Retrievers
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CompositeRetrievalMode = "routing" or "full"
Enum for the mode of composite retrieval.
CompositeRetrievalResult = object { image_nodes, nodes, page_figure_nodes }
The image nodes retrieved by the pipeline for the given query. Deprecated - will soon be replaced with 'page_screenshot_nodes'.
node: object { file_id, image_size, page_index, metadata }
file_id: string
The ID of the file that the page screenshot was taken from
image_size: number
The size of the image in bytes
page_index: number
The index of the page for which the screenshot is taken (0-indexed)
metadata: optional map[unknown]
Metadata for the screenshot
score: number
The score of the screenshot node
nodes: optional array of object { node, class_name, score }
The retrieved nodes from the composite retrieval.
node: object { id, end_char_idx, pipeline_id, 5 more }
id: string
The ID of the retrieved node.
end_char_idx: number
The end character index of the retrieved node in the document
pipeline_id: string
The ID of the pipeline this node was retrieved from.
retriever_id: string
The ID of the retriever this node was retrieved from.
retriever_pipeline_name: string
The name of the retrieval pipeline this node was retrieved from.
start_char_idx: number
The start character index of the retrieved node in the document
text: string
The text of the retrieved node.
metadata: optional map[unknown]
Metadata associated with the retrieved node.
The page figure nodes retrieved by the pipeline for the given query.
node: object { confidence, figure_name, figure_size, 4 more }
confidence: number
The confidence of the figure
figure_name: string
The name of the figure
figure_size: number
The size of the figure in bytes
file_id: string
The ID of the file that the figure was taken from
page_index: number
The index of the page for which the figure is taken (0-indexed)
is_likely_noise: optional boolean
Whether the figure is likely to be noise
metadata: optional map[unknown]
Metadata for the figure
score: number
The score of the figure node
ReRankConfig = object { top_n, type }
top_n: optional number
The number of nodes to retrieve after reranking over retrieved nodes from all retrieval tools.
type: optional "system_default" or "llm" or "cohere" or 3 more
The type of reranker to use.
Retriever = object { id, name, project_id, 3 more }
An entity that retrieves context nodes from several sub RetrieverTools.
id: string
Unique identifier
name: string
A name for the retriever tool. Will default to the pipeline name if not provided.
project_id: string
The ID of the project this retriever resides in.
created_at: optional string
Creation datetime
pipelines: optional array of RetrieverPipeline { description, name, pipeline_id, preset_retrieval_parameters }
The pipelines this retriever uses.
description: string
A description of the retriever tool.
name: string
A name for the retriever tool. Will default to the pipeline name if not provided.
pipeline_id: string
The ID of the pipeline this tool uses.
preset_retrieval_parameters: optional PresetRetrievalParams { alpha, class_name, dense_similarity_cutoff, 11 more }
Parameters for retrieval configuration.
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.
dense_similarity_cutoff: optional number
Minimum similarity score wrt query for retrieval
dense_similarity_top_k: optional number
Number of nodes for dense retrieval.
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).
rerank_top_n: optional number
Number of reranked nodes for returning.
The retrieval mode for the query.
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.
Metadata filters for vector stores.
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
value: number or string or array of string or 2 more
operator: optional "==" or ">" or "<" or 11 more
Vector store filter operator.
MetadataFilters = object { filters, condition }
Metadata filters for vector stores.
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
value: number or string or array of string or 2 more
operator: optional "==" or ">" or "<" or 11 more
Vector store filter operator.
condition: optional "and" or "or" or "not"
Vector store filter conditions to combine different filters.
condition: optional "and" or "or" or "not"
Vector store filter conditions to combine different filters.
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.
sparse_similarity_top_k: optional number
Number of nodes for sparse retrieval.
updated_at: optional string
Update datetime
RetrieverCreate = object { name, pipelines }
name: string
A name for the retriever tool. Will default to the pipeline name if not provided.
pipelines: optional array of RetrieverPipeline { description, name, pipeline_id, preset_retrieval_parameters }
The pipelines this retriever uses.
description: string
A description of the retriever tool.
name: string
A name for the retriever tool. Will default to the pipeline name if not provided.
pipeline_id: string
The ID of the pipeline this tool uses.
preset_retrieval_parameters: optional PresetRetrievalParams { alpha, class_name, dense_similarity_cutoff, 11 more }
Parameters for retrieval configuration.
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.
dense_similarity_cutoff: optional number
Minimum similarity score wrt query for retrieval
dense_similarity_top_k: optional number
Number of nodes for dense retrieval.
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).
rerank_top_n: optional number
Number of reranked nodes for returning.
The retrieval mode for the query.
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.
Metadata filters for vector stores.
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
value: number or string or array of string or 2 more
operator: optional "==" or ">" or "<" or 11 more
Vector store filter operator.
MetadataFilters = object { filters, condition }
Metadata filters for vector stores.
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
value: number or string or array of string or 2 more
operator: optional "==" or ">" or "<" or 11 more
Vector store filter operator.
condition: optional "and" or "or" or "not"
Vector store filter conditions to combine different filters.
condition: optional "and" or "or" or "not"
Vector store filter conditions to combine different filters.
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.
sparse_similarity_top_k: optional number
Number of nodes for sparse retrieval.
RetrieverPipeline = object { description, name, pipeline_id, preset_retrieval_parameters }
description: string
A description of the retriever tool.
name: string
A name for the retriever tool. Will default to the pipeline name if not provided.
pipeline_id: string
The ID of the pipeline this tool uses.
preset_retrieval_parameters: optional PresetRetrievalParams { alpha, class_name, dense_similarity_cutoff, 11 more }
Parameters for retrieval configuration.
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.
dense_similarity_cutoff: optional number
Minimum similarity score wrt query for retrieval
dense_similarity_top_k: optional number
Number of nodes for dense retrieval.
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).
rerank_top_n: optional number
Number of reranked nodes for returning.
The retrieval mode for the query.
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.
Metadata filters for vector stores.
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
value: number or string or array of string or 2 more
operator: optional "==" or ">" or "<" or 11 more
Vector store filter operator.
MetadataFilters = object { filters, condition }
Metadata filters for vector stores.
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
value: number or string or array of string or 2 more
operator: optional "==" or ">" or "<" or 11 more
Vector store filter operator.
condition: optional "and" or "or" or "not"
Vector store filter conditions to combine different filters.
condition: optional "and" or "or" or "not"
Vector store filter conditions to combine different filters.
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.
sparse_similarity_top_k: optional number
Number of nodes for sparse retrieval.