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[ElasticsearchStore] Improve migration text to ElasticsearchStore (#11158)
We noticed that as we have been moving developers to the new `ElasticsearchStore` implementation, we want to keep the ElasticVectorSearch class still available as developers transition slowly to the new store. To speed up this process, I updated the blurb giving them a better recommendation of why they should use ElasticsearchStore.
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@ -1,4 +1,3 @@
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"""[DEPRECATED] Please use ElasticsearchStore instead."""
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from __future__ import annotations
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from __future__ import annotations
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import uuid
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import uuid
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@ -51,9 +50,19 @@ def _default_script_query(query_vector: List[float], filter: Optional[dict]) ->
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}
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}
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@deprecated("0.0.265", alternative="ElasticsearchStore class.", pending=True)
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class ElasticVectorSearch(VectorStore):
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class ElasticVectorSearch(VectorStore):
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"""[DEPRECATED] `Elasticsearch` vector store.
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"""
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ElasticVectorSearch uses the brute force method of searching on vectors.
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Recommended to use ElasticsearchStore instead, which gives you the option
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to uses the approx HNSW algorithm which performs better on large datasets.
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ElasticsearchStore also supports metadata filtering, customising the
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query retriever and much more!
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You can read more on ElasticsearchStore:
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https://python.langchain.com/docs/integrations/vectorstores/elasticsearch
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To connect to an `Elasticsearch` instance that does not require
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To connect to an `Elasticsearch` instance that does not require
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login credentials, pass the Elasticsearch URL and index name along with the
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login credentials, pass the Elasticsearch URL and index name along with the
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@ -339,10 +348,17 @@ class ElasticVectorSearch(VectorStore):
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self.client.delete(index=self.index_name, id=id)
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self.client.delete(index=self.index_name, id=id)
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@deprecated("0.0.265", alternative="ElasticsearchStore class.", pending=True)
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class ElasticKnnSearch(VectorStore):
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class ElasticKnnSearch(VectorStore):
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"""[DEPRECATED] `Elasticsearch` with k-nearest neighbor search
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"""[DEPRECATED] `Elasticsearch` with k-nearest neighbor search
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(`k-NN`) vector store.
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(`k-NN`) vector store.
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Recommended to use ElasticsearchStore instead, which supports
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metadata filtering, customising the query retriever and much more!
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You can read more on ElasticsearchStore:
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https://python.langchain.com/docs/integrations/vectorstores/elasticsearch
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It creates an Elasticsearch index of text data that
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It creates an Elasticsearch index of text data that
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can be searched using k-NN search. The text data is transformed into
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can be searched using k-NN search. The text data is transformed into
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vector embeddings using a provided embedding model, and these embeddings
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vector embeddings using a provided embedding model, and these embeddings
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