ArangoEmbeddingRetriever

Retrieve documents from an ArangoDocumentStore using vector similarity search. Use this component in query pipelines to find semantically similar documents based on dense embeddings.

Embedding Models in Query Pipelines and Indexes

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

This means the embedders for your indexing and query 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 against documents stored in ArangoDB.
• Supports cosine, dot product, and L2 similarity functions defined on the document store.
• Applies optional metadata filters at retrieval time.
• Requires ArangoDB 3.12 or later for vector search.

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. First, configure an ArangoDocumentStore in your pipeline.
2. Drag the ArangoEmbeddingRetriever component onto the canvas from the Component Library.
3. Connect an embedder component to provide query_embedding as input.
4. Connect the retriever output to downstream components such as PromptBuilder.

Connections

ArangoEmbeddingRetriever 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

  ArangoEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.arangodb.embedding_retriever.ArangoEmbeddingRetriever
    init_parameters:
      document_store: ArangoDocumentStore
      top_k: 5

Using the Component in a Pipeline

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

  document_store:
    type: haystack_integrations.document_stores.arangodb.document_store.ArangoDocumentStore
    init_parameters:
      host: http://localhost:8529
      database: haystack
      username:
        type: env_var
        env_vars:
        - ARANGO_USERNAME
        strict: false
      password:
        type: env_var
        env_vars:
        - ARANGO_PASSWORD
        strict: false
      embedding_dimension: 384

  retriever:
    type: haystack_integrations.components.retrievers.arangodb.embedding_retriever.ArangoEmbeddingRetriever
    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.
top_k	Optional[int]	The maximum number of documents to retrieve. Overrides the init-time value.
filters	Optional[Dict[str, Any]]	Filters to apply when retrieving documents. Overrides the init-time value.

Outputs

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

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	ArangoDocumentStore		The ArangoDB document store to retrieve documents from.
top_k	int	10	The maximum number of documents to retrieve.
filters	Optional[Dict[str, Any]]	None	Default filters to apply when retrieving documents.

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

Related Information

• ArangoDocumentStore
• Pipeline Components