Skip to main content
Version: 2.29

Azure DocumentDB

haystack_integrations.components.retrievers.azure_documentdb.embedding_retriever​

AzureDocumentDBEmbeddingRetriever​

Retrieve documents from Azure DocumentDB using cosmosSearch vector similarity.

init​

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

Create the embedding retriever.

Parameters:

  • document_store (AzureDocumentDBDocumentStore) – Azure DocumentDB document store to query.
  • filters (dict[str, Any] | None) – Default Haystack metadata filters.
  • top_k (int) – Maximum number of documents to return.
  • filter_policy (str | FilterPolicy) – Policy for combining initialization and runtime filters.

to_dict​

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

Serialize this component to a dictionary.

Returns:

  • dict[str, Any] – Serialized retriever configuration.

from_dict​

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

Deserialize this component from a dictionary.

Parameters:

  • data (dict[str, Any]) – Serialized retriever configuration.

Returns:

  • AzureDocumentDBEmbeddingRetriever – The deserialized retriever.

close​

python
close() -> None

Release synchronous document-store resources.

close_async​

python
close_async() -> None

Release asynchronous document-store resources.

run​

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

Retrieve documents by vector similarity.

Parameters:

  • query_embedding (list[float]) – Query vector.
  • filters (dict[str, Any] | None) – Runtime Haystack metadata filters.
  • top_k (int | None) – Runtime maximum number of documents.

Returns:

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

run_async​

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

Asynchronously retrieve documents by vector similarity.

Parameters:

  • query_embedding (list[float]) – Query vector.
  • filters (dict[str, Any] | None) – Runtime Haystack metadata filters.
  • top_k (int | None) – Runtime maximum number of documents.

Returns:

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

haystack_integrations.components.retrievers.azure_documentdb.full_text_retriever​

AzureDocumentDBFullTextRetriever​

Retrieve documents using Azure DocumentDB BM25 full-text search, currently a gated preview.

init​

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

Create the full-text retriever.

Parameters:

  • document_store (AzureDocumentDBDocumentStore) – Azure DocumentDB document store to query.
  • filters (dict[str, Any] | None) – Default Haystack metadata filters.
  • top_k (int) – Maximum number of documents to return.
  • filter_policy (str | FilterPolicy) – Policy for combining initialization and runtime filters.

to_dict​

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

Serialize this component to a dictionary.

Returns:

  • dict[str, Any] – Serialized retriever configuration.

from_dict​

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

Deserialize this component from a dictionary.

Parameters:

  • data (dict[str, Any]) – Serialized retriever configuration.

Returns:

  • AzureDocumentDBFullTextRetriever – The deserialized retriever.

close​

python
close() -> None

Release synchronous document-store resources.

close_async​

python
close_async() -> None

Release asynchronous document-store resources.

run​

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

Retrieve documents by BM25 keyword search.

Parameters:

  • query (str | list[str]) – Query string or strings.
  • fuzzy (dict[str, int] | None) – Azure DocumentDB fuzzy-search options such as maxEdits.
  • filters (dict[str, Any] | None) – Runtime Haystack metadata filters.
  • top_k (int | None) – Runtime maximum number of documents.

Returns:

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

run_async​

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

Asynchronously retrieve documents by BM25 keyword search.

Parameters:

  • query (str | list[str]) – Query string or strings.
  • fuzzy (dict[str, int] | None) – Azure DocumentDB fuzzy-search options such as maxEdits.
  • filters (dict[str, Any] | None) – Runtime Haystack metadata filters.
  • top_k (int | None) – Runtime maximum number of documents.

Returns:

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

haystack_integrations.document_stores.azure_documentdb.document_store​

AzureIdentityTokenCallback​

Bases: OIDCCallback

Fetch Microsoft Entra access tokens for PyMongo's OIDC authentication.

fetch​

python
fetch(context: OIDCCallbackContext) -> OIDCCallbackResult

Fetch an access token for Azure DocumentDB.

Parameters:

  • context (OIDCCallbackContext) – PyMongo OIDC callback context.

Returns:

  • OIDCCallbackResult – The OIDC callback result containing a Microsoft Entra access token.

AzureDocumentDBDocumentStore​

A Haystack document store backed by Azure DocumentDB.

The default authentication mode uses Microsoft Entra ID through DefaultAzureCredential. Supply the Azure DocumentDB cluster name with cluster_name or the AZURE_DOCUMENTDB_CLUSTER_NAME environment variable.

A connection string can be supplied through mongo_connection_string or AZURE_DOCUMENTDB_CONNECTION_STRING for local development and integration tests. Connection strings can contain credentials and aren't recommended for production workloads.

The collection must already exist. For embedding retrieval, create a cosmosSearch vector index by calling create_vector_index or provisioning it separately. Filtered vector search also requires a regular index for every filtered metadata field, such as meta.category. Values used with >, >=, <, or <= must be numbers or ISO-formatted date strings.

Usage:

python
from haystack_integrations.document_stores.azure_documentdb import AzureDocumentDBDocumentStore

document_store = AzureDocumentDBDocumentStore(database_name="haystack", collection_name="documents")
document_store.create_vector_index(dimensions=1536, similarity="COS")

init​

python
__init__(
    *,
    database_name: str,
    collection_name: str,
    vector_search_index: str = "haystack_vector_index",
    full_text_search_index: str | None = None,
    cluster_name: str | None = None,
    mongo_connection_string: Secret | None = Secret.from_env_var(
        "AZURE_DOCUMENTDB_CONNECTION_STRING", strict=False
    ),
    azure_token_credential: TokenCredential | None = None,
    embedding_field: str = "embedding",
    content_field: str = "content"
) -> None

Create an Azure DocumentDB document store.

Parameters:

  • database_name (str) – Name of the existing database.
  • collection_name (str) – Name of the existing collection.
  • vector_search_index (str) – Name used when creating the vector index. Azure DocumentDB selects vector indexes by path at query time, so this name is not included in vector search queries.
  • full_text_search_index (str | None) – Name of an Azure DocumentDB full-text search index. Full-text search is currently a gated preview and must be enabled on the cluster before using the full-text retriever.
  • cluster_name (str | None) – Azure DocumentDB cluster name. If omitted, AZURE_DOCUMENTDB_CLUSTER_NAME is used.
  • mongo_connection_string (Secret | None) – Optional MongoDB connection string intended only for local development and integration tests. Microsoft Entra authentication is used when this value is absent.
  • azure_token_credential (TokenCredential | None) – Azure credential used for Microsoft Entra authentication. If omitted, DefaultAzureCredential is used.
  • embedding_field (str) – Field containing document embeddings.
  • content_field (str) – Field containing document content.

Raises:

  • ValueError – If database, collection, or field names are invalid.

connection​

python
connection: MongoClient | AsyncMongoClient

Return the active Azure DocumentDB client.

Returns:

  • MongoClient | AsyncMongoClient – The synchronous or asynchronous PyMongo client.

Raises:

  • DocumentStoreError – If no connection has been established.

collection​

python
collection: Collection | AsyncCollection

Return the active Azure DocumentDB collection.

Returns:

  • Collection | AsyncCollection – The synchronous or asynchronous PyMongo collection.

Raises:

  • DocumentStoreError – If no collection has been initialized.

close​

python
close() -> None

Release synchronous client resources.

close_async​

python
close_async() -> None

Release asynchronous client resources.

to_dict​

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

Serialize this document store to a dictionary.

Returns:

  • dict[str, Any] – Serialized document-store configuration.

from_dict​

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

Deserialize this document store from a dictionary.

Parameters:

  • data (dict[str, Any]) – Serialized document-store configuration.

Returns:

  • AzureDocumentDBDocumentStore – The deserialized document store.

count_documents​

python
count_documents() -> int

Return the number of documents in the store.

Returns:

  • int – The number of documents.

count_documents_async​

python
count_documents_async() -> int

Asynchronously return the number of documents in the store.

Returns:

  • int – The number of documents.

filter_documents​

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

Return documents matching Haystack metadata filters.

Parameters:

  • filters (dict[str, Any] | None) – Haystack metadata filters. Strings in ordered comparisons must be ISO-formatted dates.

Returns:

  • list[Document] – Documents matching the filters.

filter_documents_async​

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

Asynchronously return documents matching Haystack metadata filters.

Parameters:

  • filters (dict[str, Any] | None) – Haystack metadata filters. Strings in ordered comparisons must be ISO-formatted dates.

Returns:

  • list[Document] – Documents matching the filters.

write_documents​

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

Write documents to Azure DocumentDB using the requested duplicate policy.

Parameters:

  • documents (list[Document]) – Documents to write.
  • policy (DuplicatePolicy) – How to handle documents whose IDs already exist.

Returns:

  • int – The number of documents written.

Raises:

  • ValueError – If documents contains an object that is not a Document.
  • DuplicateDocumentError – If a duplicate ID is written with DuplicatePolicy.FAIL.

write_documents_async​

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

Asynchronously write documents using the requested duplicate policy.

Parameters:

  • documents (list[Document]) – Documents to write.
  • policy (DuplicatePolicy) – How to handle documents whose IDs already exist.

Returns:

  • int – The number of documents written.

Raises:

  • ValueError – If documents contains an object that is not a Document.
  • DuplicateDocumentError – If a duplicate ID is written with DuplicatePolicy.FAIL.

delete_documents​

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

Delete documents with matching Haystack IDs.

Parameters:

  • document_ids (list[str]) – IDs of documents to delete.

delete_documents_async​

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

Asynchronously delete documents with matching Haystack IDs.

Parameters:

  • document_ids (list[str]) – IDs of documents to delete.

delete_by_filter​

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

Delete documents matching filters.

Parameters:

  • filters (dict[str, Any]) – Haystack metadata filters selecting documents to delete.

Returns:

  • int – The number of documents deleted.

delete_by_filter_async​

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

Asynchronously delete documents matching filters.

Parameters:

  • filters (dict[str, Any]) – Haystack metadata filters selecting documents to delete.

Returns:

  • int – The number of documents deleted.

update_by_filter​

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

Update metadata on documents matching filters.

Parameters:

  • filters (dict[str, Any]) – Haystack metadata filters selecting documents to update.
  • meta (dict[str, Any]) – Metadata fields and values to set.

Returns:

  • int – The number of documents updated.

update_by_filter_async​

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

Asynchronously update metadata on documents matching filters.

Parameters:

  • filters (dict[str, Any]) – Haystack metadata filters selecting documents to update.
  • meta (dict[str, Any]) – Metadata fields and values to set.

Returns:

  • int – The number of documents updated.

delete_all_documents​

python
delete_all_documents(*, recreate_collection: bool = False) -> None

Delete all documents, optionally recreating the collection.

Parameters:

  • recreate_collection (bool) – Drop and recreate the collection instead of deleting documents individually.

delete_all_documents_async​

python
delete_all_documents_async(*, recreate_collection: bool = False) -> None

Asynchronously delete all documents, optionally recreating the collection.

Parameters:

  • recreate_collection (bool) – Drop and recreate the collection instead of deleting documents individually.

create_vector_index​

python
create_vector_index(
    *,
    dimensions: int,
    similarity: Literal["COS", "L2", "IP"] = "COS",
    kind: Literal[
        "vector-ivf", "vector-hnsw", "vector-diskann"
    ] = "vector-hnsw",
    **index_options: Any
) -> None

Create the configured Azure DocumentDB cosmosSearch vector index.

Parameters:

  • dimensions (int) – Number of dimensions in each embedding.
  • similarity (Literal['COS', 'L2', 'IP']) – Similarity metric: cosine (COS), Euclidean (L2), or inner product (IP).
  • kind (Literal['vector-ivf', 'vector-hnsw', 'vector-diskann']) – Vector index algorithm.
  • index_options (Any) – Algorithm-specific Azure DocumentDB index options.

Raises:

  • ValueError – If dimensions is not positive.
  • DocumentStoreError – If index creation fails.

create_vector_index_async​

python
create_vector_index_async(
    *,
    dimensions: int,
    similarity: Literal["COS", "L2", "IP"] = "COS",
    kind: Literal[
        "vector-ivf", "vector-hnsw", "vector-diskann"
    ] = "vector-hnsw",
    **index_options: Any
) -> None

Asynchronously create the configured cosmosSearch vector index.

Parameters:

  • dimensions (int) – Number of dimensions in each embedding.
  • similarity (Literal['COS', 'L2', 'IP']) – Similarity metric: cosine (COS), Euclidean (L2), or inner product (IP).
  • kind (Literal['vector-ivf', 'vector-hnsw', 'vector-diskann']) – Vector index algorithm.
  • index_options (Any) – Algorithm-specific Azure DocumentDB index options.

Raises:

  • ValueError – If dimensions is not positive.
  • DocumentStoreError – If index creation fails.

haystack_integrations.document_stores.azure_documentdb.filters​