Elasticsearch

Provides functionality to interact with Elasticsearch for storing and querying document embeddings. It facilitates adding documents, performing similarity searches, and deleting documents from an Elasticsearch index.

API Reference
pip install elasticsearch

from spyder_index.vector_stores import ElasticsearchVectorStore
ElasticsearchVectorStore(index_name, es_hostname, es_user, es_password, dims_length, embedding, batch_size, ssl, distance_strategy, text_field, vector_field)

Initialize a ElasticsearchVectorStore.

  • index_name: The name of the Elasticsearch index.

  • es_hostname: The hostname of the Elasticsearch instance.

  • es_user: The username for authentication.

  • es_password: The password for authentication.

  • dims_length: The length of the embedding dimensions.

  • embedding: An instance of embeddings.

  • batch_size: The batch size for bulk operations. Defaults to 200.

  • ssl: Whether to use SSL. Defaults to False.

  • distance_strategy: The distance strategy for similarity search. Defaults to cosine.

  • text_field: The name of the field containing text. Defaults to text.

  • vector_field: The name of the field containing vector embeddings. Defaults to embedding.

add_documents(documents, create_index_if_not_exists)

Adds documents to the Elasticsearch index.

  • documents: A list of Document objects to add to the index.

  • create_index_if_not_exists: Whether to create the index if it doesn’t exist. Defaults to True.

similarity_search(query, top_k)

Performs a similarity search based on the documents most similar to the query.

  • query: The query text.

  • top_k`: The number of top results to return. Defaults to 4.

delete(ids)

Deletes documents from the Elasticsearch index.

  • ids: A list of document IDs to delete. Defaults to None.