AlloyDBEmbeddingRetriever

Retrieve documents from an AlloyDBDocumentStore 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 using embeddings stored in AlloyDB.
• Supports cosine similarity, inner product, and L2 distance similarity functions.
• Supports exact nearest neighbor (ENN) and HNSW approximate nearest neighbor (ANN) search strategies defined on the document store.
• Configurable filter policy to merge or replace filters at query time.

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 AlloyDBDocumentStore in your pipeline.
2. Drag the AlloyDBEmbeddingRetriever 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

AlloyDBEmbeddingRetriever 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

  AlloyDBEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.alloydb.embedding_retriever.AlloyDBEmbeddingRetriever
    init_parameters:
      document_store: AlloyDBDocumentStore
      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.alloydb.document_store.AlloyDBDocumentStore
    init_parameters:
      instance_uri:
        type: env_var
        env_vars:
        - ALLOYDB_INSTANCE_URI
        strict: true
      user:
        type: env_var
        env_vars:
        - ALLOYDB_USER
        strict: true
      password:
        type: env_var
        env_vars:
        - ALLOYDB_PASSWORD
        strict: false
      db: my_database
      embedding_dimension: 384

  retriever:
    type: haystack_integrations.components.retrievers.alloydb.embedding_retriever.AlloyDBEmbeddingRetriever
    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.
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.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	AlloyDBDocumentStore		The AlloyDB 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.
vector_function	Optional[str]	None	Override the document store's vector similarity function. One of cosine_similarity, inner_product, or l2_distance.
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.
vector_function	Optional[str]	None	Override the vector similarity function for this query.

Related Information

• AlloyDBDocumentStore
• AlloyDBKeywordRetriever
• Pipeline Components