@ -1,4 +1,3 @@
""" [DEPRECATED] Please use ElasticsearchStore instead. """
from __future__ import annotations
import uuid
@ -51,9 +50,19 @@ def _default_script_query(query_vector: List[float], filter: Optional[dict]) ->
}
@deprecated ( " 0.0.265 " , alternative = " ElasticsearchStore class. " , pending = True )
class ElasticVectorSearch ( VectorStore ) :
""" [DEPRECATED] `Elasticsearch` vector store.
"""
ElasticVectorSearch uses the brute force method of searching on vectors .
Recommended to use ElasticsearchStore instead , which gives you the option
to uses the approx HNSW algorithm which performs better on large datasets .
ElasticsearchStore also supports metadata filtering , customising the
query retriever and much more !
You can read more on ElasticsearchStore :
https : / / python . langchain . com / docs / integrations / vectorstores / elasticsearch
To connect to an ` Elasticsearch ` instance that does not require
login credentials , pass the Elasticsearch URL and index name along with the
@ -339,10 +348,17 @@ class ElasticVectorSearch(VectorStore):
self . client . delete ( index = self . index_name , id = id )
@deprecated ( " 0.0.265 " , alternative = " ElasticsearchStore class. " , pending = True )
class ElasticKnnSearch ( VectorStore ) :
""" [DEPRECATED] `Elasticsearch` with k-nearest neighbor search
( ` k - NN ` ) vector store .
Recommended to use ElasticsearchStore instead , which supports
metadata filtering , customising the query retriever and much more !
You can read more on ElasticsearchStore :
https : / / python . langchain . com / docs / integrations / vectorstores / elasticsearch
It creates an Elasticsearch index of text data that
can be searched using k - NN search . The text data is transformed into
vector embeddings using a provided embedding model , and these embeddings