diff --git a/docarray/array/array/array.py b/docarray/array/array/array.py
index 9f78e29cb16..056a962a0a9 100644
--- a/docarray/array/array/array.py
+++ b/docarray/array/array/array.py
@@ -23,7 +23,7 @@
 from docarray.array.array.io import IOMixinArray
 from docarray.base_document import AnyDocument, BaseDocument
 from docarray.typing import NdArray
-from docarray.utils.misc import is_torch_available
+from docarray.utils.misc import is_np_int, is_torch_available
 
 if TYPE_CHECKING:
     from pydantic import BaseConfig
@@ -56,17 +56,6 @@ def _delegate_meth(self, *args, **kwargs):
     return _delegate_meth
 
 
-def _is_np_int(item: Any) -> bool:
-    dtype = getattr(item, 'dtype', None)
-    ndim = getattr(item, 'ndim', None)
-    if dtype is not None and ndim is not None:
-        try:
-            return ndim == 0 and np.issubdtype(dtype, np.integer)
-        except TypeError:
-            return False
-    return False  # this is unreachable, but mypy wants it
-
-
 class DocumentArray(IOMixinArray, AnyDocumentArray[T_doc]):
     """
      DocumentArray is a container of Documents.
@@ -255,7 +244,7 @@ def _normalize_index_item(
             return item
 
         # numpy index types
-        if _is_np_int(item):
+        if is_np_int(item):
             return item.item()
 
         index_has_getitem = hasattr(item, '__getitem__')
diff --git a/docarray/doc_index/__init__.py b/docarray/doc_index/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/docarray/doc_index/abstract_doc_index.py b/docarray/doc_index/abstract_doc_index.py
new file mode 100644
index 00000000000..99557d78b5d
--- /dev/null
+++ b/docarray/doc_index/abstract_doc_index.py
@@ -0,0 +1,678 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field, replace
+from functools import wraps
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Generator,
+    Generic,
+    List,
+    NamedTuple,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+
+import numpy as np
+from typing_inspect import is_union_type
+
+from docarray import BaseDocument, DocumentArray
+from docarray.array.abstract_array import AnyDocumentArray
+from docarray.typing import AnyTensor
+from docarray.utils.find import FindResult
+from docarray.utils.misc import torch_imported
+
+if TYPE_CHECKING:
+    from pydantic.fields import ModelField
+
+if torch_imported:
+    import torch
+
+TSchema = TypeVar('TSchema', bound=BaseDocument)
+
+
+class FindResultBatched(NamedTuple):
+    documents: List[DocumentArray]
+    scores: np.ndarray
+
+
+def _delegate_to_query(method_name: str, func: Callable):
+    @wraps(func)
+    def inner(self, *args, **kwargs):
+        if args:
+            raise ValueError(
+                f'Positional arguments are not supported for '
+                f'{type(self)}.`{method_name}`.'
+                f' Use keyword arguments instead.'
+            )
+        self._queries.append((method_name, kwargs))
+        return self
+
+    return inner
+
+
+def _raise_not_composable(name):
+    def _inner(*args, **kwargs):
+        raise NotImplementedError(
+            f'`{name}` is not usable through the query builder of this Document Store. '
+            f'But you can call `doc_store.{name}()` directly.'
+        )
+
+    return _inner
+
+
+@dataclass
+class _Column:
+    docarray_type: Type
+    db_type: Any
+    n_dim: Optional[int]
+    config: Dict[str, Any]
+
+
+class composable:
+    """Decorator that marks methods in a DocumentIndex as composable,
+    i.e. they can be used in a query builder.
+    """
+
+    def __init__(self, fn):
+        self.fn = fn
+
+    def __set_name__(self, owner, name):
+        if name.startswith('_') and not name.startswith('__'):
+            public_name = name[1:]
+        else:
+            public_name = name
+        setattr(
+            owner.QueryBuilder, public_name, _delegate_to_query(public_name, self.fn)
+        )
+        setattr(owner, name, self.fn)
+
+
+class BaseDocumentIndex(ABC, Generic[TSchema]):
+    """Abstract class for all Document Stores"""
+
+    # the BaseDocument that defines the schema of the store
+    # for subclasses this is filled automatically
+    _schema: Optional[Type[BaseDocument]] = None
+
+    def __init__(self, db_config=None, **kwargs):
+        if self._schema is None:
+            raise ValueError(
+                'A DocumentIndex must be typed with a Document type.'
+                'To do so, use the syntax: DocumentIndex[DocumentType]'
+            )
+        self._db_config = db_config or self.DBConfig(**kwargs)
+        if not isinstance(self._db_config, self.DBConfig):
+            raise ValueError(f'db_config must be of type {self.DBConfig}')
+        self._runtime_config = self.RuntimeConfig()
+        self._columns: Dict[str, _Column] = self._create_columns(self._schema)
+
+    ###############################################
+    # Inner classes for query builder and configs #
+    # Subclasses must subclass & implement these  #
+    ###############################################
+
+    class QueryBuilder(ABC):
+        def __init__(self):
+            # list of tuples (method name, kwargs)
+            # no need to populate this, it's done automatically
+            self._queries: List[Tuple[str, Dict]] = []
+
+        @abstractmethod
+        def build(self, *args, **kwargs) -> Any:
+            """Build the DB specific query object.
+            The DB specific implementation can leverage self._queries to do so.
+            The output of this should be able to be passed to execute_query().
+            """
+            ...
+
+        # no need to implement the methods below
+        # they are handled automatically by the `composable` decorator
+        find = _raise_not_composable('find')
+        filter = _raise_not_composable('filter')
+        text_search = _raise_not_composable('text_search')
+        find_batched = _raise_not_composable('find_batched')
+        filter_batched = _raise_not_composable('filter_batched')
+        text_search_batched = _raise_not_composable('text_search_batched')
+
+    @dataclass
+    class DBConfig(ABC):
+        ...
+
+    @dataclass
+    class RuntimeConfig(ABC):
+        # default configurations for every column type
+        # a dictionary from a column type (DB specific) to a dictionary
+        # of default configurations for that type
+        # These configs are used if no configs are specified in the `Field(...)`
+        # of a field in the Document schema (`cls._schema`)
+        # Example: `default_column_config['VARCHAR'] = {'length': 255}`
+        default_column_config: Dict[Type, Dict[str, Any]] = field(default_factory=dict)
+
+    #####################################
+    # Abstract methods                  #
+    # Subclasses must implement these   #
+    #####################################
+
+    @abstractmethod
+    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.
+        """
+        ...
+
+    @abstractmethod
+    def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]):
+        """Index a document into the store"""
+        # `column_to_data` is a dictionary from column name to a generator
+        # that yields the data for that column.
+        # If you want to work directly on documents, you can implement index() instead
+        # If you implement index(), _index() only needs a dummy implementation.
+        ...
+
+    @abstractmethod
+    def num_docs(self) -> int:
+        """Return the number of indexed documents"""
+        ...
+
+    @abstractmethod
+    def _del_items(self, doc_ids: Sequence[str]):
+        """Delete Documents from the index.
+
+        :param doc_ids: ids to delete from the Document Store
+        """
+        ...
+
+    @abstractmethod
+    def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]:
+        """Get Documents from the index, by `id`.
+        If no document is found, a KeyError is raised.
+
+        :param doc_ids: ids to get from the Document Index
+        :return: Sequence of Documents, sorted corresponding to the order of `doc_ids`. Duplicate `doc_ids` can be omitted in the output.
+        """
+        ...
+
+    @abstractmethod
+    def execute_query(self, query: Any, *args, **kwargs) -> Any:
+        """
+        Execute a query on the database.
+        This is intended as a pass-through to the underlying database, so that users
+        can enjoy anything that is not available through our API.
+
+        Also, this is the method that the output of the query builder is passed to.
+
+        :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
+        """
+        ...
+
+    @abstractmethod
+    def _find(
+        self,
+        query: np.ndarray,
+        search_field: str,
+        limit: int,
+    ) -> FindResult:
+        """Find documents in the index
+
+        :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
+        :return: a named tuple containing `documents` and `scores`
+        """
+        # NOTE: in standard implementations,
+        # `search_field` is equal to the column name to search on
+        ...
+
+    @abstractmethod
+    def _find_batched(
+        self,
+        query: np.ndarray,
+        search_field: str,
+        limit: int,
+    ) -> FindResultBatched:
+        """Find documents in the index
+
+        :param query: 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
+        :return: a named tuple containing `documents` and `scores`
+        """
+        ...
+
+    @abstractmethod
+    def _filter(
+        self,
+        filter_query: Any,
+        limit: int,
+    ) -> DocumentArray:
+        """Find documents in the index based on a filter query
+
+        :param filter_query: the DB specific filter query to execute
+        :param limit: maximum number of documents to return
+        :return: a DocumentArray containing the documents that match the filter query
+        """
+        ...
+
+    @abstractmethod
+    def _filter_batched(
+        self,
+        filter_queries: Any,
+        limit: int,
+    ) -> List[DocumentArray]:
+        """Find documents in the index based on multiple filter queries.
+        Each query is considered individually, and results are returned per query.
+
+        :param filter_queries: the DB specific filter queries to execute
+        :param limit: maximum number of documents to return per query
+        :return: List of DocumentArrays containing the documents
+            that match the filter queries
+        """
+        ...
+
+    @abstractmethod
+    def _text_search(
+        self,
+        query: str,
+        search_field: str,
+        limit: int,
+    ) -> FindResult:
+        """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 return
+        :return: a named tuple containing `documents` and `scores`
+        """
+        # NOTE: in standard implementations,
+        # `search_field` is equal to the column name to search on
+        ...
+
+    @abstractmethod
+    def _text_search_batched(
+        self,
+        queries: Sequence[str],
+        search_field: str,
+        limit: int,
+    ) -> FindResultBatched:
+        """Find documents in the index based on a text search query
+
+        :param queries: The texts to search for
+        :param search_field: name of the field to search on
+        :param limit: maximum number of documents to return per query
+        :return: a named tuple containing `documents` and `scores`
+        """
+        # NOTE: in standard implementations,
+        # `search_field` is equal to the column name to search on
+        ...
+
+    ####################################################
+    # Optional overrides                               #
+    # Subclasses may or may not need to change these #
+    ####################################################
+
+    def __getitem__(
+        self, key: Union[str, Sequence[str]]
+    ) -> Union[TSchema, DocumentArray[TSchema]]:
+        """Get one or multiple Documents into the index, by `id`.
+        If no document is found, a KeyError is raised.
+
+        :param key: id or ids to get from the Document Index
+        """
+        # normalize input
+        if isinstance(key, str):
+            return_singleton = True
+            key = [key]
+        else:
+            return_singleton = False
+        # retrieve data
+        doc_sequence = self._get_items(key)
+        # cast output
+        if isinstance(doc_sequence, DocumentArray):
+            out_da: DocumentArray[TSchema] = doc_sequence
+        else:
+            da_cls = DocumentArray.__class_getitem__(
+                cast(Type[BaseDocument], self._schema)
+            )
+            out_da = da_cls(doc_sequence)
+        return out_da[0] if return_singleton else out_da  # type: ignore
+
+    def __delitem__(self, key: Union[str, Sequence[str]]):
+        """Delete one or multiple Documents from the index, by `id`.
+        If no document is found, a KeyError is raised.
+
+        :param key: id or ids to delete from the Document Index
+        """
+        if isinstance(key, str):
+            key = [key]
+        self._del_items(key)
+
+    def configure(self, runtime_config=None, **kwargs):
+        """
+        Configure the DocumentIndex.
+        You can either pass a config object to `config` or pass individual config
+        parameters as keyword arguments.
+        If a configuration object is passed, it will replace the current configuration.
+        If keyword arguments are passed, they will update the current configuration.
+
+        :param runtime_config: the configuration to apply
+        :param kwargs: individual configuration parameters
+        """
+        if runtime_config is None:
+            self._runtime_config = replace(self._runtime_config, **kwargs)
+        else:
+            if not isinstance(runtime_config, self.RuntimeConfig):
+                raise ValueError(f'runtime_config must be of type {self.RuntimeConfig}')
+            self._runtime_config = runtime_config
+
+    def index(self, docs: Union[BaseDocument, Sequence[BaseDocument]], **kwargs):
+        """Index Documents into the index.
+
+        :param docs: Documents to index
+        """
+        data_by_columns = self._get_col_value_dict(docs)
+        self._index(data_by_columns, **kwargs)  # type: ignore
+
+    def find(
+        self,
+        query: Union[AnyTensor, BaseDocument],
+        search_field: str = 'embedding',
+        limit: int = 10,
+        **kwargs,
+    ) -> FindResult:
+        """Find documents in the index using nearest neighbor search.
+
+        :param query: query vector for KNN/ANN search.
+            Can be either a tensor-like (np.array, torch.Tensor, etc.)
+            with a single axis, or a Document
+        :param search_field: name of the field to search on.
+            Documents in the index are retrieved based on this similarity
+            of this field to the query.
+        :param limit: maximum number of documents to return
+        :return: a named tuple containing `documents` and `scores`
+        """
+        if isinstance(query, BaseDocument):
+            query_vec = self._get_values_by_column([query], search_field)[0]
+        else:
+            query_vec = query
+        query_vec_np = self._to_numpy(query_vec)
+        return self._find(
+            query_vec_np, search_field=search_field, limit=limit, **kwargs  # type: ignore
+        )
+
+    def find_batched(
+        self,
+        queries: Union[AnyTensor, DocumentArray],
+        search_field: str = 'embedding',
+        limit: int = 10,
+        **kwargs,
+    ) -> FindResultBatched:
+        """Find documents in the index using nearest neighbor search.
+
+        :param queries: query vector for KNN/ANN search.
+            Can be either a tensor-like (np.array, torch.Tensor, etc.) with a,
+            or a DocumentArray.
+            If a tensor-like is passed, it should have shape (batch_size, vector_dim)
+        :param search_field: name of the field to search on.
+            Documents in the index are retrieved based on this similarity
+            of this field to the query.
+        :param limit: maximum number of documents to return per query
+        :return: a named tuple containing `documents` and `scores`
+        """
+        if isinstance(queries, Sequence):
+            query_vec_list = self._get_values_by_column(queries, search_field)
+            query_vec_np = np.stack(
+                tuple(self._to_numpy(query_vec) for query_vec in query_vec_list)
+            )
+        else:
+            query_vec_np = self._to_numpy(queries)
+
+        return self._find_batched(
+            query_vec_np, search_field=search_field, limit=limit, **kwargs  # type: ignore
+        )
+
+    def filter(
+        self,
+        filter_query: Any,
+        limit: int = 10,
+        **kwargs,
+    ) -> DocumentArray:
+        """Find documents in the index based on a filter query
+
+        :param filter_query: the DB specific filter query to execute
+        :param limit: maximum number of documents to return
+        :return: a DocumentArray containing the documents that match the filter query
+        """
+        return self._filter(filter_query, limit=limit, **kwargs)  # type: ignore
+
+    def filter_batched(
+        self,
+        filter_queries: Any,
+        limit: int = 10,
+        **kwargs,
+    ) -> List[DocumentArray]:
+        """Find documents in the index based on multiple filter queries.
+
+        :param filter_queries: the DB specific filter query to execute
+        :param limit: maximum number of documents to return
+        :return: a DocumentArray containing the documents that match the filter query
+        """
+        return self._filter_batched(filter_queries, limit=limit, **kwargs)  # type: ignore
+
+    def text_search(
+        self,
+        query: Union[str, BaseDocument],
+        search_field: str = 'text',
+        limit: int = 10,
+        **kwargs,
+    ) -> FindResult:
+        """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 return
+        :return: a named tuple containing `documents` and `scores`
+        """
+        if isinstance(query, BaseDocument):
+            query_text = self._get_values_by_column([query], search_field)[0]
+        else:
+            query_text = query
+        return self._text_search(
+            query_text, search_field=search_field, limit=limit, **kwargs  # type: ignore
+        )
+
+    def text_search_batched(
+        self,
+        queries: Union[Sequence[str], Sequence[BaseDocument]],
+        search_field: str = 'embedding',
+        limit: int = 10,
+        **kwargs,
+    ) -> FindResultBatched:
+        """Find documents in the index based on a text search query.
+
+        :param queries: The texts to search for
+        :param search_field: name of the field to search on
+        :param limit: maximum number of documents to return
+        :return: a named tuple containing `documents` and `scores`
+        """
+        if isinstance(queries[0], BaseDocument):
+            query_docs: Sequence[BaseDocument] = cast(Sequence[BaseDocument], queries)
+            query_texts: Sequence[str] = self._get_values_by_column(
+                query_docs, search_field
+            )
+        else:
+            query_texts = cast(Sequence[str], queries)
+        return self._text_search_batched(
+            query_texts, search_field=search_field, limit=limit, **kwargs  # type: ignore
+        )
+
+    ##########################################################
+    # Helper methods                                         #
+    # These might be useful in your subclass implementation  #
+    ##########################################################
+
+    @staticmethod
+    def _get_values_by_column(docs: Sequence[BaseDocument], col_name: str) -> List[Any]:
+        """Get the value of a column of a document.
+
+        :param docs: The DocumentArray to get the values from
+        :param col_name: The name of the column, e.g. 'text' or 'image__tensor'
+        :return: The value of the column of `doc`
+        """
+        leaf_vals = []
+        for doc in docs:
+            if '__' in col_name:
+                fields = col_name.split('__')
+                leaf_doc: BaseDocument = doc
+                for f in fields[:-1]:
+                    leaf_doc = getattr(leaf_doc, f)
+                leaf_vals.append(getattr(leaf_doc, fields[-1]))
+            else:
+                leaf_vals.append(getattr(doc, col_name))
+        return leaf_vals
+
+    def _get_col_value_dict(
+        self, docs: Union[BaseDocument, Sequence[BaseDocument]]
+    ) -> Dict[str, Generator[Any, None, None]]:
+        """
+        Get all data from a (sequence of) document(s), flattened out by column.
+        :param docs: The document(s) to get the data from
+        :return: A dictionary mapping column names to a generator of values
+        """
+        if isinstance(docs, BaseDocument):
+            docs_seq: Sequence[BaseDocument] = [docs]
+        else:
+            docs_seq = docs
+        if not self._is_schema_compatible(docs_seq):
+            raise ValueError(
+                'The schema of the documents to be indexed is not compatible'
+                ' with the schema of the index.'
+            )
+
+        def _col_gen(col_name: str):
+            return (self._get_values_by_column([doc], col_name)[0] for doc in docs_seq)
+
+        return {col_name: _col_gen(col_name) for col_name in self._columns}
+
+    ##################################################
+    # Behind-the-scenes magic                        #
+    # Subclasses should not need to implement these  #
+    ##################################################
+    def __class_getitem__(cls, item: Type[TSchema]):
+        if not isinstance(item, type):
+            # do nothing
+            # enables use in static contexts with type vars, e.g. as type annotation
+            return Generic.__class_getitem__.__func__(cls, item)  # type: ignore
+        if not issubclass(item, BaseDocument):
+            raise ValueError(
+                f'{cls.__name__}[item] `item` should be a Document not a {item} '
+            )
+
+        class _DocumentIndexTyped(cls):  # type: ignore
+            _schema: Type[TSchema] = item
+
+        _DocumentIndexTyped.__name__ = f'{cls.__name__}[{item.__name__}]'
+        _DocumentIndexTyped.__qualname__ = f'{cls.__qualname__}[{item.__name__}]'
+
+        return _DocumentIndexTyped
+
+    def build_query(self) -> QueryBuilder:
+        """
+        Build a query for this DocumentIndex.
+
+        :return: a new `QueryBuilder` object for this DocumentIndex
+        """
+        return self.QueryBuilder()  # type: ignore
+
+    def _create_columns(self, schema: Type[BaseDocument]) -> Dict[str, _Column]:
+        columns: Dict[str, _Column] = dict()
+        for field_name, field_ in schema.__fields__.items():
+            t_ = schema._get_field_type(field_name)
+            if is_union_type(t_):
+                raise ValueError(
+                    'Union types are not supported in the schema of a DocumentIndex.'
+                    f' Instead of using type {t_} use a single specific type.'
+                )
+            elif issubclass(t_, AnyDocumentArray):
+                raise ValueError(
+                    'Indexing field of DocumentArray type (=subindex)'
+                    'is not yet supported.'
+                )
+            elif issubclass(t_, BaseDocument):
+                columns = dict(
+                    columns,
+                    **{
+                        f'{field_name}__{nested_name}': t
+                        for nested_name, t in self._create_columns(t_).items()
+                    },
+                )
+            else:
+                columns[field_name] = self._create_single_column(field_, t_)
+        return columns
+
+    def _create_single_column(self, field: 'ModelField', type_: Type) -> _Column:
+        db_type = self.python_type_to_db_type(type_)
+        config = self._runtime_config.default_column_config[db_type].copy()
+        custom_config = field.field_info.extra
+        config.update(custom_config)
+        # parse n_dim from parametrized tensor type
+        if (
+            hasattr(type_, '__docarray_target_shape__')
+            and type_.__docarray_target_shape__
+        ):
+            if len(type_.__docarray_target_shape__) == 1:
+                n_dim = type_.__docarray_target_shape__[0]
+            else:
+                n_dim = type_.__docarray_target_shape__
+        else:
+            n_dim = None
+        return _Column(docarray_type=type_, db_type=db_type, config=config, n_dim=n_dim)
+
+    def _is_schema_compatible(self, docs: Sequence[BaseDocument]) -> bool:
+        """Flatten a DocumentArray into a DocumentArray of the schema type."""
+        reference_col_db_types = [
+            (name, col.db_type) for name, col in self._columns.items()
+        ]
+        if isinstance(docs, AnyDocumentArray):
+            input_columns = self._create_columns(docs.document_type)
+            input_col_db_types = [
+                (name, col.db_type) for name, col in input_columns.items()
+            ]
+            # this could be relaxed in the future,
+            # see schema translation ideas in the design doc
+            return reference_col_db_types == input_col_db_types
+        else:
+            for d in docs:
+                input_columns = self._create_columns(type(d))
+                input_col_db_types = [
+                    (name, col.db_type) for name, col in input_columns.items()
+                ]
+                # this could be relaxed in the future,
+                # see schema translation ideas in the design doc
+                if reference_col_db_types != input_col_db_types:
+                    return False
+            return True
+
+    def _to_numpy(self, val: Any) -> Any:
+        if isinstance(val, np.ndarray):
+            return val
+        elif isinstance(val, (list, tuple)):
+            return np.array(val)
+        elif torch_imported and isinstance(val, torch.Tensor):
+            return val.numpy()
+        else:
+            raise ValueError(f'Unsupported input type for {type(self)}: {type(val)}')
diff --git a/docarray/doc_index/backends/__init__.py b/docarray/doc_index/backends/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/docarray/doc_index/backends/hnswlib_doc_index.py b/docarray/doc_index/backends/hnswlib_doc_index.py
new file mode 100644
index 00000000000..45a358a032a
--- /dev/null
+++ b/docarray/doc_index/backends/hnswlib_doc_index.py
@@ -0,0 +1,384 @@
+import hashlib
+import os
+import sqlite3
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    Generic,
+    List,
+    Sequence,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+
+import hnswlib
+import numpy as np
+
+import docarray.typing
+from docarray import BaseDocument, DocumentArray
+from docarray.doc_index.abstract_doc_index import (
+    BaseDocumentIndex,
+    FindResultBatched,
+    _Column,
+)
+from docarray.proto import DocumentProto
+from docarray.utils.filter import filter as da_filter
+from docarray.utils.find import FindResult
+from docarray.utils.misc import is_np_int, torch_imported
+
+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='HnswDocumentIndex')
+
+HNSWLIB_PY_VEC_TYPES = [list, tuple, np.ndarray]
+if torch_imported:
+    import torch
+
+    HNSWLIB_PY_VEC_TYPES.append(torch.Tensor)
+
+
+class HnswDocumentIndex(BaseDocumentIndex, Generic[TSchema]):
+    def __init__(self, db_config=None, **kwargs):
+        super().__init__(db_config=db_config, **kwargs)
+        self._db_config = cast(HnswDocumentIndex.DBConfig, self._db_config)
+        self._work_dir = self._db_config.work_dir
+        load_existing = os.path.exists(self._work_dir) and os.listdir(self._work_dir)
+        Path(self._work_dir).mkdir(parents=True, exist_ok=True)
+
+        # HNSWLib setup
+        self._index_construct_params = ('space', 'dim')
+        self._index_init_params = (
+            'max_elements',
+            'ef_construction',
+            'M',
+            'allow_replace_deleted',
+        )
+
+        self._hnsw_locations = {
+            col_name: os.path.join(self._work_dir, f'{col_name}.bin')
+            for col_name, col in self._columns.items()
+            if col.config
+        }
+        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
+            if load_existing:
+                self._hnsw_indices[col_name] = self._load_index(col_name, col)
+            else:
+                self._hnsw_indices[col_name] = self._create_index(col)
+
+        # SQLite setup
+        self._sqlite_db_path = os.path.join(self._work_dir, 'docs_sqlite.db')
+        self._sqlite_conn = sqlite3.connect(self._sqlite_db_path)
+        self._sqlite_cursor = self._sqlite_conn.cursor()
+        self._create_docs_table()
+        self._sqlite_conn.commit()
+
+    ###############################################
+    # 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):
+        work_dir: str = '.'
+
+    @dataclass
+    class RuntimeConfig(BaseDocumentIndex.RuntimeConfig):
+        default_column_config: Dict[Type, Dict[str, Any]] = field(
+            default_factory=lambda: {
+                np.ndarray: {
+                    'dim': 128,
+                    'space': 'l2',  # 'l2', 'ip', 'cosine'
+                    'max_elements': 1024,
+                    'ef_construction': 200,
+                    'ef': 10,
+                    'M': 16,
+                    'allow_replace_deleted': True,
+                    'num_threads': 1,
+                },
+                # `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 HNSWLIB_PY_VEC_TYPES:
+            if issubclass(python_type, allowed_type):
+                return np.ndarray
+
+        if python_type == docarray.typing.ID:
+            return None
+
+        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"""
+        if kwargs:
+            raise ValueError(f'{list(kwargs.keys())} are not valid keyword arguments')
+        doc_seq = docs if isinstance(docs, Sequence) else [docs]
+        data_by_columns = self._get_col_value_dict(doc_seq)
+        hashed_ids = tuple(self._to_hashed_id(doc.id) for doc in doc_seq)
+
+        # indexing into HNSWLib and SQLite sequentially
+        # could be improved by processing in parallel
+        for col_name, index in self._hnsw_indices.items():
+            data = data_by_columns[col_name]
+            data_np = [self._to_numpy(arr) for arr in data]
+            data_stacked = np.stack(data_np)
+            index.add_items(data_stacked, ids=hashed_ids)
+            index.save_index(self._hnsw_locations[col_name])
+
+        self._send_docs_to_sqlite(doc_seq)
+        self._sqlite_conn.commit()
+
+    def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any:
+        if args or kwargs:
+            raise ValueError(
+                f'args and kwargs not supported for `execute_query` on {type(self)}'
+            )
+
+        ann_docs = DocumentArray.__class_getitem__(
+            cast(Type[BaseDocument], self._schema)
+        )([])
+        filter_conditions = []
+        doc_to_score: Dict[BaseDocument, Any] = {}
+        for op, op_kwargs in query:
+            if op == 'find':
+                docs, scores = self.find(**op_kwargs)
+                ann_docs.extend(docs)
+                doc_to_score.update(zip(docs.__getattribute__('id'), scores))
+            elif op == 'filter':
+                filter_conditions.append(op_kwargs['filter_query'])
+
+        docs_filtered = ann_docs
+        for cond in filter_conditions:
+            da_cls = DocumentArray.__class_getitem__(
+                cast(Type[BaseDocument], self._schema)
+            )
+            docs_filtered = da_cls(da_filter(docs_filtered, cond))
+
+        docs_and_scores = zip(
+            docs_filtered, (doc_to_score[doc.id] for doc in docs_filtered)
+        )
+        docs_sorted = sorted(docs_and_scores, key=lambda x: x[1])
+        out_docs, out_scores = zip(*docs_sorted)
+        return FindResult(documents=out_docs, scores=out_scores)
+
+    def _find_batched(
+        self,
+        query: np.ndarray,
+        search_field: str,
+        limit: int,
+    ) -> FindResultBatched:
+        index = self._hnsw_indices[search_field]
+        labels, distances = index.knn_query(query, k=limit)
+        result_das = [
+            self._get_docs_sqlite_hashed_id(
+                ids_per_query.tolist(),
+            )
+            for ids_per_query in labels
+        ]
+        return FindResultBatched(documents=result_das, scores=distances)
+
+    @composable
+    def _find(self, query: np.ndarray, search_field: str, limit: int) -> FindResult:
+        query_batched = np.expand_dims(query, axis=0)
+        docs, scores = self._find_batched(query_batched, search_field, limit)
+        return FindResult(documents=docs[0], scores=scores[0])
+
+    @composable
+    def _filter(
+        self,
+        filter_query: Any,
+        limit: int,
+    ) -> DocumentArray:
+
+        raise NotImplementedError(
+            f'{type(self)} does not support filter-only queries.'
+            f' To perform post-filtering on a query, use'
+            f' `build_query()` and `execute_query()`.'
+        )
+
+    def _filter_batched(
+        self,
+        filter_queries: Any,
+        limit: int,
+    ) -> List[DocumentArray]:
+        raise NotImplementedError(
+            f'{type(self)} does not support filter-only queries.'
+            f' To perform post-filtering on a query, use'
+            f' `build_query()` and `execute_query()`.'
+        )
+
+    def _text_search(
+        self,
+        query: str,
+        search_field: str,
+        limit: int,
+    ) -> FindResult:
+        raise NotImplementedError(f'{type(self)} does not support text search.')
+
+    def _text_search_batched(
+        self,
+        queries: Sequence[str],
+        search_field: str,
+        limit: int,
+    ) -> FindResultBatched:
+        raise NotImplementedError(f'{type(self)} does not support text search.')
+
+    def _del_items(self, doc_ids: Sequence[str]):
+        # delete from the indices
+        try:
+            for doc_id in doc_ids:
+                id_ = self._to_hashed_id(doc_id)
+                for col_name, index in self._hnsw_indices.items():
+                    index.mark_deleted(id_)
+        except RuntimeError:
+            raise KeyError(f'No document with id {doc_ids} found')
+
+        self._delete_docs_from_sqlite(doc_ids)
+        self._sqlite_conn.commit()
+
+    def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]:
+        out_docs = self._get_docs_sqlite_doc_id(doc_ids)
+        if len(out_docs) == 0:
+            raise KeyError(f'No document with id {doc_ids} found')
+        return out_docs  # type: ignore
+
+    def num_docs(self) -> int:
+        return self._get_num_docs_sqlite()
+
+    ###############################################
+    # Helpers                                     #
+    ###############################################
+
+    # general helpers
+    @staticmethod
+    def _to_hashed_id(doc_id: str) -> int:
+        # https://stackoverflow.com/questions/16008670/how-to-hash-a-string-into-8-digits
+        # hashing to 18 digits avoids overflow of sqlite INTEGER
+        return int(hashlib.sha256(doc_id.encode('utf-8')).hexdigest(), 16) % 10**18
+
+    def _load_index(self, col_name: str, col: '_Column') -> hnswlib.Index:
+        """Load an existing HNSW index from disk."""
+        index = self._create_index_class(col)
+        index.load_index(self._hnsw_locations[col_name])
+        return index
+
+    def _to_numpy(self, val: Any) -> Any:
+        if isinstance(val, np.ndarray):
+            return val
+        elif isinstance(val, (list, tuple)):
+            return np.array(val)
+        elif torch_imported and isinstance(val, torch.Tensor):
+            return val.numpy()
+        else:
+            raise ValueError(f'Unsupported input type for {type(self)}: {type(val)}')
+
+    # HNSWLib helpers
+    def _create_index_class(self, col: '_Column') -> hnswlib.Index:
+        """Create an instance of hnswlib.Index without initializing it."""
+        construct_params = dict(
+            (k, col.config[k]) for k in self._index_construct_params
+        )
+        if col.n_dim:
+            construct_params['dim'] = col.n_dim
+        return hnswlib.Index(**construct_params)
+
+    def _create_index(self, col: '_Column') -> hnswlib.Index:
+        """Create a new HNSW index for a column, and initialize it."""
+        index = self._create_index_class(col)
+        init_params = dict((k, col.config[k]) for k in self._index_init_params)
+        index.init_index(**init_params)
+        index.set_ef(col.config['ef'])
+        index.set_num_threads(col.config['num_threads'])
+        return index
+
+    # SQLite helpers
+    def _create_docs_table(self):
+        self._sqlite_cursor.execute(
+            'CREATE TABLE IF NOT EXISTS docs (doc_id INTEGER PRIMARY KEY, data BLOB)'
+        )
+
+    def _send_docs_to_sqlite(self, docs: Sequence[BaseDocument]):
+        ids = (self._to_hashed_id(doc.id) for doc in docs)
+        self._sqlite_cursor.executemany(
+            'INSERT INTO docs VALUES (?, ?)',
+            ((id_, self._doc_to_bytes(doc)) for id_, doc in zip(ids, docs)),
+        )
+
+    def _get_docs_sqlite_unsorted(self, univ_ids: Sequence[int]):
+        for id_ in univ_ids:
+            # I hope this protects from injection attacks
+            # properly binding with '?' doesn't work for some reason
+            assert isinstance(id_, int) or is_np_int(id_)
+        sql_id_list = '(' + ', '.join(str(id_) for id_ in univ_ids) + ')'
+        self._sqlite_cursor.execute(
+            'SELECT data FROM docs WHERE doc_id IN %s' % sql_id_list,
+        )
+        rows = self._sqlite_cursor.fetchall()
+        da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema))
+        return da_cls([self._doc_from_bytes(row[0]) for row in rows])
+
+    def _get_docs_sqlite_doc_id(self, doc_ids: Sequence[str]) -> DocumentArray[TSchema]:
+        hashed_ids = tuple(self._to_hashed_id(id_) for id_ in doc_ids)
+        docs_unsorted = self._get_docs_sqlite_unsorted(hashed_ids)
+        da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema))
+        return da_cls(sorted(docs_unsorted, key=lambda doc: doc_ids.index(doc.id)))
+
+    def _get_docs_sqlite_hashed_id(self, hashed_ids: Sequence[int]) -> DocumentArray:
+        docs_unsorted = self._get_docs_sqlite_unsorted(hashed_ids)
+
+        def _in_position(doc):
+            return hashed_ids.index(self._to_hashed_id(doc.id))
+
+        da_cls = DocumentArray.__class_getitem__(cast(Type[BaseDocument], self._schema))
+        return da_cls(sorted(docs_unsorted, key=_in_position))
+
+    def _delete_docs_from_sqlite(self, doc_ids: Sequence[Union[str, int]]):
+        ids = tuple(
+            self._to_hashed_id(id_) if isinstance(id_, str) else id_ for id_ in doc_ids
+        )
+        self._sqlite_cursor.execute(
+            'DELETE FROM docs WHERE doc_id IN (%s)' % ','.join('?' * len(ids)),
+            ids,
+        )
+
+    def _get_num_docs_sqlite(self) -> int:
+        self._sqlite_cursor.execute('SELECT COUNT(*) FROM docs')
+        return self._sqlite_cursor.fetchone()[0]
+
+    # serialization helpers
+    def _doc_to_bytes(self, doc: BaseDocument) -> bytes:
+        return doc.to_protobuf().SerializeToString()
+
+    def _doc_from_bytes(self, data: bytes) -> BaseDocument:
+        schema_cls = cast(Type[BaseDocument], self._schema)
+        return schema_cls.from_protobuf(DocumentProto.FromString(data))
diff --git a/docarray/utils/misc.py b/docarray/utils/misc.py
index a7318792e31..99ed9b6696a 100644
--- a/docarray/utils/misc.py
+++ b/docarray/utils/misc.py
@@ -1,3 +1,7 @@
+from typing import Any
+
+import numpy as np
+
 try:
     import torch  # noqa: F401
 except ImportError:
@@ -18,6 +22,17 @@ def is_torch_available():
     return torch_imported
 
 
+def is_np_int(item: Any) -> bool:
+    dtype = getattr(item, 'dtype', None)
+    ndim = getattr(item, 'ndim', None)
+    if dtype is not None and ndim is not None:
+        try:
+            return ndim == 0 and np.issubdtype(dtype, np.integer)
+        except TypeError:
+            return False
+    return False  # this is unreachable, but mypy wants it
+
+
 def is_tf_available():
     return tf_imported
 
diff --git a/docs/index_init.md b/docs/index_init.md
index 1183a3c8855..3a95ad802cf 100644
--- a/docs/index_init.md
+++ b/docs/index_init.md
@@ -7,6 +7,7 @@
 api_public
 api/docarray
 tutorials/mutlimodal_training_and_serving.md
+tutroials/add_doc_index.md
 CONTRIBUTING.md
 ```
 
diff --git a/docs/tutorials/add_doc_index.md b/docs/tutorials/add_doc_index.md
new file mode 100644
index 00000000000..d08864532e3
--- /dev/null
+++ b/docs/tutorials/add_doc_index.md
@@ -0,0 +1,269 @@
+# Add a new Document Index
+
+In DocArray there exists the concept of _Document Index_, a class that takes `Document`s, optionally persists them,
+and makes them searchable.
+
+There are different Document Indexes leveraging different backends, such as Weaviate, Qdrant, HNSWLit etc.
+
+This Document shows how to add a new Document Index to DocArray.
+
+That process can be broken down into a number of basic steps:
+
+1. Create a new class that inherits from `BaseDocumentIndex`
+2. Declare default configurations for your Document Index
+3. Implement abstract methods for indexing, searching, and deleting
+4. Implement a Query Builder for your Document Index
+
+For an end-to-end example of this process, you can check out the [existing HNSWLib Document Index implementation](https://github.com/docarray/docarray/pull/1124).
+
+## Create a new Document Index class
+
+To get started, create a new class that inherits from `BaseDocumentIndex` and `typing.Generic`:
+
+
+```python
+TSchema = TypeVar('TSchema', bound=BaseDocument)
+
+
+class MyDocumentIndex(BaseDocumentIndex, Generic[TSchema]):
+    ...
+```
+
+Here, `TSchema` is a type variable representing the schema of the Document Index, which is a `Document`.
+You can use it in type hints of methods and attributes.
+
+### Create the constructor
+
+You can write an `__init__` method for your Document Index where you set up all the needed bells and whistles:
+
+```python
+def __init__(self, db_config=None, **kwargs):
+    super().__init__(db_config=db_config, **kwargs)
+    ...
+```
+
+Make sure that you call the `super().__init__` method, which will do some basic initialization for you.
+
+Overall, the following attributes will be set up automatically and be available to you (more info in the dedicated sections below):
+- `self._schema`
+- `self._db_config`
+- `self._runtime_config`
+- `self._columns`
+
+### The `_schema`
+
+When a user instantiates a Document Index, they do so in a parametric way, like so:
+
+```python
+class Inner(BaseDocument):
+    embedding: NdArray[512]
+
+
+class MyDoc(BaseDocument):
+    tensor: NdArray[100]
+    other_tensor: NdArray = Field(dim=10, space='cosine')
+    description: str
+    inner: Inner
+
+
+store = MyDocumentIndex[MyDoc]()
+```
+
+In this case, `store` would have a class attribute `_schema` that is the `MyDoc` class.
+This is done automatically for you, and you can use it in your implementation.
+
+### The `_db_config`
+
+The `_db_config` is a dataclass that contains all "static" configurations of your Document Index.
+Users can pass these configurations to the `__init__` method of your Document Index, and `self._db_config` will be populated
+for you, so that you can use it in your implementation.
+
+You can declare allowed fields and default values for your `_db_config`, but you will see that later.
+
+### The `_runtime_config`
+
+The `_runtime_config` is a dataclass that contains all "dynamic" configurations of your Document Index.
+Users can pass these configurations to the `.configure()` method of your Document Index, and `self._runtime_config` will be populated
+for you, so that you can use it in your implementation.
+
+You can declare allowed fields and default values for your `_runtime_config `, but you will see that later.
+
+### The `_columns`
+
+`self._columns` is a dictionary that contains information about all columns in your Document Index instance.
+
+This information is automatically extracted from `self._schema`, and populated for you.
+
+Concretely, `self._columns: Dict[str, _Column]` maps from a column name to a `_Column` dataclass.
+
+For the `MyDoc` schema above, the column names would be `tensor`, `other_tensor`, `description`, `id`, `inner__embedding`, and `inner__id`.
+These are the key of `self._columns`.
+
+The values of `self._columns` are `_Column` dataclasses, which have the following form:
+
+```python
+@dataclass
+class _Column:
+    docarray_type: Type
+    db_type: Any
+    n_dim: Optional[int]
+    config: Dict[str, Any]
+```
+
+- `docarray_type` is the type of the column in DocArray, e.g. `NdArray` or `str`
+- `db_type` is the type of the column in the Document Index, e.g. `np.ndarray` or `str`. You can customize the mapping from `docarray_type` to `db_type`, as we will see later.
+- `config` is a dictionary of configurations for the column. For example, for the `other_tensor` column above, this would contain the `space` and `dim` configurations.
+- `n_dim` is the dimensionality of the column, e.g. `100` for a 100-dimensional vector. See further guidance on this below.
+
+Again, these are automatically populated for you, so you can just use them in your implementation.
+
+### Properly handle `n_dim`
+
+`_Column.n_dim` is automatically obtained from type parametrizations of the form `NdArray[100]`;
+if there isn't such a parametrization, `n_dim` of the columns will be `None`.
+
+You should also provide another way of defining the dimensionality of your columns, specifically by exposing a parameter in `Field(...)` (see example schema at the top).
+
+This leads to four possible scenarios:
+
+**Scenario 1: Only `n_dim` is defined**
+
+Imagine the user defines a schema like the following:
+
+```python
+class MyDoc(BaseDocument):
+    tensor: NdArray[100]
+
+
+index = MyDocumentIndex[MyDoc]()
+```
+
+In that case, the following will be true: `self._columns['tensor'].n_dim == 100` and `self._columns['tensor'].config == {}`.
+The `tensor` column in your backend should be configured to have dimensionality 100.
+
+**Scenario 2: Only `Field(...)` is defined**
+
+Imagine the user defines a schema like the following:
+
+```python
+class MyDoc(BaseDocument):
+    tensor: NdArray = Field(dim=50)
+
+
+index = MyDocumentIndex[MyDoc]()
+```
+
+In that case, the following will be true: `self._columns['tensor'].n_dim is None` and `self._columns['tensor'].config['dim'] == 50`.
+The `tensor` column in your backend should be configured to have dimensionality 50.
+
+**Scenario 3: Both `n_dim` and `Field(...)` are defined**
+
+Imagine the user defines a schema like the following:
+
+```python
+class MyDoc(BaseDocument):
+    tensor: NdArray[100] = Field(dim=50)
+
+
+index = MyDocumentIndex[MyDoc]()
+```
+
+In that case, the following will be true: `self._columns['tensor'].n_dim == 100` and `self._columns['tensor'].config['dim'] == 50`.
+The `tensor` column in your backend should be configured to have dimensionality 100, as **`n_dim` takes precedence over `Field(...)`**.
+
+**Scenario 4: Neither `n_dim` nor `Field(...)` are defined**
+
+Imagine the user defines a schema like the following:
+
+```python
+class MyDoc(BaseDocument):
+    tensor: NdArray
+
+
+index = MyDocumentIndex[MyDoc]()
+```
+
+In that case, the following will be true: `self._columns['tensor'].n_dim is None` and `self._columns['tensor'].config == {}`.
+If your backend can handle tensor/embedding columns without defined dimensionality, you should leverage that mechanism.
+Otherwise, raise an Exception.
+
+## Declare default configurations
+
+We already made reference to the `_db_config` and `_runtime_config` attributes.
+
+In order to define what can be stored in them, and what the default values are, you need to create two inner classes:
+
+```python
+@dataclass
+class DBConfig(BaseDocumentIndex.DBConfig):
+    ...
+
+
+@dataclass
+class RuntimeConfig(BaseDocumentIndex.RuntimeConfig):
+    default_column_config: Dict[Type, Dict[str, Any]] = ...
+```
+
+Note that:
+- `DBConfig` inherits from `BaseDocumentIndex.DBConfig` and `RuntimeConfig` inherits from `BaseDocumentIndex.RuntimeConfig`
+- All fields in each dataclass need to have default values. Choose these sensibly, as they will be used if the user does not specify a value.
+
+### The `DBConfig` class
+
+The `DBConfig` class is used to define the static configurations of your Document Index.
+These are configurations that are tied to the database (or library) running in the background, such as `host`, `port`, etc.
+Here you should put everything that the user cannot or should not change after initialization.
+
+### The `RuntimeConfig` class
+
+The `RuntimeConfig` class is used to define the dynamic configurations of your Document Index.
+These are configurations that can be changed at runtime, for example default behaviours such as batch sizes, consistency levels, etc.
+
+It is a common pattern to allow such parameters both in the `RuntimeConfig`, where they will act as global defaults, and
+in specific methods (`index`, `find`, etc.), where they will act as local overrides.
+
+**Important**: Every `RuntimeConfig` needs to contain a `default_column_config` field.
+This is a dictionary that, for each possible column type in your database, defines a default configuration for that column type.
+This will automatically be passed to a `_Column` whenever a user does not manually specify a configuration for that column.
+
+For example, in the `MyDoc` schema above, the `tensor` `_Column` would have a default configuration specified for `np.ndarray` columns.
+
+What is actually contained in these type-dependant configurations is up to you (and database specific).
+For example, for `np.ndarray` columns you could define the configurations `index_type` and `metric_type`,
+and for `varchar` columns you could define a `max_length` configuration.
+
+It is probably best to see this in action, so you should check out the `HnswDocumentIndex` implementation.
+
+## Implement abstract methods for indexing, searching, and deleting
+
+After you've done the basic setup above, you can jump into the good stuff: implementing the actual indexing, searching, and deleting.
+
+In general, the following is true:
+- For every method that you need to implement, there is a public variant (e.g. `index`) and a private variant (e.g. `_index`)
+- You should usually implement the private variant, which is called by the already implemented public variant. This should make your life easier, because some preprocessing and data normalization will already be done for you.
+- You can, however, also implement the public variant directly, if you want to do something special.
+  - **Caution**: While this is a perfectly fine thing to do, it might create more maintenance work for you in the future, because the public variant defined in the `BaseDocumentIndex` might change in the future, and you will have to update your implementation accordingly.
+
+Further:
+- You don't absolutely have to implement everything. If a feature (e.g. `text_search`) is not supported by your backend, just raise a `NotImplementedError` in the corresponding method.
+- Many methods come in a "singular" variant (e.g. `find`) and a "batched" variant (e.g. `find_batched`).
+  - The "singular" variant expects a single input, be it an ANN query, a text query, a filter, etc., and return matches and scores for that single input
+  - The "batched" variant expects a batch of inputs, and returns of matches and scores for each input
+- Your implementations of, e.g., `_find()`, `_index()` etc. are allowed to take additional optional keyword arguments.
+  These can then be used to control DB specific behaviours, such as consistency levels, batch sizes, etc. As mentioned above, it is good practice to mirror these arguments in `self.RuntimeConfig`.
+
+Overall, you're asked to implement the methods that appear after the `Abstract methods; Subclasses must implement these`
+comment in the `BaseDocumentIndex` class.
+The details of each method should become clear from the docstrings and type hints.
+
+### The `python_type_to_db_type` method
+
+This method is slightly special, because 1) it is not exposed to the user, and 2) you absolutely have to implement it.
+
+It is intended to do the following: It takes a type of a field in the store's schema (e.g. `NdArray` for `tensor`), and returns the corresponding type in the database (e.g. `np.ndarray`).
+The `BaseDocumentIndex` class uses this information to create and populate the `_Columns` in `self._columns`.
+
+## Implement a Query Builder for your Document Index
+
+This part of the process is still slightly WIP, so let's worry about it once we get there, shall we? ;)
+
diff --git a/poetry.lock b/poetry.lock
index dd068b989c8..8b8d2526924 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,3 @@
-# This file is automatically @generated by Poetry and should not be changed by hand.
-
 [[package]]
 name = "anyio"
 version = "3.6.2"
@@ -7,10 +5,6 @@ description = "High level compatibility layer for multiple asynchronous event lo
 category = "main"
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-]
 
 [package.dependencies]
 idna = ">=2.8"
@@ -29,10 +23,6 @@ description = "Disable App Nap on macOS >= 10.9"
 category = "dev"
 optional = false
 python-versions = "*"
-files = [
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 [[package]]
 name = "argon2-cffi"
@@ -41,10 +31,6 @@ description = "The secure Argon2 password hashing algorithm."
 category = "dev"
 optional = false
 python-versions = ">=3.6"
-files = [
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 argon2-cffi-bindings = "*"
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 cffi = ">=1.0.1"
@@ -100,10 +63,6 @@ description = "Classes Without Boilerplate"
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 optional = false
 python-versions = ">=3.5"
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 [package.dependencies]
 pycparser = "*"
@@ -373,10 +177,6 @@ description = "Validate configuration and produce human readable error messages.
 category = "dev"
 optional = false
 python-versions = ">=3.6.1"
-files = [
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 [[package]]
 name = "charset-normalizer"
@@ -385,10 +185,6 @@ description = "The Real First Universal Charset Detector. Open, modern and activ
 category = "dev"
 optional = false
 python-versions = ">=3.5.0"
-files = [
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-]
 
 [package.extras]
 unicode-backport = ["unicodedata2"]
@@ -400,10 +196,6 @@ description = "Composable command line interface toolkit"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
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-]
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
@@ -416,10 +208,6 @@ description = "Cross-platform colored terminal text."
 category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
-files = [
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-]
 
 [[package]]
 name = "commonmark"
@@ -428,10 +216,6 @@ description = "Python parser for the CommonMark Markdown spec"
 category = "main"
 optional = false
 python-versions = "*"
-files = [
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 [package.extras]
 test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"]
@@ -443,26 +227,6 @@ description = "An implementation of the Debug Adapter Protocol for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
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-    {file = "debugpy-1.6.3.zip", hash = "sha256:e8922090514a890eec99cfb991bab872dd2e353ebb793164d5f01c362b9a40bf"},
-]
 
 [[package]]
 name = "decorator"
@@ -471,10 +235,6 @@ description = "Decorators for Humans"
 category = "dev"
 optional = false
 python-versions = ">=3.5"
-files = [
-    {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
-    {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
-]
 
 [[package]]
 name = "defusedxml"
@@ -483,10 +243,6 @@ description = "XML bomb protection for Python stdlib modules"
 category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-files = [
-    {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"},
-    {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
-]
 
 [[package]]
 name = "distlib"
@@ -495,10 +251,6 @@ description = "Distribution utilities"
 category = "dev"
 optional = false
 python-versions = "*"
-files = [
-    {file = "distlib-0.3.6-py2.py3-none-any.whl", hash = "sha256:f35c4b692542ca110de7ef0bea44d73981caeb34ca0b9b6b2e6d7790dda8f80e"},
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-]
 
 [[package]]
 name = "entrypoints"
@@ -507,10 +259,6 @@ description = "Discover and load entry points from installed packages."
 category = "dev"
 optional = false
 python-versions = ">=3.6"
-files = [
-    {file = "entrypoints-0.4-py3-none-any.whl", hash = "sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f"},
-    {file = "entrypoints-0.4.tar.gz", hash = "sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4"},
-]
 
 [[package]]
 name = "exceptiongroup"
@@ -519,10 +267,6 @@ description = "Backport of PEP 654 (exception groups)"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
-    {file = "exceptiongroup-1.1.0-py3-none-any.whl", hash = "sha256:327cbda3da756e2de031a3107b81ab7b3770a602c4d16ca618298c526f4bec1e"},
-    {file = "exceptiongroup-1.1.0.tar.gz", hash = "sha256:bcb67d800a4497e1b404c2dd44fca47d3b7a5e5433dbab67f96c1a685cdfdf23"},
-]
 
 [package.extras]
 test = ["pytest (>=6)"]
@@ -534,10 +278,6 @@ description = "FastAPI framework, high performance, easy to learn, fast to code,
 category = "main"
 optional = true
 python-versions = ">=3.7"
-files = [
-    {file = "fastapi-0.87.0-py3-none-any.whl", hash = "sha256:254453a2e22f64e2a1b4e1d8baf67d239e55b6c8165c079d25746a5220c81bb4"},
-    {file = "fastapi-0.87.0.tar.gz", hash = "sha256:07032e53df9a57165047b4f38731c38bdcc3be5493220471015e2b4b51b486a4"},
-]
 
 [package.dependencies]
 pydantic = ">=1.6.2,<1.7 || >1.7,<1.7.1 || >1.7.1,<1.7.2 || >1.7.2,<1.7.3 || >1.7.3,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0"
@@ -556,10 +296,6 @@ description = "Fastest Python implementation of JSON schema"
 category = "dev"
 optional = false
 python-versions = "*"
-files = [
-    {file = "fastjsonschema-2.16.2-py3-none-any.whl", hash = "sha256:21f918e8d9a1a4ba9c22e09574ba72267a6762d47822db9add95f6454e51cc1c"},
-    {file = "fastjsonschema-2.16.2.tar.gz", hash = "sha256:01e366f25d9047816fe3d288cbfc3e10541daf0af2044763f3d0ade42476da18"},
-]
 
 [package.extras]
 devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
@@ -571,10 +307,6 @@ description = "A platform independent file lock."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
-    {file = "filelock-3.8.0-py3-none-any.whl", hash = "sha256:617eb4e5eedc82fc5f47b6d61e4d11cb837c56cb4544e39081099fa17ad109d4"},
-    {file = "filelock-3.8.0.tar.gz", hash = "sha256:55447caa666f2198c5b6b13a26d2084d26fa5b115c00d065664b2124680c4edc"},
-]
 
 [package.extras]
 docs = ["furo (>=2022.6.21)", "sphinx (>=5.1.1)", "sphinx-autodoc-typehints (>=1.19.1)"]
@@ -587,14 +319,21 @@ description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
-    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
-    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
-]
 
 [package.dependencies]
 typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
 
+[[package]]
+name = "hnswlib"
+version = "0.7.0"
+description = "hnswlib"
+category = "main"
+optional = true
+python-versions = "*"
+
+[package.dependencies]
+numpy = "*"
+
 [[package]]
 name = "httpcore"
 version = "0.16.1"
@@ -602,10 +341,6 @@ description = "A minimal low-level HTTP client."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
-    {file = "httpcore-0.16.1-py3-none-any.whl", hash = "sha256:8d393db683cc8e35cc6ecb02577c5e1abfedde52b38316d038932a84b4875ecb"},
-    {file = "httpcore-0.16.1.tar.gz", hash = "sha256:3d3143ff5e1656a5740ea2f0c167e8e9d48c5a9bbd7f00ad1f8cff5711b08543"},
-]
 
 [package.dependencies]
 anyio = ">=3.0,<5.0"
@@ -624,10 +359,6 @@ description = "The next generation HTTP client."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
-    {file = "httpx-0.23.1-py3-none-any.whl", hash = "sha256:0b9b1f0ee18b9978d637b0776bfd7f54e2ca278e063e3586d8f01cda89e042a8"},
-    {file = "httpx-0.23.1.tar.gz", hash = "sha256:202ae15319be24efe9a8bd4ed4360e68fde7b38bcc2ce87088d416f026667d19"},
-]
 
 [package.dependencies]
 certifi = "*"
@@ -648,10 +379,6 @@ description = "File identification library for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
-    {file = "identify-2.5.8-py2.py3-none-any.whl", hash = "sha256:48b7925fe122720088aeb7a6c34f17b27e706b72c61070f27fe3789094233440"},
-    {file = "identify-2.5.8.tar.gz", hash = "sha256:7a214a10313b9489a0d61467db2856ae8d0b8306fc923e03a9effa53d8aedc58"},
-]
 
 [package.extras]
 license = ["ukkonen"]
@@ -663,10 +390,6 @@ description = "Internationalized Domain Names in Applications (IDNA)"
 category = "main"
 optional = false
 python-versions = ">=3.5"
-files = [
-    {file = "idna-3.4-py3-none-any.whl", hash = "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2"},
-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
-]
 
 [[package]]
 name = "importlib-metadata"
@@ -675,10 +398,6 @@ description = "Read metadata from Python packages"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
-    {file = "importlib_metadata-5.0.0-py3-none-any.whl", hash = "sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43"},
-    {file = "importlib_metadata-5.0.0.tar.gz", hash = "sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab"},
-]
 
 [package.dependencies]
 typing-extensions = {version = ">=3.6.4", markers = "python_version < \"3.8\""}
@@ -696,10 +415,6 @@ description = "Read resources from Python packages"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
-    {file = "importlib_resources-5.10.0-py3-none-any.whl", hash = "sha256:ee17ec648f85480d523596ce49eae8ead87d5631ae1551f913c0100b5edd3437"},
-    {file = "importlib_resources-5.10.0.tar.gz", hash = "sha256:c01b1b94210d9849f286b86bb51bcea7cd56dde0600d8db721d7b81330711668"},
-]
 
 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
@@ -715,10 +430,6 @@ description = "iniconfig: brain-dead simple config-ini parsing"
 category = "dev"
 optional = false
 python-versions = "*"
-files = [
-    {file = "iniconfig-1.1.1-py2.py3-none-any.whl", hash = "sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3"},
-    {file = "iniconfig-1.1.1.tar.gz", hash = "sha256:bc3af051d7d14b2ee5ef9969666def0cd1a000e121eaea580d4a313df4b37f32"},
-]
 
 [[package]]
 name = "ipykernel"
@@ -727,10 +438,6 @@ description = "IPython Kernel for Jupyter"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
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 [package.extras]
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@@ -1782,10 +1083,6 @@ description = "Resolve a name to an object."
 category = "dev"
 optional = false
 python-versions = ">=3.6"
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 [[package]]
 name = "platformdirs"
@@ -1794,10 +1091,6 @@ description = "A small Python package for determining appropriate platform-speci
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 python-versions = ">=3.7"
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@@ -1810,10 +1103,6 @@ description = "plugin and hook calling mechanisms for python"
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 [package.dependencies]
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@@ -1828,10 +1117,6 @@ description = "A framework for managing and maintaining multi-language pre-commi
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 [package.dependencies]
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@@ -1849,10 +1134,6 @@ description = "Python client for the Prometheus monitoring system."
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 [package.extras]
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@@ -1864,10 +1145,6 @@ description = "Library for building powerful interactive command lines in Python
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@@ -2462,10 +1425,6 @@ description = "Python 2 and 3 compatibility utilities"
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@@ -2474,10 +1433,6 @@ description = "Sniff out which async library your code is running under"
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 name = "soupsieve"
@@ -2486,10 +1441,6 @@ description = "A modern CSS selector implementation for Beautiful Soup."
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@@ -2498,10 +1449,6 @@ description = "The little ASGI library that shines."
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@@ -2538,10 +1481,6 @@ description = "A tiny CSS parser"
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@@ -2557,10 +1496,6 @@ description = "Python Library for Tom's Obvious, Minimal Language"
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@@ -2569,10 +1504,6 @@ description = "A lil' TOML parser"
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 nvidia-cublas-cu11 = "11.10.3.66"
@@ -2622,19 +1530,6 @@ description = "Tornado is a Python web framework and asynchronous networking lib
 category = "dev"
 optional = false
 python-versions = ">= 3.7"
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 [[package]]
 name = "traitlets"
@@ -2643,10 +1538,6 @@ description = ""
 category = "dev"
 optional = false
 python-versions = ">=3.7"
-files = [
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 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
@@ -2659,10 +1550,6 @@ description = "Import, export, process, analyze and view triangular meshes."
 category = "main"
 optional = true
 python-versions = "*"
-files = [
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 [package.dependencies]
 numpy = "*"
@@ -2679,32 +1566,6 @@ description = "a fork of Python 2 and 3 ast modules with type comment support"
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 optional = false
 python-versions = ">=3.6"
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 [[package]]
 name = "types-pillow"
@@ -2713,10 +1574,6 @@ description = "Typing stubs for Pillow"
 category = "main"
 optional = true
 python-versions = "*"
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 [[package]]
 name = "types-protobuf"
@@ -2725,10 +1582,6 @@ description = "Typing stubs for protobuf"
 category = "dev"
 optional = false
 python-versions = "*"
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 [[package]]
 name = "types-requests"
@@ -2737,10 +1590,6 @@ description = "Typing stubs for requests"
 category = "main"
 optional = false
 python-versions = "*"
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 [package.dependencies]
 types-urllib3 = "<1.27"
@@ -2752,10 +1601,6 @@ description = "Typing stubs for urllib3"
 category = "main"
 optional = false
 python-versions = "*"
-files = [
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 name = "typing-extensions"
@@ -2764,10 +1609,6 @@ description = "Backported and Experimental Type Hints for Python 3.7+"
 category = "main"
 optional = false
 python-versions = ">=3.7"
-files = [
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 name = "typing-inspect"
@@ -2776,10 +1617,6 @@ description = "Runtime inspection utilities for typing module."
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 optional = false
 python-versions = "*"
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 mypy-extensions = ">=0.3.0"
@@ -2792,10 +1629,6 @@ description = "HTTP library with thread-safe connection pooling, file post, and
 category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
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@@ -2809,10 +1642,6 @@ description = "The lightning-fast ASGI server."
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 click = ">=7.0"
@@ -2829,10 +1658,6 @@ description = "Virtual Python Environment builder"
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@@ -2851,10 +1676,6 @@ description = "Measures the displayed width of unicode strings in a terminal"
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 python-versions = "*"
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 name = "webencodings"
@@ -2863,10 +1684,6 @@ description = "Character encoding aliases for legacy web content"
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 python-versions = "*"
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 name = "websocket-client"
@@ -2875,10 +1692,6 @@ description = "WebSocket client for Python with low level API options"
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@@ -2892,10 +1705,6 @@ description = "A built-package format for Python"
 category = "main"
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@@ -2907,10 +1716,6 @@ description = "Backport of pathlib-compatible object wrapper for zip files"
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 python-versions = ">=3.7"
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@@ -2919,6 +1724,7 @@ testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools"
 [extras]
 audio = ["pydub"]
 common = ["protobuf", "lz4"]
+hnswlib = ["hnswlib"]
 image = ["pillow", "types-pillow"]
 mesh = ["trimesh"]
 pandas = ["pandas"]
@@ -2927,6 +1733,1215 @@ video = ["av"]
 web = ["fastapi"]
 
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+    {file = "types-Pillow-9.3.0.1.tar.gz", hash = "sha256:f3b7cada3fa496c78d75253c6b1f07a843d625f42e5639b320a72acaff6f7cfb"},
+    {file = "types_Pillow-9.3.0.1-py3-none-any.whl", hash = "sha256:79837755fe9659f29efd1016e9903ac4a500e0c73260483f07296bd6ca47668b"},
+]
+types-protobuf = [
+    {file = "types-protobuf-3.20.4.5.tar.gz", hash = "sha256:e9b45008d106e1d10cc77a29d2d344b85c0f01e2e643aaccf32f69e9e81b0cdd"},
+    {file = "types_protobuf-3.20.4.5-py3-none-any.whl", hash = "sha256:97af5ce70d890fdb94cb0c906f5a6624ca2fef58bc04e27990a25509e992a950"},
+]
+types-requests = [
+    {file = "types-requests-2.28.11.7.tar.gz", hash = "sha256:0ae38633734990d019b80f5463dfa164ebd3581998ac8435f526da6fe4d598c3"},
+    {file = "types_requests-2.28.11.7-py3-none-any.whl", hash = "sha256:b6a2fca8109f4fdba33052f11ed86102bddb2338519e1827387137fefc66a98b"},
+]
+types-urllib3 = [
+    {file = "types-urllib3-1.26.25.4.tar.gz", hash = "sha256:eec5556428eec862b1ac578fb69aab3877995a99ffec9e5a12cf7fbd0cc9daee"},
+    {file = "types_urllib3-1.26.25.4-py3-none-any.whl", hash = "sha256:ed6b9e8a8be488796f72306889a06a3fc3cb1aa99af02ab8afb50144d7317e49"},
+]
+typing-extensions = [
+    {file = "typing_extensions-4.4.0-py3-none-any.whl", hash = "sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e"},
+    {file = "typing_extensions-4.4.0.tar.gz", hash = "sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa"},
+]
+typing-inspect = [
+    {file = "typing_inspect-0.8.0-py3-none-any.whl", hash = "sha256:5fbf9c1e65d4fa01e701fe12a5bca6c6e08a4ffd5bc60bfac028253a447c5188"},
+    {file = "typing_inspect-0.8.0.tar.gz", hash = "sha256:8b1ff0c400943b6145df8119c41c244ca8207f1f10c9c057aeed1560e4806e3d"},
+]
+urllib3 = [
+    {file = "urllib3-1.26.14-py2.py3-none-any.whl", hash = "sha256:75edcdc2f7d85b137124a6c3c9fc3933cdeaa12ecb9a6a959f22797a0feca7e1"},
+    {file = "urllib3-1.26.14.tar.gz", hash = "sha256:076907bf8fd355cde77728471316625a4d2f7e713c125f51953bb5b3eecf4f72"},
+]
+uvicorn = [
+    {file = "uvicorn-0.19.0-py3-none-any.whl", hash = "sha256:cc277f7e73435748e69e075a721841f7c4a95dba06d12a72fe9874acced16f6f"},
+    {file = "uvicorn-0.19.0.tar.gz", hash = "sha256:cf538f3018536edb1f4a826311137ab4944ed741d52aeb98846f52215de57f25"},
+]
+virtualenv = [
+    {file = "virtualenv-20.16.7-py3-none-any.whl", hash = "sha256:efd66b00386fdb7dbe4822d172303f40cd05e50e01740b19ea42425cbe653e29"},
+    {file = "virtualenv-20.16.7.tar.gz", hash = "sha256:8691e3ff9387f743e00f6bb20f70121f5e4f596cae754531f2b3b3a1b1ac696e"},
+]
+wcwidth = [
+    {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
+    {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
+]
+webencodings = [
+    {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
+    {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
+]
+websocket-client = [
+    {file = "websocket-client-1.4.2.tar.gz", hash = "sha256:d6e8f90ca8e2dd4e8027c4561adeb9456b54044312dba655e7cae652ceb9ae59"},
+    {file = "websocket_client-1.4.2-py3-none-any.whl", hash = "sha256:d6b06432f184438d99ac1f456eaf22fe1ade524c3dd16e661142dc54e9cba574"},
+]
+wheel = [
+    {file = "wheel-0.38.4-py3-none-any.whl", hash = "sha256:b60533f3f5d530e971d6737ca6d58681ee434818fab630c83a734bb10c083ce8"},
+    {file = "wheel-0.38.4.tar.gz", hash = "sha256:965f5259b566725405b05e7cf774052044b1ed30119b5d586b2703aafe8719ac"},
+]
+zipp = [
+    {file = "zipp-3.10.0-py3-none-any.whl", hash = "sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1"},
+    {file = "zipp-3.10.0.tar.gz", hash = "sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8"},
+]
diff --git a/pyproject.toml b/pyproject.toml
index a6bd44e4657..7148623672b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,6 +20,7 @@ types-requests = ">=2.28.11.6"
 av = {version = ">=10.0.0", optional = true}
 fastapi = {version = ">=0.87.0", optional = true }
 rich = ">=13.1.0"
+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 }
@@ -32,6 +33,7 @@ video = ["av"]
 audio = ["pydub"]
 mesh = ["trimesh"]
 web = ["fastapi"]
+hnswlib = ["hnswlib"]
 pandas = ["pandas"]
 
 [tool.poetry.dev-dependencies]
@@ -70,6 +72,10 @@ ignore_missing_imports = true
 module = "trimesh"
 ignore_missing_imports = true
 
+[[tool.mypy.overrides]]
+module = "hnswlib"
+ignore_missing_imports = true
+
 [[tool.mypy.overrides]]
 module = "typing_inspect"
 ignore_missing_imports = true
@@ -96,5 +102,6 @@ markers = [
     "internet: marks tests as requiring internet (deselect with '-m \"not internet\"')",
     "asyncio: marks that run async tests",
     "proto: mark tests that run with proto",
-    "tensorflow: marks test using tensorflow and proto 3"
+    "tensorflow: marks test using tensorflow and proto 3",
+    "doc_index: marks test using a document index"
 ]
diff --git a/tests/doc_index/__init__.py b/tests/doc_index/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/doc_index/base_classes/__init__.py b/tests/doc_index/base_classes/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/doc_index/base_classes/test_base_doc_store.py b/tests/doc_index/base_classes/test_base_doc_store.py
new file mode 100644
index 00000000000..e244c4f491c
--- /dev/null
+++ b/tests/doc_index/base_classes/test_base_doc_store.py
@@ -0,0 +1,278 @@
+from dataclasses import dataclass, field
+from typing import Any, Dict, Type
+
+import numpy as np
+import pytest
+from pydantic import Field
+
+from docarray import BaseDocument, DocumentArray
+from docarray.doc_index.abstract_doc_index import BaseDocumentIndex
+from docarray.typing import ID, NdArray
+
+pytestmark = pytest.mark.doc_index
+
+
+class SimpleDoc(BaseDocument):
+    tens: NdArray[10] = Field(dim=1000)
+
+
+class FlatDoc(BaseDocument):
+    tens_one: NdArray = Field(dim=10)
+    tens_two: NdArray = Field(dim=50)
+
+
+class NestedDoc(BaseDocument):
+    d: SimpleDoc
+
+
+class DeepNestedDoc(BaseDocument):
+    d: NestedDoc
+
+
+class FakeQueryBuilder:
+    ...
+
+
+def _identity(*x, **y):
+    return x, y
+
+
+class DummyDocIndex(BaseDocumentIndex):
+    @dataclass
+    class RuntimeConfig(BaseDocumentIndex.RuntimeConfig):
+        default_column_config: Dict[Type, Dict[str, Any]] = field(
+            default_factory=lambda: {str: {'hi': 'there'}, np.ndarray: {'you': 'good?'}}
+        )
+
+    @dataclass
+    class DBConfig(BaseDocumentIndex.DBConfig):
+        work_dir: str = '.'
+
+    class QueryBuilder(BaseDocumentIndex.QueryBuilder):
+        def build(self):
+            return self._queries
+
+    def python_type_to_db_type(self, x):
+        return str
+
+    _index = _identity
+    num_docs = _identity
+    _del_items = _identity
+    _get_items = _identity
+    execute_query = _identity
+    _find = _identity
+    _find_batched = _identity
+    _filter = _identity
+    _filter_batched = _identity
+    _text_search = _identity
+    _text_search_batched = _identity
+
+
+def test_parametrization():
+    with pytest.raises(ValueError):
+        DummyDocIndex()
+
+    store = DummyDocIndex[SimpleDoc]()
+    assert store._schema is SimpleDoc
+
+
+def test_build_query():
+    store = DummyDocIndex[SimpleDoc]()
+    q = store.build_query()
+    assert isinstance(q, store.QueryBuilder)
+
+
+def test_create_columns():
+    # Simple doc
+    store = DummyDocIndex[SimpleDoc]()
+    assert list(store._columns.keys()) == ['id', 'tens']
+
+    assert store._columns['id'].docarray_type == ID
+    assert store._columns['id'].db_type == str
+    assert store._columns['id'].n_dim is None
+    assert store._columns['id'].config == {'hi': 'there'}
+
+    assert issubclass(store._columns['tens'].docarray_type, NdArray)
+    assert store._columns['tens'].db_type == str
+    assert store._columns['tens'].n_dim == 10
+    assert store._columns['tens'].config == {'dim': 1000, 'hi': 'there'}
+
+    # Flat doc
+    store = DummyDocIndex[FlatDoc]()
+    assert list(store._columns.keys()) == ['id', 'tens_one', 'tens_two']
+
+    assert store._columns['id'].docarray_type == ID
+    assert store._columns['id'].db_type == str
+    assert store._columns['id'].n_dim is None
+    assert store._columns['id'].config == {'hi': 'there'}
+
+    assert issubclass(store._columns['tens_one'].docarray_type, NdArray)
+    assert store._columns['tens_one'].db_type == str
+    assert store._columns['tens_one'].n_dim is None
+    assert store._columns['tens_one'].config == {'dim': 10, 'hi': 'there'}
+
+    assert issubclass(store._columns['tens_two'].docarray_type, NdArray)
+    assert store._columns['tens_two'].db_type == str
+    assert store._columns['tens_two'].n_dim is None
+    assert store._columns['tens_two'].config == {'dim': 50, 'hi': 'there'}
+
+    # Nested doc
+    store = DummyDocIndex[NestedDoc]()
+    assert list(store._columns.keys()) == ['id', 'd__id', 'd__tens']
+
+    assert store._columns['id'].docarray_type == ID
+    assert store._columns['id'].db_type == str
+    assert store._columns['id'].n_dim is None
+    assert store._columns['id'].config == {'hi': 'there'}
+
+    assert issubclass(store._columns['d__tens'].docarray_type, NdArray)
+    assert store._columns['d__tens'].db_type == str
+    assert store._columns['d__tens'].n_dim == 10
+    assert store._columns['d__tens'].config == {'dim': 1000, 'hi': 'there'}
+
+
+def test_is_schema_compatible():
+    class OtherSimpleDoc(SimpleDoc):
+        ...
+
+    class OtherFlatDoc(FlatDoc):
+        ...
+
+    class OtherNestedDoc(NestedDoc):
+        ...
+
+    store = DummyDocIndex[SimpleDoc]()
+    assert store._is_schema_compatible([SimpleDoc(tens=np.random.random((10,)))])
+    assert store._is_schema_compatible(
+        DocumentArray[SimpleDoc]([SimpleDoc(tens=np.random.random((10,)))])
+    )
+    assert store._is_schema_compatible([OtherSimpleDoc(tens=np.random.random((10,)))])
+    assert store._is_schema_compatible(
+        DocumentArray[OtherSimpleDoc]([OtherSimpleDoc(tens=np.random.random((10,)))])
+    )
+    assert not store._is_schema_compatible(
+        [FlatDoc(tens_one=np.random.random((10,)), tens_two=np.random.random((50,)))]
+    )
+    assert not store._is_schema_compatible(
+        DocumentArray[FlatDoc](
+            [
+                FlatDoc(
+                    tens_one=np.random.random((10,)), tens_two=np.random.random((50,))
+                )
+            ]
+        )
+    )
+
+    store = DummyDocIndex[FlatDoc]()
+    assert store._is_schema_compatible(
+        [FlatDoc(tens_one=np.random.random((10,)), tens_two=np.random.random((50,)))]
+    )
+    assert store._is_schema_compatible(
+        DocumentArray[FlatDoc](
+            [
+                FlatDoc(
+                    tens_one=np.random.random((10,)), tens_two=np.random.random((50,))
+                )
+            ]
+        )
+    )
+    assert store._is_schema_compatible(
+        [
+            OtherFlatDoc(
+                tens_one=np.random.random((10,)), tens_two=np.random.random((50,))
+            )
+        ]
+    )
+    assert store._is_schema_compatible(
+        DocumentArray[OtherFlatDoc](
+            [
+                OtherFlatDoc(
+                    tens_one=np.random.random((10,)), tens_two=np.random.random((50,))
+                )
+            ]
+        )
+    )
+    assert not store._is_schema_compatible([SimpleDoc(tens=np.random.random((10,)))])
+    assert not store._is_schema_compatible(
+        DocumentArray[SimpleDoc]([SimpleDoc(tens=np.random.random((10,)))])
+    )
+
+    store = DummyDocIndex[NestedDoc]()
+    assert store._is_schema_compatible(
+        [NestedDoc(d=SimpleDoc(tens=np.random.random((10,))))]
+    )
+    assert store._is_schema_compatible(
+        DocumentArray[NestedDoc]([NestedDoc(d=SimpleDoc(tens=np.random.random((10,))))])
+    )
+    assert store._is_schema_compatible(
+        [OtherNestedDoc(d=OtherSimpleDoc(tens=np.random.random((10,))))]
+    )
+    assert store._is_schema_compatible(
+        DocumentArray[OtherNestedDoc](
+            [OtherNestedDoc(d=OtherSimpleDoc(tens=np.random.random((10,))))]
+        )
+    )
+    assert not store._is_schema_compatible([SimpleDoc(tens=np.random.random((10,)))])
+    assert not store._is_schema_compatible(
+        DocumentArray[SimpleDoc]([SimpleDoc(tens=np.random.random((10,)))])
+    )
+
+
+def test_get_value():
+    t = np.random.random((10,))
+
+    doc = SimpleDoc(tens=t)
+    assert np.all(DummyDocIndex._get_values_by_column([doc], 'tens')[0] == t)
+
+    doc = FlatDoc(tens_one=t, tens_two=np.random.random((50,)))
+    assert np.all(DummyDocIndex._get_values_by_column([doc], 'tens_one')[0] == t)
+
+    doc = NestedDoc(d=SimpleDoc(tens=t))
+    assert np.all(DummyDocIndex._get_values_by_column([doc], 'd__tens')[0] == t)
+
+    doc = DeepNestedDoc(d=NestedDoc(d=SimpleDoc(tens=t)))
+    assert np.all(DummyDocIndex._get_values_by_column([doc], 'd__d__tens')[0] == t)
+
+    vals = DummyDocIndex._get_values_by_column([doc, doc], 'd__d__tens')
+    assert np.all(vals[0] == t)
+    assert np.all(vals[1] == t)
+
+
+def test_get_data_by_columns():
+    store = DummyDocIndex[SimpleDoc]()
+    docs = [SimpleDoc(tens=np.random.random((10,))) for _ in range(10)]
+    data_by_columns = store._get_col_value_dict(docs)
+    assert list(data_by_columns.keys()) == ['id', 'tens']
+    assert list(data_by_columns['id']) == [doc.id for doc in docs]
+    assert list(data_by_columns['tens']) == [doc.tens for doc in docs]
+
+    store = DummyDocIndex[FlatDoc]()
+    docs = [
+        FlatDoc(tens_one=np.random.random((10,)), tens_two=np.random.random((50,)))
+        for _ in range(10)
+    ]
+    data_by_columns = store._get_col_value_dict(docs)
+    assert list(data_by_columns.keys()) == ['id', 'tens_one', 'tens_two']
+    assert list(data_by_columns['id']) == [doc.id for doc in docs]
+    assert list(data_by_columns['tens_one']) == [doc.tens_one for doc in docs]
+    assert list(data_by_columns['tens_two']) == [doc.tens_two for doc in docs]
+
+    store = DummyDocIndex[NestedDoc]()
+    docs = [NestedDoc(d=SimpleDoc(tens=np.random.random((10,)))) for _ in range(10)]
+    data_by_columns = store._get_col_value_dict(docs)
+    assert list(data_by_columns.keys()) == ['id', 'd__id', 'd__tens']
+    assert list(data_by_columns['id']) == [doc.id for doc in docs]
+    assert list(data_by_columns['d__id']) == [doc.d.id for doc in docs]
+    assert list(data_by_columns['d__tens']) == [doc.d.tens for doc in docs]
+
+    store = DummyDocIndex[DeepNestedDoc]()
+    docs = [
+        DeepNestedDoc(d=NestedDoc(d=SimpleDoc(tens=np.random.random((10,)))))
+        for _ in range(10)
+    ]
+    data_by_columns = store._get_col_value_dict(docs)
+    assert list(data_by_columns.keys()) == ['id', 'd__id', 'd__d__id', 'd__d__tens']
+    assert list(data_by_columns['id']) == [doc.id for doc in docs]
+    assert list(data_by_columns['d__id']) == [doc.d.id for doc in docs]
+    assert list(data_by_columns['d__d__id']) == [doc.d.d.id for doc in docs]
+    assert list(data_by_columns['d__d__tens']) == [doc.d.d.tens for doc in docs]
diff --git a/tests/doc_index/base_classes/test_base_query_builder.py b/tests/doc_index/base_classes/test_base_query_builder.py
new file mode 100644
index 00000000000..d44837b9075
--- /dev/null
+++ b/tests/doc_index/base_classes/test_base_query_builder.py
@@ -0,0 +1,79 @@
+import pytest
+
+from docarray.doc_index.abstract_doc_index import BaseDocumentIndex, composable
+
+pytestmark = pytest.mark.doc_index
+
+
+class DummyQueryBuilder(BaseDocumentIndex.QueryBuilder):
+    def build(self):
+        return self._queries
+
+
+def _identity(*x, **y):
+    return x, y
+
+
+class DummyDocIndex(BaseDocumentIndex):
+    _query_builder_cls = DummyQueryBuilder
+    _db_config_cls = None
+    _runtime_config_cls = None
+
+    def python_type_to_db_type(self, x):
+        return str
+
+    index = _identity
+    num_docs = _identity
+    _del_items = _identity
+    _get_items = _identity
+    execute_query = _identity
+
+    @composable
+    def find(self, *args, **kwargs):
+        return _identity(*args, **kwargs)
+
+    @composable
+    def filter(self, *args, **kwargs):
+        return _identity(*args, **kwargs)
+
+    @composable
+    def text_search(self, *args, **kwargs):
+        return _identity(*args, **kwargs)
+
+    find_batched = _identity
+    filter_batched = _identity
+    text_search_batched = _identity
+
+
+def test_collect_calls():
+    qb = DummyQueryBuilder()
+    qb.find(find='find', param='param')
+    qb.filter(filter='filter', param='param')
+    qb.text_search(text='text', param='param')
+
+    collected_queries = qb._queries
+    assert collected_queries[0] == ('find', {'find': 'find', 'param': 'param'})
+    assert collected_queries[1] == ('filter', {'filter': 'filter', 'param': 'param'})
+    assert collected_queries[2] == ('text_search', {'text': 'text', 'param': 'param'})
+
+
+def test_fluent_interface():
+    qb = DummyQueryBuilder()
+    qb.find(find='find', param='param').filter(
+        filter='filter', param='param'
+    ).text_search(text='text', param='param')
+
+    collected_queries = qb._queries
+    assert collected_queries[0] == ('find', {'find': 'find', 'param': 'param'})
+    assert collected_queries[1] == ('filter', {'filter': 'filter', 'param': 'param'})
+    assert collected_queries[2] == ('text_search', {'text': 'text', 'param': 'param'})
+
+
+def test_not_composable_raises():
+    qb = DummyQueryBuilder()
+    with pytest.raises(NotImplementedError):
+        qb.filter_batched(filter_batched='filter_batched', param='param')
+    with pytest.raises(NotImplementedError):
+        qb.text_search_batched(text_batched='text_batched', param='param')
+    with pytest.raises(NotImplementedError):
+        qb.find_batched(find_batched='find_batched', param='param')
diff --git a/tests/doc_index/base_classes/test_configs.py b/tests/doc_index/base_classes/test_configs.py
new file mode 100644
index 00000000000..ef1867d140a
--- /dev/null
+++ b/tests/doc_index/base_classes/test_configs.py
@@ -0,0 +1,114 @@
+from dataclasses import dataclass, field
+from typing import Any, Dict, Type
+
+import pytest
+from pydantic import Field
+
+from docarray import BaseDocument
+from docarray.doc_index.abstract_doc_index import BaseDocumentIndex
+from docarray.typing import NdArray
+
+pytestmark = pytest.mark.doc_index
+
+
+class SimpleDoc(BaseDocument):
+    tens: NdArray[10] = Field(dim=1000)
+
+
+class FakeQueryBuilder:
+    ...
+
+
+@dataclass
+class DBConfig(BaseDocumentIndex.DBConfig):
+    work_dir: str = '.'
+    other: int = 5
+
+
+@dataclass
+class RuntimeConfig(BaseDocumentIndex.RuntimeConfig):
+    default_column_config: Dict[Type, Dict[str, Any]] = field(
+        default_factory=lambda: {
+            str: {
+                'dim': 128,
+                'space': 'l2',
+            },
+        }
+    )
+    default_ef: int = 50
+
+
+def _identity(*x, **y):
+    return x, y
+
+
+class DummyDocIndex(BaseDocumentIndex):
+    DBConfig = DBConfig
+    RuntimeConfig = RuntimeConfig
+
+    def python_type_to_db_type(self, x):
+        return str
+
+    _index = _identity
+    num_docs = _identity
+    _del_items = _identity
+    _get_items = _identity
+    execute_query = _identity
+    _find = _identity
+    _find_batched = _identity
+    _filter = _identity
+    _filter_batched = _identity
+    _text_search = _identity
+    _text_search_batched = _identity
+
+
+def test_defaults():
+    store = DummyDocIndex[SimpleDoc]()
+    assert store._db_config.other == 5
+    assert store._db_config.work_dir == '.'
+    assert store._runtime_config.default_column_config[str] == {
+        'dim': 128,
+        'space': 'l2',
+    }
+
+
+def test_set_by_class():
+    # change all settings
+    store = DummyDocIndex[SimpleDoc](DBConfig(work_dir='hi', other=10))
+    assert store._db_config.other == 10
+    assert store._db_config.work_dir == 'hi'
+    store.configure(RuntimeConfig(default_column_config={}, default_ef=10))
+    assert store._runtime_config.default_column_config == {}
+
+    # change only some settings
+    store = DummyDocIndex[SimpleDoc](DBConfig(work_dir='hi'))
+    assert store._db_config.other == 5
+    assert store._db_config.work_dir == 'hi'
+    store.configure(RuntimeConfig(default_column_config={}))
+    assert store._runtime_config.default_column_config == {}
+
+
+def test_set_by_kwargs():
+    # change all settings
+    store = DummyDocIndex[SimpleDoc](work_dir='hi', other=10)
+    assert store._db_config.other == 10
+    assert store._db_config.work_dir == 'hi'
+    store.configure(default_column_config={}, default_ef=10)
+    assert store._runtime_config.default_column_config == {}
+
+    # change only some settings
+    store = DummyDocIndex[SimpleDoc](work_dir='hi')
+    assert store._db_config.other == 5
+    assert store._db_config.work_dir == 'hi'
+    store.configure(default_column_config={})
+    assert store._runtime_config.default_column_config == {}
+
+
+def test_default_column_config():
+    store = DummyDocIndex[SimpleDoc]()
+    assert store._runtime_config.default_column_config == {
+        str: {
+            'dim': 128,
+            'space': 'l2',
+        },
+    }
diff --git a/tests/doc_index/hnswlib/__init__.py b/tests/doc_index/hnswlib/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/doc_index/hnswlib/test_find.py b/tests/doc_index/hnswlib/test_find.py
new file mode 100644
index 00000000000..147d842286a
--- /dev/null
+++ b/tests/doc_index/hnswlib/test_find.py
@@ -0,0 +1,151 @@
+import numpy as np
+import pytest
+from pydantic import Field
+
+from docarray import BaseDocument
+from docarray.doc_index.backends.hnswlib_doc_index import HnswDocumentIndex
+from docarray.typing import NdArray
+
+pytestmark = [pytest.mark.slow, pytest.mark.doc_index]
+
+
+class SimpleDoc(BaseDocument):
+    tens: NdArray[10] = Field(dim=1000)
+
+
+class FlatDoc(BaseDocument):
+    tens_one: NdArray = Field(dim=10)
+    tens_two: NdArray = Field(dim=50)
+
+
+class NestedDoc(BaseDocument):
+    d: SimpleDoc
+
+
+class DeepNestedDoc(BaseDocument):
+    d: NestedDoc
+
+
+@pytest.mark.parametrize('space', ['cosine', 'l2', 'ip'])
+def test_find_simple_schema(tmp_path, space):
+    class SimpleSchema(BaseDocument):
+        tens: NdArray[10] = Field(space=space)
+
+    store = HnswDocumentIndex[SimpleSchema](work_dir=str(tmp_path))
+
+    index_docs = [SimpleDoc(tens=np.zeros(10)) for _ in range(10)]
+    index_docs.append(SimpleDoc(tens=np.ones(10)))
+    store.index(index_docs)
+
+    query = SimpleDoc(tens=np.ones(10))
+
+    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)
+    for result in docs[1:]:
+        assert np.allclose(result.tens, np.zeros(10))
+
+
+@pytest.mark.parametrize('space', ['cosine', 'l2', 'ip'])
+def test_find_flat_schema(tmp_path, space):
+    class FlatSchema(BaseDocument):
+        tens_one: NdArray = Field(dim=10, space=space)
+        tens_two: NdArray = Field(dim=50, space=space)
+
+    store = HnswDocumentIndex[FlatSchema](work_dir=str(tmp_path))
+
+    index_docs = [
+        FlatDoc(tens_one=np.zeros(10), tens_two=np.zeros(50)) for _ in range(10)
+    ]
+    index_docs.append(FlatDoc(tens_one=np.zeros(10), tens_two=np.ones(50)))
+    index_docs.append(FlatDoc(tens_one=np.ones(10), tens_two=np.zeros(50)))
+    store.index(index_docs)
+
+    query = FlatDoc(tens_one=np.ones(10), tens_two=np.ones(50))
+
+    # 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[-2].id
+    assert np.allclose(docs[0].tens_one, index_docs[-2].tens_one)
+    assert np.allclose(docs[0].tens_two, index_docs[-2].tens_two)
+
+
+@pytest.mark.parametrize('space', ['cosine', 'l2', 'ip'])
+def test_find_nested_schema(tmp_path, space):
+    class SimpleDoc(BaseDocument):
+        tens: NdArray[10] = Field(space=space)
+
+    class NestedDoc(BaseDocument):
+        d: SimpleDoc
+        tens: NdArray[10] = Field(space=space)
+
+    class DeepNestedDoc(BaseDocument):
+        d: NestedDoc
+        tens: NdArray = Field(space=space, dim=10)
+
+    store = HnswDocumentIndex[DeepNestedDoc](work_dir=str(tmp_path))
+
+    index_docs = [
+        DeepNestedDoc(
+            d=NestedDoc(d=SimpleDoc(tens=np.zeros(10)), tens=np.zeros(10)),
+            tens=np.zeros(10),
+        )
+        for _ in range(10)
+    ]
+    index_docs.append(
+        DeepNestedDoc(
+            d=NestedDoc(d=SimpleDoc(tens=np.ones(10)), tens=np.zeros(10)),
+            tens=np.zeros(10),
+        )
+    )
+    index_docs.append(
+        DeepNestedDoc(
+            d=NestedDoc(d=SimpleDoc(tens=np.zeros(10)), tens=np.ones(10)),
+            tens=np.zeros(10),
+        )
+    )
+    index_docs.append(
+        DeepNestedDoc(
+            d=NestedDoc(d=SimpleDoc(tens=np.zeros(10)), tens=np.zeros(10)),
+            tens=np.ones(10),
+        )
+    )
+    store.index(index_docs)
+
+    query = DeepNestedDoc(
+        d=NestedDoc(d=SimpleDoc(tens=np.ones(10)), tens=np.ones(10)), tens=np.ones(10)
+    )
+
+    # find on root level
+    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[-2].id
+    assert np.allclose(docs[0].d.tens, index_docs[-2].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[-3].id
+    assert np.allclose(docs[0].d.d.tens, index_docs[-3].d.d.tens)
diff --git a/tests/doc_index/hnswlib/test_index_get_del.py b/tests/doc_index/hnswlib/test_index_get_del.py
new file mode 100644
index 00000000000..72fecc66b9a
--- /dev/null
+++ b/tests/doc_index/hnswlib/test_index_get_del.py
@@ -0,0 +1,246 @@
+import numpy as np
+import pytest
+from pydantic import Field
+
+from docarray import BaseDocument, DocumentArray
+from docarray.doc_index.backends.hnswlib_doc_index import HnswDocumentIndex
+from docarray.typing import NdArray
+
+pytestmark = [pytest.mark.slow, pytest.mark.doc_index]
+
+
+class SimpleDoc(BaseDocument):
+    tens: NdArray[10] = Field(dim=1000)
+
+
+class FlatDoc(BaseDocument):
+    tens_one: NdArray = Field(dim=10)
+    tens_two: NdArray = Field(dim=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, tmp_path, use_docarray):
+    store = HnswDocumentIndex[SimpleDoc](work_dir=str(tmp_path))
+    if use_docarray:
+        ten_simple_docs = DocumentArray[SimpleDoc](ten_simple_docs)
+
+    store.index(ten_simple_docs)
+    assert store.num_docs() == 10
+    for index in store._hnsw_indices.values():
+        assert index.get_current_count() == 10
+
+
+@pytest.mark.parametrize('use_docarray', [True, False])
+def test_index_flat_schema(ten_flat_docs, tmp_path, use_docarray):
+    store = HnswDocumentIndex[FlatDoc](work_dir=str(tmp_path))
+    if use_docarray:
+        ten_flat_docs = DocumentArray[FlatDoc](ten_flat_docs)
+
+    store.index(ten_flat_docs)
+    assert store.num_docs() == 10
+    for index in store._hnsw_indices.values():
+        assert index.get_current_count() == 10
+
+
+@pytest.mark.parametrize('use_docarray', [True, False])
+def test_index_nested_schema(ten_nested_docs, tmp_path, use_docarray):
+    store = HnswDocumentIndex[NestedDoc](work_dir=str(tmp_path))
+    if use_docarray:
+        ten_nested_docs = DocumentArray[NestedDoc](ten_nested_docs)
+
+    store.index(ten_nested_docs)
+    assert store.num_docs() == 10
+    for index in store._hnsw_indices.values():
+        assert index.get_current_count() == 10
+
+
+def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
+    simple_path = tmp_path / 'simple'
+    flat_path = tmp_path / 'flat'
+    nested_path = tmp_path / 'nested'
+
+    # simple
+    store = HnswDocumentIndex[SimpleDoc](work_dir=str(simple_path))
+    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 = HnswDocumentIndex[FlatDoc](work_dir=str(flat_path))
+    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 = HnswDocumentIndex[NestedDoc](work_dir=str(nested_path))
+    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, tmp_path):
+    simple_path = tmp_path / 'simple'
+    flat_path = tmp_path / 'flat'
+    nested_path = tmp_path / 'nested'
+    docs_to_get_idx = [0, 2, 4, 6, 8]
+
+    # simple
+    store = HnswDocumentIndex[SimpleDoc](work_dir=str(simple_path))
+    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 = HnswDocumentIndex[FlatDoc](work_dir=str(flat_path))
+    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 = HnswDocumentIndex[NestedDoc](work_dir=str(nested_path))
+    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, ten_flat_docs, ten_nested_docs, tmp_path):
+    store = HnswDocumentIndex[SimpleDoc](work_dir=str(tmp_path))
+    store.index(ten_simple_docs)
+
+    with pytest.raises(KeyError):
+        store['not_a_real_id']
+
+
+def test_del_single(ten_simple_docs, ten_flat_docs, ten_nested_docs, tmp_path):
+    store = HnswDocumentIndex[SimpleDoc](work_dir=str(tmp_path))
+    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, ten_flat_docs, ten_nested_docs, tmp_path):
+    docs_to_del_idx = [0, 2, 4, 6, 8]
+
+    store = HnswDocumentIndex[SimpleDoc](work_dir=str(tmp_path))
+    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, tmp_path):
+    store = HnswDocumentIndex[SimpleDoc](work_dir=str(tmp_path))
+    store.index(ten_simple_docs)
+
+    with pytest.raises(KeyError):
+        del store['not_a_real_id']
+
+
+def test_num_docs(ten_simple_docs, tmp_path):
+    store = HnswDocumentIndex[SimpleDoc](work_dir=str(tmp_path))
+    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