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CloudAstraDBVectorStore = object { 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
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
keyspace: optional string
The keyspace to use. If not provided, 'default_keyspace'
CloudAzStorageBlobDataSource = object { 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: optional string
The Azure Storage Blob account key to use for authentication.
account_name: optional string
The Azure Storage Blob account name to use for authentication.
blob: optional string
The blob name to read from.
client_id: optional string
The Azure AD client ID to use for authentication.
client_secret: optional string
The Azure AD client secret to use for authentication.
prefix: optional string
The prefix of the Azure Storage Blob objects to read from.
tenant_id: optional string
The Azure AD tenant ID to use for authentication.
CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }
Cloud Azure AI Search Vector Store.
CloudBoxDataSource = object { authentication_mechanism, class_name, client_id, 6 more }
authentication_mechanism: "developer_token" or "ccg"
The type of authentication to use (Developer Token or CCG)
client_id: optional string
Box API key used for identifying the application the user is authenticating with
client_secret: optional string
Box API secret used for making auth requests.
developer_token: optional string
Developer token for authentication if authentication_mechanism is 'developer_token'.
enterprise_id: optional string
Box Enterprise ID, if provided authenticates as service.
folder_id: optional string
The ID of the Box folder to read from.
user_id: optional string
Box User ID, if provided authenticates as user.
CloudConfluenceDataSource = object { 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: optional string
The API token to use for authentication.
cql: optional string
The CQL query to use for fetching pages.
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 boolean
Whether to skip failed batches/lists and continue processing
index_restricted_pages: optional boolean
Whether to index restricted pages.
keep_markdown_format: optional boolean
Whether to keep the markdown format.
label: optional string
The label to use for fetching pages.
page_ids: optional string
The page IDs of the Confluence to read from.
space_key: optional string
The space key to read from.
user_name: optional string
The username to use for authentication.
CloudJiraDataSource = object { 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: optional string
The API/ Access Token used for Basic, PAT and OAuth2 authentication.
cloud_id: optional string
The cloud ID, used in case of OAuth2.
email: optional string
The email address to use for authentication.
server_url: optional string
The server url for Jira Cloud.
CloudJiraDataSourceV2 = object { 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: optional string
The API Access Token used for Basic, PAT and OAuth2 authentication.
api_version: optional "2" or "3"
Jira REST API version to use (2 or 3). 3 supports Atlassian Document Format (ADF).
cloud_id: optional string
The cloud ID, used in case of OAuth2.
email: optional string
The email address to use for authentication.
expand: optional string
Fields to expand in the response.
fields: optional array of string
List of fields to retrieve from Jira. If None, retrieves all fields.
get_permissions: optional boolean
Whether to fetch project role permissions and issue-level security
requests_per_minute: optional number
Rate limit for Jira API requests per minute.
CloudMilvusVectorStore = object { uri, token, class_name, 3 more }
Cloud Milvus Vector Store.
CloudMongoDBAtlasVectorSearch = object { 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
CloudNotionPageDataSource = object { integration_token, class_name, database_ids, 2 more }
integration_token: string
The integration token to use for authentication.
database_ids: optional string
The Notion Database Id to read content from.
page_ids: optional string
The Page ID's of the Notion to read from.
CloudOneDriveDataSource = object { 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.
tenant_id: string
The tenant ID to use for authentication.
user_principal_name: string
The user principal name to use for authentication.
folder_id: optional string
The ID of the OneDrive folder to read from.
folder_path: optional string
The path of the OneDrive folder to read from.
required_exts: optional array of string
The list of required file extensions.
CloudPineconeVectorStore = object { 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
CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }
hnsw_settings: optional PgVectorHnswSettings { distance_method, ef_construction, ef_search, 2 more }
HNSW settings for PGVector.
distance_method: optional "l2" or "ip" or "cosine" or 3 more
The distance method to use.
ef_construction: optional number
The number of edges to use during the construction phase.
ef_search: optional number
The number of edges to use during the search phase.
m: optional number
The number of bi-directional links created for each new element.
vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"
The type of vector to use.
CloudQdrantVectorStore = object { 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
CloudS3DataSource = object { bucket, aws_access_id, aws_access_secret, 5 more }
bucket: string
The name of the S3 bucket to read from.
aws_access_id: optional string
The AWS access ID to use for authentication.
aws_access_secret: optional string
The AWS access secret to use for authentication.
prefix: optional string
The prefix of the S3 objects to read from.
regex_pattern: optional string
The regex pattern to filter S3 objects. Must be a valid regex pattern.
s3_endpoint_url: optional string
The S3 endpoint URL to use for authentication.
CloudSharepointDataSource = object { 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.
tenant_id: string
The tenant ID to use for authentication.
drive_name: optional string
The name of the Sharepoint drive to read from.
exclude_path_patterns: optional array of string
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$', '^~']
folder_id: optional string
The ID of the Sharepoint folder to read from.
folder_path: optional string
The path of the Sharepoint folder to read from.
get_permissions: optional boolean
Whether to get permissions for the sharepoint site.
include_path_patterns: optional array of string
List of regex patterns for file paths to include. Full paths (including filename) must match at least one pattern to be included. Example: ['/reports/', '/docs/..pdf$', '^Report..pdf$']
required_exts: optional array of string
The list of required file extensions.
site_id: optional string
The ID of the SharePoint site to download from.
site_name: optional string
The name of the SharePoint site to download from.
CloudSlackDataSource = object { slack_token, channel_ids, channel_patterns, 6 more }
slack_token: string
Slack Bot Token.
channel_ids: optional string
Slack Channel.
channel_patterns: optional string
Slack Channel name pattern.
earliest_date: optional string
Earliest date.
earliest_date_timestamp: optional number
Earliest date timestamp.
latest_date: optional string
Latest date.
latest_date_timestamp: optional number
Latest date timestamp.
FailureHandlingConfig = object { skip_list_failures }
Configuration for handling different types of failures during data source processing.
skip_list_failures: optional boolean
Whether to skip failed batches/lists and continue processing
PgVectorHnswSettings = object { distance_method, ef_construction, ef_search, 2 more }
HNSW settings for PGVector.
distance_method: optional "l2" or "ip" or "cosine" or 3 more
The distance method to use.
ef_construction: optional number
The number of edges to use during the construction phase.
ef_search: optional number
The number of edges to use during the search phase.
m: optional number
The number of bi-directional links created for each new element.
vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"
The type of vector to use.