ElasticsearchEmbeddingsCache
features and configurations head to the API reference.
ElasticsearchEmbeddingsCache
is a ByteStore
implementation that uses your Elasticsearch instance for efficient storage and retrieval of embeddings.
Class | Package | Local | JS support | Package downloads | Package latest |
---|---|---|---|---|---|
ElasticsearchEmbeddingsCache | langchain-elasticsearch | ✅ | ❌ |
ElasticsearchEmbeddingsCache
byte store, you’ll need an Elasticsearch cluster. You can set one up locally or create an Elastic account.
ElasticsearchEmbeddingsCache
integration lives in the langchain-elasticsearch
package:
mset
method:
mdelete
method:
ByteStores
, you can use an ElasticsearchEmbeddingsCache
instance for persistent caching in document ingestion for RAG.
However, cached vectors won’t be searchable by default. The developer can customize the building of the Elasticsearch document in order to add indexed vector field.
This can be done by subclassing and overriding methods:
ElasticsearchEmbeddingsCache
features and configurations, head to the API reference: python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html