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 |
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 |
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
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|
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
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|
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
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|
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
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|
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
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|
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
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|
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
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