WeaviateDocumentIndex

docarray.index.backends.weaviate.WeaviateDocumentIndex

Bases: BaseDocIndex, Generic[TSchema]

Source code in docarray/index/backends/weaviate.py

class WeaviateDocumentIndex(BaseDocIndex, Generic[TSchema]):
    def __init__(self, db_config=None, **kwargs) -> None:
        """Initialize WeaviateDocumentIndex"""

        self.embedding_column: Optional[str] = None
        self.properties: Optional[List[str]] = None
        # keep track of the column name that contains the bytes
        # type because we will store them as a base64 encoded string
        # in weaviate
        self.bytes_columns: List[str] = []
        # keep track of the array columns that are not embeddings because we will
        # convert them to python lists before uploading to weaviate
        self.nonembedding_array_columns: List[str] = []
        super().__init__(db_config=db_config, **kwargs)
        self._db_config: WeaviateDocumentIndex.DBConfig = cast(
            WeaviateDocumentIndex.DBConfig, self._db_config
        )
        self._runtime_config: WeaviateDocumentIndex.RuntimeConfig = cast(
            WeaviateDocumentIndex.RuntimeConfig, self._runtime_config
        )

        if self._db_config.embedded_options:
            self._client = weaviate.Client(
                embedded_options=self._db_config.embedded_options
            )
        else:
            self._client = weaviate.Client(
                self._db_config.host, auth_client_secret=self._build_auth_credentials()
            )

        self._configure_client()
        self._validate_columns()
        self._set_embedding_column()
        self._set_properties()
        self._create_schema()

    @property
    def index_name(self):
        default_index_name = self._schema.__name__ if self._schema is not None else None
        if default_index_name is None:
            raise ValueError(
                'A WeaviateDocumentIndex must be typed with a Document type.'
                'To do so, use the syntax: WeaviateDocumentIndex[DocumentType]'
            )

        return self._db_config.index_name or default_index_name

    def _set_properties(self) -> None:
        field_overwrites = {"id": DOCUMENTID}

        self.properties = [
            field_overwrites.get(k, k)
            for k, v in self._column_infos.items()
            if v.config.get('is_embedding', False) is False
            and not issubclass(v.docarray_type, AnyDocArray)
        ]

    def _validate_columns(self) -> None:
        # must have at most one column with property is_embedding=True
        # and that column must be of type WEAVIATE_PY_VEC_TYPES
        # TODO: update when https://github.com/weaviate/weaviate/issues/2424
        # is implemented and discuss best interface to signal which column(s)
        # should be used for embeddings
        num_embedding_columns = 0

        for column_name, column_info in self._column_infos.items():
            if column_info.config.get('is_embedding', False):
                num_embedding_columns += 1
                # if db_type is not 'number[]', then that means the type of the column in
                # the given schema is not one of WEAVIATE_PY_VEC_TYPES
                # note: the mapping between a column's type in the schema to a weaviate type
                # is handled by the python_type_to_db_type method
                if column_info.db_type != 'number[]':
                    raise ValueError(
                        f'Column {column_name} is marked as embedding but is not of type {WEAVIATE_PY_VEC_TYPES}'
                    )

        if num_embedding_columns > 1:
            raise ValueError(
                f'Only one column can be marked as embedding but found {num_embedding_columns} columns marked as embedding'
            )

    def _set_embedding_column(self) -> None:
        for column_name, column_info in self._column_infos.items():
            if column_info.config.get('is_embedding', False):
                self.embedding_column = column_name
                break

    def _configure_client(self) -> None:
        self._client.batch.configure(**self._runtime_config.batch_config)

    def _build_auth_credentials(self):
        dbconfig = self._db_config

        if dbconfig.auth_api_key:
            return weaviate.auth.AuthApiKey(api_key=dbconfig.auth_api_key)
        elif dbconfig.username and dbconfig.password:
            return weaviate.auth.AuthClientPassword(
                dbconfig.username, dbconfig.password, dbconfig.scopes
            )
        else:
            return None

    def configure(self, runtime_config=None, **kwargs) -> None:
        """
        Configure the WeaviateDocumentIndex.
        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
        """
        super().configure(runtime_config, **kwargs)
        self._configure_client()

    def _create_schema(self) -> None:
        schema: Dict[str, Any] = {}

        properties = []
        column_infos = self._column_infos

        for column_name, column_info in column_infos.items():
            # in weaviate, we do not create a property for the doc's embeddings
            if issubclass(column_info.docarray_type, AnyDocArray):
                continue
            if column_name == self.embedding_column:
                continue
            if column_info.db_type == 'blob':
                self.bytes_columns.append(column_name)
            if column_info.db_type == 'number[]':
                self.nonembedding_array_columns.append(column_name)
            prop = {
                "name": column_name
                if column_name != 'id'
                else DOCUMENTID,  # in weaviate, id and _id is a reserved keyword
                "dataType": [column_info.db_type],
            }
            properties.append(prop)

        # TODO: What is the best way to specify other config that is part of schema?
        # e.g. invertedIndexConfig, shardingConfig, moduleConfig, vectorIndexConfig
        #       and configure replication
        # we will update base on user feedback
        schema["properties"] = properties
        schema["class"] = self.index_name

        # TODO: Use exists() instead of contains() when available
        #       see https://github.com/weaviate/weaviate-python-client/issues/232
        if self._client.schema.contains(schema):
            logging.warning(
                f"Found index {self.index_name} with schema {schema}. Will reuse existing schema."
            )
        else:
            self._client.schema.create_class(schema)

    @dataclass
    class DBConfig(BaseDocIndex.DBConfig):
        """Dataclass that contains all "static" configurations of WeaviateDocumentIndex."""

        host: str = 'http://localhost:8080'
        index_name: Optional[str] = None
        username: Optional[str] = None
        password: Optional[str] = None
        scopes: List[str] = field(default_factory=lambda: ["offline_access"])
        auth_api_key: Optional[str] = None
        embedded_options: Optional[EmbeddedOptions] = None

    @dataclass
    class RuntimeConfig(BaseDocIndex.RuntimeConfig):
        """Dataclass that contains all "dynamic" configurations of WeaviateDocumentIndex."""

        default_column_config: Dict[Any, Dict[str, Any]] = field(
            default_factory=lambda: {
                np.ndarray: {},
                docarray.typing.ID: {},
                'string': {},
                'text': {},
                'int': {},
                'number': {},
                'boolean': {},
                'number[]': {},
                'blob': {},
            }
        )

        batch_config: Dict[str, Any] = field(
            default_factory=lambda: DEFAULT_BATCH_CONFIG
        )

    def _del_items(self, doc_ids: Sequence[str]):
        has_matches = True

        operands = [
            {"path": [DOCUMENTID], "operator": "Equal", "valueString": doc_id}
            for doc_id in doc_ids
        ]
        where_filter = {
            "operator": "Or",
            "operands": operands,
        }

        # do a loop because there is a limit to how many objects can be deleted at
        # in a single query
        # see: https://weaviate.io/developers/weaviate/api/rest/batch#maximum-number-of-deletes-per-query
        while has_matches:
            results = self._client.batch.delete_objects(
                class_name=self.index_name,
                where=where_filter,
            )

            has_matches = results["results"]["matches"]

    def _filter(self, filter_query: Any, limit: int) -> Union[DocList, List[Dict]]:
        self._overwrite_id(filter_query)

        results = (
            self._client.query.get(self.index_name, self.properties)
            .with_additional("vector")
            .with_where(filter_query)
            .with_limit(limit)
            .do()
        )

        docs = results["data"]["Get"][self.index_name]

        return [self._parse_weaviate_result(doc) for doc in docs]

    def _filter_batched(
        self, filter_queries: Any, limit: int
    ) -> Union[List[DocList], List[List[Dict]]]:
        for filter_query in filter_queries:
            self._overwrite_id(filter_query)

        qs = [
            self._client.query.get(self.index_name, self.properties)
            .with_additional("vector")
            .with_where(filter_query)
            .with_limit(limit)
            .with_alias(f'query_{i}')
            for i, filter_query in enumerate(filter_queries)
        ]

        batched_results = self._client.query.multi_get(qs).do()

        return [
            [self._parse_weaviate_result(doc) for doc in batched_result]
            for batched_result in batched_results["data"]["Get"].values()
        ]

    def find(
        self,
        query: Union[AnyTensor, BaseDoc],
        search_field: str = '',
        limit: int = 10,
        **kwargs,
    ):
        """
        Find k-nearest neighbors of the query.

        :param query: query vector for KNN/ANN search. Has single axis.
        :param search_field: name of the field to search on
        :param limit: maximum number of documents to return per query
        :return: a named tuple containing `documents` and `scores`
        """
        self._logger.debug('Executing `find`')
        if search_field != '':
            raise ValueError(
                'Argument search_field is not supported for WeaviateDocumentIndex.\nSet search_field to an empty string to proceed.'
            )
        embedding_field = self._get_embedding_field()
        if isinstance(query, BaseDoc):
            query_vec = self._get_values_by_column([query], embedding_field)[0]
        else:
            query_vec = query
        query_vec_np = self._to_numpy(query_vec)
        docs, scores = self._find(
            query_vec_np, search_field=search_field, limit=limit, **kwargs
        )

        if isinstance(docs, List):
            docs = self._dict_list_to_docarray(docs)

        return FindResult(documents=docs, scores=scores)

    def _overwrite_id(self, where_filter):
        """
        Overwrite the id field in the where filter to DOCUMENTID
        if the "id" field is present in the path
        """
        for key, value in where_filter.items():
            if key == "path" and value == ["id"]:
                where_filter[key] = [DOCUMENTID]
            elif isinstance(value, dict):
                self._overwrite_id(value)
            elif isinstance(value, list):
                for item in value:
                    if isinstance(item, dict):
                        self._overwrite_id(item)

    def _find(
        self,
        query: np.ndarray,
        limit: int,
        search_field: str = '',
        score_name: Literal["certainty", "distance"] = "certainty",
        score_threshold: Optional[float] = None,
    ) -> _FindResult:
        index_name = self.index_name
        if search_field:
            logging.warning(
                'The search_field argument is not supported for the WeaviateDocumentIndex and will be ignored.'
            )
        near_vector: Dict[str, Any] = {
            "vector": query,
        }
        if score_threshold:
            near_vector[score_name] = score_threshold

        results = (
            self._client.query.get(index_name, self.properties)
            .with_near_vector(
                near_vector,
            )
            .with_limit(limit)
            .with_additional([score_name, "vector"])
            .do()
        )

        docs, scores = self._format_response(
            results["data"]["Get"][index_name], score_name
        )
        return _FindResult(docs, parse_obj_as(NdArray, scores))

    def _format_response(
        self, results, score_name
    ) -> Tuple[List[Dict[Any, Any]], List[Any]]:
        """
        Format the response from Weaviate into a Tuple of DocList and scores
        """

        documents = []
        scores = []

        for result in results:
            score = result["_additional"][score_name]
            scores.append(score)

            document = self._parse_weaviate_result(result)
            documents.append(document)

        return documents, scores

    def find_batched(
        self,
        queries: Union[AnyTensor, DocList],
        search_field: str = '',
        limit: int = 10,
        **kwargs: Any,
    ) -> 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 DocList.
            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`
        """
        self._logger.debug('Executing `find_batched`')
        if search_field != '':
            raise ValueError(
                'Argument search_field is not supported for WeaviateDocumentIndex.\nSet search_field to an empty string to proceed.'
            )
        embedding_field = self._get_embedding_field()

        if isinstance(queries, Sequence):
            query_vec_list = self._get_values_by_column(queries, embedding_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)

        da_list, scores = self._find_batched(
            query_vec_np, search_field=search_field, limit=limit, **kwargs
        )

        if len(da_list) > 0 and isinstance(da_list[0], List):
            da_list = [self._dict_list_to_docarray(docs) for docs in da_list]

        return FindResultBatched(documents=da_list, scores=scores)  # type: ignore

    def _find_batched(
        self,
        queries: np.ndarray,
        limit: int,
        search_field: str = '',
        score_name: Literal["certainty", "distance"] = "certainty",
        score_threshold: Optional[float] = None,
    ) -> _FindResultBatched:
        qs = []
        for i, query in enumerate(queries):
            near_vector: Dict[str, Any] = {"vector": query}

            if score_threshold:
                near_vector[score_name] = score_threshold

            q = (
                self._client.query.get(self.index_name, self.properties)
                .with_near_vector(near_vector)
                .with_limit(limit)
                .with_additional([score_name, "vector"])
                .with_alias(f'query_{i}')
            )

            qs.append(q)

        results = self._client.query.multi_get(qs).do()

        docs_and_scores = [
            self._format_response(result, score_name)
            for result in results["data"]["Get"].values()
        ]

        docs, scores = zip(*docs_and_scores)
        return _FindResultBatched(list(docs), list(scores))

    def _get_items(self, doc_ids: Sequence[str]) -> List[Dict]:
        # TODO: warn when doc_ids > QUERY_MAXIMUM_RESULTS after
        #       https://github.com/weaviate/weaviate/issues/2792
        #       is implemented
        operands = [
            {"path": [DOCUMENTID], "operator": "Equal", "valueString": doc_id}
            for doc_id in doc_ids
        ]
        where_filter = {
            "operator": "Or",
            "operands": operands,
        }

        results = (
            self._client.query.get(self.index_name, self.properties)
            .with_where(where_filter)
            .with_additional("vector")
            .do()
        )

        docs = [
            self._parse_weaviate_result(doc)
            for doc in results["data"]["Get"][self.index_name]
        ]

        return docs

    def _rewrite_documentid(self, document: Dict):
        doc = document.copy()

        # rewrite the id to DOCUMENTID
        document_id = doc.pop('id')
        doc[DOCUMENTID] = document_id

        return doc

    def _parse_weaviate_result(self, result: Dict) -> Dict:
        """
        Parse the result from weaviate to a format that is compatible with the schema
        that was used to initialize weaviate with.
        """

        result = result.copy()

        # rewrite the DOCUMENTID to id
        if DOCUMENTID in result:
            result['id'] = result.pop(DOCUMENTID)

        # take the vector from the _additional field
        if '_additional' in result and self.embedding_column:
            additional_fields = result.pop('_additional')
            if 'vector' in additional_fields:
                result[self.embedding_column] = additional_fields['vector']

        # convert any base64 encoded bytes column to bytes
        self._decode_base64_properties_to_bytes(result)

        return result

    def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]):
        self._index_subindex(column_to_data)

        docs = self._transpose_col_value_dict(column_to_data)
        index_name = self.index_name

        with self._client.batch as batch:
            for doc in docs:
                parsed_doc = self._rewrite_documentid(doc)
                self._encode_bytes_columns_to_base64(parsed_doc)
                self._convert_nonembedding_array_to_list(parsed_doc)
                vector = (
                    parsed_doc.pop(self.embedding_column)
                    if self.embedding_column
                    else None
                )

                batch.add_data_object(
                    uuid=weaviate.util.generate_uuid5(parsed_doc, index_name),
                    data_object=parsed_doc,
                    class_name=index_name,
                    vector=vector,
                )

    def _text_search(
        self, query: str, limit: int, search_field: str = ''
    ) -> _FindResult:
        index_name = self.index_name
        bm25 = {"query": query, "properties": [search_field]}

        results = (
            self._client.query.get(index_name, self.properties)
            .with_bm25(bm25)
            .with_limit(limit)
            .with_additional(["score", "vector"])
            .do()
        )

        docs, scores = self._format_response(
            results["data"]["Get"][index_name], "score"
        )

        return _FindResult(documents=docs, scores=parse_obj_as(NdArray, scores))

    def _text_search_batched(
        self, queries: Sequence[str], limit: int, search_field: str = ''
    ) -> _FindResultBatched:
        qs = []
        for i, query in enumerate(queries):
            bm25 = {"query": query, "properties": [search_field]}

            q = (
                self._client.query.get(self.index_name, self.properties)
                .with_bm25(bm25)
                .with_limit(limit)
                .with_additional(["score", "vector"])
                .with_alias(f'query_{i}')
            )

            qs.append(q)

        results = self._client.query.multi_get(qs).do()

        docs_and_scores = [
            self._format_response(result, "score")
            for result in results["data"]["Get"].values()
        ]

        docs, scores = zip(*docs_and_scores)
        return _FindResultBatched(list(docs), list(scores))

    def execute_query(self, query: Any, *args, **kwargs) -> Any:
        """
        Execute a query on the WeaviateDocumentIndex.

        Can take two kinds of inputs:

        1. A native query of the underlying database. This is meant as a passthrough so that you
        can enjoy any functionality that is not available through the Document index API.
        2. The output of this Document index' `QueryBuilder.build()` method.

        :param query: the query to execute
        :param args: positional arguments to pass to the query
        :param kwargs: keyword arguments to pass to the query
        :return: the result of the query
        """
        da_class = DocList.__class_getitem__(cast(Type[BaseDoc], self._schema))

        if isinstance(query, self.QueryBuilder):
            batched_results = self._client.query.multi_get(query._queries).do()
            batched_docs = batched_results["data"]["Get"].values()

            def f(doc):
                # TODO: use
                # return self._schema(**self._parse_weaviate_result(doc))
                # when https://github.com/weaviate/weaviate/issues/2858
                # is fixed
                return self._schema.from_view(self._parse_weaviate_result(doc))  # type: ignore

            results = [
                da_class([f(doc) for doc in batched_doc])
                for batched_doc in batched_docs
            ]
            return results if len(results) > 1 else results[0]

        # TODO: validate graphql query string before sending it to weaviate
        if isinstance(query, str):
            return self._client.query.raw(query)

    def num_docs(self) -> int:
        """
        Get the number of documents.
        """
        index_name = self.index_name
        result = self._client.query.aggregate(index_name).with_meta_count().do()
        # TODO: decorator to check for errors
        total_docs = result["data"]["Aggregate"][index_name][0]["meta"]["count"]

        return total_docs

    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.
        """
        for allowed_type in WEAVIATE_PY_VEC_TYPES:
            if issubclass(python_type, allowed_type):
                return 'number[]'

        py_weaviate_type_map = {
            docarray.typing.ID: 'string',
            str: 'text',
            int: 'int',
            float: 'number',
            bool: 'boolean',
            np.ndarray: 'number[]',
            bytes: 'blob',
        }

        for py_type, weaviate_type in py_weaviate_type_map.items():
            if issubclass(python_type, py_type):
                return weaviate_type

        raise ValueError(f'Unsupported column type for {type(self)}: {python_type}')

    def build_query(self) -> BaseDocIndex.QueryBuilder:
        """
        Build a query for WeaviateDocumentIndex.
        :return: QueryBuilder object
        """
        return self.QueryBuilder(self)

    def _get_embedding_field(self):
        for colname, colinfo in self._column_infos.items():
            # no need to check for missing is_embedding attribute because this check
            # is done when the index is created
            if colinfo.config.get('is_embedding', None):
                return colname

        # just to pass mypy
        return ""

    def _encode_bytes_columns_to_base64(self, doc):
        for column in self.bytes_columns:
            if doc[column] is not None:
                doc[column] = base64.b64encode(doc[column]).decode("utf-8")

    def _decode_base64_properties_to_bytes(self, doc):
        for column in self.bytes_columns:
            if doc[column] is not None:
                doc[column] = base64.b64decode(doc[column])

    def _convert_nonembedding_array_to_list(self, doc):
        for column in self.nonembedding_array_columns:
            if doc[column] is not None:
                doc[column] = doc[column].tolist()

    def _filter_by_parent_id(self, id: str) -> Optional[List[str]]:
        results = (
            self._client.query.get(self._db_config.index_name, ['docarrayid'])
            .with_where(
                {'path': ['parent_id'], 'operator': 'Equal', 'valueString': f'{id}'}
            )
            .do()
        )

        ids = [
            res['docarrayid']
            for res in results['data']['Get'][self._db_config.index_name]
        ]
        return ids

    class QueryBuilder(BaseDocIndex.QueryBuilder):
        def __init__(self, document_index):
            self._queries = [
                document_index._client.query.get(
                    document_index.index_name, document_index.properties
                )
            ]

        def build(self, *args, **kwargs) -> Any:
            """Build the query object."""
            num_queries = len(self._queries)

            for i in range(num_queries):
                q = self._queries[i]
                if self._is_hybrid_query(q):
                    self._make_proper_hybrid_query(q)
                q.with_additional(["vector"]).with_alias(f'query_{i}')

            return self

        def _is_hybrid_query(self, query: weaviate.gql.get.GetBuilder) -> bool:
            """
            Checks if a query has been composed with both a with_bm25 and a with_near_vector verb
            """
            if not query._near_ask:
                return False
            else:
                return query._bm25 and query._near_ask._content.get("vector", None)

        def _make_proper_hybrid_query(
            self, query: weaviate.gql.get.GetBuilder
        ) -> weaviate.gql.get.GetBuilder:
            """
            Modifies a query to be a proper hybrid query.

            In weaviate, a query with with_bm25 and with_near_vector verb is not a hybrid query.
            We need to use the with_hybrid verb to make it a hybrid query.
            """

            text_query = query._bm25.query
            vector_query = query._near_ask._content["vector"]
            hybrid_query = weaviate.gql.get.Hybrid(
                query=text_query, vector=vector_query, alpha=0.5
            )

            query._bm25 = None
            query._near_ask = None
            query._hybrid = hybrid_query

        def _overwrite_id(self, where_filter):
            """
            Overwrite the id field in the where filter to DOCUMENTID
            if the "id" field is present in the path
            """
            for key, value in where_filter.items():
                if key == "path" and value == ["id"]:
                    where_filter[key] = [DOCUMENTID]
                elif isinstance(value, dict):
                    self._overwrite_id(value)
                elif isinstance(value, list):
                    for item in value:
                        if isinstance(item, dict):
                            self._overwrite_id(item)

        def find(
            self,
            query,
            score_name: Literal["certainty", "distance"] = "certainty",
            score_threshold: Optional[float] = None,
            **kwargs,
        ) -> Any:
            """
            Find k-nearest neighbors of the query.

            :param query: query vector for search. Has single axis.
            :param score_name: either `"certainty"` (default) or `"distance"`
            :param score_threshold: the threshold of the score
            :return: self
            """
            if kwargs.get('search_field'):
                logging.warning(
                    'The search_field argument is not supported for the WeaviateDocumentIndex and will be ignored.'
                )

            near_vector = {
                "vector": query,
            }
            if score_threshold:
                near_vector[score_name] = score_threshold

            self._queries[0] = self._queries[0].with_near_vector(near_vector)
            return self

        def find_batched(
            self,
            queries,
            score_name: Literal["certainty", "distance"] = "certainty",
            score_threshold: Optional[float] = None,
        ) -> Any:
            """Find k-nearest neighbors of the query vectors.

            :param queries: query vector for KNN/ANN search.
                Can be either a tensor-like (np.array, torch.Tensor, etc.) with a,
                or a DocList.
                If a tensor-like is passed, it should have shape `(batch_size, vector_dim)`
            :param score_name: either `"certainty"` (default) or `"distance"`
            :param score_threshold: the threshold of the score
            :return: self
            """
            adj_queries, adj_clauses = self._resize_queries_and_clauses(
                self._queries, queries
            )
            new_queries = []

            for query, clause in zip(adj_queries, adj_clauses):
                near_vector = {
                    "vector": clause,
                }
                if score_threshold:
                    near_vector[score_name] = score_threshold

                new_queries.append(query.with_near_vector(near_vector))

            self._queries = new_queries

            return self

        def filter(self, where_filter: Any) -> Any:
            """Find documents in the index based on a filter query
            :param where_filter: a filter
            :return: self
            """
            where_filter = where_filter.copy()
            self._overwrite_id(where_filter)
            self._queries[0] = self._queries[0].with_where(where_filter)
            return self

        def filter_batched(self, filters) -> Any:
            """Find documents in the index based on a filter query
            :param filters: filters
            :return: self
            """
            adj_queries, adj_clauses = self._resize_queries_and_clauses(
                self._queries, filters
            )
            new_queries = []

            for query, clause in zip(adj_queries, adj_clauses):
                clause = clause.copy()
                self._overwrite_id(clause)
                new_queries.append(query.with_where(clause))

            self._queries = new_queries

            return self

        def text_search(self, query: str, search_field: Optional[str] = None) -> Any:
            """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
            :return: self
            """
            bm25: Dict[str, Any] = {"query": query}
            if search_field:
                bm25["properties"] = [search_field]
            self._queries[0] = self._queries[0].with_bm25(**bm25)
            return self

        def text_search_batched(
            self, queries: Sequence[str], search_field: Optional[str] = None
        ) -> Any:
            """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
            :return: self
            """
            adj_queries, adj_clauses = self._resize_queries_and_clauses(
                self._queries, queries
            )
            new_queries = []

            for query, clause in zip(adj_queries, adj_clauses):
                bm25 = {"query": clause}
                if search_field:
                    bm25["properties"] = [search_field]
                new_queries.append(query.with_bm25(**bm25))

            self._queries = new_queries

            return self

        def limit(self, limit: int) -> Any:
            self._queries = [query.with_limit(limit) for query in self._queries]
            return self

        def _resize_queries_and_clauses(self, queries, clauses):
            """
            Adjust the length and content of queries and clauses so that we can compose
            them element-wise
            """
            num_clauses = len(clauses)
            num_queries = len(queries)

            # if there's only one clause, then we assume that it should be applied
            # to every query
            if num_clauses == 1:
                return queries, clauses * num_queries
            # if there's only one query, then we can lengthen it to match the number
            # of clauses
            elif num_queries == 1:
                return [copy.deepcopy(queries[0]) for _ in range(num_clauses)], clauses
            # if the number of queries and clauses is the same, then we can just
            # return them as-is
            elif num_clauses == num_queries:
                return queries, clauses
            else:
                raise ValueError(
                    f"Can't compose {num_clauses} clauses with {num_queries} queries"
                )

DBConfig dataclass

Bases: BaseDocIndex.DBConfig

Dataclass that contains all "static" configurations of WeaviateDocumentIndex.

Source code in docarray/index/backends/weaviate.py

@dataclass
class DBConfig(BaseDocIndex.DBConfig):
    """Dataclass that contains all "static" configurations of WeaviateDocumentIndex."""

    host: str = 'http://localhost:8080'
    index_name: Optional[str] = None
    username: Optional[str] = None
    password: Optional[str] = None
    scopes: List[str] = field(default_factory=lambda: ["offline_access"])
    auth_api_key: Optional[str] = None
    embedded_options: Optional[EmbeddedOptions] = None

QueryBuilder

Bases: BaseDocIndex.QueryBuilder

Source code in docarray/index/backends/weaviate.py

class QueryBuilder(BaseDocIndex.QueryBuilder):
    def __init__(self, document_index):
        self._queries = [
            document_index._client.query.get(
                document_index.index_name, document_index.properties
            )
        ]

    def build(self, *args, **kwargs) -> Any:
        """Build the query object."""
        num_queries = len(self._queries)

        for i in range(num_queries):
            q = self._queries[i]
            if self._is_hybrid_query(q):
                self._make_proper_hybrid_query(q)
            q.with_additional(["vector"]).with_alias(f'query_{i}')

        return self

    def _is_hybrid_query(self, query: weaviate.gql.get.GetBuilder) -> bool:
        """
        Checks if a query has been composed with both a with_bm25 and a with_near_vector verb
        """
        if not query._near_ask:
            return False
        else:
            return query._bm25 and query._near_ask._content.get("vector", None)

    def _make_proper_hybrid_query(
        self, query: weaviate.gql.get.GetBuilder
    ) -> weaviate.gql.get.GetBuilder:
        """
        Modifies a query to be a proper hybrid query.

        In weaviate, a query with with_bm25 and with_near_vector verb is not a hybrid query.
        We need to use the with_hybrid verb to make it a hybrid query.
        """

        text_query = query._bm25.query
        vector_query = query._near_ask._content["vector"]
        hybrid_query = weaviate.gql.get.Hybrid(
            query=text_query, vector=vector_query, alpha=0.5
        )

        query._bm25 = None
        query._near_ask = None
        query._hybrid = hybrid_query

    def _overwrite_id(self, where_filter):
        """
        Overwrite the id field in the where filter to DOCUMENTID
        if the "id" field is present in the path
        """
        for key, value in where_filter.items():
            if key == "path" and value == ["id"]:
                where_filter[key] = [DOCUMENTID]
            elif isinstance(value, dict):
                self._overwrite_id(value)
            elif isinstance(value, list):
                for item in value:
                    if isinstance(item, dict):
                        self._overwrite_id(item)

    def find(
        self,
        query,
        score_name: Literal["certainty", "distance"] = "certainty",
        score_threshold: Optional[float] = None,
        **kwargs,
    ) -> Any:
        """
        Find k-nearest neighbors of the query.

        :param query: query vector for search. Has single axis.
        :param score_name: either `"certainty"` (default) or `"distance"`
        :param score_threshold: the threshold of the score
        :return: self
        """
        if kwargs.get('search_field'):
            logging.warning(
                'The search_field argument is not supported for the WeaviateDocumentIndex and will be ignored.'
            )

        near_vector = {
            "vector": query,
        }
        if score_threshold:
            near_vector[score_name] = score_threshold

        self._queries[0] = self._queries[0].with_near_vector(near_vector)
        return self

    def find_batched(
        self,
        queries,
        score_name: Literal["certainty", "distance"] = "certainty",
        score_threshold: Optional[float] = None,
    ) -> Any:
        """Find k-nearest neighbors of the query vectors.

        :param queries: query vector for KNN/ANN search.
            Can be either a tensor-like (np.array, torch.Tensor, etc.) with a,
            or a DocList.
            If a tensor-like is passed, it should have shape `(batch_size, vector_dim)`
        :param score_name: either `"certainty"` (default) or `"distance"`
        :param score_threshold: the threshold of the score
        :return: self
        """
        adj_queries, adj_clauses = self._resize_queries_and_clauses(
            self._queries, queries
        )
        new_queries = []

        for query, clause in zip(adj_queries, adj_clauses):
            near_vector = {
                "vector": clause,
            }
            if score_threshold:
                near_vector[score_name] = score_threshold

            new_queries.append(query.with_near_vector(near_vector))

        self._queries = new_queries

        return self

    def filter(self, where_filter: Any) -> Any:
        """Find documents in the index based on a filter query
        :param where_filter: a filter
        :return: self
        """
        where_filter = where_filter.copy()
        self._overwrite_id(where_filter)
        self._queries[0] = self._queries[0].with_where(where_filter)
        return self

    def filter_batched(self, filters) -> Any:
        """Find documents in the index based on a filter query
        :param filters: filters
        :return: self
        """
        adj_queries, adj_clauses = self._resize_queries_and_clauses(
            self._queries, filters
        )
        new_queries = []

        for query, clause in zip(adj_queries, adj_clauses):
            clause = clause.copy()
            self._overwrite_id(clause)
            new_queries.append(query.with_where(clause))

        self._queries = new_queries

        return self

    def text_search(self, query: str, search_field: Optional[str] = None) -> Any:
        """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
        :return: self
        """
        bm25: Dict[str, Any] = {"query": query}
        if search_field:
            bm25["properties"] = [search_field]
        self._queries[0] = self._queries[0].with_bm25(**bm25)
        return self

    def text_search_batched(
        self, queries: Sequence[str], search_field: Optional[str] = None
    ) -> Any:
        """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
        :return: self
        """
        adj_queries, adj_clauses = self._resize_queries_and_clauses(
            self._queries, queries
        )
        new_queries = []

        for query, clause in zip(adj_queries, adj_clauses):
            bm25 = {"query": clause}
            if search_field:
                bm25["properties"] = [search_field]
            new_queries.append(query.with_bm25(**bm25))

        self._queries = new_queries

        return self

    def limit(self, limit: int) -> Any:
        self._queries = [query.with_limit(limit) for query in self._queries]
        return self

    def _resize_queries_and_clauses(self, queries, clauses):
        """
        Adjust the length and content of queries and clauses so that we can compose
        them element-wise
        """
        num_clauses = len(clauses)
        num_queries = len(queries)

        # if there's only one clause, then we assume that it should be applied
        # to every query
        if num_clauses == 1:
            return queries, clauses * num_queries
        # if there's only one query, then we can lengthen it to match the number
        # of clauses
        elif num_queries == 1:
            return [copy.deepcopy(queries[0]) for _ in range(num_clauses)], clauses
        # if the number of queries and clauses is the same, then we can just
        # return them as-is
        elif num_clauses == num_queries:
            return queries, clauses
        else:
            raise ValueError(
                f"Can't compose {num_clauses} clauses with {num_queries} queries"
            )

build(*args, **kwargs)

Build the query object.

Source code in docarray/index/backends/weaviate.py

def build(self, *args, **kwargs) -> Any:
    """Build the query object."""
    num_queries = len(self._queries)

    for i in range(num_queries):
        q = self._queries[i]
        if self._is_hybrid_query(q):
            self._make_proper_hybrid_query(q)
        q.with_additional(["vector"]).with_alias(f'query_{i}')

    return self

filter(where_filter)

Find documents in the index based on a filter query

Parameters:

Name	Type	Description	Default
where_filter	Any	a filter	required

Returns:

Type	Description
Any	self

Source code in docarray/index/backends/weaviate.py

def filter(self, where_filter: Any) -> Any:
    """Find documents in the index based on a filter query
    :param where_filter: a filter
    :return: self
    """
    where_filter = where_filter.copy()
    self._overwrite_id(where_filter)
    self._queries[0] = self._queries[0].with_where(where_filter)
    return self

filter_batched(filters)

Find documents in the index based on a filter query

Parameters:

Name	Type	Description	Default
filters		filters	required

Returns:

Type	Description
Any	self

Source code in docarray/index/backends/weaviate.py

def filter_batched(self, filters) -> Any:
    """Find documents in the index based on a filter query
    :param filters: filters
    :return: self
    """
    adj_queries, adj_clauses = self._resize_queries_and_clauses(
        self._queries, filters
    )
    new_queries = []

    for query, clause in zip(adj_queries, adj_clauses):
        clause = clause.copy()
        self._overwrite_id(clause)
        new_queries.append(query.with_where(clause))

    self._queries = new_queries

    return self

find(query, score_name='certainty', score_threshold=None, **kwargs)

Find k-nearest neighbors of the query.

Parameters:

Name	Type	Description	Default
query		query vector for search. Has single axis.	required
score_name	Literal[certainty, distance]	either "certainty" (default) or "distance"	'certainty'
score_threshold	Optional[float]	the threshold of the score	None

Returns:

Type	Description
Any	self

Source code in docarray/index/backends/weaviate.py

def find(
    self,
    query,
    score_name: Literal["certainty", "distance"] = "certainty",
    score_threshold: Optional[float] = None,
    **kwargs,
) -> Any:
    """
    Find k-nearest neighbors of the query.

    :param query: query vector for search. Has single axis.
    :param score_name: either `"certainty"` (default) or `"distance"`
    :param score_threshold: the threshold of the score
    :return: self
    """
    if kwargs.get('search_field'):
        logging.warning(
            'The search_field argument is not supported for the WeaviateDocumentIndex and will be ignored.'
        )

    near_vector = {
        "vector": query,
    }
    if score_threshold:
        near_vector[score_name] = score_threshold

    self._queries[0] = self._queries[0].with_near_vector(near_vector)
    return self

find_batched(queries, score_name='certainty', score_threshold=None)

Find k-nearest neighbors of the query vectors.

Parameters:

Name	Type	Description	Default
queries		query vector for KNN/ANN search. Can be either a tensor-like (np.array, torch.Tensor, etc.) with a, or a DocList. If a tensor-like is passed, it should have shape (batch_size, vector_dim)	required
score_name	Literal[certainty, distance]	either "certainty" (default) or "distance"	'certainty'
score_threshold	Optional[float]	the threshold of the score	None

Returns:

Type	Description
Any	self

Source code in docarray/index/backends/weaviate.py

def find_batched(
    self,
    queries,
    score_name: Literal["certainty", "distance"] = "certainty",
    score_threshold: Optional[float] = None,
) -> Any:
    """Find k-nearest neighbors of the query vectors.

    :param queries: query vector for KNN/ANN search.
        Can be either a tensor-like (np.array, torch.Tensor, etc.) with a,
        or a DocList.
        If a tensor-like is passed, it should have shape `(batch_size, vector_dim)`
    :param score_name: either `"certainty"` (default) or `"distance"`
    :param score_threshold: the threshold of the score
    :return: self
    """
    adj_queries, adj_clauses = self._resize_queries_and_clauses(
        self._queries, queries
    )
    new_queries = []

    for query, clause in zip(adj_queries, adj_clauses):
        near_vector = {
            "vector": clause,
        }
        if score_threshold:
            near_vector[score_name] = score_threshold

        new_queries.append(query.with_near_vector(near_vector))

    self._queries = new_queries

    return self

text_search(query, search_field=None)

Find documents in the index based on a text search query

Parameters:

Name	Type	Description	Default
query	str	The text to search for	required
search_field	Optional[str]	name of the field to search on	None

Returns:

Type	Description
Any	self

Source code in docarray/index/backends/weaviate.py

def text_search(self, query: str, search_field: Optional[str] = None) -> Any:
    """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
    :return: self
    """
    bm25: Dict[str, Any] = {"query": query}
    if search_field:
        bm25["properties"] = [search_field]
    self._queries[0] = self._queries[0].with_bm25(**bm25)
    return self

text_search_batched(queries, search_field=None)

Find documents in the index based on a text search query

Parameters:

Name	Type	Description	Default
queries	Sequence[str]	The texts to search for	required
search_field	Optional[str]	name of the field to search on	None

Returns:

Type	Description
Any	self

Source code in docarray/index/backends/weaviate.py

def text_search_batched(
    self, queries: Sequence[str], search_field: Optional[str] = None
) -> Any:
    """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
    :return: self
    """
    adj_queries, adj_clauses = self._resize_queries_and_clauses(
        self._queries, queries
    )
    new_queries = []

    for query, clause in zip(adj_queries, adj_clauses):
        bm25 = {"query": clause}
        if search_field:
            bm25["properties"] = [search_field]
        new_queries.append(query.with_bm25(**bm25))

    self._queries = new_queries

    return self

RuntimeConfig dataclass

Bases: BaseDocIndex.RuntimeConfig

Dataclass that contains all "dynamic" configurations of WeaviateDocumentIndex.

Source code in docarray/index/backends/weaviate.py

@dataclass
class RuntimeConfig(BaseDocIndex.RuntimeConfig):
    """Dataclass that contains all "dynamic" configurations of WeaviateDocumentIndex."""

    default_column_config: Dict[Any, Dict[str, Any]] = field(
        default_factory=lambda: {
            np.ndarray: {},
            docarray.typing.ID: {},
            'string': {},
            'text': {},
            'int': {},
            'number': {},
            'boolean': {},
            'number[]': {},
            'blob': {},
        }
    )

    batch_config: Dict[str, Any] = field(
        default_factory=lambda: DEFAULT_BATCH_CONFIG
    )

__init__(db_config=None, **kwargs)

Initialize WeaviateDocumentIndex

Source code in docarray/index/backends/weaviate.py

def __init__(self, db_config=None, **kwargs) -> None:
    """Initialize WeaviateDocumentIndex"""

    self.embedding_column: Optional[str] = None
    self.properties: Optional[List[str]] = None
    # keep track of the column name that contains the bytes
    # type because we will store them as a base64 encoded string
    # in weaviate
    self.bytes_columns: List[str] = []
    # keep track of the array columns that are not embeddings because we will
    # convert them to python lists before uploading to weaviate
    self.nonembedding_array_columns: List[str] = []
    super().__init__(db_config=db_config, **kwargs)
    self._db_config: WeaviateDocumentIndex.DBConfig = cast(
        WeaviateDocumentIndex.DBConfig, self._db_config
    )
    self._runtime_config: WeaviateDocumentIndex.RuntimeConfig = cast(
        WeaviateDocumentIndex.RuntimeConfig, self._runtime_config
    )

    if self._db_config.embedded_options:
        self._client = weaviate.Client(
            embedded_options=self._db_config.embedded_options
        )
    else:
        self._client = weaviate.Client(
            self._db_config.host, auth_client_secret=self._build_auth_credentials()
        )

    self._configure_client()
    self._validate_columns()
    self._set_embedding_column()
    self._set_properties()
    self._create_schema()

build_query()

Build a query for WeaviateDocumentIndex.

Returns:

Type	Description
BaseDocIndex.QueryBuilder	QueryBuilder object

Source code in docarray/index/backends/weaviate.py

def build_query(self) -> BaseDocIndex.QueryBuilder:
    """
    Build a query for WeaviateDocumentIndex.
    :return: QueryBuilder object
    """
    return self.QueryBuilder(self)

configure(runtime_config=None, **kwargs)

Configure the WeaviateDocumentIndex. 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.

Parameters:

Name	Type	Description	Default
runtime_config		the configuration to apply	None
kwargs		individual configuration parameters	{}

Source code in docarray/index/backends/weaviate.py

def configure(self, runtime_config=None, **kwargs) -> None:
    """
    Configure the WeaviateDocumentIndex.
    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
    """
    super().configure(runtime_config, **kwargs)
    self._configure_client()

execute_query(query, *args, **kwargs)

Execute a query on the WeaviateDocumentIndex.

Can take two kinds of inputs:

1. A native query of the underlying database. This is meant as a passthrough so that you can enjoy any functionality that is not available through the Document index API.
2. The output of this Document index' QueryBuilder.build() method.

Parameters:

Name	Type	Description	Default
query	Any	the query to execute	required
args		positional arguments to pass to the query	()
kwargs		keyword arguments to pass to the query	{}

Returns:

Type	Description
Any	the result of the query

Source code in docarray/index/backends/weaviate.py

def execute_query(self, query: Any, *args, **kwargs) -> Any:
    """
    Execute a query on the WeaviateDocumentIndex.

    Can take two kinds of inputs:

    1. A native query of the underlying database. This is meant as a passthrough so that you
    can enjoy any functionality that is not available through the Document index API.
    2. The output of this Document index' `QueryBuilder.build()` method.

    :param query: the query to execute
    :param args: positional arguments to pass to the query
    :param kwargs: keyword arguments to pass to the query
    :return: the result of the query
    """
    da_class = DocList.__class_getitem__(cast(Type[BaseDoc], self._schema))

    if isinstance(query, self.QueryBuilder):
        batched_results = self._client.query.multi_get(query._queries).do()
        batched_docs = batched_results["data"]["Get"].values()

        def f(doc):
            # TODO: use
            # return self._schema(**self._parse_weaviate_result(doc))
            # when https://github.com/weaviate/weaviate/issues/2858
            # is fixed
            return self._schema.from_view(self._parse_weaviate_result(doc))  # type: ignore

        results = [
            da_class([f(doc) for doc in batched_doc])
            for batched_doc in batched_docs
        ]
        return results if len(results) > 1 else results[0]

    # TODO: validate graphql query string before sending it to weaviate
    if isinstance(query, str):
        return self._client.query.raw(query)

find(query, search_field='', limit=10, **kwargs)

Find k-nearest neighbors of the query.

Parameters:

Name	Type	Description	Default
query	Union[AnyTensor, BaseDoc]	query vector for KNN/ANN search. Has single axis.	required
search_field	str	name of the field to search on	''
limit	int	maximum number of documents to return per query	10

Returns:

Type	Description
	a named tuple containing documents and scores

Source code in docarray/index/backends/weaviate.py

def find(
    self,
    query: Union[AnyTensor, BaseDoc],
    search_field: str = '',
    limit: int = 10,
    **kwargs,
):
    """
    Find k-nearest neighbors of the query.

    :param query: query vector for KNN/ANN search. Has single axis.
    :param search_field: name of the field to search on
    :param limit: maximum number of documents to return per query
    :return: a named tuple containing `documents` and `scores`
    """
    self._logger.debug('Executing `find`')
    if search_field != '':
        raise ValueError(
            'Argument search_field is not supported for WeaviateDocumentIndex.\nSet search_field to an empty string to proceed.'
        )
    embedding_field = self._get_embedding_field()
    if isinstance(query, BaseDoc):
        query_vec = self._get_values_by_column([query], embedding_field)[0]
    else:
        query_vec = query
    query_vec_np = self._to_numpy(query_vec)
    docs, scores = self._find(
        query_vec_np, search_field=search_field, limit=limit, **kwargs
    )

    if isinstance(docs, List):
        docs = self._dict_list_to_docarray(docs)

    return FindResult(documents=docs, scores=scores)

find_batched(queries, search_field='', limit=10, **kwargs)

Find documents in the index using nearest neighbor search.

Parameters:

Name	Type	Description	Default
queries	Union[AnyTensor, DocList]	query vector for KNN/ANN search. Can be either a tensor-like (np.array, torch.Tensor, etc.) with a, or a DocList. If a tensor-like is passed, it should have shape (batch_size, vector_dim)	required
search_field	str	name of the field to search on. Documents in the index are retrieved based on this similarity of this field to the query.	''
limit	int	maximum number of documents to return per query	10

Returns:

Type	Description
FindResultBatched	a named tuple containing documents and scores

Source code in docarray/index/backends/weaviate.py

def find_batched(
    self,
    queries: Union[AnyTensor, DocList],
    search_field: str = '',
    limit: int = 10,
    **kwargs: Any,
) -> 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 DocList.
        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`
    """
    self._logger.debug('Executing `find_batched`')
    if search_field != '':
        raise ValueError(
            'Argument search_field is not supported for WeaviateDocumentIndex.\nSet search_field to an empty string to proceed.'
        )
    embedding_field = self._get_embedding_field()

    if isinstance(queries, Sequence):
        query_vec_list = self._get_values_by_column(queries, embedding_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)

    da_list, scores = self._find_batched(
        query_vec_np, search_field=search_field, limit=limit, **kwargs
    )

    if len(da_list) > 0 and isinstance(da_list[0], List):
        da_list = [self._dict_list_to_docarray(docs) for docs in da_list]

    return FindResultBatched(documents=da_list, scores=scores)  # type: ignore

num_docs()

Get the number of documents.

Source code in docarray/index/backends/weaviate.py

def num_docs(self) -> int:
    """
    Get the number of documents.
    """
    index_name = self.index_name
    result = self._client.query.aggregate(index_name).with_meta_count().do()
    # TODO: decorator to check for errors
    total_docs = result["data"]["Aggregate"][index_name][0]["meta"]["count"]

    return total_docs

python_type_to_db_type(python_type)

Map python type to database type. Takes any python type and returns the corresponding database column type.

Parameters:

Name	Type	Description	Default
python_type	Type	a python type.	required

Returns:

Type	Description
Any	the corresponding database column type, or None if python_type is not supported.

Source code in docarray/index/backends/weaviate.py

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.
    """
    for allowed_type in WEAVIATE_PY_VEC_TYPES:
        if issubclass(python_type, allowed_type):
            return 'number[]'

    py_weaviate_type_map = {
        docarray.typing.ID: 'string',
        str: 'text',
        int: 'int',
        float: 'number',
        bool: 'boolean',
        np.ndarray: 'number[]',
        bytes: 'blob',
    }

    for py_type, weaviate_type in py_weaviate_type_map.items():
        if issubclass(python_type, py_type):
            return weaviate_type

    raise ValueError(f'Unsupported column type for {type(self)}: {python_type}')