MariaDBEmbeddingRetriever

Retrieve documents from a MariaDBDocumentStore using vector similarity search. Use this component in query pipelines to find semantically similar documents using MariaDB's native VECTOR support and MHNSW indexing.

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 approximate nearest-neighbor (ANN) vector search using MariaDB's native VECTOR datatype with MHNSW indexing.
• Supports cosine and Euclidean distance functions via VEC_DISTANCE_COSINE and VEC_DISTANCE_EUCLIDEAN.
• Optional score threshold to exclude documents below a minimum similarity score.
• Configurable filter policy to merge or replace filters at query time.
• Requires MariaDB 11.7 or later for native VECTOR support.

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. Set the MARIADB_USER and MARIADB_PASSWORD environment variables with your MariaDB credentials.
2. First, configure a MariaDBDocumentStore in your pipeline with create_vector_index: true for ANN search.
3. Drag the MariaDBEmbeddingRetriever 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

MariaDBEmbeddingRetriever 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

  MariaDBEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.mariadb.embedding_retriever.MariaDBEmbeddingRetriever
    init_parameters:
      document_store: MariaDBDocumentStore
      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.mariadb.document_store.MariaDBDocumentStore
    init_parameters:
      host: 127.0.0.1
      port: 3306
      database: haystack
      embedding_dimension: 384
      distance: cosine
      create_vector_index: true

  retriever:
    type: haystack_integrations.components.retrievers.mariadb.embedding_retriever.MariaDBEmbeddingRetriever
    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.
score_threshold	Optional[float]	Minimum score to include a document. 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	MariaDBDocumentStore		The MariaDB 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.
score_threshold	Optional[float]	None	Minimum similarity score to include a document. Documents below this score are excluded.
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.
score_threshold	Optional[float]	None	Minimum similarity score. Overrides the init-time value.

Related Information

• MariaDBKeywordRetriever
• Pipeline Components