Ibm Db

haystack_integrations.components.retrievers.ibm_db.embedding_retriever

IBMDb2EmbeddingRetriever

Retrieves documents from a IBMDb2DocumentStore using vector similarity.

Use inside a Haystack pipeline after a text embedder:

python

pipeline.add_component("embedder", SentenceTransformersTextEmbedder())
pipeline.add_component("retriever", IBMDb2EmbeddingRetriever(
    document_store=store, top_k=5
))
pipeline.connect("embedder.embedding", "retriever.query_embedding")

init

python

__init__(
    *,
    document_store: IBMDb2DocumentStore,
    filters: dict[str, Any] | None = None,
    top_k: int = 10,
    filter_policy: FilterPolicy = FilterPolicy.REPLACE
) -> None

Initialize the IBMDb2EmbeddingRetriever.

Parameters:

• document_store (IBMDb2DocumentStore) – An instance of IBMDb2DocumentStore.
• filters (dict[str, Any] | None) – Filters applied to the retrieved Documents.
• top_k (int) – Maximum number of Documents to return.
• filter_policy (FilterPolicy) – Policy to determine how filters are applied.

Raises:

• TypeError – If document_store is not an instance of IBMDb2DocumentStore.

run

python

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

Retrieve documents by vector similarity.

Parameters:

• query_embedding (list[float]) – Dense float vector from an embedder component.
• filters (dict[str, Any] | None) – Runtime filters, merged with constructor filters according to filter_policy.
• top_k (int | None) – Override the constructor top_k for this call.

Returns:

• dict[str, list[Document]] – A dictionary with key documents containing a list of matching :class:Document objects.

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]) -> IBMDb2EmbeddingRetriever

Deserializes the component from a dictionary.

Parameters:

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

Returns:

• IBMDb2EmbeddingRetriever – Deserialized component.

haystack_integrations.document_stores.ibm_db.document_store

IBM DB2 Document Store for Haystack.

IBMDb2DocumentStore

IBM DB2 Document Store for Haystack using vector search capabilities.

This document store uses IBM DB2's native vector search functionality to store and retrieve documents with embeddings.

init

python

__init__(
    *,
    database: str,
    hostname: str,
    username: Secret = Secret.from_env_var("DB2_USERNAME"),
    password: Secret = Secret.from_env_var("DB2_PASSWORD"),
    port: int = 50000,
    protocol: str = "TCPIP",
    schema: str | None = None,
    use_ssl: bool = False,
    ssl_certificate: str | None = None,
    connection_options: dict[str, Any] | None = None,
    table_name: str = "haystack_documents",
    embedding_dim: int = 768,
    distance_metric: Literal["EUCLIDEAN", "COSINE", "MANHATTAN"] = "COSINE",
    recreate_table: bool = False
)

Initialize the IBM DB2 Document Store.

Parameters:

• database (str) – Database name
• hostname (str) – Database server hostname
• username (Secret) – Database username as a Secret, e.g. Secret.from_env_var("DB2_USERNAME").
• password (Secret) – Database password as a Secret, e.g. Secret.from_env_var("DB2_PASSWORD").
• port (int) – Database server port (default: 50000)
• protocol (str) – Connection protocol (default: "TCPIP")
• schema (str | None) – Database schema (optional)
• use_ssl (bool) – Enable SSL/TLS connection (default: False)
• ssl_certificate (str | None) – Path to SSL certificate file (optional, required if use_ssl is True)
• connection_options (dict[str, Any] | None) – Additional connection options as dict (optional)
• table_name (str) – Name of the table to store documents (default: "haystack_documents")
• embedding_dim (int) – Dimension of embedding vectors (default: 768)
• distance_metric (Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']) – Distance metric for similarity search (default: "COSINE")
• recreate_table (bool) – If True, drop and recreate the table (default: False)

count_documents

python

count_documents() -> int

Count all documents in the store.

Returns:

• int – Number of documents

count_documents_by_filter

python

count_documents_by_filter(filters: dict[str, Any] | None = None) -> int

Count documents that match the provided filters.

Parameters:

• filters (dict[str, Any] | None) – Filters to apply. See Haystack documentation for filter syntax.

Returns:

• int – Number of documents matching the filters

write_documents

python

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

Write documents to the store.

Parameters:

• documents (list[Document]) – List of documents to write
• policy (DuplicatePolicy) – Policy for handling duplicate documents

Returns:

• int – Number of documents written

Raises:

• ValueError – If documents is not a list of Document objects or has invalid embeddings
• TypeError – If embeddings have invalid types
• DuplicateDocumentError – If a document with the same id already exists and policy is FAIL or NONE

filter_documents

python

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

Filter documents using SQL-based metadata and field conditions.

Parameters:

• filters (dict[str, Any] | None) – Optional filter dictionary to constrain the returned documents.

Returns:

• list[Document] – List of matching documents.

delete_documents

python

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

Delete documents by their IDs.

Parameters:

• document_ids (list[str]) – List of document IDs to delete

delete_by_filter

python

delete_by_filter(filters: dict[str, Any] | None = None) -> int

Delete documents that match the provided filters.

Parameters:

• filters (dict[str, Any] | None) – Filters to apply. See Haystack documentation for filter syntax.

Returns:

• int – Number of documents deleted

delete_all_documents

python

delete_all_documents(recreate_index: bool = False) -> int

Delete all documents from the document store.

Parameters:

• recreate_index (bool) – If True, recreate the table after deletion

Returns:

• int – Number of documents deleted

update_by_filter

python

update_by_filter(
    filters: dict[str, Any] | None = None, meta: dict[str, Any] | None = None
) -> int

Update documents that match the provided filters.

Parameters:

• filters (dict[str, Any] | None) – Filters to apply. See Haystack documentation for filter syntax.
• meta (dict[str, Any] | None) – Dictionary of metadata fields to update

Returns:

• int – Number of documents updated

get_metadata_field_unique_values

python

get_metadata_field_unique_values(field: str) -> list[Any]

Get all unique values for a given metadata field.

Parameters:

• field (str) – The metadata field name (can include 'meta.' prefix)

Returns:

• list[Any] – List of unique values for the field

get_metadata_field_min_max

python

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

Get the minimum and maximum values for a numeric metadata field.

Parameters:

• field (str) – The metadata field name (can include 'meta.' prefix)

Returns:

• dict[str, Any] – Dictionary with 'min' and 'max' keys

get_metadata_fields_info

python

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

Get information about all metadata fields including their types.

Returns:

• dict[str, dict[str, Any]] – Dictionary mapping field names to their type information

count_unique_metadata_by_filter

python

count_unique_metadata_by_filter(
    filters: dict[str, Any] | None = None,
    metadata_fields: list[str] | None = None,
) -> dict[str, int]

Count unique values for specified metadata fields, optionally filtered.

Parameters:

• filters (dict[str, Any] | None) – Optional filters to apply before counting
• metadata_fields (list[str] | None) – List of metadata field names to count unique values for

Returns:

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

to_dict

python

to_dict() -> dict[str, Any]

Serialize the document store to a dictionary.

Returns:

• dict[str, Any] – Dictionary representation

from_dict

python

from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore

Deserialize the document store from a dictionary.

Parameters:

• data (dict[str, Any]) – Dictionary representation

Returns:

• IBMDb2DocumentStore – IBMDb2DocumentStore instance