# Sync ## Sync Pipeline `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. ### Parameters - `SyncCreateParams parameters` - `required string pipelineID` ### Returns - `class Pipeline:` Schema for a pipeline. - `required string ID` Unique identifier - `required EmbeddingConfig EmbeddingConfig` - `class AzureOpenAIEmbeddingConfig:` - `AzureOpenAIEmbedding Component` Configuration for the Azure OpenAI embedding model. - `IReadOnlyDictionary 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? 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. - `Long MaxRetries` Maximum number of retries. - `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. - `Type Type` Type of the embedding model. - `"AZURE_EMBEDDING"AzureEmbedding` - `class BedrockEmbeddingConfig:` - `BedrockEmbedding Component` Configuration for the Bedrock embedding model. - `IReadOnlyDictionary 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. - `Long MaxRetries` The maximum number of API retries. - `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. - `"BEDROCK_EMBEDDING"BedrockEmbedding` - `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. - `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. - `"COHERE_EMBEDDING"CohereEmbedding` - `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. - `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. - `"GEMINI_EMBEDDING"GeminiEmbedding` - `class HuggingFaceInferenceApiEmbeddingConfig:` - `HuggingFaceInferenceApiEmbedding Component` 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. - `string` - `Boolean` - `string ClassName` - `IReadOnlyDictionary? Cookies` Additional cookies to send to the server. - `Long EmbedBatchSize` The batch size for embedding calls. - `IReadOnlyDictionary? 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. - `"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. - `"HUGGINGFACE_API_EMBEDDING"HuggingfaceApiEmbedding` - `class ManagedOpenAIEmbedding:` - `Component Component` Configuration for the Managed OpenAI embedding model. - `string ClassName` - `Long EmbedBatchSize` The batch size for embedding calls. - `ModelName ModelName` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"OpenAITextEmbedding3Small` - `Long? NumWorkers` The number of workers to use for async embedding calls. - `Type Type` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"ManagedOpenAIEmbedding` - `class OpenAIEmbeddingConfig:` - `OpenAIEmbedding Component` Configuration for the OpenAI embedding model. - `IReadOnlyDictionary 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? 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. - `Long MaxRetries` Maximum number of retries. - `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. - `Type Type` Type of the embedding model. - `"OPENAI_EMBEDDING"OpenAIEmbedding` - `class VertexAIEmbeddingConfig:` - `VertexTextEmbedding Component` 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 AdditionalKwargs` Additional kwargs for the Vertex. - `string ClassName` - `Long EmbedBatchSize` The batch size for embedding calls. - `EmbedMode EmbedMode` The embedding mode to use. - `"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. - `"VERTEXAI_EMBEDDING"VertexaiEmbedding` - `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 - `DataSink? DataSink` Schema for a data sink. - `required string ID` Unique identifier - `required Component Component` Component that implements the data sink - `IReadOnlyDictionary` - `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 - `required string IndexName` - `string ClassName` - `IReadOnlyDictionary? InsertKwargs` - `string? Namespace` - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `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. - `"cosine"Cosine` - `"hamming"Hamming` - `"ip"IP` - `"jaccard"Jaccard` - `"l1"L1` - `"l2"L2` - `Long EfConstruction` The number of edges to use during the construction phase. - `Long EfSearch` The number of edges to use during the search phase. - `Long M` The number of bi-directional links created for each new element. - `VectorType VectorType` The type of vector to use. - `"bit"Bit` - `"half_vec"HalfVec` - `"sparse_vec"SparseVec` - `"vector"Vector` - `Boolean? HybridSearch` - `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 ClientKwargs` - `Long MaxRetries` - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `class CloudAzureAISearchVectorStore:` Cloud Azure AI Search Vector Store. - `required string SearchServiceApiKey` - `required string SearchServiceEndpoint` - `string ClassName` - `string? ClientID` - `string? ClientSecret` - `Long? EmbeddingDimension` - `IReadOnlyDictionary? FilterableMetadataFieldKeys` - `string? IndexName` - `string? SearchServiceApiVersion` - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `string? TenantID` - `class CloudMongoDBAtlasVectorSearch:` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `required string CollectionName` - `required string DBName` - `required string MongoDBUri` - `string ClassName` - `Long? EmbeddingDimension` - `string? FulltextIndexName` - `Boolean SupportsNestedMetadataFilters` - `string? VectorIndexName` - `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 - `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` - `trueTrue` - `required string Name` The name of the data sink. - `required string ProjectID` - `required SinkType SinkType` - `"ASTRA_DB"AstraDB` - `"AZUREAI_SEARCH"AzureaiSearch` - `"MILVUS"Milvus` - `"MONGODB_ATLAS"MongoDBAtlas` - `"PINECONE"Pinecone` - `"POSTGRES"Postgres` - `"QDRANT"Qdrant` - `DateTimeOffset? CreatedAt` Creation datetime - `DateTimeOffset? UpdatedAt` Update datetime - `EmbeddingModelConfig? EmbeddingModelConfig` Schema for an embedding model config. - `required string ID` Unique identifier - `required EmbeddingConfig EmbeddingConfig` The embedding configuration for the embedding model config. - `class AzureOpenAIEmbeddingConfig:` - `class BedrockEmbeddingConfig:` - `class CohereEmbeddingConfig:` - `class GeminiEmbeddingConfig:` - `class HuggingFaceInferenceApiEmbeddingConfig:` - `class OpenAIEmbeddingConfig:` - `class VertexAIEmbeddingConfig:` - `required string Name` The name of the embedding model config. - `required string ProjectID` - `DateTimeOffset? CreatedAt` Creation datetime - `DateTimeOffset? UpdatedAt` Update datetime - `string? EmbeddingModelConfigID` The ID of the EmbeddingModelConfig this pipeline is using. - `LlamaParseParameters? LlamaParseParameters` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `Boolean? AdaptiveLongTable` - `Boolean? AggressiveTableExtraction` - `Boolean? AnnotateLinks` - `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` - `"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 Languages` - `"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. - `"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. - `"critical"Critical` - `"high"High` - `"low"Low` - `"medium"Medium` - `string? ProjectID` - `Boolean? RemoveHiddenText` - `FailPageMode? ReplaceFailedPageMode` Enum for representing the different available page error handling modes. - `"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? WebhookConfigurations` Outbound webhook endpoints to notify on job status changes - `IReadOnlyList? WebhookEvents` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"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? 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. - `PipelineMetadataConfig? MetadataConfig` Metadata configuration for the pipeline. - `IReadOnlyList ExcludedEmbedMetadataKeys` List of metadata keys to exclude from embeddings - `IReadOnlyList ExcludedLlmMetadataKeys` List of metadata keys to exclude from LLM during retrieval - `PipelineType PipelineType` Type of pipeline. Either PLAYGROUND or MANAGED. - `"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. - `string ClassName` - `Double? DenseSimilarityCutoff` Minimum similarity score wrt query for retrieval - `Long? DenseSimilarityTopK` Number of nodes for dense retrieval. - `Boolean? EnableReranking` Enable reranking for retrieval - `Long? FilesTopK` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `Long? RerankTopN` Number of reranked nodes for returning. - `RetrievalMode RetrievalMode` The retrieval mode for the query. - `"auto_routed"AutoRouted` - `"chunks"Chunks` - `"files_via_content"FilesViaContent` - `"files_via_metadata"FilesViaMetadata` - `Boolean 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 Filters` - `class MetadataFilter:` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `required string Key` - `required Value? Value` - `Double` - `string` - `IReadOnlyList` - `IReadOnlyList` - `IReadOnlyList` - `Operator Operator` Vector store filter operator. - `"!="` - `"<"` - `"<="` - `"=="` - `">"` - `">="` - `"all"All` - `"any"Any` - `"contains"Contains` - `"in"In` - `"is_empty"IsEmpty` - `"nin"Nin` - `"text_match"TextMatch` - `"text_match_insensitive"TextMatchInsensitive` - `class MetadataFilters:` Metadata filters for vector stores. - `Condition? Condition` Vector store filter conditions to combine different filters. - `"and"And` - `"not"Not` - `"or"Or` - `IReadOnlyDictionary? SearchFiltersInferenceSchema` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `IReadOnlyDictionary` - `IReadOnlyList` - `string` - `Double` - `Boolean` - `Long? SparseSimilarityTopK` Number of nodes for sparse retrieval. - `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). - `"auto"Auto` - `"bm25"Bm25` - `"splade"Splade` - `Status? Status` Status of the pipeline. - `"CREATED"Created` - `"DELETING"Deleting` - `TransformConfig TransformConfig` Configuration for the transformation. - `class AutoTransformConfig:` - `Long ChunkOverlap` Chunk overlap for the transformation. - `Long ChunkSize` Chunk size for the transformation. - `Mode Mode` - `"auto"Auto` - `class AdvancedModeTransformConfig:` - `ChunkingConfig ChunkingConfig` Configuration for the chunking. - `class NoneChunkingConfig:` - `Mode Mode` - `"none"None` - `class CharacterChunkingConfig:` - `Long ChunkOverlap` - `Long ChunkSize` - `Mode Mode` - `"character"Character` - `class TokenChunkingConfig:` - `Long ChunkOverlap` - `Long ChunkSize` - `Mode Mode` - `"token"Token` - `string Separator` - `class SentenceChunkingConfig:` - `Long ChunkOverlap` - `Long ChunkSize` - `Mode Mode` - `"sentence"Sentence` - `string ParagraphSeparator` - `string Separator` - `class SemanticChunkingConfig:` - `Long BreakpointPercentileThreshold` - `Long BufferSize` - `Mode Mode` - `"semantic"Semantic` - `Mode Mode` - `"advanced"Advanced` - `SegmentationConfig SegmentationConfig` Configuration for the segmentation. - `class NoneSegmentationConfig:` - `Mode Mode` - `"none"None` - `class PageSegmentationConfig:` - `Mode Mode` - `"page"Page` - `string PageSeparator` - `class ElementSegmentationConfig:` - `Mode Mode` - `"element"Element` - `DateTimeOffset? UpdatedAt` Update datetime ### Example ```csharp SyncCreateParams parameters = new() { PipelineID = "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e" }; var pipeline = await client.Pipelines.Sync.Create(parameters); Console.WriteLine(pipeline); ``` #### Response ```json { "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" } ``` ## Cancel Pipeline Sync `Pipeline Pipelines.Sync.Cancel(SyncCancelParamsparameters, CancellationTokencancellationToken = default)` **post** `/api/v1/pipelines/{pipeline_id}/sync/cancel` Cancel all running sync jobs for a pipeline. ### Parameters - `SyncCancelParams parameters` - `required string pipelineID` ### Returns - `class Pipeline:` Schema for a pipeline. - `required string ID` Unique identifier - `required EmbeddingConfig EmbeddingConfig` - `class AzureOpenAIEmbeddingConfig:` - `AzureOpenAIEmbedding Component` Configuration for the Azure OpenAI embedding model. - `IReadOnlyDictionary 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? 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. - `Long MaxRetries` Maximum number of retries. - `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. - `Type Type` Type of the embedding model. - `"AZURE_EMBEDDING"AzureEmbedding` - `class BedrockEmbeddingConfig:` - `BedrockEmbedding Component` Configuration for the Bedrock embedding model. - `IReadOnlyDictionary 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. - `Long MaxRetries` The maximum number of API retries. - `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. - `"BEDROCK_EMBEDDING"BedrockEmbedding` - `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. - `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. - `"COHERE_EMBEDDING"CohereEmbedding` - `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. - `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. - `"GEMINI_EMBEDDING"GeminiEmbedding` - `class HuggingFaceInferenceApiEmbeddingConfig:` - `HuggingFaceInferenceApiEmbedding Component` 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. - `string` - `Boolean` - `string ClassName` - `IReadOnlyDictionary? Cookies` Additional cookies to send to the server. - `Long EmbedBatchSize` The batch size for embedding calls. - `IReadOnlyDictionary? 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. - `"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. - `"HUGGINGFACE_API_EMBEDDING"HuggingfaceApiEmbedding` - `class ManagedOpenAIEmbedding:` - `Component Component` Configuration for the Managed OpenAI embedding model. - `string ClassName` - `Long EmbedBatchSize` The batch size for embedding calls. - `ModelName ModelName` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"OpenAITextEmbedding3Small` - `Long? NumWorkers` The number of workers to use for async embedding calls. - `Type Type` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"ManagedOpenAIEmbedding` - `class OpenAIEmbeddingConfig:` - `OpenAIEmbedding Component` Configuration for the OpenAI embedding model. - `IReadOnlyDictionary 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? 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. - `Long MaxRetries` Maximum number of retries. - `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. - `Type Type` Type of the embedding model. - `"OPENAI_EMBEDDING"OpenAIEmbedding` - `class VertexAIEmbeddingConfig:` - `VertexTextEmbedding Component` 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 AdditionalKwargs` Additional kwargs for the Vertex. - `string ClassName` - `Long EmbedBatchSize` The batch size for embedding calls. - `EmbedMode EmbedMode` The embedding mode to use. - `"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. - `"VERTEXAI_EMBEDDING"VertexaiEmbedding` - `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 - `DataSink? DataSink` Schema for a data sink. - `required string ID` Unique identifier - `required Component Component` Component that implements the data sink - `IReadOnlyDictionary` - `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 - `required string IndexName` - `string ClassName` - `IReadOnlyDictionary? InsertKwargs` - `string? Namespace` - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `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. - `"cosine"Cosine` - `"hamming"Hamming` - `"ip"IP` - `"jaccard"Jaccard` - `"l1"L1` - `"l2"L2` - `Long EfConstruction` The number of edges to use during the construction phase. - `Long EfSearch` The number of edges to use during the search phase. - `Long M` The number of bi-directional links created for each new element. - `VectorType VectorType` The type of vector to use. - `"bit"Bit` - `"half_vec"HalfVec` - `"sparse_vec"SparseVec` - `"vector"Vector` - `Boolean? HybridSearch` - `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 ClientKwargs` - `Long MaxRetries` - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `class CloudAzureAISearchVectorStore:` Cloud Azure AI Search Vector Store. - `required string SearchServiceApiKey` - `required string SearchServiceEndpoint` - `string ClassName` - `string? ClientID` - `string? ClientSecret` - `Long? EmbeddingDimension` - `IReadOnlyDictionary? FilterableMetadataFieldKeys` - `string? IndexName` - `string? SearchServiceApiVersion` - `SupportsNestedMetadataFilters SupportsNestedMetadataFilters` - `trueTrue` - `string? TenantID` - `class CloudMongoDBAtlasVectorSearch:` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `required string CollectionName` - `required string DBName` - `required string MongoDBUri` - `string ClassName` - `Long? EmbeddingDimension` - `string? FulltextIndexName` - `Boolean SupportsNestedMetadataFilters` - `string? VectorIndexName` - `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 - `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` - `trueTrue` - `required string Name` The name of the data sink. - `required string ProjectID` - `required SinkType SinkType` - `"ASTRA_DB"AstraDB` - `"AZUREAI_SEARCH"AzureaiSearch` - `"MILVUS"Milvus` - `"MONGODB_ATLAS"MongoDBAtlas` - `"PINECONE"Pinecone` - `"POSTGRES"Postgres` - `"QDRANT"Qdrant` - `DateTimeOffset? CreatedAt` Creation datetime - `DateTimeOffset? UpdatedAt` Update datetime - `EmbeddingModelConfig? EmbeddingModelConfig` Schema for an embedding model config. - `required string ID` Unique identifier - `required EmbeddingConfig EmbeddingConfig` The embedding configuration for the embedding model config. - `class AzureOpenAIEmbeddingConfig:` - `class BedrockEmbeddingConfig:` - `class CohereEmbeddingConfig:` - `class GeminiEmbeddingConfig:` - `class HuggingFaceInferenceApiEmbeddingConfig:` - `class OpenAIEmbeddingConfig:` - `class VertexAIEmbeddingConfig:` - `required string Name` The name of the embedding model config. - `required string ProjectID` - `DateTimeOffset? CreatedAt` Creation datetime - `DateTimeOffset? UpdatedAt` Update datetime - `string? EmbeddingModelConfigID` The ID of the EmbeddingModelConfig this pipeline is using. - `LlamaParseParameters? LlamaParseParameters` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `Boolean? AdaptiveLongTable` - `Boolean? AggressiveTableExtraction` - `Boolean? AnnotateLinks` - `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` - `"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 Languages` - `"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. - `"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. - `"critical"Critical` - `"high"High` - `"low"Low` - `"medium"Medium` - `string? ProjectID` - `Boolean? RemoveHiddenText` - `FailPageMode? ReplaceFailedPageMode` Enum for representing the different available page error handling modes. - `"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? WebhookConfigurations` Outbound webhook endpoints to notify on job status changes - `IReadOnlyList? WebhookEvents` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"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? 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. - `PipelineMetadataConfig? MetadataConfig` Metadata configuration for the pipeline. - `IReadOnlyList ExcludedEmbedMetadataKeys` List of metadata keys to exclude from embeddings - `IReadOnlyList ExcludedLlmMetadataKeys` List of metadata keys to exclude from LLM during retrieval - `PipelineType PipelineType` Type of pipeline. Either PLAYGROUND or MANAGED. - `"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. - `string ClassName` - `Double? DenseSimilarityCutoff` Minimum similarity score wrt query for retrieval - `Long? DenseSimilarityTopK` Number of nodes for dense retrieval. - `Boolean? EnableReranking` Enable reranking for retrieval - `Long? FilesTopK` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `Long? RerankTopN` Number of reranked nodes for returning. - `RetrievalMode RetrievalMode` The retrieval mode for the query. - `"auto_routed"AutoRouted` - `"chunks"Chunks` - `"files_via_content"FilesViaContent` - `"files_via_metadata"FilesViaMetadata` - `Boolean 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 Filters` - `class MetadataFilter:` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `required string Key` - `required Value? Value` - `Double` - `string` - `IReadOnlyList` - `IReadOnlyList` - `IReadOnlyList` - `Operator Operator` Vector store filter operator. - `"!="` - `"<"` - `"<="` - `"=="` - `">"` - `">="` - `"all"All` - `"any"Any` - `"contains"Contains` - `"in"In` - `"is_empty"IsEmpty` - `"nin"Nin` - `"text_match"TextMatch` - `"text_match_insensitive"TextMatchInsensitive` - `class MetadataFilters:` Metadata filters for vector stores. - `Condition? Condition` Vector store filter conditions to combine different filters. - `"and"And` - `"not"Not` - `"or"Or` - `IReadOnlyDictionary? SearchFiltersInferenceSchema` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `IReadOnlyDictionary` - `IReadOnlyList` - `string` - `Double` - `Boolean` - `Long? SparseSimilarityTopK` Number of nodes for sparse retrieval. - `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). - `"auto"Auto` - `"bm25"Bm25` - `"splade"Splade` - `Status? Status` Status of the pipeline. - `"CREATED"Created` - `"DELETING"Deleting` - `TransformConfig TransformConfig` Configuration for the transformation. - `class AutoTransformConfig:` - `Long ChunkOverlap` Chunk overlap for the transformation. - `Long ChunkSize` Chunk size for the transformation. - `Mode Mode` - `"auto"Auto` - `class AdvancedModeTransformConfig:` - `ChunkingConfig ChunkingConfig` Configuration for the chunking. - `class NoneChunkingConfig:` - `Mode Mode` - `"none"None` - `class CharacterChunkingConfig:` - `Long ChunkOverlap` - `Long ChunkSize` - `Mode Mode` - `"character"Character` - `class TokenChunkingConfig:` - `Long ChunkOverlap` - `Long ChunkSize` - `Mode Mode` - `"token"Token` - `string Separator` - `class SentenceChunkingConfig:` - `Long ChunkOverlap` - `Long ChunkSize` - `Mode Mode` - `"sentence"Sentence` - `string ParagraphSeparator` - `string Separator` - `class SemanticChunkingConfig:` - `Long BreakpointPercentileThreshold` - `Long BufferSize` - `Mode Mode` - `"semantic"Semantic` - `Mode Mode` - `"advanced"Advanced` - `SegmentationConfig SegmentationConfig` Configuration for the segmentation. - `class NoneSegmentationConfig:` - `Mode Mode` - `"none"None` - `class PageSegmentationConfig:` - `Mode Mode` - `"page"Page` - `string PageSeparator` - `class ElementSegmentationConfig:` - `Mode Mode` - `"element"Element` - `DateTimeOffset? UpdatedAt` Update datetime ### Example ```csharp SyncCancelParams parameters = new() { PipelineID = "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e" }; var pipeline = await client.Pipelines.Sync.Cancel(parameters); Console.WriteLine(pipeline); ``` #### Response ```json { "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" } ```