ApproxRetrievalStrategy#

class langchain_elasticsearch.vectorstores.ApproxRetrievalStrategy(query_model_id: str | None = None, hybrid: bool | None = False, rrf: dict | bool | None = True)[source]#

Deprecated since version 0.2.0: Use DenseVectorStrategy instead.

Approximate retrieval strategy using the HNSW algorithm.

Methods

__init__([query_model_id, hybrid, rrf])

before_index_setup(client, text_field, ...)

Executes before the index is created.

index(dims_length, vector_query_field, ...)

Create the mapping for the Elasticsearch index.

query(query_vector, query, k, fetch_k, ...)

Executes when a search is performed on the store.

require_inference()

Returns whether or not the strategy requires inference to be performed on the text before it is added to the index.
Parameters:

  • query_model_id (str | None) –

  • hybrid (bool | None) –

  • rrf (dict | bool | None) –

__init__(query_model_id: str | None = None, hybrid: bool | None = False, rrf: dict | bool | None = True)[source]#
Parameters:

  • query_model_id (str | None) –

  • hybrid (bool | None) –

  • rrf (dict | bool | None) –

before_index_setup(client: Elasticsearch, text_field: str, vector_query_field: str) → None[source]#

Executes before the index is created. Used for setting up any required Elasticsearch resources like a pipeline.

Parameters:

  • client (Elasticsearch) – The Elasticsearch client.

  • text_field (str) – The field containing the text data in the index.

  • vector_query_field (str) – The field containing the vector representations in the index.

Return type:

None

index(dims_length: int | None, vector_query_field: str, text_field: str, similarity: DistanceStrategy | None) → Dict[source]#

Create the mapping for the Elasticsearch index.

Parameters:

  • dims_length (int | None) –

  • vector_query_field (str) –

  • text_field (str) –

  • similarity (DistanceStrategy | None) –

Return type:

Dict

query(query_vector: List[float] | None, query: str | None, k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: DistanceStrategy | None) → Dict[source]#

Executes when a search is performed on the store.

Parameters:

  • query_vector (List[float] | None) – The query vector, or None if not using vector-based query.

  • query (str | None) – The text query, or None if not using text-based query.

  • k (int) – The total number of results to retrieve.

  • fetch_k (int) – The number of results to fetch initially.

  • vector_query_field (str) – The field containing the vector representations in the index.

  • text_field (str) – The field containing the text data in the index.

  • filter (List[dict]) – List of filter clauses to apply to the query.

  • similarity (DistanceStrategy | None) – The similarity strategy to use, or None if not using one.

Returns:

The Elasticsearch query body.

Return type:

Dict

require_inference() → bool#

Returns whether or not the strategy requires inference to be performed on the text before it is added to the index.

Returns:

Whether or not the strategy requires inference to be performed on the text before it is added to the index.

Return type:

bool

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