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class AzureOpenAIEmbedding:
class AzureOpenAIEmbeddingConfig:
Configuration for the Azure OpenAI embedding model.
class DataSinkCreate:
Schema for creating a data sink.
Component component
Component that implements the data sink
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
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
class CloudMongoDBAtlasVectorSearch:
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 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’
class GeminiEmbedding:
Optional<Long> outputDimensionality
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.
class GeminiEmbeddingConfig:
Configuration for the Gemini embedding model.
Optional<Long> outputDimensionality
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.
class HuggingFaceInferenceApiEmbedding:
Optional<Headers> headers
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.
class HuggingFaceInferenceApiEmbeddingConfig:
Configuration for the HuggingFace Inference API embedding model.
Optional<Headers> headers
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.
class LlamaParseParameters:
Optional<List<WebhookConfiguration>> webhookConfigurations
Outbound webhook endpoints to notify on job status changes
Optional<List<WebhookEvent>> webhookEvents
Events to subscribe to (e.g. ‘parse.success’, ‘extract.error’). If null, all events are delivered.
Optional<WebhookHeaders> webhookHeaders
Custom HTTP headers sent with each webhook request (e.g. auth tokens)
class MetadataFilters:
Metadata filters for vector stores.
List<Filter> filters
class MetadataFilter:
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
class OpenAIEmbedding:
class OpenAIEmbeddingConfig:
Configuration for the OpenAI embedding model.
class Pipeline:
Schema for a pipeline.
EmbeddingConfig embeddingConfig
class ManagedOpenAIEmbedding:
class AzureOpenAIEmbeddingConfig:
Configuration for the Azure OpenAI embedding model.
class CohereEmbeddingConfig:
class GeminiEmbeddingConfig:
Configuration for the Gemini embedding model.
Optional<Long> outputDimensionality
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.
class HuggingFaceInferenceApiEmbeddingConfig:
Configuration for the HuggingFace Inference API embedding model.
Optional<Headers> headers
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.
class OpenAIEmbeddingConfig:
Configuration for the OpenAI embedding model.
class VertexAiEmbeddingConfig:
Schema for a data sink.
Component component
Component that implements the data sink
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
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
class CloudMongoDBAtlasVectorSearch:
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 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’
Optional<EmbeddingModelConfig> embeddingModelConfig
Schema for an embedding model config.
EmbeddingConfig embeddingConfig
The embedding configuration for the embedding model config.
class AzureOpenAIEmbeddingConfig:
Configuration for the Azure OpenAI embedding model.
class CohereEmbeddingConfig:
class GeminiEmbeddingConfig:
Configuration for the Gemini embedding model.
Optional<Long> outputDimensionality
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.
class HuggingFaceInferenceApiEmbeddingConfig:
Configuration for the HuggingFace Inference API embedding model.
Optional<Headers> headers
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.
class OpenAIEmbeddingConfig:
Configuration for the OpenAI embedding model.
class VertexAiEmbeddingConfig:
Optional<String> embeddingModelConfigId
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.
Optional<List<WebhookConfiguration>> webhookConfigurations
Outbound webhook endpoints to notify on job status changes
Optional<List<WebhookEvent>> webhookEvents
Events to subscribe to (e.g. ‘parse.success’, ‘extract.error’). If null, all events are delivered.
Optional<WebhookHeaders> webhookHeaders
Custom HTTP headers sent with each webhook request (e.g. auth tokens)
Optional<String> managedPipelineId
The ID of the ManagedPipeline this playground pipeline is linked to.
Preset retrieval parameters for the pipeline.
Optional<Double> alpha
Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.
Optional<Double> denseSimilarityCutoff
Minimum similarity score wrt query for retrieval
Optional<Long> filesTopK
Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).
Metadata filters for vector stores.
List<Filter> filters
class MetadataFilter:
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
Optional<TransformConfig> transformConfig
class PipelineCreate:
Schema for creating a pipeline.
Schema for creating a data sink.
Component component
Component that implements the data sink
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
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
class CloudMongoDBAtlasVectorSearch:
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 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’
Optional<String> dataSinkId
Data sink ID. When provided instead of data_sink, the data sink will be looked up by ID.
Optional<EmbeddingConfig> embeddingConfig
class AzureOpenAIEmbeddingConfig:
Configuration for the Azure OpenAI embedding model.
class CohereEmbeddingConfig:
class GeminiEmbeddingConfig:
Configuration for the Gemini embedding model.
Optional<Long> outputDimensionality
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.
class HuggingFaceInferenceApiEmbeddingConfig:
Configuration for the HuggingFace Inference API embedding model.
Optional<Headers> headers
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.
class OpenAIEmbeddingConfig:
Configuration for the OpenAI embedding model.
class VertexAiEmbeddingConfig:
Optional<String> embeddingModelConfigId
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.
Optional<List<WebhookConfiguration>> webhookConfigurations
Outbound webhook endpoints to notify on job status changes
Optional<List<WebhookEvent>> webhookEvents
Events to subscribe to (e.g. ‘parse.success’, ‘extract.error’). If null, all events are delivered.
Optional<WebhookHeaders> webhookHeaders
Custom HTTP headers sent with each webhook request (e.g. auth tokens)
Optional<String> managedPipelineId
The ID of the ManagedPipeline this playground pipeline is linked to.
Preset retrieval parameters for the pipeline.
Optional<Double> alpha
Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.
Optional<Double> denseSimilarityCutoff
Minimum similarity score wrt query for retrieval
Optional<Long> filesTopK
Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).
Metadata filters for vector stores.
List<Filter> filters
class MetadataFilter:
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
Optional<TransformConfig> transformConfig
class PresetRetrievalParams:
Schema for the search params for an retrieval execution that can be preset for a pipeline.
Optional<Double> alpha
Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.
Optional<Double> denseSimilarityCutoff
Minimum similarity score wrt query for retrieval
Optional<Long> filesTopK
Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).
Metadata filters for vector stores.
List<Filter> filters
class MetadataFilter:
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
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ModelsExpand Collapse
class PipelineDataSource:
Schema for a data source in a pipeline.
Component component
Component that implements the data source
class CloudAzStorageBlobDataSource:
class CloudSharepointDataSource:
Optional<List<String>> excludePathPatterns
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$’, ’^~’]
class CloudConfluenceDataSource:
class CloudJiraDataSource:
class CloudJiraDataSourceV2:
class CloudBoxDataSource:
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ModelsExpand Collapse
class TextNode:
Provided for backward compatibility.
Optional<List<String>> excludedEmbedMetadataKeys
Metadata keys that are excluded from text for the embed model.
Optional<List<String>> excludedLlmMetadataKeys
Metadata keys that are excluded from text for the LLM.
Optional<String> metadataTemplate
Template for how metadata is formatted, with {key} and {value} placeholders.