From edf408ab7238dc2416380d473b85c7d1593f4de1 Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 1 Mar 2023 20:47:10 +0800 Subject: [PATCH 01/44] feat: __init__ of ElasticDocumentIndex Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 220 ++++++++++++++++++ 1 file changed, 220 insertions(+) create mode 100644 docarray/doc_index/backends/elastic_doc_index.py diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py new file mode 100644 index 00000000000..94ab69402b7 --- /dev/null +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -0,0 +1,220 @@ +import uuid +from dataclasses import dataclass, field +from typing import ( + TYPE_CHECKING, + Any, + Dict, + Generic, + List, + Mapping, + Optional, + Sequence, + Tuple, + Type, + TypeVar, + Union, + cast, +) + +import hnswlib +import numpy as np +from elasticsearch import Elasticsearch + +import docarray.typing +from docarray import BaseDocument, DocumentArray +from docarray.doc_index.abstract_doc_index import ( + BaseDocumentIndex, + FindResultBatched, + _Column, +) +from docarray.utils.find import FindResult +from docarray.utils.misc import torch_imported + +# mypy: ignore-errors + +if TYPE_CHECKING: + + def composable(fn): # type: ignore + return fn + +else: + # static type checkers do not like callable objects as decorators + from docarray.doc_index.abstract_doc_index import composable + +TSchema = TypeVar('TSchema', bound=BaseDocument) +T = TypeVar('T', bound='ElasticDocumentIndex') + +ELASTIC_PY_VEC_TYPES = [list, tuple, np.ndarray] +if torch_imported: + import torch + + ELASTIC_PY_VEC_TYPES.append(torch.Tensor) + + +class ElasticDocumentIndex(BaseDocumentIndex, Generic[TSchema]): + def __init__(self, db_config=None, **kwargs): + + if db_config.index_name is None: + id = uuid.uuid4().hex + db_config.index_name = 'index__' + id + + super().__init__(db_config=db_config, **kwargs) + self._db_config = cast(ElasticDocumentIndex.DBConfig, self._db_config) + + self._client = Elasticsearch( + hosts=self._db_config.hosts, + **self._db_config.es_config, + ) + + # ElasticSearh index setup + self._index_init_params = ('dims', 'similarity', 'type') + self._index_options = ( + 'm', + 'ef_construction', + ) + + self._hnsw_indices = {} + for col_name, col in self._columns.items(): + if not col.config: + continue # do not create column index if no config is given + self._hnsw_indices[col_name] = self._create_index(col) + + mappings = {'dynamic': 'true', '_source': {'enabled': 'true'}, 'properties': {}} + for col_name, index in self._hnsw_indices.items(): + mappings['properties'][col_name] = index + + if self._client.indices.exists(index=self._db_config.index_name): + self._client.indices.put_mapping( + index=self._db_config.index_name, put_mapping=mappings['properties'] + ) + else: + self._client.indices.create( + index=self._db_config.index_name, mappings=mappings + ) + + self._client.indices.refresh(index=self._config.index_name) + + ############################################### + # Inner classes for query builder and configs # + ############################################### + class QueryBuilder(BaseDocumentIndex.QueryBuilder): + def build(self, *args, **kwargs) -> Any: + return self._queries + + @dataclass + class DBConfig(BaseDocumentIndex.DBConfig): + hosts: Union[ + str, List[Union[str, Mapping[str, Union[str, int]]]], None + ] = 'http://localhost:9200' + index_name: Optional[str] = None + es_config: Dict[str, Any] = field(default_factory=dict) + + @dataclass + class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): + default_column_config: Dict[Type, Dict[str, Any]] = field( + default_factory=lambda: { + np.ndarray: { + 'type': 'dense_vector', + 'dim': 128, + 'similarity': 'cosine', # 'l2_norm', 'dot_product', 'cosine' + 'm': 16, + 'ef_construction': 100, + }, + # `None` is not a Type, but we allow it here anyway + None: {}, # type: ignore + } + ) + + ############################################### + # Implementation of abstract methods # + ############################################### + + def python_type_to_db_type(self, python_type: Type) -> Any: + """Map python type to database type.""" + for allowed_type in ELASTIC_PY_VEC_TYPES: + if issubclass(python_type, allowed_type): + return list + + if python_type == docarray.typing.ID: + return None # TODO(johannes): handle this + + raise ValueError(f'Unsupported column type for {type(self)}: {python_type}') + + def _index(self, column_data_dic, **kwargs): + ... + + def index(self, docs: Union[BaseDocument, Sequence[BaseDocument]], **kwargs): + """Index a document into the store""" + ... + + def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: + ... + + def _find_batched( + self, + query: np.ndarray, + search_field: str, + limit: int, + **kwargs, + ) -> FindResultBatched: + ... + + @composable + def _find( + self, query: np.ndarray, search_field: str, limit: int, **kwargs + ) -> FindResult: + ... + + @composable + def _filter( + self, + *args, + **kwargs, + ) -> DocumentArray: + ... + + def _filter_batched( + self, + filter_queries: Any, + limit: int, + **kwargs, + ) -> List[DocumentArray]: + ... + + def _text_search( + self, + query: str, + search_field: str, + limit: int, + **kwargs, + ) -> FindResult: + ... + + def _text_search_batched( + self, + queries: Sequence[str], + search_field: str, + limit: int, + **kwargs, + ) -> FindResultBatched: + ... + + #################################################### + # Optional overrides # + #################################################### + + ############################################### + # Helpers # + ############################################### + + # general helpers + @staticmethod + + # ElasticSearch helpers + def _create_index(self, col: '_Column') -> hnswlib.Index: + """Create a new HNSW index for a column, and initialize it.""" + index = dict((k, col.config[k]) for k in self._index_init_params) + if col.n_dim: + index['dims'] = col.n_dim + index['index_options'] = dict((k, col.config[k]) for k in self._index_options) + return index From 4aa76c57bb89507e4722809094517bb2a8372d8f Mon Sep 17 00:00:00 2001 From: AnneY Date: Thu, 2 Mar 2023 16:22:02 +0800 Subject: [PATCH 02/44] feat: add index func Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 75 ++++++++++++++++--- 1 file changed, 63 insertions(+), 12 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 94ab69402b7..5b1d0abcad9 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -1,10 +1,12 @@ import uuid +import warnings from dataclasses import dataclass, field from typing import ( TYPE_CHECKING, Any, Dict, Generic, + Iterable, List, Mapping, Optional, @@ -16,9 +18,9 @@ cast, ) -import hnswlib import numpy as np from elasticsearch import Elasticsearch +from elasticsearch.helpers import parallel_bulk import docarray.typing from docarray import BaseDocument, DocumentArray @@ -53,12 +55,11 @@ def composable(fn): # type: ignore class ElasticDocumentIndex(BaseDocumentIndex, Generic[TSchema]): def __init__(self, db_config=None, **kwargs): - - if db_config.index_name is None: + super().__init__(db_config=db_config, **kwargs) + if self._db_config.index_name is None: id = uuid.uuid4().hex - db_config.index_name = 'index__' + id + self._db_config.index_name = 'index__' + id - super().__init__(db_config=db_config, **kwargs) self._db_config = cast(ElasticDocumentIndex.DBConfig, self._db_config) self._client = Elasticsearch( @@ -92,7 +93,7 @@ def __init__(self, db_config=None, **kwargs): index=self._db_config.index_name, mappings=mappings ) - self._client.indices.refresh(index=self._config.index_name) + self._refresh(self._db_config.index_name) ############################################### # Inner classes for query builder and configs # @@ -115,7 +116,7 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): default_factory=lambda: { np.ndarray: { 'type': 'dense_vector', - 'dim': 128, + 'dims': 128, 'similarity': 'cosine', # 'l2_norm', 'dot_product', 'cosine' 'm': 16, 'ef_construction': 100, @@ -133,7 +134,7 @@ def python_type_to_db_type(self, python_type: Type) -> Any: """Map python type to database type.""" for allowed_type in ELASTIC_PY_VEC_TYPES: if issubclass(python_type, allowed_type): - return list + return np.ndarray if python_type == docarray.typing.ID: return None # TODO(johannes): handle this @@ -141,10 +142,16 @@ def python_type_to_db_type(self, python_type: Type) -> Any: raise ValueError(f'Unsupported column type for {type(self)}: {python_type}') def _index(self, column_data_dic, **kwargs): + # not needed, we implement `index` directly ... - def index(self, docs: Union[BaseDocument, Sequence[BaseDocument]], **kwargs): - """Index a document into the store""" + def num_docs(self) -> int: + return self._client.count(index=self._db_config.index_name)['count'] + + def _del_items(self, doc_ids: Sequence[str]): + ... + + def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: ... def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: @@ -203,18 +210,62 @@ def _text_search_batched( # Optional overrides # #################################################### + def index(self, docs: Union[BaseDocument, Sequence[BaseDocument]], **kwargs): + """Index a document into the store""" + if kwargs: + raise ValueError(f'{list(kwargs.keys())} are not valid keyword arguments') + doc_seq = docs if isinstance(docs, Sequence) else [docs] + requests = [] + + for doc in doc_seq: + request = { + '_index': self._db_config.index_name, + '_id': doc.id, + 'blob': doc.to_base64(), + } + for col_name, col in self._columns.items(): + if not col.config: + continue + request[col_name] = doc[col_name].tolist() + requests.append(request) + + self._send_requests(request) + self._refresh(self._db_config.index_name) + ############################################### # Helpers # ############################################### # general helpers - @staticmethod # ElasticSearch helpers - def _create_index(self, col: '_Column') -> hnswlib.Index: + def _create_index(self, col: '_Column') -> Dict[str, Any]: """Create a new HNSW index for a column, and initialize it.""" index = dict((k, col.config[k]) for k in self._index_init_params) if col.n_dim: index['dims'] = col.n_dim index['index_options'] = dict((k, col.config[k]) for k in self._index_options) return index + + def _send_requests(self, request: Iterable[Dict[str, Any]], **kwargs) -> List[Dict]: + """Send bulk request to Elastic and gather the successful info""" + + # TODO chunk_size + + accumulated_info = [] + for success, info in parallel_bulk( + self._client, + request, + raise_on_error=False, + raise_on_exception=False, + **kwargs, + ): + if not success: + warnings.warn(str(info)) + else: + accumulated_info.append(info) + + return accumulated_info + + def _refresh(self, index_name: str): + self._client.indices.refresh(index=index_name) From 409f3ffd6b7e96d5e5c8a1d7af143df38ec4958c Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 3 Mar 2023 09:45:39 +0800 Subject: [PATCH 03/44] feat: get and del funcs Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 47 +++++++++++++++++-- 1 file changed, 42 insertions(+), 5 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 5b1d0abcad9..469b03bbfe9 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -46,6 +46,8 @@ def composable(fn): # type: ignore TSchema = TypeVar('TSchema', bound=BaseDocument) T = TypeVar('T', bound='ElasticDocumentIndex') +MAX_ES_RETURNED_DOCS = 10000 + ELASTIC_PY_VEC_TYPES = [list, tuple, np.ndarray] if torch_imported: import torch @@ -123,6 +125,7 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): }, # `None` is not a Type, but we allow it here anyway None: {}, # type: ignore + # TODO: add support for other types } ) @@ -149,10 +152,41 @@ def num_docs(self) -> int: return self._client.count(index=self._db_config.index_name)['count'] def _del_items(self, doc_ids: Sequence[str]): - ... + # TODO: check if this works when id doesn't exist + requests = [] + for _id in doc_ids: + requests.append( + {'_op_type': 'delete', '_index': self._db_config.index_name, '_id': _id} + ) + + self._send_requests(requests) + self._refresh(self._db_config.index_name) def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: - ... + accumulated_docs = [] + accumulated_docs_id_not_found = [] + + for pos in range(0, len(doc_ids), self.MAX_ES_RETURNED_DOCS): + + es_docs = self._client.mget( + index=self._config.index_name, + ids=doc_ids[pos : pos + self.MAX_ES_RETURNED_DOCS], + )['docs'] + + for doc in es_docs: + if doc['found']: + accumulated_docs.append( + BaseDocument.from_base64(doc['_source']['blob']) + ) + else: + accumulated_docs_id_not_found.append(doc['_id']) + + if accumulated_docs_id_not_found: + raise Warning(f'No document with id {accumulated_docs_id_not_found} found') + + da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) + + return da_cls(accumulated_docs) def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: ... @@ -221,16 +255,19 @@ def index(self, docs: Union[BaseDocument, Sequence[BaseDocument]], **kwargs): request = { '_index': self._db_config.index_name, '_id': doc.id, - 'blob': doc.to_base64(), + 'blob': doc.to_base64(), # TODO deceide if we want to store the blob } + # TODO change here when more types are supported for col_name, col in self._columns.items(): if not col.config: continue request[col_name] = doc[col_name].tolist() requests.append(request) - self._send_requests(request) - self._refresh(self._db_config.index_name) + self._send_requests(requests) + self._refresh( + self._db_config.index_name + ) # TODO add runtime config for efficient refresh ############################################### # Helpers # From 4e53d0c28b639b0ea4424c6b3f76fc929bb59c78 Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 3 Mar 2023 22:13:07 +0800 Subject: [PATCH 04/44] fix: init and index creation Signed-off-by: AnneY --- docarray/doc_index/backends/elastic_doc_index.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 469b03bbfe9..6c05e58999a 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -70,12 +70,13 @@ def __init__(self, db_config=None, **kwargs): ) # ElasticSearh index setup - self._index_init_params = ('dims', 'similarity', 'type') + self._index_init_params = ('dims', 'similarity', 'type', 'index') self._index_options = ( 'm', 'ef_construction', ) + # TODO check if index should be stored in self._hnsw_indices self._hnsw_indices = {} for col_name, col in self._columns.items(): if not col.config: @@ -88,7 +89,7 @@ def __init__(self, db_config=None, **kwargs): if self._client.indices.exists(index=self._db_config.index_name): self._client.indices.put_mapping( - index=self._db_config.index_name, put_mapping=mappings['properties'] + index=self._db_config.index_name, properties=mappings['properties'] ) else: self._client.indices.create( @@ -118,6 +119,7 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): default_factory=lambda: { np.ndarray: { 'type': 'dense_vector', + 'index': True, 'dims': 128, 'similarity': 'cosine', # 'l2_norm', 'dot_product', 'cosine' 'm': 16, @@ -282,6 +284,9 @@ def _create_index(self, col: '_Column') -> Dict[str, Any]: if col.n_dim: index['dims'] = col.n_dim index['index_options'] = dict((k, col.config[k]) for k in self._index_options) + index['index_options'][ + 'type' + ] = 'hnsw' # TODO dict key 'type' is confilct with property's 'type' return index def _send_requests(self, request: Iterable[Dict[str, Any]], **kwargs) -> List[Dict]: From f7b775d20dffdbc88defc87aa240348b89ea5517 Mon Sep 17 00:00:00 2001 From: AnneY Date: Sat, 4 Mar 2023 12:03:25 +0800 Subject: [PATCH 05/44] fix: __init__ and _index Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 58 +++++++++---------- 1 file changed, 27 insertions(+), 31 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 6c05e58999a..584d95558c5 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -5,6 +5,7 @@ TYPE_CHECKING, Any, Dict, + Generator, Generic, Iterable, List, @@ -27,7 +28,7 @@ from docarray.doc_index.abstract_doc_index import ( BaseDocumentIndex, FindResultBatched, - _Column, + _ColumnInfo, ) from docarray.utils.find import FindResult from docarray.utils.misc import torch_imported @@ -78,7 +79,7 @@ def __init__(self, db_config=None, **kwargs): # TODO check if index should be stored in self._hnsw_indices self._hnsw_indices = {} - for col_name, col in self._columns.items(): + for col_name, col in self._column_infos.items(): if not col.config: continue # do not create column index if no config is given self._hnsw_indices[col_name] = self._create_index(col) @@ -146,9 +147,29 @@ def python_type_to_db_type(self, python_type: Type) -> Any: raise ValueError(f'Unsupported column type for {type(self)}: {python_type}') - def _index(self, column_data_dic, **kwargs): - # not needed, we implement `index` directly - ... + def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): + + data = self._transpose_col_value_dict(column_to_data) + requests = [] + + for row in data: + request = { + '_index': self._db_config.index_name, + '_id': row['id'], + # 'blob': row.to_base64(), # TODO deceide if we want to store the blob + } + # TODO change here when more types are supported + for col_name, col in self._column_infos.items(): + if not col.config: + continue + request[col_name] = row[col_name].tolist() + requests.append(request) + + self._send_requests(requests) + + self._refresh( + self._db_config.index_name + ) # TODO add runtime config for efficient refresh def num_docs(self) -> int: return self._client.count(index=self._db_config.index_name)['count'] @@ -246,31 +267,6 @@ def _text_search_batched( # Optional overrides # #################################################### - def index(self, docs: Union[BaseDocument, Sequence[BaseDocument]], **kwargs): - """Index a document into the store""" - if kwargs: - raise ValueError(f'{list(kwargs.keys())} are not valid keyword arguments') - doc_seq = docs if isinstance(docs, Sequence) else [docs] - requests = [] - - for doc in doc_seq: - request = { - '_index': self._db_config.index_name, - '_id': doc.id, - 'blob': doc.to_base64(), # TODO deceide if we want to store the blob - } - # TODO change here when more types are supported - for col_name, col in self._columns.items(): - if not col.config: - continue - request[col_name] = doc[col_name].tolist() - requests.append(request) - - self._send_requests(requests) - self._refresh( - self._db_config.index_name - ) # TODO add runtime config for efficient refresh - ############################################### # Helpers # ############################################### @@ -278,7 +274,7 @@ def index(self, docs: Union[BaseDocument, Sequence[BaseDocument]], **kwargs): # general helpers # ElasticSearch helpers - def _create_index(self, col: '_Column') -> Dict[str, Any]: + def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: """Create a new HNSW index for a column, and initialize it.""" index = dict((k, col.config[k]) for k in self._index_init_params) if col.n_dim: From 0cc93f775f6a00c96ce66a3fd3f7bb42e6bfe370 Mon Sep 17 00:00:00 2001 From: AnneY Date: Sat, 4 Mar 2023 14:15:52 +0800 Subject: [PATCH 06/44] fix: _get_items Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 28 ++++++++++--------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 584d95558c5..b2e510dab26 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -189,26 +189,28 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: accumulated_docs = [] accumulated_docs_id_not_found = [] - for pos in range(0, len(doc_ids), self.MAX_ES_RETURNED_DOCS): + for pos in range(0, len(doc_ids), MAX_ES_RETURNED_DOCS): - es_docs = self._client.mget( - index=self._config.index_name, - ids=doc_ids[pos : pos + self.MAX_ES_RETURNED_DOCS], + es_rows = self._client.mget( + index=self._db_config.index_name, + ids=doc_ids[pos : pos + MAX_ES_RETURNED_DOCS], )['docs'] - for doc in es_docs: - if doc['found']: - accumulated_docs.append( - BaseDocument.from_base64(doc['_source']['blob']) - ) + for row in es_rows: + if row['found']: + doc_dict = row['_source'] + doc_dict['id'] = row['_id'] + schema_cls = cast(Type[BaseDocument], self._schema) + accumulated_docs.append(schema_cls(**doc_dict)) else: - accumulated_docs_id_not_found.append(doc['_id']) - + accumulated_docs_id_not_found.append(row['_id']) + # TODO decide the warning or error here if accumulated_docs_id_not_found: - raise Warning(f'No document with id {accumulated_docs_id_not_found} found') + warnings.warn( + f'No document with id {accumulated_docs_id_not_found} found', Warning + ) da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) - return da_cls(accumulated_docs) def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: From 9c5f70dbd417ade9ffdf5d78b6365f6781ef6a7e Mon Sep 17 00:00:00 2001 From: AnneY Date: Sat, 4 Mar 2023 19:44:41 +0800 Subject: [PATCH 07/44] feat: add _find Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 43 +++++++++++++++++-- 1 file changed, 39 insertions(+), 4 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index b2e510dab26..50b62cb375a 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -200,18 +200,19 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: if row['found']: doc_dict = row['_source'] doc_dict['id'] = row['_id'] - schema_cls = cast(Type[BaseDocument], self._schema) - accumulated_docs.append(schema_cls(**doc_dict)) + accumulated_docs.append(doc_dict) else: accumulated_docs_id_not_found.append(row['_id']) + # TODO decide the warning or error here if accumulated_docs_id_not_found: warnings.warn( f'No document with id {accumulated_docs_id_not_found} found', Warning ) + doc_list = self._convert_to_doc_list(accumulated_docs) da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) - return da_cls(accumulated_docs) + return da_cls(doc_list) def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: ... @@ -229,7 +230,31 @@ def _find_batched( def _find( self, query: np.ndarray, search_field: str, limit: int, **kwargs ) -> FindResult: - ... + knn_query = { + 'field': search_field, + 'query_vector': query, + 'k': limit, + 'num_candidates': 10000 + if 'num_candidates' not in kwargs + else kwargs['num_candidates'], + } + + resp = self._client.search( + index=self._db_config.index_name, + knn=knn_query, + ) + + docs = [] + scores = [] + for result in resp['hits']['hits']: + doc_dict = result['_source'] + doc_dict['id'] = result['_id'] + docs.append(doc_dict) + scores.append(result['_score']) + + doc_list = self._convert_to_doc_list(docs) + da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) + return FindResult(documents=da_cls(doc_list), scores=np.array(scores)) @composable def _filter( @@ -287,6 +312,16 @@ def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: ] = 'hnsw' # TODO dict key 'type' is confilct with property's 'type' return index + def _convert_to_doc_list(self, docs: List[Dict[str, Any]]) -> List[BaseDocument]: + """Convert a list of docs to a list of Document objects.""" + schema_cls = cast(Type[BaseDocument], self._schema) + doc_list = [] + + for doc_dict in docs: + doc_list.append(schema_cls(**doc_dict)) + + return doc_list + def _send_requests(self, request: Iterable[Dict[str, Any]], **kwargs) -> List[Dict]: """Send bulk request to Elastic and gather the successful info""" From 310c8eb69adb93e9fdfc6f8c33bc06838eafc29e Mon Sep 17 00:00:00 2001 From: AnneY Date: Mon, 6 Mar 2023 23:07:09 +0800 Subject: [PATCH 08/44] feat: add filter text and their batch version Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 134 ++++++++++++------ 1 file changed, 92 insertions(+), 42 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 50b62cb375a..65a15480c65 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -77,16 +77,11 @@ def __init__(self, db_config=None, **kwargs): 'ef_construction', ) - # TODO check if index should be stored in self._hnsw_indices - self._hnsw_indices = {} + mappings = {'dynamic': 'true', '_source': {'enabled': 'true'}, 'properties': {}} for col_name, col in self._column_infos.items(): if not col.config: continue # do not create column index if no config is given - self._hnsw_indices[col_name] = self._create_index(col) - - mappings = {'dynamic': 'true', '_source': {'enabled': 'true'}, 'properties': {}} - for col_name, index in self._hnsw_indices.items(): - mappings['properties'][col_name] = index + mappings['properties'][col_name] = self._create_index(col) if self._client.indices.exists(index=self._db_config.index_name): self._client.indices.put_mapping( @@ -156,16 +151,18 @@ def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): request = { '_index': self._db_config.index_name, '_id': row['id'], - # 'blob': row.to_base64(), # TODO deceide if we want to store the blob + # '_blob': row.to_base64(), # TODO blob is stored for nested doc } # TODO change here when more types are supported for col_name, col in self._column_infos.items(): if not col.config: continue - request[col_name] = row[col_name].tolist() + request[col_name] = row[col_name] requests.append(request) - self._send_requests(requests) + _, warning_info = self._send_requests(requests) + for info in warning_info: + warnings.warn(str(info)) self._refresh( self._db_config.index_name @@ -175,14 +172,19 @@ def num_docs(self) -> int: return self._client.count(index=self._db_config.index_name)['count'] def _del_items(self, doc_ids: Sequence[str]): - # TODO: check if this works when id doesn't exist requests = [] for _id in doc_ids: requests.append( {'_op_type': 'delete', '_index': self._db_config.index_name, '_id': _id} ) - self._send_requests(requests) + _, warning_info = self._send_requests(requests) + + # raise warning if some ids are not found + if warning_info: + ids = [info['delete']['_id'] for info in warning_info] + warnings.warn(f'No document with id {ids} found') + self._refresh(self._db_config.index_name) def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: @@ -204,11 +206,9 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: else: accumulated_docs_id_not_found.append(row['_id']) - # TODO decide the warning or error here + # raise warning if some ids are not found if accumulated_docs_id_not_found: - warnings.warn( - f'No document with id {accumulated_docs_id_not_found} found', Warning - ) + warnings.warn(f'No document with id {accumulated_docs_id_not_found} found') doc_list = self._convert_to_doc_list(accumulated_docs) da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) @@ -217,15 +217,6 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: ... - def _find_batched( - self, - query: np.ndarray, - search_field: str, - limit: int, - **kwargs, - ) -> FindResultBatched: - ... - @composable def _find( self, query: np.ndarray, search_field: str, limit: int, **kwargs @@ -256,50 +247,108 @@ def _find( da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) return FindResult(documents=da_cls(doc_list), scores=np.array(scores)) + def _find_batched( + self, + queries: np.ndarray, + search_field: str, + limit: int, + ) -> FindResultBatched: + result_das = [] + result_scores = [] + + for query in queries: + documents, scores = self._find(query, search_field, limit) + result_das.append(documents) + result_scores.append(scores) + + result_scores = np.array(result_scores) + return FindResultBatched(documents=result_das, scores=result_scores) + @composable def _filter( self, - *args, - **kwargs, + filter_query: Any, + limit: int, ) -> DocumentArray: - ... + resp = self._client.search( + index=self._db_config.index_name, + query=filter_query, + size=limit, + ) + + docs = [] + for result in resp['hits']['hits'][:limit]: + doc_dict = result['_source'] + doc_dict['id'] = result['_id'] + docs.append(doc_dict) + + doc_list = self._convert_to_doc_list(docs) + da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) + return da_cls(doc_list) def _filter_batched( self, filter_queries: Any, limit: int, - **kwargs, ) -> List[DocumentArray]: - ... + result_das = [] + for query in filter_queries: + result_das.append(self._filter(query, limit)) + return result_das def _text_search( self, query: str, search_field: str, limit: int, - **kwargs, ) -> FindResult: - ... + query = { + "bool": { + "must": [ + {"match": {search_field: query}}, + ], + } + } + + resp = self._client.search( + index=self._db_config.index_name, + query=query, + size=limit, + ) + + docs = [] + scores = [] + for result in resp['hits']['hits']: + doc_dict = result['_source'] + doc_dict['id'] = result['_id'] + docs.append(doc_dict) + scores.append(result['_score']) + + doc_list = self._convert_to_doc_list(docs) + da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) + return FindResult(documents=da_cls(doc_list), scores=np.array(scores)) def _text_search_batched( self, queries: Sequence[str], search_field: str, limit: int, - **kwargs, ) -> FindResultBatched: - ... + result_das = [] + result_scores = [] + + for query in queries: + documents, scores = self._text_search(query, search_field, limit) + result_das.append(documents) + result_scores.append(scores) - #################################################### - # Optional overrides # - #################################################### + result_scores = np.array(result_scores) + return FindResultBatched(documents=result_das, scores=result_scores) ############################################### # Helpers # ############################################### - # general helpers - # ElasticSearch helpers def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: """Create a new HNSW index for a column, and initialize it.""" @@ -309,7 +358,7 @@ def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: index['index_options'] = dict((k, col.config[k]) for k in self._index_options) index['index_options'][ 'type' - ] = 'hnsw' # TODO dict key 'type' is confilct with property's 'type' + ] = 'hnsw' # dict key 'type' is confilct with the 'type' of index return index def _convert_to_doc_list(self, docs: List[Dict[str, Any]]) -> List[BaseDocument]: @@ -328,6 +377,7 @@ def _send_requests(self, request: Iterable[Dict[str, Any]], **kwargs) -> List[Di # TODO chunk_size accumulated_info = [] + warning_info = [] for success, info in parallel_bulk( self._client, request, @@ -336,11 +386,11 @@ def _send_requests(self, request: Iterable[Dict[str, Any]], **kwargs) -> List[Di **kwargs, ): if not success: - warnings.warn(str(info)) + warning_info.append(info) else: accumulated_info.append(info) - return accumulated_info + return accumulated_info, warning_info def _refresh(self, index_name: str): self._client.indices.refresh(index=index_name) From 11aa3c083d578f9215e9c86c452fb6093c562c03 Mon Sep 17 00:00:00 2001 From: AnneY Date: Tue, 7 Mar 2023 14:31:50 +0800 Subject: [PATCH 09/44] feat: store id and get nested doc Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 56 +++++++++++++------ 1 file changed, 39 insertions(+), 17 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 65a15480c65..a28cef479f2 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -71,11 +71,9 @@ def __init__(self, db_config=None, **kwargs): ) # ElasticSearh index setup - self._index_init_params = ('dims', 'similarity', 'type', 'index') - self._index_options = ( - 'm', - 'ef_construction', - ) + self._index_init_params = ('type',) + self._index_vector_params = ('dims', 'similarity', 'index') + self._index_vector_options = ('m', 'ef_construction') mappings = {'dynamic': 'true', '_source': {'enabled': 'true'}, 'properties': {}} for col_name, col in self._column_infos.items(): @@ -121,6 +119,7 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): 'm': 16, 'ef_construction': 100, }, + docarray.typing.ID: {'type': 'keyword'}, # `None` is not a Type, but we allow it here anyway None: {}, # type: ignore # TODO: add support for other types @@ -138,7 +137,7 @@ def python_type_to_db_type(self, python_type: Type) -> Any: return np.ndarray if python_type == docarray.typing.ID: - return None # TODO(johannes): handle this + return docarray.typing.ID raise ValueError(f'Unsupported column type for {type(self)}: {python_type}') @@ -151,7 +150,6 @@ def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): request = { '_index': self._db_config.index_name, '_id': row['id'], - # '_blob': row.to_base64(), # TODO blob is stored for nested doc } # TODO change here when more types are supported for col_name, col in self._column_infos.items(): @@ -201,7 +199,6 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: for row in es_rows: if row['found']: doc_dict = row['_source'] - doc_dict['id'] = row['_id'] accumulated_docs.append(doc_dict) else: accumulated_docs_id_not_found.append(row['_id']) @@ -353,24 +350,49 @@ def _text_search_batched( def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: """Create a new HNSW index for a column, and initialize it.""" index = dict((k, col.config[k]) for k in self._index_init_params) - if col.n_dim: - index['dims'] = col.n_dim - index['index_options'] = dict((k, col.config[k]) for k in self._index_options) - index['index_options'][ - 'type' - ] = 'hnsw' # dict key 'type' is confilct with the 'type' of index + if col.db_type == np.ndarray: + for k in self._index_vector_params: + index[k] = col.config[k] + if col.n_dim: + index['dims'] = col.n_dim + index['index_options'] = dict( + (k, col.config[k]) for k in self._index_vector_options + ) + index['index_options']['type'] = 'hnsw' return index def _convert_to_doc_list(self, docs: List[Dict[str, Any]]) -> List[BaseDocument]: """Convert a list of docs to a list of Document objects.""" - schema_cls = cast(Type[BaseDocument], self._schema) - doc_list = [] + doc_list = [] for doc_dict in docs: - doc_list.append(schema_cls(**doc_dict)) + doc = self._convert_dict_to_doc(doc_dict, self._schema) + doc_list.append(doc) return doc_list + def _convert_dict_to_doc( + self, doc_dict: Dict[str, Any], schema: Type[BaseDocument] + ) -> BaseDocument: + """Convert a dict to a Document object.""" + + for field_name, _ in schema.__fields__.items(): + t_ = schema._get_field_type(field_name) + if issubclass(t_, BaseDocument): + inner_dict = {} + + fields = [ + key for key in doc_dict.keys() if key.startswith(f'{field_name}__') + ] + for key in fields: + nested_name = key.replace(f'{field_name}__', '') + inner_dict[nested_name] = doc_dict.pop(key) + + doc_dict[field_name] = self._convert_dict_to_doc(inner_dict, t_) + + schema_cls = cast(Type[BaseDocument], schema) + return schema_cls(**doc_dict) + def _send_requests(self, request: Iterable[Dict[str, Any]], **kwargs) -> List[Dict]: """Send bulk request to Elastic and gather the successful info""" From 9da99954eda5d6e22974b558c8154e642ee26954 Mon Sep 17 00:00:00 2001 From: AnneY Date: Tue, 7 Mar 2023 15:58:50 +0800 Subject: [PATCH 10/44] fix: vector cannot be all zero Signed-off-by: AnneY --- docarray/doc_index/backends/elastic_doc_index.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index a28cef479f2..5b22f044fbb 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -155,6 +155,8 @@ def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): for col_name, col in self._column_infos.items(): if not col.config: continue + if col.db_type == np.ndarray and np.all(row[col_name] == 0): + row[col_name] = row[col_name] + 1.0e-9 request[col_name] = row[col_name] requests.append(request) From ede075fba4f38debf9ed9df47db8baf7f1d915f4 Mon Sep 17 00:00:00 2001 From: AnneY Date: Tue, 7 Mar 2023 16:09:58 +0800 Subject: [PATCH 11/44] fix: __getitem__ raise error Signed-off-by: AnneY --- docarray/doc_index/abstract_doc_index.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/docarray/doc_index/abstract_doc_index.py b/docarray/doc_index/abstract_doc_index.py index c5a9557689b..cf2b4a10dd2 100644 --- a/docarray/doc_index/abstract_doc_index.py +++ b/docarray/doc_index/abstract_doc_index.py @@ -241,13 +241,13 @@ def _find( @abstractmethod def _find_batched( self, - query: np.ndarray, + queries: np.ndarray, search_field: str, limit: int, ) -> FindResultBatched: """Find documents in the index - :param query: query vectors for KNN/ANN search. + :param queries: query vectors for KNN/ANN search. Has shape (batch_size, vector_dim) :param search_field: name of the field to search on :param limit: maximum number of documents to return @@ -350,7 +350,13 @@ def __getitem__( cast(Type[BaseDocument], self._schema) ) out_da = da_cls(doc_sequence) - return out_da[0] if return_singleton else out_da + + if return_singleton: + if len(out_da) == 0: + raise KeyError(f'No document with id {key[0]} found') + return out_da[0] + + return out_da def __delitem__(self, key: Union[str, Sequence[str]]): """Delete one or multiple Documents from the index, by `id`. From 2a6782f410fb0c00552e4ee312dad33912e685e2 Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 10 Mar 2023 14:02:50 +0800 Subject: [PATCH 12/44] feat: support more python types Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 90 +++++-------------- 1 file changed, 20 insertions(+), 70 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 5b22f044fbb..e9a667c51f0 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -2,7 +2,6 @@ import warnings from dataclasses import dataclass, field from typing import ( - TYPE_CHECKING, Any, Dict, Generator, @@ -27,29 +26,19 @@ from docarray import BaseDocument, DocumentArray from docarray.doc_index.abstract_doc_index import ( BaseDocumentIndex, - FindResultBatched, _ColumnInfo, + _FindResultBatched, ) -from docarray.utils.find import FindResult +from docarray.utils.find import _FindResult from docarray.utils.misc import torch_imported -# mypy: ignore-errors - -if TYPE_CHECKING: - - def composable(fn): # type: ignore - return fn - -else: - # static type checkers do not like callable objects as decorators - from docarray.doc_index.abstract_doc_index import composable - TSchema = TypeVar('TSchema', bound=BaseDocument) T = TypeVar('T', bound='ElasticDocumentIndex') MAX_ES_RETURNED_DOCS = 10000 ELASTIC_PY_VEC_TYPES = [list, tuple, np.ndarray] +ELASTIC_PY_TYPES = [bool, int, float, str, docarray.typing.ID] if torch_imported: import torch @@ -120,9 +109,12 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): 'ef_construction': 100, }, docarray.typing.ID: {'type': 'keyword'}, + bool: {'type': 'boolean'}, + int: {'type': 'integer'}, + float: {'type': 'float'}, + str: {'type': 'text'}, # `None` is not a Type, but we allow it here anyway None: {}, # type: ignore - # TODO: add support for other types } ) @@ -136,8 +128,8 @@ def python_type_to_db_type(self, python_type: Type) -> Any: if issubclass(python_type, allowed_type): return np.ndarray - if python_type == docarray.typing.ID: - return docarray.typing.ID + if python_type in ELASTIC_PY_TYPES: + return python_type raise ValueError(f'Unsupported column type for {type(self)}: {python_type}') @@ -209,17 +201,14 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: if accumulated_docs_id_not_found: warnings.warn(f'No document with id {accumulated_docs_id_not_found} found') - doc_list = self._convert_to_doc_list(accumulated_docs) - da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) - return da_cls(doc_list) + return accumulated_docs def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: ... - @composable def _find( self, query: np.ndarray, search_field: str, limit: int, **kwargs - ) -> FindResult: + ) -> _FindResult: knn_query = { 'field': search_field, 'query_vector': query, @@ -242,16 +231,14 @@ def _find( docs.append(doc_dict) scores.append(result['_score']) - doc_list = self._convert_to_doc_list(docs) - da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) - return FindResult(documents=da_cls(doc_list), scores=np.array(scores)) + return _FindResult(documents=docs, scores=np.array(scores)) def _find_batched( self, queries: np.ndarray, search_field: str, limit: int, - ) -> FindResultBatched: + ) -> _FindResultBatched: result_das = [] result_scores = [] @@ -261,14 +248,13 @@ def _find_batched( result_scores.append(scores) result_scores = np.array(result_scores) - return FindResultBatched(documents=result_das, scores=result_scores) + return _FindResultBatched(documents=result_das, scores=result_scores) - @composable def _filter( self, filter_query: Any, limit: int, - ) -> DocumentArray: + ) -> List[Dict]: resp = self._client.search( index=self._db_config.index_name, query=filter_query, @@ -281,9 +267,7 @@ def _filter( doc_dict['id'] = result['_id'] docs.append(doc_dict) - doc_list = self._convert_to_doc_list(docs) - da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) - return da_cls(doc_list) + return docs def _filter_batched( self, @@ -300,7 +284,7 @@ def _text_search( query: str, search_field: str, limit: int, - ) -> FindResult: + ) -> _FindResult: query = { "bool": { "must": [ @@ -323,16 +307,14 @@ def _text_search( docs.append(doc_dict) scores.append(result['_score']) - doc_list = self._convert_to_doc_list(docs) - da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema)) - return FindResult(documents=da_cls(doc_list), scores=np.array(scores)) + return _FindResult(documents=docs, scores=np.array(scores)) def _text_search_batched( self, queries: Sequence[str], search_field: str, limit: int, - ) -> FindResultBatched: + ) -> _FindResultBatched: result_das = [] result_scores = [] @@ -342,7 +324,7 @@ def _text_search_batched( result_scores.append(scores) result_scores = np.array(result_scores) - return FindResultBatched(documents=result_das, scores=result_scores) + return _FindResultBatched(documents=result_das, scores=result_scores) ############################################### # Helpers # @@ -363,38 +345,6 @@ def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: index['index_options']['type'] = 'hnsw' return index - def _convert_to_doc_list(self, docs: List[Dict[str, Any]]) -> List[BaseDocument]: - """Convert a list of docs to a list of Document objects.""" - - doc_list = [] - for doc_dict in docs: - doc = self._convert_dict_to_doc(doc_dict, self._schema) - doc_list.append(doc) - - return doc_list - - def _convert_dict_to_doc( - self, doc_dict: Dict[str, Any], schema: Type[BaseDocument] - ) -> BaseDocument: - """Convert a dict to a Document object.""" - - for field_name, _ in schema.__fields__.items(): - t_ = schema._get_field_type(field_name) - if issubclass(t_, BaseDocument): - inner_dict = {} - - fields = [ - key for key in doc_dict.keys() if key.startswith(f'{field_name}__') - ] - for key in fields: - nested_name = key.replace(f'{field_name}__', '') - inner_dict[nested_name] = doc_dict.pop(key) - - doc_dict[field_name] = self._convert_dict_to_doc(inner_dict, t_) - - schema_cls = cast(Type[BaseDocument], schema) - return schema_cls(**doc_dict) - def _send_requests(self, request: Iterable[Dict[str, Any]], **kwargs) -> List[Dict]: """Send bulk request to Elastic and gather the successful info""" From 79cac767ff8e5f849b43fcb0428ee244f840a043 Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 10 Mar 2023 15:03:20 +0800 Subject: [PATCH 13/44] fix: mypy Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 75 ++++++++++--------- 1 file changed, 39 insertions(+), 36 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index e9a667c51f0..1717baafb13 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -23,7 +23,7 @@ from elasticsearch.helpers import parallel_bulk import docarray.typing -from docarray import BaseDocument, DocumentArray +from docarray import BaseDocument from docarray.doc_index.abstract_doc_index import ( BaseDocumentIndex, _ColumnInfo, @@ -48,11 +48,13 @@ class ElasticDocumentIndex(BaseDocumentIndex, Generic[TSchema]): def __init__(self, db_config=None, **kwargs): super().__init__(db_config=db_config, **kwargs) + self._db_config = cast(ElasticDocumentIndex.DBConfig, self._db_config) + if self._db_config.index_name is None: id = uuid.uuid4().hex self._db_config.index_name = 'index__' + id - self._db_config = cast(ElasticDocumentIndex.DBConfig, self._db_config) + self._index_name = self._db_config.index_name self._client = Elasticsearch( hosts=self._db_config.hosts, @@ -64,34 +66,37 @@ def __init__(self, db_config=None, **kwargs): self._index_vector_params = ('dims', 'similarity', 'index') self._index_vector_options = ('m', 'ef_construction') - mappings = {'dynamic': 'true', '_source': {'enabled': 'true'}, 'properties': {}} + mappings: Dict[str, Any] = { + 'dynamic': True, + '_source': {'enabled': 'true'}, + 'properties': {}, + } + for col_name, col in self._column_infos.items(): if not col.config: continue # do not create column index if no config is given mappings['properties'][col_name] = self._create_index(col) - if self._client.indices.exists(index=self._db_config.index_name): + if self._client.indices.exists(index=self._index_name): # type: ignore self._client.indices.put_mapping( - index=self._db_config.index_name, properties=mappings['properties'] + index=self._index_name, properties=mappings['properties'] ) else: - self._client.indices.create( - index=self._db_config.index_name, mappings=mappings - ) + self._client.indices.create(index=self._index_name, mappings=mappings) - self._refresh(self._db_config.index_name) + self._refresh(self._index_name) ############################################### # Inner classes for query builder and configs # ############################################### - class QueryBuilder(BaseDocumentIndex.QueryBuilder): - def build(self, *args, **kwargs) -> Any: - return self._queries + # TODO add class QueryBuilder @dataclass class DBConfig(BaseDocumentIndex.DBConfig): + from elastic_transport import NodeConfig + hosts: Union[ - str, List[Union[str, Mapping[str, Union[str, int]]]], None + str, List[Union[str, Mapping[str, Union[str, int]], NodeConfig]], None ] = 'http://localhost:9200' index_name: Optional[str] = None es_config: Dict[str, Any] = field(default_factory=dict) @@ -135,12 +140,12 @@ def python_type_to_db_type(self, python_type: Type) -> Any: def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): - data = self._transpose_col_value_dict(column_to_data) + data = self._transpose_col_value_dict(column_to_data) # type: ignore requests = [] for row in data: request = { - '_index': self._db_config.index_name, + '_index': self._index_name, '_id': row['id'], } # TODO change here when more types are supported @@ -156,18 +161,16 @@ def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): for info in warning_info: warnings.warn(str(info)) - self._refresh( - self._db_config.index_name - ) # TODO add runtime config for efficient refresh + self._refresh(self._index_name) # TODO add runtime config for efficient refresh def num_docs(self) -> int: - return self._client.count(index=self._db_config.index_name)['count'] + return self._client.count(index=self._index_name)['count'] def _del_items(self, doc_ids: Sequence[str]): requests = [] for _id in doc_ids: requests.append( - {'_op_type': 'delete', '_index': self._db_config.index_name, '_id': _id} + {'_op_type': 'delete', '_index': self._index_name, '_id': _id} ) _, warning_info = self._send_requests(requests) @@ -177,7 +180,7 @@ def _del_items(self, doc_ids: Sequence[str]): ids = [info['delete']['_id'] for info in warning_info] warnings.warn(f'No document with id {ids} found') - self._refresh(self._db_config.index_name) + self._refresh(self._index_name) def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: accumulated_docs = [] @@ -186,8 +189,8 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: for pos in range(0, len(doc_ids), MAX_ES_RETURNED_DOCS): es_rows = self._client.mget( - index=self._db_config.index_name, - ids=doc_ids[pos : pos + MAX_ES_RETURNED_DOCS], + index=self._index_name, + ids=doc_ids[pos : pos + MAX_ES_RETURNED_DOCS], # type: ignore )['docs'] for row in es_rows: @@ -219,7 +222,7 @@ def _find( } resp = self._client.search( - index=self._db_config.index_name, + index=self._index_name, knn=knn_query, ) @@ -231,7 +234,7 @@ def _find( docs.append(doc_dict) scores.append(result['_score']) - return _FindResult(documents=docs, scores=np.array(scores)) + return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore def _find_batched( self, @@ -247,8 +250,7 @@ def _find_batched( result_das.append(documents) result_scores.append(scores) - result_scores = np.array(result_scores) - return _FindResultBatched(documents=result_das, scores=result_scores) + return _FindResultBatched(documents=result_das, scores=np.array(result_scores)) # type: ignore def _filter( self, @@ -256,7 +258,7 @@ def _filter( limit: int, ) -> List[Dict]: resp = self._client.search( - index=self._db_config.index_name, + index=self._index_name, query=filter_query, size=limit, ) @@ -273,7 +275,7 @@ def _filter_batched( self, filter_queries: Any, limit: int, - ) -> List[DocumentArray]: + ) -> List[List[Dict]]: result_das = [] for query in filter_queries: result_das.append(self._filter(query, limit)) @@ -285,7 +287,7 @@ def _text_search( search_field: str, limit: int, ) -> _FindResult: - query = { + search_query = { "bool": { "must": [ {"match": {search_field: query}}, @@ -294,8 +296,8 @@ def _text_search( } resp = self._client.search( - index=self._db_config.index_name, - query=query, + index=self._index_name, + query=search_query, size=limit, ) @@ -307,7 +309,7 @@ def _text_search( docs.append(doc_dict) scores.append(result['_score']) - return _FindResult(documents=docs, scores=np.array(scores)) + return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore def _text_search_batched( self, @@ -323,8 +325,7 @@ def _text_search_batched( result_das.append(documents) result_scores.append(scores) - result_scores = np.array(result_scores) - return _FindResultBatched(documents=result_das, scores=result_scores) + return _FindResultBatched(documents=result_das, scores=np.array(result_scores)) # type: ignore ############################################### # Helpers # @@ -345,7 +346,9 @@ def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: index['index_options']['type'] = 'hnsw' return index - def _send_requests(self, request: Iterable[Dict[str, Any]], **kwargs) -> List[Dict]: + def _send_requests( + self, request: Iterable[Dict[str, Any]], **kwargs + ) -> Tuple[List[Dict], List[Any]]: """Send bulk request to Elastic and gather the successful info""" # TODO chunk_size From dc1f988158d382c8187d623befaa1ea93b021092 Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 10 Mar 2023 15:07:38 +0800 Subject: [PATCH 14/44] test: elastic index tests Signed-off-by: AnneY --- tests/doc_index/elastic/test_find.py | 232 ++++++++++++++++++ tests/doc_index/elastic/test_index_get_del.py | 231 +++++++++++++++++ 2 files changed, 463 insertions(+) create mode 100644 tests/doc_index/elastic/test_find.py create mode 100644 tests/doc_index/elastic/test_index_get_del.py diff --git a/tests/doc_index/elastic/test_find.py b/tests/doc_index/elastic/test_find.py new file mode 100644 index 00000000000..e1545dfb8a3 --- /dev/null +++ b/tests/doc_index/elastic/test_find.py @@ -0,0 +1,232 @@ +import numpy as np +import pytest +from pydantic import Field + +from docarray import BaseDocument +from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex +from docarray.typing import NdArray + +pytestmark = [pytest.mark.slow, pytest.mark.doc_index] + + +class SimpleDoc(BaseDocument): + tens: NdArray[10] = Field(dims=1000) + + +class FlatDoc(BaseDocument): + tens_one: NdArray = Field(dims=10) + tens_two: NdArray = Field(dims=50) + + +class NestedDoc(BaseDocument): + d: SimpleDoc + + +class DeepNestedDoc(BaseDocument): + d: NestedDoc + + +@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) +def test_find_simple_schema(similarity): + class SimpleSchema(BaseDocument): + tens: NdArray[10] = Field(similarity=similarity) + + store = ElasticDocumentIndex[SimpleSchema]() + + index_docs = [] + for _ in range(10): + vec = np.random.rand(10) + if similarity == 'dot_product': + vec = vec / np.linalg.norm(vec) + index_docs.append(SimpleDoc(tens=vec)) + store.index(index_docs) + + query = index_docs[-1] + docs, scores = store.find(query, search_field='tens', limit=5) + + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].tens, index_docs[-1].tens) + + +@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) +def test_find_flat_schema(similarity): + class FlatSchema(BaseDocument): + tens_one: NdArray = Field(dims=10, similarity=similarity) + tens_two: NdArray = Field(dims=50, similarity=similarity) + + store = ElasticDocumentIndex[FlatSchema]() + + index_docs = [] + for _ in range(10): + vec_one = np.random.rand(10) + vec_two = np.random.rand(50) + if similarity == 'dot_product': + vec_one = vec_one / np.linalg.norm(vec_one) + vec_two = vec_two / np.linalg.norm(vec_two) + index_docs.append(FlatDoc(tens_one=vec_one, tens_two=vec_two)) + + store.index(index_docs) + + query = index_docs[-1] + + # find on tens_one + docs, scores = store.find(query, search_field='tens_one', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) + assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) + + # find on tens_two + docs, scores = store.find(query, search_field='tens_two', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) + assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) + + +@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) +def test_find_nested_schema(similarity): + class SimpleDoc(BaseDocument): + tens: NdArray[10] = Field(similarity=similarity) + + class NestedDoc(BaseDocument): + d: SimpleDoc + tens: NdArray[10] = Field(similarity=similarity) + + class DeepNestedDoc(BaseDocument): + d: NestedDoc + tens: NdArray = Field(similarity=similarity, dims=10) + + store = ElasticDocumentIndex[DeepNestedDoc]() + + index_docs = [] + for _ in range(10): + vec_simple = np.random.rand(10) + vec_nested = np.random.rand(10) + vec_deep = np.random.rand(10) + if similarity == 'dot_product': + vec_simple = vec_simple / np.linalg.norm(vec_simple) + vec_nested = vec_nested / np.linalg.norm(vec_nested) + vec_deep = vec_deep / np.linalg.norm(vec_deep) + index_docs.append( + DeepNestedDoc( + d=NestedDoc(d=SimpleDoc(tens=vec_simple), tens=vec_nested), + tens=vec_deep, + ) + ) + + store.index(index_docs) + + query = index_docs[-1] + + # find on root level + docs, scores = store.find(query, search_field='tens', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].tens, index_docs[-1].tens) + + # find on first nesting level + docs, scores = store.find(query, search_field='d__tens', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].d.tens, index_docs[-1].d.tens) + + # find on second nesting level + docs, scores = store.find(query, search_field='d__d__tens', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].d.d.tens, index_docs[-1].d.d.tens) + + +def test_find_batched(): + store = ElasticDocumentIndex[SimpleDoc]() + + index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] + store.index(index_docs) + + queries = index_docs[-2:] + docs_batched, scores_batched = store.find_batched( + queries, search_field='tens', limit=5 + ) + + for docs, scores, query in zip(docs_batched, scores_batched, queries): + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == query.id + assert np.allclose(docs[0].tens, query.tens) + + +def test_filter(): + import itertools + + class MyDoc(BaseDocument): + A: bool + B: int + C: float + + store = ElasticDocumentIndex[MyDoc]() + + A_list = [True, False] + B_list = [1, 2] + C_list = [1.5, 2.5] + + # cross product of all possible combinations + combinations = itertools.product(A_list, B_list, C_list) + index_docs = [MyDoc(A=A, B=B, C=C) for A, B, C in combinations] + store.index(index_docs) + + filter_query = {'term': {'A': True}} + docs = store.filter(filter_query) + assert len(docs) > 0 + for doc in docs: + assert doc.A + + filter_query = {'term': {'B': 1}} + docs = store.filter(filter_query) + assert len(docs) > 0 + for doc in docs: + assert doc.B == 1 + + filter_query = {'term': {'C': 1.5}} + docs = store.filter(filter_query) + assert len(docs) > 0 + for doc in docs: + assert doc.C == 1.5 + + +def test_text_search(): + class MyDoc(BaseDocument): + text: str + + store = ElasticDocumentIndex[MyDoc]() + index_docs = [ + MyDoc(text='hello world'), + MyDoc(text='never gonna give you up'), + MyDoc(text='we are the world'), + ] + store.index(index_docs) + + query = 'world' + docs, scores = store.text_search(query, search_field='text') + + assert len(docs) == 2 + assert len(scores) == 2 + assert docs[0].text.index(query) >= 0 + assert docs[1].text.index(query) >= 0 + + queries = ['world', 'never'] + docs, scores = store.text_search_batched(queries, search_field='text') + print(scores) + for query, da, score in zip(queries, docs, scores): + print(score) + assert len(da) > 0 + assert len(score) > 0 + for doc in da: + assert doc.text.index(query) >= 0 diff --git a/tests/doc_index/elastic/test_index_get_del.py b/tests/doc_index/elastic/test_index_get_del.py new file mode 100644 index 00000000000..c26f5ce5998 --- /dev/null +++ b/tests/doc_index/elastic/test_index_get_del.py @@ -0,0 +1,231 @@ +import numpy as np +import pytest +from pydantic import Field + +from docarray import BaseDocument, DocumentArray +from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex +from docarray.typing import NdArray + + +class SimpleDoc(BaseDocument): + tens: NdArray[10] = Field(dims=1000) + + +class FlatDoc(BaseDocument): + tens_one: NdArray = Field(dims=10) + tens_two: NdArray = Field(dims=50) + + +class NestedDoc(BaseDocument): + d: SimpleDoc + + +class DeepNestedDoc(BaseDocument): + d: NestedDoc + + +@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): + store = ElasticDocumentIndex[SimpleDoc]() + if use_docarray: + ten_simple_docs = DocumentArray[SimpleDoc](ten_simple_docs) + + store.index(ten_simple_docs) + assert store.num_docs() == 10 + + +@pytest.mark.parametrize('use_docarray', [True, False]) +def test_index_flat_schema(ten_flat_docs, use_docarray): + store = ElasticDocumentIndex[FlatDoc]() + if use_docarray: + ten_flat_docs = DocumentArray[FlatDoc](ten_flat_docs) + + store.index(ten_flat_docs) + assert store.num_docs() == 10 + + +@pytest.mark.parametrize('use_docarray', [True, False]) +def test_index_nested_schema(ten_nested_docs, use_docarray): + store = ElasticDocumentIndex[NestedDoc]() + if use_docarray: + ten_nested_docs = DocumentArray[NestedDoc](ten_nested_docs) + + store.index(ten_nested_docs) + assert store.num_docs() == 10 + + +def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): + # simple + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + assert store.num_docs() == 10 + for d in ten_simple_docs: + id_ = d.id + assert store[id_].id == id_ + assert np.all(store[id_].tens == d.tens) + + # flat + store = ElasticDocumentIndex[FlatDoc]() + store.index(ten_flat_docs) + + assert store.num_docs() == 10 + for d in ten_flat_docs: + id_ = d.id + assert store[id_].id == id_ + assert np.all(store[id_].tens_one == d.tens_one) + assert np.all(store[id_].tens_two == d.tens_two) + + # nested + store = ElasticDocumentIndex[NestedDoc]() + store.index(ten_nested_docs) + + assert store.num_docs() == 10 + for d in ten_nested_docs: + id_ = d.id + assert store[id_].id == id_ + assert store[id_].d.id == d.d.id + assert np.all(store[id_].d.tens == d.d.tens) + + +def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): + docs_to_get_idx = [0, 2, 4, 6, 8] + + # simple + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + assert store.num_docs() == 10 + docs_to_get = [ten_simple_docs[i] for i in docs_to_get_idx] + ids_to_get = [d.id for d in docs_to_get] + retrieved_docs = store[ids_to_get] + for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): + assert d_out.id == id_ + assert np.all(d_out.tens == d_in.tens) + + # flat + store = ElasticDocumentIndex[FlatDoc]() + store.index(ten_flat_docs) + + assert store.num_docs() == 10 + docs_to_get = [ten_flat_docs[i] for i in docs_to_get_idx] + ids_to_get = [d.id for d in docs_to_get] + retrieved_docs = store[ids_to_get] + for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): + assert d_out.id == id_ + assert np.all(d_out.tens_one == d_in.tens_one) + assert np.all(d_out.tens_two == d_in.tens_two) + + # nested + store = ElasticDocumentIndex[NestedDoc]() + store.index(ten_nested_docs) + + assert store.num_docs() == 10 + docs_to_get = [ten_nested_docs[i] for i in docs_to_get_idx] + ids_to_get = [d.id for d in docs_to_get] + retrieved_docs = store[ids_to_get] + for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): + assert d_out.id == id_ + assert d_out.d.id == d_in.d.id + assert np.all(d_out.d.tens == d_in.d.tens) + + +def test_get_key_error(ten_simple_docs): + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + with pytest.raises(KeyError): + store['not_a_real_id'] + + +def test_del_single(ten_simple_docs): + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + # delete once + assert store.num_docs() == 10 + del store[ten_simple_docs[0].id] + assert store.num_docs() == 9 + for i, d in enumerate(ten_simple_docs): + id_ = d.id + if i == 0: # deleted + with pytest.raises(KeyError): + store[id_] + else: + assert store[id_].id == id_ + assert np.all(store[id_].tens == d.tens) + # delete again + del store[ten_simple_docs[3].id] + assert store.num_docs() == 8 + for i, d in enumerate(ten_simple_docs): + id_ = d.id + if i in (0, 3): # deleted + with pytest.raises(KeyError): + store[id_] + else: + assert store[id_].id == id_ + assert np.all(store[id_].tens == d.tens) + + +def test_del_multiple(ten_simple_docs): + docs_to_del_idx = [0, 2, 4, 6, 8] + + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + assert store.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 store[ids_to_del] + for i, doc in enumerate(ten_simple_docs): + if i in docs_to_del_idx: + with pytest.raises(KeyError): + store[doc.id] + else: + assert store[doc.id].id == doc.id + assert np.all(store[doc.id].tens == doc.tens) + + +def test_del_key_error(ten_simple_docs, ten_flat_docs, ten_nested_docs): + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + with pytest.warns(UserWarning): + del store['not_a_real_id'] + + +def test_num_docs(ten_simple_docs): + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + assert store.num_docs() == 10 + + del store[ten_simple_docs[0].id] + assert store.num_docs() == 9 + + del store[ten_simple_docs[3].id, ten_simple_docs[5].id] + assert store.num_docs() == 7 + + more_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(5)] + store.index(more_docs) + assert store.num_docs() == 12 + + del store[more_docs[2].id, ten_simple_docs[7].id] + assert store.num_docs() == 10 From 70c0da98b5a2bb598c91ee726b3c9eba9c93f173 Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 10 Mar 2023 15:36:38 +0800 Subject: [PATCH 15/44] test: comment scripts before ci setup Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 2 +- .../doc_index/backends/hnswlib_doc_index.py | 4 +- tests/doc_index/elastic/test_find.py | 413 +++++++++--------- tests/doc_index/elastic/test_index_get_del.py | 377 ++++++++-------- 4 files changed, 399 insertions(+), 397 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 1717baafb13..28b69eda93e 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -19,6 +19,7 @@ ) import numpy as np +from elastic_transport import NodeConfig from elasticsearch import Elasticsearch from elasticsearch.helpers import parallel_bulk @@ -93,7 +94,6 @@ def __init__(self, db_config=None, **kwargs): @dataclass class DBConfig(BaseDocumentIndex.DBConfig): - from elastic_transport import NodeConfig hosts: Union[ str, List[Union[str, Mapping[str, Union[str, int]], NodeConfig]], None diff --git a/docarray/doc_index/backends/hnswlib_doc_index.py b/docarray/doc_index/backends/hnswlib_doc_index.py index d80e022e5f5..0b532caea86 100644 --- a/docarray/doc_index/backends/hnswlib_doc_index.py +++ b/docarray/doc_index/backends/hnswlib_doc_index.py @@ -212,12 +212,12 @@ def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: def _find_batched( self, - query: np.ndarray, + queries: np.ndarray, search_field: str, limit: int, ) -> _FindResultBatched: index = self._hnsw_indices[search_field] - labels, distances = index.knn_query(query, k=limit) + labels, distances = index.knn_query(queries, k=limit) result_das = [ self._get_docs_sqlite_hashed_id( ids_per_query.tolist(), diff --git a/tests/doc_index/elastic/test_find.py b/tests/doc_index/elastic/test_find.py index e1545dfb8a3..334d746943c 100644 --- a/tests/doc_index/elastic/test_find.py +++ b/tests/doc_index/elastic/test_find.py @@ -1,9 +1,10 @@ -import numpy as np +# import numpy as np import pytest from pydantic import Field from docarray import BaseDocument -from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex + +# from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex from docarray.typing import NdArray pytestmark = [pytest.mark.slow, pytest.mark.doc_index] @@ -26,207 +27,207 @@ class DeepNestedDoc(BaseDocument): d: NestedDoc -@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) -def test_find_simple_schema(similarity): - class SimpleSchema(BaseDocument): - tens: NdArray[10] = Field(similarity=similarity) - - store = ElasticDocumentIndex[SimpleSchema]() - - index_docs = [] - for _ in range(10): - vec = np.random.rand(10) - if similarity == 'dot_product': - vec = vec / np.linalg.norm(vec) - index_docs.append(SimpleDoc(tens=vec)) - store.index(index_docs) - - query = index_docs[-1] - docs, scores = store.find(query, search_field='tens', limit=5) - - assert len(docs) == 5 - assert len(scores) == 5 - assert docs[0].id == index_docs[-1].id - assert np.allclose(docs[0].tens, index_docs[-1].tens) - - -@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) -def test_find_flat_schema(similarity): - class FlatSchema(BaseDocument): - tens_one: NdArray = Field(dims=10, similarity=similarity) - tens_two: NdArray = Field(dims=50, similarity=similarity) - - store = ElasticDocumentIndex[FlatSchema]() - - index_docs = [] - for _ in range(10): - vec_one = np.random.rand(10) - vec_two = np.random.rand(50) - if similarity == 'dot_product': - vec_one = vec_one / np.linalg.norm(vec_one) - vec_two = vec_two / np.linalg.norm(vec_two) - index_docs.append(FlatDoc(tens_one=vec_one, tens_two=vec_two)) - - store.index(index_docs) - - query = index_docs[-1] - - # find on tens_one - docs, scores = store.find(query, search_field='tens_one', limit=5) - assert len(docs) == 5 - assert len(scores) == 5 - assert docs[0].id == index_docs[-1].id - assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) - assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) - - # find on tens_two - docs, scores = store.find(query, search_field='tens_two', limit=5) - assert len(docs) == 5 - assert len(scores) == 5 - assert docs[0].id == index_docs[-1].id - assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) - assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) - - -@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) -def test_find_nested_schema(similarity): - class SimpleDoc(BaseDocument): - tens: NdArray[10] = Field(similarity=similarity) - - class NestedDoc(BaseDocument): - d: SimpleDoc - tens: NdArray[10] = Field(similarity=similarity) - - class DeepNestedDoc(BaseDocument): - d: NestedDoc - tens: NdArray = Field(similarity=similarity, dims=10) - - store = ElasticDocumentIndex[DeepNestedDoc]() - - index_docs = [] - for _ in range(10): - vec_simple = np.random.rand(10) - vec_nested = np.random.rand(10) - vec_deep = np.random.rand(10) - if similarity == 'dot_product': - vec_simple = vec_simple / np.linalg.norm(vec_simple) - vec_nested = vec_nested / np.linalg.norm(vec_nested) - vec_deep = vec_deep / np.linalg.norm(vec_deep) - index_docs.append( - DeepNestedDoc( - d=NestedDoc(d=SimpleDoc(tens=vec_simple), tens=vec_nested), - tens=vec_deep, - ) - ) - - store.index(index_docs) - - query = index_docs[-1] - - # find on root level - docs, scores = store.find(query, search_field='tens', limit=5) - assert len(docs) == 5 - assert len(scores) == 5 - assert docs[0].id == index_docs[-1].id - assert np.allclose(docs[0].tens, index_docs[-1].tens) - - # find on first nesting level - docs, scores = store.find(query, search_field='d__tens', limit=5) - assert len(docs) == 5 - assert len(scores) == 5 - assert docs[0].id == index_docs[-1].id - assert np.allclose(docs[0].d.tens, index_docs[-1].d.tens) - - # find on second nesting level - docs, scores = store.find(query, search_field='d__d__tens', limit=5) - assert len(docs) == 5 - assert len(scores) == 5 - assert docs[0].id == index_docs[-1].id - assert np.allclose(docs[0].d.d.tens, index_docs[-1].d.d.tens) - - -def test_find_batched(): - store = ElasticDocumentIndex[SimpleDoc]() - - index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] - store.index(index_docs) - - queries = index_docs[-2:] - docs_batched, scores_batched = store.find_batched( - queries, search_field='tens', limit=5 - ) - - for docs, scores, query in zip(docs_batched, scores_batched, queries): - assert len(docs) == 5 - assert len(scores) == 5 - assert docs[0].id == query.id - assert np.allclose(docs[0].tens, query.tens) - - -def test_filter(): - import itertools - - class MyDoc(BaseDocument): - A: bool - B: int - C: float - - store = ElasticDocumentIndex[MyDoc]() - - A_list = [True, False] - B_list = [1, 2] - C_list = [1.5, 2.5] - - # cross product of all possible combinations - combinations = itertools.product(A_list, B_list, C_list) - index_docs = [MyDoc(A=A, B=B, C=C) for A, B, C in combinations] - store.index(index_docs) - - filter_query = {'term': {'A': True}} - docs = store.filter(filter_query) - assert len(docs) > 0 - for doc in docs: - assert doc.A - - filter_query = {'term': {'B': 1}} - docs = store.filter(filter_query) - assert len(docs) > 0 - for doc in docs: - assert doc.B == 1 - - filter_query = {'term': {'C': 1.5}} - docs = store.filter(filter_query) - assert len(docs) > 0 - for doc in docs: - assert doc.C == 1.5 - - -def test_text_search(): - class MyDoc(BaseDocument): - text: str - - store = ElasticDocumentIndex[MyDoc]() - index_docs = [ - MyDoc(text='hello world'), - MyDoc(text='never gonna give you up'), - MyDoc(text='we are the world'), - ] - store.index(index_docs) - - query = 'world' - docs, scores = store.text_search(query, search_field='text') - - assert len(docs) == 2 - assert len(scores) == 2 - assert docs[0].text.index(query) >= 0 - assert docs[1].text.index(query) >= 0 - - queries = ['world', 'never'] - docs, scores = store.text_search_batched(queries, search_field='text') - print(scores) - for query, da, score in zip(queries, docs, scores): - print(score) - assert len(da) > 0 - assert len(score) > 0 - for doc in da: - assert doc.text.index(query) >= 0 +# @pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) +# def test_find_simple_schema(similarity): +# class SimpleSchema(BaseDocument): +# tens: NdArray[10] = Field(similarity=similarity) + +# store = ElasticDocumentIndex[SimpleSchema]() + +# index_docs = [] +# for _ in range(10): +# vec = np.random.rand(10) +# if similarity == 'dot_product': +# vec = vec / np.linalg.norm(vec) +# index_docs.append(SimpleDoc(tens=vec)) +# store.index(index_docs) + +# query = index_docs[-1] +# docs, scores = store.find(query, search_field='tens', limit=5) + +# assert len(docs) == 5 +# assert len(scores) == 5 +# assert docs[0].id == index_docs[-1].id +# assert np.allclose(docs[0].tens, index_docs[-1].tens) + + +# @pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) +# def test_find_flat_schema(similarity): +# class FlatSchema(BaseDocument): +# tens_one: NdArray = Field(dims=10, similarity=similarity) +# tens_two: NdArray = Field(dims=50, similarity=similarity) + +# store = ElasticDocumentIndex[FlatSchema]() + +# index_docs = [] +# for _ in range(10): +# vec_one = np.random.rand(10) +# vec_two = np.random.rand(50) +# if similarity == 'dot_product': +# vec_one = vec_one / np.linalg.norm(vec_one) +# vec_two = vec_two / np.linalg.norm(vec_two) +# index_docs.append(FlatDoc(tens_one=vec_one, tens_two=vec_two)) + +# store.index(index_docs) + +# query = index_docs[-1] + +# # find on tens_one +# docs, scores = store.find(query, search_field='tens_one', limit=5) +# assert len(docs) == 5 +# assert len(scores) == 5 +# assert docs[0].id == index_docs[-1].id +# assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) +# assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) + +# # find on tens_two +# docs, scores = store.find(query, search_field='tens_two', limit=5) +# assert len(docs) == 5 +# assert len(scores) == 5 +# assert docs[0].id == index_docs[-1].id +# assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) +# assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) + + +# @pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) +# def test_find_nested_schema(similarity): +# class SimpleDoc(BaseDocument): +# tens: NdArray[10] = Field(similarity=similarity) + +# class NestedDoc(BaseDocument): +# d: SimpleDoc +# tens: NdArray[10] = Field(similarity=similarity) + +# class DeepNestedDoc(BaseDocument): +# d: NestedDoc +# tens: NdArray = Field(similarity=similarity, dims=10) + +# store = ElasticDocumentIndex[DeepNestedDoc]() + +# index_docs = [] +# for _ in range(10): +# vec_simple = np.random.rand(10) +# vec_nested = np.random.rand(10) +# vec_deep = np.random.rand(10) +# if similarity == 'dot_product': +# vec_simple = vec_simple / np.linalg.norm(vec_simple) +# vec_nested = vec_nested / np.linalg.norm(vec_nested) +# vec_deep = vec_deep / np.linalg.norm(vec_deep) +# index_docs.append( +# DeepNestedDoc( +# d=NestedDoc(d=SimpleDoc(tens=vec_simple), tens=vec_nested), +# tens=vec_deep, +# ) +# ) + +# store.index(index_docs) + +# query = index_docs[-1] + +# # find on root level +# docs, scores = store.find(query, search_field='tens', limit=5) +# assert len(docs) == 5 +# assert len(scores) == 5 +# assert docs[0].id == index_docs[-1].id +# assert np.allclose(docs[0].tens, index_docs[-1].tens) + +# # find on first nesting level +# docs, scores = store.find(query, search_field='d__tens', limit=5) +# assert len(docs) == 5 +# assert len(scores) == 5 +# assert docs[0].id == index_docs[-1].id +# assert np.allclose(docs[0].d.tens, index_docs[-1].d.tens) + +# # find on second nesting level +# docs, scores = store.find(query, search_field='d__d__tens', limit=5) +# assert len(docs) == 5 +# assert len(scores) == 5 +# assert docs[0].id == index_docs[-1].id +# assert np.allclose(docs[0].d.d.tens, index_docs[-1].d.d.tens) + + +# def test_find_batched(): +# store = ElasticDocumentIndex[SimpleDoc]() + +# index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] +# store.index(index_docs) + +# queries = index_docs[-2:] +# docs_batched, scores_batched = store.find_batched( +# queries, search_field='tens', limit=5 +# ) + +# for docs, scores, query in zip(docs_batched, scores_batched, queries): +# assert len(docs) == 5 +# assert len(scores) == 5 +# assert docs[0].id == query.id +# assert np.allclose(docs[0].tens, query.tens) + + +# def test_filter(): +# import itertools + +# class MyDoc(BaseDocument): +# A: bool +# B: int +# C: float + +# store = ElasticDocumentIndex[MyDoc]() + +# A_list = [True, False] +# B_list = [1, 2] +# C_list = [1.5, 2.5] + +# # cross product of all possible combinations +# combinations = itertools.product(A_list, B_list, C_list) +# index_docs = [MyDoc(A=A, B=B, C=C) for A, B, C in combinations] +# store.index(index_docs) + +# filter_query = {'term': {'A': True}} +# docs = store.filter(filter_query) +# assert len(docs) > 0 +# for doc in docs: +# assert doc.A + +# filter_query = {'term': {'B': 1}} +# docs = store.filter(filter_query) +# assert len(docs) > 0 +# for doc in docs: +# assert doc.B == 1 + +# filter_query = {'term': {'C': 1.5}} +# docs = store.filter(filter_query) +# assert len(docs) > 0 +# for doc in docs: +# assert doc.C == 1.5 + + +# def test_text_search(): +# class MyDoc(BaseDocument): +# text: str + +# store = ElasticDocumentIndex[MyDoc]() +# index_docs = [ +# MyDoc(text='hello world'), +# MyDoc(text='never gonna give you up'), +# MyDoc(text='we are the world'), +# ] +# store.index(index_docs) + +# query = 'world' +# docs, scores = store.text_search(query, search_field='text') + +# assert len(docs) == 2 +# assert len(scores) == 2 +# assert docs[0].text.index(query) >= 0 +# assert docs[1].text.index(query) >= 0 + +# queries = ['world', 'never'] +# docs, scores = store.text_search_batched(queries, search_field='text') +# print(scores) +# for query, da, score in zip(queries, docs, scores): +# print(score) +# assert len(da) > 0 +# assert len(score) > 0 +# for doc in da: +# assert doc.text.index(query) >= 0 diff --git a/tests/doc_index/elastic/test_index_get_del.py b/tests/doc_index/elastic/test_index_get_del.py index c26f5ce5998..bf16c36c5fc 100644 --- a/tests/doc_index/elastic/test_index_get_del.py +++ b/tests/doc_index/elastic/test_index_get_del.py @@ -2,8 +2,9 @@ import pytest from pydantic import Field -from docarray import BaseDocument, DocumentArray -from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex +from docarray import BaseDocument # , DocumentArray + +# from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex from docarray.typing import NdArray @@ -42,190 +43,190 @@ 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): - store = ElasticDocumentIndex[SimpleDoc]() - if use_docarray: - ten_simple_docs = DocumentArray[SimpleDoc](ten_simple_docs) - - store.index(ten_simple_docs) - assert store.num_docs() == 10 - - -@pytest.mark.parametrize('use_docarray', [True, False]) -def test_index_flat_schema(ten_flat_docs, use_docarray): - store = ElasticDocumentIndex[FlatDoc]() - if use_docarray: - ten_flat_docs = DocumentArray[FlatDoc](ten_flat_docs) +# @pytest.mark.parametrize('use_docarray', [True, False]) +# def test_index_simple_schema(ten_simple_docs, use_docarray): +# store = ElasticDocumentIndex[SimpleDoc]() +# if use_docarray: +# ten_simple_docs = DocumentArray[SimpleDoc](ten_simple_docs) + +# store.index(ten_simple_docs) +# assert store.num_docs() == 10 + + +# @pytest.mark.parametrize('use_docarray', [True, False]) +# def test_index_flat_schema(ten_flat_docs, use_docarray): +# store = ElasticDocumentIndex[FlatDoc]() +# if use_docarray: +# ten_flat_docs = DocumentArray[FlatDoc](ten_flat_docs) - store.index(ten_flat_docs) - assert store.num_docs() == 10 - - -@pytest.mark.parametrize('use_docarray', [True, False]) -def test_index_nested_schema(ten_nested_docs, use_docarray): - store = ElasticDocumentIndex[NestedDoc]() - if use_docarray: - ten_nested_docs = DocumentArray[NestedDoc](ten_nested_docs) - - store.index(ten_nested_docs) - assert store.num_docs() == 10 - - -def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): - # simple - store = ElasticDocumentIndex[SimpleDoc]() - store.index(ten_simple_docs) - - assert store.num_docs() == 10 - for d in ten_simple_docs: - id_ = d.id - assert store[id_].id == id_ - assert np.all(store[id_].tens == d.tens) - - # flat - store = ElasticDocumentIndex[FlatDoc]() - store.index(ten_flat_docs) - - assert store.num_docs() == 10 - for d in ten_flat_docs: - id_ = d.id - assert store[id_].id == id_ - assert np.all(store[id_].tens_one == d.tens_one) - assert np.all(store[id_].tens_two == d.tens_two) - - # nested - store = ElasticDocumentIndex[NestedDoc]() - store.index(ten_nested_docs) - - assert store.num_docs() == 10 - for d in ten_nested_docs: - id_ = d.id - assert store[id_].id == id_ - assert store[id_].d.id == d.d.id - assert np.all(store[id_].d.tens == d.d.tens) - - -def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): - docs_to_get_idx = [0, 2, 4, 6, 8] - - # simple - store = ElasticDocumentIndex[SimpleDoc]() - store.index(ten_simple_docs) - - assert store.num_docs() == 10 - docs_to_get = [ten_simple_docs[i] for i in docs_to_get_idx] - ids_to_get = [d.id for d in docs_to_get] - retrieved_docs = store[ids_to_get] - for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): - assert d_out.id == id_ - assert np.all(d_out.tens == d_in.tens) - - # flat - store = ElasticDocumentIndex[FlatDoc]() - store.index(ten_flat_docs) - - assert store.num_docs() == 10 - docs_to_get = [ten_flat_docs[i] for i in docs_to_get_idx] - ids_to_get = [d.id for d in docs_to_get] - retrieved_docs = store[ids_to_get] - for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): - assert d_out.id == id_ - assert np.all(d_out.tens_one == d_in.tens_one) - assert np.all(d_out.tens_two == d_in.tens_two) - - # nested - store = ElasticDocumentIndex[NestedDoc]() - store.index(ten_nested_docs) - - assert store.num_docs() == 10 - docs_to_get = [ten_nested_docs[i] for i in docs_to_get_idx] - ids_to_get = [d.id for d in docs_to_get] - retrieved_docs = store[ids_to_get] - for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): - assert d_out.id == id_ - assert d_out.d.id == d_in.d.id - assert np.all(d_out.d.tens == d_in.d.tens) - - -def test_get_key_error(ten_simple_docs): - store = ElasticDocumentIndex[SimpleDoc]() - store.index(ten_simple_docs) - - with pytest.raises(KeyError): - store['not_a_real_id'] - - -def test_del_single(ten_simple_docs): - store = ElasticDocumentIndex[SimpleDoc]() - store.index(ten_simple_docs) - # delete once - assert store.num_docs() == 10 - del store[ten_simple_docs[0].id] - assert store.num_docs() == 9 - for i, d in enumerate(ten_simple_docs): - id_ = d.id - if i == 0: # deleted - with pytest.raises(KeyError): - store[id_] - else: - assert store[id_].id == id_ - assert np.all(store[id_].tens == d.tens) - # delete again - del store[ten_simple_docs[3].id] - assert store.num_docs() == 8 - for i, d in enumerate(ten_simple_docs): - id_ = d.id - if i in (0, 3): # deleted - with pytest.raises(KeyError): - store[id_] - else: - assert store[id_].id == id_ - assert np.all(store[id_].tens == d.tens) - - -def test_del_multiple(ten_simple_docs): - docs_to_del_idx = [0, 2, 4, 6, 8] - - store = ElasticDocumentIndex[SimpleDoc]() - store.index(ten_simple_docs) - - assert store.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 store[ids_to_del] - for i, doc in enumerate(ten_simple_docs): - if i in docs_to_del_idx: - with pytest.raises(KeyError): - store[doc.id] - else: - assert store[doc.id].id == doc.id - assert np.all(store[doc.id].tens == doc.tens) - - -def test_del_key_error(ten_simple_docs, ten_flat_docs, ten_nested_docs): - store = ElasticDocumentIndex[SimpleDoc]() - store.index(ten_simple_docs) - - with pytest.warns(UserWarning): - del store['not_a_real_id'] - - -def test_num_docs(ten_simple_docs): - store = ElasticDocumentIndex[SimpleDoc]() - store.index(ten_simple_docs) - - assert store.num_docs() == 10 - - del store[ten_simple_docs[0].id] - assert store.num_docs() == 9 - - del store[ten_simple_docs[3].id, ten_simple_docs[5].id] - assert store.num_docs() == 7 - - more_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(5)] - store.index(more_docs) - assert store.num_docs() == 12 - - del store[more_docs[2].id, ten_simple_docs[7].id] - assert store.num_docs() == 10 +# store.index(ten_flat_docs) +# assert store.num_docs() == 10 + + +# @pytest.mark.parametrize('use_docarray', [True, False]) +# def test_index_nested_schema(ten_nested_docs, use_docarray): +# store = ElasticDocumentIndex[NestedDoc]() +# if use_docarray: +# ten_nested_docs = DocumentArray[NestedDoc](ten_nested_docs) + +# store.index(ten_nested_docs) +# assert store.num_docs() == 10 + + +# def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): +# # simple +# store = ElasticDocumentIndex[SimpleDoc]() +# store.index(ten_simple_docs) + +# assert store.num_docs() == 10 +# for d in ten_simple_docs: +# id_ = d.id +# assert store[id_].id == id_ +# assert np.all(store[id_].tens == d.tens) + +# # flat +# store = ElasticDocumentIndex[FlatDoc]() +# store.index(ten_flat_docs) + +# assert store.num_docs() == 10 +# for d in ten_flat_docs: +# id_ = d.id +# assert store[id_].id == id_ +# assert np.all(store[id_].tens_one == d.tens_one) +# assert np.all(store[id_].tens_two == d.tens_two) + +# # nested +# store = ElasticDocumentIndex[NestedDoc]() +# store.index(ten_nested_docs) + +# assert store.num_docs() == 10 +# for d in ten_nested_docs: +# id_ = d.id +# assert store[id_].id == id_ +# assert store[id_].d.id == d.d.id +# assert np.all(store[id_].d.tens == d.d.tens) + + +# def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): +# docs_to_get_idx = [0, 2, 4, 6, 8] + +# # simple +# store = ElasticDocumentIndex[SimpleDoc]() +# store.index(ten_simple_docs) + +# assert store.num_docs() == 10 +# docs_to_get = [ten_simple_docs[i] for i in docs_to_get_idx] +# ids_to_get = [d.id for d in docs_to_get] +# retrieved_docs = store[ids_to_get] +# for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): +# assert d_out.id == id_ +# assert np.all(d_out.tens == d_in.tens) + +# # flat +# store = ElasticDocumentIndex[FlatDoc]() +# store.index(ten_flat_docs) + +# assert store.num_docs() == 10 +# docs_to_get = [ten_flat_docs[i] for i in docs_to_get_idx] +# ids_to_get = [d.id for d in docs_to_get] +# retrieved_docs = store[ids_to_get] +# for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): +# assert d_out.id == id_ +# assert np.all(d_out.tens_one == d_in.tens_one) +# assert np.all(d_out.tens_two == d_in.tens_two) + +# # nested +# store = ElasticDocumentIndex[NestedDoc]() +# store.index(ten_nested_docs) + +# assert store.num_docs() == 10 +# docs_to_get = [ten_nested_docs[i] for i in docs_to_get_idx] +# ids_to_get = [d.id for d in docs_to_get] +# retrieved_docs = store[ids_to_get] +# for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): +# assert d_out.id == id_ +# assert d_out.d.id == d_in.d.id +# assert np.all(d_out.d.tens == d_in.d.tens) + + +# def test_get_key_error(ten_simple_docs): +# store = ElasticDocumentIndex[SimpleDoc]() +# store.index(ten_simple_docs) + +# with pytest.raises(KeyError): +# store['not_a_real_id'] + + +# def test_del_single(ten_simple_docs): +# store = ElasticDocumentIndex[SimpleDoc]() +# store.index(ten_simple_docs) +# # delete once +# assert store.num_docs() == 10 +# del store[ten_simple_docs[0].id] +# assert store.num_docs() == 9 +# for i, d in enumerate(ten_simple_docs): +# id_ = d.id +# if i == 0: # deleted +# with pytest.raises(KeyError): +# store[id_] +# else: +# assert store[id_].id == id_ +# assert np.all(store[id_].tens == d.tens) +# # delete again +# del store[ten_simple_docs[3].id] +# assert store.num_docs() == 8 +# for i, d in enumerate(ten_simple_docs): +# id_ = d.id +# if i in (0, 3): # deleted +# with pytest.raises(KeyError): +# store[id_] +# else: +# assert store[id_].id == id_ +# assert np.all(store[id_].tens == d.tens) + + +# def test_del_multiple(ten_simple_docs): +# docs_to_del_idx = [0, 2, 4, 6, 8] + +# store = ElasticDocumentIndex[SimpleDoc]() +# store.index(ten_simple_docs) + +# assert store.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 store[ids_to_del] +# for i, doc in enumerate(ten_simple_docs): +# if i in docs_to_del_idx: +# with pytest.raises(KeyError): +# store[doc.id] +# else: +# assert store[doc.id].id == doc.id +# assert np.all(store[doc.id].tens == doc.tens) + + +# def test_del_key_error(ten_simple_docs, ten_flat_docs, ten_nested_docs): +# store = ElasticDocumentIndex[SimpleDoc]() +# store.index(ten_simple_docs) + +# with pytest.warns(UserWarning): +# del store['not_a_real_id'] + + +# def test_num_docs(ten_simple_docs): +# store = ElasticDocumentIndex[SimpleDoc]() +# store.index(ten_simple_docs) + +# assert store.num_docs() == 10 + +# del store[ten_simple_docs[0].id] +# assert store.num_docs() == 9 + +# del store[ten_simple_docs[3].id, ten_simple_docs[5].id] +# assert store.num_docs() == 7 + +# more_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(5)] +# store.index(more_docs) +# assert store.num_docs() == 12 + +# del store[more_docs[2].id, ten_simple_docs[7].id] +# assert store.num_docs() == 10 From 2991d5272383c58cb530bbf4674e5d0e05c35caf Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 10 Mar 2023 22:29:19 +0800 Subject: [PATCH 16/44] chore: add elasticsearch dependency to poetry Signed-off-by: AnneY --- poetry.lock | 47 +++++++++++++++++++++++++++++++++++++++++++---- pyproject.toml | 4 +++- 2 files changed, 46 insertions(+), 5 deletions(-) diff --git a/poetry.lock b/poetry.lock index 8b8d2526924..b90cf1a21c5 100644 --- a/poetry.lock +++ b/poetry.lock @@ -155,7 +155,7 @@ dev = ["Sphinx (==4.3.2)", "black (==22.3.0)", "build (==0.8.0)", "flake8 (==4.0 name = "certifi" version = "2022.9.24" description = "Python package for providing Mozilla's CA Bundle." -category = "dev" +category = "main" optional = false python-versions = ">=3.6" @@ -252,6 +252,36 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "elastic-transport" +version = "8.4.0" +description = "Transport classes and utilities shared among Python Elastic client libraries" +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +certifi = "*" +urllib3 = ">=1.26.2,<2" + +[package.extras] +develop = ["aiohttp", "mock", "pytest", "pytest-asyncio", "pytest-cov", "pytest-httpserver", "pytest-mock", "requests", "trustme"] + +[[package]] +name = "elasticsearch" +version = "8.6.2" +description = "Python client for Elasticsearch" +category = "main" +optional = true +python-versions = ">=3.6, <4" + +[package.dependencies] +elastic-transport = ">=8,<9" + +[package.extras] +async = ["aiohttp (>=3,<4)"] +requests = ["requests (>=2.4.0,<3.0.0)"] + [[package]] name = "entrypoints" version = "0.4" @@ -1626,7 +1656,7 @@ typing-extensions = ">=3.7.4" name = "urllib3" version = "1.26.14" description = "HTTP library with thread-safe connection pooling, file post, and more." -category = "dev" +category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*" @@ -1724,6 +1754,7 @@ testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools" [extras] audio = ["pydub"] common = ["protobuf", "lz4"] +elasticsearch = ["elasticsearch"] hnswlib = ["hnswlib"] image = ["pillow", "types-pillow"] mesh = ["trimesh"] @@ -1734,8 +1765,8 @@ web = ["fastapi"] [metadata] lock-version = "1.1" -python-versions = ">=3.7" -content-hash = "3ec1e886d794ed803736bc2b49a626c600a97bda8a3db734b53604c10f08d252" +python-versions = ">=3.7,<4" +content-hash = "762e0d498828bc3c3c82ee22ffefce7a5690cc542ac77af517856373ba63291d" [metadata.files] anyio = [ @@ -1984,6 +2015,14 @@ distlib = [ {file = "distlib-0.3.6-py2.py3-none-any.whl", hash = "sha256:f35c4b692542ca110de7ef0bea44d73981caeb34ca0b9b6b2e6d7790dda8f80e"}, {file = "distlib-0.3.6.tar.gz", hash = "sha256:14bad2d9b04d3a36127ac97f30b12a19268f211063d8f8ee4f47108896e11b46"}, ] +elastic-transport = [ + {file = "elastic-transport-8.4.0.tar.gz", hash = "sha256:b9ad708ceb7fcdbc6b30a96f886609a109f042c0b9d9f2e44403b3133ba7ff10"}, + {file = "elastic_transport-8.4.0-py3-none-any.whl", hash = "sha256:19db271ab79c9f70f8c43f8f5b5111408781a6176b54ab2e54d713b6d9ceb815"}, +] +elasticsearch = [ + {file = "elasticsearch-8.6.2-py3-none-any.whl", hash = "sha256:8ccbebd9a0f6f523c7db67bb54863dde8bdb93daae4ff97f7c814e0500a73e84"}, + {file = "elasticsearch-8.6.2.tar.gz", hash = "sha256:084458e84caa91e3ad807b68aa82c022e785bead853a3b125641a25e894a1d47"}, +] entrypoints = [ {file = "entrypoints-0.4-py3-none-any.whl", hash = "sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f"}, {file = "entrypoints-0.4.tar.gz", hash = "sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4"}, diff --git a/pyproject.toml b/pyproject.toml index 29bec14bf00..bfaeba19316 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors=['DocArray'] license='Apache 2.0' [tool.poetry.dependencies] -python = ">=3.7" +python = ">=3.7,<4" pydantic = ">=1.10.2" numpy = ">=1.17.3" protobuf = { version = ">=3.19.0", optional = true } @@ -24,6 +24,7 @@ hnswlib = {version = ">=0.6.2", optional = true } lz4 = {version= ">=1.0.0", optional = true} pydub = {version = "^0.25.1", optional = true } pandas = {version = ">=1.1.0", optional = true } +elasticsearch = {version = "^8.6.2", optional = true} [tool.poetry.extras] common = ["protobuf", "lz4"] @@ -35,6 +36,7 @@ mesh = ["trimesh"] web = ["fastapi"] hnswlib = ["hnswlib"] pandas = ["pandas"] +elasticsearch = ["elasticsearch"] [tool.poetry.dev-dependencies] pytest = ">=7.0" From a0f90bbd80ec142f09683123220a56966d230a54 Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 10 Mar 2023 23:24:05 +0800 Subject: [PATCH 17/44] test: elastic index ci setup Signed-off-by: AnneY --- tests/doc_index/elastic/docker-compose.yml | 16 + tests/doc_index/elastic/fixture.py | 48 ++ tests/doc_index/elastic/test_find.py | 436 +++++++++--------- tests/doc_index/elastic/test_index_get_del.py | 416 ++++++++--------- 4 files changed, 480 insertions(+), 436 deletions(-) create mode 100644 tests/doc_index/elastic/docker-compose.yml create mode 100644 tests/doc_index/elastic/fixture.py diff --git a/tests/doc_index/elastic/docker-compose.yml b/tests/doc_index/elastic/docker-compose.yml new file mode 100644 index 00000000000..70eedba34f5 --- /dev/null +++ b/tests/doc_index/elastic/docker-compose.yml @@ -0,0 +1,16 @@ +version: "3.3" +services: + elastic: + image: docker.elastic.co/elasticsearch/elasticsearch:8.6.2 + environment: + - xpack.security.enabled=false + - discovery.type=single-node + - ES_JAVA_OPTS=-Xmx1024m + ports: + - "9200:9200" + networks: + - elastic + +networks: + elastic: + name: elastic \ No newline at end of file diff --git a/tests/doc_index/elastic/fixture.py b/tests/doc_index/elastic/fixture.py new file mode 100644 index 00000000000..b7f5b9c802a --- /dev/null +++ b/tests/doc_index/elastic/fixture.py @@ -0,0 +1,48 @@ +import os +import time + +import pytest +from pydantic import Field + +from docarray import BaseDocument +from docarray.typing import NdArray + +pytestmark = [pytest.mark.slow, pytest.mark.doc_index] + + +class SimpleDoc(BaseDocument): + tens: NdArray[10] = Field(dims=1000) + + +class FlatDoc(BaseDocument): + tens_one: NdArray = Field(dims=10) + tens_two: NdArray = Field(dims=50) + + +class NestedDoc(BaseDocument): + d: SimpleDoc + + +class DeepNestedDoc(BaseDocument): + d: NestedDoc + + +cur_dir = os.path.dirname(os.path.abspath(__file__)) +compose_yml = os.path.abspath(os.path.join(cur_dir, 'docker-compose.yml')) + + +@pytest.fixture(scope='module', autouse=True) +def start_storage(): + os.system(f"docker-compose -f {compose_yml} up -d --remove-orphans") + _wait_for_es() + + yield + os.system(f"docker-compose -f {compose_yml} down --remove-orphans") + + +def _wait_for_es(): + from elasticsearch import Elasticsearch + + es = Elasticsearch(hosts='http://localhost:9200/') + while not es.ping(): + time.sleep(0.5) diff --git a/tests/doc_index/elastic/test_find.py b/tests/doc_index/elastic/test_find.py index 334d746943c..44b3b6a1b6a 100644 --- a/tests/doc_index/elastic/test_find.py +++ b/tests/doc_index/elastic/test_find.py @@ -1,233 +1,213 @@ -# import numpy as np +import numpy as np import pytest from pydantic import Field from docarray import BaseDocument - -# from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex +from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex from docarray.typing import NdArray - -pytestmark = [pytest.mark.slow, pytest.mark.doc_index] - - -class SimpleDoc(BaseDocument): - tens: NdArray[10] = Field(dims=1000) - - -class FlatDoc(BaseDocument): - tens_one: NdArray = Field(dims=10) - tens_two: NdArray = Field(dims=50) - - -class NestedDoc(BaseDocument): - d: SimpleDoc - - -class DeepNestedDoc(BaseDocument): - d: NestedDoc - - -# @pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) -# def test_find_simple_schema(similarity): -# class SimpleSchema(BaseDocument): -# tens: NdArray[10] = Field(similarity=similarity) - -# store = ElasticDocumentIndex[SimpleSchema]() - -# index_docs = [] -# for _ in range(10): -# vec = np.random.rand(10) -# if similarity == 'dot_product': -# vec = vec / np.linalg.norm(vec) -# index_docs.append(SimpleDoc(tens=vec)) -# store.index(index_docs) - -# query = index_docs[-1] -# docs, scores = store.find(query, search_field='tens', limit=5) - -# assert len(docs) == 5 -# assert len(scores) == 5 -# assert docs[0].id == index_docs[-1].id -# assert np.allclose(docs[0].tens, index_docs[-1].tens) - - -# @pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) -# def test_find_flat_schema(similarity): -# class FlatSchema(BaseDocument): -# tens_one: NdArray = Field(dims=10, similarity=similarity) -# tens_two: NdArray = Field(dims=50, similarity=similarity) - -# store = ElasticDocumentIndex[FlatSchema]() - -# index_docs = [] -# for _ in range(10): -# vec_one = np.random.rand(10) -# vec_two = np.random.rand(50) -# if similarity == 'dot_product': -# vec_one = vec_one / np.linalg.norm(vec_one) -# vec_two = vec_two / np.linalg.norm(vec_two) -# index_docs.append(FlatDoc(tens_one=vec_one, tens_two=vec_two)) - -# store.index(index_docs) - -# query = index_docs[-1] - -# # find on tens_one -# docs, scores = store.find(query, search_field='tens_one', limit=5) -# assert len(docs) == 5 -# assert len(scores) == 5 -# assert docs[0].id == index_docs[-1].id -# assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) -# assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) - -# # find on tens_two -# docs, scores = store.find(query, search_field='tens_two', limit=5) -# assert len(docs) == 5 -# assert len(scores) == 5 -# assert docs[0].id == index_docs[-1].id -# assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) -# assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) - - -# @pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) -# def test_find_nested_schema(similarity): -# class SimpleDoc(BaseDocument): -# tens: NdArray[10] = Field(similarity=similarity) - -# class NestedDoc(BaseDocument): -# d: SimpleDoc -# tens: NdArray[10] = Field(similarity=similarity) - -# class DeepNestedDoc(BaseDocument): -# d: NestedDoc -# tens: NdArray = Field(similarity=similarity, dims=10) - -# store = ElasticDocumentIndex[DeepNestedDoc]() - -# index_docs = [] -# for _ in range(10): -# vec_simple = np.random.rand(10) -# vec_nested = np.random.rand(10) -# vec_deep = np.random.rand(10) -# if similarity == 'dot_product': -# vec_simple = vec_simple / np.linalg.norm(vec_simple) -# vec_nested = vec_nested / np.linalg.norm(vec_nested) -# vec_deep = vec_deep / np.linalg.norm(vec_deep) -# index_docs.append( -# DeepNestedDoc( -# d=NestedDoc(d=SimpleDoc(tens=vec_simple), tens=vec_nested), -# tens=vec_deep, -# ) -# ) - -# store.index(index_docs) - -# query = index_docs[-1] - -# # find on root level -# docs, scores = store.find(query, search_field='tens', limit=5) -# assert len(docs) == 5 -# assert len(scores) == 5 -# assert docs[0].id == index_docs[-1].id -# assert np.allclose(docs[0].tens, index_docs[-1].tens) - -# # find on first nesting level -# docs, scores = store.find(query, search_field='d__tens', limit=5) -# assert len(docs) == 5 -# assert len(scores) == 5 -# assert docs[0].id == index_docs[-1].id -# assert np.allclose(docs[0].d.tens, index_docs[-1].d.tens) - -# # find on second nesting level -# docs, scores = store.find(query, search_field='d__d__tens', limit=5) -# assert len(docs) == 5 -# assert len(scores) == 5 -# assert docs[0].id == index_docs[-1].id -# assert np.allclose(docs[0].d.d.tens, index_docs[-1].d.d.tens) - - -# def test_find_batched(): -# store = ElasticDocumentIndex[SimpleDoc]() - -# index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] -# store.index(index_docs) - -# queries = index_docs[-2:] -# docs_batched, scores_batched = store.find_batched( -# queries, search_field='tens', limit=5 -# ) - -# for docs, scores, query in zip(docs_batched, scores_batched, queries): -# assert len(docs) == 5 -# assert len(scores) == 5 -# assert docs[0].id == query.id -# assert np.allclose(docs[0].tens, query.tens) - - -# def test_filter(): -# import itertools - -# class MyDoc(BaseDocument): -# A: bool -# B: int -# C: float - -# store = ElasticDocumentIndex[MyDoc]() - -# A_list = [True, False] -# B_list = [1, 2] -# C_list = [1.5, 2.5] - -# # cross product of all possible combinations -# combinations = itertools.product(A_list, B_list, C_list) -# index_docs = [MyDoc(A=A, B=B, C=C) for A, B, C in combinations] -# store.index(index_docs) - -# filter_query = {'term': {'A': True}} -# docs = store.filter(filter_query) -# assert len(docs) > 0 -# for doc in docs: -# assert doc.A - -# filter_query = {'term': {'B': 1}} -# docs = store.filter(filter_query) -# assert len(docs) > 0 -# for doc in docs: -# assert doc.B == 1 - -# filter_query = {'term': {'C': 1.5}} -# docs = store.filter(filter_query) -# assert len(docs) > 0 -# for doc in docs: -# assert doc.C == 1.5 - - -# def test_text_search(): -# class MyDoc(BaseDocument): -# text: str - -# store = ElasticDocumentIndex[MyDoc]() -# index_docs = [ -# MyDoc(text='hello world'), -# MyDoc(text='never gonna give you up'), -# MyDoc(text='we are the world'), -# ] -# store.index(index_docs) - -# query = 'world' -# docs, scores = store.text_search(query, search_field='text') - -# assert len(docs) == 2 -# assert len(scores) == 2 -# assert docs[0].text.index(query) >= 0 -# assert docs[1].text.index(query) >= 0 - -# queries = ['world', 'never'] -# docs, scores = store.text_search_batched(queries, search_field='text') -# print(scores) -# for query, da, score in zip(queries, docs, scores): -# print(score) -# assert len(da) > 0 -# assert len(score) > 0 -# for doc in da: -# assert doc.text.index(query) >= 0 +from tests.doc_index.elastic.fixture import start_storage # noqa: F401 +from tests.doc_index.elastic.fixture import FlatDoc, SimpleDoc + + +@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) +def test_find_simple_schema(similarity): + class SimpleSchema(BaseDocument): + tens: NdArray[10] = Field(similarity=similarity) + + store = ElasticDocumentIndex[SimpleSchema]() + + index_docs = [] + for _ in range(10): + vec = np.random.rand(10) + if similarity == 'dot_product': + vec = vec / np.linalg.norm(vec) + index_docs.append(SimpleDoc(tens=vec)) + store.index(index_docs) + + query = index_docs[-1] + docs, scores = store.find(query, search_field='tens', limit=5) + + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].tens, index_docs[-1].tens) + + +@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) +def test_find_flat_schema(similarity): + class FlatSchema(BaseDocument): + tens_one: NdArray = Field(dims=10, similarity=similarity) + tens_two: NdArray = Field(dims=50, similarity=similarity) + + store = ElasticDocumentIndex[FlatSchema]() + + index_docs = [] + for _ in range(10): + vec_one = np.random.rand(10) + vec_two = np.random.rand(50) + if similarity == 'dot_product': + vec_one = vec_one / np.linalg.norm(vec_one) + vec_two = vec_two / np.linalg.norm(vec_two) + index_docs.append(FlatDoc(tens_one=vec_one, tens_two=vec_two)) + + store.index(index_docs) + + query = index_docs[-1] + + # find on tens_one + docs, scores = store.find(query, search_field='tens_one', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) + assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) + + # find on tens_two + docs, scores = store.find(query, search_field='tens_two', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].tens_one, index_docs[-1].tens_one) + assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) + + +@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) +def test_find_nested_schema(similarity): + class SimpleDoc(BaseDocument): + tens: NdArray[10] = Field(similarity=similarity) + + class NestedDoc(BaseDocument): + d: SimpleDoc + tens: NdArray[10] = Field(similarity=similarity) + + class DeepNestedDoc(BaseDocument): + d: NestedDoc + tens: NdArray = Field(similarity=similarity, dims=10) + + store = ElasticDocumentIndex[DeepNestedDoc]() + + index_docs = [] + for _ in range(10): + vec_simple = np.random.rand(10) + vec_nested = np.random.rand(10) + vec_deep = np.random.rand(10) + if similarity == 'dot_product': + vec_simple = vec_simple / np.linalg.norm(vec_simple) + vec_nested = vec_nested / np.linalg.norm(vec_nested) + vec_deep = vec_deep / np.linalg.norm(vec_deep) + index_docs.append( + DeepNestedDoc( + d=NestedDoc(d=SimpleDoc(tens=vec_simple), tens=vec_nested), + tens=vec_deep, + ) + ) + + store.index(index_docs) + + query = index_docs[-1] + + # find on root level + docs, scores = store.find(query, search_field='tens', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].tens, index_docs[-1].tens) + + # find on first nesting level + docs, scores = store.find(query, search_field='d__tens', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].d.tens, index_docs[-1].d.tens) + + # find on second nesting level + docs, scores = store.find(query, search_field='d__d__tens', limit=5) + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == index_docs[-1].id + assert np.allclose(docs[0].d.d.tens, index_docs[-1].d.d.tens) + + +def test_find_batched(): + store = ElasticDocumentIndex[SimpleDoc]() + + index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] + store.index(index_docs) + + queries = index_docs[-2:] + docs_batched, scores_batched = store.find_batched( + queries, search_field='tens', limit=5 + ) + + for docs, scores, query in zip(docs_batched, scores_batched, queries): + assert len(docs) == 5 + assert len(scores) == 5 + assert docs[0].id == query.id + assert np.allclose(docs[0].tens, query.tens) + + +def test_filter(): + import itertools + + class MyDoc(BaseDocument): + A: bool + B: int + C: float + + store = ElasticDocumentIndex[MyDoc]() + + A_list = [True, False] + B_list = [1, 2] + C_list = [1.5, 2.5] + + # cross product of all possible combinations + combinations = itertools.product(A_list, B_list, C_list) + index_docs = [MyDoc(A=A, B=B, C=C) for A, B, C in combinations] + store.index(index_docs) + + filter_query = {'term': {'A': True}} + docs = store.filter(filter_query) + assert len(docs) > 0 + for doc in docs: + assert doc.A + + filter_query = {'term': {'B': 1}} + docs = store.filter(filter_query) + assert len(docs) > 0 + for doc in docs: + assert doc.B == 1 + + filter_query = {'term': {'C': 1.5}} + docs = store.filter(filter_query) + assert len(docs) > 0 + for doc in docs: + assert doc.C == 1.5 + + +def test_text_search(): + class MyDoc(BaseDocument): + text: str + + store = ElasticDocumentIndex[MyDoc]() + index_docs = [ + MyDoc(text='hello world'), + MyDoc(text='never gonna give you up'), + MyDoc(text='we are the world'), + ] + store.index(index_docs) + + query = 'world' + docs, scores = store.text_search(query, search_field='text') + + assert len(docs) == 2 + assert len(scores) == 2 + assert docs[0].text.index(query) >= 0 + assert docs[1].text.index(query) >= 0 + + queries = ['world', 'never'] + docs, scores = store.text_search_batched(queries, search_field='text') + for query, da, score in zip(queries, docs, scores): + assert len(da) > 0 + assert len(score) > 0 + for doc in da: + assert doc.text.index(query) >= 0 diff --git a/tests/doc_index/elastic/test_index_get_del.py b/tests/doc_index/elastic/test_index_get_del.py index bf16c36c5fc..19e6b244f7f 100644 --- a/tests/doc_index/elastic/test_index_get_del.py +++ b/tests/doc_index/elastic/test_index_get_del.py @@ -1,28 +1,10 @@ import numpy as np import pytest -from pydantic import Field -from docarray import BaseDocument # , DocumentArray - -# from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex -from docarray.typing import NdArray - - -class SimpleDoc(BaseDocument): - tens: NdArray[10] = Field(dims=1000) - - -class FlatDoc(BaseDocument): - tens_one: NdArray = Field(dims=10) - tens_two: NdArray = Field(dims=50) - - -class NestedDoc(BaseDocument): - d: SimpleDoc - - -class DeepNestedDoc(BaseDocument): - d: NestedDoc +from docarray import DocumentArray +from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex +from tests.doc_index.elastic.fixture import start_storage # noqa: F401 +from tests.doc_index.elastic.fixture import DeepNestedDoc, FlatDoc, NestedDoc, SimpleDoc @pytest.fixture @@ -43,190 +25,208 @@ 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): -# store = ElasticDocumentIndex[SimpleDoc]() -# if use_docarray: -# ten_simple_docs = DocumentArray[SimpleDoc](ten_simple_docs) - -# store.index(ten_simple_docs) -# assert store.num_docs() == 10 - - -# @pytest.mark.parametrize('use_docarray', [True, False]) -# def test_index_flat_schema(ten_flat_docs, use_docarray): -# store = ElasticDocumentIndex[FlatDoc]() -# if use_docarray: -# ten_flat_docs = DocumentArray[FlatDoc](ten_flat_docs) +@pytest.fixture +def ten_deep_nested_docs(): + return [ + DeepNestedDoc(d=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): + store = ElasticDocumentIndex[SimpleDoc]() + if use_docarray: + ten_simple_docs = DocumentArray[SimpleDoc](ten_simple_docs) + + store.index(ten_simple_docs) + assert store.num_docs() == 10 + + +@pytest.mark.parametrize('use_docarray', [True, False]) +def test_index_flat_schema(ten_flat_docs, use_docarray): + store = ElasticDocumentIndex[FlatDoc]() + if use_docarray: + ten_flat_docs = DocumentArray[FlatDoc](ten_flat_docs) + + store.index(ten_flat_docs) + assert store.num_docs() == 10 + + +@pytest.mark.parametrize('use_docarray', [True, False]) +def test_index_nested_schema(ten_nested_docs, use_docarray): + store = ElasticDocumentIndex[NestedDoc]() + if use_docarray: + ten_nested_docs = DocumentArray[NestedDoc](ten_nested_docs) + + store.index(ten_nested_docs) + assert store.num_docs() == 10 -# store.index(ten_flat_docs) -# assert store.num_docs() == 10 - - -# @pytest.mark.parametrize('use_docarray', [True, False]) -# def test_index_nested_schema(ten_nested_docs, use_docarray): -# store = ElasticDocumentIndex[NestedDoc]() -# if use_docarray: -# ten_nested_docs = DocumentArray[NestedDoc](ten_nested_docs) - -# store.index(ten_nested_docs) -# assert store.num_docs() == 10 - - -# def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): -# # simple -# store = ElasticDocumentIndex[SimpleDoc]() -# store.index(ten_simple_docs) - -# assert store.num_docs() == 10 -# for d in ten_simple_docs: -# id_ = d.id -# assert store[id_].id == id_ -# assert np.all(store[id_].tens == d.tens) - -# # flat -# store = ElasticDocumentIndex[FlatDoc]() -# store.index(ten_flat_docs) - -# assert store.num_docs() == 10 -# for d in ten_flat_docs: -# id_ = d.id -# assert store[id_].id == id_ -# assert np.all(store[id_].tens_one == d.tens_one) -# assert np.all(store[id_].tens_two == d.tens_two) - -# # nested -# store = ElasticDocumentIndex[NestedDoc]() -# store.index(ten_nested_docs) - -# assert store.num_docs() == 10 -# for d in ten_nested_docs: -# id_ = d.id -# assert store[id_].id == id_ -# assert store[id_].d.id == d.d.id -# assert np.all(store[id_].d.tens == d.d.tens) - - -# def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): -# docs_to_get_idx = [0, 2, 4, 6, 8] - -# # simple -# store = ElasticDocumentIndex[SimpleDoc]() -# store.index(ten_simple_docs) - -# assert store.num_docs() == 10 -# docs_to_get = [ten_simple_docs[i] for i in docs_to_get_idx] -# ids_to_get = [d.id for d in docs_to_get] -# retrieved_docs = store[ids_to_get] -# for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): -# assert d_out.id == id_ -# assert np.all(d_out.tens == d_in.tens) - -# # flat -# store = ElasticDocumentIndex[FlatDoc]() -# store.index(ten_flat_docs) - -# assert store.num_docs() == 10 -# docs_to_get = [ten_flat_docs[i] for i in docs_to_get_idx] -# ids_to_get = [d.id for d in docs_to_get] -# retrieved_docs = store[ids_to_get] -# for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): -# assert d_out.id == id_ -# assert np.all(d_out.tens_one == d_in.tens_one) -# assert np.all(d_out.tens_two == d_in.tens_two) - -# # nested -# store = ElasticDocumentIndex[NestedDoc]() -# store.index(ten_nested_docs) - -# assert store.num_docs() == 10 -# docs_to_get = [ten_nested_docs[i] for i in docs_to_get_idx] -# ids_to_get = [d.id for d in docs_to_get] -# retrieved_docs = store[ids_to_get] -# for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): -# assert d_out.id == id_ -# assert d_out.d.id == d_in.d.id -# assert np.all(d_out.d.tens == d_in.d.tens) - - -# def test_get_key_error(ten_simple_docs): -# store = ElasticDocumentIndex[SimpleDoc]() -# store.index(ten_simple_docs) - -# with pytest.raises(KeyError): -# store['not_a_real_id'] - - -# def test_del_single(ten_simple_docs): -# store = ElasticDocumentIndex[SimpleDoc]() -# store.index(ten_simple_docs) -# # delete once -# assert store.num_docs() == 10 -# del store[ten_simple_docs[0].id] -# assert store.num_docs() == 9 -# for i, d in enumerate(ten_simple_docs): -# id_ = d.id -# if i == 0: # deleted -# with pytest.raises(KeyError): -# store[id_] -# else: -# assert store[id_].id == id_ -# assert np.all(store[id_].tens == d.tens) -# # delete again -# del store[ten_simple_docs[3].id] -# assert store.num_docs() == 8 -# for i, d in enumerate(ten_simple_docs): -# id_ = d.id -# if i in (0, 3): # deleted -# with pytest.raises(KeyError): -# store[id_] -# else: -# assert store[id_].id == id_ -# assert np.all(store[id_].tens == d.tens) - - -# def test_del_multiple(ten_simple_docs): -# docs_to_del_idx = [0, 2, 4, 6, 8] - -# store = ElasticDocumentIndex[SimpleDoc]() -# store.index(ten_simple_docs) - -# assert store.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 store[ids_to_del] -# for i, doc in enumerate(ten_simple_docs): -# if i in docs_to_del_idx: -# with pytest.raises(KeyError): -# store[doc.id] -# else: -# assert store[doc.id].id == doc.id -# assert np.all(store[doc.id].tens == doc.tens) - - -# def test_del_key_error(ten_simple_docs, ten_flat_docs, ten_nested_docs): -# store = ElasticDocumentIndex[SimpleDoc]() -# store.index(ten_simple_docs) - -# with pytest.warns(UserWarning): -# del store['not_a_real_id'] - - -# def test_num_docs(ten_simple_docs): -# store = ElasticDocumentIndex[SimpleDoc]() -# store.index(ten_simple_docs) - -# assert store.num_docs() == 10 - -# del store[ten_simple_docs[0].id] -# assert store.num_docs() == 9 - -# del store[ten_simple_docs[3].id, ten_simple_docs[5].id] -# assert store.num_docs() == 7 - -# more_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(5)] -# store.index(more_docs) -# assert store.num_docs() == 12 - -# del store[more_docs[2].id, ten_simple_docs[7].id] -# assert store.num_docs() == 10 + +@pytest.mark.parametrize('use_docarray', [True, False]) +def test_index_deep_nested_schema(ten_deep_nested_docs, use_docarray): + store = ElasticDocumentIndex[DeepNestedDoc]() + if use_docarray: + ten_deep_nested_docs = DocumentArray[DeepNestedDoc](ten_deep_nested_docs) + + store.index(ten_deep_nested_docs) + assert store.num_docs() == 10 + + +def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): + # simple + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + assert store.num_docs() == 10 + for d in ten_simple_docs: + id_ = d.id + assert store[id_].id == id_ + assert np.all(store[id_].tens == d.tens) + + # flat + store = ElasticDocumentIndex[FlatDoc]() + store.index(ten_flat_docs) + + assert store.num_docs() == 10 + for d in ten_flat_docs: + id_ = d.id + assert store[id_].id == id_ + assert np.all(store[id_].tens_one == d.tens_one) + assert np.all(store[id_].tens_two == d.tens_two) + + # nested + store = ElasticDocumentIndex[NestedDoc]() + store.index(ten_nested_docs) + + assert store.num_docs() == 10 + for d in ten_nested_docs: + id_ = d.id + assert store[id_].id == id_ + assert store[id_].d.id == d.d.id + assert np.all(store[id_].d.tens == d.d.tens) + + +def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): + docs_to_get_idx = [0, 2, 4, 6, 8] + + # simple + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + assert store.num_docs() == 10 + docs_to_get = [ten_simple_docs[i] for i in docs_to_get_idx] + ids_to_get = [d.id for d in docs_to_get] + retrieved_docs = store[ids_to_get] + for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): + assert d_out.id == id_ + assert np.all(d_out.tens == d_in.tens) + + # flat + store = ElasticDocumentIndex[FlatDoc]() + store.index(ten_flat_docs) + + assert store.num_docs() == 10 + docs_to_get = [ten_flat_docs[i] for i in docs_to_get_idx] + ids_to_get = [d.id for d in docs_to_get] + retrieved_docs = store[ids_to_get] + for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): + assert d_out.id == id_ + assert np.all(d_out.tens_one == d_in.tens_one) + assert np.all(d_out.tens_two == d_in.tens_two) + + # nested + store = ElasticDocumentIndex[NestedDoc]() + store.index(ten_nested_docs) + + assert store.num_docs() == 10 + docs_to_get = [ten_nested_docs[i] for i in docs_to_get_idx] + ids_to_get = [d.id for d in docs_to_get] + retrieved_docs = store[ids_to_get] + for id_, d_in, d_out in zip(ids_to_get, docs_to_get, retrieved_docs): + assert d_out.id == id_ + assert d_out.d.id == d_in.d.id + assert np.all(d_out.d.tens == d_in.d.tens) + + +def test_get_key_error(ten_simple_docs): + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + with pytest.raises(KeyError): + store['not_a_real_id'] + + +def test_del_single(ten_simple_docs): + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + # delete once + assert store.num_docs() == 10 + del store[ten_simple_docs[0].id] + assert store.num_docs() == 9 + for i, d in enumerate(ten_simple_docs): + id_ = d.id + if i == 0: # deleted + with pytest.raises(KeyError): + store[id_] + else: + assert store[id_].id == id_ + assert np.all(store[id_].tens == d.tens) + # delete again + del store[ten_simple_docs[3].id] + assert store.num_docs() == 8 + for i, d in enumerate(ten_simple_docs): + id_ = d.id + if i in (0, 3): # deleted + with pytest.raises(KeyError): + store[id_] + else: + assert store[id_].id == id_ + assert np.all(store[id_].tens == d.tens) + + +def test_del_multiple(ten_simple_docs): + docs_to_del_idx = [0, 2, 4, 6, 8] + + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + assert store.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 store[ids_to_del] + for i, doc in enumerate(ten_simple_docs): + if i in docs_to_del_idx: + with pytest.raises(KeyError): + store[doc.id] + else: + assert store[doc.id].id == doc.id + assert np.all(store[doc.id].tens == doc.tens) + + +def test_del_key_error(ten_simple_docs): + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + with pytest.warns(UserWarning): + del store['not_a_real_id'] + + +def test_num_docs(ten_simple_docs): + store = ElasticDocumentIndex[SimpleDoc]() + store.index(ten_simple_docs) + + assert store.num_docs() == 10 + + del store[ten_simple_docs[0].id] + assert store.num_docs() == 9 + + del store[ten_simple_docs[3].id, ten_simple_docs[5].id] + assert store.num_docs() == 7 + + more_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(5)] + store.index(more_docs) + assert store.num_docs() == 12 + + del store[more_docs[2].id, ten_simple_docs[7].id] + assert store.num_docs() == 10 From 6a161610088830786df8e8b70d2a45f2b8a19a69 Mon Sep 17 00:00:00 2001 From: AnneY Date: Sat, 11 Mar 2023 16:22:04 +0800 Subject: [PATCH 18/44] feat: add num_candidates to rumtime config Signed-off-by: AnneY --- docarray/doc_index/backends/elastic_doc_index.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 28b69eda93e..705e9d9ca80 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -112,6 +112,7 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): 'similarity': 'cosine', # 'l2_norm', 'dot_product', 'cosine' 'm': 16, 'ef_construction': 100, + 'num_candidates': 10000, }, docarray.typing.ID: {'type': 'keyword'}, bool: {'type': 'boolean'}, @@ -209,16 +210,14 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: ... - def _find( - self, query: np.ndarray, search_field: str, limit: int, **kwargs - ) -> _FindResult: + def _find(self, query: np.ndarray, search_field: str, limit: int) -> _FindResult: knn_query = { 'field': search_field, 'query_vector': query, 'k': limit, - 'num_candidates': 10000 - if 'num_candidates' not in kwargs - else kwargs['num_candidates'], + 'num_candidates': self._runtime_config.default_column_config[np.ndarray][ + 'num_candidates' + ], } resp = self._client.search( From 36cfce4057c20fab65428694218bf818d08c819a Mon Sep 17 00:00:00 2001 From: AnneY Date: Sat, 11 Mar 2023 16:54:07 +0800 Subject: [PATCH 19/44] fix: let user pass index_settings Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 31 ++++++++++--------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 705e9d9ca80..bb22224435a 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -36,8 +36,6 @@ TSchema = TypeVar('TSchema', bound=BaseDocument) T = TypeVar('T', bound='ElasticDocumentIndex') -MAX_ES_RETURNED_DOCS = 10000 - ELASTIC_PY_VEC_TYPES = [list, tuple, np.ndarray] ELASTIC_PY_TYPES = [bool, int, float, str, docarray.typing.ID] if torch_imported: @@ -85,6 +83,11 @@ def __init__(self, db_config=None, **kwargs): else: self._client.indices.create(index=self._index_name, mappings=mappings) + if len(self._db_config.index_settings): + self._client.indices.put_settings( + index=self._index_name, settings=self._db_config.index_settings + ) + self._refresh(self._index_name) ############################################### @@ -100,6 +103,7 @@ class DBConfig(BaseDocumentIndex.DBConfig): ] = 'http://localhost:9200' index_name: Optional[str] = None es_config: Dict[str, Any] = field(default_factory=dict) + index_settings: Dict[str, Any] = field(default_factory=dict) @dataclass class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): @@ -187,19 +191,17 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: accumulated_docs = [] accumulated_docs_id_not_found = [] - for pos in range(0, len(doc_ids), MAX_ES_RETURNED_DOCS): - - es_rows = self._client.mget( - index=self._index_name, - ids=doc_ids[pos : pos + MAX_ES_RETURNED_DOCS], # type: ignore - )['docs'] + es_rows = self._client.mget( + index=self._index_name, + ids=doc_ids, # type: ignore + )['docs'] - for row in es_rows: - if row['found']: - doc_dict = row['_source'] - accumulated_docs.append(doc_dict) - else: - accumulated_docs_id_not_found.append(row['_id']) + for row in es_rows: + if row['found']: + doc_dict = row['_source'] + accumulated_docs.append(doc_dict) + else: + accumulated_docs_id_not_found.append(row['_id']) # raise warning if some ids are not found if accumulated_docs_id_not_found: @@ -223,6 +225,7 @@ def _find(self, query: np.ndarray, search_field: str, limit: int) -> _FindResult resp = self._client.search( index=self._index_name, knn=knn_query, + size=limit, ) docs = [] From c5df1c03761acee63340d4774d36ba6ce9cd1185 Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 15 Mar 2023 14:14:35 +0800 Subject: [PATCH 20/44] feat: degrade to v7 and add query builder Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 179 ++++++++++++------ poetry.lock | 46 +++-- pyproject.toml | 3 +- tests/doc_index/elastic/fixture.py | 18 +- .../elastic/{ => v7}/docker-compose.yml | 2 +- tests/doc_index/elastic/{ => v7}/test_find.py | 140 +++++++++----- .../elastic/{ => v7}/test_index_get_del.py | 2 +- 7 files changed, 261 insertions(+), 129 deletions(-) rename tests/doc_index/elastic/{ => v7}/docker-compose.yml (80%) rename tests/doc_index/elastic/{ => v7}/test_find.py (61%) rename tests/doc_index/elastic/{ => v7}/test_index_get_del.py (98%) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index bb22224435a..adbbfc23104 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -22,6 +22,7 @@ from elastic_transport import NodeConfig from elasticsearch import Elasticsearch from elasticsearch.helpers import parallel_bulk +from elasticsearch_dsl import Search # type: ignore import docarray.typing from docarray import BaseDocument @@ -29,6 +30,7 @@ BaseDocumentIndex, _ColumnInfo, _FindResultBatched, + _raise_not_composable, ) from docarray.utils.find import _FindResult from docarray.utils.misc import torch_imported @@ -44,6 +46,16 @@ ELASTIC_PY_VEC_TYPES.append(torch.Tensor) +def _raise_has_substitue(name): + def _inner(self, *args, **kwargs): + raise NotImplementedError( + f'`{name}` is replaced by query() through the query builder of this Document Index ({type(self)}). ' + f'But you can call `{type(self)}.{name}()` directly.' + ) + + return _inner + + class ElasticDocumentIndex(BaseDocumentIndex, Generic[TSchema]): def __init__(self, db_config=None, **kwargs): super().__init__(db_config=db_config, **kwargs) @@ -62,30 +74,31 @@ def __init__(self, db_config=None, **kwargs): # ElasticSearh index setup self._index_init_params = ('type',) - self._index_vector_params = ('dims', 'similarity', 'index') - self._index_vector_options = ('m', 'ef_construction') + self._index_vector_params = ('dims',) - mappings: Dict[str, Any] = { - 'dynamic': True, - '_source': {'enabled': 'true'}, - 'properties': {}, + body: Dict[str, Any] = { + 'mappings': { + 'dynamic': True, + '_source': {'enabled': 'true'}, + 'properties': {}, + } } for col_name, col in self._column_infos.items(): if not col.config: continue # do not create column index if no config is given - mappings['properties'][col_name] = self._create_index(col) + body['mappings']['properties'][col_name] = self._create_index(col) if self._client.indices.exists(index=self._index_name): # type: ignore self._client.indices.put_mapping( - index=self._index_name, properties=mappings['properties'] + index=self._index_name, body=body['mappings'] ) else: - self._client.indices.create(index=self._index_name, mappings=mappings) + self._client.indices.create(index=self._index_name, body=body) if len(self._db_config.index_settings): self._client.indices.put_settings( - index=self._index_name, settings=self._db_config.index_settings + index=self._index_name, body=self._db_config.index_settings ) self._refresh(self._index_name) @@ -93,11 +106,53 @@ def __init__(self, db_config=None, **kwargs): ############################################### # Inner classes for query builder and configs # ############################################### - # TODO add class QueryBuilder + + class QueryBuilder(BaseDocumentIndex.QueryBuilder): + def __init__(self, outer_instance, **kwargs): + super().__init__() + self._search = Search( + using=outer_instance._client, index=outer_instance._index_name, **kwargs + ) + + def build(self, *args, **kwargs) -> Any: + return self + + def query(self, *args, **kwargs): + self._search = self._search.query(*args, **kwargs) + return self + + def filter(self, *args, **kwargs): + self._search = self._search.filter(*args, **kwargs) + return self + + def exclude(self, *args, **kwargs): + self._search = self._search.exclude(*args, **kwargs) + return self + + def script_fields(self, **kwargs): + self._search = self._search.script_fields(**kwargs) + return self + + def sort(self, *args, **kwargs): + self._search = self._search.sort(*args, **kwargs) + return self + + # TODO: add more methods + + find = _raise_has_substitue('find') + text_search = _raise_has_substitue('text_search') + find_batched = _raise_not_composable('find_batched') + filter_batched = _raise_not_composable('find_batched') + text_search_batched = _raise_not_composable('text_search') + + def build_query(self, **kwargs) -> QueryBuilder: + """ + Build a query for this DocumentIndex. + """ + return self.QueryBuilder(self, **kwargs) # type: ignore @dataclass class DBConfig(BaseDocumentIndex.DBConfig): - hosts: Union[ str, List[Union[str, Mapping[str, Union[str, int]], NodeConfig]], None ] = 'http://localhost:9200' @@ -111,12 +166,7 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): default_factory=lambda: { np.ndarray: { 'type': 'dense_vector', - 'index': True, 'dims': 128, - 'similarity': 'cosine', # 'l2_norm', 'dot_product', 'cosine' - 'm': 16, - 'ef_construction': 100, - 'num_candidates': 10000, }, docarray.typing.ID: {'type': 'keyword'}, bool: {'type': 'boolean'}, @@ -153,7 +203,6 @@ def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): '_index': self._index_name, '_id': row['id'], } - # TODO change here when more types are supported for col_name, col in self._column_infos.items(): if not col.config: continue @@ -193,7 +242,7 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: es_rows = self._client.mget( index=self._index_name, - ids=doc_ids, # type: ignore + body={'ids': doc_ids}, # type: ignore )['docs'] for row in es_rows: @@ -209,32 +258,37 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: return accumulated_docs - def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any: - ... + def execute_query(self, query: QueryBuilder, *args, **kwargs) -> Any: + if args or kwargs: + raise ValueError( + f'args and kwargs not supported for `execute_query` on {type(self)}' + ) + + resp = query._search.execute() + docs, scores = self._format_response(resp) + + return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore def _find(self, query: np.ndarray, search_field: str, limit: int) -> _FindResult: - knn_query = { - 'field': search_field, - 'query_vector': query, - 'k': limit, - 'num_candidates': self._runtime_config.default_column_config[np.ndarray][ - 'num_candidates' - ], + body = { + 'size': limit, + 'query': { + 'script_score': { + 'query': {'match_all': {}}, + 'script': { + 'source': f"cosineSimilarity(params.query_vector, '{search_field}') + 1.0", + 'params': {'query_vector': query}, + }, + } + }, } resp = self._client.search( index=self._index_name, - knn=knn_query, - size=limit, + body=body, ) - docs = [] - scores = [] - for result in resp['hits']['hits']: - doc_dict = result['_source'] - doc_dict['id'] = result['_id'] - docs.append(doc_dict) - scores.append(result['_score']) + docs, scores = self._format_response(resp) return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore @@ -259,17 +313,17 @@ def _filter( filter_query: Any, limit: int, ) -> List[Dict]: + body = { + 'size': limit, + 'query': filter_query, + } + resp = self._client.search( index=self._index_name, - query=filter_query, - size=limit, + body=body, ) - docs = [] - for result in resp['hits']['hits'][:limit]: - doc_dict = result['_source'] - doc_dict['id'] = result['_id'] - docs.append(doc_dict) + docs, _ = self._format_response(resp) return docs @@ -289,6 +343,7 @@ def _text_search( search_field: str, limit: int, ) -> _FindResult: + search_query = { "bool": { "must": [ @@ -296,20 +351,17 @@ def _text_search( ], } } + body = { + 'size': limit, + 'query': search_query, + } resp = self._client.search( index=self._index_name, - query=search_query, - size=limit, + body=body, ) - docs = [] - scores = [] - for result in resp['hits']['hits']: - doc_dict = result['_source'] - doc_dict['id'] = result['_id'] - docs.append(doc_dict) - scores.append(result['_score']) + docs, scores = self._format_response(resp) return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore @@ -342,10 +394,6 @@ def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: index[k] = col.config[k] if col.n_dim: index['dims'] = col.n_dim - index['index_options'] = dict( - (k, col.config[k]) for k in self._index_vector_options - ) - index['index_options']['type'] = 'hnsw' return index def _send_requests( @@ -371,5 +419,22 @@ def _send_requests( return accumulated_info, warning_info + def _format_response(self, response: Any) -> Tuple[List[Dict], List[float]]: + docs = [] + scores = [] + for result in response['hits']['hits']: + if not isinstance(result, dict): + result = result.to_dict() + + if result.get('_source', None): + doc_dict = result['_source'] + else: + doc_dict = result['fields'] + doc_dict['id'] = result['_id'] + docs.append(doc_dict) + scores.append(result['_score']) + + return docs, scores + def _refresh(self, index_name: str): self._client.indices.refresh(index=index_name) diff --git a/poetry.lock b/poetry.lock index b90cf1a21c5..d4a1764238c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -253,34 +253,38 @@ optional = false python-versions = "*" [[package]] -name = "elastic-transport" -version = "8.4.0" -description = "Transport classes and utilities shared among Python Elastic client libraries" +name = "elasticsearch" +version = "7.10.1" +description = "Python client for Elasticsearch" category = "main" -optional = true -python-versions = ">=3.6" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4" [package.dependencies] certifi = "*" -urllib3 = ">=1.26.2,<2" +urllib3 = ">=1.21.1,<2" [package.extras] -develop = ["aiohttp", "mock", "pytest", "pytest-asyncio", "pytest-cov", "pytest-httpserver", "pytest-mock", "requests", "trustme"] +async = ["aiohttp (>=3,<4)"] +develop = ["black", "coverage", "jinja2", "mock", "pytest", "pytest-cov", "pyyaml", "requests (>=2.0.0,<3.0.0)", "sphinx (<1.7)", "sphinx-rtd-theme"] +docs = ["sphinx (<1.7)", "sphinx-rtd-theme"] +requests = ["requests (>=2.4.0,<3.0.0)"] [[package]] -name = "elasticsearch" -version = "8.6.2" +name = "elasticsearch-dsl" +version = "7.4.1" description = "Python client for Elasticsearch" category = "main" -optional = true -python-versions = ">=3.6, <4" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [package.dependencies] -elastic-transport = ">=8,<9" +elasticsearch = ">=7.0.0,<8.0.0" +python-dateutil = "*" +six = "*" [package.extras] -async = ["aiohttp (>=3,<4)"] -requests = ["requests (>=2.4.0,<3.0.0)"] +develop = ["coverage (<5.0.0)", "mock", "pytest (>=3.0.0)", "pytest-cov", "pytest-mock (<3.0.0)", "pytz", "sphinx", "sphinx-rtd-theme"] [[package]] name = "entrypoints" @@ -1766,7 +1770,7 @@ web = ["fastapi"] [metadata] lock-version = "1.1" python-versions = ">=3.7,<4" -content-hash = "762e0d498828bc3c3c82ee22ffefce7a5690cc542ac77af517856373ba63291d" +content-hash = "2c64e8e6bea549e5ccdde4b3fdcd212604c43bba5cf64f503261060f81f1ab0e" [metadata.files] anyio = [ @@ -2015,13 +2019,13 @@ distlib = [ {file = "distlib-0.3.6-py2.py3-none-any.whl", hash = "sha256:f35c4b692542ca110de7ef0bea44d73981caeb34ca0b9b6b2e6d7790dda8f80e"}, {file = "distlib-0.3.6.tar.gz", hash = "sha256:14bad2d9b04d3a36127ac97f30b12a19268f211063d8f8ee4f47108896e11b46"}, ] -elastic-transport = [ - {file = "elastic-transport-8.4.0.tar.gz", hash = "sha256:b9ad708ceb7fcdbc6b30a96f886609a109f042c0b9d9f2e44403b3133ba7ff10"}, - {file = "elastic_transport-8.4.0-py3-none-any.whl", hash = "sha256:19db271ab79c9f70f8c43f8f5b5111408781a6176b54ab2e54d713b6d9ceb815"}, -] elasticsearch = [ - {file = "elasticsearch-8.6.2-py3-none-any.whl", hash = "sha256:8ccbebd9a0f6f523c7db67bb54863dde8bdb93daae4ff97f7c814e0500a73e84"}, - {file = "elasticsearch-8.6.2.tar.gz", hash = "sha256:084458e84caa91e3ad807b68aa82c022e785bead853a3b125641a25e894a1d47"}, + {file = "elasticsearch-7.10.1-py2.py3-none-any.whl", hash = "sha256:4ebd34fd223b31c99d9f3b6b6236d3ac18b3046191a37231e8235b06ae7db955"}, + {file = "elasticsearch-7.10.1.tar.gz", hash = "sha256:a725dd923d349ca0652cf95d6ce23d952e2153740cf4ab6daf4a2d804feeed48"}, +] +elasticsearch-dsl = [ + {file = "elasticsearch-dsl-7.4.1.tar.gz", hash = "sha256:07ee9c87dc28cc3cae2daa19401e1e18a172174ad9e5ca67938f752e3902a1d5"}, + {file = "elasticsearch_dsl-7.4.1-py2.py3-none-any.whl", hash = "sha256:97f79239a252be7c4cce554c29e64695d7ef6a4828372316a5e5ff815e7a7498"}, ] entrypoints = [ {file = "entrypoints-0.4-py3-none-any.whl", hash = "sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f"}, diff --git a/pyproject.toml b/pyproject.toml index 07309b5ff4a..8d1f4672553 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,8 @@ hnswlib = {version = ">=0.6.2", optional = true } lz4 = {version= ">=1.0.0", optional = true} pydub = {version = "^0.25.1", optional = true } pandas = {version = ">=1.1.0", optional = true } -elasticsearch = {version = "^8.6.2", optional = true} +elasticsearch = "7.10.1" +elasticsearch-dsl = "^7.4.1" [tool.poetry.extras] common = ["protobuf", "lz4"] diff --git a/tests/doc_index/elastic/fixture.py b/tests/doc_index/elastic/fixture.py index b7f5b9c802a..3862b04f8d4 100644 --- a/tests/doc_index/elastic/fixture.py +++ b/tests/doc_index/elastic/fixture.py @@ -28,16 +28,26 @@ class DeepNestedDoc(BaseDocument): cur_dir = os.path.dirname(os.path.abspath(__file__)) -compose_yml = os.path.abspath(os.path.join(cur_dir, 'docker-compose.yml')) +compose_yml_v7 = os.path.abspath(os.path.join(cur_dir, 'v7/docker-compose.yml')) +compose_yml_v8 = os.path.abspath(os.path.join(cur_dir, 'v8/docker-compose.yml')) @pytest.fixture(scope='module', autouse=True) -def start_storage(): - os.system(f"docker-compose -f {compose_yml} up -d --remove-orphans") +def start_storage_v7(): + os.system(f"docker-compose -f {compose_yml_v7} up -d --remove-orphans") _wait_for_es() yield - os.system(f"docker-compose -f {compose_yml} down --remove-orphans") + os.system(f"docker-compose -f {compose_yml_v7} down --remove-orphans") + + +@pytest.fixture(scope='module', autouse=True) +def start_storage_v8(): + os.system(f"docker-compose -f {compose_yml_v8} up -d --remove-orphans") + _wait_for_es() + + yield + os.system(f"docker-compose -f {compose_yml_v8} down --remove-orphans") def _wait_for_es(): diff --git a/tests/doc_index/elastic/docker-compose.yml b/tests/doc_index/elastic/v7/docker-compose.yml similarity index 80% rename from tests/doc_index/elastic/docker-compose.yml rename to tests/doc_index/elastic/v7/docker-compose.yml index 70eedba34f5..f4dd8a49d0b 100644 --- a/tests/doc_index/elastic/docker-compose.yml +++ b/tests/doc_index/elastic/v7/docker-compose.yml @@ -1,7 +1,7 @@ version: "3.3" services: elastic: - image: docker.elastic.co/elasticsearch/elasticsearch:8.6.2 + image: docker.elastic.co/elasticsearch/elasticsearch:7.10.2 environment: - xpack.security.enabled=false - discovery.type=single-node diff --git a/tests/doc_index/elastic/test_find.py b/tests/doc_index/elastic/v7/test_find.py similarity index 61% rename from tests/doc_index/elastic/test_find.py rename to tests/doc_index/elastic/v7/test_find.py index 44b3b6a1b6a..3486641c3ee 100644 --- a/tests/doc_index/elastic/test_find.py +++ b/tests/doc_index/elastic/v7/test_find.py @@ -1,27 +1,20 @@ import numpy as np -import pytest from pydantic import Field from docarray import BaseDocument from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex from docarray.typing import NdArray -from tests.doc_index.elastic.fixture import start_storage # noqa: F401 +from tests.doc_index.elastic.fixture import start_storage_v7 # noqa: F401 from tests.doc_index.elastic.fixture import FlatDoc, SimpleDoc -@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) -def test_find_simple_schema(similarity): +def test_find_simple_schema(): class SimpleSchema(BaseDocument): - tens: NdArray[10] = Field(similarity=similarity) + tens: NdArray[10] store = ElasticDocumentIndex[SimpleSchema]() - index_docs = [] - for _ in range(10): - vec = np.random.rand(10) - if similarity == 'dot_product': - vec = vec / np.linalg.norm(vec) - index_docs.append(SimpleDoc(tens=vec)) + index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] store.index(index_docs) query = index_docs[-1] @@ -29,27 +22,24 @@ class SimpleSchema(BaseDocument): assert len(docs) == 5 assert len(scores) == 5 + print([doc.id for doc in docs]) + print(scores) + assert docs[0].id == index_docs[-1].id assert np.allclose(docs[0].tens, index_docs[-1].tens) -@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) -def test_find_flat_schema(similarity): +def test_find_flat_schema(): class FlatSchema(BaseDocument): - tens_one: NdArray = Field(dims=10, similarity=similarity) - tens_two: NdArray = Field(dims=50, similarity=similarity) + tens_one: NdArray = Field(dims=10) + tens_two: NdArray = Field(dims=50) store = ElasticDocumentIndex[FlatSchema]() - index_docs = [] - for _ in range(10): - vec_one = np.random.rand(10) - vec_two = np.random.rand(50) - if similarity == 'dot_product': - vec_one = vec_one / np.linalg.norm(vec_one) - vec_two = vec_two / np.linalg.norm(vec_two) - index_docs.append(FlatDoc(tens_one=vec_one, tens_two=vec_two)) - + index_docs = [ + FlatDoc(tens_one=np.random.rand(10), tens_two=np.random.rand(50)) + for _ in range(10) + ] store.index(index_docs) query = index_docs[-1] @@ -71,37 +61,27 @@ class FlatSchema(BaseDocument): assert np.allclose(docs[0].tens_two, index_docs[-1].tens_two) -@pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product']) -def test_find_nested_schema(similarity): +def test_find_nested_schema(): class SimpleDoc(BaseDocument): - tens: NdArray[10] = Field(similarity=similarity) + tens: NdArray[10] class NestedDoc(BaseDocument): d: SimpleDoc - tens: NdArray[10] = Field(similarity=similarity) + tens: NdArray[10] class DeepNestedDoc(BaseDocument): d: NestedDoc - tens: NdArray = Field(similarity=similarity, dims=10) + tens: NdArray = Field(dims=10) store = ElasticDocumentIndex[DeepNestedDoc]() - index_docs = [] - for _ in range(10): - vec_simple = np.random.rand(10) - vec_nested = np.random.rand(10) - vec_deep = np.random.rand(10) - if similarity == 'dot_product': - vec_simple = vec_simple / np.linalg.norm(vec_simple) - vec_nested = vec_nested / np.linalg.norm(vec_nested) - vec_deep = vec_deep / np.linalg.norm(vec_deep) - index_docs.append( - DeepNestedDoc( - d=NestedDoc(d=SimpleDoc(tens=vec_simple), tens=vec_nested), - tens=vec_deep, - ) + index_docs = [ + DeepNestedDoc( + d=NestedDoc(d=SimpleDoc(tens=np.random.rand(10)), tens=np.random.rand(10)), + tens=np.random.rand(10), ) - + for _ in range(10) + ] store.index(index_docs) query = index_docs[-1] @@ -211,3 +191,75 @@ class MyDoc(BaseDocument): assert len(score) > 0 for doc in da: assert doc.text.index(query) >= 0 + + +def test_query_builder(): + class MyDoc(BaseDocument): + tens: NdArray[10] = Field(dims=10) + num: int + text: str + + store = ElasticDocumentIndex[MyDoc]() + index_docs = [ + MyDoc( + id=f'{i}', tens=np.random.rand(10), num=int(i / 2), text=f'text {int(i/2)}' + ) + for i in range(10) + ] + store.index(index_docs) + + # build_query + q = store.build_query() + assert isinstance(q, store.QueryBuilder) + + # filter + q = store.build_query().filter('term', num=0).build() + docs, _ = store.execute_query(q) + assert [doc['id'] for doc in docs] == ['0', '1'] + + # exclude + q = store.build_query(extra={"size": 3}).exclude('term', num=0).build() + docs, _ = store.execute_query(q) + assert [doc['id'] for doc in docs] == ['2', '3', '4'] + + # script_fields + q = ( + store.build_query(extra={"size": 3}) + .script_fields(bigger_num="doc['num'].value + 10") + .build() + ) + docs, _ = store.execute_query(q) + assert [doc['bigger_num'][0] for doc in docs] == [10, 10, 11] + + # query (text search) + q = store.build_query().query('match', text='0').build() + docs, _ = store.execute_query(q) + assert [doc['id'] for doc in docs] == ['0', '1'] + + # query (find) + from elasticsearch_dsl import Q + + d = { + "script_score": { + "query": {"match_all": {}}, + "script": { + "source": "cosineSimilarity(params.query_vector, 'tens') + 1.0", + "params": {'query_vector': index_docs[-1].tens}, + }, + } + } + q = store.build_query().query(Q(d)).sort("_score").build() + docs, scores = store.execute_query(q) + assert docs[0]['id'] == index_docs[-1].id + assert np.allclose(docs[0]['tens'], index_docs[-1].tens) + + # combination + q = ( + store.build_query() + .query(Q(d)) + .exclude('term', num=5) + .filter(Q("term", num=0) | Q("term", num=1)) + .build() + ) + docs, _ = store.execute_query(q) + assert sorted([doc['id'] for doc in docs]) == ['0', '1', '2', '3'] diff --git a/tests/doc_index/elastic/test_index_get_del.py b/tests/doc_index/elastic/v7/test_index_get_del.py similarity index 98% rename from tests/doc_index/elastic/test_index_get_del.py rename to tests/doc_index/elastic/v7/test_index_get_del.py index 19e6b244f7f..b141f547233 100644 --- a/tests/doc_index/elastic/test_index_get_del.py +++ b/tests/doc_index/elastic/v7/test_index_get_del.py @@ -3,7 +3,7 @@ from docarray import DocumentArray from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex -from tests.doc_index.elastic.fixture import start_storage # noqa: F401 +from tests.doc_index.elastic.fixture import start_storage_v7 # noqa: F401 from tests.doc_index.elastic.fixture import DeepNestedDoc, FlatDoc, NestedDoc, SimpleDoc From 389b082a7ad133992b228f825bd1c3b7ea6c9574 Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 15 Mar 2023 14:25:01 +0800 Subject: [PATCH 21/44] fix: remove elastic_transport Signed-off-by: AnneY --- docarray/doc_index/backends/elastic_doc_index.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index adbbfc23104..d4146507b78 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -8,7 +8,6 @@ Generic, Iterable, List, - Mapping, Optional, Sequence, Tuple, @@ -19,7 +18,6 @@ ) import numpy as np -from elastic_transport import NodeConfig from elasticsearch import Elasticsearch from elasticsearch.helpers import parallel_bulk from elasticsearch_dsl import Search # type: ignore @@ -153,9 +151,7 @@ def build_query(self, **kwargs) -> QueryBuilder: @dataclass class DBConfig(BaseDocumentIndex.DBConfig): - hosts: Union[ - str, List[Union[str, Mapping[str, Union[str, int]], NodeConfig]], None - ] = 'http://localhost:9200' + hosts: Union[str, List[str], None] = 'http://localhost:9200' index_name: Optional[str] = None es_config: Dict[str, Any] = field(default_factory=dict) index_settings: Dict[str, Any] = field(default_factory=dict) From bffcd57a69af5fee573e86bb22548ac0dc6cf86d Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 15 Mar 2023 21:03:45 +0800 Subject: [PATCH 22/44] feat: minor features Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 30 ++++++++++++++----- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index d4146507b78..1a5a6188a8f 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -1,3 +1,4 @@ +import os import uuid import warnings from dataclasses import dataclass, field @@ -48,7 +49,7 @@ def _raise_has_substitue(name): def _inner(self, *args, **kwargs): raise NotImplementedError( f'`{name}` is replaced by query() through the query builder of this Document Index ({type(self)}). ' - f'But you can call `{type(self)}.{name}()` directly.' + f'You can also call `{type(self)}.{name}()` directly.' ) return _inner @@ -70,6 +71,11 @@ def __init__(self, db_config=None, **kwargs): **self._db_config.es_config, ) + # compatibility + self._server_version = self._client.info()['version']['number'] + if int(self._server_version.split('.')[0]) >= 8: + os.environ['ELASTIC_CLIENT_APIVERSIONING'] = '1' + # ElasticSearh index setup self._index_init_params = ('type',) self._index_vector_params = ('dims',) @@ -189,7 +195,11 @@ def python_type_to_db_type(self, python_type: Type) -> Any: raise ValueError(f'Unsupported column type for {type(self)}: {python_type}') - def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): + def _index( + self, + column_to_data: Dict[str, Generator[Any, None, None]], + refresh: bool = True, + ): data = self._transpose_col_value_dict(column_to_data) # type: ignore requests = [] @@ -211,7 +221,8 @@ def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]): for info in warning_info: warnings.warn(str(info)) - self._refresh(self._index_name) # TODO add runtime config for efficient refresh + if refresh: + self._refresh(self._index_name) def num_docs(self) -> int: return self._client.count(index=self._index_name)['count'] @@ -266,13 +277,18 @@ def execute_query(self, query: QueryBuilder, *args, **kwargs) -> Any: return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore def _find(self, query: np.ndarray, search_field: str, limit: int) -> _FindResult: + if int(self._server_version.split('.')[0]) >= 8: + warnings.warn( + 'You are using Elasticsearch 8.0+ and the current client is 7.10.1. HNSW based vector search is not supported and the find method has a default implementation.' + ) + body = { 'size': limit, 'query': { 'script_score': { 'query': {'match_all': {}}, 'script': { - 'source': f"cosineSimilarity(params.query_vector, '{search_field}') + 1.0", + 'source': f'cosineSimilarity(params.query_vector, \'{search_field}\') + 1.0', 'params': {'query_vector': query}, }, } @@ -341,9 +357,9 @@ def _text_search( ) -> _FindResult: search_query = { - "bool": { - "must": [ - {"match": {search_field: query}}, + 'bool': { + 'must': [ + {'match': {search_field: query}}, ], } } From 8622d00c7fc42a1411510964e3a02aa2e45976ea Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 17 Mar 2023 10:53:58 +0800 Subject: [PATCH 23/44] refactor: style fix Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 2 +- tests/doc_index/elastic/v7/test_find.py | 26 +++++++++---------- 2 files changed, 13 insertions(+), 15 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 1a5a6188a8f..eb6847c1d80 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -322,7 +322,7 @@ def _find_batched( def _filter( self, - filter_query: Any, + filter_query: Dict[str, Any], limit: int, ) -> List[Dict]: body = { diff --git a/tests/doc_index/elastic/v7/test_find.py b/tests/doc_index/elastic/v7/test_find.py index 3486641c3ee..63d73b4f716 100644 --- a/tests/doc_index/elastic/v7/test_find.py +++ b/tests/doc_index/elastic/v7/test_find.py @@ -201,9 +201,7 @@ class MyDoc(BaseDocument): store = ElasticDocumentIndex[MyDoc]() index_docs = [ - MyDoc( - id=f'{i}', tens=np.random.rand(10), num=int(i / 2), text=f'text {int(i/2)}' - ) + MyDoc(id=f'{i}', tens=np.ones(10) * i, num=int(i / 2), text=f'text {int(i/2)}') for i in range(10) ] store.index(index_docs) @@ -218,14 +216,14 @@ class MyDoc(BaseDocument): assert [doc['id'] for doc in docs] == ['0', '1'] # exclude - q = store.build_query(extra={"size": 3}).exclude('term', num=0).build() + q = store.build_query(extra={'size': 3}).exclude('term', num=0).build() docs, _ = store.execute_query(q) assert [doc['id'] for doc in docs] == ['2', '3', '4'] # script_fields q = ( - store.build_query(extra={"size": 3}) - .script_fields(bigger_num="doc['num'].value + 10") + store.build_query(extra={'size': 3}) + .script_fields(bigger_num='doc[\'num\'].value + 10') .build() ) docs, _ = store.execute_query(q) @@ -240,17 +238,17 @@ class MyDoc(BaseDocument): from elasticsearch_dsl import Q d = { - "script_score": { - "query": {"match_all": {}}, - "script": { - "source": "cosineSimilarity(params.query_vector, 'tens') + 1.0", - "params": {'query_vector': index_docs[-1].tens}, + 'script_score': { + 'query': {'match_all': {}}, + 'script': { + 'source': '1 / (1 + l2norm(params.query_vector, \'tens\'))', + 'params': {'query_vector': index_docs[-1].tens}, }, } } - q = store.build_query().query(Q(d)).sort("_score").build() + q = store.build_query(extra={'size': 5}).query(Q(d)).sort('_score').build() docs, scores = store.execute_query(q) - assert docs[0]['id'] == index_docs[-1].id + assert [doc['id'] for doc in docs] == ['9', '8', '7', '6', '5'] assert np.allclose(docs[0]['tens'], index_docs[-1].tens) # combination @@ -258,7 +256,7 @@ class MyDoc(BaseDocument): store.build_query() .query(Q(d)) .exclude('term', num=5) - .filter(Q("term", num=0) | Q("term", num=1)) + .filter(Q('term', num=0) | Q('term', num=1)) .build() ) docs, _ = store.execute_query(q) From 343caa336548d6ffdff539cf224cadadc7cf947c Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 17 Mar 2023 11:09:52 +0800 Subject: [PATCH 24/44] fix: fix mypy Signed-off-by: AnneY --- docarray/doc_index/backends/elastic_doc_index.py | 14 ++++++++------ docarray/doc_index/backends/hnswlib_doc_index.py | 2 +- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index eb6847c1d80..48542fc5acd 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -276,7 +276,9 @@ def execute_query(self, query: QueryBuilder, *args, **kwargs) -> Any: return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore - def _find(self, query: np.ndarray, search_field: str, limit: int) -> _FindResult: + def _find( + self, query: np.ndarray, limit: int, search_field: str = '' + ) -> _FindResult: if int(self._server_version.split('.')[0]) >= 8: warnings.warn( 'You are using Elasticsearch 8.0+ and the current client is 7.10.1. HNSW based vector search is not supported and the find method has a default implementation.' @@ -307,14 +309,14 @@ def _find(self, query: np.ndarray, search_field: str, limit: int) -> _FindResult def _find_batched( self, queries: np.ndarray, - search_field: str, limit: int, + search_field: str = '', ) -> _FindResultBatched: result_das = [] result_scores = [] for query in queries: - documents, scores = self._find(query, search_field, limit) + documents, scores = self._find(query, limit, search_field) result_das.append(documents) result_scores.append(scores) @@ -352,8 +354,8 @@ def _filter_batched( def _text_search( self, query: str, - search_field: str, limit: int, + search_field: str = '', ) -> _FindResult: search_query = { @@ -380,14 +382,14 @@ def _text_search( def _text_search_batched( self, queries: Sequence[str], - search_field: str, limit: int, + search_field: str = '', ) -> _FindResultBatched: result_das = [] result_scores = [] for query in queries: - documents, scores = self._text_search(query, search_field, limit) + documents, scores = self._text_search(query, limit, search_field) result_das.append(documents) result_scores.append(scores) diff --git a/docarray/doc_index/backends/hnswlib_doc_index.py b/docarray/doc_index/backends/hnswlib_doc_index.py index bf35f5dfae6..444cab3c9dd 100644 --- a/docarray/doc_index/backends/hnswlib_doc_index.py +++ b/docarray/doc_index/backends/hnswlib_doc_index.py @@ -231,7 +231,7 @@ def _find( ) -> _FindResult: query_batched = np.expand_dims(query, axis=0) docs, scores = self._find_batched( - query=query_batched, limit=limit, search_field=search_field + queries=query_batched, limit=limit, search_field=search_field ) return _FindResult(documents=docs[0], scores=scores[0]) From cf65798f99e3d1892b503d29fec51e389d87bb91 Mon Sep 17 00:00:00 2001 From: AnneY Date: Mon, 20 Mar 2023 14:30:52 +0800 Subject: [PATCH 25/44] feat: add chunk size to runtime config Signed-off-by: AnneY --- docarray/doc_index/backends/elastic_doc_index.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 48542fc5acd..879e5b57fc3 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -177,7 +177,8 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): str: {'type': 'text'}, # `None` is not a Type, but we allow it here anyway None: {}, # type: ignore - } + }, + chunk_size=500, ) ############################################### @@ -424,6 +425,7 @@ def _send_requests( request, raise_on_error=False, raise_on_exception=False, + chunk_size=self._runtime_config.chunk_size, **kwargs, ): if not success: From 81bbc570108e4dd3d0743a1074864cb3930f3bab Mon Sep 17 00:00:00 2001 From: AnneY Date: Mon, 20 Mar 2023 15:22:37 +0800 Subject: [PATCH 26/44] fix: chunk size Signed-off-by: AnneY --- docarray/doc_index/backends/elastic_doc_index.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 879e5b57fc3..d8e9421f517 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -177,9 +177,9 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): str: {'type': 'text'}, # `None` is not a Type, but we allow it here anyway None: {}, # type: ignore - }, - chunk_size=500, + } ) + chunk_size: int = 500 ############################################### # Implementation of abstract methods # @@ -416,8 +416,6 @@ def _send_requests( ) -> Tuple[List[Dict], List[Any]]: """Send bulk request to Elastic and gather the successful info""" - # TODO chunk_size - accumulated_info = [] warning_info = [] for success, info in parallel_bulk( @@ -425,7 +423,7 @@ def _send_requests( request, raise_on_error=False, raise_on_exception=False, - chunk_size=self._runtime_config.chunk_size, + chunk_size=self._runtime_config.chunk_size, # type: ignore **kwargs, ): if not success: From 0bbe52f6a88e314ba81fcef9b9a8bef97e9b1d5f Mon Sep 17 00:00:00 2001 From: AnneY Date: Tue, 21 Mar 2023 09:52:36 +0800 Subject: [PATCH 27/44] feat: add chunk_size to funcs Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 24 ++++++++++++------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index d8e9421f517..775cdd75c35 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -93,7 +93,7 @@ def __init__(self, db_config=None, **kwargs): continue # do not create column index if no config is given body['mappings']['properties'][col_name] = self._create_index(col) - if self._client.indices.exists(index=self._index_name): # type: ignore + if self._client.indices.exists(index=self._index_name): self._client.indices.put_mapping( index=self._index_name, body=body['mappings'] ) @@ -153,7 +153,7 @@ def build_query(self, **kwargs) -> QueryBuilder: """ Build a query for this DocumentIndex. """ - return self.QueryBuilder(self, **kwargs) # type: ignore + return self.QueryBuilder(self, **kwargs) @dataclass class DBConfig(BaseDocumentIndex.DBConfig): @@ -200,6 +200,7 @@ def _index( self, column_to_data: Dict[str, Generator[Any, None, None]], refresh: bool = True, + chunk_size: Optional[int] = None, ): data = self._transpose_col_value_dict(column_to_data) # type: ignore @@ -218,7 +219,7 @@ def _index( request[col_name] = row[col_name] requests.append(request) - _, warning_info = self._send_requests(requests) + _, warning_info = self._send_requests(requests, chunk_size) for info in warning_info: warnings.warn(str(info)) @@ -228,14 +229,18 @@ def _index( def num_docs(self) -> int: return self._client.count(index=self._index_name)['count'] - def _del_items(self, doc_ids: Sequence[str]): + def _del_items( + self, + doc_ids: Sequence[str], + chunk_size: Optional[int] = None, + ): requests = [] for _id in doc_ids: requests.append( {'_op_type': 'delete', '_index': self._index_name, '_id': _id} ) - _, warning_info = self._send_requests(requests) + _, warning_info = self._send_requests(requests, chunk_size) # raise warning if some ids are not found if warning_info: @@ -250,7 +255,7 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: es_rows = self._client.mget( index=self._index_name, - body={'ids': doc_ids}, # type: ignore + body={'ids': doc_ids}, )['docs'] for row in es_rows: @@ -412,7 +417,10 @@ def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: return index def _send_requests( - self, request: Iterable[Dict[str, Any]], **kwargs + self, + request: Iterable[Dict[str, Any]], + chunk_size: Optional[int] = None, + **kwargs, ) -> Tuple[List[Dict], List[Any]]: """Send bulk request to Elastic and gather the successful info""" @@ -423,7 +431,7 @@ def _send_requests( request, raise_on_error=False, raise_on_exception=False, - chunk_size=self._runtime_config.chunk_size, # type: ignore + chunk_size=chunk_size if chunk_size else self._runtime_config.chunk_size, # type: ignore **kwargs, ): if not success: From 5c79ef1173b3d3fef65864705784a3924c9ad2ae Mon Sep 17 00:00:00 2001 From: AnneY Date: Tue, 21 Mar 2023 11:04:05 +0800 Subject: [PATCH 28/44] feat: rewrite elastic v7 query builder Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 86 +++++++++++-------- poetry.lock | 22 +---- pyproject.toml | 1 - tests/doc_index/elastic/v7/test_find.py | 86 ++++++++++--------- 4 files changed, 98 insertions(+), 97 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 775cdd75c35..319cc4ec61c 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -1,6 +1,7 @@ import os import uuid import warnings +from collections import defaultdict from dataclasses import dataclass, field from typing import ( Any, @@ -21,7 +22,6 @@ import numpy as np from elasticsearch import Elasticsearch from elasticsearch.helpers import parallel_bulk -from elasticsearch_dsl import Search # type: ignore import docarray.typing from docarray import BaseDocument @@ -31,6 +31,7 @@ _FindResultBatched, _raise_not_composable, ) +from docarray.typing import AnyTensor from docarray.utils.find import _FindResult from docarray.utils.misc import torch_imported @@ -45,16 +46,6 @@ ELASTIC_PY_VEC_TYPES.append(torch.Tensor) -def _raise_has_substitue(name): - def _inner(self, *args, **kwargs): - raise NotImplementedError( - f'`{name}` is replaced by query() through the query builder of this Document Index ({type(self)}). ' - f'You can also call `{type(self)}.{name}()` directly.' - ) - - return _inner - - class ElasticDocumentIndex(BaseDocumentIndex, Generic[TSchema]): def __init__(self, db_config=None, **kwargs): super().__init__(db_config=db_config, **kwargs) @@ -114,37 +105,60 @@ def __init__(self, db_config=None, **kwargs): class QueryBuilder(BaseDocumentIndex.QueryBuilder): def __init__(self, outer_instance, **kwargs): super().__init__() - self._search = Search( - using=outer_instance._client, index=outer_instance._index_name, **kwargs - ) + self._outer_instance = outer_instance + self._query: Dict[str, Any] = { + 'query': defaultdict(lambda: defaultdict(list)) + } def build(self, *args, **kwargs) -> Any: + if ( + 'script_score' in self._query['query'] + and 'bool' in self._query['query'] + and len(self._query['query']['bool']) > 0 + ): + self._query['query']['script_score']['query'] = {} + self._query['query']['script_score']['query']['bool'] = self._query[ + 'query' + ]['bool'] + del self._query['query']['bool'] + + return self._query + + def find( + self, + query: Union[AnyTensor, BaseDocument], + search_field: str = 'embedding', + limit: int = 10, + ): + if isinstance(query, BaseDocument): + query_vec = BaseDocumentIndex._get_values_by_column( + [query], search_field + )[0] + else: + query_vec = query + query_vec_np = BaseDocumentIndex._to_numpy(self._outer_instance, query_vec) + self._query['size'] = limit + self._query['query']['script_score'] = { + 'query': {'match_all': {}}, + 'script': { + 'source': f'cosineSimilarity(params.query_vector, \'{search_field}\') + 1.0', + 'params': {'query_vector': query_vec_np}, + }, + } return self - def query(self, *args, **kwargs): - self._search = self._search.query(*args, **kwargs) - return self - - def filter(self, *args, **kwargs): - self._search = self._search.filter(*args, **kwargs) - return self - - def exclude(self, *args, **kwargs): - self._search = self._search.exclude(*args, **kwargs) - return self - - def script_fields(self, **kwargs): - self._search = self._search.script_fields(**kwargs) + def filter(self, query: Dict[str, Any], limit: int = 10): + self._query['size'] = limit + self._query['query']['bool']['filter'].append(query) return self - def sort(self, *args, **kwargs): - self._search = self._search.sort(*args, **kwargs) + def text_search(self, query: str, search_field: str = 'text', limit: int = 10): + self._query['size'] = limit + self._query['query']['bool']['must'].append( + {'match': {search_field: query}} + ) return self - # TODO: add more methods - - find = _raise_has_substitue('find') - text_search = _raise_has_substitue('text_search') find_batched = _raise_not_composable('find_batched') filter_batched = _raise_not_composable('find_batched') text_search_batched = _raise_not_composable('text_search') @@ -271,13 +285,13 @@ def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]: return accumulated_docs - def execute_query(self, query: QueryBuilder, *args, **kwargs) -> Any: + def execute_query(self, query: Dict[str, Any], *args, **kwargs) -> Any: if args or kwargs: raise ValueError( f'args and kwargs not supported for `execute_query` on {type(self)}' ) - resp = query._search.execute() + resp = self._client.search(index=self._index_name, body=query) docs, scores = self._format_response(resp) return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore diff --git a/poetry.lock b/poetry.lock index d4a1764238c..7e306763456 100644 --- a/poetry.lock +++ b/poetry.lock @@ -270,22 +270,6 @@ develop = ["black", "coverage", "jinja2", "mock", "pytest", "pytest-cov", "pyyam docs = ["sphinx (<1.7)", "sphinx-rtd-theme"] requests = ["requests (>=2.4.0,<3.0.0)"] -[[package]] -name = "elasticsearch-dsl" -version = "7.4.1" -description = "Python client for Elasticsearch" -category = "main" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" - -[package.dependencies] -elasticsearch = ">=7.0.0,<8.0.0" -python-dateutil = "*" -six = "*" - -[package.extras] -develop = ["coverage (<5.0.0)", "mock", "pytest (>=3.0.0)", "pytest-cov", "pytest-mock (<3.0.0)", "pytz", "sphinx", "sphinx-rtd-theme"] - [[package]] name = "entrypoints" version = "0.4" @@ -1770,7 +1754,7 @@ web = ["fastapi"] [metadata] lock-version = "1.1" python-versions = ">=3.7,<4" -content-hash = "2c64e8e6bea549e5ccdde4b3fdcd212604c43bba5cf64f503261060f81f1ab0e" +content-hash = "3bfd6b61a807faba5e9590871dfb62831d58bd2d3bb1a13bff5ba109250dcbb8" [metadata.files] anyio = [ @@ -2023,10 +2007,6 @@ elasticsearch = [ {file = "elasticsearch-7.10.1-py2.py3-none-any.whl", hash = "sha256:4ebd34fd223b31c99d9f3b6b6236d3ac18b3046191a37231e8235b06ae7db955"}, {file = "elasticsearch-7.10.1.tar.gz", hash = "sha256:a725dd923d349ca0652cf95d6ce23d952e2153740cf4ab6daf4a2d804feeed48"}, ] -elasticsearch-dsl = [ - {file = "elasticsearch-dsl-7.4.1.tar.gz", hash = "sha256:07ee9c87dc28cc3cae2daa19401e1e18a172174ad9e5ca67938f752e3902a1d5"}, - {file = "elasticsearch_dsl-7.4.1-py2.py3-none-any.whl", hash = "sha256:97f79239a252be7c4cce554c29e64695d7ef6a4828372316a5e5ff815e7a7498"}, -] entrypoints = [ {file = "entrypoints-0.4-py3-none-any.whl", hash = "sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f"}, {file = "entrypoints-0.4.tar.gz", hash = "sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4"}, diff --git a/pyproject.toml b/pyproject.toml index 8d1f4672553..ce5c3a62467 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,6 @@ lz4 = {version= ">=1.0.0", optional = true} pydub = {version = "^0.25.1", optional = true } pandas = {version = ">=1.1.0", optional = true } elasticsearch = "7.10.1" -elasticsearch-dsl = "^7.4.1" [tool.poetry.extras] common = ["protobuf", "lz4"] diff --git a/tests/doc_index/elastic/v7/test_find.py b/tests/doc_index/elastic/v7/test_find.py index 63d73b4f716..ad00c68ac90 100644 --- a/tests/doc_index/elastic/v7/test_find.py +++ b/tests/doc_index/elastic/v7/test_find.py @@ -195,13 +195,15 @@ class MyDoc(BaseDocument): def test_query_builder(): class MyDoc(BaseDocument): - tens: NdArray[10] = Field(dims=10) + tens: NdArray[10] num: int text: str - store = ElasticDocumentIndex[MyDoc]() + store = ElasticDocumentIndex[MyDoc](index_name='tmp') index_docs = [ - MyDoc(id=f'{i}', tens=np.ones(10) * i, num=int(i / 2), text=f'text {int(i/2)}') + MyDoc( + id=f'{i}', tens=np.random.rand(10), num=int(i / 2), text=f'text {int(i/2)}' + ) for i in range(10) ] store.index(index_docs) @@ -211,53 +213,59 @@ class MyDoc(BaseDocument): assert isinstance(q, store.QueryBuilder) # filter - q = store.build_query().filter('term', num=0).build() + q = store.build_query().filter({'term': {'num': 0}}).build() docs, _ = store.execute_query(q) assert [doc['id'] for doc in docs] == ['0', '1'] - # exclude - q = store.build_query(extra={'size': 3}).exclude('term', num=0).build() + # find + q = store.build_query().find(index_docs[-1], search_field='tens', limit=3).build() + docs, scores = store.execute_query(q) + assert len(docs) == 3 + assert len(scores) == 3 + assert docs[0]['id'] == index_docs[-1].id + assert np.allclose(docs[0]['tens'], index_docs[-1].tens) + + # text search + q = store.build_query().text_search('0', search_field='text').build() docs, _ = store.execute_query(q) - assert [doc['id'] for doc in docs] == ['2', '3', '4'] + assert [doc['id'] for doc in docs] == ['0', '1'] - # script_fields + # combination q = ( - store.build_query(extra={'size': 3}) - .script_fields(bigger_num='doc[\'num\'].value + 10') + store.build_query() + .filter({'range': {'num': {'lte': 3}}}) + .find(index_docs[-1], search_field='tens') + .text_search('0', search_field='text') .build() ) docs, _ = store.execute_query(q) - assert [doc['bigger_num'][0] for doc in docs] == [10, 10, 11] - - # query (text search) - q = store.build_query().query('match', text='0').build() - docs, _ = store.execute_query(q) - assert [doc['id'] for doc in docs] == ['0', '1'] + assert sorted([doc['id'] for doc in docs]) == ['0', '1'] - # query (find) - from elasticsearch_dsl import Q + # direct + index_docs = [ + MyDoc(id=f'{i}', tens=np.ones(10) * i, num=int(i / 2), text=f'text {int(i/2)}') + for i in range(10) + ] + store.index(index_docs) - d = { - 'script_score': { - 'query': {'match_all': {}}, - 'script': { - 'source': '1 / (1 + l2norm(params.query_vector, \'tens\'))', - 'params': {'query_vector': index_docs[-1].tens}, - }, + query = { + 'query': { + 'script_score': { + 'query': { + 'bool': { + 'filter': [ + {'range': {'num': {'gte': 2}}}, + {'range': {'num': {'lte': 3}}}, + ], + }, + }, + 'script': { + 'source': '1 / (1 + l2norm(params.query_vector, \'tens\'))', + 'params': {'query_vector': index_docs[-1].tens}, + }, + } } } - q = store.build_query(extra={'size': 5}).query(Q(d)).sort('_score').build() - docs, scores = store.execute_query(q) - assert [doc['id'] for doc in docs] == ['9', '8', '7', '6', '5'] - assert np.allclose(docs[0]['tens'], index_docs[-1].tens) - # combination - q = ( - store.build_query() - .query(Q(d)) - .exclude('term', num=5) - .filter(Q('term', num=0) | Q('term', num=1)) - .build() - ) - docs, _ = store.execute_query(q) - assert sorted([doc['id'] for doc in docs]) == ['0', '1', '2', '3'] + docs, _ = store.execute_query(query) + assert [doc['id'] for doc in docs] == ['7', '6', '5', '4'] From 0f5873d36a731fd4cbae8e9ef8b17e33da92b2b8 Mon Sep 17 00:00:00 2001 From: AnneY Date: Tue, 21 Mar 2023 12:12:49 +0800 Subject: [PATCH 29/44] fix: poetry Signed-off-by: AnneY --- poetry.lock | 2032 +++++++++++++++++++++++++++++++-------------------- 1 file changed, 1237 insertions(+), 795 deletions(-) diff --git a/poetry.lock b/poetry.lock index b5978c87d8e..c0ee094ea95 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,31 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand. + +[[package]] +name = "aiofiles" +version = "22.1.0" +description = "File support for asyncio." +category = "dev" +optional = false +python-versions = ">=3.7,<4.0" +files = [ + {file = "aiofiles-22.1.0-py3-none-any.whl", hash = "sha256:1142fa8e80dbae46bb6339573ad4c8c0841358f79c6eb50a493dceca14621bad"}, + {file = "aiofiles-22.1.0.tar.gz", hash = "sha256:9107f1ca0b2a5553987a94a3c9959fe5b491fdf731389aa5b7b1bd0733e32de6"}, +] + +[[package]] +name = "aiosqlite" +version = "0.18.0" +description = "asyncio bridge to the standard sqlite3 module" +category = "dev" +optional = false +python-versions = ">=3.7" +files = [ + {file = "aiosqlite-0.18.0-py3-none-any.whl", hash = "sha256:c3511b841e3a2c5614900ba1d179f366826857586f78abd75e7cbeb88e75a557"}, + {file = "aiosqlite-0.18.0.tar.gz", hash = "sha256:faa843ef5fb08bafe9a9b3859012d3d9d6f77ce3637899de20606b7fc39aa213"}, +] + +[package.dependencies] +typing_extensions = {version = ">=4.0", markers = "python_version < \"3.8\""} [[package]] name = "anyio" @@ -93,23 +120,40 @@ 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" +category = "dev" +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" +typing-extensions = {version = "*", markers = "python_version < \"3.8\""} + [[package]] name = "attrs" -version = "22.1.0" +version = "22.2.0" description = "Classes Without Boilerplate" category = "dev" optional = false -python-versions = ">=3.5" +python-versions = ">=3.6" files = [ - 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(>=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)"] [extras] audio = ["pydub"] -common = ["protobuf", "lz4"] +common = ["lz4", "protobuf"] elasticsearch = ["elasticsearch"] hnswlib = ["hnswlib"] image = ["pillow", "types-pillow"] @@ -3334,5 +3776,5 @@ web = ["fastapi"] [metadata] lock-version = "2.0" -python-versions = ">=3.7" -content-hash = "60dc7dedebd775c6fe3f45ddd2869a07df2c28bbc83420e875eb61e118b064b2" +python-versions = ">=3.7,<4" +content-hash = "be22e90913da070a566fe43ab37e7e4d4facec7682e7cc5d9c881d1cde70e29d" From 2e850c0c01fcc74c5790b4ba2e99b67a62088fc0 Mon Sep 17 00:00:00 2001 From: AnneY Date: Tue, 21 Mar 2023 22:51:19 +0800 Subject: [PATCH 30/44] fix: db_type should be elastic types Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 52 +++++++++++-------- 1 file changed, 30 insertions(+), 22 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 319cc4ec61c..0819f6e6d33 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -38,8 +38,10 @@ TSchema = TypeVar('TSchema', bound=BaseDocument) T = TypeVar('T', bound='ElasticDocumentIndex') -ELASTIC_PY_VEC_TYPES = [list, tuple, np.ndarray] -ELASTIC_PY_TYPES = [bool, int, float, str, docarray.typing.ID] +ELASTIC_PY_VEC_TYPES: List[Any] = [ + np.ndarray, +] + if torch_imported: import torch @@ -68,7 +70,6 @@ def __init__(self, db_config=None, **kwargs): os.environ['ELASTIC_CLIENT_APIVERSIONING'] = '1' # ElasticSearh index setup - self._index_init_params = ('type',) self._index_vector_params = ('dims',) body: Dict[str, Any] = { @@ -80,8 +81,8 @@ def __init__(self, db_config=None, **kwargs): } for col_name, col in self._column_infos.items(): - if not col.config: - continue # do not create column index if no config is given + # if not col.config: + # continue # do not create column index if no config is given body['mappings']['properties'][col_name] = self._create_index(col) if self._client.indices.exists(index=self._index_name): @@ -178,17 +179,14 @@ class DBConfig(BaseDocumentIndex.DBConfig): @dataclass class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): - default_column_config: Dict[Type, Dict[str, Any]] = field( + default_column_config: Dict[Any, Dict[str, Any]] = field( default_factory=lambda: { - np.ndarray: { - 'type': 'dense_vector', - 'dims': 128, - }, - docarray.typing.ID: {'type': 'keyword'}, - bool: {'type': 'boolean'}, - int: {'type': 'integer'}, - float: {'type': 'float'}, - str: {'type': 'text'}, + 'dense_vector': {'dims': 128}, + 'keyword': {}, + 'boolean': {}, + 'integer': {}, + 'float': {}, + 'text': {}, # `None` is not a Type, but we allow it here anyway None: {}, # type: ignore } @@ -201,12 +199,21 @@ class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): def python_type_to_db_type(self, python_type: Type) -> Any: """Map python type to database type.""" + for allowed_type in ELASTIC_PY_VEC_TYPES: if issubclass(python_type, allowed_type): - return np.ndarray + return 'dense_vector' + + elastic_py_types = { + bool: 'boolean', + int: 'integer', + float: 'float', + str: 'text', + docarray.typing.ID: 'keyword', + } - if python_type in ELASTIC_PY_TYPES: - return python_type + if python_type in elastic_py_types: + return elastic_py_types[python_type] raise ValueError(f'Unsupported column type for {type(self)}: {python_type}') @@ -226,8 +233,6 @@ def _index( '_id': row['id'], } for col_name, col in self._column_infos.items(): - if not col.config: - continue if col.db_type == np.ndarray and np.all(row[col_name] == 0): row[col_name] = row[col_name] + 1.0e-9 request[col_name] = row[col_name] @@ -422,12 +427,15 @@ def _text_search_batched( # ElasticSearch helpers def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: """Create a new HNSW index for a column, and initialize it.""" - index = dict((k, col.config[k]) for k in self._index_init_params) - if col.db_type == np.ndarray: + + index = {'type': col.config['type'] if 'type' in col.config else col.db_type} + + if col.db_type == 'dense_vector': for k in self._index_vector_params: index[k] = col.config[k] if col.n_dim: index['dims'] = col.n_dim + return index def _send_requests( From 956078946aa694a83278c0ef7b32da348b3de27b Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 22 Mar 2023 11:23:33 +0800 Subject: [PATCH 31/44] fix: minor adjustment Signed-off-by: AnneY --- .../doc_index/backends/elastic_doc_index.py | 4 +-- tests/doc_index/elastic/v7/test_find.py | 29 +++++++------------ 2 files changed, 12 insertions(+), 21 deletions(-) diff --git a/docarray/doc_index/backends/elastic_doc_index.py b/docarray/doc_index/backends/elastic_doc_index.py index 0819f6e6d33..f994e51ea1d 100644 --- a/docarray/doc_index/backends/elastic_doc_index.py +++ b/docarray/doc_index/backends/elastic_doc_index.py @@ -83,7 +83,7 @@ def __init__(self, db_config=None, **kwargs): for col_name, col in self._column_infos.items(): # if not col.config: # continue # do not create column index if no config is given - body['mappings']['properties'][col_name] = self._create_index(col) + body['mappings']['properties'][col_name] = self._create_index_mapping(col) if self._client.indices.exists(index=self._index_name): self._client.indices.put_mapping( @@ -425,7 +425,7 @@ def _text_search_batched( ############################################### # ElasticSearch helpers - def _create_index(self, col: '_ColumnInfo') -> Dict[str, Any]: + def _create_index_mapping(self, col: '_ColumnInfo') -> Dict[str, Any]: """Create a new HNSW index for a column, and initialize it.""" index = {'type': col.config['type'] if 'type' in col.config else col.db_type} diff --git a/tests/doc_index/elastic/v7/test_find.py b/tests/doc_index/elastic/v7/test_find.py index ad00c68ac90..2d1428e2494 100644 --- a/tests/doc_index/elastic/v7/test_find.py +++ b/tests/doc_index/elastic/v7/test_find.py @@ -127,8 +127,6 @@ def test_find_batched(): def test_filter(): - import itertools - class MyDoc(BaseDocument): A: bool B: int @@ -136,13 +134,7 @@ class MyDoc(BaseDocument): store = ElasticDocumentIndex[MyDoc]() - A_list = [True, False] - B_list = [1, 2] - C_list = [1.5, 2.5] - - # cross product of all possible combinations - combinations = itertools.product(A_list, B_list, C_list) - index_docs = [MyDoc(A=A, B=B, C=C) for A, B, C in combinations] + index_docs = [MyDoc(id=f'{i}', A=(i % 2 == 0), B=i, C=i + 0.5) for i in range(10)] store.index(index_docs) filter_query = {'term': {'A': True}} @@ -151,17 +143,16 @@ class MyDoc(BaseDocument): for doc in docs: assert doc.A - filter_query = {'term': {'B': 1}} - docs = store.filter(filter_query) - assert len(docs) > 0 - for doc in docs: - assert doc.B == 1 - - filter_query = {'term': {'C': 1.5}} + filter_query = { + "bool": { + "filter": [ + {"terms": {"B": [3, 4, 7, 8]}}, + {"range": {"C": {"gte": 3, "lte": 5}}}, + ] + } + } docs = store.filter(filter_query) - assert len(docs) > 0 - for doc in docs: - assert doc.C == 1.5 + assert [doc.id for doc in docs] == ['3', '4'] def test_text_search(): From 6beeb32ab252105d86dd302716f3f642f0fb4f16 Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 22 Mar 2023 21:39:48 +0800 Subject: [PATCH 32/44] refactor: rename elastic index files Signed-off-by: AnneY --- docarray/index/__init__.py | 3 ++- .../index/backends/{elastic_doc_index.py => elastic.py} | 2 +- tests/{doc_index => index}/elastic/fixture.py | 0 tests/{doc_index => index}/elastic/v7/docker-compose.yml | 0 tests/{doc_index => index}/elastic/v7/test_find.py | 6 +++--- tests/{doc_index => index}/elastic/v7/test_index_get_del.py | 6 +++--- 6 files changed, 9 insertions(+), 8 deletions(-) rename docarray/index/backends/{elastic_doc_index.py => elastic.py} (99%) rename tests/{doc_index => index}/elastic/fixture.py (100%) rename tests/{doc_index => index}/elastic/v7/docker-compose.yml (100%) rename tests/{doc_index => index}/elastic/v7/test_find.py (97%) rename tests/{doc_index => index}/elastic/v7/test_index_get_del.py (96%) diff --git a/docarray/index/__init__.py b/docarray/index/__init__.py index 5fdbf8ad736..c58c359576a 100644 --- a/docarray/index/__init__.py +++ b/docarray/index/__init__.py @@ -1,3 +1,4 @@ +from docarray.index.backends.elastic import ElasticDocumentIndex from docarray.index.backends.hnswlib import HnswDocumentIndex -__all__ = ['HnswDocumentIndex'] +__all__ = ['HnswDocumentIndex', 'ElasticDocumentIndex'] diff --git a/docarray/index/backends/elastic_doc_index.py b/docarray/index/backends/elastic.py similarity index 99% rename from docarray/index/backends/elastic_doc_index.py rename to docarray/index/backends/elastic.py index f994e51ea1d..ab255d414ae 100644 --- a/docarray/index/backends/elastic_doc_index.py +++ b/docarray/index/backends/elastic.py @@ -25,7 +25,7 @@ import docarray.typing from docarray import BaseDocument -from docarray.doc_index.abstract_doc_index import ( +from docarray.index.abstract import ( BaseDocumentIndex, _ColumnInfo, _FindResultBatched, diff --git a/tests/doc_index/elastic/fixture.py b/tests/index/elastic/fixture.py similarity index 100% rename from tests/doc_index/elastic/fixture.py rename to tests/index/elastic/fixture.py diff --git a/tests/doc_index/elastic/v7/docker-compose.yml b/tests/index/elastic/v7/docker-compose.yml similarity index 100% rename from tests/doc_index/elastic/v7/docker-compose.yml rename to tests/index/elastic/v7/docker-compose.yml diff --git a/tests/doc_index/elastic/v7/test_find.py b/tests/index/elastic/v7/test_find.py similarity index 97% rename from tests/doc_index/elastic/v7/test_find.py rename to tests/index/elastic/v7/test_find.py index 2d1428e2494..45c96e02d75 100644 --- a/tests/doc_index/elastic/v7/test_find.py +++ b/tests/index/elastic/v7/test_find.py @@ -2,10 +2,10 @@ from pydantic import Field from docarray import BaseDocument -from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex +from docarray.index import ElasticDocumentIndex from docarray.typing import NdArray -from tests.doc_index.elastic.fixture import start_storage_v7 # noqa: F401 -from tests.doc_index.elastic.fixture import FlatDoc, SimpleDoc +from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 +from tests.index.elastic.fixture import FlatDoc, SimpleDoc def test_find_simple_schema(): diff --git a/tests/doc_index/elastic/v7/test_index_get_del.py b/tests/index/elastic/v7/test_index_get_del.py similarity index 96% rename from tests/doc_index/elastic/v7/test_index_get_del.py rename to tests/index/elastic/v7/test_index_get_del.py index b141f547233..642c0affa05 100644 --- a/tests/doc_index/elastic/v7/test_index_get_del.py +++ b/tests/index/elastic/v7/test_index_get_del.py @@ -2,9 +2,9 @@ import pytest from docarray import DocumentArray -from docarray.doc_index.backends.elastic_doc_index import ElasticDocumentIndex -from tests.doc_index.elastic.fixture import start_storage_v7 # noqa: F401 -from tests.doc_index.elastic.fixture import DeepNestedDoc, FlatDoc, NestedDoc, SimpleDoc +from docarray.index import ElasticDocumentIndex +from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 +from tests.index.elastic.fixture import DeepNestedDoc, FlatDoc, NestedDoc, SimpleDoc @pytest.fixture From fad699bbc00bea6a8c92001373586a19750e77e8 Mon Sep 17 00:00:00 2001 From: AnneY Date: Thu, 23 Mar 2023 14:18:37 +0800 Subject: [PATCH 33/44] refactor: remove comments Signed-off-by: AnneY --- docarray/index/backends/elastic.py | 6 +----- tests/index/elastic/v7/test_find.py | 2 -- 2 files changed, 1 insertion(+), 7 deletions(-) diff --git a/docarray/index/backends/elastic.py b/docarray/index/backends/elastic.py index ab255d414ae..cfc2a433fb3 100644 --- a/docarray/index/backends/elastic.py +++ b/docarray/index/backends/elastic.py @@ -38,9 +38,7 @@ TSchema = TypeVar('TSchema', bound=BaseDocument) T = TypeVar('T', bound='ElasticDocumentIndex') -ELASTIC_PY_VEC_TYPES: List[Any] = [ - np.ndarray, -] +ELASTIC_PY_VEC_TYPES: List[Any] = [np.ndarray] if torch_imported: import torch @@ -81,8 +79,6 @@ def __init__(self, db_config=None, **kwargs): } for col_name, col in self._column_infos.items(): - # if not col.config: - # continue # do not create column index if no config is given body['mappings']['properties'][col_name] = self._create_index_mapping(col) if self._client.indices.exists(index=self._index_name): diff --git a/tests/index/elastic/v7/test_find.py b/tests/index/elastic/v7/test_find.py index 45c96e02d75..547a69340cb 100644 --- a/tests/index/elastic/v7/test_find.py +++ b/tests/index/elastic/v7/test_find.py @@ -22,8 +22,6 @@ class SimpleSchema(BaseDocument): assert len(docs) == 5 assert len(scores) == 5 - print([doc.id for doc in docs]) - print(scores) assert docs[0].id == index_docs[-1].id assert np.allclose(docs[0].tens, index_docs[-1].tens) From b3dd13b567c10e0d2c3bb9f2ba9f7719a666b721 Mon Sep 17 00:00:00 2001 From: AnneY Date: Thu, 23 Mar 2023 21:59:43 +0800 Subject: [PATCH 34/44] feat: rename, batch operations, etc Signed-off-by: AnneY --- docarray/index/__init__.py | 4 +- docarray/index/backends/elastic.py | 134 +++++++++++-------- tests/index/elastic/v7/test_find.py | 16 +-- tests/index/elastic/v7/test_index_get_del.py | 32 ++--- 4 files changed, 104 insertions(+), 82 deletions(-) diff --git a/docarray/index/__init__.py b/docarray/index/__init__.py index c58c359576a..48f18b5ceca 100644 --- a/docarray/index/__init__.py +++ b/docarray/index/__init__.py @@ -1,4 +1,4 @@ -from docarray.index.backends.elastic import ElasticDocumentIndex +from docarray.index.backends.elastic import ElasticDocIndex from docarray.index.backends.hnswlib import HnswDocumentIndex -__all__ = ['HnswDocumentIndex', 'ElasticDocumentIndex'] +__all__ = ['HnswDocumentIndex', 'ElasticDocIndex'] diff --git a/docarray/index/backends/elastic.py b/docarray/index/backends/elastic.py index cfc2a433fb3..7e3e4eaf57f 100644 --- a/docarray/index/backends/elastic.py +++ b/docarray/index/backends/elastic.py @@ -22,6 +22,7 @@ import numpy as np from elasticsearch import Elasticsearch from elasticsearch.helpers import parallel_bulk +from pydantic import parse_obj_as import docarray.typing from docarray import BaseDocument @@ -32,11 +33,12 @@ _raise_not_composable, ) from docarray.typing import AnyTensor +from docarray.typing.tensor.ndarray import NdArray from docarray.utils.find import _FindResult from docarray.utils.misc import torch_imported TSchema = TypeVar('TSchema', bound=BaseDocument) -T = TypeVar('T', bound='ElasticDocumentIndex') +T = TypeVar('T', bound='ElasticDocIndex') ELASTIC_PY_VEC_TYPES: List[Any] = [np.ndarray] @@ -46,10 +48,10 @@ ELASTIC_PY_VEC_TYPES.append(torch.Tensor) -class ElasticDocumentIndex(BaseDocumentIndex, Generic[TSchema]): +class ElasticDocIndex(BaseDocumentIndex, Generic[TSchema]): def __init__(self, db_config=None, **kwargs): super().__init__(db_config=db_config, **kwargs) - self._db_config = cast(ElasticDocumentIndex.DBConfig, self._db_config) + self._db_config = cast(ElasticDocIndex.DBConfig, self._db_config) if self._db_config.index_name is None: id = uuid.uuid4().hex @@ -135,13 +137,10 @@ def find( query_vec = query query_vec_np = BaseDocumentIndex._to_numpy(self._outer_instance, query_vec) self._query['size'] = limit - self._query['query']['script_score'] = { - 'query': {'match_all': {}}, - 'script': { - 'source': f'cosineSimilarity(params.query_vector, \'{search_field}\') + 1.0', - 'params': {'query_vector': query_vec_np}, - }, - } + self._query['query']['script_score'] = ElasticDocIndex._form_search_body( + query_vec_np, limit, search_field + )['query']['script_score'] + return self def filter(self, query: Dict[str, Any], limit: int = 10): @@ -295,28 +294,17 @@ def execute_query(self, query: Dict[str, Any], *args, **kwargs) -> Any: resp = self._client.search(index=self._index_name, body=query) docs, scores = self._format_response(resp) - return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore + return _FindResult(documents=docs, scores=scores) def _find( self, query: np.ndarray, limit: int, search_field: str = '' ) -> _FindResult: if int(self._server_version.split('.')[0]) >= 8: warnings.warn( - 'You are using Elasticsearch 8.0+ and the current client is 7.10.1. HNSW based vector search is not supported and the find method has a default implementation.' + 'You are using Elasticsearch 8.0+ and the current client is 7.10.1. HNSW based vector search is not supported and the find method has a default implementation using exhaustive KNN search with cosineSimilarity, which may result in slow performance.' ) - body = { - 'size': limit, - 'query': { - 'script_score': { - 'query': {'match_all': {}}, - 'script': { - 'source': f'cosineSimilarity(params.query_vector, \'{search_field}\') + 1.0', - 'params': {'query_vector': query}, - }, - } - }, - } + body = self._form_search_body(query, limit, search_field) resp = self._client.search( index=self._index_name, @@ -325,7 +313,7 @@ def _find( docs, scores = self._format_response(resp) - return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore + return _FindResult(documents=docs, scores=scores) def _find_batched( self, @@ -333,15 +321,18 @@ def _find_batched( limit: int, search_field: str = '', ) -> _FindResultBatched: - result_das = [] - result_scores = [] - + request = [] for query in queries: - documents, scores = self._find(query, limit, search_field) - result_das.append(documents) - result_scores.append(scores) + head = {'index': self._index_name} + body = self._form_search_body(query, limit, search_field) + request.extend([head, body]) - return _FindResultBatched(documents=result_das, scores=np.array(result_scores)) # type: ignore + responses = self._client.msearch(body=request) + + das, scores = zip( + *[self._format_response(resp) for resp in responses['responses']] + ) + return _FindResultBatched(documents=list(das), scores=np.array(scores)) def _filter( self, @@ -367,10 +358,16 @@ def _filter_batched( filter_queries: Any, limit: int, ) -> List[List[Dict]]: - result_das = [] + request = [] for query in filter_queries: - result_das.append(self._filter(query, limit)) - return result_das + head = {'index': self._index_name} + body = {'query': query, 'size': limit} + request.extend([head, body]) + + responses = self._client.msearch(body=request) + das, _ = zip(*[self._format_response(resp) for resp in responses['responses']]) + + return list(das) def _text_search( self, @@ -379,17 +376,7 @@ def _text_search( search_field: str = '', ) -> _FindResult: - search_query = { - 'bool': { - 'must': [ - {'match': {search_field: query}}, - ], - } - } - body = { - 'size': limit, - 'query': search_query, - } + body = self._form_text_search_body(query, limit, search_field) resp = self._client.search( index=self._index_name, @@ -398,7 +385,7 @@ def _text_search( docs, scores = self._format_response(resp) - return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore + return _FindResult(documents=docs, scores=scores) def _text_search_batched( self, @@ -406,15 +393,18 @@ def _text_search_batched( limit: int, search_field: str = '', ) -> _FindResultBatched: - result_das = [] - result_scores = [] - + request = [] for query in queries: - documents, scores = self._text_search(query, limit, search_field) - result_das.append(documents) - result_scores.append(scores) + head = {'index': self._index_name} + body = self._form_text_search_body(query, limit, search_field) + request.extend([head, body]) + + responses = self._client.msearch(body=request) - return _FindResultBatched(documents=result_das, scores=np.array(result_scores)) # type: ignore + das, scores = zip( + *[self._format_response(resp) for resp in responses['responses']] + ) + return _FindResultBatched(documents=list(das), scores=np.array(scores)) ############################################### # Helpers # @@ -459,7 +449,39 @@ def _send_requests( return accumulated_info, warning_info - def _format_response(self, response: Any) -> Tuple[List[Dict], List[float]]: + @staticmethod + def _form_search_body( + query: np.ndarray, limit: int, search_field: str = '' + ) -> Dict[str, Any]: + body = { + 'size': limit, + 'query': { + 'script_score': { + 'query': {'match_all': {}}, + 'script': { + 'source': f'cosineSimilarity(params.query_vector, \'{search_field}\') + 1.0', + 'params': {'query_vector': query}, + }, + } + }, + } + return body + + @staticmethod + def _form_text_search_body( + query: str, limit: int, search_field: str = '' + ) -> Dict[str, Any]: + body = { + 'size': limit, + 'query': { + 'bool': { + 'must': {'match': {search_field: query}}, + } + }, + } + return body + + def _format_response(self, response: Any) -> Tuple[List[Dict], NdArray]: docs = [] scores = [] for result in response['hits']['hits']: @@ -474,7 +496,7 @@ def _format_response(self, response: Any) -> Tuple[List[Dict], List[float]]: docs.append(doc_dict) scores.append(result['_score']) - return docs, scores + return docs, parse_obj_as(NdArray, scores) def _refresh(self, index_name: str): self._client.indices.refresh(index=index_name) diff --git a/tests/index/elastic/v7/test_find.py b/tests/index/elastic/v7/test_find.py index 547a69340cb..23b560baf87 100644 --- a/tests/index/elastic/v7/test_find.py +++ b/tests/index/elastic/v7/test_find.py @@ -2,7 +2,7 @@ from pydantic import Field from docarray import BaseDocument -from docarray.index import ElasticDocumentIndex +from docarray.index import ElasticDocIndex from docarray.typing import NdArray from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 from tests.index.elastic.fixture import FlatDoc, SimpleDoc @@ -12,7 +12,7 @@ def test_find_simple_schema(): class SimpleSchema(BaseDocument): tens: NdArray[10] - store = ElasticDocumentIndex[SimpleSchema]() + store = ElasticDocIndex[SimpleSchema]() index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] store.index(index_docs) @@ -32,7 +32,7 @@ class FlatSchema(BaseDocument): tens_one: NdArray = Field(dims=10) tens_two: NdArray = Field(dims=50) - store = ElasticDocumentIndex[FlatSchema]() + store = ElasticDocIndex[FlatSchema]() index_docs = [ FlatDoc(tens_one=np.random.rand(10), tens_two=np.random.rand(50)) @@ -71,7 +71,7 @@ class DeepNestedDoc(BaseDocument): d: NestedDoc tens: NdArray = Field(dims=10) - store = ElasticDocumentIndex[DeepNestedDoc]() + store = ElasticDocIndex[DeepNestedDoc]() index_docs = [ DeepNestedDoc( @@ -107,7 +107,7 @@ class DeepNestedDoc(BaseDocument): def test_find_batched(): - store = ElasticDocumentIndex[SimpleDoc]() + store = ElasticDocIndex[SimpleDoc]() index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] store.index(index_docs) @@ -130,7 +130,7 @@ class MyDoc(BaseDocument): B: int C: float - store = ElasticDocumentIndex[MyDoc]() + store = ElasticDocIndex[MyDoc]() index_docs = [MyDoc(id=f'{i}', A=(i % 2 == 0), B=i, C=i + 0.5) for i in range(10)] store.index(index_docs) @@ -157,7 +157,7 @@ def test_text_search(): class MyDoc(BaseDocument): text: str - store = ElasticDocumentIndex[MyDoc]() + store = ElasticDocIndex[MyDoc]() index_docs = [ MyDoc(text='hello world'), MyDoc(text='never gonna give you up'), @@ -188,7 +188,7 @@ class MyDoc(BaseDocument): num: int text: str - store = ElasticDocumentIndex[MyDoc](index_name='tmp') + store = ElasticDocIndex[MyDoc]() index_docs = [ MyDoc( id=f'{i}', tens=np.random.rand(10), num=int(i / 2), text=f'text {int(i/2)}' diff --git a/tests/index/elastic/v7/test_index_get_del.py b/tests/index/elastic/v7/test_index_get_del.py index 642c0affa05..f8e79b13044 100644 --- a/tests/index/elastic/v7/test_index_get_del.py +++ b/tests/index/elastic/v7/test_index_get_del.py @@ -2,7 +2,7 @@ import pytest from docarray import DocumentArray -from docarray.index import ElasticDocumentIndex +from docarray.index import ElasticDocIndex from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 from tests.index.elastic.fixture import DeepNestedDoc, FlatDoc, NestedDoc, SimpleDoc @@ -35,7 +35,7 @@ def ten_deep_nested_docs(): @pytest.mark.parametrize('use_docarray', [True, False]) def test_index_simple_schema(ten_simple_docs, use_docarray): - store = ElasticDocumentIndex[SimpleDoc]() + store = ElasticDocIndex[SimpleDoc]() if use_docarray: ten_simple_docs = DocumentArray[SimpleDoc](ten_simple_docs) @@ -45,7 +45,7 @@ def test_index_simple_schema(ten_simple_docs, use_docarray): @pytest.mark.parametrize('use_docarray', [True, False]) def test_index_flat_schema(ten_flat_docs, use_docarray): - store = ElasticDocumentIndex[FlatDoc]() + store = ElasticDocIndex[FlatDoc]() if use_docarray: ten_flat_docs = DocumentArray[FlatDoc](ten_flat_docs) @@ -55,7 +55,7 @@ def test_index_flat_schema(ten_flat_docs, use_docarray): @pytest.mark.parametrize('use_docarray', [True, False]) def test_index_nested_schema(ten_nested_docs, use_docarray): - store = ElasticDocumentIndex[NestedDoc]() + store = ElasticDocIndex[NestedDoc]() if use_docarray: ten_nested_docs = DocumentArray[NestedDoc](ten_nested_docs) @@ -65,7 +65,7 @@ def test_index_nested_schema(ten_nested_docs, use_docarray): @pytest.mark.parametrize('use_docarray', [True, False]) def test_index_deep_nested_schema(ten_deep_nested_docs, use_docarray): - store = ElasticDocumentIndex[DeepNestedDoc]() + store = ElasticDocIndex[DeepNestedDoc]() if use_docarray: ten_deep_nested_docs = DocumentArray[DeepNestedDoc](ten_deep_nested_docs) @@ -75,7 +75,7 @@ def test_index_deep_nested_schema(ten_deep_nested_docs, use_docarray): def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): # simple - store = ElasticDocumentIndex[SimpleDoc]() + store = ElasticDocIndex[SimpleDoc]() store.index(ten_simple_docs) assert store.num_docs() == 10 @@ -85,7 +85,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): assert np.all(store[id_].tens == d.tens) # flat - store = ElasticDocumentIndex[FlatDoc]() + store = ElasticDocIndex[FlatDoc]() store.index(ten_flat_docs) assert store.num_docs() == 10 @@ -96,7 +96,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): assert np.all(store[id_].tens_two == d.tens_two) # nested - store = ElasticDocumentIndex[NestedDoc]() + store = ElasticDocIndex[NestedDoc]() store.index(ten_nested_docs) assert store.num_docs() == 10 @@ -111,7 +111,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): docs_to_get_idx = [0, 2, 4, 6, 8] # simple - store = ElasticDocumentIndex[SimpleDoc]() + store = ElasticDocIndex[SimpleDoc]() store.index(ten_simple_docs) assert store.num_docs() == 10 @@ -123,7 +123,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): assert np.all(d_out.tens == d_in.tens) # flat - store = ElasticDocumentIndex[FlatDoc]() + store = ElasticDocIndex[FlatDoc]() store.index(ten_flat_docs) assert store.num_docs() == 10 @@ -136,7 +136,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): assert np.all(d_out.tens_two == d_in.tens_two) # nested - store = ElasticDocumentIndex[NestedDoc]() + store = ElasticDocIndex[NestedDoc]() store.index(ten_nested_docs) assert store.num_docs() == 10 @@ -150,7 +150,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): def test_get_key_error(ten_simple_docs): - store = ElasticDocumentIndex[SimpleDoc]() + store = ElasticDocIndex[SimpleDoc]() store.index(ten_simple_docs) with pytest.raises(KeyError): @@ -158,7 +158,7 @@ def test_get_key_error(ten_simple_docs): def test_del_single(ten_simple_docs): - store = ElasticDocumentIndex[SimpleDoc]() + store = ElasticDocIndex[SimpleDoc]() store.index(ten_simple_docs) # delete once assert store.num_docs() == 10 @@ -188,7 +188,7 @@ def test_del_single(ten_simple_docs): def test_del_multiple(ten_simple_docs): docs_to_del_idx = [0, 2, 4, 6, 8] - store = ElasticDocumentIndex[SimpleDoc]() + store = ElasticDocIndex[SimpleDoc]() store.index(ten_simple_docs) assert store.num_docs() == 10 @@ -205,7 +205,7 @@ def test_del_multiple(ten_simple_docs): def test_del_key_error(ten_simple_docs): - store = ElasticDocumentIndex[SimpleDoc]() + store = ElasticDocIndex[SimpleDoc]() store.index(ten_simple_docs) with pytest.warns(UserWarning): @@ -213,7 +213,7 @@ def test_del_key_error(ten_simple_docs): def test_num_docs(ten_simple_docs): - store = ElasticDocumentIndex[SimpleDoc]() + store = ElasticDocIndex[SimpleDoc]() store.index(ten_simple_docs) assert store.num_docs() == 10 From 0476e6b7b3c94cbe2c76ef70e7f4c6a4de532f56 Mon Sep 17 00:00:00 2001 From: AnneY Date: Thu, 23 Mar 2023 22:58:43 +0800 Subject: [PATCH 35/44] test: add test for persistency and col config Signed-off-by: AnneY --- tests/index/elastic/v7/test_find.py | 30 +++++++++++++++++--- tests/index/elastic/v7/test_index_get_del.py | 8 ++++++ 2 files changed, 34 insertions(+), 4 deletions(-) diff --git a/tests/index/elastic/v7/test_find.py b/tests/index/elastic/v7/test_find.py index 23b560baf87..b047e448049 100644 --- a/tests/index/elastic/v7/test_find.py +++ b/tests/index/elastic/v7/test_find.py @@ -142,10 +142,10 @@ class MyDoc(BaseDocument): assert doc.A filter_query = { - "bool": { - "filter": [ - {"terms": {"B": [3, 4, 7, 8]}}, - {"range": {"C": {"gte": 3, "lte": 5}}}, + 'bool': { + 'filter': [ + {'terms': {'B': [3, 4, 7, 8]}}, + {'range': {'C': {'gte': 3, 'lte': 5}}}, ] } } @@ -182,6 +182,28 @@ class MyDoc(BaseDocument): assert doc.text.index(query) >= 0 +def test_column_config(): + class MyDoc(BaseDocument): + text: str + color: str = Field(col_type='keyword') + + store = ElasticDocIndex[MyDoc]() + index_docs = [ + MyDoc(id='0', text='hello world', color='red'), + MyDoc(id='1', text='never gonna give you up', color='blue'), + MyDoc(id='2', text='we are the world', color='green'), + ] + store.index(index_docs) + + query = 'world' + docs, _ = store.text_search(query, search_field='text') + assert [doc.id for doc in docs] == ['0', '2'] + + filter_query = {'terms': {'color': ['red', 'blue']}} + docs = store.filter(filter_query) + assert [doc.id for doc in docs] == ['0', '1'] + + def test_query_builder(): class MyDoc(BaseDocument): tens: NdArray[10] diff --git a/tests/index/elastic/v7/test_index_get_del.py b/tests/index/elastic/v7/test_index_get_del.py index f8e79b13044..60ba94e5d52 100644 --- a/tests/index/elastic/v7/test_index_get_del.py +++ b/tests/index/elastic/v7/test_index_get_del.py @@ -157,6 +157,14 @@ def test_get_key_error(ten_simple_docs): store['not_a_real_id'] +def test_persisting(ten_simple_docs): + store = ElasticDocIndex[SimpleDoc](index_name='test_persisting') + store.index(ten_simple_docs) + + store2 = ElasticDocIndex[SimpleDoc](index_name='test_persisting') + assert store2.num_docs() == 10 + + def test_del_single(ten_simple_docs): store = ElasticDocIndex[SimpleDoc]() store.index(ten_simple_docs) From e8a8c92375f53c3f05ca914ee2c02e59e7af2e16 Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 24 Mar 2023 13:18:52 +0800 Subject: [PATCH 36/44] feat: support more field types and subclass Signed-off-by: AnneY --- docarray/index/backends/elastic.py | 63 ++++++++++++++++++++++++++---- 1 file changed, 55 insertions(+), 8 deletions(-) diff --git a/docarray/index/backends/elastic.py b/docarray/index/backends/elastic.py index 7e3e4eaf57f..1f2476db9ba 100644 --- a/docarray/index/backends/elastic.py +++ b/docarray/index/backends/elastic.py @@ -35,18 +35,26 @@ from docarray.typing import AnyTensor from docarray.typing.tensor.ndarray import NdArray from docarray.utils.find import _FindResult -from docarray.utils.misc import torch_imported +from docarray.utils.misc import is_tf_available, is_torch_available TSchema = TypeVar('TSchema', bound=BaseDocument) T = TypeVar('T', bound='ElasticDocIndex') ELASTIC_PY_VEC_TYPES: List[Any] = [np.ndarray] -if torch_imported: +if is_torch_available(): import torch ELASTIC_PY_VEC_TYPES.append(torch.Tensor) +if is_tf_available(): + import tensorflow as tf # type: ignore + + from docarray.typing import TensorFlowTensor + + ELASTIC_PY_VEC_TYPES.append(tf.Tensor) + ELASTIC_PY_VEC_TYPES.append(TensorFlowTensor) + class ElasticDocIndex(BaseDocumentIndex, Generic[TSchema]): def __init__(self, db_config=None, **kwargs): @@ -176,12 +184,46 @@ class DBConfig(BaseDocumentIndex.DBConfig): class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): default_column_config: Dict[Any, Dict[str, Any]] = field( default_factory=lambda: { - 'dense_vector': {'dims': 128}, - 'keyword': {}, + 'binary': {}, 'boolean': {}, + 'keyword': {}, + 'long': {}, 'integer': {}, + 'short': {}, + 'byte': {}, + 'double': {}, 'float': {}, + 'half_float': {}, + 'scaled_float': {}, + 'unsigned_long': {}, + 'dates': {}, + 'alias': {}, + 'object': {}, + 'flattened': {}, + 'nested': {}, + 'join': {}, + 'integer_range': {}, + 'float_range': {}, + 'long_range': {}, + 'double_range': {}, + 'date_range': {}, + 'ip': {}, + 'version': {}, + 'histogram': {}, 'text': {}, + 'annotated_text': {}, + 'completion': {}, + 'search_as_you_type': {}, + 'token_count': {}, + 'dense_vector': {'dims': 128}, + 'sparse_vector': {}, + 'rank_feature': {}, + 'rank_features': {}, + 'geo_point': {}, + 'geo_shape': {}, + 'point': {}, + 'shape': {}, + 'percolator': {}, # `None` is not a Type, but we allow it here anyway None: {}, # type: ignore } @@ -203,12 +245,15 @@ def python_type_to_db_type(self, python_type: Type) -> Any: bool: 'boolean', int: 'integer', float: 'float', - str: 'text', docarray.typing.ID: 'keyword', + str: 'text', + bytes: 'binary', + dict: 'object', } - if python_type in elastic_py_types: - return elastic_py_types[python_type] + for type in elastic_py_types.keys(): + if issubclass(python_type, type): + return elastic_py_types[type] raise ValueError(f'Unsupported column type for {type(self)}: {python_type}') @@ -228,8 +273,10 @@ def _index( '_id': row['id'], } for col_name, col in self._column_infos.items(): - if col.db_type == np.ndarray and np.all(row[col_name] == 0): + if col.db_type == 'dense_vector' and np.all(row[col_name] == 0): row[col_name] = row[col_name] + 1.0e-9 + if row[col_name] is None: + continue request[col_name] = row[col_name] requests.append(request) From c62da11f2c3676931722d674ca25271fc475946f Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 24 Mar 2023 23:12:48 +0800 Subject: [PATCH 37/44] feat: support more python types Signed-off-by: AnneY --- docarray/index/backends/elastic.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/docarray/index/backends/elastic.py b/docarray/index/backends/elastic.py index 1f2476db9ba..08f655240fa 100644 --- a/docarray/index/backends/elastic.py +++ b/docarray/index/backends/elastic.py @@ -40,7 +40,7 @@ TSchema = TypeVar('TSchema', bound=BaseDocument) T = TypeVar('T', bound='ElasticDocIndex') -ELASTIC_PY_VEC_TYPES: List[Any] = [np.ndarray] +ELASTIC_PY_VEC_TYPES: List[Any] = [list, tuple, np.ndarray] if is_torch_available(): import torch @@ -77,9 +77,6 @@ def __init__(self, db_config=None, **kwargs): if int(self._server_version.split('.')[0]) >= 8: os.environ['ELASTIC_CLIENT_APIVERSIONING'] = '1' - # ElasticSearh index setup - self._index_vector_params = ('dims',) - body: Dict[str, Any] = { 'mappings': { 'dynamic': True, @@ -242,10 +239,11 @@ def python_type_to_db_type(self, python_type: Type) -> Any: return 'dense_vector' elastic_py_types = { + docarray.typing.ID: 'keyword', + docarray.typing.AnyUrl: 'keyword', bool: 'boolean', int: 'integer', float: 'float', - docarray.typing.ID: 'keyword', str: 'text', bytes: 'binary', dict: 'object', @@ -461,13 +459,12 @@ def _text_search_batched( def _create_index_mapping(self, col: '_ColumnInfo') -> Dict[str, Any]: """Create a new HNSW index for a column, and initialize it.""" - index = {'type': col.config['type'] if 'type' in col.config else col.db_type} + index = col.config.copy() + if 'type' not in index: + index['type'] = col.db_type - if col.db_type == 'dense_vector': - for k in self._index_vector_params: - index[k] = col.config[k] - if col.n_dim: - index['dims'] = col.n_dim + if col.db_type == 'dense_vector' and col.n_dim: + index['dims'] = col.n_dim return index From 31de3373af5d3e0bea4aae936e3de498c9dc4fe6 Mon Sep 17 00:00:00 2001 From: AnneY Date: Fri, 24 Mar 2023 23:14:25 +0800 Subject: [PATCH 38/44] test: tf, tensor and more elastic field types Signed-off-by: AnneY --- tests/index/elastic/v7/test_column_config.py | 128 +++++++++++++++++++ tests/index/elastic/v7/test_find.py | 82 ++++++++---- 2 files changed, 187 insertions(+), 23 deletions(-) create mode 100644 tests/index/elastic/v7/test_column_config.py diff --git a/tests/index/elastic/v7/test_column_config.py b/tests/index/elastic/v7/test_column_config.py new file mode 100644 index 00000000000..1df25e3967c --- /dev/null +++ b/tests/index/elastic/v7/test_column_config.py @@ -0,0 +1,128 @@ +from pydantic import Field + +from docarray import BaseDocument +from docarray.index import ElasticDocIndex +from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 + + +def test_column_config(): + class MyDoc(BaseDocument): + text: str + color: str = Field(col_type='keyword') + + store = ElasticDocIndex[MyDoc]() + index_docs = [ + MyDoc(id='0', text='hello world', color='red'), + MyDoc(id='1', text='never gonna give you up', color='blue'), + MyDoc(id='2', text='we are the world', color='green'), + ] + store.index(index_docs) + + query = 'world' + docs, _ = store.text_search(query, search_field='text') + assert [doc.id for doc in docs] == ['0', '2'] + + filter_query = {'terms': {'color': ['red', 'blue']}} + docs = store.filter(filter_query) + assert [doc.id for doc in docs] == ['0', '1'] + + +def test_field_object(): + class MyDoc(BaseDocument): + manager: dict = Field( + properties={ + 'age': {'type': 'integer'}, + 'name': { + 'properties': { + 'first': {'type': 'keyword'}, + 'last': {'type': 'keyword'}, + } + }, + } + ) + + store = ElasticDocIndex[MyDoc]() + doc = [ + MyDoc(manager={'age': 25, 'name': {'first': 'Rachel', 'last': 'Green'}}), + MyDoc(manager={'age': 30, 'name': {'first': 'Monica', 'last': 'Geller'}}), + MyDoc(manager={'age': 35, 'name': {'first': 'Phoebe', 'last': 'Buffay'}}), + ] + store.index(doc) + id_ = doc[0].id + assert store[id_].id == id_ + assert store[id_].manager == doc[0].manager + + filter_query = {'range': {'manager.age': {'gte': 30}}} + docs = store.filter(filter_query) + assert [doc.id for doc in docs] == [doc[1].id, doc[2].id] + + +def test_field_geo_point(): + class MyDoc(BaseDocument): + location: dict = Field(col_type='geo_point') + + store = ElasticDocIndex[MyDoc]() + doc = [ + MyDoc(location={'lat': 40.12, 'lon': -72.34}), + MyDoc(location={'lat': 41.12, 'lon': -73.34}), + MyDoc(location={'lat': 42.12, 'lon': -74.34}), + ] + store.index(doc) + + query = { + 'query': { + 'geo_bounding_box': { + 'location': { + 'top_left': {'lat': 42, 'lon': -74}, + 'bottom_right': {'lat': 40, 'lon': -72}, + } + } + }, + } + + docs, _ = store.execute_query(query) + assert [doc['id'] for doc in docs] == [doc[0].id, doc[1].id] + + +def test_field_range(): + class MyDoc(BaseDocument): + expected_attendees: dict = Field(col_type='integer_range') + time_frame: dict = Field(col_type='date_range', format='yyyy-MM-dd') + + store = ElasticDocIndex[MyDoc]() + doc = [ + MyDoc( + expected_attendees={'gte': 10, 'lt': 20}, + time_frame={'gte': '2023-01-01', 'lt': '2023-02-01'}, + ), + MyDoc( + expected_attendees={'gte': 20, 'lt': 30}, + time_frame={'gte': '2023-02-01', 'lt': '2023-03-01'}, + ), + MyDoc( + expected_attendees={'gte': 30, 'lt': 40}, + time_frame={'gte': '2023-03-01', 'lt': '2023-04-01'}, + ), + ] + store.index(doc) + + query = { + 'query': { + 'bool': { + 'should': [ + {'term': {'expected_attendees': {'value': 15}}}, + { + 'range': { + 'time_frame': { + 'gte': '2023-02-05', + 'lt': '2023-02-10', + 'relation': 'contains', + } + } + }, + ] + } + }, + } + docs, _ = store.execute_query(query) + assert [doc['id'] for doc in docs] == [doc[0].id, doc[1].id] diff --git a/tests/index/elastic/v7/test_find.py b/tests/index/elastic/v7/test_find.py index b047e448049..3735ea861f7 100644 --- a/tests/index/elastic/v7/test_find.py +++ b/tests/index/elastic/v7/test_find.py @@ -1,9 +1,10 @@ import numpy as np +import torch from pydantic import Field from docarray import BaseDocument from docarray.index import ElasticDocIndex -from docarray.typing import NdArray +from docarray.typing import NdArray, TorchTensor from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 from tests.index.elastic.fixture import FlatDoc, SimpleDoc @@ -106,6 +107,63 @@ class DeepNestedDoc(BaseDocument): assert np.allclose(docs[0].d.d.tens, index_docs[-1].d.d.tens) +def test_find_torch(): + class TorchDoc(BaseDocument): + tens: TorchTensor[10] + + store = ElasticDocIndex[TorchDoc]() + + # A dense_vector field stores dense vectors of float values. + index_docs = [ + TorchDoc(tens=np.random.rand(10).astype(dtype=np.float32)) for _ in range(10) + ] + store.index(index_docs) + + for doc in index_docs: + assert isinstance(doc.tens, TorchTensor) + + query = index_docs[-1] + docs, scores = store.find(query, search_field='tens', limit=5) + + assert len(docs) == 5 + assert len(scores) == 5 + for doc in docs: + assert isinstance(doc.tens, TorchTensor) + + assert docs[0].id == index_docs[-1].id + assert torch.allclose(docs[0].tens, index_docs[-1].tens) + + +def test_find_tensorflow(): + from docarray.typing import TensorFlowTensor + + class TfDoc(BaseDocument): + tens: TensorFlowTensor[10] + + store = ElasticDocIndex[TfDoc]() + + index_docs = [ + TfDoc(tens=np.random.rand(10).astype(dtype=np.float32)) for _ in range(10) + ] + store.index(index_docs) + + for doc in index_docs: + assert isinstance(doc.tens, TensorFlowTensor) + + query = index_docs[-1] + docs, scores = store.find(query, search_field='tens', limit=5) + + assert len(docs) == 5 + assert len(scores) == 5 + for doc in docs: + assert isinstance(doc.tens, TensorFlowTensor) + + assert docs[0].id == index_docs[-1].id + assert np.allclose( + docs[0].tens.unwrap().numpy(), index_docs[-1].tens.unwrap().numpy() + ) + + def test_find_batched(): store = ElasticDocIndex[SimpleDoc]() @@ -182,28 +240,6 @@ class MyDoc(BaseDocument): assert doc.text.index(query) >= 0 -def test_column_config(): - class MyDoc(BaseDocument): - text: str - color: str = Field(col_type='keyword') - - store = ElasticDocIndex[MyDoc]() - index_docs = [ - MyDoc(id='0', text='hello world', color='red'), - MyDoc(id='1', text='never gonna give you up', color='blue'), - MyDoc(id='2', text='we are the world', color='green'), - ] - store.index(index_docs) - - query = 'world' - docs, _ = store.text_search(query, search_field='text') - assert [doc.id for doc in docs] == ['0', '2'] - - filter_query = {'terms': {'color': ['red', 'blue']}} - docs = store.filter(filter_query) - assert [doc.id for doc in docs] == ['0', '1'] - - def test_query_builder(): class MyDoc(BaseDocument): tens: NdArray[10] From 9938ea4d8272deaf80e669b543f7fbc601e5a6a5 Mon Sep 17 00:00:00 2001 From: Anne Yang Date: Fri, 24 Mar 2023 23:16:36 +0800 Subject: [PATCH 39/44] fix: elastic should be optional in toml Co-authored-by: Charlotte Gerhaher Signed-off-by: Anne Yang --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 64ecbadd1e3..f140d6afcfb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ hnswlib = {version = ">=0.6.2", optional = true } lz4 = {version= ">=1.0.0", optional = true} pydub = {version = "^0.25.1", optional = true } pandas = {version = ">=1.1.0", optional = true } -elasticsearch = "7.10.1" +elasticsearch = {version = "7.10.1", optional = true } [tool.poetry.extras] common = ["protobuf", "lz4"] From 2038a39278809280d39a7f716ccc9f4609c8aa05 Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 29 Mar 2023 20:35:39 +0800 Subject: [PATCH 40/44] refactor: rename class Signed-off-by: AnneY --- docarray/index/__init__.py | 4 +-- docarray/index/backends/elastic.py | 32 ++++++++--------- tests/index/elastic/v7/test_column_config.py | 10 +++--- tests/index/elastic/v7/test_find.py | 20 +++++------ tests/index/elastic/v7/test_index_get_del.py | 36 ++++++++++---------- 5 files changed, 50 insertions(+), 52 deletions(-) diff --git a/docarray/index/__init__.py b/docarray/index/__init__.py index 48f18b5ceca..4a9986e1a97 100644 --- a/docarray/index/__init__.py +++ b/docarray/index/__init__.py @@ -1,4 +1,4 @@ -from docarray.index.backends.elastic import ElasticDocIndex +from docarray.index.backends.elastic import ElasticV7DocIndex from docarray.index.backends.hnswlib import HnswDocumentIndex -__all__ = ['HnswDocumentIndex', 'ElasticDocIndex'] +__all__ = ['HnswDocumentIndex', 'ElasticV7DocIndex'] diff --git a/docarray/index/backends/elastic.py b/docarray/index/backends/elastic.py index 08f655240fa..086caa9c66f 100644 --- a/docarray/index/backends/elastic.py +++ b/docarray/index/backends/elastic.py @@ -25,20 +25,20 @@ from pydantic import parse_obj_as import docarray.typing -from docarray import BaseDocument +from docarray import BaseDoc from docarray.index.abstract import ( - BaseDocumentIndex, + BaseDocIndex, _ColumnInfo, _FindResultBatched, _raise_not_composable, ) from docarray.typing import AnyTensor from docarray.typing.tensor.ndarray import NdArray +from docarray.utils._internal.misc import is_tf_available, is_torch_available from docarray.utils.find import _FindResult -from docarray.utils.misc import is_tf_available, is_torch_available -TSchema = TypeVar('TSchema', bound=BaseDocument) -T = TypeVar('T', bound='ElasticDocIndex') +TSchema = TypeVar('TSchema', bound=BaseDoc) +T = TypeVar('T', bound='ElasticV7DocIndex') ELASTIC_PY_VEC_TYPES: List[Any] = [list, tuple, np.ndarray] @@ -56,10 +56,10 @@ ELASTIC_PY_VEC_TYPES.append(TensorFlowTensor) -class ElasticDocIndex(BaseDocumentIndex, Generic[TSchema]): +class ElasticV7DocIndex(BaseDocIndex, Generic[TSchema]): def __init__(self, db_config=None, **kwargs): super().__init__(db_config=db_config, **kwargs) - self._db_config = cast(ElasticDocIndex.DBConfig, self._db_config) + self._db_config = cast(ElasticV7DocIndex.DBConfig, self._db_config) if self._db_config.index_name is None: id = uuid.uuid4().hex @@ -106,7 +106,7 @@ def __init__(self, db_config=None, **kwargs): # Inner classes for query builder and configs # ############################################### - class QueryBuilder(BaseDocumentIndex.QueryBuilder): + class QueryBuilder(BaseDocIndex.QueryBuilder): def __init__(self, outer_instance, **kwargs): super().__init__() self._outer_instance = outer_instance @@ -130,19 +130,17 @@ def build(self, *args, **kwargs) -> Any: def find( self, - query: Union[AnyTensor, BaseDocument], + query: Union[AnyTensor, BaseDoc], search_field: str = 'embedding', limit: int = 10, ): - if isinstance(query, BaseDocument): - query_vec = BaseDocumentIndex._get_values_by_column( - [query], search_field - )[0] + if isinstance(query, BaseDoc): + query_vec = BaseDocIndex._get_values_by_column([query], search_field)[0] else: query_vec = query - query_vec_np = BaseDocumentIndex._to_numpy(self._outer_instance, query_vec) + query_vec_np = BaseDocIndex._to_numpy(self._outer_instance, query_vec) self._query['size'] = limit - self._query['query']['script_score'] = ElasticDocIndex._form_search_body( + self._query['query']['script_score'] = ElasticV7DocIndex._form_search_body( query_vec_np, limit, search_field )['query']['script_score'] @@ -171,14 +169,14 @@ def build_query(self, **kwargs) -> QueryBuilder: return self.QueryBuilder(self, **kwargs) @dataclass - class DBConfig(BaseDocumentIndex.DBConfig): + class DBConfig(BaseDocIndex.DBConfig): hosts: Union[str, List[str], None] = 'http://localhost:9200' index_name: Optional[str] = None es_config: Dict[str, Any] = field(default_factory=dict) index_settings: Dict[str, Any] = field(default_factory=dict) @dataclass - class RuntimeConfig(BaseDocumentIndex.RuntimeConfig): + class RuntimeConfig(BaseDocIndex.RuntimeConfig): default_column_config: Dict[Any, Dict[str, Any]] = field( default_factory=lambda: { 'binary': {}, diff --git a/tests/index/elastic/v7/test_column_config.py b/tests/index/elastic/v7/test_column_config.py index 1df25e3967c..bd53ae2361b 100644 --- a/tests/index/elastic/v7/test_column_config.py +++ b/tests/index/elastic/v7/test_column_config.py @@ -1,7 +1,7 @@ from pydantic import Field from docarray import BaseDocument -from docarray.index import ElasticDocIndex +from docarray.index import ElasticV7DocIndex from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 @@ -10,7 +10,7 @@ class MyDoc(BaseDocument): text: str color: str = Field(col_type='keyword') - store = ElasticDocIndex[MyDoc]() + store = ElasticV7DocIndex[MyDoc]() index_docs = [ MyDoc(id='0', text='hello world', color='red'), MyDoc(id='1', text='never gonna give you up', color='blue'), @@ -41,7 +41,7 @@ class MyDoc(BaseDocument): } ) - store = ElasticDocIndex[MyDoc]() + store = ElasticV7DocIndex[MyDoc]() doc = [ MyDoc(manager={'age': 25, 'name': {'first': 'Rachel', 'last': 'Green'}}), MyDoc(manager={'age': 30, 'name': {'first': 'Monica', 'last': 'Geller'}}), @@ -61,7 +61,7 @@ def test_field_geo_point(): class MyDoc(BaseDocument): location: dict = Field(col_type='geo_point') - store = ElasticDocIndex[MyDoc]() + store = ElasticV7DocIndex[MyDoc]() doc = [ MyDoc(location={'lat': 40.12, 'lon': -72.34}), MyDoc(location={'lat': 41.12, 'lon': -73.34}), @@ -89,7 +89,7 @@ class MyDoc(BaseDocument): expected_attendees: dict = Field(col_type='integer_range') time_frame: dict = Field(col_type='date_range', format='yyyy-MM-dd') - store = ElasticDocIndex[MyDoc]() + store = ElasticV7DocIndex[MyDoc]() doc = [ MyDoc( expected_attendees={'gte': 10, 'lt': 20}, diff --git a/tests/index/elastic/v7/test_find.py b/tests/index/elastic/v7/test_find.py index 3735ea861f7..c8aac617804 100644 --- a/tests/index/elastic/v7/test_find.py +++ b/tests/index/elastic/v7/test_find.py @@ -3,7 +3,7 @@ from pydantic import Field from docarray import BaseDocument -from docarray.index import ElasticDocIndex +from docarray.index import ElasticV7DocIndex from docarray.typing import NdArray, TorchTensor from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 from tests.index.elastic.fixture import FlatDoc, SimpleDoc @@ -13,7 +13,7 @@ def test_find_simple_schema(): class SimpleSchema(BaseDocument): tens: NdArray[10] - store = ElasticDocIndex[SimpleSchema]() + store = ElasticV7DocIndex[SimpleSchema]() index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] store.index(index_docs) @@ -33,7 +33,7 @@ class FlatSchema(BaseDocument): tens_one: NdArray = Field(dims=10) tens_two: NdArray = Field(dims=50) - store = ElasticDocIndex[FlatSchema]() + store = ElasticV7DocIndex[FlatSchema]() index_docs = [ FlatDoc(tens_one=np.random.rand(10), tens_two=np.random.rand(50)) @@ -72,7 +72,7 @@ class DeepNestedDoc(BaseDocument): d: NestedDoc tens: NdArray = Field(dims=10) - store = ElasticDocIndex[DeepNestedDoc]() + store = ElasticV7DocIndex[DeepNestedDoc]() index_docs = [ DeepNestedDoc( @@ -111,7 +111,7 @@ def test_find_torch(): class TorchDoc(BaseDocument): tens: TorchTensor[10] - store = ElasticDocIndex[TorchDoc]() + store = ElasticV7DocIndex[TorchDoc]() # A dense_vector field stores dense vectors of float values. index_docs = [ @@ -140,7 +140,7 @@ def test_find_tensorflow(): class TfDoc(BaseDocument): tens: TensorFlowTensor[10] - store = ElasticDocIndex[TfDoc]() + store = ElasticV7DocIndex[TfDoc]() index_docs = [ TfDoc(tens=np.random.rand(10).astype(dtype=np.float32)) for _ in range(10) @@ -165,7 +165,7 @@ class TfDoc(BaseDocument): def test_find_batched(): - store = ElasticDocIndex[SimpleDoc]() + store = ElasticV7DocIndex[SimpleDoc]() index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)] store.index(index_docs) @@ -188,7 +188,7 @@ class MyDoc(BaseDocument): B: int C: float - store = ElasticDocIndex[MyDoc]() + store = ElasticV7DocIndex[MyDoc]() index_docs = [MyDoc(id=f'{i}', A=(i % 2 == 0), B=i, C=i + 0.5) for i in range(10)] store.index(index_docs) @@ -215,7 +215,7 @@ def test_text_search(): class MyDoc(BaseDocument): text: str - store = ElasticDocIndex[MyDoc]() + store = ElasticV7DocIndex[MyDoc]() index_docs = [ MyDoc(text='hello world'), MyDoc(text='never gonna give you up'), @@ -246,7 +246,7 @@ class MyDoc(BaseDocument): num: int text: str - store = ElasticDocIndex[MyDoc]() + store = ElasticV7DocIndex[MyDoc]() index_docs = [ MyDoc( id=f'{i}', tens=np.random.rand(10), num=int(i / 2), text=f'text {int(i/2)}' diff --git a/tests/index/elastic/v7/test_index_get_del.py b/tests/index/elastic/v7/test_index_get_del.py index 60ba94e5d52..772c7e6e55f 100644 --- a/tests/index/elastic/v7/test_index_get_del.py +++ b/tests/index/elastic/v7/test_index_get_del.py @@ -2,7 +2,7 @@ import pytest from docarray import DocumentArray -from docarray.index import ElasticDocIndex +from docarray.index import ElasticV7DocIndex from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 from tests.index.elastic.fixture import DeepNestedDoc, FlatDoc, NestedDoc, SimpleDoc @@ -35,7 +35,7 @@ def ten_deep_nested_docs(): @pytest.mark.parametrize('use_docarray', [True, False]) def test_index_simple_schema(ten_simple_docs, use_docarray): - store = ElasticDocIndex[SimpleDoc]() + store = ElasticV7DocIndex[SimpleDoc]() if use_docarray: ten_simple_docs = DocumentArray[SimpleDoc](ten_simple_docs) @@ -45,7 +45,7 @@ def test_index_simple_schema(ten_simple_docs, use_docarray): @pytest.mark.parametrize('use_docarray', [True, False]) def test_index_flat_schema(ten_flat_docs, use_docarray): - store = ElasticDocIndex[FlatDoc]() + store = ElasticV7DocIndex[FlatDoc]() if use_docarray: ten_flat_docs = DocumentArray[FlatDoc](ten_flat_docs) @@ -55,7 +55,7 @@ def test_index_flat_schema(ten_flat_docs, use_docarray): @pytest.mark.parametrize('use_docarray', [True, False]) def test_index_nested_schema(ten_nested_docs, use_docarray): - store = ElasticDocIndex[NestedDoc]() + store = ElasticV7DocIndex[NestedDoc]() if use_docarray: ten_nested_docs = DocumentArray[NestedDoc](ten_nested_docs) @@ -65,7 +65,7 @@ def test_index_nested_schema(ten_nested_docs, use_docarray): @pytest.mark.parametrize('use_docarray', [True, False]) def test_index_deep_nested_schema(ten_deep_nested_docs, use_docarray): - store = ElasticDocIndex[DeepNestedDoc]() + store = ElasticV7DocIndex[DeepNestedDoc]() if use_docarray: ten_deep_nested_docs = DocumentArray[DeepNestedDoc](ten_deep_nested_docs) @@ -75,7 +75,7 @@ def test_index_deep_nested_schema(ten_deep_nested_docs, use_docarray): def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): # simple - store = ElasticDocIndex[SimpleDoc]() + store = ElasticV7DocIndex[SimpleDoc]() store.index(ten_simple_docs) assert store.num_docs() == 10 @@ -85,7 +85,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): assert np.all(store[id_].tens == d.tens) # flat - store = ElasticDocIndex[FlatDoc]() + store = ElasticV7DocIndex[FlatDoc]() store.index(ten_flat_docs) assert store.num_docs() == 10 @@ -96,7 +96,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs): assert np.all(store[id_].tens_two == d.tens_two) # nested - store = ElasticDocIndex[NestedDoc]() + store = ElasticV7DocIndex[NestedDoc]() store.index(ten_nested_docs) assert store.num_docs() == 10 @@ -111,7 +111,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): docs_to_get_idx = [0, 2, 4, 6, 8] # simple - store = ElasticDocIndex[SimpleDoc]() + store = ElasticV7DocIndex[SimpleDoc]() store.index(ten_simple_docs) assert store.num_docs() == 10 @@ -123,7 +123,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): assert np.all(d_out.tens == d_in.tens) # flat - store = ElasticDocIndex[FlatDoc]() + store = ElasticV7DocIndex[FlatDoc]() store.index(ten_flat_docs) assert store.num_docs() == 10 @@ -136,7 +136,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): assert np.all(d_out.tens_two == d_in.tens_two) # nested - store = ElasticDocIndex[NestedDoc]() + store = ElasticV7DocIndex[NestedDoc]() store.index(ten_nested_docs) assert store.num_docs() == 10 @@ -150,7 +150,7 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs): def test_get_key_error(ten_simple_docs): - store = ElasticDocIndex[SimpleDoc]() + store = ElasticV7DocIndex[SimpleDoc]() store.index(ten_simple_docs) with pytest.raises(KeyError): @@ -158,15 +158,15 @@ def test_get_key_error(ten_simple_docs): def test_persisting(ten_simple_docs): - store = ElasticDocIndex[SimpleDoc](index_name='test_persisting') + store = ElasticV7DocIndex[SimpleDoc](index_name='test_persisting') store.index(ten_simple_docs) - store2 = ElasticDocIndex[SimpleDoc](index_name='test_persisting') + store2 = ElasticV7DocIndex[SimpleDoc](index_name='test_persisting') assert store2.num_docs() == 10 def test_del_single(ten_simple_docs): - store = ElasticDocIndex[SimpleDoc]() + store = ElasticV7DocIndex[SimpleDoc]() store.index(ten_simple_docs) # delete once assert store.num_docs() == 10 @@ -196,7 +196,7 @@ def test_del_single(ten_simple_docs): def test_del_multiple(ten_simple_docs): docs_to_del_idx = [0, 2, 4, 6, 8] - store = ElasticDocIndex[SimpleDoc]() + store = ElasticV7DocIndex[SimpleDoc]() store.index(ten_simple_docs) assert store.num_docs() == 10 @@ -213,7 +213,7 @@ def test_del_multiple(ten_simple_docs): def test_del_key_error(ten_simple_docs): - store = ElasticDocIndex[SimpleDoc]() + store = ElasticV7DocIndex[SimpleDoc]() store.index(ten_simple_docs) with pytest.warns(UserWarning): @@ -221,7 +221,7 @@ def test_del_key_error(ten_simple_docs): def test_num_docs(ten_simple_docs): - store = ElasticDocIndex[SimpleDoc]() + store = ElasticV7DocIndex[SimpleDoc]() store.index(ten_simple_docs) assert store.num_docs() == 10 From 0f74e5cf387c3b5b4fa64cf9cb46662c0e0e2aa0 Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 29 Mar 2023 20:54:36 +0800 Subject: [PATCH 41/44] fix: change Dict to Mapping Signed-off-by: AnneY --- docarray/index/abstract.py | 3 ++- docarray/index/backends/elastic.py | 5 +++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/docarray/index/abstract.py b/docarray/index/abstract.py index 5b00c78affb..a037b070527 100644 --- a/docarray/index/abstract.py +++ b/docarray/index/abstract.py @@ -9,6 +9,7 @@ Generic, Iterable, List, + Mapping, NamedTuple, Optional, Sequence, @@ -593,7 +594,7 @@ def _get_values_by_column(docs: Sequence[BaseDoc], col_name: str) -> List[Any]: @staticmethod def _transpose_col_value_dict( - col_value_dict: Dict[str, Iterable[Any]] + col_value_dict: Mapping[str, Iterable[Any]] ) -> Generator[Dict[str, Any], None, None]: """'Transpose' the output of `_get_col_value_dict()`: Yield rows of columns, where each row represent one Document. Since a generator is returned, this process comes at negligible cost. diff --git a/docarray/index/backends/elastic.py b/docarray/index/backends/elastic.py index 086caa9c66f..1351737671b 100644 --- a/docarray/index/backends/elastic.py +++ b/docarray/index/backends/elastic.py @@ -10,6 +10,7 @@ Generic, Iterable, List, + Mapping, Optional, Sequence, Tuple, @@ -255,12 +256,12 @@ def python_type_to_db_type(self, python_type: Type) -> Any: def _index( self, - column_to_data: Dict[str, Generator[Any, None, None]], + column_to_data: Mapping[str, Generator[Any, None, None]], refresh: bool = True, chunk_size: Optional[int] = None, ): - data = self._transpose_col_value_dict(column_to_data) # type: ignore + data = self._transpose_col_value_dict(column_to_data) requests = [] for row in data: From 641050b2d44ba5413399e1a1d9ab9370af23d373 Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 29 Mar 2023 21:05:19 +0800 Subject: [PATCH 42/44] fix: add AbstractTensor Signed-off-by: AnneY --- docarray/index/backends/elastic.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docarray/index/backends/elastic.py b/docarray/index/backends/elastic.py index 1351737671b..deefc3b2a86 100644 --- a/docarray/index/backends/elastic.py +++ b/docarray/index/backends/elastic.py @@ -34,6 +34,7 @@ _raise_not_composable, ) from docarray.typing import AnyTensor +from docarray.typing.tensor.abstract_tensor import AbstractTensor from docarray.typing.tensor.ndarray import NdArray from docarray.utils._internal.misc import is_tf_available, is_torch_available from docarray.utils.find import _FindResult @@ -41,7 +42,7 @@ TSchema = TypeVar('TSchema', bound=BaseDoc) T = TypeVar('T', bound='ElasticV7DocIndex') -ELASTIC_PY_VEC_TYPES: List[Any] = [list, tuple, np.ndarray] +ELASTIC_PY_VEC_TYPES: List[Any] = [list, tuple, np.ndarray, AbstractTensor] if is_torch_available(): import torch From 295140b1de4988fcba914206690e9d7589507bb2 Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 29 Mar 2023 21:05:54 +0800 Subject: [PATCH 43/44] test: rename class and add tests Signed-off-by: AnneY --- tests/index/elastic/fixture.py | 10 ++-- tests/index/elastic/v7/test_column_config.py | 10 ++-- tests/index/elastic/v7/test_find.py | 22 ++++----- tests/index/elastic/v7/test_index_get_del.py | 50 ++++++++++++++++++-- 4 files changed, 66 insertions(+), 26 deletions(-) diff --git a/tests/index/elastic/fixture.py b/tests/index/elastic/fixture.py index 3862b04f8d4..1caa31da2a6 100644 --- a/tests/index/elastic/fixture.py +++ b/tests/index/elastic/fixture.py @@ -4,26 +4,26 @@ import pytest from pydantic import Field -from docarray import BaseDocument +from docarray import BaseDoc from docarray.typing import NdArray pytestmark = [pytest.mark.slow, pytest.mark.doc_index] -class SimpleDoc(BaseDocument): +class SimpleDoc(BaseDoc): tens: NdArray[10] = Field(dims=1000) -class FlatDoc(BaseDocument): +class FlatDoc(BaseDoc): tens_one: NdArray = Field(dims=10) tens_two: NdArray = Field(dims=50) -class NestedDoc(BaseDocument): +class NestedDoc(BaseDoc): d: SimpleDoc -class DeepNestedDoc(BaseDocument): +class DeepNestedDoc(BaseDoc): d: NestedDoc diff --git a/tests/index/elastic/v7/test_column_config.py b/tests/index/elastic/v7/test_column_config.py index bd53ae2361b..80f42c6a347 100644 --- a/tests/index/elastic/v7/test_column_config.py +++ b/tests/index/elastic/v7/test_column_config.py @@ -1,12 +1,12 @@ from pydantic import Field -from docarray import BaseDocument +from docarray import BaseDoc from docarray.index import ElasticV7DocIndex from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 def test_column_config(): - class MyDoc(BaseDocument): + class MyDoc(BaseDoc): text: str color: str = Field(col_type='keyword') @@ -28,7 +28,7 @@ class MyDoc(BaseDocument): def test_field_object(): - class MyDoc(BaseDocument): + class MyDoc(BaseDoc): manager: dict = Field( properties={ 'age': {'type': 'integer'}, @@ -58,7 +58,7 @@ class MyDoc(BaseDocument): def test_field_geo_point(): - class MyDoc(BaseDocument): + class MyDoc(BaseDoc): location: dict = Field(col_type='geo_point') store = ElasticV7DocIndex[MyDoc]() @@ -85,7 +85,7 @@ class MyDoc(BaseDocument): def test_field_range(): - class MyDoc(BaseDocument): + class MyDoc(BaseDoc): expected_attendees: dict = Field(col_type='integer_range') time_frame: dict = Field(col_type='date_range', format='yyyy-MM-dd') diff --git a/tests/index/elastic/v7/test_find.py b/tests/index/elastic/v7/test_find.py index c8aac617804..562b6f68326 100644 --- a/tests/index/elastic/v7/test_find.py +++ b/tests/index/elastic/v7/test_find.py @@ -2,7 +2,7 @@ import torch from pydantic import Field -from docarray import BaseDocument +from docarray import BaseDoc from docarray.index import ElasticV7DocIndex from docarray.typing import NdArray, TorchTensor from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 @@ -10,7 +10,7 @@ def test_find_simple_schema(): - class SimpleSchema(BaseDocument): + class SimpleSchema(BaseDoc): tens: NdArray[10] store = ElasticV7DocIndex[SimpleSchema]() @@ -29,7 +29,7 @@ class SimpleSchema(BaseDocument): def test_find_flat_schema(): - class FlatSchema(BaseDocument): + class FlatSchema(BaseDoc): tens_one: NdArray = Field(dims=10) tens_two: NdArray = Field(dims=50) @@ -61,14 +61,14 @@ class FlatSchema(BaseDocument): def test_find_nested_schema(): - class SimpleDoc(BaseDocument): + class SimpleDoc(BaseDoc): tens: NdArray[10] - class NestedDoc(BaseDocument): + class NestedDoc(BaseDoc): d: SimpleDoc tens: NdArray[10] - class DeepNestedDoc(BaseDocument): + class DeepNestedDoc(BaseDoc): d: NestedDoc tens: NdArray = Field(dims=10) @@ -108,7 +108,7 @@ class DeepNestedDoc(BaseDocument): def test_find_torch(): - class TorchDoc(BaseDocument): + class TorchDoc(BaseDoc): tens: TorchTensor[10] store = ElasticV7DocIndex[TorchDoc]() @@ -137,7 +137,7 @@ class TorchDoc(BaseDocument): def test_find_tensorflow(): from docarray.typing import TensorFlowTensor - class TfDoc(BaseDocument): + class TfDoc(BaseDoc): tens: TensorFlowTensor[10] store = ElasticV7DocIndex[TfDoc]() @@ -183,7 +183,7 @@ def test_find_batched(): def test_filter(): - class MyDoc(BaseDocument): + class MyDoc(BaseDoc): A: bool B: int C: float @@ -212,7 +212,7 @@ class MyDoc(BaseDocument): def test_text_search(): - class MyDoc(BaseDocument): + class MyDoc(BaseDoc): text: str store = ElasticV7DocIndex[MyDoc]() @@ -241,7 +241,7 @@ class MyDoc(BaseDocument): def test_query_builder(): - class MyDoc(BaseDocument): + class MyDoc(BaseDoc): tens: NdArray[10] num: int text: str diff --git a/tests/index/elastic/v7/test_index_get_del.py b/tests/index/elastic/v7/test_index_get_del.py index 772c7e6e55f..8389e28ffbb 100644 --- a/tests/index/elastic/v7/test_index_get_del.py +++ b/tests/index/elastic/v7/test_index_get_del.py @@ -1,8 +1,12 @@ +from typing import Union + import numpy as np import pytest -from docarray import DocumentArray +from docarray import BaseDoc, DocArray +from docarray.documents import ImageDoc, TextDoc from docarray.index import ElasticV7DocIndex +from docarray.typing import NdArray from tests.index.elastic.fixture import start_storage_v7 # noqa: F401 from tests.index.elastic.fixture import DeepNestedDoc, FlatDoc, NestedDoc, SimpleDoc @@ -37,7 +41,7 @@ def ten_deep_nested_docs(): def test_index_simple_schema(ten_simple_docs, use_docarray): store = ElasticV7DocIndex[SimpleDoc]() if use_docarray: - ten_simple_docs = DocumentArray[SimpleDoc](ten_simple_docs) + ten_simple_docs = DocArray[SimpleDoc](ten_simple_docs) store.index(ten_simple_docs) assert store.num_docs() == 10 @@ -47,7 +51,7 @@ def test_index_simple_schema(ten_simple_docs, use_docarray): def test_index_flat_schema(ten_flat_docs, use_docarray): store = ElasticV7DocIndex[FlatDoc]() if use_docarray: - ten_flat_docs = DocumentArray[FlatDoc](ten_flat_docs) + ten_flat_docs = DocArray[FlatDoc](ten_flat_docs) store.index(ten_flat_docs) assert store.num_docs() == 10 @@ -57,7 +61,7 @@ def test_index_flat_schema(ten_flat_docs, use_docarray): def test_index_nested_schema(ten_nested_docs, use_docarray): store = ElasticV7DocIndex[NestedDoc]() if use_docarray: - ten_nested_docs = DocumentArray[NestedDoc](ten_nested_docs) + ten_nested_docs = DocArray[NestedDoc](ten_nested_docs) store.index(ten_nested_docs) assert store.num_docs() == 10 @@ -67,7 +71,7 @@ def test_index_nested_schema(ten_nested_docs, use_docarray): def test_index_deep_nested_schema(ten_deep_nested_docs, use_docarray): store = ElasticV7DocIndex[DeepNestedDoc]() if use_docarray: - ten_deep_nested_docs = DocumentArray[DeepNestedDoc](ten_deep_nested_docs) + ten_deep_nested_docs = DocArray[DeepNestedDoc](ten_deep_nested_docs) store.index(ten_deep_nested_docs) assert store.num_docs() == 10 @@ -238,3 +242,39 @@ def test_num_docs(ten_simple_docs): del store[more_docs[2].id, ten_simple_docs[7].id] assert store.num_docs() == 10 + + +def test_index_union_doc(): + class MyDoc(BaseDoc): + tensor: Union[NdArray, str] + + class MySchema(BaseDoc): + tensor: NdArray + + store = ElasticV7DocIndex[MySchema]() + doc = [MyDoc(tensor=np.random.randn(128))] + store.index(doc) + + id_ = doc[0].id + assert store[id_].id == id_ + assert np.all(store[id_].tensor == doc[0].tensor) + + +def test_index_multi_modal_doc(): + class MyMultiModalDoc(BaseDoc): + image: ImageDoc + text: TextDoc + + store = ElasticV7DocIndex[MyMultiModalDoc]() + + doc = [ + MyMultiModalDoc( + image=ImageDoc(embedding=np.random.randn(128)), text=TextDoc(text='hello') + ) + ] + store.index(doc) + + id_ = doc[0].id + assert store[id_].id == id_ + assert np.all(store[id_].image.embedding == doc[0].image.embedding) + assert store[id_].text.text == doc[0].text.text From 4a6868ae9b01481e02714b8fbb7da025bbfcc98d Mon Sep 17 00:00:00 2001 From: AnneY Date: Wed, 29 Mar 2023 21:11:29 +0800 Subject: [PATCH 44/44] fix: poetry Signed-off-by: AnneY --- poetry.lock | 1923 +++++++++++++++++++++------------------------------ 1 file changed, 782 insertions(+), 1141 deletions(-) diff --git a/poetry.lock b/poetry.lock index 51fa3f6ff11..93569cb83c4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,32 +1,5 @@ # This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand. -[[package]] -name = "aiofiles" -version = "22.1.0" -description = "File support for asyncio." -category = "dev" -optional = false -python-versions = ">=3.7,<4.0" -files = [ - 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-[package.extras] -test = ["mypy", "pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)"] - -[[package]] -name = "zipp" -version = "3.15.0" -description = "Backport of pathlib-compatible object wrapper for zip files" -category = "main" -optional = false -python-versions = ">=3.7" -files = [ - {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"}, - {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"}, + {file = "zipp-3.10.0-py3-none-any.whl", hash = "sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1"}, + {file = "zipp-3.10.0.tar.gz", hash = "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"] -elasticsearch = ["elasticsearch"] aws = ["smart-open"] -common = ["protobuf", "lz4"] -full = ["protobuf", "lz4", "pillow", "types-pillow", "av", "pydub", "trimesh"] +common = ["lz4", "protobuf"] +elasticsearch = ["elasticsearch"] +full = ["av", "lz4", "pillow", "protobuf", "pydub", "trimesh", "types-pillow"] hnswlib = ["hnswlib"] image = ["pillow", "types-pillow"] jac = ["jina-hubble-sdk"] @@ -4412,4 +4053,4 @@ web = ["fastapi"] [metadata] lock-version = "2.0" python-versions = ">=3.7,<4.0" -content-hash = "c5b13c9b48aa9edf9d494ce8ba91cfdd9f78d4220ae758ac8a74b69963fc7253" +content-hash = "3388af41b53300637299b44bd9ea94a18a44368bb1006ee16e04d35c99a238b6"