WeaviateEmbeddingRetriever

Retrieves documents from Weaviate using vector similarity search on query embeddings.

Key Features

• Vector similarity search from Weaviate's vector database.
• Distance and certainty threshold support for controlling result quality.
• Filter support to narrow down the search space.
• Configurable number of results with top_k.

Configuration

1. Drag the WeaviateEmbeddingRetriever component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Configure the document_store with your Weaviate instance URL.
4. Go to the Advanced tab to configure top_k, filters, distance, and certainty.

Connections

WeaviateEmbeddingRetriever accepts a query embedding as input. It outputs a list of retrieved documents.

Connect a text embedder to its query_embedding input. Connect its documents output to a PromptBuilder, Ranker, or answer builder.

Usage Example

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

Parameters

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]

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.