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type AdvancedModeTransformConfig struct{…}
ChunkingConfig AdvancedModeTransformConfigChunkingConfigUnionoptional
type AzureOpenAIEmbedding struct{…}
type AzureOpenAIEmbeddingConfig struct{…}
Configuration for the Azure OpenAI embedding model.
type DataSinkCreate struct{…}
Schema for creating a data sink.
Component DataSinkCreateComponentUnion
Component that implements the data sink
type CloudPineconeVectorStore struct{…}
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
type CloudPostgresVectorStore struct{…}
type CloudQdrantVectorStore struct{…}
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
type CloudMongoDBAtlasVectorSearch struct{…}
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
type CloudAstraDBVectorStore struct{…}
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’
type GeminiEmbedding struct{…}
OutputDimensionality int64optional
Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.
type GeminiEmbeddingConfig struct{…}
Configuration for the Gemini embedding model.
OutputDimensionality int64optional
Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.
type HuggingFaceInferenceAPIEmbedding struct{…}
Headers map[string, string]optional
Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.
Pooling HuggingFaceInferenceAPIEmbeddingPoolingoptional
Enum of possible pooling choices with pooling behaviors.
type HuggingFaceInferenceAPIEmbeddingConfig struct{…}
Configuration for the HuggingFace Inference API embedding model.
Headers map[string, string]optional
Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.
Pooling HuggingFaceInferenceAPIEmbeddingPoolingoptional
Enum of possible pooling choices with pooling behaviors.
type LlamaParseParametersResp struct{…}
Priority LlamaParseParametersPriorityoptional
The priority for the request. This field may be ignored or overwritten depending on the organization tier.
WebhookConfigurations []LlamaParseParametersWebhookConfigurationRespoptional
Outbound webhook endpoints to notify on job status changes
WebhookEvents []stringoptional
Events to subscribe to (e.g. ‘parse.success’, ‘extract.error’). If null, all events are delivered.
type LlmParametersResp struct{…}
ModelName LlmParametersModelNameoptional
The name of the model to use for LLM completions.
type ManagedIngestionStatusResponse struct{…}
Status ManagedIngestionStatusResponseStatus
Status of the ingestion.
Error []ManagedIngestionStatusResponseErroroptional
List of errors that occurred during ingestion.
Step string
Name of the job that failed.
type MetadataFilters struct{…}
Metadata filters for vector stores.
Filters []MetadataFiltersFilterUnion
type MetadataFiltersFilterMetadataFilter struct{…}
Comprehensive metadata filter for vector stores to support more operators.
Value uses Strict types, as int, float and str are compatible types and were all converted to string before.
See: https://docs.pydantic.dev/latest/usage/types/#strict-types
Operator stringoptional
Vector store filter operator.
type OpenAIEmbedding struct{…}
type OpenAIEmbeddingConfig struct{…}
Configuration for the OpenAI embedding model.
type Pipeline struct{…}
Schema for a pipeline.
EmbeddingConfig PipelineEmbeddingConfigUnion
type PipelineEmbeddingConfigManagedOpenAIEmbedding struct{…}
type AzureOpenAIEmbeddingConfig struct{…}
Configuration for the Azure OpenAI embedding model.
type CohereEmbeddingConfig struct{…}
type GeminiEmbeddingConfig struct{…}
Configuration for the Gemini embedding model.
OutputDimensionality int64optional
Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.
type HuggingFaceInferenceAPIEmbeddingConfig struct{…}
Configuration for the HuggingFace Inference API embedding model.
Headers map[string, string]optional
Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.
Pooling HuggingFaceInferenceAPIEmbeddingPoolingoptional
Enum of possible pooling choices with pooling behaviors.
type OpenAIEmbeddingConfig struct{…}
Configuration for the OpenAI embedding model.
type VertexAIEmbeddingConfig struct{…}
Schema for a data sink.
Component DataSinkComponentUnion
Component that implements the data sink
type CloudPineconeVectorStore struct{…}
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
type CloudPostgresVectorStore struct{…}
type CloudQdrantVectorStore struct{…}
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
type CloudMongoDBAtlasVectorSearch struct{…}
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
type CloudAstraDBVectorStore struct{…}
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’
EmbeddingModelConfig PipelineEmbeddingModelConfigoptional
Schema for an embedding model config.
EmbeddingConfig PipelineEmbeddingModelConfigEmbeddingConfigUnion
The embedding configuration for the embedding model config.
type AzureOpenAIEmbeddingConfig struct{…}
Configuration for the Azure OpenAI embedding model.
type CohereEmbeddingConfig struct{…}
type GeminiEmbeddingConfig struct{…}
Configuration for the Gemini embedding model.
OutputDimensionality int64optional
Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.
type HuggingFaceInferenceAPIEmbeddingConfig struct{…}
Configuration for the HuggingFace Inference API embedding model.
Headers map[string, string]optional
Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.
Pooling HuggingFaceInferenceAPIEmbeddingPoolingoptional
Enum of possible pooling choices with pooling behaviors.
type OpenAIEmbeddingConfig struct{…}
Configuration for the OpenAI embedding model.
type VertexAIEmbeddingConfig struct{…}
EmbeddingModelConfigID stringoptional
The ID of the EmbeddingModelConfig this pipeline is using.
Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.
Priority LlamaParseParametersPriorityoptional
The priority for the request. This field may be ignored or overwritten depending on the organization tier.
WebhookConfigurations []LlamaParseParametersWebhookConfigurationRespoptional
Outbound webhook endpoints to notify on job status changes
WebhookEvents []stringoptional
Events to subscribe to (e.g. ‘parse.success’, ‘extract.error’). If null, all events are delivered.
ManagedPipelineID stringoptional
The ID of the ManagedPipeline this playground pipeline is linked to.
Preset retrieval parameters for the pipeline.
Alpha float64optional
Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.
DenseSimilarityCutoff float64optional
Minimum similarity score wrt query for retrieval
FilesTopK int64optional
Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).
Metadata filters for vector stores.
Filters []MetadataFiltersFilterUnion
type MetadataFiltersFilterMetadataFilter struct{…}
Comprehensive metadata filter for vector stores to support more operators.
Value uses Strict types, as int, float and str are compatible types and were all converted to string before.
See: https://docs.pydantic.dev/latest/usage/types/#strict-types
Operator stringoptional
Vector store filter operator.
Configuration for sparse embedding models used in hybrid search.
This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks.
TransformConfig PipelineTransformConfigUnionoptional
Configuration for the transformation.
type AdvancedModeTransformConfig struct{…}
ChunkingConfig AdvancedModeTransformConfigChunkingConfigUnionoptional
type PipelineCreate struct{…}
Schema for creating a pipeline.
Schema for creating a data sink.
Component DataSinkCreateComponentUnion
Component that implements the data sink
type CloudPineconeVectorStore struct{…}
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
type CloudPostgresVectorStore struct{…}
type CloudQdrantVectorStore struct{…}
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
type CloudMongoDBAtlasVectorSearch struct{…}
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
type CloudAstraDBVectorStore struct{…}
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’
DataSinkID stringoptional
Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID.
EmbeddingConfig PipelineCreateEmbeddingConfigUnionoptional
type AzureOpenAIEmbeddingConfig struct{…}
Configuration for the Azure OpenAI embedding model.
type CohereEmbeddingConfig struct{…}
type GeminiEmbeddingConfig struct{…}
Configuration for the Gemini embedding model.
OutputDimensionality int64optional
Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001.
type HuggingFaceInferenceAPIEmbeddingConfig struct{…}
Configuration for the HuggingFace Inference API embedding model.
Headers map[string, string]optional
Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values.
Pooling HuggingFaceInferenceAPIEmbeddingPoolingoptional
Enum of possible pooling choices with pooling behaviors.
type OpenAIEmbeddingConfig struct{…}
Configuration for the OpenAI embedding model.
type VertexAIEmbeddingConfig struct{…}
EmbeddingModelConfigID stringoptional
Embedding model config ID. When provided instead of embedding_config, the embedding model config will be looked up by ID.
Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.
Priority LlamaParseParametersPriorityoptional
The priority for the request. This field may be ignored or overwritten depending on the organization tier.
WebhookConfigurations []LlamaParseParametersWebhookConfigurationRespoptional
Outbound webhook endpoints to notify on job status changes
WebhookEvents []stringoptional
Events to subscribe to (e.g. ‘parse.success’, ‘extract.error’). If null, all events are delivered.
ManagedPipelineID stringoptional
The ID of the ManagedPipeline this playground pipeline is linked to.
Preset retrieval parameters for the pipeline.
Alpha float64optional
Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.
DenseSimilarityCutoff float64optional
Minimum similarity score wrt query for retrieval
FilesTopK int64optional
Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).
Metadata filters for vector stores.
Filters []MetadataFiltersFilterUnion
type MetadataFiltersFilterMetadataFilter struct{…}
Comprehensive metadata filter for vector stores to support more operators.
Value uses Strict types, as int, float and str are compatible types and were all converted to string before.
See: https://docs.pydantic.dev/latest/usage/types/#strict-types
Operator stringoptional
Vector store filter operator.
Configuration for sparse embedding models used in hybrid search.
This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks.
TransformConfig PipelineCreateTransformConfigUnionoptional
Configuration for the transformation.
type AdvancedModeTransformConfig struct{…}
ChunkingConfig AdvancedModeTransformConfigChunkingConfigUnionoptional
type PresetRetrievalParamsResp struct{…}
Schema for the search params for an retrieval execution that can be preset for a pipeline.
Alpha float64optional
Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.
DenseSimilarityCutoff float64optional
Minimum similarity score wrt query for retrieval
FilesTopK int64optional
Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).
Metadata filters for vector stores.
Filters []MetadataFiltersFilterUnion
type MetadataFiltersFilterMetadataFilter struct{…}
Comprehensive metadata filter for vector stores to support more operators.
Value uses Strict types, as int, float and str are compatible types and were all converted to string before.
See: https://docs.pydantic.dev/latest/usage/types/#strict-types
Operator stringoptional
Vector store filter operator.
type SparseModelConfig struct{…}
Configuration for sparse embedding models used in hybrid search.
This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks.
PipelinesSync
Sync Pipeline
Cancel Pipeline Sync
PipelinesData Sources
List Pipeline Data Sources
Add Data Sources To Pipeline
Update Pipeline Data Source
Get Pipeline Data Source Status
Sync Pipeline Data Source
ModelsExpand Collapse
type PipelineDataSource struct{…}
Schema for a data source in a pipeline.
Component PipelineDataSourceComponentUnion
Component that implements the data source
type CloudAzStorageBlobDataSource struct{…}
type CloudSharepointDataSource struct{…}
ExcludePathPatterns []stringoptional
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$’, ’^~’]
type CloudConfluenceDataSource struct{…}
type CloudJiraDataSource struct{…}
type CloudJiraDataSourceV2 struct{…}
type CloudBoxDataSource struct{…}
AuthenticationMechanism CloudBoxDataSourceAuthenticationMechanism
The type of authentication to use (Developer Token or CCG)
ClientID stringoptional
Box API key used for identifying the application the user is authenticating with
SourceType PipelineDataSourceSourceType
Status PipelineDataSourceStatusoptional
The status of the data source in the pipeline.
Version metadata for the data source
ReaderVersion DataSourceReaderVersionMetadataReaderVersionoptional
PipelinesImages
List File Page Screenshots
Get File Page Screenshot
Get File Page Figure
List File Pages Figures
PipelinesFiles
Get Pipeline File Status Counts
Get Pipeline File Status
Add Files To Pipeline Api
Update Pipeline File
Delete Pipeline File
List Pipeline Files2
ModelsExpand Collapse
type PipelineFile struct{…}
A file associated with a pipeline.
PipelinesMetadata
Import Pipeline Metadata
Delete Pipeline Files Metadata
PipelinesDocuments
Create Batch Pipeline Documents
Paginated List Pipeline Documents
Get Pipeline Document
Delete Pipeline Document
Get Pipeline Document Status
Sync Pipeline Document
List Pipeline Document Chunks
Upsert Batch Pipeline Documents
ModelsExpand Collapse
type TextNode struct{…}
Provided for backward compatibility.
ExcludedEmbedMetadataKeys []stringoptional
Metadata keys that are excluded from text for the embed model.
MetadataTemplate stringoptional
Template for how metadata is formatted, with {key} and {value} placeholders.
Relationships map[string, TextNodeRelationshipUnion]optional
A mapping of relationships to other node information.