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Version: 3.1-unstable

MariaDB

haystack_integrations.components.retrievers.mariadb.embedding_retriever​

MariaDBEmbeddingRetriever​

Retrieves documents from MariaDBDocumentStore using vector similarity search.

Uses MariaDB's native VEC_DISTANCE_COSINE or VEC_DISTANCE_EUCLIDEAN functions with MHNSW indexing for efficient approximate nearest-neighbour search.

Usage example​

python
from haystack_integrations.document_stores.mariadb import MariaDBDocumentStore
from haystack_integrations.components.retrievers.mariadb import MariaDBEmbeddingRetriever

store = MariaDBDocumentStore(host="localhost", database="haystack", embedding_dimension=768)
retriever = MariaDBEmbeddingRetriever(document_store=store, top_k=5)
result = retriever.run(query_embedding=[0.1] * 768)
documents = result["documents"]

init​

python
__init__(
    *,
    document_store: MariaDBDocumentStore,
    filters: dict[str, Any] | None = None,
    top_k: int = 10,
    score_threshold: float | None = None,
    filter_policy: str | FilterPolicy = FilterPolicy.REPLACE
) -> None

Initialize the MariaDBEmbeddingRetriever.

Parameters:

  • document_store (MariaDBDocumentStore) – A MariaDBDocumentStore instance.
  • filters (dict[str, Any] | None) – Default Haystack metadata filters applied to every query.
  • top_k (int) – Maximum number of documents to return.
  • score_threshold (float | None) – Minimum score to include a document. Documents below this score are excluded.
  • filter_policy (str | FilterPolicy) – How runtime filters interact with init-time filters.

Raises:

  • ValueError – If document_store is not a MariaDBDocumentStore.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the component to a dictionary.

from_dict​

python
from_dict(data: dict[str, Any]) -> MariaDBEmbeddingRetriever

Deserialize the component from a dictionary.

run​

python
run(
    query_embedding: list[float],
    filters: dict[str, Any] | None = None,
    top_k: int | None = None,
    score_threshold: float | None = None,
) -> dict[str, list[Document]]

Retrieve documents similar to the query embedding.

Parameters:

  • query_embedding (list[float]) – The query vector.
  • filters (dict[str, Any] | None) – Runtime filters merged with init-time filters per filter_policy.
  • top_k (int | None) – Override the retriever's top_k.
  • score_threshold (float | None) – Override the retriever's score_threshold.

Returns:

  • dict[str, list[Document]] – Dictionary with "documents" key containing the ranked results.

haystack_integrations.components.retrievers.mariadb.keyword_retriever​

MariaDBKeywordRetriever​

Retrieves documents from MariaDBDocumentStore using full-text keyword search.

Uses MariaDB's MATCH ... AGAINST full-text search in natural language mode, backed by a FULLTEXT index on the content column.

Usage example​

python
from haystack_integrations.document_stores.mariadb import MariaDBDocumentStore
from haystack_integrations.components.retrievers.mariadb import MariaDBKeywordRetriever

store = MariaDBDocumentStore(host="localhost", database="haystack", embedding_dimension=768)
retriever = MariaDBKeywordRetriever(document_store=store, top_k=5)
result = retriever.run(query="climate change")
documents = result["documents"]

init​

python
__init__(
    *,
    document_store: MariaDBDocumentStore,
    filters: dict[str, Any] | None = None,
    top_k: int = 10,
    filter_policy: str | FilterPolicy = FilterPolicy.REPLACE
) -> None

Initialize the MariaDBKeywordRetriever.

Parameters:

  • document_store (MariaDBDocumentStore) – A MariaDBDocumentStore instance.
  • filters (dict[str, Any] | None) – Default Haystack metadata filters.
  • top_k (int) – Maximum number of documents to return.
  • filter_policy (str | FilterPolicy) – How runtime filters interact with init-time filters.

Raises:

  • ValueError – If document_store is not a MariaDBDocumentStore.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the component to a dictionary.

from_dict​

python
from_dict(data: dict[str, Any]) -> MariaDBKeywordRetriever

Deserialize the component from a dictionary.

run​

python
run(
    query: str, filters: dict[str, Any] | None = None, top_k: int | None = None
) -> dict[str, list[Document]]

Retrieve documents matching the query via full-text search.

Parameters:

  • query (str) – The keyword query string.
  • filters (dict[str, Any] | None) – Runtime filters merged with init-time filters per filter_policy.
  • top_k (int | None) – Override the retriever's top_k.

Returns:

  • dict[str, list[Document]] – Dictionary with "documents" key containing results ranked by relevance.

haystack_integrations.document_stores.mariadb.document_store​

MariaDBDocumentStore​

A Document Store backed by MariaDB 11.7+ using native VECTOR support.

Uses MariaDB's VECTOR datatype with MHNSW indexing for approximate nearest-neighbour vector search, and MATCH ... AGAINST for full-text keyword search.

Usage example​

python
from haystack_integrations.document_stores.mariadb import MariaDBDocumentStore

store = MariaDBDocumentStore(
    host="localhost",
    port=3306,
    database="haystack",
    embedding_dimension=768,
)
store.write_documents(documents)

init​

python
__init__(
    *,
    host: str = "localhost",
    port: int = 3306,
    database: str = "haystack",
    user: Secret = Secret.from_env_var("MARIADB_USER"),
    password: Secret = Secret.from_env_var("MARIADB_PASSWORD"),
    table_name: str = "haystack_documents",
    recreate_table: bool = False,
    embedding_dimension: int = 768,
    distance: str = "cosine",
    create_vector_index: bool = False
) -> None

Initialize the MariaDBDocumentStore.

Parameters:

  • host (str) – MariaDB host.
  • port (int) – MariaDB port.
  • database (str) – Database name.
  • user (Secret) – Database user, read from the MARIADB_USER environment variable.
  • password (Secret) – Database password, read from the MARIADB_PASSWORD environment variable.
  • table_name (str) – Table used to store documents. Must contain only letters, digits, and underscores.
  • recreate_table (bool) – Drop and recreate the table on init. Deletes all data.
  • embedding_dimension (int) – Dimension of embedding vectors. Applied only when the table is created; ignored on an existing table.
  • distance (str) – Distance function for vector similarity — "cosine" or "euclidean". Applied only when the table is created; ignored on an existing table.
  • create_vector_index (bool) – If True, creates an MHNSW vector index for fast ANN search. Requires every document to have a non-null embedding. Applied only when the table is created; ignored on an existing table.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize this document store to a dictionary.

Returns:

  • dict[str, Any] – Dictionary with serialized data.

from_dict​

python
from_dict(data: dict[str, Any]) -> MariaDBDocumentStore

Deserialize this document store from a dictionary.

Parameters:

  • data (dict[str, Any]) – Dictionary to deserialize from.

Returns:

  • MariaDBDocumentStore – Deserialized document store.

close​

python
close() -> None

Release the associated synchronous resources.

delete_table​

python
delete_table() -> None

Drop the documents table

count_documents​

python
count_documents() -> int

Return how many documents are present in the document store.

Returns:

  • int – Number of documents in the document store.

filter_documents​

python
filter_documents(filters: dict[str, Any] | None = None) -> list[Document]

Return the documents that match the filters provided.

For a detailed specification of the filters, refer to the documentation.

Parameters:

  • filters (dict[str, Any] | None) – The filters to apply to the document list.

Returns:

  • list[Document] – A list of Documents that match the given filters.

Raises:

  • TypeError – If filters is not a dictionary.
  • ValueError – If filters syntax is invalid.

write_documents​

python
write_documents(
    documents: list[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE
) -> int

Write documents to the store.

Parameters:

  • documents (list[Document]) – A list of Documents to write to the document store.
  • policy (DuplicatePolicy) – The duplicate policy to use when writing documents.

Returns:

  • int – The number of documents written to the document store.

Raises:

  • ValueError – If documents contains objects that are not of type Document.
  • DuplicateDocumentError – If a document with the same id already exists in the document store and the policy is set to DuplicatePolicy.FAIL (or not specified).
  • DocumentStoreError – If the write operation fails for any other reason.

delete_documents​

python
delete_documents(document_ids: list[str]) -> None

Delete documents that match the provided document_ids from the document store.

Parameters:

  • document_ids (list[str]) – The document ids to delete.