01/24] feat: init commit for milvus storage backend Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 124 ++++++++++++++++++++++++++++++ pyproject.toml | 1 + 2 files changed, 125 insertions(+) create mode 100644 docarray/index/backends/milvus.py diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py new file mode 100644 index 00000000000..3decb2ab223 --- /dev/null +++ b/docarray/index/backends/milvus.py @@ -0,0 +1,124 @@ +from dataclasses import dataclass +from typing import ( + Any, + Dict, + Generic, + List, + Optional, + Sequence, + Type, + TypeVar, + Union, + cast, +) + +import numpy as np +from pymilvus import Collection, DataType, connections, utility + +from docarray import BaseDoc, DocList +from docarray.index.abstract import BaseDocIndex +from docarray.typing import AnyTensor +from docarray.utils._internal._typing import safe_issubclass +from docarray.utils.find import _FindResult, _FindResultBatched + +TSchema = TypeVar('TSchema', bound=BaseDoc) + + +class MilvusDocumentIndex(BaseDocIndex, Generic[TSchema]): + def __init__(self, db_config=None, **kwargs): + super().__init__(db_config, **kwargs) + self._db_config: MilvusDocumentIndex.DBConfig = cast( + MilvusDocumentIndex.DBConfig, self._db_config + ) + + self._client = connections.connect( + db_name="default", + host=self._db_config.host, + user=self._db_config.user, + password=self._db_config.password, + token=self._db_config.token, + ) + + self._connection = self._init_index() + + @dataclass + class DBConfig(BaseDocIndex.DBConfig): + host: str = "localhost" + port: int = 19530 + user: Optional[str] = "" + password: Optional[str] = "" + token: Optional[str] = "" + index_name = None + + def python_type_to_db_type(self, python_type: Type) -> Any: + type_map = { + int: DataType.INT32, + float: DataType.FLOAT, + str: DataType.STRING, + bytes: DataType.STRING, + np.ndarray: DataType.FLOAT_VECTOR, + list: DataType.FLOAT_VECTOR, + AnyTensor: DataType.FLOAT_VECTOR, + } + + for py_type, db_type in type_map.items(type_map): + if safe_issubclass(python_type, py_type): + return db_type + + raise ValueError("No corresponding milvus type") + + def _init_index(self) -> Collection: + # TODO: Loading schema into Milvus Collection + if not utility.has_collection("docs"): + # INIT schema + pass + return Collection("docs").load() + + def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): + pass + + def num_docs(self) -> int: + return self._connection.num_entities + + def _del_items(self, doc_ids: Sequence[str]): + pass + + def _get_items( + self, doc_ids: Sequence[str] + ) -> Union[Sequence[TSchema], Sequence[Dict[str, Any]]]: + pass + + def execute_query(self, query: Any, *args, **kwargs) -> Any: + pass + + def _find( + self, + query: np.ndarray, + limit: int, + search_field: str = '', + ) -> _FindResult: + pass + + def _find_batched( + self, + queries: np.ndarray, + limit: int, + search_field: str = '', + ) -> _FindResultBatched: + pass + + def filter( + self, + filter_query: Any, + limit: int = 10, + **kwargs, + ) -> DocList: + pass + + def filter_batched( + self, + filter_queries: Any, + limit: int = 10, + **kwargs, + ) -> List[DocList]: + pass diff --git a/pyproject.toml b/pyproject.toml index 1dd7e8d7e78..516589f2c62 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,6 +58,7 @@ smart-open = {version = ">=6.3.0", extras = ["s3"], optional = true} jina-hubble-sdk = {version = ">=0.34.0", optional = true} elastic-transport = {version ="^8.4.0", optional = true } qdrant-client = {version = ">=1.1.4", python = "<3.12", optional = true } +pymilvus = {version = "^2.2.12", optional = true } [tool.poetry.extras] proto = ["protobuf", "lz4"] From 888cb73307a8f129abd39cd9f0f7f668b182c096 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Mon, 26 Jun 2023 18:32:47 +0800 Subject: [PATCH 02/24] feat: milvus index & get items Signed-off-by: maxwelljin2 --- docarray/index/__init__.py | 3 + docarray/index/backends/milvus.py | 288 ++- docarray/utils/_internal/_typing.py | 2 +- poetry.lock | 3244 +++++++++++++++------------ 4 files changed, 2093 insertions(+), 1444 deletions(-) diff --git a/docarray/index/__init__.py b/docarray/index/__init__.py index 9e4dbde474a..4186f2b6fc9 100644 --- a/docarray/index/__init__.py +++ b/docarray/index/__init__.py @@ -34,6 +34,9 @@ def __getattr__(name: str): elif name == 'WeaviateDocumentIndex': import_library('weaviate', raise_error=True) import docarray.index.backends.weaviate as lib + elif name == "MilvusDocumentIndex": + import_library('pymilvus', raise_error=True) + import docarray.index.backends.milvus as lib else: raise ImportError( f'cannot import name \'{name}\' from \'{_get_path_from_docarray_root_level(__file__)}\'' diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index 3decb2ab223..9bf36733163 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -1,7 +1,12 @@ -from dataclasses import dataclass +import re +import uuid +from collections import defaultdict +from dataclasses import dataclass, field from typing import ( + TYPE_CHECKING, Any, Dict, + Generator, Generic, List, Optional, @@ -12,21 +17,50 @@ cast, ) -import numpy as np -from pymilvus import Collection, DataType, connections, utility - from docarray import BaseDoc, DocList from docarray.index.abstract import BaseDocIndex from docarray.typing import AnyTensor +from docarray.typing.id import ID +from docarray.typing.tensor.abstract_tensor import AbstractTensor from docarray.utils._internal._typing import safe_issubclass +from docarray.utils._internal.misc import import_library from docarray.utils.find import _FindResult, _FindResultBatched +if TYPE_CHECKING: + import numpy as np + import tensorflow as tf # type: ignore + import torch + from pymilvus import ( + Collection, + CollectionSchema, + DataType, + FieldSchema, + connections, + utility, + ) +else: + hnswlib = import_library('hnswlib', raise_error=True) + torch = import_library('torch', raise_error=False) + tf = import_library('tensorflow', raise_error=False) + np = import_library('numpy', raise_error=False) + from pymilvus import ( + Collection, + CollectionSchema, + DataType, + FieldSchema, + connections, + utility, + ) + +ID_VARCHAR_LEN = 1024 +SERIALIZED_VARCHAR_LEN = 65_535 # Maximum length that Milvus allows for a VARCHAR field + TSchema = TypeVar('TSchema', bound=BaseDoc) class MilvusDocumentIndex(BaseDocIndex, Generic[TSchema]): - def __init__(self, db_config=None, **kwargs): - super().__init__(db_config, **kwargs) + def __init__(self, db_config=None, index_name=None, **kwargs): + super().__init__(db_config=db_config, **kwargs) self._db_config: MilvusDocumentIndex.DBConfig = cast( MilvusDocumentIndex.DBConfig, self._db_config ) @@ -39,57 +73,240 @@ def __init__(self, db_config=None, **kwargs): token=self._db_config.token, ) - self._connection = self._init_index() + self._db_config.index_name = index_name + self._validate_columns() + self._create_collection_name() + self._collection = self._init_index() + self._build_index() + self._logger.info(f"{self.__class__.__name__} has been initialized") @dataclass class DBConfig(BaseDocIndex.DBConfig): + collection_name: str = None host: str = "localhost" port: int = 19530 user: Optional[str] = "" password: Optional[str] = "" token: Optional[str] = "" - index_name = None + index_name: str = None + index_type: str = "IVF_FLAT" + index_metric: str = "L2" + index_params: Dict = field(default_factory=lambda: {"nlist": 1024}) + default_column_config: Dict[Type, Dict[str, Any]] = field( + default_factory=lambda: defaultdict(dict) + ) def python_type_to_db_type(self, python_type: Type) -> Any: type_map = { - int: DataType.INT32, + int: DataType.INT64, float: DataType.FLOAT, - str: DataType.STRING, - bytes: DataType.STRING, + str: DataType.VARCHAR, + bytes: DataType.VARCHAR, np.ndarray: DataType.FLOAT_VECTOR, list: DataType.FLOAT_VECTOR, AnyTensor: DataType.FLOAT_VECTOR, + AbstractTensor: DataType.FLOAT_VECTOR, } - for py_type, db_type in type_map.items(type_map): + if issubclass(python_type, ID): + return DataType.INT64 # Primary key must be int64 + + for py_type, db_type in type_map.items(): if safe_issubclass(python_type, py_type): return db_type raise ValueError("No corresponding milvus type") def _init_index(self) -> Collection: - # TODO: Loading schema into Milvus Collection - if not utility.has_collection("docs"): - # INIT schema - pass - return Collection("docs").load() + if not utility.has_collection(self._db_config.collection_name): + print(self._db_config.collection_name) + fields = [ + FieldSchema( + name="id", + dtype=DataType.INT64, + is_primary=True, + auto_id=True, + ) + ] # Initiliaze the id + fields.extend( + [ + FieldSchema( + name="doc_id" if column_name == "id" else column_name, + dtype=DataType.VARCHAR if column_name == "id" else info.db_type, + is_primary=False, + **( + {'max_length': 256} + if info.db_type == DataType.VARCHAR or column_name == "id" + else {} + ), + **( + {'dim': info.n_dim} + if info.db_type == DataType.FLOAT_VECTOR + else {} + ), + ) + for column_name, info in self._column_infos.items() + ] + ) + self._logger.info("Collection has been created") + return Collection( + name=self._db_config.collection_name, + schema=CollectionSchema( + fields=fields, + description="Collection Schema", + ), + using='default', + ) + + return Collection(self._db_config.collection_name) + + def _create_collection_name(self): + if self._db_config.collection_name is None: + id = uuid.uuid4().hex + self._db_config.collection_name = f"{self.__class__.__name__}__" + id + + self._db_config.collection_name = ''.join( + re.findall('[a-zA-Z0-9_]', self._db_config.collection_name) + ) + + def _validate_columns(self): + """ + Validates if the DataFrame contains at least one vector column + (Milvus' requirement) and checks that each vector column has + dimension information specified. + """ + vector_columns_exist = any( + safe_issubclass(info.docarray_type, AbstractTensor) + for info in self._column_infos.values() + ) + + if not vector_columns_exist: + raise ValueError( + "No vector columns found. Ensure that at least one column is of a vector type" + ) + + for column_name, info in self._column_infos.items(): + if info.n_dim is None and safe_issubclass( + info.docarray_type, AbstractTensor + ): + raise ValueError( + f"Dimension information is missing for column '{column_name}' which is vector type" + ) + + def _build_index(self): + index = { + "index_type": self._db_config.index_type, + "metric_type": self._db_config.index_metric, + "params": self._db_config.index_params, + } + + self._collection.create_index(self._db_config.index_name, index) + self._logger.info( + f"Index '{self._db_config.index_name}' has been successfully created" + ) def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): - pass + """Index Documents into the index. - def num_docs(self) -> int: - return self._connection.num_entities + !!! note + Passing a sequence of Documents that is not a DocList + (such as a List of Docs) comes at a performance penalty. + This is because the Index needs to check compatibility between itself and + the data. With a DocList as input this is a single check; for other inputs + compatibility needs to be checked for every Document individually. - def _del_items(self, doc_ids: Sequence[str]): - pass + :param docs: Documents to index. + """ + + docs = self._validate_docs(docs) + entities = [[] for _ in range(len(self._column_infos.items()))] + + for i in range(len(docs)): + for j, (column_name, info) in enumerate(self._column_infos.items()): + column_value = docs[i].__getattr__(column_name) + if isinstance(column_value, (tf.Tensor, torch.Tensor, np.ndarray)): + column_value = self._convert_to_vector(column_value) + entities[j].append(column_value) + + self._collection.insert(entities) + self._collection.flush() + self._logger.info(f"{len(docs)} documents has been indexed") + + def _convert_to_vector(self, column_value: AbstractTensor) -> Sequence[float]: + """ + Converts a column value to a float vector. + + The database can only store float vectors, so this method is used to convert + TensorFlow or PyTorch tensors to a format compatible with the database. + + :param column_value: The column value to be converted. + :return: The converted float vector. + """ + if len(column_value.shape) != 1: + raise ValueError( + 'Unsupported: Milvus backend only supports one-dimensional vectors' + ) + + if isinstance(column_value, np.ndarray): + return column_value.astype(float).tolist() + elif torch.is_tensor(column_value): + return column_value.float().numpy().tolist() + elif tf.is_tensor(column_value): + return column_value.numpy().astype(float).tolist() + + def num_docs(self) -> int: + return self._collection.num_entities def _get_items( self, doc_ids: Sequence[str] ) -> Union[Sequence[TSchema], Sequence[Dict[str, Any]]]: - pass + self._collection.load() + ret = self._collection.query( + expr="doc_id in " + str([id for id in doc_ids]), + offset=0, + limit=self.num_docs(), + output_fields=[ + column_name for column_name, _ in self._column_infos.items() + ], + ) - def execute_query(self, query: Any, *args, **kwargs) -> Any: - pass + return DocList[self._schema]([self._schema(**ret[i]) for i in range(len(ret))]) + + def _del_items(self, doc_ids: Sequence[str]): + ... + + def _filter( + self, + filter_query: Any, + limit: int, + ) -> Union[DocList, List[Dict]]: + ... + + def _filter_batched( + self, + filter_queries: Any, + limit: int, + ) -> Union[List[DocList], List[List[Dict]]]: + ... + + def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): + raise NotImplementedError + + def _text_search( + self, + query: str, + limit: int, + search_field: str = '', + ) -> _FindResult: + ... + + def _text_search_batched( + self, + queries: Sequence[str], + limit: int, + search_field: str = '', + ) -> _FindResultBatched: + ... def _find( self, @@ -97,7 +314,7 @@ def _find( limit: int, search_field: str = '', ) -> _FindResult: - pass + ... def _find_batched( self, @@ -105,20 +322,7 @@ def _find_batched( limit: int, search_field: str = '', ) -> _FindResultBatched: - pass - - def filter( - self, - filter_query: Any, - limit: int = 10, - **kwargs, - ) -> DocList: - pass + ... - def filter_batched( - self, - filter_queries: Any, - limit: int = 10, - **kwargs, - ) -> List[DocList]: - pass + def execute_query(self, query: Any, *args, **kwargs) -> Any: + ... diff --git a/docarray/utils/_internal/_typing.py b/docarray/utils/_internal/_typing.py index 69babecea10..216a1714810 100644 --- a/docarray/utils/_internal/_typing.py +++ b/docarray/utils/_internal/_typing.py @@ -51,7 +51,7 @@ def safe_issubclass(x: type, a_tuple: type) -> bool: or is_typevar(x) or (type(x) == ForwardRef) or is_typevar(x) - or x == ID + or x is ID ): return False return issubclass(x, a_tuple) diff --git a/poetry.lock b/poetry.lock index 222ef3ec30d..472b6c21bcb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.0 and should not be changed by hand. [[package]] name = "aiohttp" @@ -124,23 +124,24 @@ frozenlist = ">=1.1.0" [[package]] name = "anyio" -version = "3.6.2" +version = "3.7.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false -python-versions = ">=3.6.2" +python-versions = ">=3.7" files = [ - {file = "anyio-3.6.2-py3-none-any.whl", hash = "sha256:fbbe32bd270d2a2ef3ed1c5d45041250284e31fc0a4df4a5a6071842051a51e3"}, - {file = "anyio-3.6.2.tar.gz", hash = "sha256:25ea0d673ae30af41a0c442f81cf3b38c7e79fdc7b60335a4c14e05eb0947421"}, + {file = "anyio-3.7.0-py3-none-any.whl", hash = "sha256:eddca883c4175f14df8aedce21054bfca3adb70ffe76a9f607aef9d7fa2ea7f0"}, + {file = "anyio-3.7.0.tar.gz", hash = "sha256:275d9973793619a5374e1c89a4f4ad3f4b0a5510a2b5b939444bee8f4c4d37ce"}, ] [package.dependencies] +exceptiongroup = {version = "*", markers = "python_version < \"3.11\""} idna = ">=2.8" sniffio = ">=1.1" [package.extras] -doc = ["packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] -test = ["contextlib2", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (<0.15)", "uvloop (>=0.15)"] -trio = ["trio (>=0.16,<0.22)"] +doc = ["Sphinx (>=6.1.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme", "sphinxcontrib-jquery"] +test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (<0.22)"] [[package]] name = "appnope" @@ -209,6 +210,51 @@ cffi = ">=1.0.1" dev = ["cogapp", "pre-commit", "pytest", "wheel"] tests = ["pytest"] +[[package]] +name = "arrow" +version = "1.2.3" +description = "Better dates & times for Python" +optional = false +python-versions = ">=3.6" +files = [ + {file = "arrow-1.2.3-py3-none-any.whl", hash = "sha256:5a49ab92e3b7b71d96cd6bfcc4df14efefc9dfa96ea19045815914a6ab6b1fe2"}, + {file = "arrow-1.2.3.tar.gz", hash = "sha256:3934b30ca1b9f292376d9db15b19446088d12ec58629bc3f0da28fd55fb633a1"}, +] + +[package.dependencies] +python-dateutil = ">=2.7.0" + +[[package]] +name = "asttokens" +version = "2.2.1" +description = "Annotate AST trees with source code positions" +optional = false +python-versions = "*" +files = [ + {file = "asttokens-2.2.1-py2.py3-none-any.whl", hash = "sha256:6b0ac9e93fb0335014d382b8fa9b3afa7df546984258005da0b9e7095b3deb1c"}, + {file = "asttokens-2.2.1.tar.gz", hash = "sha256:4622110b2a6f30b77e1473affaa97e711bc2f07d3f10848420ff1898edbe94f3"}, +] + +[package.dependencies] +six = "*" + +[package.extras] +test = ["astroid", "pytest"] + +[[package]] +name = "async-lru" +version = "2.0.2" +description = "Simple LRU cache for asyncio" +optional = false +python-versions = ">=3.8" +files = [ + {file = "async-lru-2.0.2.tar.gz", hash = "sha256:3b87ec4f2460c52cc7916a0138cc606b584c75d1ef7d661853c95d1d3acb869a"}, + {file = "async_lru-2.0.2-py3-none-any.whl", hash = "sha256:d7c2b873e9af5c5a1f0a87a6c145e7e0b4eb92342b7235dda9dd5b10e950d6e2"}, +] + +[package.dependencies] +typing-extensions = ">=4.0.0" + [[package]] name = "async-timeout" version = "4.0.2" @@ -222,30 +268,31 @@ files = [ [[package]] name = "attrs" -version = "22.1.0" +version = "23.1.0" description = "Classes Without Boilerplate" optional = false -python-versions = ">=3.5" +python-versions = ">=3.7" files = [ - 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"sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"] +testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] [extras] audio = ["pydub"] @@ -5093,4 +4651,4 @@ web = ["fastapi"] [metadata] lock-version = "2.0" python-versions = ">=3.8,<4.0" -content-hash = "d9599e163b07217ddbee0d95626bddd403677a4f12c580711a7a17dc72ddd565" +content-hash = "efa11671865be91f94b93c6988ac1d348f8cad1b3c9314ca989ac7f471fce497" From b47924cb8a97c993f8537e14f24536dbc9b1c0ff Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Tue, 27 Jun 2023 18:11:39 +0800 Subject: [PATCH 04/24] feat: add vector search Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 103 +++++++++++++++++++++++------- 1 file changed, 79 insertions(+), 24 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index 9bf36733163..5236fa5787a 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -23,13 +23,23 @@ from docarray.typing.id import ID from docarray.typing.tensor.abstract_tensor import AbstractTensor from docarray.utils._internal._typing import safe_issubclass -from docarray.utils._internal.misc import import_library +from docarray.utils._internal.misc import ( + import_library, + is_tf_available, + is_torch_available, +) from docarray.utils.find import _FindResult, _FindResultBatched +torch_available, tf_available = is_torch_available(), is_tf_available() + +if torch_available: + import torch + +if tf_available: + import tensorflow as tf # type: ignore + if TYPE_CHECKING: import numpy as np - import tensorflow as tf # type: ignore - import torch from pymilvus import ( Collection, CollectionSchema, @@ -40,8 +50,6 @@ ) else: hnswlib = import_library('hnswlib', raise_error=True) - torch = import_library('torch', raise_error=False) - tf = import_library('tensorflow', raise_error=False) np = import_library('numpy', raise_error=False) from pymilvus import ( Collection, @@ -83,6 +91,7 @@ def __init__(self, db_config=None, index_name=None, **kwargs): @dataclass class DBConfig(BaseDocIndex.DBConfig): collection_name: str = None + collection_description: str = "" host: str = "localhost" port: int = 19530 user: Optional[str] = "" @@ -92,6 +101,14 @@ class DBConfig(BaseDocIndex.DBConfig): index_type: str = "IVF_FLAT" index_metric: str = "L2" index_params: Dict = field(default_factory=lambda: {"nlist": 1024}) + search_params: Dict = field( + default_factory=lambda: { + "metric_type": "L2", + "params": {"nprobe": 10}, + "offset": 5, + } + ) + serialize_config: Dict = field(default_factory=lambda: {"protocol": "protobuf"}) default_column_config: Dict[Type, Dict[str, Any]] = field( default_factory=lambda: defaultdict(dict) ) @@ -119,24 +136,28 @@ def python_type_to_db_type(self, python_type: Type) -> Any: def _init_index(self) -> Collection: if not utility.has_collection(self._db_config.collection_name): - print(self._db_config.collection_name) fields = [ + FieldSchema( + name="serialized", + dtype=DataType.VARCHAR, + max_length=SERIALIZED_VARCHAR_LEN, + ), FieldSchema( name="id", - dtype=DataType.INT64, + dtype=DataType.VARCHAR, is_primary=True, - auto_id=True, - ) + max_length=256, + ), ] # Initiliaze the id fields.extend( [ FieldSchema( - name="doc_id" if column_name == "id" else column_name, - dtype=DataType.VARCHAR if column_name == "id" else info.db_type, + name=column_name, + dtype=info.db_type, is_primary=False, **( {'max_length': 256} - if info.db_type == DataType.VARCHAR or column_name == "id" + if info.db_type == DataType.VARCHAR else {} ), **( @@ -146,6 +167,7 @@ def _init_index(self) -> Collection: ), ) for column_name, info in self._column_infos.items() + if column_name != "id" ] ) self._logger.info("Collection has been created") @@ -153,7 +175,7 @@ def _init_index(self) -> Collection: name=self._db_config.collection_name, schema=CollectionSchema( fields=fields, - description="Collection Schema", + description=self._db_config.collection_description, ), using='default', ) @@ -219,14 +241,15 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): """ docs = self._validate_docs(docs) - entities = [[] for _ in range(len(self._column_infos.items()))] + entities = [[] for _ in range(len(self._column_infos.items()) + 1)] for i in range(len(docs)): + entities[0].append(docs[i].to_base64(**self._db_config.serialize_config)) for j, (column_name, info) in enumerate(self._column_infos.items()): column_value = docs[i].__getattr__(column_name) if isinstance(column_value, (tf.Tensor, torch.Tensor, np.ndarray)): column_value = self._convert_to_vector(column_value) - entities[j].append(column_value) + entities[j + 1].append(column_value) self._collection.insert(entities) self._collection.flush() @@ -242,6 +265,9 @@ def _convert_to_vector(self, column_value: AbstractTensor) -> Sequence[float]: :param column_value: The column value to be converted. :return: The converted float vector. """ + if type(column_value) is list: + return column_value + if len(column_value.shape) != 1: raise ValueError( 'Unsupported: Milvus backend only supports one-dimensional vectors' @@ -249,9 +275,9 @@ def _convert_to_vector(self, column_value: AbstractTensor) -> Sequence[float]: if isinstance(column_value, np.ndarray): return column_value.astype(float).tolist() - elif torch.is_tensor(column_value): + elif torch_available and torch.is_tensor(column_value): return column_value.float().numpy().tolist() - elif tf.is_tensor(column_value): + elif tf_available and tf.is_tensor(column_value): return column_value.numpy().astype(float).tolist() def num_docs(self) -> int: @@ -262,18 +288,22 @@ def _get_items( ) -> Union[Sequence[TSchema], Sequence[Dict[str, Any]]]: self._collection.load() ret = self._collection.query( - expr="doc_id in " + str([id for id in doc_ids]), + expr="id in " + str([id for id in doc_ids]), offset=0, limit=self.num_docs(), - output_fields=[ - column_name for column_name, _ in self._column_infos.items() - ], + output_fields=["serialized"], ) - return DocList[self._schema]([self._schema(**ret[i]) for i in range(len(ret))]) + return [ + self._schema.from_base64( + ret[i]["serialized"], **self._db_config.serialize_config + ) + for i in range(len(ret)) + ] def _del_items(self, doc_ids: Sequence[str]): - ... + self._collection.delete(expr="id in " + str([id for id in doc_ids])) + self._logger.info(f"{len(doc_ids)} documents has been deleted") def _filter( self, @@ -314,7 +344,32 @@ def _find( limit: int, search_field: str = '', ) -> _FindResult: - ... + self._collection.load() + + results = self._collection.search( + data=self._convert_to_vector(query), + anns_field=search_field, + param=self._db_config.search_params, + limit=limit, + expr=None, + output_fields=["serialized"], + consistency_level="Strong", + ) + + self._collection.release() + results = next(iter(results), None) # Only consider the first element + + return _FindResult( + documents=DocList[self._schema]( + [ + self._schema.from_base64( + hit.entity.get('serialized'), **self._db_config.serialize_config + ) + for hit in results + ] + ), + scores=[hit.score for hit in results], + ) def _find_batched( self, From c444626a954aeaf24510da6b72c9f0d9e7201e93 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Wed, 28 Jun 2023 12:23:29 +0800 Subject: [PATCH 05/24] fix: release resources after loading Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 58 +++++++++++++++++++++++++------ 1 file changed, 48 insertions(+), 10 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index 5236fa5787a..134ac6a78a4 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -19,6 +19,7 @@ from docarray import BaseDoc, DocList from docarray.index.abstract import BaseDocIndex +from docarray.index.backends.helper import _execute_find_and_filter_query from docarray.typing import AnyTensor from docarray.typing.id import ID from docarray.typing.tensor.abstract_tensor import AbstractTensor @@ -46,6 +47,7 @@ DataType, FieldSchema, connections, + loading_progress, utility, ) else: @@ -57,6 +59,7 @@ DataType, FieldSchema, connections, + loading_progress, utility, ) @@ -286,19 +289,19 @@ def num_docs(self) -> int: def _get_items( self, doc_ids: Sequence[str] ) -> Union[Sequence[TSchema], Sequence[Dict[str, Any]]]: - self._collection.load() - ret = self._collection.query( - expr="id in " + str([id for id in doc_ids]), - offset=0, - limit=self.num_docs(), - output_fields=["serialized"], - ) + with self.loaded_collection(): + result = self._collection.query( + expr="id in " + str([id for id in doc_ids]), + offset=0, + limit=self.num_docs(), + output_fields=["serialized"], + ) return [ self._schema.from_base64( - ret[i]["serialized"], **self._db_config.serialize_config + result[i]["serialized"], **self._db_config.serialize_config ) - for i in range(len(ret)) + for i in range(len(result)) ] def _del_items(self, doc_ids: Sequence[str]): @@ -380,4 +383,39 @@ def _find_batched( ... def execute_query(self, query: Any, *args, **kwargs) -> Any: - ... + if args or kwargs: + raise ValueError( + f'args and kwargs not supported for `execute_query` on {type(self)}' + ) + find_res = _execute_find_and_filter_query( + doc_index=self, + query=query, + ) + return find_res + + def loaded_collection(self, collection=None): + """ + Context manager to load a collection and release it after the context is exited. + If the collection is already loaded when entering, it will not be released while exiting. + + :param collection: the collection to load. If None, the main collection of this indexer is used. + :return: Context manager for the provided collection. + """ + + class LoadedCollectionManager: + def __init__(self, coll): + self._collection = coll + self._loaded_when_enter = False + + def __enter__(self): + self._loaded_when_enter = ( + loading_progress(self._collection.name)['loading_progress'] != '0%' + ) + self._collection.load() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if not self._loaded_when_enter: + self._collection.release() + + return LoadedCollectionManager(collection if collection else self._collection) From 87a75d4bfaf73798e5b5ddee07abf05a101c61a2 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Wed, 28 Jun 2023 14:47:17 +0800 Subject: [PATCH 06/24] feat: add filters Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 109 +++++++++++++++--------------- 1 file changed, 55 insertions(+), 54 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index 134ac6a78a4..18005cc1a44 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -47,7 +47,6 @@ DataType, FieldSchema, connections, - loading_progress, utility, ) else: @@ -59,7 +58,6 @@ DataType, FieldSchema, connections, - loading_progress, utility, ) @@ -84,6 +82,8 @@ def __init__(self, db_config=None, index_name=None, **kwargs): token=self._db_config.token, ) + self._loaded = False + self._db_config.index_name = index_name self._validate_columns() self._create_collection_name() @@ -289,20 +289,16 @@ def num_docs(self) -> int: def _get_items( self, doc_ids: Sequence[str] ) -> Union[Sequence[TSchema], Sequence[Dict[str, Any]]]: - with self.loaded_collection(): - result = self._collection.query( - expr="id in " + str([id for id in doc_ids]), - offset=0, - limit=self.num_docs(), - output_fields=["serialized"], - ) + self._check_loaded() - return [ - self._schema.from_base64( - result[i]["serialized"], **self._db_config.serialize_config - ) - for i in range(len(result)) - ] + result = self._collection.query( + expr="id in " + str([id for id in doc_ids]), + offset=0, + limit=self.num_docs(), + output_fields=["serialized"], + ) + + return self._docs_from_query_response(result) def _del_items(self, doc_ids: Sequence[str]): self._collection.delete(expr="id in " + str([id for id in doc_ids])) @@ -313,14 +309,25 @@ def _filter( filter_query: Any, limit: int, ) -> Union[DocList, List[Dict]]: - ... + self._check_loaded() + + result = self._collection.query( + expr=filter_query, + offset=0, + limit=min(limit, self.num_docs()), + output_fields=["serialized"], + ) + + return self._docs_from_query_response(result) def _filter_batched( self, filter_queries: Any, limit: int, ) -> Union[List[DocList], List[List[Dict]]]: - ... + return [ + self._filter(filter_query=query, limit=limit) for query in filter_queries + ] def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): raise NotImplementedError @@ -331,7 +338,7 @@ def _text_search( limit: int, search_field: str = '', ) -> _FindResult: - ... + raise NotImplementedError(f'{type(self)} does not support text search.') def _text_search_batched( self, @@ -339,7 +346,7 @@ def _text_search_batched( limit: int, search_field: str = '', ) -> _FindResultBatched: - ... + raise NotImplementedError(f'{type(self)} does not support text search.') def _find( self, @@ -347,7 +354,7 @@ def _find( limit: int, search_field: str = '', ) -> _FindResult: - self._collection.load() + self._check_loaded() results = self._collection.search( data=self._convert_to_vector(query), @@ -359,20 +366,9 @@ def _find( consistency_level="Strong", ) - self._collection.release() results = next(iter(results), None) # Only consider the first element - return _FindResult( - documents=DocList[self._schema]( - [ - self._schema.from_base64( - hit.entity.get('serialized'), **self._db_config.serialize_config - ) - for hit in results - ] - ), - scores=[hit.score for hit in results], - ) + return self._docs_from_find_response(results) def _find_batched( self, @@ -393,29 +389,34 @@ def execute_query(self, query: Any, *args, **kwargs) -> Any: ) return find_res - def loaded_collection(self, collection=None): - """ - Context manager to load a collection and release it after the context is exited. - If the collection is already loaded when entering, it will not be released while exiting. - - :param collection: the collection to load. If None, the main collection of this indexer is used. - :return: Context manager for the provided collection. - """ + def _check_loaded(self): + self._collection.load() and setattr( + self, "_loaded", True + ) if not self._loaded else None - class LoadedCollectionManager: - def __init__(self, coll): - self._collection = coll - self._loaded_when_enter = False - - def __enter__(self): - self._loaded_when_enter = ( - loading_progress(self._collection.name)['loading_progress'] != '0%' + def _docs_from_query_response(self, result: Sequence[Dict]) -> Sequence[TSchema]: + return DocList[self._schema]( + [ + self._schema.from_base64( + result[i]["serialized"], **self._db_config.serialize_config ) - self._collection.load() - return self + for i in range(len(result)) + ] + ) - def __exit__(self, exc_type, exc_val, exc_tb): - if not self._loaded_when_enter: - self._collection.release() + def _docs_from_find_response(self, result: str) -> _FindResult: + return _FindResult( + documents=DocList[self._schema]( + [ + self._schema.from_base64( + hit.entity.get('serialized'), **self._db_config.serialize_config + ) + for hit in result + ] + ), + scores=[hit.score for hit in result], + ) - return LoadedCollectionManager(collection if collection else self._collection) + def __exit__(self, exc_type, exc_val, exc_tb): + self._collection.release() + self._loaded = False From 79ae266dd376eab15cf80ce170eb600a30caa5fd Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Thu, 29 Jun 2023 14:42:25 +0800 Subject: [PATCH 07/24] feat: batch find for mivlus backend Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 48 +++++++++++++++++++++++++++---- 1 file changed, 42 insertions(+), 6 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index 18005cc1a44..de56d78486b 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -50,7 +50,6 @@ utility, ) else: - hnswlib = import_library('hnswlib', raise_error=True) np = import_library('numpy', raise_error=False) from pymilvus import ( Collection, @@ -250,7 +249,7 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): entities[0].append(docs[i].to_base64(**self._db_config.serialize_config)) for j, (column_name, info) in enumerate(self._column_infos.items()): column_value = docs[i].__getattr__(column_name) - if isinstance(column_value, (tf.Tensor, torch.Tensor, np.ndarray)): + if info.db_type == DataType.FLOAT_VECTOR: column_value = self._convert_to_vector(column_value) entities[j + 1].append(column_value) @@ -271,17 +270,20 @@ def _convert_to_vector(self, column_value: AbstractTensor) -> Sequence[float]: if type(column_value) is list: return column_value + ''' + # tensorflow don't have shape property. will change this check later if len(column_value.shape) != 1: raise ValueError( 'Unsupported: Milvus backend only supports one-dimensional vectors' ) + ''' if isinstance(column_value, np.ndarray): return column_value.astype(float).tolist() elif torch_available and torch.is_tensor(column_value): return column_value.float().numpy().tolist() - elif tf_available and tf.is_tensor(column_value): - return column_value.numpy().astype(float).tolist() + elif tf_available and tf.is_tensor(column_value.tensor): + return column_value.tensor.numpy().astype(float).tolist() def num_docs(self) -> int: return self._collection.num_entities @@ -357,7 +359,7 @@ def _find( self._check_loaded() results = self._collection.search( - data=self._convert_to_vector(query), + data=[self._convert_to_vector(query)], anns_field=search_field, param=self._db_config.search_params, limit=limit, @@ -366,6 +368,8 @@ def _find( consistency_level="Strong", ) + self._collection.release() + results = next(iter(results), None) # Only consider the first element return self._docs_from_find_response(results) @@ -376,7 +380,28 @@ def _find_batched( limit: int, search_field: str = '', ) -> _FindResultBatched: - ... + self._check_loaded() + + results = self._collection.search( + data=[self._convert_to_vector(query) for query in queries], + anns_field=search_field, + param=self._db_config.search_params, + limit=limit, + expr=None, + output_fields=["serialized"], + consistency_level="Strong", + ) + + self._collection.release() + + documents, scores = zip( + *[self._docs_from_find_response(result) for result in results] + ) + + return _FindResultBatched( + documents=list(documents), + scores=list(scores), + ) def execute_query(self, query: Any, *args, **kwargs) -> Any: if args or kwargs: @@ -417,6 +442,17 @@ def _docs_from_find_response(self, result: str) -> _FindResult: scores=[hit.score for hit in result], ) + def __contains__(self, item) -> bool: + self._check_loaded() + + result = self._collection.query( + expr="id in " + str([item.id]), + offset=0, + output_fields=["serialized"], + ) + + return len(result) > 0 + def __exit__(self, exc_type, exc_val, exc_tb): self._collection.release() self._loaded = False From 7376b90a98aa49dbe3112665ddbe558278f1d273 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Thu, 29 Jun 2023 14:52:43 +0800 Subject: [PATCH 08/24] test: find/batch find Signed-off-by: maxwelljin2 --- tests/index/milvus/test_find.py | 137 ++++++++++++++++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 tests/index/milvus/test_find.py diff --git a/tests/index/milvus/test_find.py b/tests/index/milvus/test_find.py new file mode 100644 index 00000000000..dc3ca009b7e --- /dev/null +++ b/tests/index/milvus/test_find.py @@ -0,0 +1,137 @@ +import numpy as np +import pytest +from pydantic import Field + +from docarray import BaseDoc, DocList +from docarray.index import MilvusDocumentIndex +from docarray.typing import NdArray, TorchTensor + +pytestmark = [pytest.mark.slow, pytest.mark.index] + + +class SimpleDoc(BaseDoc): + tens: NdArray[10] = Field(dim=1000) # type: ignore[valid-type] + + +class FlatDoc(BaseDoc): + tens_one: NdArray = Field(dim=10) + tens_two: NdArray = Field(dim=50) + + +class TorchDoc(BaseDoc): + tens: TorchTensor[10] # type: ignore[valid-type] + + +@pytest.mark.parametrize('space', ['cosine', 'l2', 'ip']) +def test_find_simple_schema(space): + class SimpleSchema(BaseDoc): + tens: NdArray[10] = Field(space=space) # type: ignore[valid-type] + + index = MilvusDocumentIndex[SimpleSchema](index_name="tens") + + index_docs = [SimpleDoc(tens=np.zeros(10)) for _ in range(10)] + index_docs.append(SimpleDoc(tens=np.ones(10))) + index.index(index_docs) + + query = SimpleDoc(tens=np.ones(10)) + + docs, scores = index.find(query, search_field='tens', limit=5) + + assert len(docs) == 5 + assert len(scores) == 5 + + +def test_find_torch(): + index = MilvusDocumentIndex[TorchDoc](index_name="tens") + + index_docs = [TorchDoc(tens=np.zeros(10)) for _ in range(10)] + index_docs.append(TorchDoc(tens=np.ones(10))) + index.index(index_docs) + + for doc in index_docs: + assert isinstance(doc.tens, TorchTensor) + + query = TorchDoc(tens=np.ones(10)) + + result_docs, scores = index.find(query, search_field='tens', limit=5) + + assert len(result_docs) == 5 + assert len(scores) == 5 + for doc in result_docs: + assert isinstance(doc.tens, TorchTensor) + + +@pytest.mark.tensorflow +def test_find_tensorflow(): + from docarray.typing import TensorFlowTensor + + class TfDoc(BaseDoc): + tens: TensorFlowTensor[10] # type: ignore[valid-type] + + index = MilvusDocumentIndex[TfDoc](index_name="tens") + + index_docs = [TfDoc(tens=np.random.rand(10)) for _ in range(10)] + index.index(index_docs) + + for doc in index_docs: + assert isinstance(doc.tens, TensorFlowTensor) + + query = index_docs[-1] + docs, scores = index.find(query, search_field='tens', limit=5) + + assert len(docs) == 5 + assert len(scores) == 5 + for doc in docs: + assert isinstance(doc.tens, TensorFlowTensor) + + +def test_find_batched(): # noqa: F811 + class SimpleSchema(BaseDoc): + tens: NdArray[10] + + index = MilvusDocumentIndex[SimpleSchema](index_name="tens") + + index_docs = [SimpleDoc(tens=vector) for vector in np.identity(10)] + index.index(index_docs) + + queries = DocList[SimpleDoc]( + [ + SimpleDoc( + tens=np.array([0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + ), + SimpleDoc( + tens=np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]) + ), + ] + ) + + docs, scores = index.find_batched(queries, search_field='tens', limit=1) + + assert len(docs) == 2 + assert len(docs[0]) == 1 + assert len(docs[1]) == 1 + assert len(scores) == 2 + assert len(scores[0]) == 1 + assert len(scores[1]) == 1 + + +def test_contain(): + class SimpleDoc(BaseDoc): + tens: NdArray[10] + + class SimpleSchema(BaseDoc): + tens: NdArray[10] + + index = MilvusDocumentIndex[SimpleSchema](index_name="tens") + index_docs = [SimpleDoc(tens=np.zeros(10)) for _ in range(10)] + + assert (index_docs[0] in index) is False + + index.index(index_docs) + + for doc in index_docs: + assert (doc in index) is True + + index_docs_new = [SimpleDoc(tens=np.zeros(10)) for _ in range(10)] + for doc in index_docs_new: + assert (doc in index) is False From 8dde5a11c2ed9dcc7d7b46c10a0779c09404afbb Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Thu, 29 Jun 2023 14:56:00 +0800 Subject: [PATCH 09/24] chore: single quote Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index de56d78486b..3048124ae53 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -88,7 +88,7 @@ def __init__(self, db_config=None, index_name=None, **kwargs): self._create_collection_name() self._collection = self._init_index() self._build_index() - self._logger.info(f"{self.__class__.__name__} has been initialized") + self._logger.info(f'{self.__class__.__name__} has been initialized') @dataclass class DBConfig(BaseDocIndex.DBConfig): @@ -300,6 +300,8 @@ def _get_items( output_fields=["serialized"], ) + self._collection.release() + return self._docs_from_query_response(result) def _del_items(self, doc_ids: Sequence[str]): @@ -320,6 +322,8 @@ def _filter( output_fields=["serialized"], ) + self._collection.release() + return self._docs_from_query_response(result) def _filter_batched( From e7f28fd0e2d36f53d4ba4c89c12c7aae54d29a75 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Thu, 29 Jun 2023 14:56:33 +0800 Subject: [PATCH 10/24] chore: single quote Signed-off-by: maxwelljin2 --- docarray/index/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docarray/index/__init__.py b/docarray/index/__init__.py index 4186f2b6fc9..eeb8077d43b 100644 --- a/docarray/index/__init__.py +++ b/docarray/index/__init__.py @@ -34,7 +34,7 @@ def __getattr__(name: str): elif name == 'WeaviateDocumentIndex': import_library('weaviate', raise_error=True) import docarray.index.backends.weaviate as lib - elif name == "MilvusDocumentIndex": + elif name == 'MilvusDocumentIndex': import_library('pymilvus', raise_error=True) import docarray.index.backends.milvus as lib else: From f3c64a3918d444fa4b83da2cbb0d7b2dbc11ff1f Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Thu, 29 Jun 2023 15:17:29 +0800 Subject: [PATCH 11/24] test: filter Signed-off-by: maxwelljin2 --- tests/index/milvus/test_filter.py | 35 +++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 tests/index/milvus/test_filter.py diff --git a/tests/index/milvus/test_filter.py b/tests/index/milvus/test_filter.py new file mode 100644 index 00000000000..7ce1de81d09 --- /dev/null +++ b/tests/index/milvus/test_filter.py @@ -0,0 +1,35 @@ +import numpy as np +from pydantic import Field + +from docarray import BaseDoc +from docarray.index import MilvusDocumentIndex +from docarray.typing import NdArray + + +class SimpleDoc(BaseDoc): + embedding: NdArray[10] = Field(dim=1000) # type: ignore[valid-type] + number: int + + +def test_filter_range(): # noqa: F811 + class SimpleSchema(BaseDoc): + embedding: NdArray[10] = Field(space='cosine') # type: ignore[valid-type] + number: int + + index = MilvusDocumentIndex[SimpleSchema](index_name="embedding") + + index_docs = [ + SimpleDoc( + embedding=np.zeros(10), + number=i, + ) + for i in range(10) + ] + index.index(index_docs) + + docs = index.filter("number > 8", limit=5) + + assert len(docs) == 1 + + docs = index.filter(f"id == '{index_docs[0].id}'", limit=5) + assert docs[0].id == index_docs[0].id From 030dc0d747dff98915d3ebdbd497a6032e4aeff9 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Thu, 29 Jun 2023 15:46:00 +0800 Subject: [PATCH 12/24] refractor: remove to_vector function Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 48 +++---------------------------- 1 file changed, 4 insertions(+), 44 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index 3048124ae53..90c1b010609 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -24,21 +24,9 @@ from docarray.typing.id import ID from docarray.typing.tensor.abstract_tensor import AbstractTensor from docarray.utils._internal._typing import safe_issubclass -from docarray.utils._internal.misc import ( - import_library, - is_tf_available, - is_torch_available, -) +from docarray.utils._internal.misc import import_library from docarray.utils.find import _FindResult, _FindResultBatched -torch_available, tf_available = is_torch_available(), is_tf_available() - -if torch_available: - import torch - -if tf_available: - import tensorflow as tf # type: ignore - if TYPE_CHECKING: import numpy as np from pymilvus import ( @@ -250,41 +238,13 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): for j, (column_name, info) in enumerate(self._column_infos.items()): column_value = docs[i].__getattr__(column_name) if info.db_type == DataType.FLOAT_VECTOR: - column_value = self._convert_to_vector(column_value) + column_value = self._to_numpy(column_value) entities[j + 1].append(column_value) self._collection.insert(entities) self._collection.flush() self._logger.info(f"{len(docs)} documents has been indexed") - def _convert_to_vector(self, column_value: AbstractTensor) -> Sequence[float]: - """ - Converts a column value to a float vector. - - The database can only store float vectors, so this method is used to convert - TensorFlow or PyTorch tensors to a format compatible with the database. - - :param column_value: The column value to be converted. - :return: The converted float vector. - """ - if type(column_value) is list: - return column_value - - ''' - # tensorflow don't have shape property. will change this check later - if len(column_value.shape) != 1: - raise ValueError( - 'Unsupported: Milvus backend only supports one-dimensional vectors' - ) - ''' - - if isinstance(column_value, np.ndarray): - return column_value.astype(float).tolist() - elif torch_available and torch.is_tensor(column_value): - return column_value.float().numpy().tolist() - elif tf_available and tf.is_tensor(column_value.tensor): - return column_value.tensor.numpy().astype(float).tolist() - def num_docs(self) -> int: return self._collection.num_entities @@ -363,7 +323,7 @@ def _find( self._check_loaded() results = self._collection.search( - data=[self._convert_to_vector(query)], + data=[query], anns_field=search_field, param=self._db_config.search_params, limit=limit, @@ -387,7 +347,7 @@ def _find_batched( self._check_loaded() results = self._collection.search( - data=[self._convert_to_vector(query) for query in queries], + data=queries, anns_field=search_field, param=self._db_config.search_params, limit=limit, From 0695a3bf3da9fd1cf70e2d98e3b060343e7b58ce Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Thu, 29 Jun 2023 16:10:50 +0800 Subject: [PATCH 13/24] chore: add comments Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 52 +++++++++++++++++++++++++++++-- 1 file changed, 50 insertions(+), 2 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index 90c1b010609..a4889353845 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -56,6 +56,7 @@ class MilvusDocumentIndex(BaseDocIndex, Generic[TSchema]): def __init__(self, db_config=None, index_name=None, **kwargs): + """Initialize MilvusDocumentIndex""" super().__init__(db_config=db_config, **kwargs) self._db_config: MilvusDocumentIndex.DBConfig = cast( MilvusDocumentIndex.DBConfig, self._db_config @@ -80,6 +81,8 @@ def __init__(self, db_config=None, index_name=None, **kwargs): @dataclass class DBConfig(BaseDocIndex.DBConfig): + """Dataclass that contains all "static" configurations of MilvusDocumentIndex.""" + collection_name: str = None collection_description: str = "" host: str = "localhost" @@ -104,6 +107,13 @@ class DBConfig(BaseDocIndex.DBConfig): ) def python_type_to_db_type(self, python_type: Type) -> Any: + """Map python type to database type. + Takes any python type and returns the corresponding database column type. + + :param python_type: a python type. + :return: the corresponding database column type, or None if ``python_type`` + is not supported. + """ type_map = { int: DataType.INT64, float: DataType.FLOAT, @@ -122,9 +132,19 @@ def python_type_to_db_type(self, python_type: Type) -> Any: if safe_issubclass(python_type, py_type): return db_type - raise ValueError("No corresponding milvus type") + return None def _init_index(self) -> Collection: + """ + This function initializes or retrieves a Milvus collection with a specified schema, + storing documents as serialized data and using the document's ID as the collection's ID + , while inheriting other schema properties from the indexer's schema. + + !!! note + Milvus framework currently only supports a single vector column, and multiple vector + columns are not supported. + """ + if not utility.has_collection(self._db_config.collection_name): fields = [ FieldSchema( @@ -173,6 +193,11 @@ def _init_index(self) -> Collection: return Collection(self._db_config.collection_name) def _create_collection_name(self): + """ + This function generates a unique and sanitized name for the collection, + , ensuring a unique identifier is used if the user does not specify a + collection name. + """ if self._db_config.collection_name is None: id = uuid.uuid4().hex self._db_config.collection_name = f"{self.__class__.__name__}__" + id @@ -194,7 +219,7 @@ def _validate_columns(self): if not vector_columns_exist: raise ValueError( - "No vector columns found. Ensure that at least one column is of a vector type" + "No vector columns found. Ensure that one column is of a vector type" ) for column_name, info in self._column_infos.items(): @@ -206,6 +231,11 @@ def _validate_columns(self): ) def _build_index(self): + """ + Sets up an index configuration for a specific column index, which is + required by the Milvus backend. + """ + index = { "index_type": self._db_config.index_type, "metric_type": self._db_config.index_metric, @@ -246,11 +276,23 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): self._logger.info(f"{len(docs)} documents has been indexed") def num_docs(self) -> int: + """ + Get the number of documents. + """ return self._collection.num_entities def _get_items( self, doc_ids: Sequence[str] ) -> Union[Sequence[TSchema], Sequence[Dict[str, Any]]]: + """Get Documents from the index, by `id`. + If no document is found, a KeyError is raised. + + :param doc_ids: ids to get from the Document index + :param raw: if raw, output the new_schema type (with parent id) + :return: Sequence of Documents, sorted corresponding to the order of `doc_ids`. + Duplicate `doc_ids` can be omitted in the output. + """ + self._check_loaded() result = self._collection.query( @@ -265,6 +307,10 @@ def _get_items( return self._docs_from_query_response(result) def _del_items(self, doc_ids: Sequence[str]): + """Delete Documents from the index. + + :param doc_ids: ids to delete from the Document Store + """ self._collection.delete(expr="id in " + str([id for id in doc_ids])) self._logger.info(f"{len(doc_ids)} documents has been deleted") @@ -379,6 +425,8 @@ def execute_query(self, query: Any, *args, **kwargs) -> Any: return find_res def _check_loaded(self): + """This function checks if the collection is loaded and loads it if necessary""" + self._collection.load() and setattr( self, "_loaded", True ) if not self._loaded else None From f23b1819068a19c9526bde65e8e2a3cd8cc6365b Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Thu, 29 Jun 2023 16:39:20 +0800 Subject: [PATCH 14/24] chore: update lock Signed-off-by: maxwelljin2 --- poetry.lock | 557 +++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 533 insertions(+), 24 deletions(-) diff --git a/poetry.lock b/poetry.lock index 222ef3ec30d..5363ae85966 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.0 and should not be changed by hand. 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"efa11671865be91f94b93c6988ac1d348f8cad1b3c9314ca989ac7f471fce497" +content-hash = "d9599e163b07217ddbee0d95626bddd403677a4f12c580711a7a17dc72ddd565" From 1b2e08c0f2be507fe684f178fb517ec981a7aebb Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Thu, 29 Jun 2023 18:41:43 +0800 Subject: [PATCH 15/24] fix: num of docs not precise Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 43 +++++++++++++++++++++++++------ 1 file changed, 35 insertions(+), 8 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index a4889353845..40510cd6536 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -94,6 +94,7 @@ class DBConfig(BaseDocIndex.DBConfig): index_type: str = "IVF_FLAT" index_metric: str = "L2" index_params: Dict = field(default_factory=lambda: {"nlist": 1024}) + consistency_level: str = 'Session' search_params: Dict = field( default_factory=lambda: { "metric_type": "L2", @@ -156,9 +157,9 @@ def _init_index(self) -> Collection: name="id", dtype=DataType.VARCHAR, is_primary=True, - max_length=256, - ), - ] # Initiliaze the id + max_length=ID_VARCHAR_LEN, + ), # id represents the document id + ] fields.extend( [ FieldSchema( @@ -278,8 +279,20 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): def num_docs(self) -> int: """ Get the number of documents. + + !!! note + Cannot use Milvus' num_entities method because it's not precise + especially after delete ops (#15201 issue in Milvus) """ - return self._collection.num_entities + self._check_loaded() + + result = self._collection.query( + expr=self._always_true_expr("id"), + offset=0, + output_fields=["serialized"], + ) + + return len(result) def _get_items( self, doc_ids: Sequence[str] @@ -298,8 +311,8 @@ def _get_items( result = self._collection.query( expr="id in " + str([id for id in doc_ids]), offset=0, - limit=self.num_docs(), output_fields=["serialized"], + consistency_level=self._db_config.consistency_level, ) self._collection.release() @@ -311,7 +324,11 @@ def _del_items(self, doc_ids: Sequence[str]): :param doc_ids: ids to delete from the Document Store """ - self._collection.delete(expr="id in " + str([id for id in doc_ids])) + self._collection.delete( + expr="id in " + str([id for id in doc_ids]), + consistency_level=self._db_config.consistency_level, + ) + self._logger.info(f"{len(doc_ids)} documents has been deleted") def _filter( @@ -375,7 +392,7 @@ def _find( limit=limit, expr=None, output_fields=["serialized"], - consistency_level="Strong", + consistency_level=self._db_config.consistency_level, ) self._collection.release() @@ -399,7 +416,7 @@ def _find_batched( limit=limit, expr=None, output_fields=["serialized"], - consistency_level="Strong", + consistency_level=self._db_config.consistency_level, ) self._collection.release() @@ -454,6 +471,16 @@ def _docs_from_find_response(self, result: str) -> _FindResult: scores=[hit.score for hit in result], ) + def _always_true_expr(self, primary_key: str) -> str: + """ + Returns a Milvus expression that is always true, thus allowing for the retrieval of all entries in a Collection + Assumes that the primary key is of type DataType.VARCHAR + + :param primary_key: the name of the primary key + :return: a Milvus expression that is always true for that primary key + """ + return f'({primary_key} in ["1"]) or ({primary_key} not in ["1"])' + def __contains__(self, item) -> bool: self._check_loaded() From 06af4713306c5b1b02669c5872fe63ffd8cbeb29 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Fri, 30 Jun 2023 11:48:58 +0800 Subject: [PATCH 16/24] test: add flat document test Signed-off-by: maxwelljin2 --- tests/index/milvus/test_find.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/tests/index/milvus/test_find.py b/tests/index/milvus/test_find.py index dc3ca009b7e..05207321e47 100644 --- a/tests/index/milvus/test_find.py +++ b/tests/index/milvus/test_find.py @@ -135,3 +135,26 @@ class SimpleSchema(BaseDoc): index_docs_new = [SimpleDoc(tens=np.zeros(10)) for _ in range(10)] for doc in index_docs_new: assert (doc in index) is False + + +@pytest.mark.parametrize('space', ['cosine', 'l2', 'ip']) +def test_find_flat_schema(space): + class FlatSchema(BaseDoc): + tens_one: NdArray[10] = Field(space=space) + tens_two: NdArray[50] = Field(space=space) + + index = MilvusDocumentIndex[FlatSchema](index_name="tens_one") + + index_docs = [ + FlatDoc(tens_one=np.zeros(10), tens_two=np.zeros(50)) for _ in range(10) + ] + index_docs.append(FlatDoc(tens_one=np.zeros(10), tens_two=np.ones(50))) + index_docs.append(FlatDoc(tens_one=np.ones(10), tens_two=np.zeros(50))) + index.index(index_docs) + + query = FlatDoc(tens_one=np.ones(10), tens_two=np.ones(50)) + + # find on tens_one + docs, scores = index.find(query, search_field='tens_one', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 From 9b96009f3e51d9e0dd15fa996a360d631ed01434 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Fri, 30 Jun 2023 11:50:21 +0800 Subject: [PATCH 17/24] feat: support multiple vectors Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index 40510cd6536..efc9e9454df 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -142,8 +142,8 @@ def _init_index(self) -> Collection: , while inheriting other schema properties from the indexer's schema. !!! note - Milvus framework currently only supports a single vector column, and multiple vector - columns are not supported. + Milvus framework currently only supports a single vector column, and only one vector + column can store in the schema (others are stored in the serialized data) """ if not utility.has_collection(self._db_config.collection_name): @@ -179,6 +179,10 @@ def _init_index(self) -> Collection: ) for column_name, info in self._column_infos.items() if column_name != "id" + and ( + info.db_type == DataType.FLOAT_VECTOR + and column_name == self._db_config.index_name + ) ] ) self._logger.info("Collection has been created") @@ -262,15 +266,20 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): """ docs = self._validate_docs(docs) - entities = [[] for _ in range(len(self._column_infos.items()) + 1)] + entities = [[] for _ in range(len(self._collection.schema))] for i in range(len(docs)): entities[0].append(docs[i].to_base64(**self._db_config.serialize_config)) - for j, (column_name, info) in enumerate(self._column_infos.items()): + entity_index = 1 + for column_name, info in self._column_infos.items(): column_value = docs[i].__getattr__(column_name) if info.db_type == DataType.FLOAT_VECTOR: + if column_name != self._db_config.index_name: + continue column_value = self._to_numpy(column_value) - entities[j + 1].append(column_value) + + entities[entity_index].append(column_value) + entity_index += 1 self._collection.insert(entities) self._collection.flush() From 7ab088b71e8104caf1c285f107c95d5a6fd87038 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Fri, 30 Jun 2023 12:01:59 +0800 Subject: [PATCH 18/24] feat: support multi-dimensional vector for milvus Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index efc9e9454df..6a8f78731bf 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -224,7 +224,7 @@ def _validate_columns(self): if not vector_columns_exist: raise ValueError( - "No vector columns found. Ensure that one column is of a vector type" + "No vector columns found. Ensure that at least one column is of a vector type" ) for column_name, info in self._column_infos.items(): @@ -276,7 +276,7 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): if info.db_type == DataType.FLOAT_VECTOR: if column_name != self._db_config.index_name: continue - column_value = self._to_numpy(column_value) + column_value = self._map_embedding(column_value) entities[entity_index].append(column_value) entity_index += 1 @@ -490,6 +490,24 @@ def _always_true_expr(self, primary_key: str) -> str: """ return f'({primary_key} in ["1"]) or ({primary_key} not in ["1"])' + def _map_embedding(self, embedding: Optional[AnyTensor]) -> AnyTensor: + """ + Milvus exclusively supports one-dimensional vectors. If multi-dimensional + vectors are provided, they will be automatically flattened to ensure compatibility. + + :param embedding: The original raw embedding, which can be in the form of a TensorFlow or PyTorch tensor. + :return embedding: A one-dimensional numpy array representing the flattened version of the original embedding. + """ + + if embedding is not None: + embedding = self._to_numpy(embedding) + + if embedding.ndim > 1: + embedding = np.asarray(embedding).squeeze() + else: + embedding = np.zeros(self._config.n_dim) + return embedding + def __contains__(self, item) -> bool: self._check_loaded() From 87f35ac2ae2970a21331c8eb8c1be53f56dcbdbe Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Fri, 30 Jun 2023 14:43:34 +0800 Subject: [PATCH 19/24] feat: support nested doc Signed-off-by: maxwelljin2 --- docarray/index/backends/milvus.py | 15 ++++++--- tests/index/milvus/test_find.py | 51 +++++++++++++++++++++++++++++++ 2 files changed, 61 insertions(+), 5 deletions(-) diff --git a/docarray/index/backends/milvus.py b/docarray/index/backends/milvus.py index 6a8f78731bf..49c3ad6b57a 100644 --- a/docarray/index/backends/milvus.py +++ b/docarray/index/backends/milvus.py @@ -127,7 +127,7 @@ def python_type_to_db_type(self, python_type: Type) -> Any: } if issubclass(python_type, ID): - return DataType.INT64 # Primary key must be int64 + return DataType.VARCHAR for py_type, db_type in type_map.items(): if safe_issubclass(python_type, py_type): @@ -179,10 +179,10 @@ def _init_index(self) -> Collection: ) for column_name, info in self._column_infos.items() if column_name != "id" - and ( + and not ( info.db_type == DataType.FLOAT_VECTOR - and column_name == self._db_config.index_name - ) + and column_name != self._db_config.index_name + ) # Only store one vector field in column ] ) self._logger.info("Collection has been created") @@ -265,6 +265,11 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): :param docs: Documents to index. """ + n_docs = 1 if isinstance(docs, BaseDoc) else len(docs) + self._logger.debug(f'Indexing {n_docs} documents') + docs_validated = self._validate_docs(docs) + self._update_subindex_data(docs_validated) + docs = self._validate_docs(docs) entities = [[] for _ in range(len(self._collection.schema))] @@ -272,7 +277,7 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs): entities[0].append(docs[i].to_base64(**self._db_config.serialize_config)) entity_index = 1 for column_name, info in self._column_infos.items(): - column_value = docs[i].__getattr__(column_name) + column_value = self._get_values_by_column([docs[i]], column_name)[0] if info.db_type == DataType.FLOAT_VECTOR: if column_name != self._db_config.index_name: continue diff --git a/tests/index/milvus/test_find.py b/tests/index/milvus/test_find.py index 05207321e47..cbb5418c2e3 100644 --- a/tests/index/milvus/test_find.py +++ b/tests/index/milvus/test_find.py @@ -158,3 +158,54 @@ class FlatSchema(BaseDoc): docs, scores = index.find(query, search_field='tens_one', limit=5) assert len(docs) == 5 assert len(scores) == 5 + + +def test_find_nested_schema(): + class SimpleDoc(BaseDoc): + tens: NdArray[10] # type: ignore[valid-type] + + class NestedDoc(BaseDoc): + d: SimpleDoc + tens: NdArray[10] # type: ignore[valid-type] + + class DeepNestedDoc(BaseDoc): + d: NestedDoc + tens: NdArray[10] + + index = MilvusDocumentIndex[DeepNestedDoc](index_name="tens") + + index_docs = [ + DeepNestedDoc( + d=NestedDoc(d=SimpleDoc(tens=np.zeros(10)), tens=np.zeros(10)), + tens=np.zeros(10), + ) + for _ in range(10) + ] + index_docs.append( + DeepNestedDoc( + d=NestedDoc(d=SimpleDoc(tens=np.ones(10)), tens=np.zeros(10)), + tens=np.zeros(10), + ) + ) + index_docs.append( + DeepNestedDoc( + d=NestedDoc(d=SimpleDoc(tens=np.zeros(10)), tens=np.ones(10)), + tens=np.zeros(10), + ) + ) + index_docs.append( + DeepNestedDoc( + d=NestedDoc(d=SimpleDoc(tens=np.zeros(10)), tens=np.zeros(10)), + tens=np.ones(10), + ) + ) + index.index(index_docs) + + query = DeepNestedDoc( + d=NestedDoc(d=SimpleDoc(tens=np.ones(10)), tens=np.ones(10)), tens=np.ones(10) + ) + + # find on root level (only support one level now) + docs, scores = index.find(query, search_field='tens', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 From fc72ab0c5d5b97417bee954f394c56829d97a787 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Fri, 30 Jun 2023 15:08:40 +0800 Subject: [PATCH 20/24] test: test index get/del Signed-off-by: maxwelljin2 --- tests/index/milvus/test_index_get_del.py | 143 +++++++++++++++++++++++ 1 file changed, 143 insertions(+) create mode 100644 tests/index/milvus/test_index_get_del.py diff --git a/tests/index/milvus/test_index_get_del.py b/tests/index/milvus/test_index_get_del.py new file mode 100644 index 00000000000..cdef1deb3de --- /dev/null +++ b/tests/index/milvus/test_index_get_del.py @@ -0,0 +1,143 @@ +import numpy as np +import pytest +import torch + +from docarray import BaseDoc, DocList +from docarray.index import MilvusDocumentIndex +from docarray.typing import NdArray, TorchTensor + +pytestmark = [pytest.mark.slow, pytest.mark.index] + + +class SimpleDoc(BaseDoc): + tens: NdArray[10] + + +class FlatDoc(BaseDoc): + tens_one: NdArray[10] + tens_two: NdArray[50] + + +class NestedDoc(BaseDoc): + d: SimpleDoc + + +class DeepNestedDoc(BaseDoc): + d: NestedDoc + + +class TorchDoc(BaseDoc): + tens: TorchTensor[10] # type: ignore[valid-type] + + +@pytest.fixture +def ten_simple_docs(): + return [SimpleDoc(tens=np.random.randn(10)) for _ in range(10)] + + +@pytest.fixture +def ten_flat_docs(): + return [ + FlatDoc(tens_one=np.random.randn(10), tens_two=np.random.randn(50)) + for _ in range(10) + ] + + +@pytest.fixture +def ten_nested_docs(): + return [NestedDoc(d=SimpleDoc(tens=np.random.randn(10))) for _ in range(10)] + + +@pytest.mark.parametrize('use_docarray', [True, False]) +def test_index_simple_schema(ten_simple_docs, use_docarray): # noqa: F811 + index = MilvusDocumentIndex[SimpleDoc](index_name='tens') + if use_docarray: + ten_simple_docs = DocList[SimpleDoc](ten_simple_docs) + + index.index(ten_simple_docs) + assert index.num_docs() == 10 + + +@pytest.mark.parametrize('use_docarray', [True, False]) +def test_index_flat_schema(ten_flat_docs, use_docarray): # noqa: F811 + index = MilvusDocumentIndex[FlatDoc](index_name='tens_one') + if use_docarray: + ten_flat_docs = DocList[FlatDoc](ten_flat_docs) + + index.index(ten_flat_docs) + assert index.num_docs() == 10 + + +def test_index_torch(): + docs = [TorchDoc(tens=np.random.randn(10)) for _ in range(10)] + assert isinstance(docs[0].tens, torch.Tensor) + assert isinstance(docs[0].tens, TorchTensor) + + index = MilvusDocumentIndex[TorchDoc](index_name='tens') + + index.index(docs) + assert index.num_docs() == 10 + + +def test_del_single(ten_simple_docs): # noqa: F811 + index = MilvusDocumentIndex[SimpleDoc](index_name='tens') + index.index(ten_simple_docs) + # delete once + assert index.num_docs() == 10 + del index[ten_simple_docs[0].id] + assert index.num_docs() == 9 + for i, d in enumerate(ten_simple_docs): + id_ = d.id + if i == 0: # deleted + with pytest.raises(KeyError): + index[id_] + else: + assert index[id_].id == id_ + # delete again + del index[ten_simple_docs[3].id] + assert index.num_docs() == 8 + for i, d in enumerate(ten_simple_docs): + id_ = d.id + if i in (0, 3): # deleted + with pytest.raises(KeyError): + index[id_] + else: + assert index[id_].id == id_ + + +def test_del_multiple(ten_simple_docs): + docs_to_del_idx = [0, 2, 4, 6, 8] + + index = MilvusDocumentIndex[SimpleDoc](index_name='tens') + index.index(ten_simple_docs) + + assert index.num_docs() == 10 + docs_to_del = [ten_simple_docs[i] for i in docs_to_del_idx] + ids_to_del = [d.id for d in docs_to_del] + del index[ids_to_del] + for i, doc in enumerate(ten_simple_docs): + if i in docs_to_del_idx: + with pytest.raises(KeyError): + index[doc.id] + else: + assert index[doc.id].id == doc.id + + +def test_num_docs(ten_simple_docs): # noqa: F811 + index = MilvusDocumentIndex[SimpleDoc](index_name='tens') + index.index(ten_simple_docs) + + assert index.num_docs() == 10 + + del index[ten_simple_docs[0].id] + assert index.num_docs() == 9 + + del index[ten_simple_docs[3].id, ten_simple_docs[5].id] + assert index.num_docs() == 7 + + more_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(5)] + index.index(more_docs) + assert index.num_docs() == 12 + + del index[more_docs[2].id, ten_simple_docs[7].id] # type: ignore[arg-type] + assert index.num_docs() == 10 From 6e1fe26422700c3c390f3c7713571f5d638ebbc6 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Fri, 30 Jun 2023 16:36:33 +0800 Subject: [PATCH 21/24] chore: update lock Signed-off-by: maxwelljin2 --- poetry.lock | 362 ++++++------------------------------------------- pyproject.toml | 3 +- 2 files changed, 42 insertions(+), 323 deletions(-) diff --git a/poetry.lock b/poetry.lock index 5363ae85966..cf1be0dd6cb 100644 --- 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\"3.11\""}, ] python-dateutil = ">=2.8.2" pytz = ">=2020.1" @@ -5151,6 +4868,7 @@ jac = ["jina-hubble-sdk"] mesh = ["trimesh"] pandas = ["pandas"] proto = ["lz4", "protobuf"] +pymilvus = ["pymilvus"] qdrant = ["qdrant-client"] torch = ["torch"] video = ["av"] @@ -5160,4 +4878,4 @@ web = ["fastapi"] [metadata] lock-version = "2.0" python-versions = ">=3.8,<4.0" -content-hash = "d9599e163b07217ddbee0d95626bddd403677a4f12c580711a7a17dc72ddd565" +content-hash = "19fa2c105f956c8fcf4bf0114c70931d69493b56c164b883581ffa6bbabcedee" diff --git a/pyproject.toml b/pyproject.toml index 516589f2c62..e73e5f1085a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -75,6 +75,7 @@ torch = ["torch"] web = ["fastapi"] qdrant = ["qdrant-client"] weaviate = ["weaviate-client"] +pymilvus = ["pymilvus"] # all full = ["protobuf", "lz4", "pandas", "pillow", "types-pillow", "av", "pydub", "trimesh"] @@ -151,4 +152,4 @@ markers = [ "index: marks test using a document index", "benchmark: marks slow benchmarking tests", "elasticv8: marks test that run with ElasticSearch v8", -] +] \ No newline at end of file From b261801e33cf3b4d818597ba9c6f374b58bb158d Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Fri, 30 Jun 2023 17:43:43 +0800 Subject: [PATCH 22/24] chore: add yaml Signed-off-by: maxwelljin2 --- tests/index/milvus/__init__.py | 0 tests/index/milvus/docker-compose.yml | 49 ++++++++++++++++++++++++ tests/index/milvus/fixtures.py | 16 ++++++++ tests/index/milvus/test_filter.py | 1 + tests/index/milvus/test_find.py | 1 + tests/index/milvus/test_index_get_del.py | 1 + 6 files changed, 68 insertions(+) create mode 100644 tests/index/milvus/__init__.py create mode 100644 tests/index/milvus/docker-compose.yml create mode 100644 tests/index/milvus/fixtures.py diff --git a/tests/index/milvus/__init__.py b/tests/index/milvus/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/index/milvus/docker-compose.yml b/tests/index/milvus/docker-compose.yml new file mode 100644 index 00000000000..7c15e82b1dc --- /dev/null +++ b/tests/index/milvus/docker-compose.yml @@ -0,0 +1,49 @@ +version: '3.5' + +services: + etcd: + container_name: milvus-etcd + image: quay.io/coreos/etcd:v3.5.5 + environment: + - ETCD_AUTO_COMPACTION_MODE=revision + - ETCD_AUTO_COMPACTION_RETENTION=1000 + - ETCD_QUOTA_BACKEND_BYTES=4294967296 + - ETCD_SNAPSHOT_COUNT=50000 + volumes: + - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/etcd:/etcd + command: etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd + + minio: + container_name: milvus-minio + image: minio/minio:RELEASE.2023-03-20T20-16-18Z + environment: + MINIO_ACCESS_KEY: minioadmin + MINIO_SECRET_KEY: minioadmin + volumes: + - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/minio:/minio_data + command: minio server /minio_data + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"] + interval: 30s + timeout: 20s + retries: 3 + + standalone: + container_name: milvus-standalone + image: milvusdb/milvus:v2.2.10 + command: ["milvus", "run", "standalone"] + environment: + ETCD_ENDPOINTS: etcd:2379 + MINIO_ADDRESS: minio:9000 + volumes: + - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/milvus:/var/lib/milvus + ports: + - "19530:19530" + - "9091:9091" + depends_on: + - "etcd" + - "minio" + +networks: + default: + name: milvus \ No newline at end of file diff --git a/tests/index/milvus/fixtures.py b/tests/index/milvus/fixtures.py new file mode 100644 index 00000000000..061ce7e474a --- /dev/null +++ b/tests/index/milvus/fixtures.py @@ -0,0 +1,16 @@ +import os +import time + +import pytest + +cur_dir = os.path.dirname(os.path.abspath(__file__)) +milvus_yml = os.path.abspath(os.path.join(cur_dir, 'docker-compose.yml')) + + +@pytest.fixture(scope='session', autouse=True) +def start_storage(): + os.system(f"docker-compose -f {milvus_yml} up -d --remove-orphans") + time.sleep(1) + + yield + os.system(f"docker-compose -f {milvus_yml} down --remove-orphans") diff --git a/tests/index/milvus/test_filter.py b/tests/index/milvus/test_filter.py index 7ce1de81d09..68334ee40ca 100644 --- a/tests/index/milvus/test_filter.py +++ b/tests/index/milvus/test_filter.py @@ -4,6 +4,7 @@ from docarray import BaseDoc from docarray.index import MilvusDocumentIndex from docarray.typing import NdArray +from tests.index.milvus.fixtures import start_storage # noqa: F401 class SimpleDoc(BaseDoc): diff --git a/tests/index/milvus/test_find.py b/tests/index/milvus/test_find.py index cbb5418c2e3..4c263cc4371 100644 --- a/tests/index/milvus/test_find.py +++ b/tests/index/milvus/test_find.py @@ -5,6 +5,7 @@ from docarray import BaseDoc, DocList from docarray.index import MilvusDocumentIndex from docarray.typing import NdArray, TorchTensor +from tests.index.milvus.fixtures import start_storage # noqa: F401 pytestmark = [pytest.mark.slow, pytest.mark.index] diff --git a/tests/index/milvus/test_index_get_del.py b/tests/index/milvus/test_index_get_del.py index cdef1deb3de..5f0ef32d104 100644 --- a/tests/index/milvus/test_index_get_del.py +++ b/tests/index/milvus/test_index_get_del.py @@ -5,6 +5,7 @@ from docarray import BaseDoc, DocList from docarray.index import MilvusDocumentIndex from docarray.typing import NdArray, TorchTensor +from tests.index.milvus.fixtures import start_storage # noqa: F401 pytestmark = [pytest.mark.slow, pytest.mark.index] From 7dc72a90d261e9685b5b8633000665b89f236435 Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Fri, 30 Jun 2023 18:03:02 +0800 Subject: [PATCH 23/24] chore: update dependency Signed-off-by: maxwelljin2 --- poetry.lock | 39 +-------------------------------------- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 39 deletions(-) diff --git a/poetry.lock b/poetry.lock index cf1be0dd6cb..e6aa5f384b5 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2553,43 +2553,6 @@ jupyter-server = ">=1.8,<3" [package.extras] test = ["pytest", "pytest-console-scripts", "pytest-tornasync"] -[[package]] -name = 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pydantic = ">=1.10.2" numpy = ">=1.17.3" -protobuf = { version = ">=3.19.0", optional = true } +protobuf = { version = ">=3.20.0", optional = true } torch = { version = ">=1.0.0", optional = true } orjson = ">=3.8.2" pillow = {version = ">=9.3.0", optional = true } From 79152a39829a0600e8782bd3b42b2aa16aa086fc Mon Sep 17 00:00:00 2001 From: maxwelljin2 Date: Fri, 30 Jun 2023 18:24:11 +0800 Subject: [PATCH 24/24] ci: change protobuf version to accomodate milvus Signed-off-by: maxwelljin2 --- .github/workflows/ci.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2a82474b005..5b980cb01ec 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -179,7 +179,7 @@ jobs: python -m pip install --upgrade pip python -m pip install poetry poetry install --all-extras - poetry run pip install protobuf==3.19.0 # we check that we support 3.19 + poetry run pip install protobuf==3.20.0 # we check that we support 3.19 sudo apt-get update sudo apt-get install --no-install-recommends ffmpeg - name: Test @@ -208,7 +208,7 @@ jobs: python -m pip install --upgrade pip python -m pip install poetry poetry install --all-extras - poetry run pip install protobuf==3.19.0 + poetry run pip install protobuf==3.20.0 poetry run pip install tensorflow==2.11.0 sudo apt-get update sudo apt-get install --no-install-recommends ffmpeg @@ -238,7 +238,7 @@ jobs: python -m pip install --upgrade pip python -m pip install poetry poetry install --all-extras - poetry run pip install protobuf==3.19.0 + poetry run pip install protobuf==3.20.0 poetry run pip install tensorflow==2.11.0 poetry run pip install elasticsearch==8.6.2 sudo apt-get update @@ -268,7 +268,7 @@ jobs: python -m pip install --upgrade pip python -m pip install poetry poetry install --all-extras - poetry run pip install protobuf==3.19.0 + poetry run pip install protobuf==3.20.0 poetry run pip install tensorflow==2.11.0 sudo apt-get update sudo apt-get install --no-install-recommends ffmpeg