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CloudAstraDBVectorStore { token, api_endpoint, collection_name, 4 more }

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: string

The Astra DB Application Token to use

formatpassword

api_endpoint: string

The Astra DB JSON API endpoint for your database

collection_name: string

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

embedding_dimension: number

Length of the embedding vectors in use

class_name?: string

keyspace?: string | null

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

supports_nested_metadata_filters?: true

CloudAzStorageBlobDataSource { account_url, container_name, account_key, 8 more }

account_url: string

The Azure Storage Blob account URL to use for authentication.

container_name: string

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

account_key?: string | null

The Azure Storage Blob account key to use for authentication.

formatpassword

account_name?: string | null

The Azure Storage Blob account name to use for authentication.

blob?: string | null

The blob name to read from.

class_name?: string

client_id?: string | null

The Azure AD client ID to use for authentication.

client_secret?: string | null

The Azure AD client secret to use for authentication.

formatpassword

prefix?: string | null

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

supports_access_control?: boolean

tenant_id?: string | null

The Azure AD tenant ID to use for authentication.

CloudAzureAISearchVectorStore { search_service_api_key, search_service_endpoint, class_name, 8 more }

Cloud Azure AI Search Vector Store.

search_service_api_key: string

search_service_endpoint: string

class_name?: string

client_id?: string | null

client_secret?: string | null

embedding_dimension?: number | null

filterable_metadata_field_keys?: Record<string, unknown> | null

index_name?: string | null

search_service_api_version?: string | null

supports_nested_metadata_filters?: true

tenant_id?: string | null

CloudBoxDataSource { authentication_mechanism, class_name, client_id, 6 more }

authentication_mechanism: "developer_token" | "ccg"

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

One of the following:

"developer_token"

"ccg"

class_name?: string

client_id?: string | null

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

client_secret?: string | null

Box API secret used for making auth requests.

formatpassword

developer_token?: string | null

Developer token for authentication if authentication_mechanism is ‘developer_token’.

formatpassword

enterprise_id?: string | null

Box Enterprise ID, if provided authenticates as service.

folder_id?: string | null

The ID of the Box folder to read from.

supports_access_control?: boolean

user_id?: string | null

Box User ID, if provided authenticates as user.

CloudConfluenceDataSource { authentication_mechanism, server_url, api_token, 10 more }

authentication_mechanism: string

Type of Authentication for connecting to Confluence APIs.

server_url: string

The server URL of the Confluence instance.

api_token?: string | null

The API token to use for authentication.

formatpassword

class_name?: string

cql?: string | null

The CQL query to use for fetching pages.

failure_handling?: FailureHandlingConfig { skip_list_failures }

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?: boolean

Whether to skip failed batches/lists and continue processing

index_restricted_pages?: boolean

Whether to index restricted pages.

keep_markdown_format?: boolean

Whether to keep the markdown format.

label?: string | null

The label to use for fetching pages.

page_ids?: string | null

The page IDs of the Confluence to read from.

space_key?: string | null

The space key to read from.

supports_access_control?: boolean

user_name?: string | null

The username to use for authentication.

CloudGoogleDriveDataSource { folder_id, class_name, service_account_key, supports_access_control }

folder_id: string

The ID of the Google Drive folder to read from.

class_name?: string

service_account_key?: Record<string, string> | null

A dictionary containing secret values

supports_access_control?: boolean

CloudJiraDataSource { authentication_mechanism, query, api_token, 5 more }

Cloud Jira Data Source integrating JiraReader.

authentication_mechanism: string

Type of Authentication for connecting to Jira APIs.

query: string

JQL (Jira Query Language) query to search.

api_token?: string | null

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

formatpassword

class_name?: string

cloud_id?: string | null

The cloud ID, used in case of OAuth2.

email?: string | null

The email address to use for authentication.

server_url?: string | null

The server url for Jira Cloud.

supports_access_control?: boolean

CloudJiraDataSourceV2 { authentication_mechanism, query, server_url, 10 more }

Cloud Jira Data Source integrating JiraReaderV2.

authentication_mechanism: string

Type of Authentication for connecting to Jira APIs.

query: string

JQL (Jira Query Language) query to search.

server_url: string

The server url for Jira Cloud.

api_token?: string | null

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

formatpassword

api_version?: "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?: string

cloud_id?: string | null

The cloud ID, used in case of OAuth2.

email?: string | null

The email address to use for authentication.

expand?: string | null

Fields to expand in the response.

fields?: Array<string> | null

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

get_permissions?: boolean

Whether to fetch project role permissions and issue-level security

requests_per_minute?: number | null

Rate limit for Jira API requests per minute.

supports_access_control?: boolean

CloudMilvusVectorStore { uri, token, class_name, 3 more }

Cloud Milvus Vector Store.

uri: string

token?: string | null

class_name?: string

collection_name?: string | null

embedding_dimension?: number | null

supports_nested_metadata_filters?: boolean

CloudMongoDBAtlasVectorSearch { collection_name, db_name, mongodb_uri, 5 more }

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

collection_name: string

db_name: string

mongodb_uri: string

class_name?: string

embedding_dimension?: number | null

fulltext_index_name?: string | null

supports_nested_metadata_filters?: boolean

vector_index_name?: string | null

CloudNotionPageDataSource { integration_token, class_name, database_ids, 2 more }

integration_token: string

The integration token to use for authentication.

formatpassword

class_name?: string

database_ids?: string | null

The Notion Database Id to read content from.

page_ids?: string | null

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

supports_access_control?: boolean

CloudOneDriveDataSource { client_id, client_secret, tenant_id, 6 more }

client_id: string

The client ID to use for authentication.

client_secret: string

The client secret to use for authentication.

formatpassword

tenant_id: string

The tenant ID to use for authentication.

user_principal_name: string

The user principal name to use for authentication.

class_name?: string

folder_id?: string | null

The ID of the OneDrive folder to read from.

folder_path?: string | null

The path of the OneDrive folder to read from.

required_exts?: Array<string> | null

The list of required file extensions.

supports_access_control?: true

CloudPineconeVectorStore { api_key, index_name, class_name, 3 more }

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: string

The API key for authenticating with Pinecone

formatpassword

index_name: string

class_name?: string

insert_kwargs?: Record<string, unknown> | null

namespace?: string | null

supports_nested_metadata_filters?: true

CloudPostgresVectorStore { database, embed_dim, host, 10 more }

database: string

embed_dim: number

host: string

password: string

port: number

schema_name: string

table_name: string

user: string

class_name?: string

hnsw_settings?: PgVectorHnswSettings { distance_method, ef_construction, ef_search, 2 more } | null

HNSW settings for PGVector.

distance_method?: "l2" | "ip" | "cosine" | 3 more

The distance method to use.

One of the following:

"l2"

"ip"

"cosine"

"l1"

"hamming"

"jaccard"

ef_construction?: number

The number of edges to use during the construction phase.

minimum1

ef_search?: number

The number of edges to use during the search phase.

minimum1

m?: number

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

minimum1

vector_type?: "vector" | "half_vec" | "bit" | "sparse_vec"

The type of vector to use.

One of the following:

"vector"

"half_vec"

"bit"

"sparse_vec"

hybrid_search?: boolean | null

perform_setup?: boolean

supports_nested_metadata_filters?: boolean

CloudQdrantVectorStore { api_key, collection_name, url, 4 more }

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: string

collection_name: string

url: string

class_name?: string

client_kwargs?: Record<string, unknown>

max_retries?: number

supports_nested_metadata_filters?: true

CloudS3DataSource { bucket, aws_access_id, aws_access_secret, 5 more }

bucket: string

The name of the S3 bucket to read from.

aws_access_id?: string | null

The AWS access ID to use for authentication.

aws_access_secret?: string | null

The AWS access secret to use for authentication.

formatpassword

class_name?: string

prefix?: string | null

The prefix of the S3 objects to read from.

regex_pattern?: string | null

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

s3_endpoint_url?: string | null

The S3 endpoint URL to use for authentication.

supports_access_control?: boolean

CloudSharepointDataSource { client_id, client_secret, tenant_id, 11 more }

client_id: string

The client ID to use for authentication.

client_secret: string

The client secret to use for authentication.

formatpassword

tenant_id: string

The tenant ID to use for authentication.

class_name?: string

drive_name?: string | null

The name of the Sharepoint drive to read from.

exclude_path_patterns?: Array<string> | null

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?: string | null

The ID of the Sharepoint folder to read from.

folder_path?: string | null

The path of the Sharepoint folder to read from.

get_permissions?: boolean | null

Whether to get permissions for the sharepoint site.

include_path_patterns?: Array<string> | null

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?: Array<string> | null

The list of required file extensions.

site_id?: string | null

The ID of the SharePoint site to download from.

site_name?: string | null

The name of the SharePoint site to download from.

supports_access_control?: true

CloudSlackDataSource { slack_token, channel_ids, channel_patterns, 6 more }

slack_token: string

Slack Bot Token.

formatpassword

channel_ids?: string | null

Slack Channel.

channel_patterns?: string | null

Slack Channel name pattern.

class_name?: string

earliest_date?: string | null

Earliest date.

earliest_date_timestamp?: number | null

Earliest date timestamp.

latest_date?: string | null

Latest date.

latest_date_timestamp?: number | null

Latest date timestamp.

supports_access_control?: boolean

FailureHandlingConfig { skip_list_failures }

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

skip_list_failures?: boolean

Whether to skip failed batches/lists and continue processing

PgVectorHnswSettings { distance_method, ef_construction, ef_search, 2 more }

HNSW settings for PGVector.

distance_method?: "l2" | "ip" | "cosine" | 3 more

The distance method to use.

One of the following:

"l2"

"ip"

"cosine"

"l1"

"hamming"

"jaccard"

ef_construction?: number

The number of edges to use during the construction phase.

minimum1

ef_search?: number

The number of edges to use during the search phase.

minimum1

m?: number

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

minimum1

vector_type?: "vector" | "half_vec" | "bit" | "sparse_vec"

The type of vector to use.

One of the following:

"vector"

"half_vec"

"bit"

"sparse_vec"