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class CloudAstraDBVectorStore: …

Cloud AstraDB Vector Store.

This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud.

Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, ‘default_keyspace’

token: str

The Astra DB Application Token to use

formatpassword
api_endpoint: str

The Astra DB JSON API endpoint for your database

collection_name: str

Collection name to use. If not existing, it will be created

embedding_dimension: int

Length of the embedding vectors in use

class_name: Optional[str]
keyspace: Optional[str]

The keyspace to use. If not provided, ‘default_keyspace’

supports_nested_metadata_filters: Optional[Literal[true]]
class CloudAzStorageBlobDataSource: …
account_url: str

The Azure Storage Blob account URL to use for authentication.

container_name: str

The name of the Azure Storage Blob container to read from.

account_key: Optional[str]

The Azure Storage Blob account key to use for authentication.

formatpassword
account_name: Optional[str]

The Azure Storage Blob account name to use for authentication.

blob: Optional[str]

The blob name to read from.

class_name: Optional[str]
client_id: Optional[str]

The Azure AD client ID to use for authentication.

client_secret: Optional[str]

The Azure AD client secret to use for authentication.

formatpassword
prefix: Optional[str]

The prefix of the Azure Storage Blob objects to read from.

supports_access_control: Optional[bool]
tenant_id: Optional[str]

The Azure AD tenant ID to use for authentication.

class CloudAzureAISearchVectorStore: …

Cloud Azure AI Search Vector Store.

search_service_api_key: str
search_service_endpoint: str
class_name: Optional[str]
client_id: Optional[str]
client_secret: Optional[str]
embedding_dimension: Optional[int]
filterable_metadata_field_keys: Optional[Dict[str, object]]
index_name: Optional[str]
search_service_api_version: Optional[str]
supports_nested_metadata_filters: Optional[Literal[true]]
tenant_id: Optional[str]
class CloudBoxDataSource: …
authentication_mechanism: Literal["developer_token", "ccg"]

The type of authentication to use (Developer Token or CCG)

One of the following:
"developer_token"
"ccg"
class_name: Optional[str]
client_id: Optional[str]

Box API key used for identifying the application the user is authenticating with

client_secret: Optional[str]

Box API secret used for making auth requests.

formatpassword
developer_token: Optional[str]

Developer token for authentication if authentication_mechanism is ‘developer_token’.

formatpassword
enterprise_id: Optional[str]

Box Enterprise ID, if provided authenticates as service.

folder_id: Optional[str]

The ID of the Box folder to read from.

supports_access_control: Optional[bool]
user_id: Optional[str]

Box User ID, if provided authenticates as user.

class CloudConfluenceDataSource: …
authentication_mechanism: str

Type of Authentication for connecting to Confluence APIs.

server_url: str

The server URL of the Confluence instance.

api_token: Optional[str]

The API token to use for authentication.

formatpassword
class_name: Optional[str]
cql: Optional[str]

The CQL query to use for fetching pages.

failure_handling: Optional[FailureHandlingConfig]

Configuration for handling failures during processing. Key-value object controlling failure handling behaviors.

Example: { “skip_list_failures”: true }

Currently supports:

  • skip_list_failures: Skip failed batches/lists and continue processing
skip_list_failures: Optional[bool]

Whether to skip failed batches/lists and continue processing

index_restricted_pages: Optional[bool]

Whether to index restricted pages.

keep_markdown_format: Optional[bool]

Whether to keep the markdown format.

label: Optional[str]

The label to use for fetching pages.

page_ids: Optional[str]

The page IDs of the Confluence to read from.

space_key: Optional[str]

The space key to read from.

supports_access_control: Optional[bool]
sync_permissions: Optional[bool]

Whether to fetch space-level permissions (allowed users/groups) and attach them to document metadata for access control. Disable for Confluence Server/Data Center versions whose permission APIs are unavailable (e.g. the JSON-RPC API removed in Data Center 9.2.6+), which otherwise surface as 401 errors during sync.

user_name: Optional[str]

The username to use for authentication.

class CloudGoogleDriveDataSource: …
folder_id: str

The ID of the Google Drive folder to read from.

class_name: Optional[str]
service_account_key: Optional[Dict[str, str]]

A dictionary containing secret values

supports_access_control: Optional[bool]
class CloudJiraDataSource: …

Cloud Jira Data Source integrating JiraReader.

authentication_mechanism: str

Type of Authentication for connecting to Jira APIs.

query: str

JQL (Jira Query Language) query to search.

api_token: Optional[str]

The API/ Access Token used for Basic, PAT and OAuth2 authentication.

formatpassword
class_name: Optional[str]
cloud_id: Optional[str]

The cloud ID, used in case of OAuth2.

email: Optional[str]

The email address to use for authentication.

server_url: Optional[str]

The server url for Jira Cloud.

supports_access_control: Optional[bool]
class CloudJiraDataSourceV2: …

Cloud Jira Data Source integrating JiraReaderV2.

authentication_mechanism: str

Type of Authentication for connecting to Jira APIs.

query: str

JQL (Jira Query Language) query to search.

server_url: str

The server url for Jira Cloud.

api_token: Optional[str]

The API Access Token used for Basic, PAT and OAuth2 authentication.

formatpassword
api_version: Optional[Literal["2", "3"]]

Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).

One of the following:
"2"
"3"
class_name: Optional[str]
cloud_id: Optional[str]

The cloud ID, used in case of OAuth2.

email: Optional[str]

The email address to use for authentication.

expand: Optional[str]

Fields to expand in the response.

fields: Optional[List[str]]

List of fields to retrieve from Jira. If None, retrieves all fields.

get_permissions: Optional[bool]

Whether to fetch project role permissions and issue-level security

requests_per_minute: Optional[int]

Rate limit for Jira API requests per minute.

supports_access_control: Optional[bool]
class CloudMilvusVectorStore: …

Cloud Milvus Vector Store.

uri: str
token: Optional[str]
class_name: Optional[str]
collection_name: Optional[str]
embedding_dimension: Optional[int]
supports_nested_metadata_filters: Optional[bool]

Cloud MongoDB Atlas Vector Store.

This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud.

Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index

class CloudNotionPageDataSource: …
integration_token: str

The integration token to use for authentication.

formatpassword
class_name: Optional[str]
database_ids: Optional[str]

The Notion Database Id to read content from.

page_ids: Optional[str]

The Page ID’s of the Notion to read from.

supports_access_control: Optional[bool]
class CloudOneDriveDataSource: …
client_id: str

The client ID to use for authentication.

client_secret: str

The client secret to use for authentication.

formatpassword
tenant_id: str

The tenant ID to use for authentication.

user_principal_name: str

The user principal name to use for authentication.

class_name: Optional[str]
folder_id: Optional[str]

The ID of the OneDrive folder to read from.

folder_path: Optional[str]

The path of the OneDrive folder to read from.

required_exts: Optional[List[str]]

The list of required file extensions.

supports_access_control: Optional[Literal[true]]
class CloudPineconeVectorStore: …

Cloud Pinecone Vector Store.

This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud.

Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion

api_key: str

The API key for authenticating with Pinecone

formatpassword
index_name: str
class_name: Optional[str]
insert_kwargs: Optional[Dict[str, object]]
namespace: Optional[str]
supports_nested_metadata_filters: Optional[Literal[true]]
class CloudPostgresVectorStore: …
database: str
embed_dim: int
host: str
password: str
port: int
schema_name: str
table_name: str
user: str
class_name: Optional[str]
hnsw_settings: Optional[PgVectorHnswSettings]

HNSW settings for PGVector.

distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]

The distance method to use.

One of the following:
"l2"
"ip"
"cosine"
"l1"
"hamming"
"jaccard"
ef_construction: Optional[int]

The number of edges to use during the construction phase.

minimum1

The number of edges to use during the search phase.

minimum1
m: Optional[int]

The number of bi-directional links created for each new element.

minimum1
vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]

The type of vector to use.

One of the following:
"vector"
"half_vec"
"bit"
"sparse_vec"
perform_setup: Optional[bool]
supports_nested_metadata_filters: Optional[bool]
class CloudQdrantVectorStore: …

Cloud Qdrant Vector Store.

This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud.

Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client

api_key: str
collection_name: str
url: str
class_name: Optional[str]
client_kwargs: Optional[Dict[str, object]]
max_retries: Optional[int]
supports_nested_metadata_filters: Optional[Literal[true]]
class CloudS3DataSource: …
bucket: str

The name of the S3 bucket to read from.

aws_access_id: Optional[str]

The AWS access ID to use for authentication.

aws_access_secret: Optional[str]

The AWS access secret to use for authentication.

formatpassword
class_name: Optional[str]
prefix: Optional[str]

The prefix of the S3 objects to read from.

regex_pattern: Optional[str]

The regex pattern to filter S3 objects. Must be a valid regex pattern.

s3_endpoint_url: Optional[str]

The S3 endpoint URL to use for authentication.

supports_access_control: Optional[bool]
class CloudSharepointDataSource: …
client_id: str

The client ID to use for authentication.

client_secret: str

The client secret to use for authentication.

formatpassword
tenant_id: str

The tenant ID to use for authentication.

class_name: Optional[str]
drive_name: Optional[str]

The name of the Sharepoint drive to read from.

exclude_path_patterns: Optional[List[str]]

List of regex patterns for file paths to exclude. Files whose paths (including filename) match any pattern will be excluded. Example: [‘/temp/’, ‘/backup/’, ‘.git/’, ‘.tmp$’, ’^~’]

folder_id: Optional[str]

The ID of the Sharepoint folder to read from.

folder_path: Optional[str]

The path of the Sharepoint folder to read from.

get_permissions: Optional[bool]

Whether to get permissions for the sharepoint site.

include_path_patterns: Optional[List[str]]

List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: [‘/reports/’, ‘/docs/..pdf$’, ‘^Report..pdf$’]

required_exts: Optional[List[str]]

The list of required file extensions.

site_id: Optional[str]

The ID of the SharePoint site to download from.

site_name: Optional[str]

The name of the SharePoint site to download from.

supports_access_control: Optional[Literal[true]]
class CloudSlackDataSource: …
slack_token: str

Slack Bot Token.

formatpassword
channel_ids: Optional[str]

Slack Channel.

channel_patterns: Optional[str]

Slack Channel name pattern.

class_name: Optional[str]
earliest_date: Optional[str]

Earliest date.

earliest_date_timestamp: Optional[float]

Earliest date timestamp.

latest_date: Optional[str]

Latest date.

latest_date_timestamp: Optional[float]

Latest date timestamp.

supports_access_control: Optional[bool]
class FailureHandlingConfig: …

Configuration for handling different types of failures during data source processing.

skip_list_failures: Optional[bool]

Whether to skip failed batches/lists and continue processing

class PgVectorHnswSettings: …

HNSW settings for PGVector.

distance_method: Optional[Literal["l2", "ip", "cosine", 3 more]]

The distance method to use.

One of the following:
"l2"
"ip"
"cosine"
"l1"
"hamming"
"jaccard"
ef_construction: Optional[int]

The number of edges to use during the construction phase.

minimum1

The number of edges to use during the search phase.

minimum1
m: Optional[int]

The number of bi-directional links created for each new element.

minimum1
vector_type: Optional[Literal["vector", "half_vec", "bit", "sparse_vec"]]

The type of vector to use.

One of the following:
"vector"
"half_vec"
"bit"
"sparse_vec"
Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/