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+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.20/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.20/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.20/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.21/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.21/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.21/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.22/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.22/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.22/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.23/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.23/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.23/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.24/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.24/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.24/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.25/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.25/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.25/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.26/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.26/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.26/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.27/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.27/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.27/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.28/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.28/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.28/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.29/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.29/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.29/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.30/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.30/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.30/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance
diff --git a/docs-website/reference_versioned_docs/version-2.31/integrations-api/ibm_db.md b/docs-website/reference_versioned_docs/version-2.31/integrations-api/ibm_db.md
new file mode 100644
index 00000000000..a1fa4f84db5
--- /dev/null
+++ b/docs-website/reference_versioned_docs/version-2.31/integrations-api/ibm_db.md
@@ -0,0 +1,374 @@
+---
+title: "Ibm Db"
+id: integrations-ibm-db
+description: "Ibm Db integration for Haystack"
+slug: "/integrations-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** (<code>IBMDb2DocumentStore</code>) – An instance of `IBMDb2DocumentStore`.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **filter_policy** (<code>FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>TypeError</code> – 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** (<code>list\[float\]</code>) – Dense float vector from an embedder component.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Runtime filters, merged with constructor filters according to filter_policy.
+- **top_k** (<code>int | None</code>) – Override the constructor top_k for this call.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2EmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>IBMDb2EmbeddingRetriever</code> – 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** (<code>str</code>) – Database name
+- **hostname** (<code>str</code>) – Database server hostname
+- **username** (<code>Secret</code>) – Database username as a `Secret`, e.g. `Secret.from_env_var("DB2_USERNAME")`.
+- **password** (<code>Secret</code>) – Database password as a `Secret`, e.g. `Secret.from_env_var("DB2_PASSWORD")`.
+- **port** (<code>int</code>) – Database server port (default: 50000)
+- **protocol** (<code>str</code>) – Connection protocol (default: "TCPIP")
+- **schema** (<code>str | None</code>) – Database schema (optional)
+- **use_ssl** (<code>bool</code>) – Enable SSL/TLS connection (default: False)
+- **ssl_certificate** (<code>str | None</code>) – Path to SSL certificate file (optional, required if use_ssl is True)
+- **connection_options** (<code>dict\[str, Any\] | None</code>) – Additional connection options as dict (optional)
+- **table_name** (<code>str</code>) – Name of the table to store documents (default: "haystack_documents")
+- **embedding_dim** (<code>int</code>) – Dimension of embedding vectors (default: 768)
+- **distance_metric** (<code>Literal['EUCLIDEAN', 'COSINE', 'MANHATTAN']</code>) – Distance metric for similarity search (default: "COSINE")
+- **recreate_table** (<code>bool</code>) – If True, drop and recreate the table (default: False)
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Count all documents in the store.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>list\[Document\]</code>) – List of documents to write
+- **policy** (<code>DuplicatePolicy</code>) – Policy for handling duplicate documents
+
+**Returns:**
+
+- <code>int</code> – Number of documents written
+
+**Raises:**
+
+- <code>ValueError</code> – If documents is not a list of Document objects or has invalid embeddings
+- <code>TypeError</code> – If embeddings have invalid types
+- <code>DuplicateDocumentError</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filter dictionary to constrain the returned documents.
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of matching documents.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>bool</code>) – If True, recreate the table after deletion
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>dict\[str, Any\] | None</code>) – Filters to apply. See Haystack documentation for filter syntax.
+- **meta** (<code>dict\[str, Any\] | None</code>) – Dictionary of metadata fields to update
+
+**Returns:**
+
+- <code>int</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>list\[Any\]</code> – 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** (<code>str</code>) – The metadata field name (can include 'meta.' prefix)
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – 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:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – 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** (<code>dict\[str, Any\] | None</code>) – Optional filters to apply before counting
+- **metadata_fields** (<code>list\[str\] | None</code>) – List of metadata field names to count unique values for
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – 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:**
+
+- <code>dict\[str, Any\]</code> – Dictionary representation
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> IBMDb2DocumentStore
+```
+
+Deserialize the document store from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary representation
+
+**Returns:**
+
+- <code>IBMDb2DocumentStore</code> – IBMDb2DocumentStore instance