langchain.vectorstores.elasticsearch.ApproxRetrievalStrategy¶
class langchain.vectorstores.elasticsearch.ApproxRetrievalStrategy(query_model_id: Optional[str] = None, hybrid: Optional[bool] = False)[source]¶
Approximate retrieval strategy using the HNSW algorithm.
Methods
__init__([query_model_id, hybrid])
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.
__init__(query_model_id: Optional[str] = None, hybrid: Optional[bool] = False)[source]¶
before_index_setup(client: Elasticsearch, text_field: str, vector_query_field: str) → None¶
Executes before the index is created. Used for setting up
any required Elasticsearch resources like a pipeline.
Parameters
client – The Elasticsearch client.
text_field – The field containing the text data in the index.
vector_query_field – The field containing the vector
representations in the index.
index(dims_length: Optional[int], vector_query_field: str, similarity: Optional[DistanceStrategy]) → Dict[source]¶
Create the mapping for the Elasticsearch index.
query(query_vector: Optional[List[float]], query: Optional[str], k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when a search is performed on the store.
Parameters
query_vector – The query vector,