WeaviateEmbeddingRetriever

A retriever that uses Weaviate's vector search to find similar documents based on the embeddings of the query.

Basic Information

• Type: haystack_integrations.components.retrievers.weaviate.embedding_retriever.WeaviateEmbeddingRetriever

Inputs

Parameter	Type	Default	Description
query_embedding	List[float]		Embedding of the query.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved Documents. The way runtime filters are applied depends on the filter_policy chosen at retriever initialization. See init method docstring for more details.
top_k	Optional[int]	None	The maximum number of documents to return.
distance	Optional[float]	None	The maximum allowed distance between Documents' embeddings.
certainty	Optional[float]	None	Normalized distance between the result item and the search vector.

Outputs

Parameter	Type	Default	Description
documents	List[Document]

Overview

Work in Progress

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

A retriever that uses Weaviate's vector search to find similar documents based on the embeddings of the query.

Usage Example

components:
  WeaviateEmbeddingRetriever:
    type: weaviate.src.haystack_integrations.components.retrievers.weaviate.embedding_retriever.WeaviateEmbeddingRetriever
    init_parameters:

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	WeaviateDocumentStore		Instance of WeaviateDocumentStore that will be used from this retriever.
filters	Optional[Dict[str, Any]]	None	Custom filters applied when running the retriever.
top_k	int	10	Maximum number of documents to return.
distance	Optional[float]	None	The maximum allowed distance between Documents' embeddings.
certainty	Optional[float]	None	Normalized distance between the result item and the search vector.
filter_policy	Union[str, FilterPolicy]	FilterPolicy.REPLACE	Policy to determine how filters are applied.

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 to the retrieved Documents. The way runtime filters are applied depends on the filter_policy chosen at retriever initialization. See init method docstring for more details.
top_k	Optional[int]	None	The maximum number of documents to return.
distance	Optional[float]	None	The maximum allowed distance between Documents' embeddings.
certainty	Optional[float]	None	Normalized distance between the result item and the search vector.