Deeplake
DeepLakeVectorStore #
Bases: BasePydanticVectorStore
The DeepLake Vector Store.
In this vector store we store the text, its embedding and
a few pieces of its metadata in a deeplake dataset. This implementation
allows the use of an already existing deeplake dataset if it is one that was created
this vector store. It also supports creating a new one if the dataset doesn't
exist or if overwrite
is set to True.
Examples:
pip install llama-index-vector-stores-deeplake
from llama_index.vector_stores.deeplake import DeepLakeVectorStore
# Create an instance of DeepLakeVectorStore
vector_store = DeepLakeVectorStore(dataset_path=dataset_path, overwrite=True)
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py
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client
property
#
client: Any
Get client.
Returns:
Name | Type | Description |
---|---|---|
Any |
Any
|
DeepLake vectorstore dataset. |
get_nodes #
get_nodes(node_ids: Optional[List[str]] = None, filters: Optional[MetadataFilters] = None) -> List[BaseNode]
Get nodes from vector store.
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py
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clear #
clear() -> None
Clear the vector store.
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py
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add #
add(nodes: List[BaseNode], **add_kwargs: Any) -> List[str]
Add the embeddings and their nodes into DeepLake.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
nodes |
List[BaseNode]
|
List of nodes with embeddings to insert. |
required |
Returns:
Type | Description |
---|---|
List[str]
|
List[str]: List of ids inserted. |
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/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 |
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/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 |
VectorStoreQuery
|
VectorStoreQuery class input, it has the following attributes: 1. query_embedding (List[float]): query embedding 2. similarity_top_k (int): top k most similar nodes |
required |
deep_memory |
bool
|
Whether to use deep memory for query execution. |
required |
Returns:
Type | Description |
---|---|
VectorStoreQueryResult
|
VectorStoreQueryResult |
Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py
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