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Developer Documentation
API Reference
Pipelines
Sync

Sync Pipeline

Deprecated
Pipeline Pipelines.Sync.Create(SyncCreateParamsparameters, CancellationTokencancellationToken = default)
POST/api/v1/pipelines/{pipeline_id}/sync

Trigger an incremental sync for a managed pipeline.

Processes new and updated documents from data sources and files, then updates the index for retrieval.

ParametersExpand Collapse
SyncCreateParams parameters
required string pipelineID
ReturnsExpand Collapse
class Pipeline:

Schema for a pipeline.

required string ID

Unique identifier

formatuuid
required EmbeddingConfig EmbeddingConfig
One of the following:
class AzureOpenAIEmbeddingConfig:

Configuration for the Azure OpenAI embedding model.

IReadOnlyDictionary<string, JsonElement> AdditionalKwargs

Additional kwargs for the OpenAI API.

string ApiBase

The base URL for Azure deployment.

string? ApiKey

The OpenAI API key.

string ApiVersion

The version for Azure OpenAI API.

string? AzureDeployment

The Azure deployment to use.

string? AzureEndpoint

The Azure endpoint to use.

string ClassName
IReadOnlyDictionary<string, string>? DefaultHeaders

The default headers for API requests.

Long? Dimensions

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

Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
Long MaxRetries

Maximum number of retries.

minimum0
string ModelName

The name of the OpenAI embedding model.

Long? NumWorkers

The number of workers to use for async embedding calls.

Boolean ReuseClient

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

Double Timeout

Timeout for each request.

minimum0
Type Type

Type of the embedding model.

class BedrockEmbeddingConfig:

Configuration for the Bedrock embedding model.

IReadOnlyDictionary<string, JsonElement> AdditionalKwargs

Additional kwargs for the bedrock client.

string? AwsAccessKeyID

AWS Access Key ID to use

string? AwsSecretAccessKey

AWS Secret Access Key to use

string? AwsSessionToken

AWS Session Token to use

string ClassName
Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
Long MaxRetries

The maximum number of API retries.

exclusiveMinimum0
string ModelName

The modelId of the Bedrock model to use.

Long? NumWorkers

The number of workers to use for async embedding calls.

string? ProfileName

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

string? RegionName

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

Double Timeout

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

Type Type

Type of the embedding model.

class CohereEmbeddingConfig:
CohereEmbedding Component

Configuration for the Cohere embedding model.

required string? ApiKey

The Cohere API key.

string ClassName
Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
string EmbeddingType

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

string? InputType

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

string ModelName

The modelId of the Cohere model to use.

Long? NumWorkers

The number of workers to use for async embedding calls.

string Truncate

Truncation type - START/ END/ NONE

Type Type

Type of the embedding model.

class GeminiEmbeddingConfig:
GeminiEmbedding Component

Configuration for the Gemini embedding model.

string? ApiBase

API base to access the model. Defaults to None.

string? ApiKey

API key to access the model. Defaults to None.

string ClassName
Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
string ModelName

The modelId of the Gemini model to use.

Long? NumWorkers

The number of workers to use for async embedding calls.

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.

string? TaskType

The task for embedding model.

string? Title

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

string? Transport

Transport to access the model. Defaults to None.

Type Type

Type of the embedding model.

class HuggingFaceInferenceApiEmbeddingConfig:

Configuration for the HuggingFace Inference API embedding model.

Token? Token

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
Boolean
string ClassName
IReadOnlyDictionary<string, string>? Cookies

Additional cookies to send to the server.

Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
IReadOnlyDictionary<string, string>? 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.

string? ModelName

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

Long? NumWorkers

The number of workers to use for async embedding calls.

Pooling? Pooling

Enum of possible pooling choices with pooling behaviors.

One of the following:
"cls"Cls
"last"Last
"mean"Mean
string? QueryInstruction

Instruction to prepend during query embedding.

string? Task

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

string? TextInstruction

Instruction to prepend during text embedding.

Double? Timeout

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 Type

Type of the embedding model.

class ManagedOpenAIEmbedding:
Component Component

Configuration for the Managed OpenAI embedding model.

string ClassName
Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
ModelName ModelName

The name of the OpenAI embedding model.

Long? NumWorkers

The number of workers to use for async embedding calls.

Type Type

Type of the embedding model.

class OpenAIEmbeddingConfig:
OpenAIEmbedding Component

Configuration for the OpenAI embedding model.

IReadOnlyDictionary<string, JsonElement> AdditionalKwargs

Additional kwargs for the OpenAI API.

string? ApiBase

The base URL for OpenAI API.

string? ApiKey

The OpenAI API key.

string? ApiVersion

The version for OpenAI API.

string ClassName
IReadOnlyDictionary<string, string>? DefaultHeaders

The default headers for API requests.

Long? Dimensions

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

Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
Long MaxRetries

Maximum number of retries.

minimum0
string ModelName

The name of the OpenAI embedding model.

Long? NumWorkers

The number of workers to use for async embedding calls.

Boolean ReuseClient

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

Double Timeout

Timeout for each request.

minimum0
Type Type

Type of the embedding model.

class VertexAIEmbeddingConfig:

Configuration for the VertexAI embedding model.

required string? ClientEmail

The client email for the VertexAI credentials.

required string Location

The default location to use when making API calls.

required string? PrivateKey

The private key for the VertexAI credentials.

required string? PrivateKeyID

The private key ID for the VertexAI credentials.

required string Project

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

required string? TokenUri

The token URI for the VertexAI credentials.

IReadOnlyDictionary<string, JsonElement> AdditionalKwargs

Additional kwargs for the Vertex.

string ClassName
Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
EmbedMode EmbedMode

The embedding mode to use.

One of the following:
"classification"Classification
"clustering"Clustering
"default"Default
"retrieval"Retrieval
"similarity"Similarity
string ModelName

The modelId of the VertexAI model to use.

Long? NumWorkers

The number of workers to use for async embedding calls.

Type Type

Type of the embedding model.

required string Name
required string ProjectID
ConfigHash? ConfigHash

Hashes for the configuration of a pipeline.

string? EmbeddingConfigHash

Hash of the embedding config.

string? ParsingConfigHash

Hash of the llama parse parameters.

string? TransformConfigHash

Hash of the transform config.

DateTimeOffset? CreatedAt

Creation datetime

formatdate-time
DataSink? DataSink

Schema for a data sink.

required string ID

Unique identifier

formatuuid
required Component Component

Component that implements the data sink

One of the following:
IReadOnlyDictionary<string, JsonElement>
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

required string ApiKey

The API key for authenticating with Pinecone

formatpassword
required string IndexName
string ClassName
IReadOnlyDictionary<string, JsonElement>? InsertKwargs
string? Namespace
SupportsNestedMetadataFilters SupportsNestedMetadataFilters
class CloudPostgresVectorStore:
required string Database
required Long EmbedDim
required string Host
required string Password
required Long Port
required string SchemaName
required string TableName
required string User
string ClassName
PgVectorHnswSettings? HnswSettings

HNSW settings for PGVector.

DistanceMethod DistanceMethod

The distance method to use.

One of the following:
"cosine"Cosine
"hamming"Hamming
"ip"IP
"jaccard"Jaccard
"l1"L1
"l2"L2
Long EfConstruction

The number of edges to use during the construction phase.

minimum1

The number of edges to use during the search phase.

minimum1
Long M

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

minimum1
VectorType VectorType

The type of vector to use.

One of the following:
"bit"Bit
"half_vec"HalfVec
"sparse_vec"SparseVec
"vector"Vector
Boolean PerformSetup
Boolean SupportsNestedMetadataFilters
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

required string ApiKey
required string CollectionName
required string Url
string ClassName
IReadOnlyDictionary<string, JsonElement> ClientKwargs
Long MaxRetries
SupportsNestedMetadataFilters SupportsNestedMetadataFilters
class CloudAzureAISearchVectorStore:

Cloud Azure AI Search Vector Store.

required string SearchServiceApiKey
required string SearchServiceEndpoint
string ClassName
string? ClientID
string? ClientSecret
Long? EmbeddingDimension
IReadOnlyDictionary<string, JsonElement>? FilterableMetadataFieldKeys
string? IndexName
string? SearchServiceApiVersion
SupportsNestedMetadataFilters SupportsNestedMetadataFilters
string? TenantID

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 CloudMilvusVectorStore:

Cloud Milvus Vector Store.

required string Uri
string? Token
string ClassName
string? CollectionName
Long? EmbeddingDimension
Boolean SupportsNestedMetadataFilters
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’

required string Token

The Astra DB Application Token to use

formatpassword
required string ApiEndpoint

The Astra DB JSON API endpoint for your database

required string CollectionName

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

required Long EmbeddingDimension

Length of the embedding vectors in use

string ClassName
string? Keyspace

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

SupportsNestedMetadataFilters SupportsNestedMetadataFilters
required string Name

The name of the data sink.

required string ProjectID
required SinkType SinkType
One of the following:
"ASTRA_DB"AstraDB
"AZUREAI_SEARCH"AzureaiSearch
"MILVUS"Milvus
"MONGODB_ATLAS"MongoDBAtlas
"PINECONE"Pinecone
"POSTGRES"Postgres
"QDRANT"Qdrant
DateTimeOffset? CreatedAt

Creation datetime

formatdate-time
DateTimeOffset? UpdatedAt

Update datetime

formatdate-time
EmbeddingModelConfig? EmbeddingModelConfig

Schema for an embedding model config.

required string ID

Unique identifier

formatuuid
required EmbeddingConfig EmbeddingConfig

The embedding configuration for the embedding model config.

One of the following:
class AzureOpenAIEmbeddingConfig:

Configuration for the Azure OpenAI embedding model.

IReadOnlyDictionary<string, JsonElement> AdditionalKwargs

Additional kwargs for the OpenAI API.

string ApiBase

The base URL for Azure deployment.

string? ApiKey

The OpenAI API key.

string ApiVersion

The version for Azure OpenAI API.

string? AzureDeployment

The Azure deployment to use.

string? AzureEndpoint

The Azure endpoint to use.

string ClassName
IReadOnlyDictionary<string, string>? DefaultHeaders

The default headers for API requests.

Long? Dimensions

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

Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
Long MaxRetries

Maximum number of retries.

minimum0
string ModelName

The name of the OpenAI embedding model.

Long? NumWorkers

The number of workers to use for async embedding calls.

Boolean ReuseClient

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

Double Timeout

Timeout for each request.

minimum0
Type Type

Type of the embedding model.

class BedrockEmbeddingConfig:

Configuration for the Bedrock embedding model.

IReadOnlyDictionary<string, JsonElement> AdditionalKwargs

Additional kwargs for the bedrock client.

string? AwsAccessKeyID

AWS Access Key ID to use

string? AwsSecretAccessKey

AWS Secret Access Key to use

string? AwsSessionToken

AWS Session Token to use

string ClassName
Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
Long MaxRetries

The maximum number of API retries.

exclusiveMinimum0
string ModelName

The modelId of the Bedrock model to use.

Long? NumWorkers

The number of workers to use for async embedding calls.

string? ProfileName

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

string? RegionName

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

Double Timeout

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

Type Type

Type of the embedding model.

class CohereEmbeddingConfig:
CohereEmbedding Component

Configuration for the Cohere embedding model.

required string? ApiKey

The Cohere API key.

string ClassName
Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
string EmbeddingType

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

string? InputType

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

string ModelName

The modelId of the Cohere model to use.

Long? NumWorkers

The number of workers to use for async embedding calls.

string Truncate

Truncation type - START/ END/ NONE

Type Type

Type of the embedding model.

class GeminiEmbeddingConfig:
GeminiEmbedding Component

Configuration for the Gemini embedding model.

string? ApiBase

API base to access the model. Defaults to None.

string? ApiKey

API key to access the model. Defaults to None.

string ClassName
Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
string ModelName

The modelId of the Gemini model to use.

Long? NumWorkers

The number of workers to use for async embedding calls.

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.

string? TaskType

The task for embedding model.

string? Title

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

string? Transport

Transport to access the model. Defaults to None.

Type Type

Type of the embedding model.

class HuggingFaceInferenceApiEmbeddingConfig:

Configuration for the HuggingFace Inference API embedding model.

Token? Token

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
Boolean
string ClassName
IReadOnlyDictionary<string, string>? Cookies

Additional cookies to send to the server.

Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
IReadOnlyDictionary<string, string>? 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.

string? ModelName

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

Long? NumWorkers

The number of workers to use for async embedding calls.

Pooling? Pooling

Enum of possible pooling choices with pooling behaviors.

One of the following:
"cls"Cls
"last"Last
"mean"Mean
string? QueryInstruction

Instruction to prepend during query embedding.

string? Task

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

string? TextInstruction

Instruction to prepend during text embedding.

Double? Timeout

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 Type

Type of the embedding model.

class OpenAIEmbeddingConfig:
OpenAIEmbedding Component

Configuration for the OpenAI embedding model.

IReadOnlyDictionary<string, JsonElement> AdditionalKwargs

Additional kwargs for the OpenAI API.

string? ApiBase

The base URL for OpenAI API.

string? ApiKey

The OpenAI API key.

string? ApiVersion

The version for OpenAI API.

string ClassName
IReadOnlyDictionary<string, string>? DefaultHeaders

The default headers for API requests.

Long? Dimensions

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

Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
Long MaxRetries

Maximum number of retries.

minimum0
string ModelName

The name of the OpenAI embedding model.

Long? NumWorkers

The number of workers to use for async embedding calls.

Boolean ReuseClient

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

Double Timeout

Timeout for each request.

minimum0
Type Type

Type of the embedding model.

class VertexAIEmbeddingConfig:

Configuration for the VertexAI embedding model.

required string? ClientEmail

The client email for the VertexAI credentials.

required string Location

The default location to use when making API calls.

required string? PrivateKey

The private key for the VertexAI credentials.

required string? PrivateKeyID

The private key ID for the VertexAI credentials.

required string Project

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

required string? TokenUri

The token URI for the VertexAI credentials.

IReadOnlyDictionary<string, JsonElement> AdditionalKwargs

Additional kwargs for the Vertex.

string ClassName
Long EmbedBatchSize

The batch size for embedding calls.

exclusiveMinimum0
maximum2048
EmbedMode EmbedMode

The embedding mode to use.

One of the following:
"classification"Classification
"clustering"Clustering
"default"Default
"retrieval"Retrieval
"similarity"Similarity
string ModelName

The modelId of the VertexAI model to use.

Long? NumWorkers

The number of workers to use for async embedding calls.

Type Type

Type of the embedding model.

required string Name

The name of the embedding model config.

required string ProjectID
DateTimeOffset? CreatedAt

Creation datetime

formatdate-time
DateTimeOffset? UpdatedAt

Update datetime

formatdate-time
string? EmbeddingModelConfigID

The ID of the EmbeddingModelConfig this pipeline is using.

formatuuid
LlamaParseParameters? LlamaParseParameters

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

Boolean? AdaptiveLongTable
Boolean? AggressiveTableExtraction
Boolean? AutoMode
string? AutoModeConfigurationJson
Boolean? AutoModeTriggerOnImageInPage
string? AutoModeTriggerOnRegexpInPage
Boolean? AutoModeTriggerOnTableInPage
string? AutoModeTriggerOnTextInPage
string? AzureOpenAIApiVersion
string? AzureOpenAIDeploymentName
string? AzureOpenAIEndpoint
string? AzureOpenAIKey
Double? BboxBottom
Double? BboxLeft
Double? BboxRight
Double? BboxTop
string? BoundingBox
Boolean? CompactMarkdownTable
string? ComplementalFormattingInstruction
string? ConfidenceScoreEffort
string? ContentGuidelineInstruction
Boolean? ContinuousMode
Boolean? DisableImageExtraction
Boolean? DisableOcr
Boolean? DisableReconstruction
Boolean? DoNotCache
Boolean? DoNotUnrollColumns
Boolean? EnableCostOptimizer
Boolean? ExtractCharts
Boolean? ExtractLayout
Boolean? ExtractPrintedPageNumber
Boolean? FastMode
string? FormattingInstruction
string? Gpt4oApiKey
Boolean? Gpt4oMode
Boolean? GuessXlsxSheetName
Boolean? HideFooters
Boolean? HideHeaders
Boolean? HighResOcr
Boolean? HtmlMakeAllElementsVisible
Boolean? HtmlRemoveFixedElements
Boolean? HtmlRemoveNavigationElements
string? HttpProxy
Boolean? IgnoreDocumentElementsForLayoutDetection
IReadOnlyList<ImagesToSave>? ImagesToSave
One of the following:
"embedded"Embedded
"layout"Layout
"screenshot"Screenshot
Boolean? InlineImagesInMarkdown
string? InputS3Path
string? InputS3Region
string? InputUrl
Boolean? InternalIsScreenshotJob
Boolean? InvalidateCache
Boolean? IsFormattingInstruction
Double? JobTimeoutExtraTimePerPageInSeconds
Double? JobTimeoutInSeconds
Boolean? KeepPageSeparatorWhenMergingTables
IReadOnlyList<ParsingLanguages> Languages
One of the following:
"abq"Abq
"ady"Ady
"af"Af
"ang"Ang
"ar"Ar
"as"As
"ava"Ava
"az"Az
"be"Be
"bg"Bg
"bgc"Bgc
"bh"Bh
"bho"Bho
"bn"Bn
"bs"Bs
"ch_sim"ChSim
"ch_tra"ChTra
"che"Che
"cs"Cs
"cy"Cy
"da"Da
"dar"Dar
"de"De
"en"En
"es"Es
"et"Et
"fa"Fa
"fr"Fr
"ga"Ga
"gom"Gom
"hi"Hi
"hr"Hr
"hu"Hu
"id"ID
"inh"Inh
"is"Is
"it"It
"ja"Ja
"kbd"Kbd
"kn"Kn
"ko"Ko
"ku"Ku
"la"La
"lbe"Lbe
"lez"Lez
"lt"Lt
"lv"Lv
"mah"Mah
"mai"Mai
"mi"Mi
"mn"Mn
"mni"Mni
"mr"Mr
"ms"Ms
"mt"Mt
"ne"Ne
"new"New
"nl"Nl
"no"No
"oc"Oc
"pi"Pi
"pl"Pl
"pt"Pt
"ro"Ro
"rs_cyrillic"RsCyrillic
"rs_latin"RsLatin
"ru"Ru
"sa"Sa
"sck"Sck
"sk"Sk
"sl"Sl
"sq"Sq
"sv"Sv
"sw"Sw
"ta"Ta
"tab"Tab
"te"Te
"th"Th
"tjk"Tjk
"tl"Tl
"tr"Tr
"ug"Ug
"uk"Uk
"ur"Ur
"uz"Uz
"vi"Vi
Boolean? LayoutAware
Boolean? LineLevelBoundingBox
string? MarkdownTableMultilineHeaderSeparator
Long? MaxPages
Long? MaxPagesEnforced
Boolean? MergeTablesAcrossPagesInMarkdown
string? Model
Boolean? OutlinedTableExtraction
Boolean? OutputPdfOfDocument
string? OutputS3PathPrefix
string? OutputS3Region
Boolean? OutputTablesAsHtml
Double? PageErrorTolerance
string? PageFooterPrefix
string? PageFooterSuffix
string? PageHeaderPrefix
string? PageHeaderSuffix
string? PagePrefix
string? PageSeparator
string? PageSuffix
ParsingMode? ParseMode

Enum for representing the mode of parsing to be used.

One of the following:
"parse_document_with_agent"ParseDocumentWithAgent
"parse_document_with_llm"ParseDocumentWithLlm
"parse_document_with_lvm"ParseDocumentWithLvm
"parse_page_with_agent"ParsePageWithAgent
"parse_page_with_layout_agent"ParsePageWithLayoutAgent
"parse_page_with_llm"ParsePageWithLlm
"parse_page_with_lvm"ParsePageWithLvm
"parse_page_without_llm"ParsePageWithoutLlm
string? ParsingInstruction
Boolean? PreciseBoundingBox
Boolean? PremiumMode
Boolean? PresentationOutOfBoundsContent
Boolean? PresentationSkipEmbeddedData
Boolean? PreserveLayoutAlignmentAcrossPages
Boolean? PreserveVerySmallText
string? Preset
Priority? Priority

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

One of the following:
"critical"Critical
"high"High
"low"Low
"medium"Medium
string? ProjectID
Boolean? RemoveHiddenText
FailPageMode? ReplaceFailedPageMode

Enum for representing the different available page error handling modes.

One of the following:
"blank_page"BlankPage
"error_message"ErrorMessage
"raw_text"RawText
string? ReplaceFailedPageWithErrorMessagePrefix
string? ReplaceFailedPageWithErrorMessageSuffix
Boolean? SaveImages
Boolean? SkipDiagonalText
Boolean? SpecializedChartParsingAgentic
Boolean? SpecializedChartParsingEfficient
Boolean? SpecializedChartParsingPlus
Boolean? SpecializedImageParsing
Boolean? SpreadsheetExtractSubTables
Boolean? SpreadsheetForceFormulaComputation
Boolean? SpreadsheetIncludeHiddenSheets
Boolean? StrictModeBuggyFont
Boolean? StrictModeImageExtraction
Boolean? StrictModeImageOcr
Boolean? StrictModeReconstruction
Boolean? StructuredOutput
string? StructuredOutputJsonSchema
string? StructuredOutputJsonSchemaName
string? SystemPrompt
string? SystemPromptAppend
Boolean? TakeScreenshot
string? TargetPages
string? Tier
Boolean? UseVendorMultimodalModel
string? UserPrompt
string? VendorMultimodalApiKey
string? VendorMultimodalModelName
string? Version
IReadOnlyList<WebhookConfiguration>? WebhookConfigurations

Outbound webhook endpoints to notify on job status changes

IReadOnlyList<WebhookEvent>? WebhookEvents

Events to subscribe to (e.g. ‘parse.success’, ‘extract.error’). If null, all events are delivered.

One of the following:
"classify.cancelled"ClassifyCancelled
"classify.error"ClassifyError
"classify.partial_success"ClassifyPartialSuccess
"classify.pending"ClassifyPending
"classify.running"ClassifyRunning
"classify.success"ClassifySuccess
"extract.cancelled"ExtractCancelled
"extract.error"ExtractError
"extract.partial_success"ExtractPartialSuccess
"extract.pending"ExtractPending
"extract.success"ExtractSuccess
"parse.cancelled"ParseCancelled
"parse.error"ParseError
"parse.partial_success"ParsePartialSuccess
"parse.pending"ParsePending
"parse.running"ParseRunning
"parse.success"ParseSuccess
"sheets.cancelled"SheetsCancelled
"sheets.error"SheetsError
"sheets.partial_success"SheetsPartialSuccess
"sheets.pending"SheetsPending
"sheets.success"SheetsSuccess
"split.cancelled"SplitCancelled
"split.error"SplitError
"split.pending"SplitPending
"split.processing"SplitProcessing
"split.success"SplitSuccess
"unmapped_event"UnmappedEvent
IReadOnlyDictionary<string, string>? WebhookHeaders

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

string? WebhookOutputFormat

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

string? WebhookSigningSecret

Shared signing secret used to sign webhook deliveries. When set, each request includes an HMAC-SHA256 signature of the request body in the ‘LC-Signature’ header (value ‘sha256=’). Recompute the HMAC over the raw request body with this secret to verify the delivery is authentic.

string? WebhookUrl

URL to receive webhook POST notifications

string? WebhookUrl
string? ManagedPipelineID

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

formatuuid
PipelineMetadataConfig? MetadataConfig

Metadata configuration for the pipeline.

IReadOnlyList<string> ExcludedEmbedMetadataKeys

List of metadata keys to exclude from embeddings

IReadOnlyList<string> ExcludedLlmMetadataKeys

List of metadata keys to exclude from LLM during retrieval

PipelineType PipelineType

Type of pipeline. Either PLAYGROUND or MANAGED.

One of the following:
"MANAGED"Managed
"PLAYGROUND"Playground
PresetRetrievalParams PresetRetrievalParameters

Preset retrieval parameters for the pipeline.

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.

maximum1
minimum0
string ClassName
Double? DenseSimilarityCutoff

Minimum similarity score wrt query for retrieval

maximum1
minimum0
Long? DenseSimilarityTopK

Number of nodes for dense retrieval.

maximum100
minimum1
Boolean? EnableReranking

Enable reranking for retrieval

Long? FilesTopK

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

maximum5
minimum1
Long? RerankTopN

Number of reranked nodes for returning.

maximum100
minimum1
RetrievalMode RetrievalMode

The retrieval mode for the query.

One of the following:
"auto_routed"AutoRouted
"chunks"Chunks
"files_via_content"FilesViaContent
"files_via_metadata"FilesViaMetadata
DeprecatedBoolean RetrieveImageNodes

Whether to retrieve image nodes.

Boolean RetrievePageFigureNodes

Whether to retrieve page figure nodes.

Boolean RetrievePageScreenshotNodes

Whether to retrieve page screenshot nodes.

MetadataFilters? SearchFilters

Metadata filters for vector stores.

required IReadOnlyList<Filter> Filters
One of the following:
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

required string Key
required Value? Value
One of the following:
Double
string
IReadOnlyList<string>
IReadOnlyList<Double>
IReadOnlyList<Long>
Operator Operator

Vector store filter operator.

One of the following:
"!="
"<"
"<="
"=="
">"
">="
"all"All
"any"Any
"contains"Contains
"in"In
"is_empty"IsEmpty
"nin"Nin
"text_match"TextMatch
"text_match_insensitive"TextMatchInsensitive
MetadataFilters
Condition? Condition

Vector store filter conditions to combine different filters.

One of the following:
"and"And
"not"Not
"or"Or
IReadOnlyDictionary<string, SearchFiltersInferenceSchema?>? SearchFiltersInferenceSchema

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

One of the following:
IReadOnlyDictionary<string, JsonElement>
IReadOnlyList<JsonElement>
string
Double
Boolean
Long? SparseSimilarityTopK

Number of nodes for sparse retrieval.

maximum100
minimum1
SparseModelConfig? SparseModelConfig

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.

string ClassName
ModelType ModelType

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:
"auto"Auto
"bm25"Bm25
"splade"Splade
Status? Status

Status of the pipeline.

One of the following:
"CREATED"Created
"DELETING"Deleting
TransformConfig TransformConfig

Configuration for the transformation.

One of the following:
class AutoTransformConfig:
Long ChunkOverlap

Chunk overlap for the transformation.

Long ChunkSize

Chunk size for the transformation.

exclusiveMinimum0
Mode Mode
class AdvancedModeTransformConfig:
ChunkingConfig ChunkingConfig

Configuration for the chunking.

One of the following:
class NoneChunkingConfig:
Mode Mode
class CharacterChunkingConfig:
Long ChunkOverlap
Long ChunkSize
Mode Mode
class TokenChunkingConfig:
Long ChunkOverlap
Long ChunkSize
Mode Mode
string Separator
class SentenceChunkingConfig:
Long ChunkOverlap
Long ChunkSize
Mode Mode
string ParagraphSeparator
string Separator
class SemanticChunkingConfig:
Long BreakpointPercentileThreshold
Long BufferSize
Mode Mode
Mode Mode
SegmentationConfig SegmentationConfig

Configuration for the segmentation.

One of the following:
class NoneSegmentationConfig:
Mode Mode
class PageSegmentationConfig:
Mode Mode
string PageSeparator
class ElementSegmentationConfig:
Mode Mode
DateTimeOffset? UpdatedAt

Update datetime

formatdate-time

Sync Pipeline

SyncCreateParams parameters = new()
{
    PipelineID = "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
};

var pipeline = await client.Pipelines.Sync.Create(parameters);

Console.WriteLine(pipeline);
{
  "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",
  "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": "ASTRA_DB",
    "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",
    "confidence_score_effort": "confidence_score_effort",
    "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": [
      "embedded"
    ],
    "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": [
      "abq"
    ],
    "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_document_with_agent",
    "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": "critical",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "blank_page",
    "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_signing_secret": "whsec_...",
        "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": "MANAGED",
  "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": "auto_routed",
    "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": "auto"
  },
  "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": {
      "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",
  "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": "ASTRA_DB",
    "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",
    "confidence_score_effort": "confidence_score_effort",
    "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": [
      "embedded"
    ],
    "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": [
      "abq"
    ],
    "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_document_with_agent",
    "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": "critical",
    "project_id": "project_id",
    "remove_hidden_text": true,
    "replace_failed_page_mode": "blank_page",
    "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_signing_secret": "whsec_...",
        "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": "MANAGED",
  "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": "auto_routed",
    "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": "auto"
  },
  "status": "CREATED",
  "transform_config": {
    "chunk_overlap": 0,
    "chunk_size": 1,
    "mode": "auto"
  },
  "updated_at": "2019-12-27T18:11:19.117Z"
}
Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/for-agents/mcp/ - Other LlamaIndex tooling for agents — the LlamaParse Platform MCP server, agent skills and plugins, and the n8n node — is mapped at https://developers.llamaindex.ai/for-agents/