ApproxRetrievalStrategy#
- class langchain_community.vectorstores.elasticsearch.ApproxRetrievalStrategy(
- query_model_id: str | None = None,
- hybrid: bool | None = False,
- rrf: dict | bool | None = True,
Deprecated since version 0.0.27: Use
Use class in langchain-elasticsearch package
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.
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,
- 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,
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,
- similarity: DistanceStrategy | None,
Create the mapping for the Elasticsearch index.
- Parameters:
dims_length (int | None)
vector_query_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,
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