Milvus

MilvusVectorStore

Bases: BasePydanticVectorStore

The Milvus Vector Store.

In this vector store we store the text, its embedding and a its metadata in a Milvus collection. This implementation allows the use of an already existing collection. It also supports creating a new one if the collection doesn't exist or if overwrite is set to True.

Parameters:

Name	Type	Description	Default
uri	str	The URI to connect to, comes in the form of "https://address:port" for Milvus or Zilliz Cloud service, or "path/to/local/milvus.db" for the lite local Milvus. Defaults to "./milvus_llamaindex.db".	'./milvus_llamaindex.db'
token	str	The token for log in. Empty if not using rbac, if using rbac it will most likely be "username:password".	''
collection_name	str	The name of the collection where data will be stored. Defaults to "llamalection".	'llamacollection'
dim	int	The dimension of the embedding vectors for the collection. Required if creating a new collection.	None
embedding_field	str	The name of the embedding field for the collection, defaults to DEFAULT_EMBEDDING_KEY.	DEFAULT_EMBEDDING_KEY
doc_id_field	str	The name of the doc_id field for the collection, defaults to DEFAULT_DOC_ID_KEY.	DEFAULT_DOC_ID_KEY
similarity_metric	str	The similarity metric to use, currently supports IP, COSINE and L2.	'IP'
consistency_level	str	Which consistency level to use for a newly created collection. Defaults to "Session".	'Session'
overwrite	bool	Whether to overwrite existing collection with same name. Defaults to False.	False
text_key	str	What key text is stored in in the passed collection. Used when bringing your own collection. Defaults to None.	None
index_config	dict	The configuration used for building the Milvus index. Defaults to None.	None
search_config	dict	The configuration used for searching the Milvus index. Note that this must be compatible with the index type specified by index_config. Defaults to None.	None
collection_properties	dict	The collection properties such as TTL (Time-To-Live) and MMAP (memory mapping). Defaults to None. It could include: - 'collection.ttl.seconds' (int): Once this property is set, data in the current collection expires in the specified time. Expired data in the collection will be cleaned up and will not be involved in searches or queries. - 'mmap.enabled' (bool): Whether to enable memory-mapped storage at the collection level.	None
batch_size	int	Configures the number of documents processed in one batch when inserting data into Milvus. Defaults to DEFAULT_BATCH_SIZE.	DEFAULT_BATCH_SIZE
enable_sparse	bool	A boolean flag indicating whether to enable support for sparse embeddings for hybrid retrieval. Defaults to False.	False
sparse_embedding_function	BaseSparseEmbeddingFunction	If enable_sparse is True, this object should be provided to convert text to a sparse embedding.	None
hybrid_ranker	str	Specifies the type of ranker used in hybrid search queries. Currently only supports ['RRFRanker','WeightedRanker']. Defaults to "RRFRanker".	'RRFRanker'
hybrid_ranker_params	dict	Configuration parameters for the hybrid ranker. The structure of this dictionary depends on the specific ranker being used: - For "RRFRanker", it should include: - 'k' (int): A parameter used in Reciprocal Rank Fusion (RRF). This value is used to calculate the rank scores as part of the RRF algorithm, which combines multiple ranking strategies into a single score to improve search relevance. - For "WeightedRanker", it expects: - 'weights' (list of float): A list of exactly two weights: 1. The weight for the dense embedding component. 2. The weight for the sparse embedding component. These weights are used to adjust the importance of the dense and sparse components of the embeddings in the hybrid retrieval process. Defaults to an empty dictionary, implying that the ranker will operate with its predefined default settings.	{}
index_management	IndexManagement	Specifies the index management strategy to use. Defaults to "create_if_not_exists".	CREATE_IF_NOT_EXISTS
scalar_field_names	list	The names of the extra scalar fields to be included in the collection schema.	None
scalar_field_types	list	The types of the extra scalar fields.	None

Raises:

Type	Description
ImportError	Unable to import pymilvus.
MilvusException	Error communicating with Milvus, more can be found in logging under Debug.

Returns:

Name	Type	Description
MilvusVectorstore		Vectorstore that supports add, delete, and query.

Examples:

pip install llama-index-vector-stores-milvus

from llama_index.vector_stores.milvus import MilvusVectorStore

# Setup MilvusVectorStore
vector_store = MilvusVectorStore(
    dim=1536,
    collection_name="your_collection_name",
    uri="http://milvus_address:port",
    token="your_milvus_token_here",
    overwrite=True
)

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py

class MilvusVectorStore(BasePydanticVectorStore):
    """The Milvus Vector Store.

    In this vector store we store the text, its embedding and
    a its metadata in a Milvus collection. This implementation
    allows the use of an already existing collection.
    It also supports creating a new one if the collection doesn't
    exist or if `overwrite` is set to True.

    Args:
        uri (str, optional): The URI to connect to, comes in the form of
            "https://address:port" for Milvus or Zilliz Cloud service,
            or "path/to/local/milvus.db" for the lite local Milvus. Defaults to
            "./milvus_llamaindex.db".
        token (str, optional): The token for log in. Empty if not using rbac, if
            using rbac it will most likely be "username:password".
        collection_name (str, optional): The name of the collection where data will be
            stored. Defaults to "llamalection".
        dim (int, optional): The dimension of the embedding vectors for the collection.
            Required if creating a new collection.
        embedding_field (str, optional): The name of the embedding field for the
            collection, defaults to DEFAULT_EMBEDDING_KEY.
        doc_id_field (str, optional): The name of the doc_id field for the collection,
            defaults to DEFAULT_DOC_ID_KEY.
        similarity_metric (str, optional): The similarity metric to use,
            currently supports IP, COSINE and L2.
        consistency_level (str, optional): Which consistency level to use for a newly
            created collection. Defaults to "Session".
        overwrite (bool, optional): Whether to overwrite existing collection with same
            name. Defaults to False.
        text_key (str, optional): What key text is stored in in the passed collection.
            Used when bringing your own collection. Defaults to None.
        index_config (dict, optional): The configuration used for building the
            Milvus index. Defaults to None.
        search_config (dict, optional): The configuration used for searching
            the Milvus index. Note that this must be compatible with the index
            type specified by `index_config`. Defaults to None.
        collection_properties (dict, optional): The collection properties such as TTL
            (Time-To-Live) and MMAP (memory mapping). Defaults to None.
            It could include:
            - 'collection.ttl.seconds' (int): Once this property is set, data in the
                current collection expires in the specified time. Expired data in the
                collection will be cleaned up and will not be involved in searches or queries.
            - 'mmap.enabled' (bool): Whether to enable memory-mapped storage at the collection level.
        batch_size (int): Configures the number of documents processed in one
            batch when inserting data into Milvus. Defaults to DEFAULT_BATCH_SIZE.
        enable_sparse (bool): A boolean flag indicating whether to enable support
            for sparse embeddings for hybrid retrieval. Defaults to False.
        sparse_embedding_function (BaseSparseEmbeddingFunction, optional): If enable_sparse
             is True, this object should be provided to convert text to a sparse embedding.
        hybrid_ranker (str): Specifies the type of ranker used in hybrid search queries.
            Currently only supports ['RRFRanker','WeightedRanker']. Defaults to "RRFRanker".
        hybrid_ranker_params (dict, optional): Configuration parameters for the hybrid ranker.
            The structure of this dictionary depends on the specific ranker being used:
            - For "RRFRanker", it should include:
                - 'k' (int): A parameter used in Reciprocal Rank Fusion (RRF). This value is used
                             to calculate the rank scores as part of the RRF algorithm, which combines
                             multiple ranking strategies into a single score to improve search relevance.
            - For "WeightedRanker", it expects:
                - 'weights' (list of float): A list of exactly two weights:
                     1. The weight for the dense embedding component.
                     2. The weight for the sparse embedding component.
                  These weights are used to adjust the importance of the dense and sparse components of the embeddings
                  in the hybrid retrieval process.
            Defaults to an empty dictionary, implying that the ranker will operate with its predefined default settings.
        index_management (IndexManagement): Specifies the index management strategy to use. Defaults to "create_if_not_exists".
        scalar_field_names (list): The names of the extra scalar fields to be included in the collection schema.
        scalar_field_types (list): The types of the extra scalar fields.

    Raises:
        ImportError: Unable to import `pymilvus`.
        MilvusException: Error communicating with Milvus, more can be found in logging
            under Debug.

    Returns:
        MilvusVectorstore: Vectorstore that supports add, delete, and query.

    Examples:
        `pip install llama-index-vector-stores-milvus`

        ```python
        from llama_index.vector_stores.milvus import MilvusVectorStore

        # Setup MilvusVectorStore
        vector_store = MilvusVectorStore(
            dim=1536,
            collection_name="your_collection_name",
            uri="http://milvus_address:port",
            token="your_milvus_token_here",
            overwrite=True
        )
        ```
    """

    stores_text: bool = True
    stores_node: bool = True

    uri: str = "./milvus_llamaindex.db"
    token: str = ""
    collection_name: str = "llamacollection"
    dim: Optional[int]
    embedding_field: str = DEFAULT_EMBEDDING_KEY
    doc_id_field: str = DEFAULT_DOC_ID_KEY
    similarity_metric: str = "IP"
    consistency_level: str = "Session"
    overwrite: bool = False
    text_key: Optional[str]
    output_fields: List[str] = Field(default_factory=list)
    index_config: Optional[dict]
    search_config: Optional[dict]
    collection_properties: Optional[dict]
    batch_size: int = DEFAULT_BATCH_SIZE
    enable_sparse: bool = False
    sparse_embedding_field: str = "sparse_embedding"
    sparse_embedding_function: Any
    hybrid_ranker: str
    hybrid_ranker_params: dict = {}
    index_management: IndexManagement = IndexManagement.CREATE_IF_NOT_EXISTS
    scalar_field_names: Optional[List[str]]
    scalar_field_types: Optional[List[DataType]]

    _milvusclient: MilvusClient = PrivateAttr()
    _collection: Any = PrivateAttr()

    def __init__(
        self,
        uri: str = "./milvus_llamaindex.db",
        token: str = "",
        collection_name: str = "llamacollection",
        dim: Optional[int] = None,
        embedding_field: str = DEFAULT_EMBEDDING_KEY,
        doc_id_field: str = DEFAULT_DOC_ID_KEY,
        similarity_metric: str = "IP",
        consistency_level: str = "Session",
        overwrite: bool = False,
        text_key: Optional[str] = None,
        output_fields: Optional[List[str]] = None,
        index_config: Optional[dict] = None,
        search_config: Optional[dict] = None,
        collection_properties: Optional[dict] = None,
        batch_size: int = DEFAULT_BATCH_SIZE,
        enable_sparse: bool = False,
        sparse_embedding_function: Optional[BaseSparseEmbeddingFunction] = None,
        hybrid_ranker: str = "RRFRanker",
        hybrid_ranker_params: dict = {},
        index_management: IndexManagement = IndexManagement.CREATE_IF_NOT_EXISTS,
        scalar_field_names: Optional[List[str]] = None,
        scalar_field_types: Optional[List[DataType]] = None,
        **kwargs: Any,
    ) -> None:
        """Init params."""
        super().__init__(
            collection_name=collection_name,
            dim=dim,
            embedding_field=embedding_field,
            doc_id_field=doc_id_field,
            consistency_level=consistency_level,
            overwrite=overwrite,
            text_key=text_key,
            output_fields=output_fields or [],
            index_config=index_config if index_config else {},
            search_config=search_config if search_config else {},
            collection_properties=collection_properties,
            batch_size=batch_size,
            enable_sparse=enable_sparse,
            sparse_embedding_function=sparse_embedding_function,
            hybrid_ranker=hybrid_ranker,
            hybrid_ranker_params=hybrid_ranker_params,
            index_management=index_management,
            scalar_field_names=scalar_field_names,
            scalar_field_types=scalar_field_types,
        )

        # Select the similarity metric
        similarity_metrics_map = {
            "ip": "IP",
            "l2": "L2",
            "euclidean": "L2",
            "cosine": "COSINE",
        }
        self.similarity_metric = similarity_metrics_map.get(
            similarity_metric.lower(), "L2"
        )
        # Connect to Milvus instance
        self._milvusclient = MilvusClient(
            uri=uri,
            token=token,
            **kwargs,  # pass additional arguments such as server_pem_path
        )
        # Delete previous collection if overwriting
        if overwrite and collection_name in self.client.list_collections():
            self._milvusclient.drop_collection(collection_name)

        # Create the collection if it does not exist
        if collection_name not in self.client.list_collections():
            if dim is None:
                raise ValueError("Dim argument required for collection creation.")
            if self.enable_sparse is False:
                # Check if custom index should be created
                if (
                    index_config is not None
                    and self.index_management is not IndexManagement.NO_VALIDATION
                ):
                    try:
                        # Prepare index
                        index_params = self.client.prepare_index_params()
                        index_type = index_config["index_type"]
                        index_params.add_index(
                            field_name=embedding_field,
                            index_type=index_type,
                            metric_type=self.similarity_metric,
                        )

                        # Create a schema according to LlamaIndex Schema.
                        schema = self._create_schema()
                        schema.verify()

                        # Using private method exposed by pymilvus client, in order to avoid creating indexes twice
                        # Reason: create_collection in pymilvus only checks schema and ignores index_config setup
                        # https://github.com/milvus-io/pymilvus/issues/2265
                        self.client._create_collection_with_schema(
                            collection_name=collection_name,
                            schema=schema,
                            index_params=index_params,
                            dimemsion=dim,
                            primary_field=MILVUS_ID_FIELD,
                            vector_field=embedding_field,
                            id_type="string",
                            max_length=65_535,
                            consistency_level=consistency_level,
                        )
                        self._collection = Collection(
                            collection_name, using=self._milvusclient._using
                        )
                    except Exception as e:
                        logger.error("Error creating collection with index_config")
                        raise NotImplementedError(
                            "Error creating collection with index_config"
                        ) from e
                else:
                    self._milvusclient.create_collection(
                        collection_name=collection_name,
                        dimension=dim,
                        primary_field_name=MILVUS_ID_FIELD,
                        vector_field_name=embedding_field,
                        id_type="string",
                        metric_type=self.similarity_metric,
                        max_length=65_535,
                        consistency_level=consistency_level,
                    )
                    self._collection = Collection(
                        collection_name, using=self._milvusclient._using
                    )

                    # Check if we have to create an index here to avoid duplicity of indexes
                    self._create_index_if_required()
            else:
                try:
                    _ = DataType.SPARSE_FLOAT_VECTOR
                except Exception as e:
                    logger.error(
                        "Hybrid retrieval is only supported in Milvus 2.4.0 or later."
                    )
                    raise NotImplementedError(
                        "Hybrid retrieval requires Milvus 2.4.0 or later."
                    ) from e
                self._create_hybrid_index(collection_name)
        else:
            self._collection = Collection(
                collection_name, using=self._milvusclient._using
            )

        # Set properties
        if collection_properties:
            if self._milvusclient.get_load_state(collection_name) == LoadState.Loaded:
                self._collection.release()
                self._collection.set_properties(properties=collection_properties)
                self._collection.load()
            else:
                self._collection.set_properties(properties=collection_properties)

        self.enable_sparse = enable_sparse
        if self.enable_sparse is True and sparse_embedding_function is None:
            logger.warning("Sparse embedding function is not provided, using default.")
            self.sparse_embedding_function = get_default_sparse_embedding_function()
        elif self.enable_sparse is True and sparse_embedding_function is not None:
            self.sparse_embedding_function = sparse_embedding_function
        else:
            pass

        logger.debug(f"Successfully created a new collection: {self.collection_name}")

    @property
    def client(self) -> Any:
        """Get client."""
        return self._milvusclient

    def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
        """Add the embeddings and their nodes into Milvus.

        Args:
            nodes (List[BaseNode]): List of nodes with embeddings
                to insert.

        Raises:
            MilvusException: Failed to insert data.

        Returns:
            List[str]: List of ids inserted.
        """
        insert_list = []
        insert_ids = []

        if self.enable_sparse is True and self.sparse_embedding_function is None:
            logger.fatal(
                "sparse_embedding_function is None when enable_sparse is True."
            )

        # Process that data we are going to insert
        for node in nodes:
            entry = node_to_metadata_dict(node)
            entry[MILVUS_ID_FIELD] = node.node_id
            entry[self.embedding_field] = node.embedding

            if self.enable_sparse is True:
                entry[
                    self.sparse_embedding_field
                ] = self.sparse_embedding_function.encode_documents([node.text])[0]

            insert_ids.append(node.node_id)
            insert_list.append(entry)

        # Insert the data into milvus
        for insert_batch in iter_batch(insert_list, self.batch_size):
            self._collection.insert(insert_batch)
        if add_kwargs.get("force_flush", False):
            self._collection.flush()
        logger.debug(
            f"Successfully inserted embeddings into: {self.collection_name} "
            f"Num Inserted: {len(insert_list)}"
        )
        return insert_ids

    def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
        """
        Delete nodes using with ref_doc_id.

        Args:
            ref_doc_id (str): The doc_id of the document to delete.

        Raises:
            MilvusException: Failed to delete the doc.
        """
        # Adds ability for multiple doc delete in future.
        doc_ids: List[str]
        if isinstance(ref_doc_id, list):
            doc_ids = ref_doc_id  # type: ignore
        else:
            doc_ids = [ref_doc_id]

        # Begin by querying for the primary keys to delete
        doc_ids = ['"' + entry + '"' for entry in doc_ids]
        entries = self._milvusclient.query(
            collection_name=self.collection_name,
            filter=f"{self.doc_id_field} in [{','.join(doc_ids)}]",
        )
        if len(entries) > 0:
            ids = [entry["id"] for entry in entries]
            self._milvusclient.delete(collection_name=self.collection_name, pks=ids)
            logger.debug(f"Successfully deleted embedding with doc_id: {doc_ids}")

    def delete_nodes(
        self,
        node_ids: Optional[List[str]] = None,
        filters: Optional[MetadataFilters] = None,
        **delete_kwargs: Any,
    ) -> None:
        """Deletes nodes.

        Args:
            node_ids (Optional[List[str]], optional): IDs of nodes to delete. Defaults to None.
            filters (Optional[MetadataFilters], optional): Metadata filters. Defaults to None.
        """
        filters_cpy = deepcopy(filters) or MetadataFilters(filters=[])

        if node_ids:
            filters_cpy.filters.append(
                MetadataFilter(key="id", value=node_ids, operator=FilterOperator.IN)
            )

        if filters_cpy is not None:
            filter = _to_milvus_filter(filters_cpy)
        else:
            filter = None

        self._milvusclient.delete(
            collection_name=self.collection_name,
            filter=filter,
            **delete_kwargs,
        )
        logger.debug(f"Successfully deleted node_ids: {node_ids}")

    def clear(self) -> None:
        """Clears db."""
        self._milvusclient.drop_collection(self.collection_name)

    def get_nodes(
        self,
        node_ids: Optional[List[str]] = None,
        filters: Optional[MetadataFilters] = None,
    ) -> List[BaseNode]:
        """Get nodes by node ids or metadata filters.

        Args:
            node_ids (Optional[List[str]], optional): IDs of nodes to retrieve. Defaults to None.
            filters (Optional[MetadataFilters], optional): Metadata filters. Defaults to None.

        Raises:
            ValueError: Neither or both of node_ids and filters are provided.

        Returns:
            List[BaseNode]:
        """
        if node_ids is None and filters is None:
            raise ValueError("Either node_ids or filters must be provided.")

        filters_cpy = deepcopy(filters) or MetadataFilters(filters=[])
        milvus_filter = _to_milvus_filter(filters_cpy)

        if node_ids is not None and milvus_filter:
            raise ValueError("Only one of node_ids or filters can be provided.")

        res = self.client.query(
            ids=node_ids, collection_name=self.collection_name, filter=milvus_filter
        )

        nodes = []
        for item in res:
            if not self.text_key:
                node = metadata_dict_to_node(item)
                node.embedding = item.get(self.embedding_field, None)
            else:
                try:
                    text = item.pop(self.text_key)
                except Exception:
                    raise ValueError(
                        "The passed in text_key value does not exist "
                        "in the retrieved entity."
                    ) from None
                embedding = item.pop(self.embedding_field, None)
                node = TextNode(
                    text=text,
                    embedding=embedding,
                    metadata=item,
                )
            nodes.append(node)
        return nodes

    def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
        """Query index for top k most similar nodes.

        Args:
            query_embedding (List[float]): query embedding
            similarity_top_k (int): top k most similar nodes
            doc_ids (Optional[List[str]]): list of doc_ids to filter by
            node_ids (Optional[List[str]]): list of node_ids to filter by
            output_fields (Optional[List[str]]): list of fields to return
            embedding_field (Optional[str]): name of embedding field
        """
        if query.mode == VectorStoreQueryMode.DEFAULT:
            pass
        elif query.mode == VectorStoreQueryMode.HYBRID:
            if self.enable_sparse is False:
                raise ValueError(f"QueryMode is HYBRID, but enable_sparse is False.")
        elif query.mode == VectorStoreQueryMode.MMR:
            pass
        else:
            raise ValueError(f"Milvus does not support {query.mode} yet.")

        expr = []
        output_fields = ["*"]

        # Parse the filter

        if query.filters is not None or "milvus_scalar_filters" in kwargs:
            expr.append(
                _to_milvus_filter(
                    query.filters,
                    (
                        kwargs["milvus_scalar_filters"]
                        if "milvus_scalar_filters" in kwargs
                        else None
                    ),
                )
            )

        # Parse any docs we are filtering on
        if query.doc_ids is not None and len(query.doc_ids) != 0:
            expr_list = ['"' + entry + '"' for entry in query.doc_ids]
            expr.append(f"{self.doc_id_field} in [{','.join(expr_list)}]")

        # Parse any nodes we are filtering on
        if query.node_ids is not None and len(query.node_ids) != 0:
            expr_list = ['"' + entry + '"' for entry in query.node_ids]
            expr.append(f"{MILVUS_ID_FIELD} in [{','.join(expr_list)}]")

        # Limit output fields
        outputs_limited = False
        if query.output_fields is not None:
            output_fields = query.output_fields
            outputs_limited = True
        elif len(self.output_fields) > 0:
            output_fields = [*self.output_fields]
            outputs_limited = True

        # Add the text key to output fields if necessary
        if self.text_key and self.text_key not in output_fields and outputs_limited:
            output_fields.append(self.text_key)

        # Convert to string expression
        string_expr = ""
        if len(expr) != 0:
            string_expr = f" and ".join(expr)

        # Perform the search
        if query.mode == VectorStoreQueryMode.DEFAULT:
            # Perform default search
            res = self._milvusclient.search(
                collection_name=self.collection_name,
                data=[query.query_embedding],
                filter=string_expr,
                limit=query.similarity_top_k,
                output_fields=output_fields,
                search_params=self.search_config,
                anns_field=self.embedding_field,
            )
            logger.debug(
                f"Successfully searched embedding in collection: {self.collection_name}"
                f" Num Results: {len(res[0])}"
            )

            nodes = []
            similarities = []
            ids = []
            # Parse the results
            for hit in res[0]:
                if not self.text_key:
                    node = metadata_dict_to_node(
                        {
                            "_node_content": hit["entity"].get("_node_content", None),
                            "_node_type": hit["entity"].get("_node_type", None),
                        }
                    )
                else:
                    try:
                        text = hit["entity"].get(self.text_key)
                    except Exception:
                        raise ValueError(
                            "The passed in text_key value does not exist "
                            "in the retrieved entity."
                        )

                    metadata = {
                        key: hit["entity"].get(key) for key in self.output_fields
                    }
                    node = TextNode(text=text, metadata=metadata)

                nodes.append(node)
                similarities.append(hit["distance"])
                ids.append(hit["id"])

        elif query.mode == VectorStoreQueryMode.MMR:
            # Perform MMR search
            mmr_threshold = kwargs.get("mmr_threshold", None)

            if (
                kwargs.get("mmr_prefetch_factor") is not None
                and kwargs.get("mmr_prefetch_k") is not None
            ):
                raise ValueError(
                    "'mmr_prefetch_factor' and 'mmr_prefetch_k' "
                    "cannot coexist in a call to query()"
                )
            else:
                if kwargs.get("mmr_prefetch_k") is not None:
                    prefetch_k0 = int(kwargs["mmr_prefetch_k"])
                else:
                    prefetch_k0 = int(
                        query.similarity_top_k
                        * kwargs.get("mmr_prefetch_factor", DEFAULT_MMR_PREFETCH_FACTOR)
                    )

            res = self._milvusclient.search(
                collection_name=self.collection_name,
                data=[query.query_embedding],
                filter=string_expr,
                limit=prefetch_k0,
                output_fields=output_fields,
                search_params=self.search_config,
                anns_field=self.embedding_field,
            )

            nodes = res[0]
            node_embeddings = []
            node_ids = []
            for node in nodes:
                node_embeddings.append(node["entity"]["embedding"])
                node_ids.append(node["id"])

            mmr_similarities, mmr_ids = get_top_k_mmr_embeddings(
                query_embedding=query.query_embedding,
                embeddings=node_embeddings,
                similarity_top_k=query.similarity_top_k,
                embedding_ids=node_ids,
                mmr_threshold=mmr_threshold,
            )

            node_dict = dict(list(zip(node_ids, nodes)))
            selected_nodes = [node_dict[id] for id in mmr_ids if id in node_dict]

            nodes = []
            # Parse the results
            for hit in selected_nodes:
                if not self.text_key:
                    node = metadata_dict_to_node(
                        {
                            "_node_content": hit["entity"].get("_node_content", None),
                            "_node_type": hit["entity"].get("_node_type", None),
                        }
                    )
                else:
                    try:
                        text = hit["entity"].get(self.text_key)
                    except Exception:
                        raise ValueError(
                            "The passed in text_key value does not exist "
                            "in the retrieved entity."
                        )

                    metadata = {
                        key: hit["entity"].get(key) for key in self.output_fields
                    }
                    node = TextNode(text=text, metadata=metadata)

                nodes.append(node)

            similarities = mmr_similarities  # Passing the MMR similarities instead of the original similarities
            ids = mmr_ids

            logger.debug(
                f"Successfully performed MMR on embeddings in collection: {self.collection_name}"
            )

        else:
            # Perform hybrid search
            sparse_emb = self.sparse_embedding_function.encode_queries(
                [query.query_str]
            )[0]
            sparse_search_params = {"metric_type": "IP"}

            sparse_req = AnnSearchRequest(
                data=[sparse_emb],
                anns_field=self.sparse_embedding_field,
                param=sparse_search_params,
                limit=query.similarity_top_k,
                expr=string_expr,  # Apply metadata filters to sparse search
            )

            dense_search_params = {
                "metric_type": self.similarity_metric,
                "params": self.search_config,
            }
            dense_emb = query.query_embedding
            dense_req = AnnSearchRequest(
                data=[dense_emb],
                anns_field=self.embedding_field,
                param=dense_search_params,
                limit=query.similarity_top_k,
                expr=string_expr,  # Apply metadata filters to dense search
            )
            ranker = None

            if WeightedRanker is None or RRFRanker is None:
                logger.error(
                    "Hybrid retrieval is only supported in Milvus 2.4.0 or later."
                )
                raise ValueError(
                    "Hybrid retrieval is only supported in Milvus 2.4.0 or later."
                )
            if self.hybrid_ranker == "WeightedRanker":
                if self.hybrid_ranker_params == {}:
                    self.hybrid_ranker_params = {"weights": [1.0, 1.0]}
                ranker = WeightedRanker(*self.hybrid_ranker_params["weights"])
            elif self.hybrid_ranker == "RRFRanker":
                if self.hybrid_ranker_params == {}:
                    self.hybrid_ranker_params = {"k": 60}
                ranker = RRFRanker(self.hybrid_ranker_params["k"])
            else:
                raise ValueError(f"Unsupported ranker: {self.hybrid_ranker}")

            res = self._collection.hybrid_search(
                [dense_req, sparse_req],
                rerank=ranker,
                limit=query.similarity_top_k,
                output_fields=output_fields,
            )

            logger.debug(
                f"Successfully searched embedding in collection: {self.collection_name}"
                f" Num Results: {len(res[0])}"
            )

            nodes = []
            similarities = []
            ids = []
            # Parse the results
            for hit in res[0]:
                if not self.text_key:
                    node = metadata_dict_to_node(
                        {
                            "_node_content": hit.entity.get("_node_content"),
                            "_node_type": hit.entity.get("_node_type"),
                        }
                    )
                else:
                    try:
                        text = hit.entity.get(self.text_key)
                    except Exception:
                        raise ValueError(
                            "The passed in text_key value does not exist "
                            "in the retrieved entity."
                        )

                    metadata = {key: hit.entity.get(key) for key in self.output_fields}
                    node = TextNode(text=text, metadata=metadata)

                nodes.append(node)
                similarities.append(hit.distance)
                ids.append(hit.id)

        return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)

    def _create_index_if_required(self) -> None:
        """
        Create or validate the index based on the index management strategy.

        This method decides whether to create or validate the index based on
        the specified index management strategy and the current state of the collection.
        """
        if self.index_management == IndexManagement.NO_VALIDATION:
            return

        if self.enable_sparse is False:
            self._create_dense_index()
        else:
            self._create_hybrid_index(self.collection_name)

    def _create_dense_index(self) -> None:
        """
        Create or recreate the dense vector index.

        This method handles the creation of the dense vector index based on
        the current index management strategy and the state of the collection.
        """
        index_exists = self._collection.has_index()

        if (
            not index_exists
            and self.index_management == IndexManagement.CREATE_IF_NOT_EXISTS
        ) or (index_exists and self.overwrite):
            if index_exists:
                self._collection.release()
                self._collection.drop_index()

            base_params: Dict[str, Any] = self.index_config.copy()
            index_type: str = base_params.pop("index_type", "FLAT")
            index_params: Dict[str, Union[str, Dict[str, Any]]] = {
                "params": base_params,
                "metric_type": self.similarity_metric,
                "index_type": index_type,
            }
            self._collection.create_index(
                self.embedding_field, index_params=index_params
            )
            self._collection.load()

    def _create_hybrid_index(self, collection_name: str) -> None:
        """
        Create or recreate the hybrid (dense and sparse) vector index.

        Args:
            collection_name (str): The name of the collection to create the index for.
        """
        # Check if the collection exists, if not, create it
        if collection_name not in self._milvusclient.list_collections():
            schema = MilvusClient.create_schema(
                auto_id=False, enable_dynamic_field=True
            )
            schema.add_field(
                field_name="id",
                datatype=DataType.VARCHAR,
                max_length=65535,
                is_primary=True,
            )
            schema.add_field(
                field_name=self.embedding_field,
                datatype=DataType.FLOAT_VECTOR,
                dim=self.dim,
            )
            schema.add_field(
                field_name=self.sparse_embedding_field,
                datatype=DataType.SPARSE_FLOAT_VECTOR,
            )
            self._milvusclient.create_collection(
                collection_name=collection_name, schema=schema
            )

        # Initialize or get the collection
        self._collection = Collection(collection_name, using=self._milvusclient._using)

        dense_index_exists = self._collection.has_index(index_name=self.embedding_field)
        sparse_index_exists = self._collection.has_index(
            index_name=self.sparse_embedding_field
        )

        if (
            (not dense_index_exists or not sparse_index_exists)
            and self.index_management == IndexManagement.CREATE_IF_NOT_EXISTS
            or (dense_index_exists and sparse_index_exists and self.overwrite)
        ):
            if dense_index_exists:
                self._collection.release()
                self._collection.drop_index(index_name=self.embedding_field)
            if sparse_index_exists:
                self._collection.drop_index(index_name=self.sparse_embedding_field)

            # Create sparse index
            sparse_index = {"index_type": "SPARSE_INVERTED_INDEX", "metric_type": "IP"}
            self._collection.create_index(self.sparse_embedding_field, sparse_index)

            # Create dense index
            base_params = self.index_config.copy()
            index_type = base_params.pop("index_type", "FLAT")
            dense_index = {
                "params": base_params,
                "metric_type": self.similarity_metric,
                "index_type": index_type,
            }
            self._collection.create_index(self.embedding_field, dense_index)

        self._collection.load()

    def _create_schema(self):
        """
        Creates the collection schema. The default fields include the id, embedding and doc_id.

        Returns: The schema of the collection
        """
        schema = MilvusClient.create_schema(auto_id=False, enable_dynamic_field=True)
        schema.add_field(
            field_name="id",
            datatype=DataType.VARCHAR,
            max_length=65_535,
            is_primary=True,
        )
        schema.add_field(
            field_name=self.embedding_field,
            datatype=DataType.FLOAT_VECTOR,
            dim=self.dim,
        )
        schema.add_field(
            field_name=self.doc_id_field,
            datatype=DataType.VARCHAR,
            max_length=65_535,
        )
        if self.scalar_field_names is not None and self.scalar_field_types is not None:
            if len(self.scalar_field_names) != len(self.scalar_field_types):
                raise ValueError(
                    "scalar_field_names and scalar_field_types must have same length."
                )

            for field_name, field_type in zip(
                self.scalar_field_names, self.scalar_field_types
            ):
                max_length = 65_535 if field_type == DataType.VARCHAR else None
                schema.add_field(
                    field_name=field_name, datatype=field_type, max_length=max_length
                )

        return schema

client property

client: Any

Get client.

add

add(nodes: List[BaseNode], **add_kwargs: Any) -> List[str]

Add the embeddings and their nodes into Milvus.

Parameters:

Name	Type	Description	Default
nodes	List[BaseNode]	List of nodes with embeddings to insert.	required

Raises:

Type	Description
MilvusException	Failed to insert data.

Returns:

Type	Description
List[str]	List[str]: List of ids inserted.

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py

def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
    """Add the embeddings and their nodes into Milvus.

    Args:
        nodes (List[BaseNode]): List of nodes with embeddings
            to insert.

    Raises:
        MilvusException: Failed to insert data.

    Returns:
        List[str]: List of ids inserted.
    """
    insert_list = []
    insert_ids = []

    if self.enable_sparse is True and self.sparse_embedding_function is None:
        logger.fatal(
            "sparse_embedding_function is None when enable_sparse is True."
        )

    # Process that data we are going to insert
    for node in nodes:
        entry = node_to_metadata_dict(node)
        entry[MILVUS_ID_FIELD] = node.node_id
        entry[self.embedding_field] = node.embedding

        if self.enable_sparse is True:
            entry[
                self.sparse_embedding_field
            ] = self.sparse_embedding_function.encode_documents([node.text])[0]

        insert_ids.append(node.node_id)
        insert_list.append(entry)

    # Insert the data into milvus
    for insert_batch in iter_batch(insert_list, self.batch_size):
        self._collection.insert(insert_batch)
    if add_kwargs.get("force_flush", False):
        self._collection.flush()
    logger.debug(
        f"Successfully inserted embeddings into: {self.collection_name} "
        f"Num Inserted: {len(insert_list)}"
    )
    return insert_ids

delete

delete(ref_doc_id: str, **delete_kwargs: Any) -> None

Delete nodes using with ref_doc_id.

Parameters:

Name	Type	Description	Default
ref_doc_id	str	The doc_id of the document to delete.	required

Raises:

Type	Description
MilvusException	Failed to delete the doc.

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py

def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
    """
    Delete nodes using with ref_doc_id.

    Args:
        ref_doc_id (str): The doc_id of the document to delete.

    Raises:
        MilvusException: Failed to delete the doc.
    """
    # Adds ability for multiple doc delete in future.
    doc_ids: List[str]
    if isinstance(ref_doc_id, list):
        doc_ids = ref_doc_id  # type: ignore
    else:
        doc_ids = [ref_doc_id]

    # Begin by querying for the primary keys to delete
    doc_ids = ['"' + entry + '"' for entry in doc_ids]
    entries = self._milvusclient.query(
        collection_name=self.collection_name,
        filter=f"{self.doc_id_field} in [{','.join(doc_ids)}]",
    )
    if len(entries) > 0:
        ids = [entry["id"] for entry in entries]
        self._milvusclient.delete(collection_name=self.collection_name, pks=ids)
        logger.debug(f"Successfully deleted embedding with doc_id: {doc_ids}")

delete_nodes

delete_nodes(node_ids: Optional[List[str]] = None, filters: Optional[MetadataFilters] = None, **delete_kwargs: Any) -> None

Deletes nodes.

Parameters:

Name	Type	Description	Default
node_ids	Optional[List[str]]	IDs of nodes to delete. Defaults to None.	None
filters	Optional[MetadataFilters]	Metadata filters. Defaults to None.	None

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py

def delete_nodes(
    self,
    node_ids: Optional[List[str]] = None,
    filters: Optional[MetadataFilters] = None,
    **delete_kwargs: Any,
) -> None:
    """Deletes nodes.

    Args:
        node_ids (Optional[List[str]], optional): IDs of nodes to delete. Defaults to None.
        filters (Optional[MetadataFilters], optional): Metadata filters. Defaults to None.
    """
    filters_cpy = deepcopy(filters) or MetadataFilters(filters=[])

    if node_ids:
        filters_cpy.filters.append(
            MetadataFilter(key="id", value=node_ids, operator=FilterOperator.IN)
        )

    if filters_cpy is not None:
        filter = _to_milvus_filter(filters_cpy)
    else:
        filter = None

    self._milvusclient.delete(
        collection_name=self.collection_name,
        filter=filter,
        **delete_kwargs,
    )
    logger.debug(f"Successfully deleted node_ids: {node_ids}")

clear

clear() -> None

Clears db.

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py

def clear(self) -> None:
    """Clears db."""
    self._milvusclient.drop_collection(self.collection_name)

get_nodes

get_nodes(node_ids: Optional[List[str]] = None, filters: Optional[MetadataFilters] = None) -> List[BaseNode]

Get nodes by node ids or metadata filters.

Parameters:

Name	Type	Description	Default
node_ids	Optional[List[str]]	IDs of nodes to retrieve. Defaults to None.	None
filters	Optional[MetadataFilters]	Metadata filters. Defaults to None.	None

Raises:

Type	Description
ValueError	Neither or both of node_ids and filters are provided.

Returns:

Type	Description
List[BaseNode]	List[BaseNode]:

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py

def get_nodes(
    self,
    node_ids: Optional[List[str]] = None,
    filters: Optional[MetadataFilters] = None,
) -> List[BaseNode]:
    """Get nodes by node ids or metadata filters.

    Args:
        node_ids (Optional[List[str]], optional): IDs of nodes to retrieve. Defaults to None.
        filters (Optional[MetadataFilters], optional): Metadata filters. Defaults to None.

    Raises:
        ValueError: Neither or both of node_ids and filters are provided.

    Returns:
        List[BaseNode]:
    """
    if node_ids is None and filters is None:
        raise ValueError("Either node_ids or filters must be provided.")

    filters_cpy = deepcopy(filters) or MetadataFilters(filters=[])
    milvus_filter = _to_milvus_filter(filters_cpy)

    if node_ids is not None and milvus_filter:
        raise ValueError("Only one of node_ids or filters can be provided.")

    res = self.client.query(
        ids=node_ids, collection_name=self.collection_name, filter=milvus_filter
    )

    nodes = []
    for item in res:
        if not self.text_key:
            node = metadata_dict_to_node(item)
            node.embedding = item.get(self.embedding_field, None)
        else:
            try:
                text = item.pop(self.text_key)
            except Exception:
                raise ValueError(
                    "The passed in text_key value does not exist "
                    "in the retrieved entity."
                ) from None
            embedding = item.pop(self.embedding_field, None)
            node = TextNode(
                text=text,
                embedding=embedding,
                metadata=item,
            )
        nodes.append(node)
    return nodes

query

query(query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters:

Name	Type	Description	Default
query_embedding	List[float]	query embedding	required
similarity_top_k	int	top k most similar nodes	required
doc_ids	Optional[List[str]]	list of doc_ids to filter by	required
node_ids	Optional[List[str]]	list of node_ids to filter by	required
output_fields	Optional[List[str]]	list of fields to return	required
embedding_field	Optional[str]	name of embedding field	required

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py

def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
    """Query index for top k most similar nodes.

    Args:
        query_embedding (List[float]): query embedding
        similarity_top_k (int): top k most similar nodes
        doc_ids (Optional[List[str]]): list of doc_ids to filter by
        node_ids (Optional[List[str]]): list of node_ids to filter by
        output_fields (Optional[List[str]]): list of fields to return
        embedding_field (Optional[str]): name of embedding field
    """
    if query.mode == VectorStoreQueryMode.DEFAULT:
        pass
    elif query.mode == VectorStoreQueryMode.HYBRID:
        if self.enable_sparse is False:
            raise ValueError(f"QueryMode is HYBRID, but enable_sparse is False.")
    elif query.mode == VectorStoreQueryMode.MMR:
        pass
    else:
        raise ValueError(f"Milvus does not support {query.mode} yet.")

    expr = []
    output_fields = ["*"]

    # Parse the filter

    if query.filters is not None or "milvus_scalar_filters" in kwargs:
        expr.append(
            _to_milvus_filter(
                query.filters,
                (
                    kwargs["milvus_scalar_filters"]
                    if "milvus_scalar_filters" in kwargs
                    else None
                ),
            )
        )

    # Parse any docs we are filtering on
    if query.doc_ids is not None and len(query.doc_ids) != 0:
        expr_list = ['"' + entry + '"' for entry in query.doc_ids]
        expr.append(f"{self.doc_id_field} in [{','.join(expr_list)}]")

    # Parse any nodes we are filtering on
    if query.node_ids is not None and len(query.node_ids) != 0:
        expr_list = ['"' + entry + '"' for entry in query.node_ids]
        expr.append(f"{MILVUS_ID_FIELD} in [{','.join(expr_list)}]")

    # Limit output fields
    outputs_limited = False
    if query.output_fields is not None:
        output_fields = query.output_fields
        outputs_limited = True
    elif len(self.output_fields) > 0:
        output_fields = [*self.output_fields]
        outputs_limited = True

    # Add the text key to output fields if necessary
    if self.text_key and self.text_key not in output_fields and outputs_limited:
        output_fields.append(self.text_key)

    # Convert to string expression
    string_expr = ""
    if len(expr) != 0:
        string_expr = f" and ".join(expr)

    # Perform the search
    if query.mode == VectorStoreQueryMode.DEFAULT:
        # Perform default search
        res = self._milvusclient.search(
            collection_name=self.collection_name,
            data=[query.query_embedding],
            filter=string_expr,
            limit=query.similarity_top_k,
            output_fields=output_fields,
            search_params=self.search_config,
            anns_field=self.embedding_field,
        )
        logger.debug(
            f"Successfully searched embedding in collection: {self.collection_name}"
            f" Num Results: {len(res[0])}"
        )

        nodes = []
        similarities = []
        ids = []
        # Parse the results
        for hit in res[0]:
            if not self.text_key:
                node = metadata_dict_to_node(
                    {
                        "_node_content": hit["entity"].get("_node_content", None),
                        "_node_type": hit["entity"].get("_node_type", None),
                    }
                )
            else:
                try:
                    text = hit["entity"].get(self.text_key)
                except Exception:
                    raise ValueError(
                        "The passed in text_key value does not exist "
                        "in the retrieved entity."
                    )

                metadata = {
                    key: hit["entity"].get(key) for key in self.output_fields
                }
                node = TextNode(text=text, metadata=metadata)

            nodes.append(node)
            similarities.append(hit["distance"])
            ids.append(hit["id"])

    elif query.mode == VectorStoreQueryMode.MMR:
        # Perform MMR search
        mmr_threshold = kwargs.get("mmr_threshold", None)

        if (
            kwargs.get("mmr_prefetch_factor") is not None
            and kwargs.get("mmr_prefetch_k") is not None
        ):
            raise ValueError(
                "'mmr_prefetch_factor' and 'mmr_prefetch_k' "
                "cannot coexist in a call to query()"
            )
        else:
            if kwargs.get("mmr_prefetch_k") is not None:
                prefetch_k0 = int(kwargs["mmr_prefetch_k"])
            else:
                prefetch_k0 = int(
                    query.similarity_top_k
                    * kwargs.get("mmr_prefetch_factor", DEFAULT_MMR_PREFETCH_FACTOR)
                )

        res = self._milvusclient.search(
            collection_name=self.collection_name,
            data=[query.query_embedding],
            filter=string_expr,
            limit=prefetch_k0,
            output_fields=output_fields,
            search_params=self.search_config,
            anns_field=self.embedding_field,
        )

        nodes = res[0]
        node_embeddings = []
        node_ids = []
        for node in nodes:
            node_embeddings.append(node["entity"]["embedding"])
            node_ids.append(node["id"])

        mmr_similarities, mmr_ids = get_top_k_mmr_embeddings(
            query_embedding=query.query_embedding,
            embeddings=node_embeddings,
            similarity_top_k=query.similarity_top_k,
            embedding_ids=node_ids,
            mmr_threshold=mmr_threshold,
        )

        node_dict = dict(list(zip(node_ids, nodes)))
        selected_nodes = [node_dict[id] for id in mmr_ids if id in node_dict]

        nodes = []
        # Parse the results
        for hit in selected_nodes:
            if not self.text_key:
                node = metadata_dict_to_node(
                    {
                        "_node_content": hit["entity"].get("_node_content", None),
                        "_node_type": hit["entity"].get("_node_type", None),
                    }
                )
            else:
                try:
                    text = hit["entity"].get(self.text_key)
                except Exception:
                    raise ValueError(
                        "The passed in text_key value does not exist "
                        "in the retrieved entity."
                    )

                metadata = {
                    key: hit["entity"].get(key) for key in self.output_fields
                }
                node = TextNode(text=text, metadata=metadata)

            nodes.append(node)

        similarities = mmr_similarities  # Passing the MMR similarities instead of the original similarities
        ids = mmr_ids

        logger.debug(
            f"Successfully performed MMR on embeddings in collection: {self.collection_name}"
        )

    else:
        # Perform hybrid search
        sparse_emb = self.sparse_embedding_function.encode_queries(
            [query.query_str]
        )[0]
        sparse_search_params = {"metric_type": "IP"}

        sparse_req = AnnSearchRequest(
            data=[sparse_emb],
            anns_field=self.sparse_embedding_field,
            param=sparse_search_params,
            limit=query.similarity_top_k,
            expr=string_expr,  # Apply metadata filters to sparse search
        )

        dense_search_params = {
            "metric_type": self.similarity_metric,
            "params": self.search_config,
        }
        dense_emb = query.query_embedding
        dense_req = AnnSearchRequest(
            data=[dense_emb],
            anns_field=self.embedding_field,
            param=dense_search_params,
            limit=query.similarity_top_k,
            expr=string_expr,  # Apply metadata filters to dense search
        )
        ranker = None

        if WeightedRanker is None or RRFRanker is None:
            logger.error(
                "Hybrid retrieval is only supported in Milvus 2.4.0 or later."
            )
            raise ValueError(
                "Hybrid retrieval is only supported in Milvus 2.4.0 or later."
            )
        if self.hybrid_ranker == "WeightedRanker":
            if self.hybrid_ranker_params == {}:
                self.hybrid_ranker_params = {"weights": [1.0, 1.0]}
            ranker = WeightedRanker(*self.hybrid_ranker_params["weights"])
        elif self.hybrid_ranker == "RRFRanker":
            if self.hybrid_ranker_params == {}:
                self.hybrid_ranker_params = {"k": 60}
            ranker = RRFRanker(self.hybrid_ranker_params["k"])
        else:
            raise ValueError(f"Unsupported ranker: {self.hybrid_ranker}")

        res = self._collection.hybrid_search(
            [dense_req, sparse_req],
            rerank=ranker,
            limit=query.similarity_top_k,
            output_fields=output_fields,
        )

        logger.debug(
            f"Successfully searched embedding in collection: {self.collection_name}"
            f" Num Results: {len(res[0])}"
        )

        nodes = []
        similarities = []
        ids = []
        # Parse the results
        for hit in res[0]:
            if not self.text_key:
                node = metadata_dict_to_node(
                    {
                        "_node_content": hit.entity.get("_node_content"),
                        "_node_type": hit.entity.get("_node_type"),
                    }
                )
            else:
                try:
                    text = hit.entity.get(self.text_key)
                except Exception:
                    raise ValueError(
                        "The passed in text_key value does not exist "
                        "in the retrieved entity."
                    )

                metadata = {key: hit.entity.get(key) for key in self.output_fields}
                node = TextNode(text=text, metadata=metadata)

            nodes.append(node)
            similarities.append(hit.distance)
            ids.append(hit.id)

    return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)