API Reference

Pipelines

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Search Pipelines

GET/api/v1/pipelines

Search for pipelines by various parameters.

Query ParametersExpand Collapse

organization_id: optional string

pipeline_name: optional string

pipeline_type: optional PipelineType

Enum for representing the type of a pipeline

Accepts one of the following:

"PLAYGROUND"

"MANAGED"

project_id: optional string

project_name: optional string

Cookie ParametersExpand Collapse

session: optional string

ReturnsExpand Collapse

id: string

Unique identifier

formatuuid

embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig { component, type } or CohereEmbeddingConfig { component, type } or 5 more

Accepts one of the following:

ManagedOpenAIEmbedding = object { component, type }

component: optional object { class_name, embed_batch_size, model_name, num_workers }

Configuration for the Managed OpenAI embedding model.

class_name: optional string

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

model_name: optional "openai-text-embedding-3-small"

The name of the OpenAI embedding model.

num_workers: optional number

The number of workers to use for async embedding calls.

type: optional "MANAGED_OPENAI_EMBEDDING"

Type of the embedding model.

AzureOpenAIEmbeddingConfig = object { component, type }

component: optional AzureOpenAIEmbedding { additional_kwargs, api_base, api_key, 12 more }

Configuration for the Azure OpenAI embedding model.

additional_kwargs: optional map[unknown]

Additional kwargs for the OpenAI API.

api_base: optional string

The base URL for Azure deployment.

api_key: optional string

The OpenAI API key.

api_version: optional string

The version for Azure OpenAI API.

azure_deployment: optional string

The Azure deployment to use.

azure_endpoint: optional string

The Azure endpoint to use.

class_name: optional string

default_headers: optional map[string]

The default headers for API requests.

dimensions: optional number

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

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

max_retries: optional number

Maximum number of retries.

minimum0

model_name: optional string

The name of the OpenAI embedding model.

num_workers: optional number

The number of workers to use for async embedding calls.

reuse_client: optional boolean

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

timeout: optional number

Timeout for each request.

minimum0

type: optional "AZURE_EMBEDDING"

Type of the embedding model.

CohereEmbeddingConfig = object { component, type }

component: optional CohereEmbedding { api_key, class_name, embed_batch_size, 5 more }

Configuration for the Cohere embedding model.

api_key: string

The Cohere API key.

class_name: optional string

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

embedding_type: optional string

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

input_type: optional string

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

model_name: optional string

The modelId of the Cohere model to use.

num_workers: optional number

The number of workers to use for async embedding calls.

truncate: optional string

Truncation type - START/ END/ NONE

type: optional "COHERE_EMBEDDING"

Type of the embedding model.

GeminiEmbeddingConfig = object { component, type }

component: optional GeminiEmbedding { api_base, api_key, class_name, 6 more }

Configuration for the Gemini embedding model.

api_base: optional string

API base to access the model. Defaults to None.

api_key: optional string

API key to access the model. Defaults to None.

class_name: optional string

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

model_name: optional string

The modelId of the Gemini model to use.

num_workers: optional number

The number of workers to use for async embedding calls.

task_type: optional string

The task for embedding model.

title: optional string

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

transport: optional string

Transport to access the model. Defaults to None.

type: optional "GEMINI_EMBEDDING"

Type of the embedding model.

HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }

component: optional HuggingFaceInferenceAPIEmbedding { token, class_name, cookies, 9 more }

Configuration for the HuggingFace Inference API embedding model.

token: optional string or boolean

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.

Accepts one of the following:

UnionMember0 = string

UnionMember1 = boolean

class_name: optional string

cookies: optional map[string]

Additional cookies to send to the server.

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

headers: optional map[string]

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.

model_name: optional string

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

num_workers: optional number

The number of workers to use for async embedding calls.

pooling: optional "cls" or "mean" or "last"

Enum of possible pooling choices with pooling behaviors.

Accepts one of the following:

"cls"

"mean"

"last"

query_instruction: optional string

Instruction to prepend during query embedding.

task: optional string

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

text_instruction: optional string

Instruction to prepend during text embedding.

timeout: optional number

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: optional "HUGGINGFACE_API_EMBEDDING"

Type of the embedding model.

OpenAIEmbeddingConfig = object { component, type }

component: optional OpenAIEmbedding { additional_kwargs, api_base, api_key, 10 more }

Configuration for the OpenAI embedding model.

additional_kwargs: optional map[unknown]

Additional kwargs for the OpenAI API.

api_base: optional string

The base URL for OpenAI API.

api_key: optional string

The OpenAI API key.

api_version: optional string

The version for OpenAI API.

class_name: optional string

default_headers: optional map[string]

The default headers for API requests.

dimensions: optional number

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

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

max_retries: optional number

Maximum number of retries.

minimum0

model_name: optional string

The name of the OpenAI embedding model.

num_workers: optional number

The number of workers to use for async embedding calls.

reuse_client: optional boolean

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

timeout: optional number

Timeout for each request.

minimum0

type: optional "OPENAI_EMBEDDING"

Type of the embedding model.

VertexAIEmbeddingConfig = object { component, type }

component: optional VertexTextEmbedding { client_email, location, private_key, 9 more }

Configuration for the VertexAI embedding model.

client_email: string

The client email for the VertexAI credentials.

location: string

The default location to use when making API calls.

private_key: string

The private key for the VertexAI credentials.

private_key_id: string

The private key ID for the VertexAI credentials.

project: string

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

token_uri: string

The token URI for the VertexAI credentials.

additional_kwargs: optional map[unknown]

Additional kwargs for the Vertex.

class_name: optional string

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

embed_mode: optional "default" or "classification" or "clustering" or 2 more

The embedding mode to use.

Accepts one of the following:

"default"

"classification"

"clustering"

"similarity"

"retrieval"

model_name: optional string

The modelId of the VertexAI model to use.

num_workers: optional number

The number of workers to use for async embedding calls.

type: optional "VERTEXAI_EMBEDDING"

Type of the embedding model.

BedrockEmbeddingConfig = object { component, type }

component: optional BedrockEmbedding { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }

Configuration for the Bedrock embedding model.

additional_kwargs: optional map[unknown]

Additional kwargs for the bedrock client.

aws_access_key_id: optional string

AWS Access Key ID to use

aws_secret_access_key: optional string

AWS Secret Access Key to use

aws_session_token: optional string

AWS Session Token to use

class_name: optional string

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

max_retries: optional number

The maximum number of API retries.

exclusiveMinimum0

model_name: optional string

The modelId of the Bedrock model to use.

num_workers: optional number

The number of workers to use for async embedding calls.

profile_name: optional string

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

region_name: optional string

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

timeout: optional number

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

type: optional "BEDROCK_EMBEDDING"

Type of the embedding model.

name: string

project_id: string

config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }

Hashes for the configuration of a pipeline.

embedding_config_hash: optional string

Hash of the embedding config.

parsing_config_hash: optional string

Hash of the llama parse parameters.

transform_config_hash: optional string

Hash of the transform config.

created_at: optional string

Creation datetime

formatdate-time

data_sink: optional DataSink { id, component, name, 4 more }

Schema for a data sink.

id: string

Unique identifier

formatuuid

component: map[unknown] or CloudPineconeVectorStore { api_key, index_name, class_name, 3 more } or CloudPostgresVectorStore { database, embed_dim, host, 10 more } or 5 more

Component that implements the data sink

Accepts one of the following:

UnionMember0 = map[unknown]

CloudPineconeVectorStore = object { api_key, index_name, class_name, 3 more }

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

api_key: string

The API key for authenticating with Pinecone

formatpassword

index_name: string

class_name: optional string

insert_kwargs: optional map[unknown]

namespace: optional string

supports_nested_metadata_filters: optional true

CloudPostgresVectorStore = object { database, embed_dim, host, 10 more }

database: string

embed_dim: number

host: string

password: string

port: number

schema_name: string

table_name: string

user: string

class_name: optional string

hnsw_settings: optional PgVectorHnswSettings { distance_method, ef_construction, ef_search, 2 more }

HNSW settings for PGVector.

distance_method: optional "l2" or "ip" or "cosine" or 3 more

The distance method to use.

Accepts one of the following:

"l2"

"ip"

"cosine"

"l1"

"hamming"

"jaccard"

ef_construction: optional number

The number of edges to use during the construction phase.

minimum1

ef_search: optional number

The number of edges to use during the search phase.

minimum1

m: optional number

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

minimum1

vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"

The type of vector to use.

Accepts one of the following:

"vector"

"half_vec"

"bit"

"sparse_vec"

hybrid_search: optional boolean

perform_setup: optional boolean

supports_nested_metadata_filters: optional boolean

CloudQdrantVectorStore = object { api_key, collection_name, url, 4 more }

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

api_key: string

collection_name: string

url: string

class_name: optional string

client_kwargs: optional map[unknown]

max_retries: optional number

supports_nested_metadata_filters: optional true

CloudAzureAISearchVectorStore = object { search_service_api_key, search_service_endpoint, class_name, 8 more }

Cloud Azure AI Search Vector Store.

search_service_api_key: string

search_service_endpoint: string

class_name: optional string

client_id: optional string

client_secret: optional string

embedding_dimension: optional number

filterable_metadata_field_keys: optional map[unknown]

index_name: optional string

search_service_api_version: optional string

supports_nested_metadata_filters: optional true

tenant_id: optional string

CloudMongoDBAtlasVectorSearch = object { collection_name, db_name, mongodb_uri, 5 more }

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

collection_name: string

db_name: string

mongodb_uri: string

class_name: optional string

embedding_dimension: optional number

fulltext_index_name: optional string

supports_nested_metadata_filters: optional boolean

vector_index_name: optional string

CloudMilvusVectorStore = object { uri, token, class_name, 3 more }

Cloud Milvus Vector Store.

uri: string

token: optional string

class_name: optional string

collection_name: optional string

embedding_dimension: optional number

supports_nested_metadata_filters: optional boolean

CloudAstraDBVectorStore = object { token, api_endpoint, collection_name, 4 more }

Cloud AstraDB Vector Store.

This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud.

Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'

token: string

The Astra DB Application Token to use

formatpassword

api_endpoint: string

The Astra DB JSON API endpoint for your database

collection_name: string

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

embedding_dimension: number

Length of the embedding vectors in use

class_name: optional string

keyspace: optional string

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

supports_nested_metadata_filters: optional true

name: string

The name of the data sink.

project_id: string

sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more

Accepts one of the following:

"PINECONE"

"POSTGRES"

"QDRANT"

"AZUREAI_SEARCH"

"MONGODB_ATLAS"

"MILVUS"

"ASTRA_DB"

created_at: optional string

Creation datetime

formatdate-time

updated_at: optional string

Update datetime

formatdate-time

embedding_model_config: optional object { id, embedding_config, name, 3 more }

Schema for an embedding model config.

id: string

Unique identifier

formatuuid

embedding_config: AzureOpenAIEmbeddingConfig { component, type } or CohereEmbeddingConfig { component, type } or GeminiEmbeddingConfig { component, type } or 4 more

The embedding configuration for the embedding model config.

Accepts one of the following:

AzureOpenAIEmbeddingConfig = object { component, type }

component: optional AzureOpenAIEmbedding { additional_kwargs, api_base, api_key, 12 more }

Configuration for the Azure OpenAI embedding model.

additional_kwargs: optional map[unknown]

Additional kwargs for the OpenAI API.

api_base: optional string

The base URL for Azure deployment.

api_key: optional string

The OpenAI API key.

api_version: optional string

The version for Azure OpenAI API.

azure_deployment: optional string

The Azure deployment to use.

azure_endpoint: optional string

The Azure endpoint to use.

class_name: optional string

default_headers: optional map[string]

The default headers for API requests.

dimensions: optional number

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

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

max_retries: optional number

Maximum number of retries.

minimum0

model_name: optional string

The name of the OpenAI embedding model.

num_workers: optional number

The number of workers to use for async embedding calls.

reuse_client: optional boolean

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

timeout: optional number

Timeout for each request.

minimum0

type: optional "AZURE_EMBEDDING"

Type of the embedding model.

CohereEmbeddingConfig = object { component, type }

component: optional CohereEmbedding { api_key, class_name, embed_batch_size, 5 more }

Configuration for the Cohere embedding model.

api_key: string

The Cohere API key.

class_name: optional string

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

embedding_type: optional string

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

input_type: optional string

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

model_name: optional string

The modelId of the Cohere model to use.

num_workers: optional number

The number of workers to use for async embedding calls.

truncate: optional string

Truncation type - START/ END/ NONE

type: optional "COHERE_EMBEDDING"

Type of the embedding model.

GeminiEmbeddingConfig = object { component, type }

component: optional GeminiEmbedding { api_base, api_key, class_name, 6 more }

Configuration for the Gemini embedding model.

api_base: optional string

API base to access the model. Defaults to None.

api_key: optional string

API key to access the model. Defaults to None.

class_name: optional string

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

model_name: optional string

The modelId of the Gemini model to use.

num_workers: optional number

The number of workers to use for async embedding calls.

task_type: optional string

The task for embedding model.

title: optional string

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

transport: optional string

Transport to access the model. Defaults to None.

type: optional "GEMINI_EMBEDDING"

Type of the embedding model.

HuggingFaceInferenceAPIEmbeddingConfig = object { component, type }

component: optional HuggingFaceInferenceAPIEmbedding { token, class_name, cookies, 9 more }

Configuration for the HuggingFace Inference API embedding model.

token: optional string or boolean

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.

Accepts one of the following:

UnionMember0 = string

UnionMember1 = boolean

class_name: optional string

cookies: optional map[string]

Additional cookies to send to the server.

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

headers: optional map[string]

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.

model_name: optional string

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

num_workers: optional number

The number of workers to use for async embedding calls.

pooling: optional "cls" or "mean" or "last"

Enum of possible pooling choices with pooling behaviors.

Accepts one of the following:

"cls"

"mean"

"last"

query_instruction: optional string

Instruction to prepend during query embedding.

task: optional string

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

text_instruction: optional string

Instruction to prepend during text embedding.

timeout: optional number

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: optional "HUGGINGFACE_API_EMBEDDING"

Type of the embedding model.

OpenAIEmbeddingConfig = object { component, type }

component: optional OpenAIEmbedding { additional_kwargs, api_base, api_key, 10 more }

Configuration for the OpenAI embedding model.

additional_kwargs: optional map[unknown]

Additional kwargs for the OpenAI API.

api_base: optional string

The base URL for OpenAI API.

api_key: optional string

The OpenAI API key.

api_version: optional string

The version for OpenAI API.

class_name: optional string

default_headers: optional map[string]

The default headers for API requests.

dimensions: optional number

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

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

max_retries: optional number

Maximum number of retries.

minimum0

model_name: optional string

The name of the OpenAI embedding model.

num_workers: optional number

The number of workers to use for async embedding calls.

reuse_client: optional boolean

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

timeout: optional number

Timeout for each request.

minimum0

type: optional "OPENAI_EMBEDDING"

Type of the embedding model.

VertexAIEmbeddingConfig = object { component, type }

component: optional VertexTextEmbedding { client_email, location, private_key, 9 more }

Configuration for the VertexAI embedding model.

client_email: string

The client email for the VertexAI credentials.

location: string

The default location to use when making API calls.

private_key: string

The private key for the VertexAI credentials.

private_key_id: string

The private key ID for the VertexAI credentials.

project: string

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

token_uri: string

The token URI for the VertexAI credentials.

additional_kwargs: optional map[unknown]

Additional kwargs for the Vertex.

class_name: optional string

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

embed_mode: optional "default" or "classification" or "clustering" or 2 more

The embedding mode to use.

Accepts one of the following:

"default"

"classification"

"clustering"

"similarity"

"retrieval"

model_name: optional string

The modelId of the VertexAI model to use.

num_workers: optional number

The number of workers to use for async embedding calls.

type: optional "VERTEXAI_EMBEDDING"

Type of the embedding model.

BedrockEmbeddingConfig = object { component, type }

component: optional BedrockEmbedding { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }

Configuration for the Bedrock embedding model.

additional_kwargs: optional map[unknown]

Additional kwargs for the bedrock client.

aws_access_key_id: optional string

AWS Access Key ID to use

aws_secret_access_key: optional string

AWS Secret Access Key to use

aws_session_token: optional string

AWS Session Token to use

class_name: optional string

embed_batch_size: optional number

The batch size for embedding calls.

maximum2048

exclusiveMinimum0

max_retries: optional number

The maximum number of API retries.

exclusiveMinimum0

model_name: optional string

The modelId of the Bedrock model to use.

num_workers: optional number

The number of workers to use for async embedding calls.

profile_name: optional string

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

region_name: optional string

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

timeout: optional number

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

type: optional "BEDROCK_EMBEDDING"

Type of the embedding model.

name: string

The name of the embedding model config.

project_id: string

created_at: optional string

Creation datetime

formatdate-time

updated_at: optional string

Update datetime

formatdate-time

embedding_model_config_id: optional string

The ID of the EmbeddingModelConfig this pipeline is using.

formatuuid

llama_parse_parameters: optional LlamaParseParameters { adaptive_long_table, aggressive_table_extraction, annotate_links, 115 more }

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

adaptive_long_table: optional boolean

aggressive_table_extraction: optional boolean

annotate_links: optional boolean

auto_mode: optional boolean

auto_mode_configuration_json: optional string

auto_mode_trigger_on_image_in_page: optional boolean

auto_mode_trigger_on_regexp_in_page: optional string

auto_mode_trigger_on_table_in_page: optional boolean

auto_mode_trigger_on_text_in_page: optional string

azure_openai_api_version: optional string

azure_openai_deployment_name: optional string

azure_openai_endpoint: optional string

azure_openai_key: optional string

bbox_bottom: optional number

bbox_left: optional number

bbox_right: optional number

bbox_top: optional number

bounding_box: optional string

compact_markdown_table: optional boolean

complemental_formatting_instruction: optional string

content_guideline_instruction: optional string

continuous_mode: optional boolean

disable_image_extraction: optional boolean

disable_ocr: optional boolean

disable_reconstruction: optional boolean

do_not_cache: optional boolean

do_not_unroll_columns: optional boolean

enable_cost_optimizer: optional boolean

extract_charts: optional boolean

extract_layout: optional boolean

extract_printed_page_number: optional boolean

fast_mode: optional boolean

formatting_instruction: optional string

gpt4o_api_key: optional string

gpt4o_mode: optional boolean

guess_xlsx_sheet_name: optional boolean

hide_footers: optional boolean

hide_headers: optional boolean

high_res_ocr: optional boolean

html_make_all_elements_visible: optional boolean

html_remove_fixed_elements: optional boolean

html_remove_navigation_elements: optional boolean

http_proxy: optional string

ignore_document_elements_for_layout_detection: optional boolean

images_to_save: optional array of "screenshot" or "embedded" or "layout"

Accepts one of the following:

"screenshot"

"embedded"

"layout"

inline_images_in_markdown: optional boolean

input_s3_path: optional string

input_s3_region: optional string

input_url: optional string

internal_is_screenshot_job: optional boolean

invalidate_cache: optional boolean

is_formatting_instruction: optional boolean

job_timeout_extra_time_per_page_in_seconds: optional number

job_timeout_in_seconds: optional number

keep_page_separator_when_merging_tables: optional boolean

languages: optional array of ParsingLanguages

Accepts one of the following:

"af"

"az"

"bs"

"cs"

"cy"

"da"

"de"

"en"

"es"

"et"

"fr"

"ga"

"hr"

"hu"

"id"

"is"

"it"

"ku"

"la"

"lt"

"lv"

"mi"

"ms"

"mt"

"nl"

"no"

"oc"

"pi"

"pl"

"pt"

"ro"

"rs_latin"

"sk"

"sl"

"sq"

"sv"

"sw"

"tl"

"tr"

"uz"

"vi"

"ar"

"fa"

"ug"

"ur"

"bn"

"as"

"mni"

"ru"

"rs_cyrillic"

"be"

"bg"

"uk"

"mn"

"abq"

"ady"

"kbd"

"ava"

"dar"

"inh"

"che"

"lbe"

"lez"

"tab"

"tjk"

"hi"

"mr"

"ne"

"bh"

"mai"

"ang"

"bho"

"mah"

"sck"

"new"

"gom"

"sa"

"bgc"

"th"

"ch_sim"

"ch_tra"

"ja"

"ko"

"ta"

"te"

"kn"

layout_aware: optional boolean

line_level_bounding_box: optional boolean

markdown_table_multiline_header_separator: optional string

max_pages: optional number

max_pages_enforced: optional number

merge_tables_across_pages_in_markdown: optional boolean

model: optional string

outlined_table_extraction: optional boolean

output_pdf_of_document: optional boolean

output_s3_path_prefix: optional string

output_s3_region: optional string

output_tables_as_HTML: optional boolean

page_error_tolerance: optional number

page_footer_prefix: optional string

page_footer_suffix: optional string

page_header_prefix: optional string

page_header_suffix: optional string

page_prefix: optional string

page_separator: optional string

page_suffix: optional string

parse_mode: optional ParsingMode

Enum for representing the mode of parsing to be used.

Accepts one of the following:

"parse_page_without_llm"

"parse_page_with_llm"

"parse_page_with_lvm"

"parse_page_with_agent"

"parse_page_with_layout_agent"

"parse_document_with_llm"

"parse_document_with_lvm"

"parse_document_with_agent"

parsing_instruction: optional string

precise_bounding_box: optional boolean

premium_mode: optional boolean

presentation_out_of_bounds_content: optional boolean

presentation_skip_embedded_data: optional boolean

preserve_layout_alignment_across_pages: optional boolean

preserve_very_small_text: optional boolean

preset: optional string

priority: optional "low" or "medium" or "high" or "critical"

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

Accepts one of the following:

"low"

"medium"

"high"

"critical"

project_id: optional string

remove_hidden_text: optional boolean

replace_failed_page_mode: optional FailPageMode

Enum for representing the different available page error handling modes.

Accepts one of the following:

"raw_text"

"blank_page"

"error_message"

replace_failed_page_with_error_message_prefix: optional string

replace_failed_page_with_error_message_suffix: optional string

save_images: optional boolean

skip_diagonal_text: optional boolean

specialized_chart_parsing_agentic: optional boolean

specialized_chart_parsing_efficient: optional boolean

specialized_chart_parsing_plus: optional boolean

specialized_image_parsing: optional boolean

spreadsheet_extract_sub_tables: optional boolean

spreadsheet_force_formula_computation: optional boolean

strict_mode_buggy_font: optional boolean

strict_mode_image_extraction: optional boolean

strict_mode_image_ocr: optional boolean

strict_mode_reconstruction: optional boolean

structured_output: optional boolean

structured_output_json_schema: optional string

structured_output_json_schema_name: optional string

system_prompt: optional string

system_prompt_append: optional string

take_screenshot: optional boolean

target_pages: optional string

tier: optional string

use_vendor_multimodal_model: optional boolean

user_prompt: optional string

vendor_multimodal_api_key: optional string

vendor_multimodal_model_name: optional string

version: optional string

webhook_configurations: optional array of WebhookConfiguration { webhook_events, webhook_headers, webhook_output_format, webhook_url }

The outbound webhook configurations

webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 13 more

List of event names to subscribe to

Accepts one of the following:

"extract.pending"

"extract.success"

"extract.error"

"extract.partial_success"

"extract.cancelled"

"parse.pending"

"parse.success"

"parse.error"

"parse.partial_success"

"parse.cancelled"

"classify.pending"

"classify.success"

"classify.error"

"classify.partial_success"

"classify.cancelled"

"unmapped_event"

webhook_headers: optional map[string]

Custom HTTP headers to include with webhook requests.

webhook_output_format: optional string

The output format to use for the webhook. Defaults to string if none supplied. Currently supported values: string, json

webhook_url: optional string

The URL to send webhook notifications to.

webhook_url: optional string

managed_pipeline_id: optional string

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

formatuuid

metadata_config: optional PipelineMetadataConfig { excluded_embed_metadata_keys, excluded_llm_metadata_keys }

Metadata configuration for the pipeline.

excluded_embed_metadata_keys: optional array of string

List of metadata keys to exclude from embeddings

excluded_llm_metadata_keys: optional array of string

List of metadata keys to exclude from LLM during retrieval

pipeline_type: optional PipelineType

Type of pipeline. Either PLAYGROUND or MANAGED.

Accepts one of the following:

"PLAYGROUND"

"MANAGED"

preset_retrieval_parameters: optional PresetRetrievalParams { alpha, class_name, dense_similarity_cutoff, 11 more }

Preset retrieval parameters for the pipeline.

alpha: optional number

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

class_name: optional string

dense_similarity_cutoff: optional number

Minimum similarity score wrt query for retrieval

maximum1

minimum0

dense_similarity_top_k: optional number

Number of nodes for dense retrieval.

maximum100

minimum1

enable_reranking: optional boolean

Enable reranking for retrieval

files_top_k: optional number

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

maximum5

minimum1

rerank_top_n: optional number

Number of reranked nodes for returning.

maximum100

minimum1

retrieval_mode: optional RetrievalMode

The retrieval mode for the query.

Accepts one of the following:

"chunks"

"files_via_metadata"

"files_via_content"

"auto_routed"

Deprecatedretrieve_image_nodes: optional boolean

Whether to retrieve image nodes.

retrieve_page_figure_nodes: optional boolean

Whether to retrieve page figure nodes.

retrieve_page_screenshot_nodes: optional boolean

Whether to retrieve page screenshot nodes.

search_filters: optional MetadataFilters { filters, condition }

Metadata filters for vector stores.

filters: array of object { key, value, operator } or MetadataFilters { filters, condition }

Accepts one of the following:

MetadataFilter = object { key, value, operator }

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

key: string

value: number or string or array of string or 2 more

Accepts one of the following:

UnionMember0 = number

UnionMember1 = string

UnionMember2 = array of string

UnionMember3 = array of number

UnionMember4 = array of number

operator: optional "==" or ">" or "<" or 11 more

Vector store filter operator.

Accepts one of the following:

"=="

">"

"<"

"!="

">="

"<="

"in"

"nin"

"any"

"all"

"text_match"

"text_match_insensitive"

"contains"

"is_empty"

MetadataFilters = object { filters, condition }

Metadata filters for vector stores.

filters: array of object { key, value, operator } or MetadataFilters { filters, condition }

Accepts one of the following:

MetadataFilter = object { key, value, operator }

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

key: string

value: number or string or array of string or 2 more

Accepts one of the following:

UnionMember0 = number

UnionMember1 = string

UnionMember2 = array of string

UnionMember3 = array of number

UnionMember4 = array of number

operator: optional "==" or ">" or "<" or 11 more

Vector store filter operator.

Accepts one of the following:

"=="

">"

"<"

"!="

">="

"<="

"in"

"nin"

"any"

"all"

"text_match"

"text_match_insensitive"

"contains"

"is_empty"

MetadataFilters { filters, condition }

condition: optional "and" or "or" or "not"

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

condition: optional "and" or "or" or "not"

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]

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

Accepts one of the following:

UnionMember0 = map[unknown]

UnionMember1 = array of unknown

UnionMember2 = string

UnionMember3 = number

UnionMember4 = boolean

sparse_similarity_top_k: optional number

Number of nodes for sparse retrieval.

maximum100

minimum1

sparse_model_config: optional SparseModelConfig { class_name, model_type }

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.

class_name: optional string

model_type: optional "splade" or "bm25" or "auto"

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).

Accepts one of the following:

"splade"

"bm25"

"auto"

status: optional "CREATED" or "DELETING"

Status of the pipeline.

Accepts one of the following:

"CREATED"

"DELETING"

transform_config: optional AutoTransformConfig { chunk_overlap, chunk_size, mode } or AdvancedModeTransformConfig { chunking_config, mode, segmentation_config }

Configuration for the transformation.

Accepts one of the following:

AutoTransformConfig = object { chunk_overlap, chunk_size, mode }

chunk_overlap: optional number

Chunk overlap for the transformation.

chunk_size: optional number

Chunk size for the transformation.

exclusiveMinimum0

mode: optional "auto"

AdvancedModeTransformConfig = object { chunking_config, mode, segmentation_config }

chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more

Configuration for the chunking.

Accepts one of the following:

NoneChunkingConfig = object { mode }

mode: optional "none"

CharacterChunkingConfig = object { chunk_overlap, chunk_size, mode }

chunk_overlap: optional number

chunk_size: optional number

mode: optional "character"

TokenChunkingConfig = object { chunk_overlap, chunk_size, mode, separator }

chunk_overlap: optional number

chunk_size: optional number

mode: optional "token"

separator: optional string

SentenceChunkingConfig = object { chunk_overlap, chunk_size, mode, 2 more }

chunk_overlap: optional number

chunk_size: optional number

mode: optional "sentence"

paragraph_separator: optional string

separator: optional string

SemanticChunkingConfig = object { breakpoint_percentile_threshold, buffer_size, mode }

breakpoint_percentile_threshold: optional number

buffer_size: optional number

mode: optional "semantic"

mode: optional "advanced"

segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }

Configuration for the segmentation.

Accepts one of the following:

NoneSegmentationConfig = object { mode }

mode: optional "none"

PageSegmentationConfig = object { mode, page_separator }

mode: optional "page"

page_separator: optional string

ElementSegmentationConfig = object { mode }

mode: optional "element"

updated_at: optional string

Update datetime

formatdate-time

Search Pipelines

HTTP

HTTP

TypeScript

Python

curl https://api.cloud.llamaindex.ai/api/v1/pipelines \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"

200 example

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

Returns Examples

200 example

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