Get Pipeline

client.Pipelines.Get(ctx, pipelineID) (*Pipeline, error)

GET/api/v1/pipelines/{pipeline_id}

Get a pipeline by ID.

ParametersExpand Collapse

pipelineID string

ReturnsExpand Collapse

type Pipeline struct{…}

Schema for a pipeline.

ID string

Unique identifier

formatuuid

EmbeddingConfig PipelineEmbeddingConfigUnion

One of the following:

type PipelineEmbeddingConfigManagedOpenAIEmbedding struct{…}

Component PipelineEmbeddingConfigManagedOpenAIEmbeddingComponentoptional

Configuration for the Managed OpenAI embedding model.

ClassName stringoptional

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

ModelName stringoptional

The name of the OpenAI embedding model.

NumWorkers int64optional

The number of workers to use for async embedding calls.

Type stringoptional

Type of the embedding model.

type AzureOpenAIEmbeddingConfig struct{…}

Component AzureOpenAIEmbeddingoptional

Configuration for the Azure OpenAI embedding model.

AdditionalKwargs map[string, any]optional

Additional kwargs for the OpenAI API.

APIBase stringoptional

The base URL for Azure deployment.

APIKey stringoptional

The OpenAI API key.

APIVersion stringoptional

The version for Azure OpenAI API.

AzureDeployment stringoptional

The Azure deployment to use.

AzureEndpoint stringoptional

The Azure endpoint to use.

ClassName stringoptional

DefaultHeaders map[string, string]optional

The default headers for API requests.

Dimensions int64optional

The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

MaxRetries int64optional

Maximum number of retries.

minimum0

ModelName stringoptional

The name of the OpenAI embedding model.

NumWorkers int64optional

The number of workers to use for async embedding calls.

ReuseClient booloptional

Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

Timeout float64optional

Timeout for each request.

minimum0

Type AzureOpenAIEmbeddingConfigTypeoptional

Type of the embedding model.

type CohereEmbeddingConfig struct{…}

Component CohereEmbeddingoptional

Configuration for the Cohere embedding model.

APIKey string

The Cohere API key.

ClassName stringoptional

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

EmbeddingType stringoptional

Embedding type. If not provided float embedding_type is used when needed.

InputType stringoptional

Model Input type. If not provided, search_document and search_query are used when needed.

ModelName stringoptional

The modelId of the Cohere model to use.

NumWorkers int64optional

The number of workers to use for async embedding calls.

Truncate stringoptional

Truncation type - START/ END/ NONE

Type CohereEmbeddingConfigTypeoptional

Type of the embedding model.

type GeminiEmbeddingConfig struct{…}

Component GeminiEmbeddingoptional

Configuration for the Gemini embedding model.

APIBase stringoptional

API base to access the model. Defaults to None.

APIKey stringoptional

API key to access the model. Defaults to None.

ClassName stringoptional

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

ModelName stringoptional

The modelId of the Gemini model to use.

NumWorkers int64optional

The number of workers to use for async embedding calls.

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.

TaskType stringoptional

The task for embedding model.

Title stringoptional

Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

Transport stringoptional

Transport to access the model. Defaults to None.

Type GeminiEmbeddingConfigTypeoptional

Type of the embedding model.

type HuggingFaceInferenceAPIEmbeddingConfig struct{…}

Component HuggingFaceInferenceAPIEmbeddingoptional

Configuration for the HuggingFace Inference API embedding model.

Token HuggingFaceInferenceAPIEmbeddingTokenUnionoptional

Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

One of the following:

string

bool

ClassName stringoptional

Cookies map[string, string]optional

Additional cookies to send to the server.

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

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.

ModelName stringoptional

Hugging Face model name. If None, the task will be used.

NumWorkers int64optional

The number of workers to use for async embedding calls.

Pooling HuggingFaceInferenceAPIEmbeddingPoolingoptional

Enum of possible pooling choices with pooling behaviors.

One of the following:

const HuggingFaceInferenceAPIEmbeddingPoolingCls HuggingFaceInferenceAPIEmbeddingPooling = "cls"

const HuggingFaceInferenceAPIEmbeddingPoolingMean HuggingFaceInferenceAPIEmbeddingPooling = "mean"

const HuggingFaceInferenceAPIEmbeddingPoolingLast HuggingFaceInferenceAPIEmbeddingPooling = "last"

QueryInstruction stringoptional

Instruction to prepend during query embedding.

Task stringoptional

Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

TextInstruction stringoptional

Instruction to prepend during text embedding.

Timeout float64optional

The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.

Type HuggingFaceInferenceAPIEmbeddingConfigTypeoptional

Type of the embedding model.

type OpenAIEmbeddingConfig struct{…}

Component OpenAIEmbeddingoptional

Configuration for the OpenAI embedding model.

AdditionalKwargs map[string, any]optional

Additional kwargs for the OpenAI API.

APIBase stringoptional

The base URL for OpenAI API.

APIKey stringoptional

The OpenAI API key.

APIVersion stringoptional

The version for OpenAI API.

ClassName stringoptional

DefaultHeaders map[string, string]optional

The default headers for API requests.

Dimensions int64optional

The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

MaxRetries int64optional

Maximum number of retries.

minimum0

ModelName stringoptional

The name of the OpenAI embedding model.

NumWorkers int64optional

The number of workers to use for async embedding calls.

ReuseClient booloptional

Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

Timeout float64optional

Timeout for each request.

minimum0

Type OpenAIEmbeddingConfigTypeoptional

Type of the embedding model.

type VertexAIEmbeddingConfig struct{…}

Component VertexTextEmbeddingoptional

Configuration for the VertexAI embedding model.

ClientEmail string

The client email for the VertexAI credentials.

Location string

The default location to use when making API calls.

PrivateKey string

The private key for the VertexAI credentials.

PrivateKeyID string

The private key ID for the VertexAI credentials.

Project string

The default GCP project to use when making Vertex API calls.

TokenUri string

The token URI for the VertexAI credentials.

AdditionalKwargs map[string, any]optional

Additional kwargs for the Vertex.

ClassName stringoptional

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

EmbedMode VertexTextEmbeddingEmbedModeoptional

The embedding mode to use.

One of the following:

const VertexTextEmbeddingEmbedModeDefault VertexTextEmbeddingEmbedMode = "default"

const VertexTextEmbeddingEmbedModeClassification VertexTextEmbeddingEmbedMode = "classification"

const VertexTextEmbeddingEmbedModeClustering VertexTextEmbeddingEmbedMode = "clustering"

const VertexTextEmbeddingEmbedModeSimilarity VertexTextEmbeddingEmbedMode = "similarity"

const VertexTextEmbeddingEmbedModeRetrieval VertexTextEmbeddingEmbedMode = "retrieval"

ModelName stringoptional

The modelId of the VertexAI model to use.

NumWorkers int64optional

The number of workers to use for async embedding calls.

Type VertexAIEmbeddingConfigTypeoptional

Type of the embedding model.

type BedrockEmbeddingConfig struct{…}

Component BedrockEmbeddingoptional

Configuration for the Bedrock embedding model.

AdditionalKwargs map[string, any]optional

Additional kwargs for the bedrock client.

AwsAccessKeyID stringoptional

AWS Access Key ID to use

AwsSecretAccessKey stringoptional

AWS Secret Access Key to use

AwsSessionToken stringoptional

AWS Session Token to use

ClassName stringoptional

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

MaxRetries int64optional

The maximum number of API retries.

exclusiveMinimum0

ModelName stringoptional

The modelId of the Bedrock model to use.

NumWorkers int64optional

The number of workers to use for async embedding calls.

ProfileName stringoptional

The name of aws profile to use. If not given, then the default profile is used.

RegionName stringoptional

AWS region name to use. Uses region configured in AWS CLI if not passed

Timeout float64optional

The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

Type BedrockEmbeddingConfigTypeoptional

Type of the embedding model.

Name string

ProjectID string

ConfigHash PipelineConfigHashoptional

Hashes for the configuration of a pipeline.

EmbeddingConfigHash stringoptional

Hash of the embedding config.

ParsingConfigHash stringoptional

Hash of the llama parse parameters.

TransformConfigHash stringoptional

Hash of the transform config.

CreatedAt Timeoptional

Creation datetime

formatdate-time

DataSink DataSinkoptional

Schema for a data sink.

ID string

Unique identifier

formatuuid

Component DataSinkComponentUnion

Component that implements the data sink

One of the following:

type DataSinkComponentMap map[string, any]

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

APIKey string

The API key for authenticating with Pinecone

formatpassword

IndexName string

ClassName stringoptional

InsertKwargs map[string, any]optional

Namespace stringoptional

SupportsNestedMetadataFilters booloptional

type CloudPostgresVectorStore struct{…}

Database string

EmbedDim int64

Host string

Password string

Port int64

SchemaName string

TableName string

User string

ClassName stringoptional

HnswSettings PgVectorHnswSettingsoptional

HNSW settings for PGVector.

DistanceMethod PgVectorHnswSettingsDistanceMethodoptional

The distance method to use.

One of the following:

const PgVectorHnswSettingsDistanceMethodL2 PgVectorHnswSettingsDistanceMethod = "l2"

const PgVectorHnswSettingsDistanceMethodIP PgVectorHnswSettingsDistanceMethod = "ip"

const PgVectorHnswSettingsDistanceMethodCosine PgVectorHnswSettingsDistanceMethod = "cosine"

const PgVectorHnswSettingsDistanceMethodL1 PgVectorHnswSettingsDistanceMethod = "l1"

const PgVectorHnswSettingsDistanceMethodHamming PgVectorHnswSettingsDistanceMethod = "hamming"

const PgVectorHnswSettingsDistanceMethodJaccard PgVectorHnswSettingsDistanceMethod = "jaccard"

EfConstruction int64optional

The number of edges to use during the construction phase.

minimum1

EfSearch int64optional

The number of edges to use during the search phase.

minimum1

M int64optional

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

minimum1

VectorType PgVectorHnswSettingsVectorTypeoptional

The type of vector to use.

One of the following:

const PgVectorHnswSettingsVectorTypeVector PgVectorHnswSettingsVectorType = "vector"

const PgVectorHnswSettingsVectorTypeHalfVec PgVectorHnswSettingsVectorType = "half_vec"

const PgVectorHnswSettingsVectorTypeBit PgVectorHnswSettingsVectorType = "bit"

const PgVectorHnswSettingsVectorTypeSparseVec PgVectorHnswSettingsVectorType = "sparse_vec"

HybridSearch booloptional

PerformSetup booloptional

SupportsNestedMetadataFilters booloptional

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

APIKey string

CollectionName string

URL string

ClassName stringoptional

ClientKwargs map[string, any]optional

MaxRetries int64optional

SupportsNestedMetadataFilters booloptional

type CloudAzureAISearchVectorStore struct{…}

Cloud Azure AI Search Vector Store.

SearchServiceAPIKey string

SearchServiceEndpoint string

ClassName stringoptional

ClientID stringoptional

ClientSecret stringoptional

EmbeddingDimension int64optional

FilterableMetadataFieldKeys map[string, any]optional

IndexName stringoptional

SearchServiceAPIVersion stringoptional

SupportsNestedMetadataFilters booloptional

TenantID stringoptional

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

CollectionName string

DBName string

MongoDBUri string

ClassName stringoptional

EmbeddingDimension int64optional

FulltextIndexName stringoptional

SupportsNestedMetadataFilters booloptional

VectorIndexName stringoptional

type CloudMilvusVectorStore struct{…}

Cloud Milvus Vector Store.

Uri string

Token stringoptional

ClassName stringoptional

CollectionName stringoptional

EmbeddingDimension int64optional

SupportsNestedMetadataFilters booloptional

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'

Token string

The Astra DB Application Token to use

formatpassword

APIEndpoint string

The Astra DB JSON API endpoint for your database

CollectionName string

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

EmbeddingDimension int64

Length of the embedding vectors in use

ClassName stringoptional

Keyspace stringoptional

The keyspace to use. If not provided, 'default_keyspace'

SupportsNestedMetadataFilters booloptional

Name string

The name of the data sink.

ProjectID string

SinkType DataSinkSinkType

One of the following:

const DataSinkSinkTypePinecone DataSinkSinkType = "PINECONE"

const DataSinkSinkTypePostgres DataSinkSinkType = "POSTGRES"

const DataSinkSinkTypeQdrant DataSinkSinkType = "QDRANT"

const DataSinkSinkTypeAzureaiSearch DataSinkSinkType = "AZUREAI_SEARCH"

const DataSinkSinkTypeMongoDBAtlas DataSinkSinkType = "MONGODB_ATLAS"

const DataSinkSinkTypeMilvus DataSinkSinkType = "MILVUS"

const DataSinkSinkTypeAstraDB DataSinkSinkType = "ASTRA_DB"

CreatedAt Timeoptional

Creation datetime

formatdate-time

UpdatedAt Timeoptional

Update datetime

formatdate-time

EmbeddingModelConfig PipelineEmbeddingModelConfigoptional

Schema for an embedding model config.

ID string

Unique identifier

formatuuid

EmbeddingConfig PipelineEmbeddingModelConfigEmbeddingConfigUnion

The embedding configuration for the embedding model config.

One of the following:

type AzureOpenAIEmbeddingConfig struct{…}

Component AzureOpenAIEmbeddingoptional

Configuration for the Azure OpenAI embedding model.

AdditionalKwargs map[string, any]optional

Additional kwargs for the OpenAI API.

APIBase stringoptional

The base URL for Azure deployment.

APIKey stringoptional

The OpenAI API key.

APIVersion stringoptional

The version for Azure OpenAI API.

AzureDeployment stringoptional

The Azure deployment to use.

AzureEndpoint stringoptional

The Azure endpoint to use.

ClassName stringoptional

DefaultHeaders map[string, string]optional

The default headers for API requests.

Dimensions int64optional

The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

MaxRetries int64optional

Maximum number of retries.

minimum0

ModelName stringoptional

The name of the OpenAI embedding model.

NumWorkers int64optional

The number of workers to use for async embedding calls.

ReuseClient booloptional

Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

Timeout float64optional

Timeout for each request.

minimum0

Type AzureOpenAIEmbeddingConfigTypeoptional

Type of the embedding model.

type CohereEmbeddingConfig struct{…}

Component CohereEmbeddingoptional

Configuration for the Cohere embedding model.

APIKey string

The Cohere API key.

ClassName stringoptional

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

EmbeddingType stringoptional

Embedding type. If not provided float embedding_type is used when needed.

InputType stringoptional

Model Input type. If not provided, search_document and search_query are used when needed.

ModelName stringoptional

The modelId of the Cohere model to use.

NumWorkers int64optional

The number of workers to use for async embedding calls.

Truncate stringoptional

Truncation type - START/ END/ NONE

Type CohereEmbeddingConfigTypeoptional

Type of the embedding model.

type GeminiEmbeddingConfig struct{…}

Component GeminiEmbeddingoptional

Configuration for the Gemini embedding model.

APIBase stringoptional

API base to access the model. Defaults to None.

APIKey stringoptional

API key to access the model. Defaults to None.

ClassName stringoptional

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

ModelName stringoptional

The modelId of the Gemini model to use.

NumWorkers int64optional

The number of workers to use for async embedding calls.

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.

TaskType stringoptional

The task for embedding model.

Title stringoptional

Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.

Transport stringoptional

Transport to access the model. Defaults to None.

Type GeminiEmbeddingConfigTypeoptional

Type of the embedding model.

type HuggingFaceInferenceAPIEmbeddingConfig struct{…}

Component HuggingFaceInferenceAPIEmbeddingoptional

Configuration for the HuggingFace Inference API embedding model.

Token HuggingFaceInferenceAPIEmbeddingTokenUnionoptional

Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server.

One of the following:

string

bool

ClassName stringoptional

Cookies map[string, string]optional

Additional cookies to send to the server.

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

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.

ModelName stringoptional

Hugging Face model name. If None, the task will be used.

NumWorkers int64optional

The number of workers to use for async embedding calls.

Pooling HuggingFaceInferenceAPIEmbeddingPoolingoptional

Enum of possible pooling choices with pooling behaviors.

One of the following:

const HuggingFaceInferenceAPIEmbeddingPoolingCls HuggingFaceInferenceAPIEmbeddingPooling = "cls"

const HuggingFaceInferenceAPIEmbeddingPoolingMean HuggingFaceInferenceAPIEmbeddingPooling = "mean"

const HuggingFaceInferenceAPIEmbeddingPoolingLast HuggingFaceInferenceAPIEmbeddingPooling = "last"

QueryInstruction stringoptional

Instruction to prepend during query embedding.

Task stringoptional

Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None.

TextInstruction stringoptional

Instruction to prepend during text embedding.

Timeout float64optional

Type HuggingFaceInferenceAPIEmbeddingConfigTypeoptional

Type of the embedding model.

type OpenAIEmbeddingConfig struct{…}

Component OpenAIEmbeddingoptional

Configuration for the OpenAI embedding model.

AdditionalKwargs map[string, any]optional

Additional kwargs for the OpenAI API.

APIBase stringoptional

The base URL for OpenAI API.

APIKey stringoptional

The OpenAI API key.

APIVersion stringoptional

The version for OpenAI API.

ClassName stringoptional

DefaultHeaders map[string, string]optional

The default headers for API requests.

Dimensions int64optional

The number of dimensions on the output embedding vectors. Works only with v3 embedding models.

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

MaxRetries int64optional

Maximum number of retries.

minimum0

ModelName stringoptional

The name of the OpenAI embedding model.

NumWorkers int64optional

The number of workers to use for async embedding calls.

ReuseClient booloptional

Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability.

Timeout float64optional

Timeout for each request.

minimum0

Type OpenAIEmbeddingConfigTypeoptional

Type of the embedding model.

type VertexAIEmbeddingConfig struct{…}

Component VertexTextEmbeddingoptional

Configuration for the VertexAI embedding model.

ClientEmail string

The client email for the VertexAI credentials.

Location string

The default location to use when making API calls.

PrivateKey string

The private key for the VertexAI credentials.

PrivateKeyID string

The private key ID for the VertexAI credentials.

Project string

The default GCP project to use when making Vertex API calls.

TokenUri string

The token URI for the VertexAI credentials.

AdditionalKwargs map[string, any]optional

Additional kwargs for the Vertex.

ClassName stringoptional

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

EmbedMode VertexTextEmbeddingEmbedModeoptional

The embedding mode to use.

One of the following:

const VertexTextEmbeddingEmbedModeDefault VertexTextEmbeddingEmbedMode = "default"

const VertexTextEmbeddingEmbedModeClassification VertexTextEmbeddingEmbedMode = "classification"

const VertexTextEmbeddingEmbedModeClustering VertexTextEmbeddingEmbedMode = "clustering"

const VertexTextEmbeddingEmbedModeSimilarity VertexTextEmbeddingEmbedMode = "similarity"

const VertexTextEmbeddingEmbedModeRetrieval VertexTextEmbeddingEmbedMode = "retrieval"

ModelName stringoptional

The modelId of the VertexAI model to use.

NumWorkers int64optional

The number of workers to use for async embedding calls.

Type VertexAIEmbeddingConfigTypeoptional

Type of the embedding model.

type BedrockEmbeddingConfig struct{…}

Component BedrockEmbeddingoptional

Configuration for the Bedrock embedding model.

AdditionalKwargs map[string, any]optional

Additional kwargs for the bedrock client.

AwsAccessKeyID stringoptional

AWS Access Key ID to use

AwsSecretAccessKey stringoptional

AWS Secret Access Key to use

AwsSessionToken stringoptional

AWS Session Token to use

ClassName stringoptional

EmbedBatchSize int64optional

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

MaxRetries int64optional

The maximum number of API retries.

exclusiveMinimum0

ModelName stringoptional

The modelId of the Bedrock model to use.

NumWorkers int64optional

The number of workers to use for async embedding calls.

ProfileName stringoptional

The name of aws profile to use. If not given, then the default profile is used.

RegionName stringoptional

AWS region name to use. Uses region configured in AWS CLI if not passed

Timeout float64optional

The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.

Type BedrockEmbeddingConfigTypeoptional

Type of the embedding model.

Name string

The name of the embedding model config.

ProjectID string

CreatedAt Timeoptional

Creation datetime

formatdate-time

UpdatedAt Timeoptional

Update datetime

formatdate-time

EmbeddingModelConfigID stringoptional

The ID of the EmbeddingModelConfig this pipeline is using.

formatuuid

LlamaParseParameters LlamaParseParametersRespoptional

Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline.

AdaptiveLongTable booloptional

AggressiveTableExtraction booloptional

AnnotateLinks booloptional

AutoMode booloptional

AutoModeConfigurationJson stringoptional

AutoModeTriggerOnImageInPage booloptional

AutoModeTriggerOnRegexpInPage stringoptional

AutoModeTriggerOnTableInPage booloptional

AutoModeTriggerOnTextInPage stringoptional

AzureOpenAIAPIVersion stringoptional

AzureOpenAIDeploymentName stringoptional

AzureOpenAIEndpoint stringoptional

AzureOpenAIKey stringoptional

BboxBottom float64optional

BboxLeft float64optional

BboxRight float64optional

BboxTop float64optional

BoundingBox stringoptional

CompactMarkdownTable booloptional

ComplementalFormattingInstruction stringoptional

ContentGuidelineInstruction stringoptional

ContinuousMode booloptional

DisableImageExtraction booloptional

DisableOcr booloptional

DisableReconstruction booloptional

DoNotCache booloptional

DoNotUnrollColumns booloptional

EnableCostOptimizer booloptional

ExtractCharts booloptional

ExtractLayout booloptional

ExtractPrintedPageNumber booloptional

FastMode booloptional

FormattingInstruction stringoptional

Gpt4oAPIKey stringoptional

Gpt4oMode booloptional

GuessXlsxSheetName booloptional

HideFooters booloptional

HideHeaders booloptional

HighResOcr booloptional

HTMLMakeAllElementsVisible booloptional

HTMLRemoveFixedElements booloptional

HTMLRemoveNavigationElements booloptional

HTTPProxy stringoptional

IgnoreDocumentElementsForLayoutDetection booloptional

ImagesToSave []stringoptional

One of the following:

const LlamaParseParametersImagesToSaveScreenshot LlamaParseParametersImagesToSave = "screenshot"

const LlamaParseParametersImagesToSaveEmbedded LlamaParseParametersImagesToSave = "embedded"

const LlamaParseParametersImagesToSaveLayout LlamaParseParametersImagesToSave = "layout"

InlineImagesInMarkdown booloptional

InputS3Path stringoptional

InputS3Region stringoptional

InputURL stringoptional

InternalIsScreenshotJob booloptional

InvalidateCache booloptional

IsFormattingInstruction booloptional

JobTimeoutExtraTimePerPageInSeconds float64optional

JobTimeoutInSeconds float64optional

KeepPageSeparatorWhenMergingTables booloptional

Languages []ParsingLanguagesoptional

One of the following:

const ParsingLanguagesAf ParsingLanguages = "af"

const ParsingLanguagesAz ParsingLanguages = "az"

const ParsingLanguagesBs ParsingLanguages = "bs"

const ParsingLanguagesCs ParsingLanguages = "cs"

const ParsingLanguagesCy ParsingLanguages = "cy"

const ParsingLanguagesDa ParsingLanguages = "da"

const ParsingLanguagesDe ParsingLanguages = "de"

const ParsingLanguagesEn ParsingLanguages = "en"

const ParsingLanguagesEs ParsingLanguages = "es"

const ParsingLanguagesEt ParsingLanguages = "et"

const ParsingLanguagesFr ParsingLanguages = "fr"

const ParsingLanguagesGa ParsingLanguages = "ga"

const ParsingLanguagesHr ParsingLanguages = "hr"

const ParsingLanguagesHu ParsingLanguages = "hu"

const ParsingLanguagesID ParsingLanguages = "id"

const ParsingLanguagesIs ParsingLanguages = "is"

const ParsingLanguagesIt ParsingLanguages = "it"

const ParsingLanguagesKu ParsingLanguages = "ku"

const ParsingLanguagesLa ParsingLanguages = "la"

const ParsingLanguagesLt ParsingLanguages = "lt"

const ParsingLanguagesLv ParsingLanguages = "lv"

const ParsingLanguagesMi ParsingLanguages = "mi"

const ParsingLanguagesMs ParsingLanguages = "ms"

const ParsingLanguagesMt ParsingLanguages = "mt"

const ParsingLanguagesNl ParsingLanguages = "nl"

const ParsingLanguagesNo ParsingLanguages = "no"

const ParsingLanguagesOc ParsingLanguages = "oc"

const ParsingLanguagesPi ParsingLanguages = "pi"

const ParsingLanguagesPl ParsingLanguages = "pl"

const ParsingLanguagesPt ParsingLanguages = "pt"

const ParsingLanguagesRo ParsingLanguages = "ro"

const ParsingLanguagesRsLatin ParsingLanguages = "rs_latin"

const ParsingLanguagesSk ParsingLanguages = "sk"

const ParsingLanguagesSl ParsingLanguages = "sl"

const ParsingLanguagesSq ParsingLanguages = "sq"

const ParsingLanguagesSv ParsingLanguages = "sv"

const ParsingLanguagesSw ParsingLanguages = "sw"

const ParsingLanguagesTl ParsingLanguages = "tl"

const ParsingLanguagesTr ParsingLanguages = "tr"

const ParsingLanguagesUz ParsingLanguages = "uz"

const ParsingLanguagesVi ParsingLanguages = "vi"

const ParsingLanguagesAr ParsingLanguages = "ar"

const ParsingLanguagesFa ParsingLanguages = "fa"

const ParsingLanguagesUg ParsingLanguages = "ug"

const ParsingLanguagesUr ParsingLanguages = "ur"

const ParsingLanguagesBn ParsingLanguages = "bn"

const ParsingLanguagesAs ParsingLanguages = "as"

const ParsingLanguagesMni ParsingLanguages = "mni"

const ParsingLanguagesRu ParsingLanguages = "ru"

const ParsingLanguagesRsCyrillic ParsingLanguages = "rs_cyrillic"

const ParsingLanguagesBe ParsingLanguages = "be"

const ParsingLanguagesBg ParsingLanguages = "bg"

const ParsingLanguagesUk ParsingLanguages = "uk"

const ParsingLanguagesMn ParsingLanguages = "mn"

const ParsingLanguagesAbq ParsingLanguages = "abq"

const ParsingLanguagesAdy ParsingLanguages = "ady"

const ParsingLanguagesKbd ParsingLanguages = "kbd"

const ParsingLanguagesAva ParsingLanguages = "ava"

const ParsingLanguagesDar ParsingLanguages = "dar"

const ParsingLanguagesInh ParsingLanguages = "inh"

const ParsingLanguagesChe ParsingLanguages = "che"

const ParsingLanguagesLbe ParsingLanguages = "lbe"

const ParsingLanguagesLez ParsingLanguages = "lez"

const ParsingLanguagesTab ParsingLanguages = "tab"

const ParsingLanguagesTjk ParsingLanguages = "tjk"

const ParsingLanguagesHi ParsingLanguages = "hi"

const ParsingLanguagesMr ParsingLanguages = "mr"

const ParsingLanguagesNe ParsingLanguages = "ne"

const ParsingLanguagesBh ParsingLanguages = "bh"

const ParsingLanguagesMai ParsingLanguages = "mai"

const ParsingLanguagesAng ParsingLanguages = "ang"

const ParsingLanguagesBho ParsingLanguages = "bho"

const ParsingLanguagesMah ParsingLanguages = "mah"

const ParsingLanguagesSck ParsingLanguages = "sck"

const ParsingLanguagesNew ParsingLanguages = "new"

const ParsingLanguagesGom ParsingLanguages = "gom"

const ParsingLanguagesSa ParsingLanguages = "sa"

const ParsingLanguagesBgc ParsingLanguages = "bgc"

const ParsingLanguagesTh ParsingLanguages = "th"

const ParsingLanguagesChSim ParsingLanguages = "ch_sim"

const ParsingLanguagesChTra ParsingLanguages = "ch_tra"

const ParsingLanguagesJa ParsingLanguages = "ja"

const ParsingLanguagesKo ParsingLanguages = "ko"

const ParsingLanguagesTa ParsingLanguages = "ta"

const ParsingLanguagesTe ParsingLanguages = "te"

const ParsingLanguagesKn ParsingLanguages = "kn"

LayoutAware booloptional

LineLevelBoundingBox booloptional

MarkdownTableMultilineHeaderSeparator stringoptional

MaxPages int64optional

MaxPagesEnforced int64optional

MergeTablesAcrossPagesInMarkdown booloptional

Model stringoptional

OutlinedTableExtraction booloptional

OutputPdfOfDocument booloptional

OutputS3PathPrefix stringoptional

OutputS3Region stringoptional

OutputTablesAsHTML booloptional

PageErrorTolerance float64optional

PageFooterPrefix stringoptional

PageFooterSuffix stringoptional

PageHeaderPrefix stringoptional

PageHeaderSuffix stringoptional

PagePrefix stringoptional

PageSeparator stringoptional

PageSuffix stringoptional

ParseMode ParsingModeoptional

Enum for representing the mode of parsing to be used.

One of the following:

const ParsingModeParsePageWithoutLlm ParsingMode = "parse_page_without_llm"

const ParsingModeParsePageWithLlm ParsingMode = "parse_page_with_llm"

const ParsingModeParsePageWithLvm ParsingMode = "parse_page_with_lvm"

const ParsingModeParsePageWithAgent ParsingMode = "parse_page_with_agent"

const ParsingModeParsePageWithLayoutAgent ParsingMode = "parse_page_with_layout_agent"

const ParsingModeParseDocumentWithLlm ParsingMode = "parse_document_with_llm"

const ParsingModeParseDocumentWithLvm ParsingMode = "parse_document_with_lvm"

const ParsingModeParseDocumentWithAgent ParsingMode = "parse_document_with_agent"

ParsingInstruction stringoptional

PreciseBoundingBox booloptional

PremiumMode booloptional

PresentationOutOfBoundsContent booloptional

PresentationSkipEmbeddedData booloptional

PreserveLayoutAlignmentAcrossPages booloptional

PreserveVerySmallText booloptional

Preset stringoptional

Priority LlamaParseParametersPriorityoptional

The priority for the request. This field may be ignored or overwritten depending on the organization tier.

One of the following:

const LlamaParseParametersPriorityLow LlamaParseParametersPriority = "low"

const LlamaParseParametersPriorityMedium LlamaParseParametersPriority = "medium"

const LlamaParseParametersPriorityHigh LlamaParseParametersPriority = "high"

const LlamaParseParametersPriorityCritical LlamaParseParametersPriority = "critical"

ProjectID stringoptional

RemoveHiddenText booloptional

ReplaceFailedPageMode FailPageModeoptional

Enum for representing the different available page error handling modes.

One of the following:

const FailPageModeRawText FailPageMode = "raw_text"

const FailPageModeBlankPage FailPageMode = "blank_page"

const FailPageModeErrorMessage FailPageMode = "error_message"

ReplaceFailedPageWithErrorMessagePrefix stringoptional

ReplaceFailedPageWithErrorMessageSuffix stringoptional

SaveImages booloptional

SkipDiagonalText booloptional

SpecializedChartParsingAgentic booloptional

SpecializedChartParsingEfficient booloptional

SpecializedChartParsingPlus booloptional

SpecializedImageParsing booloptional

SpreadsheetExtractSubTables booloptional

SpreadsheetForceFormulaComputation booloptional

SpreadsheetIncludeHiddenSheets booloptional

StrictModeBuggyFont booloptional

StrictModeImageExtraction booloptional

StrictModeImageOcr booloptional

StrictModeReconstruction booloptional

StructuredOutput booloptional

StructuredOutputJsonSchema stringoptional

StructuredOutputJsonSchemaName stringoptional

SystemPrompt stringoptional

SystemPromptAppend stringoptional

TakeScreenshot booloptional

TargetPages stringoptional

Tier stringoptional

UseVendorMultimodalModel booloptional

UserPrompt stringoptional

VendorMultimodalAPIKey stringoptional

VendorMultimodalModelName stringoptional

Version stringoptional

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.

One of the following:

const LlamaParseParametersWebhookConfigurationWebhookEventExtractPending LlamaParseParametersWebhookConfigurationWebhookEvent = "extract.pending"

const LlamaParseParametersWebhookConfigurationWebhookEventExtractSuccess LlamaParseParametersWebhookConfigurationWebhookEvent = "extract.success"

const LlamaParseParametersWebhookConfigurationWebhookEventExtractError LlamaParseParametersWebhookConfigurationWebhookEvent = "extract.error"

const LlamaParseParametersWebhookConfigurationWebhookEventExtractPartialSuccess LlamaParseParametersWebhookConfigurationWebhookEvent = "extract.partial_success"

const LlamaParseParametersWebhookConfigurationWebhookEventExtractCancelled LlamaParseParametersWebhookConfigurationWebhookEvent = "extract.cancelled"

const LlamaParseParametersWebhookConfigurationWebhookEventParsePending LlamaParseParametersWebhookConfigurationWebhookEvent = "parse.pending"

const LlamaParseParametersWebhookConfigurationWebhookEventParseRunning LlamaParseParametersWebhookConfigurationWebhookEvent = "parse.running"

const LlamaParseParametersWebhookConfigurationWebhookEventParseSuccess LlamaParseParametersWebhookConfigurationWebhookEvent = "parse.success"

const LlamaParseParametersWebhookConfigurationWebhookEventParseError LlamaParseParametersWebhookConfigurationWebhookEvent = "parse.error"

const LlamaParseParametersWebhookConfigurationWebhookEventParsePartialSuccess LlamaParseParametersWebhookConfigurationWebhookEvent = "parse.partial_success"

const LlamaParseParametersWebhookConfigurationWebhookEventParseCancelled LlamaParseParametersWebhookConfigurationWebhookEvent = "parse.cancelled"

const LlamaParseParametersWebhookConfigurationWebhookEventClassifyPending LlamaParseParametersWebhookConfigurationWebhookEvent = "classify.pending"

const LlamaParseParametersWebhookConfigurationWebhookEventClassifySuccess LlamaParseParametersWebhookConfigurationWebhookEvent = "classify.success"

const LlamaParseParametersWebhookConfigurationWebhookEventClassifyError LlamaParseParametersWebhookConfigurationWebhookEvent = "classify.error"

const LlamaParseParametersWebhookConfigurationWebhookEventClassifyPartialSuccess LlamaParseParametersWebhookConfigurationWebhookEvent = "classify.partial_success"

const LlamaParseParametersWebhookConfigurationWebhookEventClassifyCancelled LlamaParseParametersWebhookConfigurationWebhookEvent = "classify.cancelled"

const LlamaParseParametersWebhookConfigurationWebhookEventUnmappedEvent LlamaParseParametersWebhookConfigurationWebhookEvent = "unmapped_event"

WebhookHeaders map[string, string]optional

Custom HTTP headers sent with each webhook request (e.g. auth tokens)

WebhookOutputFormat stringoptional

Response format sent to the webhook: 'string' (default) or 'json'

WebhookURL stringoptional

URL to receive webhook POST notifications

WebhookURL stringoptional

ManagedPipelineID stringoptional

The ID of the ManagedPipeline this playground pipeline is linked to.

formatuuid

MetadataConfig PipelineMetadataConfigoptional

Metadata configuration for the pipeline.

ExcludedEmbedMetadataKeys []stringoptional

List of metadata keys to exclude from embeddings

ExcludedLlmMetadataKeys []stringoptional

List of metadata keys to exclude from LLM during retrieval

PipelineType PipelineTypeoptional

Type of pipeline. Either PLAYGROUND or MANAGED.

One of the following:

const PipelineTypePlayground PipelineType = "PLAYGROUND"

const PipelineTypeManaged PipelineType = "MANAGED"

PresetRetrievalParameters PresetRetrievalParamsRespoptional

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.

maximum1

minimum0

ClassName stringoptional

DenseSimilarityCutoff float64optional

Minimum similarity score wrt query for retrieval

maximum1

minimum0

DenseSimilarityTopK int64optional

Number of nodes for dense retrieval.

maximum100

minimum1

EnableReranking booloptional

Enable reranking for retrieval

FilesTopK int64optional

Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

maximum5

minimum1

RerankTopN int64optional

Number of reranked nodes for returning.

maximum100

minimum1

RetrievalMode RetrievalModeoptional

The retrieval mode for the query.

One of the following:

const RetrievalModeChunks RetrievalMode = "chunks"

const RetrievalModeFilesViaMetadata RetrievalMode = "files_via_metadata"

const RetrievalModeFilesViaContent RetrievalMode = "files_via_content"

const RetrievalModeAutoRouted RetrievalMode = "auto_routed"

DeprecatedRetrieveImageNodes booloptional

Whether to retrieve image nodes.

RetrievePageFigureNodes booloptional

Whether to retrieve page figure nodes.

RetrievePageScreenshotNodes booloptional

Whether to retrieve page screenshot nodes.

Metadata filters for vector stores.

One of the following:

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

One of the following:

Vector store filter operator.

One of the following:

Metadata filters for vector stores.

Vector store filter conditions to combine different filters.

One of the following:

SearchFiltersInferenceSchema map[string, PresetRetrievalParamsSearchFiltersInferenceSchemaUnionResp]optional

JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

One of the following:

type PresetRetrievalParamsSearchFiltersInferenceSchemaMap map[string, any]

type PresetRetrievalParamsSearchFiltersInferenceSchemaArray []any

string

float64

bool

SparseSimilarityTopK int64optional

Number of nodes for sparse retrieval.

maximum100

minimum1

SparseModelConfig SparseModelConfigoptional

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.

ClassName stringoptional

ModelType SparseModelConfigModelTypeoptional

The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade).

One of the following:

const SparseModelConfigModelTypeSplade SparseModelConfigModelType = "splade"

const SparseModelConfigModelTypeBm25 SparseModelConfigModelType = "bm25"

const SparseModelConfigModelTypeAuto SparseModelConfigModelType = "auto"

Status PipelineStatusoptional

Status of the pipeline.

One of the following:

const PipelineStatusCreated PipelineStatus = "CREATED"

const PipelineStatusDeleting PipelineStatus = "DELETING"

TransformConfig PipelineTransformConfigUnionoptional

Configuration for the transformation.

One of the following:

type AutoTransformConfig struct{…}

ChunkOverlap int64optional

Chunk overlap for the transformation.

ChunkSize int64optional

Chunk size for the transformation.

exclusiveMinimum0

Mode AutoTransformConfigModeoptional

type AdvancedModeTransformConfig struct{…}

ChunkingConfig AdvancedModeTransformConfigChunkingConfigUnionoptional

Configuration for the chunking.

One of the following:

type AdvancedModeTransformConfigChunkingConfigNoneChunkingConfig struct{…}

Mode stringoptional

type AdvancedModeTransformConfigChunkingConfigCharacterChunkingConfig struct{…}

ChunkOverlap int64optional

ChunkSize int64optional

Mode stringoptional

type AdvancedModeTransformConfigChunkingConfigTokenChunkingConfig struct{…}

ChunkOverlap int64optional

ChunkSize int64optional

Mode stringoptional

Separator stringoptional

type AdvancedModeTransformConfigChunkingConfigSentenceChunkingConfig struct{…}

ChunkOverlap int64optional

ChunkSize int64optional

Mode stringoptional

ParagraphSeparator stringoptional

Separator stringoptional

type AdvancedModeTransformConfigChunkingConfigSemanticChunkingConfig struct{…}

BreakpointPercentileThreshold int64optional

BufferSize int64optional

Mode stringoptional

Mode AdvancedModeTransformConfigModeoptional

SegmentationConfig AdvancedModeTransformConfigSegmentationConfigUnionoptional

Configuration for the segmentation.

One of the following:

type AdvancedModeTransformConfigSegmentationConfigNoneSegmentationConfig struct{…}

Mode stringoptional

type AdvancedModeTransformConfigSegmentationConfigPageSegmentationConfig struct{…}

Mode stringoptional

PageSeparator stringoptional

type AdvancedModeTransformConfigSegmentationConfigElementSegmentationConfig struct{…}

Mode stringoptional

UpdatedAt Timeoptional

Update datetime

formatdate-time

Get Pipeline

package main

import (
  "context"
  "fmt"

  "github.com/stainless-sdks/llamacloud-prod-go"
  "github.com/stainless-sdks/llamacloud-prod-go/option"
)

func main() {
  client := llamacloudprod.NewClient(
    option.WithAPIKey("My API Key"),
  )
  pipeline, err := client.Pipelines.Get(context.TODO(), "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e")
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", pipeline.ID)
}

{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "embedding_config": {
    "component": {
      "class_name": "class_name",
      "embed_batch_size": 1,
      "model_name": "openai-text-embedding-3-small",
      "num_workers": 0
    },
    "type": "MANAGED_OPENAI_EMBEDDING"
  },
  "name": "name",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "embedding_config_hash": "embedding_config_hash",
    "parsing_config_hash": "parsing_config_hash",
    "transform_config_hash": "transform_config_hash"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "data_sink": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "sink_type": "PINECONE",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "additional_kwargs": {
          "foo": "bar"
        },
        "api_base": "api_base",
        "api_key": "api_key",
        "api_version": "api_version",
        "azure_deployment": "azure_deployment",
        "azure_endpoint": "azure_endpoint",
        "class_name": "class_name",
        "default_headers": {
          "foo": "string"
        },
        "dimensions": 0,
        "embed_batch_size": 1,
        "max_retries": 0,
        "model_name": "model_name",
        "num_workers": 0,
        "reuse_client": true,
        "timeout": 0
      },
      "type": "AZURE_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "llama_parse_parameters": {
    "adaptive_long_table": true,
    "aggressive_table_extraction": true,
    "annotate_links": true,
    "auto_mode": true,
    "auto_mode_configuration_json": "auto_mode_configuration_json",
    "auto_mode_trigger_on_image_in_page": true,
    "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
    "auto_mode_trigger_on_table_in_page": true,
    "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
    "azure_openai_api_version": "azure_openai_api_version",
    "azure_openai_deployment_name": "azure_openai_deployment_name",
    "azure_openai_endpoint": "azure_openai_endpoint",
    "azure_openai_key": "azure_openai_key",
    "bbox_bottom": 0,
    "bbox_left": 0,
    "bbox_right": 0,
    "bbox_top": 0,
    "bounding_box": "bounding_box",
    "compact_markdown_table": true,
    "complemental_formatting_instruction": "complemental_formatting_instruction",
    "content_guideline_instruction": "content_guideline_instruction",
    "continuous_mode": true,
    "disable_image_extraction": true,
    "disable_ocr": true,
    "disable_reconstruction": true,
    "do_not_cache": true,
    "do_not_unroll_columns": true,
    "enable_cost_optimizer": true,
    "extract_charts": true,
    "extract_layout": true,
    "extract_printed_page_number": true,
    "fast_mode": true,
    "formatting_instruction": "formatting_instruction",
    "gpt4o_api_key": "gpt4o_api_key",
    "gpt4o_mode": true,
    "guess_xlsx_sheet_name": true,
    "hide_footers": true,
    "hide_headers": true,
    "high_res_ocr": true,
    "html_make_all_elements_visible": true,
    "html_remove_fixed_elements": true,
    "html_remove_navigation_elements": true,
    "http_proxy": "http_proxy",
    "ignore_document_elements_for_layout_detection": true,
    "images_to_save": [
      "screenshot"
    ],
    "inline_images_in_markdown": true,
    "input_s3_path": "input_s3_path",
    "input_s3_region": "input_s3_region",
    "input_url": "input_url",
    "internal_is_screenshot_job": true,
    "invalidate_cache": true,
    "is_formatting_instruction": true,
    "job_timeout_extra_time_per_page_in_seconds": 0,
    "job_timeout_in_seconds": 0,
    "keep_page_separator_when_merging_tables": true,
    "languages": [
      "af"
    ],
    "layout_aware": true,
    "line_level_bounding_box": true,
    "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
    "max_pages": 0,
    "max_pages_enforced": 0,
    "merge_tables_across_pages_in_markdown": true,
    "model": "model",
    "outlined_table_extraction": true,
    "output_pdf_of_document": true,
    "output_s3_path_prefix": "output_s3_path_prefix",
    "output_s3_region": "output_s3_region",
    "output_tables_as_HTML": true,
    "page_error_tolerance": 0,
    "page_footer_prefix": "page_footer_prefix",
    "page_footer_suffix": "page_footer_suffix",
    "page_header_prefix": "page_header_prefix",
    "page_header_suffix": "page_header_suffix",
    "page_prefix": "page_prefix",
    "page_separator": "page_separator",
    "page_suffix": "page_suffix",
    "parse_mode": "parse_page_without_llm",
    "parsing_instruction": "parsing_instruction",
    "precise_bounding_box": true,
    "premium_mode": true,
    "presentation_out_of_bounds_content": true,
    "presentation_skip_embedded_data": true,
    "preserve_layout_alignment_across_pages": true,
    "preserve_very_small_text": true,
    "preset": "preset",
    "priority": "low",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "raw_text",
    "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
    "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
    "save_images": true,
    "skip_diagonal_text": true,
    "specialized_chart_parsing_agentic": true,
    "specialized_chart_parsing_efficient": true,
    "specialized_chart_parsing_plus": true,
    "specialized_image_parsing": true,
    "spreadsheet_extract_sub_tables": true,
    "spreadsheet_force_formula_computation": true,
    "spreadsheet_include_hidden_sheets": true,
    "strict_mode_buggy_font": true,
    "strict_mode_image_extraction": true,
    "strict_mode_image_ocr": true,
    "strict_mode_reconstruction": true,
    "structured_output": true,
    "structured_output_json_schema": "structured_output_json_schema",
    "structured_output_json_schema_name": "structured_output_json_schema_name",
    "system_prompt": "system_prompt",
    "system_prompt_append": "system_prompt_append",
    "take_screenshot": true,
    "target_pages": "target_pages",
    "tier": "tier",
    "use_vendor_multimodal_model": true,
    "user_prompt": "user_prompt",
    "vendor_multimodal_api_key": "vendor_multimodal_api_key",
    "vendor_multimodal_model_name": "vendor_multimodal_model_name",
    "version": "version",
    "webhook_configurations": [
      {
        "webhook_events": [
          "parse.success",
          "parse.error"
        ],
        "webhook_headers": {
          "Authorization": "Bearer sk-..."
        },
        "webhook_output_format": "json",
        "webhook_url": "https://example.com/webhooks/llamacloud"
      }
    ],
    "webhook_url": "webhook_url"
  },
  "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "metadata_config": {
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ]
  },
  "pipeline_type": "PLAYGROUND",
  "preset_retrieval_parameters": {
    "alpha": 0,
    "class_name": "class_name",
    "dense_similarity_cutoff": 0,
    "dense_similarity_top_k": 1,
    "enable_reranking": true,
    "files_top_k": 1,
    "rerank_top_n": 1,
    "retrieval_mode": "chunks",
    "retrieve_image_nodes": true,
    "retrieve_page_figure_nodes": true,
    "retrieve_page_screenshot_nodes": true,
    "search_filters": {
      "filters": [
        {
          "key": "key",
          "value": 0,
          "operator": "=="
        }
      ],
      "condition": "and"
    },
    "search_filters_inference_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "sparse_similarity_top_k": 1
  },
  "sparse_model_config": {
    "class_name": "class_name",
    "model_type": "splade"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}

Returns Examples

{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "embedding_config": {
    "component": {
      "class_name": "class_name",
      "embed_batch_size": 1,
      "model_name": "openai-text-embedding-3-small",
      "num_workers": 0
    },
    "type": "MANAGED_OPENAI_EMBEDDING"
  },
  "name": "name",
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config_hash": {
    "embedding_config_hash": "embedding_config_hash",
    "parsing_config_hash": "parsing_config_hash",
    "transform_config_hash": "transform_config_hash"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "data_sink": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "component": {
      "foo": "bar"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "sink_type": "PINECONE",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "embedding_config": {
      "component": {
        "additional_kwargs": {
          "foo": "bar"
        },
        "api_base": "api_base",
        "api_key": "api_key",
        "api_version": "api_version",
        "azure_deployment": "azure_deployment",
        "azure_endpoint": "azure_endpoint",
        "class_name": "class_name",
        "default_headers": {
          "foo": "string"
        },
        "dimensions": 0,
        "embed_batch_size": 1,
        "max_retries": 0,
        "model_name": "model_name",
        "num_workers": 0,
        "reuse_client": true,
        "timeout": 0
      },
      "type": "AZURE_EMBEDDING"
    },
    "name": "name",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "llama_parse_parameters": {
    "adaptive_long_table": true,
    "aggressive_table_extraction": true,
    "annotate_links": true,
    "auto_mode": true,
    "auto_mode_configuration_json": "auto_mode_configuration_json",
    "auto_mode_trigger_on_image_in_page": true,
    "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page",
    "auto_mode_trigger_on_table_in_page": true,
    "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page",
    "azure_openai_api_version": "azure_openai_api_version",
    "azure_openai_deployment_name": "azure_openai_deployment_name",
    "azure_openai_endpoint": "azure_openai_endpoint",
    "azure_openai_key": "azure_openai_key",
    "bbox_bottom": 0,
    "bbox_left": 0,
    "bbox_right": 0,
    "bbox_top": 0,
    "bounding_box": "bounding_box",
    "compact_markdown_table": true,
    "complemental_formatting_instruction": "complemental_formatting_instruction",
    "content_guideline_instruction": "content_guideline_instruction",
    "continuous_mode": true,
    "disable_image_extraction": true,
    "disable_ocr": true,
    "disable_reconstruction": true,
    "do_not_cache": true,
    "do_not_unroll_columns": true,
    "enable_cost_optimizer": true,
    "extract_charts": true,
    "extract_layout": true,
    "extract_printed_page_number": true,
    "fast_mode": true,
    "formatting_instruction": "formatting_instruction",
    "gpt4o_api_key": "gpt4o_api_key",
    "gpt4o_mode": true,
    "guess_xlsx_sheet_name": true,
    "hide_footers": true,
    "hide_headers": true,
    "high_res_ocr": true,
    "html_make_all_elements_visible": true,
    "html_remove_fixed_elements": true,
    "html_remove_navigation_elements": true,
    "http_proxy": "http_proxy",
    "ignore_document_elements_for_layout_detection": true,
    "images_to_save": [
      "screenshot"
    ],
    "inline_images_in_markdown": true,
    "input_s3_path": "input_s3_path",
    "input_s3_region": "input_s3_region",
    "input_url": "input_url",
    "internal_is_screenshot_job": true,
    "invalidate_cache": true,
    "is_formatting_instruction": true,
    "job_timeout_extra_time_per_page_in_seconds": 0,
    "job_timeout_in_seconds": 0,
    "keep_page_separator_when_merging_tables": true,
    "languages": [
      "af"
    ],
    "layout_aware": true,
    "line_level_bounding_box": true,
    "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator",
    "max_pages": 0,
    "max_pages_enforced": 0,
    "merge_tables_across_pages_in_markdown": true,
    "model": "model",
    "outlined_table_extraction": true,
    "output_pdf_of_document": true,
    "output_s3_path_prefix": "output_s3_path_prefix",
    "output_s3_region": "output_s3_region",
    "output_tables_as_HTML": true,
    "page_error_tolerance": 0,
    "page_footer_prefix": "page_footer_prefix",
    "page_footer_suffix": "page_footer_suffix",
    "page_header_prefix": "page_header_prefix",
    "page_header_suffix": "page_header_suffix",
    "page_prefix": "page_prefix",
    "page_separator": "page_separator",
    "page_suffix": "page_suffix",
    "parse_mode": "parse_page_without_llm",
    "parsing_instruction": "parsing_instruction",
    "precise_bounding_box": true,
    "premium_mode": true,
    "presentation_out_of_bounds_content": true,
    "presentation_skip_embedded_data": true,
    "preserve_layout_alignment_across_pages": true,
    "preserve_very_small_text": true,
    "preset": "preset",
    "priority": "low",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "raw_text",
    "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix",
    "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix",
    "save_images": true,
    "skip_diagonal_text": true,
    "specialized_chart_parsing_agentic": true,
    "specialized_chart_parsing_efficient": true,
    "specialized_chart_parsing_plus": true,
    "specialized_image_parsing": true,
    "spreadsheet_extract_sub_tables": true,
    "spreadsheet_force_formula_computation": true,
    "spreadsheet_include_hidden_sheets": true,
    "strict_mode_buggy_font": true,
    "strict_mode_image_extraction": true,
    "strict_mode_image_ocr": true,
    "strict_mode_reconstruction": true,
    "structured_output": true,
    "structured_output_json_schema": "structured_output_json_schema",
    "structured_output_json_schema_name": "structured_output_json_schema_name",
    "system_prompt": "system_prompt",
    "system_prompt_append": "system_prompt_append",
    "take_screenshot": true,
    "target_pages": "target_pages",
    "tier": "tier",
    "use_vendor_multimodal_model": true,
    "user_prompt": "user_prompt",
    "vendor_multimodal_api_key": "vendor_multimodal_api_key",
    "vendor_multimodal_model_name": "vendor_multimodal_model_name",
    "version": "version",
    "webhook_configurations": [
      {
        "webhook_events": [
          "parse.success",
          "parse.error"
        ],
        "webhook_headers": {
          "Authorization": "Bearer sk-..."
        },
        "webhook_output_format": "json",
        "webhook_url": "https://example.com/webhooks/llamacloud"
      }
    ],
    "webhook_url": "webhook_url"
  },
  "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "metadata_config": {
    "excluded_embed_metadata_keys": [
      "string"
    ],
    "excluded_llm_metadata_keys": [
      "string"
    ]
  },
  "pipeline_type": "PLAYGROUND",
  "preset_retrieval_parameters": {
    "alpha": 0,
    "class_name": "class_name",
    "dense_similarity_cutoff": 0,
    "dense_similarity_top_k": 1,
    "enable_reranking": true,
    "files_top_k": 1,
    "rerank_top_n": 1,
    "retrieval_mode": "chunks",
    "retrieve_image_nodes": true,
    "retrieve_page_figure_nodes": true,
    "retrieve_page_screenshot_nodes": true,
    "search_filters": {
      "filters": [
        {
          "key": "key",
          "value": 0,
          "operator": "=="
        }
      ],
      "condition": "and"
    },
    "search_filters_inference_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "sparse_similarity_top_k": 1
  },
  "sparse_model_config": {
    "class_name": "class_name",
    "model_type": "splade"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}