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Version: 2.28-unstable

Oracle AI Vector Search

haystack_integrations.components.retrievers.oracle.embedding_retriever​

OracleEmbeddingRetriever​

Retrieves documents from an OracleDocumentStore using vector similarity.

Use inside a Haystack pipeline after a text embedder::

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

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.

Args: query_embedding: Dense float vector from an embedder component. filters: Runtime filters, merged with constructor filters according to filter_policy. top_k: Override the constructor top_k for this call.

Returns: {"documents": [Document, ...]}

run_async​

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

Async variant of :meth:run.

to_dict​

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

Serializes the component to a dictionary.

Returns:

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

from_dict​

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

Deserializes the component from a dictionary.

Parameters:

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

Returns:

  • OracleEmbeddingRetriever – Deserialized component.

haystack_integrations.document_stores.oracle.document_store​

OracleConnectionConfig​

Connection parameters for Oracle Database.

Supports both thin (direct TCP) and thick (wallet / ADB-S) modes. Thin mode requires no Oracle Instant Client; thick mode is activated automatically when wallet_location is provided.

to_dict​

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

Serializes the component to a dictionary.

Returns:

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

from_dict​

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

Deserializes the component from a dictionary.

Parameters:

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

Returns:

  • OracleConnectionConfig – Deserialized component.

OracleDocumentStore​

Haystack DocumentStore backed by Oracle AI Vector Search.

Requires Oracle Database 23ai or later (for VECTOR data type and IF NOT EXISTS DDL support).

Usage::

from haystack.utils import Secret
from haystack_integrations.document_stores.oracle import (
    OracleDocumentStore, OracleConnectionConfig,
)

store = OracleDocumentStore(
    connection_config=OracleConnectionConfig(
        user=Secret.from_env_var("ORACLE_USER"),
        password=Secret.from_env_var("ORACLE_PASSWORD"),
        dsn=Secret.from_env_var("ORACLE_DSN"),
    ),
    embedding_dim=1536,
)

create_hnsw_index​

python
create_hnsw_index() -> None

Create an HNSW vector index on the embedding column.

Safe to call multiple times — uses IF NOT EXISTS.

create_hnsw_index_async​

python
create_hnsw_index_async() -> None

Async variant of create_hnsw_index.

write_documents​

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

Writes documents to the document store.

Parameters:

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

Returns:

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

Raises:

  • DuplicateDocumentError – If a document with the same id already exists in the document store and the policy is set to DuplicatePolicy.FAIL or DuplicatePolicy.NONE.

write_documents_async​

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

Asynchronously writes documents to the document store.

Parameters:

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

Returns:

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

Raises:

  • DuplicateDocumentError – If a document with the same id already exists in the document store and the policy is set to DuplicatePolicy.FAIL or DuplicatePolicy.NONE.

filter_documents​

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

Returns the documents that match the filters provided.

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

Parameters:

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

Returns:

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

filter_documents_async​

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

Asynchronously returns the documents that match the filters provided.

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

Parameters:

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

Returns:

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

delete_documents​

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

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

Parameters:

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

delete_documents_async​

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

Asynchronously deletes documents that match the provided document_ids from the document store.

Parameters:

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

count_documents​

python
count_documents() -> int

Returns how many documents are present in the document store.

Returns:

  • int – Number of documents in the document store.

count_documents_async​

python
count_documents_async() -> int

Asynchronously returns how many documents are present in the document store.

Returns:

  • int – Number of documents in the document store.

to_dict​

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

Serializes the component to a dictionary.

Returns:

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

from_dict​

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

Deserializes the component from a dictionary.

Parameters:

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

Returns:

  • OracleDocumentStore – Deserialized component.