FalkorDBEmbeddingRetriever

Retrieve documents from a FalkorDBDocumentStore using vector similarity search. Use this component in query pipelines to find semantically similar documents stored in FalkorDB, a high-performance graph database optimized for GraphRAG workloads.

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 native vector search using FalkorDB's db.idx.vector.queryNodes OpenCypher extension.
• Supports cosine and Euclidean similarity functions.
• Scales raw similarity scores to the unit interval [0, 1] for consistent ranking across pipelines.
• Supports Haystack metadata filters applied as WHERE predicates on the vector search result set.
• No APOC library required — uses only FalkorDB-native syntax.

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. Start a FalkorDB server (the default port is 6379). Optionally set credentials using the password secret.
2. Configure a FalkorDBDocumentStore in your pipeline, specifying embedding_dim to match your embedding model and similarity for the distance function.
3. Drag the FalkorDBEmbeddingRetriever 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

FalkorDBEmbeddingRetriever 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

  FalkorDBEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.falkordb.embedding_retriever.FalkorDBEmbeddingRetriever
    init_parameters:
      document_store: FalkorDBDocumentStore
      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.falkordb.document_store.FalkorDBDocumentStore
    init_parameters:
      host: localhost
      port: 6379
      graph_name: haystack
      embedding_dim: 384
      similarity: cosine

  retriever:
    type: haystack_integrations.components.retrievers.falkordb.embedding_retriever.FalkorDBEmbeddingRetriever
    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 applied as a WHERE predicate on the vector search results.
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.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	FalkorDBDocumentStore		The FalkorDB document store to retrieve documents from.
filters	Optional[Dict[str, Any]]	None	Default Haystack 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

• FalkorDBCypherRetriever
• Pipeline Components