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 "http://address:port".	'http://localhost:19530'
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 and L2.	'IP'
consistency_level	str	Which consistency level to use for a newly created collection. Defaults to "Strong".	'Strong'
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

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
            "http://address:port".
        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 and L2.
        consistency_level (str, optional): Which consistency level to use for a newly
            created collection. Defaults to "Strong".
        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.

    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 = "http://localhost:19530"
    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 = "Strong"
    overwrite: bool = False
    text_key: Optional[str]
    index_config: Optional[dict]
    search_config: Optional[dict]

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

    def __init__(
        self,
        uri: str = "http://localhost:19530",
        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 = "Strong",
        overwrite: bool = False,
        text_key: Optional[str] = None,
        index_config: Optional[dict] = None,
        search_config: Optional[dict] = 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,
            index_config=index_config if index_config else {},
            search_config=search_config if search_config else {},
        )

        # Select the similarity metric
        similarity_metrics_map = {"ip": "IP", "l2": "L2", "euclidean": "L2"}
        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.")
            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=similarity_metric,
                max_length=65_535,
                consistency_level=consistency_level,
            )

        self._collection = Collection(collection_name, using=self._milvusclient._using)
        self._create_index_if_required()

        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 = []

        # 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

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

        # Insert the data into milvus
        self._collection.insert(insert_list)
        if add_kwargs.get("force_flush", False):
            self._collection.flush()
        self._create_index_if_required()
        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 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:
            raise ValueError(f"Milvus does not support {query.mode} yet.")

        expr = []
        output_fields = ["*"]

        # Parse the filter
        if query.filters is not None:
            expr.extend(_to_milvus_filter(query.filters))

        # 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
        if query.output_fields is not None:
            output_fields = query.output_fields

        # Convert to string expression
        string_expr = ""
        if len(expr) != 0:
            string_expr = f" {query.filters.condition.value} ".join(expr)

        # Perform the 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,
        )

        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."
                    )
                node = TextNode(
                    text=text,
                )
            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, force: bool = False) -> None:
        # This helper method is introduced to allow the index to be created
        # both in the constructor and in the `add` method. The `force` flag is
        # provided to ensure that the index is created in the constructor even
        # if self.overwrite is false. In the `add` method, the index is
        # recreated only if self.overwrite is true.
        if (self._collection.has_index() and self.overwrite) or force:
            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()

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 = []

    # 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

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

    # Insert the data into milvus
    self._collection.insert(insert_list)
    if add_kwargs.get("force_flush", False):
        self._collection.flush()
    self._create_index_if_required()
    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}")

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:
        raise ValueError(f"Milvus does not support {query.mode} yet.")

    expr = []
    output_fields = ["*"]

    # Parse the filter
    if query.filters is not None:
        expr.extend(_to_milvus_filter(query.filters))

    # 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
    if query.output_fields is not None:
        output_fields = query.output_fields

    # Convert to string expression
    string_expr = ""
    if len(expr) != 0:
        string_expr = f" {query.filters.condition.value} ".join(expr)

    # Perform the 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,
    )

    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."
                )
            node = TextNode(
                text=text,
            )
        nodes.append(node)
        similarities.append(hit["distance"])
        ids.append(hit["id"])

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