QdrantDocumentIndex

docarray.index.backends.qdrant.QdrantDocumentIndex

Bases: BaseDocIndex, Generic[TSchema]

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

class QdrantDocumentIndex(BaseDocIndex, Generic[TSchema]):
    UUID_NAMESPACE = uuid.UUID('3896d314-1e95-4a3a-b45a-945f9f0b541d')

    def __init__(self, db_config=None, **kwargs):
        """Initialize QdrantDocumentIndex"""
        if db_config is not None and getattr(
            db_config, 'index_name'
        ):  # this is needed for subindices
            db_config.collection_name = db_config.index_name
        super().__init__(db_config=db_config, **kwargs)
        self._db_config: QdrantDocumentIndex.DBConfig = cast(
            QdrantDocumentIndex.DBConfig, self._db_config
        )
        self._client = qdrant_client.QdrantClient(
            location=self._db_config.location,
            url=self._db_config.url,
            port=self._db_config.port,
            grpc_port=self._db_config.grpc_port,
            prefer_grpc=self._db_config.prefer_grpc,
            https=self._db_config.https,
            api_key=self._db_config.api_key,
            prefix=self._db_config.prefix,
            timeout=self._db_config.timeout,
            host=self._db_config.host,
            path=self._db_config.path,
        )
        self._initialize_collection()
        self._logger.info(f'{self.__class__.__name__} has been initialized')

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

        return self._db_config.collection_name or default_collection_name

    @property
    def index_name(self):
        return self.collection_name

    @dataclass
    class Query:
        """Dataclass describing a query."""

        vector_field: Optional[str]
        vector_query: Optional[NdArray]
        filter: Optional[rest.Filter]
        limit: int

    class QueryBuilder(BaseDocIndex.QueryBuilder):
        def __init__(
            self,
            vector_search_field: Optional[str] = None,
            vector_filters: Optional[List[NdArray]] = None,
            payload_filters: Optional[List[rest.Filter]] = None,
            text_search_filters: Optional[List[Tuple[str, str]]] = None,
        ):
            self._vector_search_field: Optional[str] = vector_search_field
            self._vector_filters: List[NdArray] = vector_filters or []
            self._payload_filters: List[rest.Filter] = payload_filters or []
            self._text_search_filters: List[Tuple[str, str]] = text_search_filters or []

        def build(self, limit: int) -> 'QdrantDocumentIndex.Query':
            """
            Build a query object for QdrantDocumentIndex.
            :return: QdrantDocumentIndex.Query object
            """
            vector_query = None
            if len(self._vector_filters) > 0:
                # If there are multiple vector queries applied, we can average them and
                # perform semantic search on a single vector instead
                vector_query = np.average(self._vector_filters, axis=0)
            merged_filter = None
            if len(self._payload_filters) > 0:
                merged_filter = rest.Filter(must=self._payload_filters)
            if len(self._text_search_filters) > 0:
                # Text search is just a special case of payload filtering, so the
                # payload filter is simply extended
                merged_filter = merged_filter or rest.Filter(must=[])
                for search_field, query in self._text_search_filters:
                    merged_filter.must.append(  # type: ignore[union-attr]
                        rest.FieldCondition(
                            key=search_field,
                            match=rest.MatchText(text=query),
                        )
                    )
            return QdrantDocumentIndex.Query(
                vector_field=self._vector_search_field,
                vector_query=vector_query,
                filter=merged_filter,
                limit=limit,
            )

        def find(  # type: ignore[override]
            self, query: NdArray, search_field: str = ''
        ) -> 'QdrantDocumentIndex.QueryBuilder':
            """
            Find k-nearest neighbors of the query.

            :param query: query vector for search. Has single axis.
            :param search_field: field to perform search on
            :return: QueryBuilder object
            """
            if self._vector_search_field and self._vector_search_field != search_field:
                raise ValueError(
                    f'Trying to call .find for search_field = {search_field}, but '
                    f'previously {self._vector_search_field} was used. Only a single '
                    f'field might be used in chained calls.'
                )
            return QdrantDocumentIndex.QueryBuilder(
                vector_search_field=search_field,
                vector_filters=self._vector_filters + [query],
                payload_filters=self._payload_filters,
                text_search_filters=self._text_search_filters,
            )

        def filter(  # type: ignore[override]
            self, filter_query: rest.Filter
        ) -> 'QdrantDocumentIndex.QueryBuilder':
            """Find documents in the index based on a filter query
            :param filter_query: a filter
            :return: QueryBuilder object
            """
            return QdrantDocumentIndex.QueryBuilder(
                vector_search_field=self._vector_search_field,
                vector_filters=self._vector_filters,
                payload_filters=self._payload_filters + [filter_query],
                text_search_filters=self._text_search_filters,
            )

        def text_search(  # type: ignore[override]
            self, query: str, search_field: str = ''
        ) -> 'QdrantDocumentIndex.QueryBuilder':
            """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: QueryBuilder object
            """
            return QdrantDocumentIndex.QueryBuilder(
                vector_search_field=self._vector_search_field,
                vector_filters=self._vector_filters,
                payload_filters=self._payload_filters,
                text_search_filters=self._text_search_filters + [(search_field, query)],
            )

        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(BaseDocIndex.DBConfig):
        """Dataclass that contains all "static" configurations of QdrantDocumentIndex."""

        location: Optional[str] = None
        url: Optional[str] = None
        port: Optional[int] = 6333
        grpc_port: int = 6334
        prefer_grpc: bool = True
        https: Optional[bool] = None
        api_key: Optional[str] = None
        prefix: Optional[str] = None
        timeout: Optional[float] = None
        host: Optional[str] = None
        path: Optional[str] = None
        collection_name: Optional[str] = None
        shard_number: Optional[int] = None
        replication_factor: Optional[int] = None
        write_consistency_factor: Optional[int] = None
        on_disk_payload: Optional[bool] = None
        hnsw_config: Optional[types.HnswConfigDiff] = None
        optimizers_config: Optional[types.OptimizersConfigDiff] = None
        wal_config: Optional[types.WalConfigDiff] = None
        quantization_config: Optional[types.QuantizationConfig] = None
        default_column_config: Dict[Type, Dict[str, Any]] = field(
            default_factory=lambda: {
                'id': {},  # type: ignore[dict-item]
                'vector': {'dim': 128},  # type: ignore[dict-item]
                'payload': {},  # type: ignore[dict-item]
            }
        )

        def __post_init__(self):
            if self.collection_name is None and self.index_name is not None:
                self.collection_name = self.index_name
            if self.index_name is None and self.collection_name is not None:
                self.index_name = self.collection_name

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

        pass

    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.
        """
        if any(safe_issubclass(python_type, vt) for vt in QDRANT_PY_VECTOR_TYPES):
            return 'vector'

        if safe_issubclass(python_type, docarray.typing.id.ID):
            return 'id'

        return 'payload'

    def _initialize_collection(self):
        try:
            self._client.get_collection(self.collection_name)
        except (UnexpectedResponse, RpcError, ValueError):
            vectors_config = {}

            for column_name, column_info in self._column_infos.items():
                if column_info.db_type == 'vector':
                    vectors_config[column_name] = self._to_qdrant_vector_params(
                        column_info
                    )

            self._client.create_collection(
                collection_name=self.collection_name,
                vectors_config=vectors_config,
                shard_number=self._db_config.shard_number,
                replication_factor=self._db_config.replication_factor,
                write_consistency_factor=self._db_config.write_consistency_factor,
                on_disk_payload=self._db_config.on_disk_payload,
                hnsw_config=self._db_config.hnsw_config,
                optimizers_config=self._db_config.optimizers_config,
                wal_config=self._db_config.wal_config,
                quantization_config=self._db_config.quantization_config,
            )
            self._client.create_payload_index(
                collection_name=self.collection_name,
                field_name='__generated_vectors',
                field_schema=rest.PayloadSchemaType.KEYWORD,
            )

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

        rows = self._transpose_col_value_dict(column_to_data)
        # TODO: add batching the documents to avoid timeouts
        points = [self._build_point_from_row(row) for row in rows]
        self._client.upsert(
            collection_name=self.collection_name,
            points=points,
        )

    def num_docs(self) -> int:
        """
        Get the number of documents.
        """
        return self._client.count(collection_name=self.collection_name).count

    def _doc_exists(self, doc_id: str) -> bool:
        response, _ = self._client.scroll(
            collection_name=self.index_name,
            scroll_filter=rest.Filter(
                must=[
                    rest.HasIdCondition(has_id=[self._to_qdrant_id(doc_id)]),
                ],
            ),
        )
        return len(response) > 0

    def _del_items(self, doc_ids: Sequence[str]):
        items = self._get_items(doc_ids)
        if len(items) < len(doc_ids):
            found_keys = set(item['id'] for item in items)  # type: ignore[index]
            missing_keys = set(doc_ids) - found_keys
            raise KeyError('Document keys could not found: %s' % ','.join(missing_keys))

        self._client.delete(
            collection_name=self.collection_name,
            points_selector=rest.PointIdsList(
                points=[self._to_qdrant_id(doc_id) for doc_id in doc_ids],
            ),
        )

    def _get_items(
        self, doc_ids: Sequence[str]
    ) -> Union[Sequence[TSchema], Sequence[Dict[str, Any]]]:
        response, _ = self._client.scroll(
            collection_name=self.collection_name,
            scroll_filter=rest.Filter(
                must=[
                    rest.HasIdCondition(
                        has_id=[self._to_qdrant_id(doc_id) for doc_id in doc_ids],
                    ),
                ],
            ),
            limit=len(doc_ids),
            with_payload=True,
            with_vectors=True,
        )
        return sorted(
            [self._convert_to_doc(point) for point in response],
            key=lambda x: doc_ids.index(x['id']),
        )

    def execute_query(self, query: Union[Query, RawQuery], *args, **kwargs) -> DocList:
        """
        Execute a query on the QdrantDocumentIndex.

        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's `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
        """
        if not isinstance(query, QdrantDocumentIndex.Query):
            points = self._execute_raw_query(query.copy())
        elif query.vector_field:
            # We perform semantic search with some vectors with Qdrant's search method
            # should be called
            points = self._client.search(  # type: ignore[assignment]
                collection_name=self.collection_name,
                query_vector=(query.vector_field, query.vector_query),  # type: ignore[arg-type]
                query_filter=rest.Filter(
                    must=[query.filter],
                    # The following filter takes care of using only those points which
                    # do not have the vector generated. Those are excluded from the
                    # search results.
                    must_not=[
                        rest.FieldCondition(
                            key='__generated_vectors',
                            match=rest.MatchValue(value=query.vector_field),
                        )
                    ],
                ),
                limit=query.limit,
                with_payload=True,
                with_vectors=True,
            )
        else:
            # Just filtering, so Qdrant's scroll has to be used instead
            points, _ = self._client.scroll(  # type: ignore[assignment]
                collection_name=self.collection_name,
                scroll_filter=query.filter,
                limit=query.limit,
                with_payload=True,
                with_vectors=True,
            )

        docs = [self._convert_to_doc(point) for point in points]
        return self._dict_list_to_docarray(docs)

    def _execute_raw_query(
        self, query: RawQuery
    ) -> Sequence[Union[rest.ScoredPoint, rest.Record]]:
        payload_filter = query.pop('filter', None)
        if payload_filter:
            payload_filter = rest.Filter.parse_obj(payload_filter)  # type: ignore[assignment]

        if 'vector' in query:
            # We perform semantic search with some vectors with Qdrant's search method
            # should be called
            search_params = query.pop('params', None)
            if search_params:
                search_params = rest.SearchParams.parse_obj(search_params)  # type: ignore[assignment]
            points = self._client.search(  # type: ignore[assignment]
                collection_name=self.collection_name,
                query_vector=query.pop('vector'),
                query_filter=payload_filter,
                search_params=search_params,
                **query,
            )
        else:
            # Just filtering, so Qdrant's scroll has to be used instead
            points, _ = self._client.scroll(  # type: ignore[assignment]
                collection_name=self.collection_name,
                scroll_filter=payload_filter,
                **query,
            )

        return points

    def _find(
        self, query: np.ndarray, limit: int, search_field: str = ''
    ) -> _FindResult:
        query_batched = np.expand_dims(query, axis=0)
        docs, scores = self._find_batched(
            queries=query_batched, limit=limit, search_field=search_field
        )
        return _FindResult(documents=docs[0], scores=scores[0])  # type: ignore[arg-type]

    def _find_batched(
        self, queries: np.ndarray, limit: int, search_field: str = ''
    ) -> _FindResultBatched:
        responses = self._client.search_batch(
            collection_name=self.collection_name,
            requests=[
                rest.SearchRequest(
                    vector=rest.NamedVector(
                        name=search_field,
                        vector=query.tolist(),  # type: ignore
                    ),
                    # The following filter takes care of using only those points which
                    # do not have the vector generated. Those are excluded from the
                    # search results.
                    filter=rest.Filter(
                        must_not=[
                            rest.FieldCondition(
                                key='__generated_vectors',
                                match=rest.MatchValue(value=search_field),
                            )
                        ]
                    ),
                    limit=limit,
                    with_vector=True,
                    with_payload=True,
                )
                for query in queries
            ],
        )
        return _FindResultBatched(
            documents=[
                [self._convert_to_doc(point) for point in response]
                for response in responses
            ],
            scores=[
                NdArray._docarray_from_native(
                    np.array([point.score for point in response])
                )
                for response in responses
            ],
        )

    def _filter(
        self, filter_query: rest.Filter, limit: int
    ) -> Union[DocList, List[Dict]]:
        query_batched = [filter_query]
        docs = self._filter_batched(filter_queries=query_batched, limit=limit)
        return docs[0]

    def _filter_batched(
        self, filter_queries: Sequence[rest.Filter], limit: int
    ) -> Union[List[DocList], List[List[Dict]]]:
        responses = []
        for filter_query in filter_queries:
            # There is no batch scroll available in Qdrant client yet, so we need to
            # perform the queries one by one. It will be changed in the future versions.
            response, _ = self._client.scroll(
                collection_name=self.collection_name,
                scroll_filter=filter_query,
                limit=limit,
                with_payload=True,
                with_vectors=True,
            )
            responses.append(response)

        return [
            [self._convert_to_doc(point) for point in response]
            for response in responses
        ]

    def _text_search(
        self, query: str, limit: int, search_field: str = ''
    ) -> _FindResult:
        query_batched = [query]
        docs, scores = self._text_search_batched(
            queries=query_batched, limit=limit, search_field=search_field
        )
        return _FindResult(documents=docs[0], scores=scores[0])  # type: ignore[arg-type]

    def _text_search_batched(
        self, queries: Sequence[str], limit: int, search_field: str = ''
    ) -> _FindResultBatched:
        filter_queries = [
            rest.Filter(
                must=[
                    rest.FieldCondition(
                        key=search_field,
                        match=rest.MatchText(text=query),
                    )
                ]
            )
            for query in queries
        ]
        documents_batched = self._filter_batched(
            filter_queries=filter_queries, limit=limit
        )

        # Qdrant does not return any scores if we just filter the objects, without using
        # semantic search over vectors. Thus, each document is scored with a value of 1
        return _FindResultBatched(
            documents=documents_batched,
            scores=[
                NdArray._docarray_from_native(np.ones(len(docs)))
                for docs in documents_batched
            ],
        )

    def _filter_by_parent_id(self, id: str) -> Optional[List[str]]:
        response, _ = self._client.scroll(
            collection_name=self.collection_name,  # type: ignore
            scroll_filter=rest.Filter(
                must=[
                    rest.FieldCondition(
                        key='parent_id', match=rest.MatchValue(value=id)
                    )
                ]
            ),
            with_payload=rest.PayloadSelectorInclude(include=['id']),
        )

        ids = [point.payload['id'] for point in response]  # type: ignore
        return ids

    def _build_point_from_row(self, row: Dict[str, Any]) -> rest.PointStruct:
        point_id = self._to_qdrant_id(row.get('id'))
        vectors: Dict[str, List[float]] = {}
        payload: Dict[str, Any] = {'__generated_vectors': []}
        for column_name, column_info in self._column_infos.items():
            if safe_issubclass(column_info.docarray_type, AnyDocArray):
                continue
            if column_info.db_type in ['id', 'payload']:
                payload[column_name] = row.get(column_name)
                continue

            vector = row.get(column_name)
            if column_info.db_type == 'vector' and vector is not None:
                vectors[column_name] = vector.tolist()
            elif column_info.db_type == 'vector' and vector is None:
                # In that case vector was not provided. Qdrant does not support optional
                # vectors - each point needs to have all the vectors already assigned.
                # Thus, we put a fake embedding with the correct dimensionality and mark
                # such point a point with a boolean flag in the payload.
                vector_size = column_info.n_dim or column_info.config.get('dim')
                vectors[column_name] = np.ones(vector_size).tolist()  # type: ignore[arg-type]
                payload['__generated_vectors'].append(column_name)
            else:
                raise ValueError(
                    f'Could not handle the conversion for column {column_name}. '
                    f'Column info: {column_info}'
                )
        return rest.PointStruct(
            id=point_id,
            vector=vectors,
            payload=payload,
        )

    def _to_qdrant_id(self, external_id: Optional[str]) -> str:
        if external_id is None:
            return uuid.uuid4().hex
        return uuid.uuid5(QdrantDocumentIndex.UUID_NAMESPACE, external_id).hex

    def _to_qdrant_vector_params(self, column_info: _ColumnInfo) -> rest.VectorParams:
        return rest.VectorParams(
            size=column_info.n_dim or column_info.config.get('dim'),
            distance=QDRANT_SPACE_MAPPING[column_info.config.get('space', 'cosine')],
        )

    def _convert_to_doc(
        self, point: Union[rest.ScoredPoint, rest.Record]
    ) -> Dict[str, Any]:
        document = cast(Dict[str, Any], point.payload)
        generated_vectors = (
            document.pop('__generated_vectors')
            if '__generated_vectors' in document
            else []
        )
        vectors = point.vector if point.vector else dict()
        if not isinstance(vectors, dict):
            vectors = {'__default__': vectors}
        for vector_name, vector in vectors.items():
            if vector_name in generated_vectors:
                # That means the vector was generated during the upload, and should not
                # be returned along the other vectors.
                pass
            document[vector_name] = vector
        return document

DBConfig dataclass

Bases: DBConfig

Dataclass that contains all "static" configurations of QdrantDocumentIndex.

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

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

    location: Optional[str] = None
    url: Optional[str] = None
    port: Optional[int] = 6333
    grpc_port: int = 6334
    prefer_grpc: bool = True
    https: Optional[bool] = None
    api_key: Optional[str] = None
    prefix: Optional[str] = None
    timeout: Optional[float] = None
    host: Optional[str] = None
    path: Optional[str] = None
    collection_name: Optional[str] = None
    shard_number: Optional[int] = None
    replication_factor: Optional[int] = None
    write_consistency_factor: Optional[int] = None
    on_disk_payload: Optional[bool] = None
    hnsw_config: Optional[types.HnswConfigDiff] = None
    optimizers_config: Optional[types.OptimizersConfigDiff] = None
    wal_config: Optional[types.WalConfigDiff] = None
    quantization_config: Optional[types.QuantizationConfig] = None
    default_column_config: Dict[Type, Dict[str, Any]] = field(
        default_factory=lambda: {
            'id': {},  # type: ignore[dict-item]
            'vector': {'dim': 128},  # type: ignore[dict-item]
            'payload': {},  # type: ignore[dict-item]
        }
    )

    def __post_init__(self):
        if self.collection_name is None and self.index_name is not None:
            self.collection_name = self.index_name
        if self.index_name is None and self.collection_name is not None:
            self.index_name = self.collection_name

Query dataclass

Dataclass describing a query.

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

@dataclass
class Query:
    """Dataclass describing a query."""

    vector_field: Optional[str]
    vector_query: Optional[NdArray]
    filter: Optional[rest.Filter]
    limit: int

QueryBuilder

Bases: QueryBuilder

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

class QueryBuilder(BaseDocIndex.QueryBuilder):
    def __init__(
        self,
        vector_search_field: Optional[str] = None,
        vector_filters: Optional[List[NdArray]] = None,
        payload_filters: Optional[List[rest.Filter]] = None,
        text_search_filters: Optional[List[Tuple[str, str]]] = None,
    ):
        self._vector_search_field: Optional[str] = vector_search_field
        self._vector_filters: List[NdArray] = vector_filters or []
        self._payload_filters: List[rest.Filter] = payload_filters or []
        self._text_search_filters: List[Tuple[str, str]] = text_search_filters or []

    def build(self, limit: int) -> 'QdrantDocumentIndex.Query':
        """
        Build a query object for QdrantDocumentIndex.
        :return: QdrantDocumentIndex.Query object
        """
        vector_query = None
        if len(self._vector_filters) > 0:
            # If there are multiple vector queries applied, we can average them and
            # perform semantic search on a single vector instead
            vector_query = np.average(self._vector_filters, axis=0)
        merged_filter = None
        if len(self._payload_filters) > 0:
            merged_filter = rest.Filter(must=self._payload_filters)
        if len(self._text_search_filters) > 0:
            # Text search is just a special case of payload filtering, so the
            # payload filter is simply extended
            merged_filter = merged_filter or rest.Filter(must=[])
            for search_field, query in self._text_search_filters:
                merged_filter.must.append(  # type: ignore[union-attr]
                    rest.FieldCondition(
                        key=search_field,
                        match=rest.MatchText(text=query),
                    )
                )
        return QdrantDocumentIndex.Query(
            vector_field=self._vector_search_field,
            vector_query=vector_query,
            filter=merged_filter,
            limit=limit,
        )

    def find(  # type: ignore[override]
        self, query: NdArray, search_field: str = ''
    ) -> 'QdrantDocumentIndex.QueryBuilder':
        """
        Find k-nearest neighbors of the query.

        :param query: query vector for search. Has single axis.
        :param search_field: field to perform search on
        :return: QueryBuilder object
        """
        if self._vector_search_field and self._vector_search_field != search_field:
            raise ValueError(
                f'Trying to call .find for search_field = {search_field}, but '
                f'previously {self._vector_search_field} was used. Only a single '
                f'field might be used in chained calls.'
            )
        return QdrantDocumentIndex.QueryBuilder(
            vector_search_field=search_field,
            vector_filters=self._vector_filters + [query],
            payload_filters=self._payload_filters,
            text_search_filters=self._text_search_filters,
        )

    def filter(  # type: ignore[override]
        self, filter_query: rest.Filter
    ) -> 'QdrantDocumentIndex.QueryBuilder':
        """Find documents in the index based on a filter query
        :param filter_query: a filter
        :return: QueryBuilder object
        """
        return QdrantDocumentIndex.QueryBuilder(
            vector_search_field=self._vector_search_field,
            vector_filters=self._vector_filters,
            payload_filters=self._payload_filters + [filter_query],
            text_search_filters=self._text_search_filters,
        )

    def text_search(  # type: ignore[override]
        self, query: str, search_field: str = ''
    ) -> 'QdrantDocumentIndex.QueryBuilder':
        """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: QueryBuilder object
        """
        return QdrantDocumentIndex.QueryBuilder(
            vector_search_field=self._vector_search_field,
            vector_filters=self._vector_filters,
            payload_filters=self._payload_filters,
            text_search_filters=self._text_search_filters + [(search_field, query)],
        )

    find_batched = _raise_not_composable('find_batched')
    filter_batched = _raise_not_composable('filter_batched')
    text_search_batched = _raise_not_composable('text_search_batched')

build(limit)

Build a query object for QdrantDocumentIndex.

Returns:

Type	Description
Query	QdrantDocumentIndex.Query object

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

def build(self, limit: int) -> 'QdrantDocumentIndex.Query':
    """
    Build a query object for QdrantDocumentIndex.
    :return: QdrantDocumentIndex.Query object
    """
    vector_query = None
    if len(self._vector_filters) > 0:
        # If there are multiple vector queries applied, we can average them and
        # perform semantic search on a single vector instead
        vector_query = np.average(self._vector_filters, axis=0)
    merged_filter = None
    if len(self._payload_filters) > 0:
        merged_filter = rest.Filter(must=self._payload_filters)
    if len(self._text_search_filters) > 0:
        # Text search is just a special case of payload filtering, so the
        # payload filter is simply extended
        merged_filter = merged_filter or rest.Filter(must=[])
        for search_field, query in self._text_search_filters:
            merged_filter.must.append(  # type: ignore[union-attr]
                rest.FieldCondition(
                    key=search_field,
                    match=rest.MatchText(text=query),
                )
            )
    return QdrantDocumentIndex.Query(
        vector_field=self._vector_search_field,
        vector_query=vector_query,
        filter=merged_filter,
        limit=limit,
    )

filter(filter_query)

Find documents in the index based on a filter query

Parameters:

Name	Type	Description	Default
filter_query	Filter	a filter	required

Returns:

Type	Description
QueryBuilder	QueryBuilder object

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

def filter(  # type: ignore[override]
    self, filter_query: rest.Filter
) -> 'QdrantDocumentIndex.QueryBuilder':
    """Find documents in the index based on a filter query
    :param filter_query: a filter
    :return: QueryBuilder object
    """
    return QdrantDocumentIndex.QueryBuilder(
        vector_search_field=self._vector_search_field,
        vector_filters=self._vector_filters,
        payload_filters=self._payload_filters + [filter_query],
        text_search_filters=self._text_search_filters,
    )

find(query, search_field='')

Find k-nearest neighbors of the query.

Parameters:

Name	Type	Description	Default
query	NdArray	query vector for search. Has single axis.	required
search_field	str	field to perform search on	''

Returns:

Type	Description
QueryBuilder	QueryBuilder object

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

def find(  # type: ignore[override]
    self, query: NdArray, search_field: str = ''
) -> 'QdrantDocumentIndex.QueryBuilder':
    """
    Find k-nearest neighbors of the query.

    :param query: query vector for search. Has single axis.
    :param search_field: field to perform search on
    :return: QueryBuilder object
    """
    if self._vector_search_field and self._vector_search_field != search_field:
        raise ValueError(
            f'Trying to call .find for search_field = {search_field}, but '
            f'previously {self._vector_search_field} was used. Only a single '
            f'field might be used in chained calls.'
        )
    return QdrantDocumentIndex.QueryBuilder(
        vector_search_field=search_field,
        vector_filters=self._vector_filters + [query],
        payload_filters=self._payload_filters,
        text_search_filters=self._text_search_filters,
    )

text_search(query, search_field='')

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	str	name of the field to search on	''

Returns:

Type	Description
QueryBuilder	QueryBuilder object

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

def text_search(  # type: ignore[override]
    self, query: str, search_field: str = ''
) -> 'QdrantDocumentIndex.QueryBuilder':
    """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: QueryBuilder object
    """
    return QdrantDocumentIndex.QueryBuilder(
        vector_search_field=self._vector_search_field,
        vector_filters=self._vector_filters,
        payload_filters=self._payload_filters,
        text_search_filters=self._text_search_filters + [(search_field, query)],
    )

RuntimeConfig dataclass

Bases: RuntimeConfig

Dataclass that contains all "dynamic" configurations of QdrantDocumentIndex.

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

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

    pass

__contains__(item)

Checks if a given document exists in the index.

Parameters:

Name	Type	Description	Default
item	BaseDoc	The document to check. It must be an instance of BaseDoc or its subclass.	required

Returns:

Type	Description
bool	True if the document exists in the index, False otherwise.

Source code in docarray/index/abstract.py

def __contains__(self, item: BaseDoc) -> bool:
    """
    Checks if a given document exists in the index.

    :param item: The document to check.
        It must be an instance of BaseDoc or its subclass.
    :return: True if the document exists in the index, False otherwise.
    """
    if safe_issubclass(type(item), BaseDoc):
        return self._doc_exists(str(item.id))
    else:
        raise TypeError(
            f"item must be an instance of BaseDoc or its subclass, not '{type(item).__name__}'"
        )

__delitem__(key)

Delete one or multiple Documents from the index, by id. If no document is found, a KeyError is raised.

Parameters:

Name	Type	Description	Default
key	Union[str, Sequence[str]]	id or ids to delete from the Document index	required

Source code in docarray/index/abstract.py

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
    """
    self._logger.info(f'Deleting documents with id(s) {key} from the index')
    if isinstance(key, str):
        key = [key]

    # delete nested data
    for field_name, type_, _ in self._flatten_schema(
        cast(Type[BaseDoc], self._schema)
    ):
        if safe_issubclass(type_, AnyDocArray):
            for doc_id in key:
                nested_docs_id = self._subindices[field_name]._filter_by_parent_id(
                    doc_id
                )
                if nested_docs_id:
                    del self._subindices[field_name][nested_docs_id]
    # delete data
    self._del_items(key)

__getitem__(key)

Get one or multiple Documents into the index, by id. If no document is found, a KeyError is raised.

Parameters:

Name	Type	Description	Default
key	Union[str, Sequence[str]]	id or ids to get from the Document index	required

Source code in docarray/index/abstract.py

def __getitem__(
    self, key: Union[str, Sequence[str]]
) -> Union[TSchema, DocList[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)

    # check data
    if len(doc_sequence) == 0:
        raise KeyError(f'No document with id {key} found')

    # retrieve nested data
    for field_name, type_, _ in self._flatten_schema(
        cast(Type[BaseDoc], self._schema)
    ):
        if safe_issubclass(type_, AnyDocArray) and isinstance(
            doc_sequence[0], Dict
        ):
            for doc in doc_sequence:
                self._get_subindex_doclist(doc, field_name)  # type: ignore

    # cast output
    if isinstance(doc_sequence, DocList):
        out_docs: DocList[TSchema] = doc_sequence
    elif isinstance(doc_sequence[0], Dict):
        out_docs = self._dict_list_to_docarray(doc_sequence)  # type: ignore
    else:
        docs_cls = DocList.__class_getitem__(cast(Type[BaseDoc], self._schema))
        out_docs = docs_cls(doc_sequence)

    return out_docs[0] if return_singleton else out_docs

__init__(db_config=None, **kwargs)

Initialize QdrantDocumentIndex

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

def __init__(self, db_config=None, **kwargs):
    """Initialize QdrantDocumentIndex"""
    if db_config is not None and getattr(
        db_config, 'index_name'
    ):  # this is needed for subindices
        db_config.collection_name = db_config.index_name
    super().__init__(db_config=db_config, **kwargs)
    self._db_config: QdrantDocumentIndex.DBConfig = cast(
        QdrantDocumentIndex.DBConfig, self._db_config
    )
    self._client = qdrant_client.QdrantClient(
        location=self._db_config.location,
        url=self._db_config.url,
        port=self._db_config.port,
        grpc_port=self._db_config.grpc_port,
        prefer_grpc=self._db_config.prefer_grpc,
        https=self._db_config.https,
        api_key=self._db_config.api_key,
        prefix=self._db_config.prefix,
        timeout=self._db_config.timeout,
        host=self._db_config.host,
        path=self._db_config.path,
    )
    self._initialize_collection()
    self._logger.info(f'{self.__class__.__name__} has been initialized')

build_query()

Build a query for this DocumentIndex.

Returns:

Type	Description
QueryBuilder	a new QueryBuilder object for this DocumentIndex

Source code in docarray/index/abstract.py

def build_query(self) -> QueryBuilder:
    """
    Build a query for this DocumentIndex.

    :return: a new `QueryBuilder` object for this DocumentIndex
    """
    return self.QueryBuilder()  # type: ignore

configure(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.

Parameters:

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

Source code in docarray/index/abstract.py

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

execute_query(query, *args, **kwargs)

Execute a query on the QdrantDocumentIndex.

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's QueryBuilder.build() method.

Parameters:

Name	Type	Description	Default
query	Union[Query, RawQuery]	the query to execute	required
args		positional arguments to pass to the query	()
kwargs		keyword arguments to pass to the query	{}

Returns:

Type	Description
DocList	the result of the query

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

def execute_query(self, query: Union[Query, RawQuery], *args, **kwargs) -> DocList:
    """
    Execute a query on the QdrantDocumentIndex.

    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's `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
    """
    if not isinstance(query, QdrantDocumentIndex.Query):
        points = self._execute_raw_query(query.copy())
    elif query.vector_field:
        # We perform semantic search with some vectors with Qdrant's search method
        # should be called
        points = self._client.search(  # type: ignore[assignment]
            collection_name=self.collection_name,
            query_vector=(query.vector_field, query.vector_query),  # type: ignore[arg-type]
            query_filter=rest.Filter(
                must=[query.filter],
                # The following filter takes care of using only those points which
                # do not have the vector generated. Those are excluded from the
                # search results.
                must_not=[
                    rest.FieldCondition(
                        key='__generated_vectors',
                        match=rest.MatchValue(value=query.vector_field),
                    )
                ],
            ),
            limit=query.limit,
            with_payload=True,
            with_vectors=True,
        )
    else:
        # Just filtering, so Qdrant's scroll has to be used instead
        points, _ = self._client.scroll(  # type: ignore[assignment]
            collection_name=self.collection_name,
            scroll_filter=query.filter,
            limit=query.limit,
            with_payload=True,
            with_vectors=True,
        )

    docs = [self._convert_to_doc(point) for point in points]
    return self._dict_list_to_docarray(docs)

filter(filter_query, limit=10, **kwargs)

Find documents in the index based on a filter query

Parameters:

Name	Type	Description	Default
filter_query	Any	the DB specific filter query to execute	required
limit	int	maximum number of documents to return	10

Returns:

Type	Description
DocList	a DocList containing the documents that match the filter query

Source code in docarray/index/abstract.py

def filter(
    self,
    filter_query: Any,
    limit: int = 10,
    **kwargs,
) -> DocList:
    """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 DocList containing the documents that match the filter query
    """
    self._logger.debug(f'Executing `filter` for the query {filter_query}')
    docs = self._filter(filter_query, limit=limit, **kwargs)

    if isinstance(docs, List) and not isinstance(docs, DocList):
        docs = self._dict_list_to_docarray(docs)

    return docs

filter_batched(filter_queries, limit=10, **kwargs)

Find documents in the index based on multiple filter queries.

Parameters:

Name	Type	Description	Default
filter_queries	Any	the DB specific filter query to execute	required
limit	int	maximum number of documents to return	10

Returns:

Type	Description
List[DocList]	a DocList containing the documents that match the filter query

Source code in docarray/index/abstract.py

def filter_batched(
    self,
    filter_queries: Any,
    limit: int = 10,
    **kwargs,
) -> List[DocList]:
    """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 DocList containing the documents that match the filter query
    """
    self._logger.debug(
        f'Executing `filter_batched` for the queries {filter_queries}'
    )
    da_list = self._filter_batched(filter_queries, 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 da_list  # type: ignore

filter_subindex(filter_query, subindex, limit=10, **kwargs)

Find documents in subindex level based on a filter query

Parameters:

Name	Type	Description	Default
filter_query	Any	the DB specific filter query to execute	required
subindex	str	name of the subindex to search on	required
limit	int	maximum number of documents to return	10

Returns:

Type	Description
DocList	a DocList containing the subindex level documents that match the filter query

Source code in docarray/index/abstract.py

def filter_subindex(
    self,
    filter_query: Any,
    subindex: str,
    limit: int = 10,
    **kwargs,
) -> DocList:
    """Find documents in subindex level based on a filter query

    :param filter_query: the DB specific filter query to execute
    :param subindex: name of the subindex to search on
    :param limit: maximum number of documents to return
    :return: a DocList containing the subindex level documents that match the filter query
    """
    self._logger.debug(
        f'Executing `filter` for the query {filter_query} in subindex {subindex}'
    )
    if '__' in subindex:
        fields = subindex.split('__')
        return self._subindices[fields[0]].filter_subindex(
            filter_query, '__'.join(fields[1:]), limit=limit, **kwargs
        )
    else:
        return self._subindices[subindex].filter(
            filter_query, limit=limit, **kwargs
        )

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

Find documents in the index using nearest neighbor search.

Parameters:

Name	Type	Description	Default
query	Union[AnyTensor, BaseDoc]	query vector for KNN/ANN search. Can be either a tensor-like (np.array, torch.Tensor, etc.) with a single axis, or a Document	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	10

Returns:

Type	Description
FindResult	a named tuple containing documents and scores

Source code in docarray/index/abstract.py

def find(
    self,
    query: Union[AnyTensor, BaseDoc],
    search_field: str = '',
    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`
    """
    self._logger.debug(f'Executing `find` for search field {search_field}')

    self._validate_search_field(search_field)
    if isinstance(query, BaseDoc):
        query_vec = self._get_values_by_column([query], search_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) and not isinstance(docs, DocList):
        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/abstract.py

def find_batched(
    self,
    queries: Union[AnyTensor, DocList],
    search_field: str = '',
    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 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(f'Executing `find_batched` for search field {search_field}')

    if search_field:
        if '__' in search_field:
            fields = search_field.split('__')
            if safe_issubclass(self._schema._get_field_annotation(fields[0]), AnyDocArray):  # type: ignore
                return self._subindices[fields[0]].find_batched(
                    queries,
                    search_field='__'.join(fields[1:]),
                    limit=limit,
                    **kwargs,
                )

    self._validate_search_field(search_field)
    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)

    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)
        and not isinstance(da_list[0], DocList)
    ):
        da_list = [self._dict_list_to_docarray(docs) for docs in da_list]

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

find_subindex(query, subindex='', search_field='', limit=10, **kwargs)

Find documents in subindex level.

Parameters:

Name	Type	Description	Default
query	Union[AnyTensor, BaseDoc]	query vector for KNN/ANN search. Can be either a tensor-like (np.array, torch.Tensor, etc.) with a single axis, or a Document	required
subindex	str	name of the subindex to search on	''
search_field	str	name of the field to search on	''
limit	int	maximum number of documents to return	10

Returns:

Type	Description
SubindexFindResult	a named tuple containing root docs, subindex docs and scores

Source code in docarray/index/abstract.py

def find_subindex(
    self,
    query: Union[AnyTensor, BaseDoc],
    subindex: str = '',
    search_field: str = '',
    limit: int = 10,
    **kwargs,
) -> SubindexFindResult:
    """Find documents in subindex level.

    :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 subindex: name of the subindex to search on
    :param search_field: name of the field to search on
    :param limit: maximum number of documents to return
    :return: a named tuple containing root docs, subindex docs and scores
    """
    self._logger.debug(f'Executing `find_subindex` for search field {search_field}')

    sub_docs, scores = self._find_subdocs(
        query, subindex=subindex, search_field=search_field, limit=limit, **kwargs
    )

    fields = subindex.split('__')
    root_ids = [
        self._get_root_doc_id(doc.id, fields[0], '__'.join(fields[1:]))
        for doc in sub_docs
    ]
    root_docs = DocList[self._schema]()  # type: ignore
    for id in root_ids:
        root_docs.append(self[id])

    return SubindexFindResult(
        root_documents=root_docs, sub_documents=sub_docs, scores=scores  # type: ignore
    )

index(docs, **kwargs)

index Documents into the index.

Note

Passing a sequence of Documents that is not a DocList (such as a List of Docs) comes at a performance penalty. This is because the Index needs to check compatibility between itself and the data. With a DocList as input this is a single check; for other inputs compatibility needs to be checked for every Document individually.

Parameters:

Name	Type	Description	Default
docs	Union[BaseDoc, Sequence[BaseDoc]]	Documents to index.	required

Source code in docarray/index/abstract.py

def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs):
    """index Documents into the index.

    !!! note
        Passing a sequence of Documents that is not a DocList
        (such as a List of Docs) comes at a performance penalty.
        This is because the Index needs to check compatibility between itself and
        the data. With a DocList as input this is a single check; for other inputs
        compatibility needs to be checked for every Document individually.

    :param docs: Documents to index.
    """
    n_docs = 1 if isinstance(docs, BaseDoc) else len(docs)
    self._logger.debug(f'Indexing {n_docs} documents')
    docs_validated = self._validate_docs(docs)
    self._update_subindex_data(docs_validated)
    data_by_columns = self._get_col_value_dict(docs_validated)
    self._index(data_by_columns, **kwargs)

num_docs()

Get the number of documents.

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

def num_docs(self) -> int:
    """
    Get the number of documents.
    """
    return self._client.count(collection_name=self.collection_name).count

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.

Source code in docarray/index/backends/qdrant.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.
    """
    if any(safe_issubclass(python_type, vt) for vt in QDRANT_PY_VECTOR_TYPES):
        return 'vector'

    if safe_issubclass(python_type, docarray.typing.id.ID):
        return 'id'

    return 'payload'

subindex_contains(item)

Checks if a given BaseDoc item is contained in the index or any of its subindices.

Parameters:

Name	Type	Description	Default
item	BaseDoc	the given BaseDoc	required

Returns:

Type	Description
bool	if the given BaseDoc item is contained in the index/subindices

Source code in docarray/index/abstract.py

def subindex_contains(self, item: BaseDoc) -> bool:
    """Checks if a given BaseDoc item is contained in the index or any of its subindices.

    :param item: the given BaseDoc
    :return: if the given BaseDoc item is contained in the index/subindices
    """
    if self._is_index_empty:
        return False

    if safe_issubclass(type(item), BaseDoc):
        return self.__contains__(item) or any(
            index.subindex_contains(item) for index in self._subindices.values()
        )
    else:
        raise TypeError(
            f"item must be an instance of BaseDoc or its subclass, not '{type(item).__name__}'"
        )

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

Find documents in the index based on a text search query.

Parameters:

Name	Type	Description	Default
query	Union[str, BaseDoc]	The text to search for	required
search_field	str	name of the field to search on	''
limit	int	maximum number of documents to return	10

Returns:

Type	Description
FindResult	a named tuple containing documents and scores

Source code in docarray/index/abstract.py

def text_search(
    self,
    query: Union[str, BaseDoc],
    search_field: str = '',
    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`
    """
    self._logger.debug(f'Executing `text_search` for search field {search_field}')
    self._validate_search_field(search_field)
    if isinstance(query, BaseDoc):
        query_text = self._get_values_by_column([query], search_field)[0]
    else:
        query_text = query
    docs, scores = self._text_search(
        query_text, search_field=search_field, limit=limit, **kwargs
    )

    if isinstance(docs, List) and not isinstance(docs, DocList):
        docs = self._dict_list_to_docarray(docs)

    return FindResult(documents=docs, scores=scores)

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

Find documents in the index based on a text search query.

Parameters:

Name	Type	Description	Default
queries	Union[Sequence[str], Sequence[BaseDoc]]	The texts to search for	required
search_field	str	name of the field to search on	''
limit	int	maximum number of documents to return	10

Returns:

Type	Description
FindResultBatched	a named tuple containing documents and scores

Source code in docarray/index/abstract.py

def text_search_batched(
    self,
    queries: Union[Sequence[str], Sequence[BaseDoc]],
    search_field: str = '',
    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`
    """
    self._logger.debug(
        f'Executing `text_search_batched` for search field {search_field}'
    )
    self._validate_search_field(search_field)
    if isinstance(queries[0], BaseDoc):
        query_docs: Sequence[BaseDoc] = cast(Sequence[BaseDoc], queries)
        query_texts: Sequence[str] = self._get_values_by_column(
            query_docs, search_field
        )
    else:
        query_texts = cast(Sequence[str], queries)
    da_list, scores = self._text_search_batched(
        query_texts, search_field=search_field, limit=limit, **kwargs
    )

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

    da_list_ = cast(List[DocList], da_list)
    return FindResultBatched(documents=da_list_, scores=scores)