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Version: 2.26

AlloyDB

haystack_integrations.components.retrievers.alloydb.embedding_retriever​

AlloyDBEmbeddingRetriever​

Retrieves documents from the AlloyDBDocumentStore by embedding similarity.

Must be connected to the AlloyDBDocumentStore.

init​

python
__init__(
    *,
    document_store: AlloyDBDocumentStore,
    filters: dict[str, Any] | None = None,
    top_k: int = 10,
    vector_function: (
        Literal["cosine_similarity", "inner_product", "l2_distance"] | None
    ) = None,
    filter_policy: str | FilterPolicy = FilterPolicy.REPLACE
) -> None

Create the AlloyDBEmbeddingRetriever component.

Parameters:

  • document_store (AlloyDBDocumentStore) – An instance of AlloyDBDocumentStore to use as the document store.
  • filters (dict[str, Any] | None) – Filters applied to the retrieved documents.
  • top_k (int) – Maximum number of documents to return.
  • vector_function (Literal['cosine_similarity', 'inner_product', 'l2_distance'] | None) – The similarity function to use when searching for similar embeddings. Overrides the vector_function set in the AlloyDBDocumentStore. "cosine_similarity" and "inner_product" are similarity functions and higher scores indicate greater similarity between the documents. "l2_distance" returns the straight-line distance between vectors, and the most similar documents are the ones with the smallest score. Important: when using the "hnsw" search strategy, make sure to use the same vector function as the one used when the HNSW index was created. If not specified, the vector_function of the AlloyDBDocumentStore is used.
  • filter_policy (str | FilterPolicy) – Policy to determine how filters are applied at query time. FilterPolicy.REPLACE (default) replaces the init filters with the run-time filters. FilterPolicy.MERGE merges the init filters with the run-time filters.

Raises:

  • ValueError – If document_store is not an instance of AlloyDBDocumentStore.

run​

python
run(
    query_embedding: list[float],
    filters: dict[str, Any] | None = None,
    top_k: int | None = None,
    vector_function: (
        Literal["cosine_similarity", "inner_product", "l2_distance"] | None
    ) = None,
) -> dict[str, list[Document]]

Retrieve documents from the AlloyDBDocumentStore by embedding similarity.

Parameters:

  • query_embedding (list[float]) – A vector representation of the query.
  • filters (dict[str, Any] | None) – Filters applied to the retrieved documents. The filter_policy set at initialization determines how these are combined with the init filters.
  • top_k (int | None) – Maximum number of documents to return. Overrides the top_k set at initialization.
  • vector_function (Literal['cosine_similarity', 'inner_product', 'l2_distance'] | None) – The similarity function to use when searching for similar embeddings. Overrides the vector_function set at initialization.

Returns:

  • dict[str, list[Document]] – A dictionary containing the documents retrieved from the document store.

to_dict​

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

Serializes the component to a dictionary.

Returns:

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

from_dict​

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

Deserializes the component from a dictionary.

Parameters:

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

Returns:

  • AlloyDBEmbeddingRetriever – Deserialized component.

haystack_integrations.components.retrievers.alloydb.keyword_retriever​

AlloyDBKeywordRetriever​

Retrieves documents from the AlloyDBDocumentStore by keyword search.

Uses PostgreSQL full-text search (to_tsvector / plainto_tsquery) to find documents. Must be connected to the AlloyDBDocumentStore.

init​

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

Create the AlloyDBKeywordRetriever component.

Parameters:

  • document_store (AlloyDBDocumentStore) – An instance of AlloyDBDocumentStore to use as the document store.
  • filters (dict[str, Any] | None) – Filters applied to the retrieved documents.
  • top_k (int) – Maximum number of documents to return.
  • filter_policy (str | FilterPolicy) – Policy to determine how filters are applied at query time. FilterPolicy.REPLACE (default) replaces the init filters with the run-time filters. FilterPolicy.MERGE merges the init filters with the run-time filters.

Raises:

  • ValueError – If document_store is not an instance of AlloyDBDocumentStore.

run​

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

Retrieve documents from the AlloyDBDocumentStore by keyword search.

Parameters:

  • query (str) – A keyword query to search for.
  • filters (dict[str, Any] | None) – Filters applied to the retrieved documents. The filter_policy set at initialization determines how these are combined with the init filters.
  • top_k (int | None) – Maximum number of documents to return. Overrides the top_k set at initialization.

Returns:

  • dict[str, list[Document]] – A dictionary containing the documents retrieved from the document store.

to_dict​

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

Serializes the component to a dictionary.

Returns:

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

from_dict​

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

Deserializes the component from a dictionary.

Parameters:

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

Returns:

  • AlloyDBKeywordRetriever – Deserialized component.

haystack_integrations.document_stores.alloydb.document_store​

AlloyDBDocumentStore​

Bases: DocumentStore

A Document Store backed by Google Cloud AlloyDB.

Uses the pgvector extension for vector search.

AlloyDB is a fully managed, PostgreSQL-compatible database service on Google Cloud. Connection is handled securely via the AlloyDB Python Connector, which provides TLS encryption and IAM-based authorization without requiring manual SSL certificate management, firewall rules, or IP allowlisting.

Filter limitations: the NOT logical operator is not supported. Use != or not in comparison operators to express negation.

Usage example:

python
import os
from haystack_integrations.document_stores.alloydb import AlloyDBDocumentStore

# Set required environment variables:
# ALLOYDB_INSTANCE_URI = "projects/MY_PROJECT/locations/MY_REGION/clusters/MY_CLUSTER/instances/MY_INSTANCE"
# ALLOYDB_USER = "my-db-user"
# ALLOYDB_PASSWORD = "my-db-password"

document_store = AlloyDBDocumentStore(
    db="my-database",
    embedding_dimension=768,
    recreate_table=True,
)

init​

python
__init__(
    *,
    instance_uri: Secret = Secret.from_env_var("ALLOYDB_INSTANCE_URI"),
    user: Secret = Secret.from_env_var("ALLOYDB_USER"),
    password: Secret = Secret.from_env_var("ALLOYDB_PASSWORD", strict=False),
    db: str = "postgres",
    enable_iam_auth: bool = False,
    ip_type: Literal["PRIVATE", "PUBLIC", "PSC"] = "PRIVATE",
    create_extension: bool = True,
    schema_name: str = "public",
    table_name: str = "haystack_documents",
    language: str = "english",
    embedding_dimension: int = 768,
    vector_function: Literal[
        "cosine_similarity", "inner_product", "l2_distance"
    ] = "cosine_similarity",
    recreate_table: bool = False,
    search_strategy: Literal[
        "exact_nearest_neighbor", "hnsw"
    ] = "exact_nearest_neighbor",
    hnsw_recreate_index_if_exists: bool = False,
    hnsw_index_creation_kwargs: dict[str, int] | None = None,
    hnsw_index_name: str = "haystack_hnsw_index",
    hnsw_ef_search: int | None = None,
    keyword_index_name: str = "haystack_keyword_index"
) -> None

Creates a new AlloyDBDocumentStore instance.

Connection to AlloyDB is established lazily on first use via the AlloyDB Python Connector. A specific table to store Haystack documents will be created if it doesn't exist yet.

Parameters:

  • instance_uri (Secret) – The AlloyDB instance URI in the format "projects/PROJECT/locations/REGION/clusters/CLUSTER/instances/INSTANCE". Read from the ALLOYDB_INSTANCE_URI environment variable by default.
  • user (Secret) – The database user. Read from the ALLOYDB_USER environment variable by default. When using IAM database authentication, use the service account email (omitting .gserviceaccount.com) or the full IAM user email.
  • password (Secret) – The database password. Read from the ALLOYDB_PASSWORD environment variable by default. Not required when enable_iam_auth=True.
  • db (str) – The name of the database to connect to. Defaults to "postgres".
  • enable_iam_auth (bool) – Whether to use IAM database authentication instead of a password. When True, password is ignored. The IAM principal must be granted the AlloyDB Client role and have an IAM database user created. See the AlloyDB documentation for details.
  • ip_type (Literal['PRIVATE', 'PUBLIC', 'PSC']) – The IP address type to use for the connection. "PRIVATE" (default) connects over a private VPC IP. "PUBLIC" connects over a public IP. "PSC" connects via Private Service Connect.
  • create_extension (bool) – Whether to create the pgvector extension if it doesn't exist. Set this to True (default) to automatically create the extension if it is missing. Creating the extension may require superuser privileges. If set to False, ensure the extension is already installed; otherwise, an error will be raised.
  • schema_name (str) – The name of the schema the table is created in. The schema must already exist.
  • table_name (str) – The name of the table to use to store Haystack documents.
  • language (str) – The language to be used to parse query and document content in keyword retrieval. To see the list of available languages, you can run the following SQL query in your PostgreSQL database: SELECT cfgname FROM pg_ts_config;.
  • embedding_dimension (int) – The dimension of the embedding.
  • vector_function (Literal['cosine_similarity', 'inner_product', 'l2_distance']) – The similarity function to use when searching for similar embeddings. "cosine_similarity" and "inner_product" are similarity functions and higher scores indicate greater similarity between the documents. "l2_distance" returns the straight-line distance between vectors, and the most similar documents are the ones with the smallest score. Important: when using the "hnsw" search strategy, an index will be created that depends on the vector_function passed here. Make sure subsequent queries will keep using the same vector similarity function in order to take advantage of the index.
  • recreate_table (bool) – Whether to recreate the table if it already exists.
  • search_strategy (Literal['exact_nearest_neighbor', 'hnsw']) – The search strategy to use when searching for similar embeddings. "exact_nearest_neighbor" provides perfect recall but can be slow for large numbers of documents. "hnsw" is an approximate nearest neighbor search strategy, which trades off some accuracy for speed; it is recommended for large numbers of documents. Important: when using the "hnsw" search strategy, an index will be created that depends on the vector_function passed here. Make sure subsequent queries will keep using the same vector similarity function in order to take advantage of the index.
  • hnsw_recreate_index_if_exists (bool) – Whether to recreate the HNSW index if it already exists. Only used if search_strategy is set to "hnsw".
  • hnsw_index_creation_kwargs (dict[str, int] | None) – Additional keyword arguments to pass to the HNSW index creation. Only used if search_strategy is set to "hnsw". Valid arguments are m and ef_construction. See the pgvector documentation for details.
  • hnsw_index_name (str) – Index name for the HNSW index.
  • hnsw_ef_search (int | None) – The ef_search parameter to use at query time. Only used if search_strategy is set to "hnsw". See the pgvector documentation.
  • keyword_index_name (str) – Index name for the keyword GIN index.

to_dict​

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

Serializes the component to a dictionary.

Returns:

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

from_dict​

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

Deserializes the component from a dictionary.

Parameters:

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

Returns:

  • AlloyDBDocumentStore – Deserialized component.

close​

python
close() -> None

Closes the database connection and the AlloyDB connector.

Call this when you are done using the document store to release resources. For long-lived applications the connector runs a background refresh thread; calling close() ensures that thread is stopped cleanly.

delete_table​

python
delete_table() -> None

Deletes the table used to store Haystack documents.

The name of the schema (schema_name) and the name of the table (table_name) are defined when initializing the AlloyDBDocumentStore.

count_documents​

python
count_documents() -> int

Returns how many documents are in the document store.

Returns:

  • int – The number of documents in the document store.

filter_documents​

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

Returns the documents that match the filters provided.

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

Filter operator support: comparison operators (==, !=, >, >=, <, <=, in, not in, like, not like) and logical operators AND and OR are fully supported. The NOT logical operator is not supported — use != or not in comparison operators instead.

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.FAIL
) -> int

Writes documents to the document 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

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

Parameters:

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

delete_all_documents​

python
delete_all_documents() -> None

Deletes all documents in the document store.

delete_by_filter​

python
delete_by_filter(filters: dict[str, Any]) -> int

Deletes all documents that match the provided filters.

Parameters:

  • filters (dict[str, Any]) – The filters to apply to select documents for deletion. For filter syntax, see Haystack metadata filtering

Returns:

  • int – The number of documents deleted.

update_by_filter​

python
update_by_filter(filters: dict[str, Any], meta: dict[str, Any]) -> int

Updates the metadata of all documents that match the provided filters.

Parameters:

  • filters (dict[str, Any]) – The filters to apply to select documents for updating. For filter syntax, see Haystack metadata filtering
  • meta (dict[str, Any]) – The metadata fields to update.

Returns:

  • int – The number of documents updated.

count_documents_by_filter​

python
count_documents_by_filter(filters: dict[str, Any]) -> int

Returns the number of documents that match the provided filters.

Parameters:

Returns:

  • int – The number of documents that match the filters.

count_unique_metadata_by_filter​

python
count_unique_metadata_by_filter(
    filters: dict[str, Any], metadata_fields: list[str]
) -> dict[str, int]

Returns the count of unique values for each specified metadata field.

Considers only documents that match the provided filters.

Parameters:

  • filters (dict[str, Any]) – The filters to apply to select documents. For filter syntax, see Haystack metadata filtering
  • metadata_fields (list[str]) – List of metadata field names to count unique values for. Field names can include or omit the "meta." prefix.

Returns:

  • dict[str, int] – A dictionary mapping field names to their unique value counts.

get_metadata_fields_info​

python
get_metadata_fields_info() -> dict[str, dict[str, str]]

Returns information about the metadata fields in the document store.

Since metadata is stored in a JSONB field, this method analyzes actual data to infer field types.

Example return:

python
{
    'category': {'type': 'text'},
    'priority': {'type': 'integer'},
}

Returns:

  • dict[str, dict[str, str]] – A dictionary mapping field names to their type information.

get_metadata_field_min_max​

python
get_metadata_field_min_max(field: str) -> dict[str, Any]

Returns the minimum and maximum values for a metadata field.

For numeric fields (integer, real), returns numeric min/max. For text and other non-numeric fields, returns lexicographic min/max using the "C" collation.

Parameters:

  • field (str) – The metadata field name (with or without the "meta." prefix).

Returns:

  • dict[str, Any] – A dictionary with min and max keys. Returns {"min": None, "max": None} when the field has no values or the store is empty.

get_metadata_field_unique_values​

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

Returns a list of unique values for a metadata field.

Parameters:

  • field (str) – The metadata field name (with or without the "meta." prefix).
  • filters (dict[str, Any] | None) – Optional filters to restrict the documents considered.

Returns:

  • list[Any] – A list of unique values for the given field.