ValkeyEmbeddingRetriever

Retrieve documents from a ValkeyDocumentStore using vector similarity search. Use this component in query pipelines to find semantically similar documents stored in Valkey, an open-source Redis-compatible key-value store with vector search capabilities.

Embedding Models in Pipelines and Indexes

The embedding model you use to embed documents in your index must be the same as the embedding model you use to embed the query in your pipeline.

This means the embedders for your indexes and pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

Key Features

• Performs vector similarity search using Valkey Search's HNSW algorithm.
• Supports L2, cosine, and inner product (ip) distance metrics.
• Supports metadata filtering on tag (string) and numeric fields.
• Supports both standalone and cluster mode connections.
• Supports both synchronous and asynchronous execution.

Configuration

Add Workspace-Level Integration

1. Click your profile icon and choose Settings.
2. Go to Workspace>Integrations.
3. Find the provider you want to connect and click Connect next to them.
4. Enter the API key and any other required details.
5. Click Connect. You can use this integration in pipelines and indexes in the current workspace.

Add Organization-Level Integration

1. Click your profile icon and choose Settings.
2. Go to Organization>Integrations.
3. Find the provider you want to connect and click Connect next to them.
4. Enter the API key and any other required details.
5. Click Connect. You can use this integration in pipelines and indexes in all workspaces in the current organization.

1. Optionally set VALKEY_USERNAME and VALKEY_PASSWORD environment variables if your Valkey instance requires authentication.
2. Configure a ValkeyDocumentStore in your pipeline, specifying index_name, embedding_dim, and any metadata_fields you want to filter on.
3. Drag the ValkeyEmbeddingRetriever component onto the canvas from the Component Library.
4. Connect an embedder component to provide query_embedding as input.
5. Connect the retriever output to downstream components such as PromptBuilder.

Connections

ValkeyEmbeddingRetriever receives a query_embedding (list of floats) from a text embedder such as SentenceTransformersTextEmbedder. It outputs a list of Document objects you can connect to PromptBuilder or other downstream components.

Source Code

To check this component's source code, open embedding_retriever.py in the Haystack Core Integrations repository.

Usage Examples

Basic Configuration

  ValkeyEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.valkey.embedding_retriever.ValkeyEmbeddingRetriever
    init_parameters:
      document_store: ValkeyDocumentStore
      top_k: 5

Using the Component in a Pipeline

# haystack-pipeline
components:
  text_embedder:
    type: haystack_integrations.components.embedders.sentence_transformers.SentenceTransformersTextEmbedder
    init_parameters:
      model: sentence-transformers/all-MiniLM-L6-v2

  document_store:
    type: haystack_integrations.document_stores.valkey.document_store.ValkeyDocumentStore
    init_parameters:
      nodes_list:
        - - localhost
          - 6379
      index_name: haystack_documents
      embedding_dim: 384
      distance_metric: cosine

  retriever:
    type: haystack_integrations.components.retrievers.valkey.embedding_retriever.ValkeyEmbeddingRetriever
    init_parameters:
      document_store: document_store
      top_k: 5

connections:
- sender: text_embedder.embedding
  receiver: retriever.query_embedding

inputs:
  query:
  - text_embedder.text

outputs:
  documents: retriever.documents

Parameters

Inputs

Parameter	Type	Description
query_embedding	List[float]	The query embedding vector to search for similar documents.
filters	Optional[Dict[str, Any]]	Filters to apply when retrieving documents. Only fields declared in metadata_fields on the document store are filterable.
top_k	Optional[int]	The maximum number of documents to retrieve. Overrides the init-time value.

Outputs

Parameter	Type	Description
documents	List[Document]	A list of the most similar documents from the document store, ordered by similarity score.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	ValkeyDocumentStore		The Valkey document store to retrieve documents from.
filters	Optional[Dict[str, Any]]	None	Default filters to apply when retrieving documents.
top_k	int	10	The maximum number of documents to retrieve.
filter_policy	FilterPolicy	FilterPolicy.REPLACE	How to handle filters passed at query time. REPLACE replaces init-time filters; MERGE combines them.

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]		The embedding vector to search with.
filters	Optional[Dict[str, Any]]	None	Runtime filters to apply.
top_k	Optional[int]	None	Maximum number of documents to retrieve. Overrides the init-time value.

Related Information

• Pipeline Components