LlamaParse Pipelines API is one of 36 APIs that LlamaParse publishes on the APIs.io network, described by a machine-readable OpenAPI specification.
Tagged areas include Pipelines. The published artifact set on APIs.io includes an OpenAPI specification, API documentation, an API reference, and a getting-started guide.
This API exposes
23 operations
across 16 paths,
and defines 90 schemas.
It is described by OpenAPI 3.2.0, at version 0.1.0.
Requests are made against a single base URL, https://api.cloud.llamaindex.ai.
The identity and technical contract details declared by the specification.
MetadataFilter
object
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 con…
3 properties
2 required
PlaygroundSession
object
A playground session for a user.
10 properties
5 required
NodeRelationship
string
Node relationships used in BaseNode class. Attributes: SOURCE: The node is the source document. PREVIOUS: The node is the previous node in the document. NEXT:…
RetrieveResults
object
Schema for the result of an retrieval execution.
8 properties
2 required
PageScreenshotNodeWithScore
object
Page screenshot metadata with score
3 properties
2 required
AdvancedModeTransformConfig
object
3 properties
ManagedOpenAIEmbeddingConfig
object
2 properties
ManagedOpenAIEmbedding
object
4 properties
ElementSegmentationConfig
object
1 property
NoneChunkingConfig
object
1 property
PipelineConfigurationHashes
object
Hashes for the configuration of a pipeline.
3 properties
DataSink
object
Schema for a data sink.
7 properties
5 required
RetrievalParams
object
Schema for the search params for an retrieval execution.
15 properties
1 required
EmbeddingModelConfig
object
Schema for an embedding model config.
6 properties
4 required
JobNameMapping
string
Enum for mapping original job names to readable names.
CloudMilvusVectorStore
object
Cloud Milvus Vector Store.
6 properties
1 required
CloudAstraDBVectorStore
object
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:…
7 properties
4 required
LLMParameters
object
6 properties
HuggingFaceInferenceAPIEmbeddingConfig
object
2 properties
PageFigureNodeWithScore
object
Page figure metadata with score
3 properties
2 required
VertexAIEmbeddingConfig
object
2 properties
AzureOpenAIEmbeddingConfig
object
2 properties
ChatData
object
3 properties
CloudMongoDBAtlasVectorSearch
object
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 LlamaCl…
8 properties
3 required
PGVectorDistanceMethod
string
Distance methods for PGVector. Docs: https://github.com/pgvector/pgvector?tab=readme-ov-filequery-options
ParsingMode
string
Enum for representing the mode of parsing to be used.
SentenceChunkingConfig
object
5 properties
MetadataFilters
object
Metadata filters for vector stores.
2 properties
1 required
WebhookConfiguration
object
Configuration for a single outbound webhook endpoint.
4 properties
CohereEmbedding
object
8 properties
1 required
PageScreenshotMetadata
object
4 properties
3 required
InputMessage
object
This is distinct from a ChatMessage because this schema is enforced by the AI Chat library used in the frontend
5 properties
2 required
ParserLanguages
string
Enum for representing the languages supported by the parser.
CloudDocument
object
Cloud document stored in S3.
7 properties
3 required
PGVectorHNSWSettings
object
HNSW settings for PGVector.
5 properties
ChatMessage
object
7 properties
3 required
FilterCondition
string
Vector store filter conditions to combine different filters.
IngestionErrorResponse
object
3 properties
3 required
CharacterChunkingConfig
object
3 properties
SemanticChunkingConfig
object
3 properties
PageSegmentationConfig
object
2 properties
AutoTransformConfig
object
3 properties
GeminiEmbeddingConfig
object
2 properties
PipelineType
string
Enum for representing the type of a pipeline
Pooling
string
Enum of possible pooling choices with pooling behaviors.
CloudPineconeVectorStore
object
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:…
6 properties
2 required
ManagedIngestionStatusResponse
object
5 properties
1 required
MessageAnnotation
object
3 properties
2 required
HTTPValidationError
object
1 property
SupportedLLMModelNames
string
PageFigureMetadata
object
7 properties
5 required
Pipeline
object
Schema for a pipeline.
18 properties
4 required
BedrockEmbeddingConfig
object
2 properties
ConfigurableDataSinkNames
string
HuggingFaceInferenceAPIEmbedding
object
12 properties
PGVectorVectorType
string
Vector storage formats for PGVector. Docs: https://github.com/pgvector/pgvector?tab=readme-ov-filequery-options
VertexTextEmbedding
object
12 properties
6 required
PaginatedListCloudDocumentsResponse
object
4 properties
4 required
FailPageMode
string
Enum for representing the different available page error handling modes.
OpenAIEmbeddingConfig
object
2 properties
NoneSegmentationConfig
object
1 property
TextNode
object
Provided for backward compatibility.
14 properties
SparseModelType
string
Enum for sparse model types supported in LlamaCloud. SPLADE: Uses HuggingFace Splade model for sparse embeddings BM25: Uses Qdrant's FastEmbed BM25 model for s…
FilterOperator
string
Vector store filter operator.
BedrockEmbedding
object
12 properties
CohereEmbeddingConfig
object
2 properties
TokenChunkingConfig
object
4 properties
OpenAIEmbedding
object
13 properties
CloudDocumentCreate
object
Create a new cloud document.
6 properties
2 required
CloudAzureAISearchVectorStore
object
Cloud Azure AI Search Vector Store.
11 properties
2 required
PipelineMetadataConfig
object
2 properties
PipelineCreate
object
Schema for creating a pipeline.
13 properties
1 required
GeminiEmbedding
object
10 properties
CloudPostgresVectorStore
object
13 properties
8 required
LlamaParseParameters
object
119 properties
MessageRole
string
Message role.
CloudQdrantVectorStore
object
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: col…
7 properties
3 required
TextNodeWithScore
object
Same as NodeWithScore but type for node is a TextNode instead of BaseNode. FastAPI doesn't accept abstract classes like BaseNode.
3 properties
1 required
RelatedNodeInfo
object
5 properties
1 required
DataSinkCreate
object
Schema for creating a data sink.
3 properties
3 required
VertexEmbeddingMode
string
Copied from llamaindex.embeddings.vertex.base.VertexEmbeddingMode since importing llamaindex.embeddings.vertex.base incurs a lot of memory usage.
AzureOpenAIEmbedding
object
15 properties
PipelineUpdate
object
Schema for updating a pipeline.
12 properties
PresetRetrievalParams
object
Schema for the search params for an retrieval execution that can be preset for a pipeline.
14 properties
ValidationError
object
5 properties
3 required
SparseModelConfig
object
Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed dat…
2 properties
ManagedIngestionStatus
string
Status of managed ingestion with partial Updates.
ChatInputParams
object
3 properties
The full machine-readable OpenAPI contract behind this narrative.
Other APIs LlamaParse publishes across the network.
