OpenSearchEmbeddingRetriever

Retrieves documents from the OpenSearchDocumentStore using a vector similarity metric.

Basic Information

• Type: haystack_integrations.opensearch.src.haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever

Inputs

Parameter	Type	Default	Description
query_embedding	List[float]		Embedding of the query.
filters	Optional[Dict[str, Any]]	None	Filters applied when fetching documents from the Document Store. Filters are applied during the approximate kNN search to ensure the Retriever returns top_k matching documents. The way runtime filters are applied depends on the filter_policy selected when initializing the Retriever.
top_k	Optional[int]	None	Maximum number of documents to return.
custom_query	Optional[Dict[str, Any]]	None	A custom OpenSearch query containing a mandatory $query_embedding and an optional $filters placeholder. An example custom_query: python { "query": { "bool": { "must": [ { "knn": { "embedding": { "vector": "$query_embedding", // mandatory query placeholder "k": 10000, } } } ], "filter": "$filters" // optional filter placeholder } } } For this custom_query, an example run() could be: python retriever.run( query_embedding=embedding, filters={ "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, {"field": "meta.quarters", "operator": "in", "value": ["Q1", "Q2"]}, ], }, )
efficient_filtering	Optional[bool]	None	If True, the filter will be applied during the approximate kNN search. This is only supported for knn engines "faiss" and "lucene" and does not work with the default "nmslib".

Outputs

Parameter	Type	Default	Description
documents	List[Document]		Dictionary with key "documents" containing the retrieved Documents. - documents: List of Document similar to query_embedding.

Overview

Work in Progress

Bear with us while we're working on adding pipeline examples and most common components connections.

Retrieves documents from the OpenSearchDocumentStore using a vector similarity metric.

Must be connected to the OpenSearchDocumentStore to run.

Usage Example

components:
  OpenSearchEmbeddingRetriever:
    type: opensearch.src.haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	OpenSearchDocumentStore		An instance of OpenSearchDocumentStore to use with the Retriever.
filters	Optional[Dict[str, Any]]	None	Filters applied when fetching documents from the Document Store. Filters are applied during the approximate kNN search to ensure the Retriever returns top_k matching documents.
top_k	int	10	Maximum number of documents to return.
filter_policy	Union[str, FilterPolicy]	FilterPolicy.REPLACE	Policy to determine how filters are applied. Possible options: - merge: Runtime filters are merged with initialization filters. - replace: Runtime filters replace initialization filters. Use this policy to change the filtering scope.
custom_query	Optional[Dict[str, Any]]	None	The custom OpenSearch query containing a mandatory $query_embedding and an optional $filters placeholder. An example custom_query: python { "query": { "bool": { "must": [ { "knn": { "embedding": { "vector": "$query_embedding", // mandatory query placeholder "k": 10000, } } } ], "filter": "$filters" // optional filter placeholder } } } For this custom_query, an example run() could be: python retriever.run( query_embedding=embedding, filters={ "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, {"field": "meta.quarters", "operator": "in", "value": ["Q1", "Q2"]}, ], }, )
raise_on_failure	bool	True	If True, raises an exception if the API call fails. If False, logs a warning and returns an empty list.
efficient_filtering	bool	False	If True, the filter will be applied during the approximate kNN search. This is only supported for knn engines "faiss" and "lucene" and does not work with the default "nmslib".

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
query_embedding	List[float]		Embedding of the query.
filters	Optional[Dict[str, Any]]	None	Filters applied when fetching documents from the Document Store. Filters are applied during the approximate kNN search to ensure the Retriever returns top_k matching documents. The way runtime filters are applied depends on the filter_policy selected when initializing the Retriever.
top_k	Optional[int]	None	Maximum number of documents to return.
custom_query	Optional[Dict[str, Any]]	None	A custom OpenSearch query containing a mandatory $query_embedding and an optional $filters placeholder. An example custom_query: python { "query": { "bool": { "must": [ { "knn": { "embedding": { "vector": "$query_embedding", // mandatory query placeholder "k": 10000, } } } ], "filter": "$filters" // optional filter placeholder } } } For this custom_query, an example run() could be: python retriever.run( query_embedding=embedding, filters={ "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, {"field": "meta.quarters", "operator": "in", "value": ["Q1", "Q2"]}, ], }, )
efficient_filtering	Optional[bool]	None	If True, the filter will be applied during the approximate kNN search. This is only supported for knn engines "faiss" and "lucene" and does not work with the default "nmslib".