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700 lines (583 loc) · 23.9 KB
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# mypy: ignore-errors
import warnings
from collections import defaultdict
from dataclasses import dataclass, field
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Generic,
Iterable,
List,
Mapping,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
cast,
)
import numpy as np
from pydantic import parse_obj_as
import docarray.typing
from docarray import BaseDoc
from docarray.array.any_array import AnyDocArray
from docarray.index.abstract import BaseDocIndex, _ColumnInfo, _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._typing import safe_issubclass
from docarray.utils._internal.misc import import_library
from docarray.utils.find import _FindResult, _FindResultBatched
TSchema = TypeVar('TSchema', bound=BaseDoc)
T = TypeVar('T', bound='ElasticDocIndex')
ELASTIC_PY_VEC_TYPES: List[Any] = [list, tuple, np.ndarray, AbstractTensor]
if TYPE_CHECKING:
import tensorflow as tf # type: ignore
import torch
from elastic_transport import NodeConfig
from elasticsearch import Elasticsearch
from elasticsearch.helpers import parallel_bulk
else:
elasticsearch = import_library('elasticsearch', raise_error=True)
from elasticsearch import Elasticsearch
from elasticsearch.helpers import parallel_bulk
elastic_transport = import_library('elastic_transport', raise_error=True)
from elastic_transport import NodeConfig
torch = import_library('torch', raise_error=False)
tf = import_library('tensorflow', raise_error=False)
if torch is not None:
ELASTIC_PY_VEC_TYPES.append(torch.Tensor)
if tf is not None:
from docarray.typing import TensorFlowTensor
ELASTIC_PY_VEC_TYPES.append(tf.Tensor)
ELASTIC_PY_VEC_TYPES.append(TensorFlowTensor)
class ElasticDocIndex(BaseDocIndex, Generic[TSchema]):
def __init__(self, db_config=None, **kwargs):
"""Initialize ElasticDocIndex"""
super().__init__(db_config=db_config, **kwargs)
self._db_config = cast(ElasticDocIndex.DBConfig, self._db_config)
self._logger.debug('Elastic Search index is being initialized')
# ElasticSearch client creation
self._client = Elasticsearch(
hosts=self._db_config.hosts,
**self._db_config.es_config,
)
self._logger.debug('ElasticSearch client has been created')
# ElasticSearh index setup
self._index_vector_params = ('dims', 'similarity', 'index')
self._index_vector_options = ('m', 'ef_construction')
mappings: Dict[str, Any] = {
'dynamic': True,
'_source': {'enabled': 'true'},
'properties': {},
}
mappings.update(self._db_config.index_mappings)
self._logger.debug('Mappings have been updated with db_config.index_mappings')
for col_name, col in self._column_infos.items():
if safe_issubclass(col.docarray_type, AnyDocArray):
continue
if col.db_type == 'dense_vector' and (
not col.n_dim and col.config['dims'] < 0
):
self._logger.info(
f'Not indexing column {col_name}, the dimensionality is not specified'
)
continue
mappings['properties'][col_name] = self._create_index_mapping(col)
self._logger.debug(f'Index mapping created for column {col_name}')
if self._client.indices.exists(index=self.index_name):
self._client_put_mapping(mappings)
self._logger.debug(f'Put mapping for index {self.index_name}')
else:
self._client_create(mappings)
self._logger.debug(f'Created new index {self.index_name} with mappings')
if len(self._db_config.index_settings):
self._client_put_settings(self._db_config.index_settings)
self._logger.debug('Updated index settings')
self._refresh(self.index_name)
self._logger.debug(f'Refreshed index {self.index_name}')
@property
def index_name(self):
default_index_name = (
self._schema.__name__.lower() if self._schema is not None else None
)
if default_index_name is None:
err_msg = (
'A ElasticDocIndex must be typed with a Document type.To do so, use the syntax: '
'ElasticDocIndex[DocumentType] '
)
self._logger.error(err_msg)
raise ValueError(err_msg)
index_name = self._db_config.index_name or default_index_name
self._logger.debug(f'Retrieved index name: {index_name}')
return index_name
###############################################
# Inner classes for query builder and configs #
###############################################
class QueryBuilder(BaseDocIndex.QueryBuilder):
def __init__(self, outer_instance, **kwargs):
super().__init__()
self._outer_instance = outer_instance
self._query: Dict[str, Any] = {
'query': defaultdict(lambda: defaultdict(list))
}
def build(self, *args, **kwargs) -> Any:
"""Build the elastic search query object."""
self._outer_instance._logger.debug(
'Building the Elastic Search query object'
)
if len(self._query['query']) == 0:
del self._query['query']
elif 'knn' in self._query:
self._query['knn']['filter'] = self._query['query']
del self._query['query']
return self._query
def find(
self,
query: Union[AnyTensor, BaseDoc],
search_field: str = 'embedding',
limit: int = 10,
num_candidates: Optional[int] = None,
):
"""
Find k-nearest neighbors of the query.
:param query: query vector for KNN/ANN search. Has single axis.
:param search_field: name of the field to search on
:param limit: maximum number of documents to return per query
:param num_candidates: number of candidates
:return: self
"""
self._outer_instance._logger.debug('Executing find query')
self._outer_instance._validate_search_field(search_field)
if isinstance(query, BaseDoc):
query_vec = BaseDocIndex._get_values_by_column([query], search_field)[0]
else:
query_vec = query
query_vec_np = BaseDocIndex._to_numpy(self._outer_instance, query_vec)
self._query['knn'] = self._outer_instance._form_search_body(
query_vec_np,
limit,
search_field,
num_candidates,
)['knn']
return self
# filter accepts Leaf/Compound query clauses
# https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html
def filter(self, query: Dict[str, Any], limit: int = 10):
"""Find documents in the index based on a filter query
:param query: the query to execute
:param limit: maximum number of documents to return
:return: self
"""
self._outer_instance._logger.debug('Executing filter query')
self._query['size'] = limit
self._query['query']['bool']['filter'].append(query)
return self
def text_search(self, query: str, search_field: str = 'text', limit: int = 10):
"""Find documents in the index based on a text search query
:param query: The text to search for
:param search_field: name of the field to search on
:param limit: maximum number of documents to find
:return: self
"""
self._outer_instance._logger.debug('Executing text search query')
self._outer_instance._validate_search_field(search_field)
self._query['size'] = limit
self._query['query']['bool']['must'].append(
{'match': {search_field: query}}
)
return self
find_batched = _raise_not_composable('find_batched')
filter_batched = _raise_not_composable('filter_batched')
text_search_batched = _raise_not_composable('text_search_batched')
def build_query(self, **kwargs) -> QueryBuilder:
"""
Build a query for ElasticDocIndex.
:param kwargs: parameters to forward to QueryBuilder initialization
:return: QueryBuilder object
"""
return self.QueryBuilder(self, **kwargs)
@dataclass
class DBConfig(BaseDocIndex.DBConfig):
"""Dataclass that contains all "static" configurations of ElasticDocIndex."""
hosts: Union[
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)
index_settings: Dict[str, Any] = field(default_factory=dict)
index_mappings: Dict[str, Any] = field(default_factory=dict)
default_column_config: Dict[Any, Dict[str, Any]] = field(default_factory=dict)
def __post_init__(self):
self.default_column_config = {
'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_range': {},
'ip': {},
'version': {},
'histogram': {},
'text': {},
'annotated_text': {},
'completion': {},
'search_as_you_type': {},
'token_count': {},
'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
}
self.default_column_config['dense_vector'] = self.dense_vector_config()
def dense_vector_config(self):
"""Get the dense vector config."""
config = {
'dims': -1,
'index': True,
'similarity': 'cosine', # 'l2_norm', 'dot_product', 'cosine'
'm': 16,
'ef_construction': 100,
'num_candidates': 10000,
}
return config
@dataclass
class RuntimeConfig(BaseDocIndex.RuntimeConfig):
"""Dataclass that contains all "dynamic" configurations of ElasticDocIndex."""
chunk_size: int = 500
###############################################
# Implementation of abstract methods #
###############################################
def python_type_to_db_type(self, python_type: Type) -> Any:
"""Map python type to database type.
Takes any python type and returns the corresponding database column type.
:param python_type: a python type.
:return: the corresponding database column type,
or None if ``python_type`` is not supported.
"""
self._logger.debug(f'Mapping Python type {python_type} to database type')
for allowed_type in ELASTIC_PY_VEC_TYPES:
if safe_issubclass(python_type, allowed_type):
self._logger.info(
f'Mapped Python type {python_type} to database type "dense_vector"'
)
return 'dense_vector'
elastic_py_types = {
docarray.typing.ID: 'keyword',
docarray.typing.AnyUrl: 'keyword',
bool: 'boolean',
int: 'integer',
float: 'float',
str: 'text',
bytes: 'binary',
dict: 'object',
}
for type in elastic_py_types.keys():
if safe_issubclass(python_type, type):
self._logger.info(
f'Mapped Python type {python_type} to database type "{elastic_py_types[type]}"'
)
return elastic_py_types[type]
err_msg = f'Unsupported column type for {type(self)}: {python_type}'
self._logger.error(err_msg)
raise ValueError(err_msg)
def _index(
self,
column_to_data: Mapping[str, Generator[Any, None, None]],
refresh: bool = True,
chunk_size: Optional[int] = None,
):
self._index_subindex(column_to_data)
data = self._transpose_col_value_dict(column_to_data)
requests = []
for row in data:
request = {
'_index': self.index_name,
'_id': row['id'],
}
for col_name, col in self._column_infos.items():
if safe_issubclass(col.docarray_type, AnyDocArray):
continue
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)
_, warning_info = self._send_requests(requests, chunk_size)
for info in warning_info:
warnings.warn(str(info))
self._logger.warning('Warning: %s', str(info))
if refresh:
self._logger.debug('Refreshing the index')
self._refresh(self.index_name)
def num_docs(self) -> int:
"""
Get the number of documents.
"""
self._logger.debug('Getting the number of documents in the index')
return self._client.count(index=self.index_name)['count']
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, chunk_size)
# 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.index_name)
def _get_items(self, doc_ids: Sequence[str]) -> Sequence[Dict[str, Any]]:
accumulated_docs = []
accumulated_docs_id_not_found = []
es_rows = self._client_mget(doc_ids)['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'])
# 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')
return accumulated_docs
def execute_query(self, query: Dict[str, Any], *args, **kwargs) -> Any:
"""
Execute a query on the ElasticDocIndex.
Can take two kinds of inputs:
1. A native query of the underlying database. This is meant as a passthrough so that you
can enjoy any functionality that is not available through the Document index API.
2. The output of this Document index' `QueryBuilder.build()` method.
:param query: the query to execute
:param args: positional arguments to pass to the query
:param kwargs: keyword arguments to pass to the query
:return: the result of the query
"""
self._logger.debug(f'Executing query: {query}')
if args or kwargs:
err_msg = (
f'args and kwargs not supported for `execute_query` on {type(self)}'
)
self._logger.error(err_msg)
raise ValueError(err_msg)
resp = self._client.search(index=self.index_name, **query)
docs, scores = self._format_response(resp)
return _FindResult(documents=docs, scores=parse_obj_as(NdArray, scores))
def _find(
self, query: np.ndarray, limit: int, search_field: str = ''
) -> _FindResult:
body = self._form_search_body(query, limit, search_field)
resp = self._client_search(**body)
docs, scores = self._format_response(resp)
return _FindResult(documents=docs, scores=parse_obj_as(NdArray, scores))
def _find_batched(
self,
queries: np.ndarray,
limit: int,
search_field: str = '',
) -> _FindResultBatched:
request = []
for query in queries:
head = {'index': self.index_name}
body = self._form_search_body(query, limit, search_field)
request.extend([head, body])
responses = self._client_msearch(request)
das, scores = zip(
*[self._format_response(resp) for resp in responses['responses']]
)
return _FindResultBatched(documents=list(das), scores=scores)
def _filter(
self,
filter_query: Dict[str, Any],
limit: int,
) -> List[Dict]:
resp = self._client_search(query=filter_query, size=limit)
docs, _ = self._format_response(resp)
return docs
def _filter_batched(
self,
filter_queries: Any,
limit: int,
) -> List[List[Dict]]:
request = []
for query in filter_queries:
head = {'index': self.index_name}
body = {'query': query, 'size': limit}
request.extend([head, body])
responses = self._client_msearch(request)
das, _ = zip(*[self._format_response(resp) for resp in responses['responses']])
return list(das)
def _text_search(
self,
query: str,
limit: int,
search_field: str = '',
) -> _FindResult:
body = self._form_text_search_body(query, limit, search_field)
resp = self._client_search(**body)
docs, scores = self._format_response(resp)
return _FindResult(documents=docs, scores=np.array(scores)) # type: ignore
def _text_search_batched(
self,
queries: Sequence[str],
limit: int,
search_field: str = '',
) -> _FindResultBatched:
request = []
for query in queries:
head = {'index': self.index_name}
body = self._form_text_search_body(query, limit, search_field)
request.extend([head, body])
responses = self._client_msearch(request)
das, scores = zip(
*[self._format_response(resp) for resp in responses['responses']]
)
return _FindResultBatched(documents=list(das), scores=scores)
def _filter_by_parent_id(self, id: str) -> List[str]:
resp = self._client_search(
query={'term': {'parent_id': id}}, fields=['id'], _source=False
)
ids = [hit['fields']['id'][0] for hit in resp['hits']['hits']]
return ids
###############################################
# Helpers #
###############################################
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}
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
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(
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"""
accumulated_info = []
warning_info = []
for success, info in parallel_bulk(
self._client,
request,
raise_on_error=False,
raise_on_exception=False,
chunk_size=chunk_size if chunk_size else self._runtime_config.chunk_size, # type: ignore
**kwargs,
):
if not success:
warning_info.append(info)
else:
accumulated_info.append(info)
return accumulated_info, warning_info
def _form_search_body(
self,
query: np.ndarray,
limit: int,
search_field: str = '',
num_candidates: Optional[int] = None,
) -> Dict[str, Any]:
if not num_candidates:
num_candidates = self._db_config.default_column_config['dense_vector'][
'num_candidates'
]
body = {
'size': limit,
'knn': {
'field': search_field,
'query_vector': query,
'k': limit,
'num_candidates': num_candidates,
},
}
return body
def _form_text_search_body(
self, 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], List[Any]]:
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, [parse_obj_as(NdArray, np.array(s)) for s in scores]
def _refresh(self, index_name: str):
self._client.indices.refresh(index=index_name)
def _doc_exists(self, doc_id: str) -> bool:
if len(doc_id) == 0:
return False
ret = self._client_mget([doc_id])
return ret["docs"][0]["found"]
###############################################
# API Wrappers #
###############################################
def _client_put_mapping(self, mappings: Dict[str, Any]):
self._client.indices.put_mapping(
index=self.index_name, properties=mappings['properties']
)
def _client_create(self, mappings: Dict[str, Any]):
self._client.indices.create(index=self.index_name, mappings=mappings)
def _client_put_settings(self, settings: Dict[str, Any]):
self._client.indices.put_settings(index=self.index_name, settings=settings)
def _client_mget(self, ids: Sequence[str]):
return self._client.mget(index=self.index_name, ids=ids)
def _client_search(self, **kwargs):
return self._client.search(index=self.index_name, **kwargs)
def _client_msearch(self, request: List[Dict[str, Any]]):
return self._client.msearch(index=self.index_name, searches=request)