InMemoryEmbeddingRetriever

Retrieves documents that are most semantically similar to the query.

Basic Information

• Type: haystack_integrations.retrievers.in_memory.embedding_retriever.InMemoryEmbeddingRetriever

Inputs

Parameter	Type	Default	Description
query_embedding	List[float]		Embedding of the query.
filters	Optional[Dict[str, Any]]	None	A dictionary with filters to narrow down the search space when retrieving documents.
top_k	Optional[int]	None	The maximum number of documents to return.
scale_score	Optional[bool]	None	When True, scales the score of retrieved documents to a range of 0 to 1, where 1 means extremely relevant. When False, uses raw similarity scores.
return_embedding	Optional[bool]	None	When True, returns the embedding of the retrieved documents. When False, returns just the documents, without their embeddings.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		The retrieved documents.

Overview

Work in Progress

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

Retrieves documents that are most semantically similar to the query.

Use this retriever with the InMemoryDocumentStore.

When using this retriever, make sure it has query and document embeddings available. In indexing pipelines, use a DocumentEmbedder to embed documents. In query pipelines, use a TextEmbedder to embed queries and send them to the retriever.

Usage Example

components:
  InMemoryEmbeddingRetriever:
    type: components.retrievers.in_memory.embedding_retriever.InMemoryEmbeddingRetriever
    init_parameters:

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	InMemoryDocumentStore		An instance of InMemoryDocumentStore where the retriever should search for relevant documents.
filters	Optional[Dict[str, Any]]	None	A dictionary with filters to narrow down the retriever's search space in the document store.
top_k	int	10	The maximum number of documents to retrieve.
scale_score	bool	False	When True, scales the score of retrieved documents to a range of 0 to 1, where 1 means extremely relevant. When False, uses raw similarity scores.
return_embedding	bool	False	When True, returns the embedding of the retrieved documents. When False, returns just the documents, without their embeddings.
filter_policy	FilterPolicy	FilterPolicy.REPLACE	The filter policy to apply during retrieval. Filter policy determines how filters are applied when retrieving documents. You can choose: - REPLACE (default): Overrides the initialization filters with the filters specified at runtime. Use this policy to dynamically change filtering for specific queries. - MERGE: Combines runtime filters with initialization filters to narrow down the search.

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	A dictionary with filters to narrow down the search space when retrieving documents.
top_k	Optional[int]	None	The maximum number of documents to return.
scale_score	Optional[bool]	None	When True, scales the score of retrieved documents to a range of 0 to 1, where 1 means extremely relevant. When False, uses raw similarity scores.
return_embedding	Optional[bool]	None	When True, returns the embedding of the retrieved documents. When False, returns just the documents, without their embeddings.