OpenSearchBM25Retriever

Fetches documents from OpenSearchDocumentStore using the keyword-based BM25 algorithm.

Basic Information

• Type: haystack_integrations.opensearch.src.haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever

Inputs

Parameter	Type	Default	Description
query	str		The query string.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved documents. The way runtime filters are applied depends on the filter_policy specified at Retriever's initialization.
all_terms_must_match	Optional[bool]	None	If True, all terms in the query string must be present in the retrieved documents.
top_k	Optional[int]	None	Maximum number of documents to return.
fuzziness	Optional[Union[int, str]]	None	Fuzziness parameter for full-text queries to apply approximate string matching. For more information, see OpenSearch fuzzy query.
scale_score	Optional[bool]	None	If True, scales the score of retrieved documents to a range between 0 and 1. This is useful when comparing documents across different indexes.
custom_query	Optional[Dict[str, Any]]	None	A custom OpenSearch query. It must include a $query and may optionally include a $filters placeholder. An example custom_query: python { "query": { "bool": { "should": [{"multi_match": { "query": "$query", // mandatory query placeholder "type": "most_fields", "fields": ["content", "title"]}}], "filter": "$filters" // optional filter placeholder } } } For this custom_query, a sample run() could be: python retriever.run( query="Why did the revenue increase?", filters={ "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, {"field": "meta.quarters", "operator": "in", "value": ["Q1", "Q2"]}, ], }, )

Outputs

Parameter	Type	Default	Description
documents	List[Document]		A dictionary containing the retrieved documents with the following structure: - documents: List of retrieved Documents.

Overview

Work in Progress

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

Fetches documents from OpenSearchDocumentStore using the keyword-based BM25 algorithm.

BM25 computes a weighted word overlap between the query string and a document to determine its similarity.

Usage Example

components:
  OpenSearchBM25Retriever:
    type: opensearch.src.haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    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 to narrow down the search for documents in the Document Store.
fuzziness	Union[int, str]	AUTO	Determines how approximate string matching is applied in full-text queries. This parameter sets the number of character edits (insertions, deletions, or substitutions) required to transform one word into another. For example, the "fuzziness" between the words "wined" and "wind" is 1 because only one edit is needed to match them. Use "AUTO" (the default) for automatic adjustment based on term length, which is optimal for most scenarios. For detailed guidance, refer to the OpenSearch fuzzy query documentation.
top_k	int	10	Maximum number of documents to return.
scale_score	bool	False	If True, scales the score of retrieved documents to a range between 0 and 1. This is useful when comparing documents across different indexes.
all_terms_must_match	bool	False	If True, all terms in the query string must be present in the retrieved documents. This is useful when searching for short text where even one term can make a difference.
filter_policy	Union[str, FilterPolicy]	FilterPolicy.REPLACE	Policy to determine how filters are applied. Possible options: - replace: Runtime filters replace initialization filters. Use this policy to change the filtering scope for specific queries. - merge: Runtime filters are merged with initialization filters.
custom_query	Optional[Dict[str, Any]]	None	The query containing a mandatory $query and an optional $filters placeholder. An example custom_query: python { "query": { "bool": { "should": [{"multi_match": { "query": "$query", // mandatory query placeholder "type": "most_fields", "fields": ["content", "title"]}}], "filter": "$filters" // optional filter placeholder } } } An example run() method for this custom_query: python retriever.run( query="Why did the revenue increase?", filters={ "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, {"field": "meta.quarters", "operator": "in", "value": ["Q1", "Q2"]}, ], }, )
raise_on_failure	bool	True	Whether to raise an exception if the API call fails. Otherwise log a warning and return an empty list.

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	str		The query string.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved documents. The way runtime filters are applied depends on the filter_policy specified at Retriever's initialization.
all_terms_must_match	Optional[bool]	None	If True, all terms in the query string must be present in the retrieved documents.
top_k	Optional[int]	None	Maximum number of documents to return.
fuzziness	Optional[Union[int, str]]	None	Fuzziness parameter for full-text queries to apply approximate string matching. For more information, see OpenSearch fuzzy query.
scale_score	Optional[bool]	None	If True, scales the score of retrieved documents to a range between 0 and 1. This is useful when comparing documents across different indexes.
custom_query	Optional[Dict[str, Any]]	None	A custom OpenSearch query. It must include a $query and may optionally include a $filters placeholder. An example custom_query: python { "query": { "bool": { "should": [{"multi_match": { "query": "$query", // mandatory query placeholder "type": "most_fields", "fields": ["content", "title"]}}], "filter": "$filters" // optional filter placeholder } } } For this custom_query, a sample run() could be: python retriever.run( query="Why did the revenue increase?", filters={ "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, {"field": "meta.quarters", "operator": "in", "value": ["Q1", "Q2"]}, ], }, )