Run Search

client.pipelines.retrieve(, , ?): PipelineRetrieveResponse { pipeline_id, retrieval_nodes, class_name, 5 more }

POST/api/v1/pipelines/{pipeline_id}/retrieve

Get retrieval results for a managed pipeline and a query

ParametersExpand Collapse

pipelineID: string

params: PipelineRetrieveParams { query, organization_id, project_id, 14 more }

query: string

Body param: The query to retrieve against.

minLength1

organization_id?: string | null

Query param

formatuuid

project_id?: string | null

Query param

formatuuid

alpha?: number | null

Body param: 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?: string

Body param

dense_similarity_cutoff?: number | null

Body param: Minimum similarity score wrt query for retrieval

maximum1

minimum0

dense_similarity_top_k?: number | null

Body param: Number of nodes for dense retrieval.

maximum100

minimum1

enable_reranking?: boolean | null

Body param: Enable reranking for retrieval

files_top_k?: number | null

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

maximum5

minimum1

rerank_top_n?: number | null

Body param: Number of reranked nodes for returning.

maximum100

minimum1

retrieval_mode?: RetrievalMode

Body param: The retrieval mode for the query.

Accepts one of the following:

"chunks"

"files_via_metadata"

"files_via_content"

"auto_routed"

Deprecatedretrieve_image_nodes?: boolean

Body param: Whether to retrieve image nodes.

retrieve_page_figure_nodes?: boolean

Body param: Whether to retrieve page figure nodes.

retrieve_page_screenshot_nodes?: boolean

Body param: Whether to retrieve page screenshot nodes.

Body param: Metadata filters for vector stores.

Accepts one of the following:

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

Accepts one of the following:

Vector store filter operator.

Accepts one of the following:

Metadata filters for vector stores.

Accepts one of the following:

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

Accepts one of the following:

Vector store filter operator.

Accepts one of the following:

Vector store filter conditions to combine different filters.

Accepts one of the following:

Vector store filter conditions to combine different filters.

Accepts one of the following:

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

Accepts one of the following:

Record<string, unknown>

Array<unknown>

string

number

boolean

sparse_similarity_top_k?: number | null

Body param: Number of nodes for sparse retrieval.

maximum100

minimum1

ReturnsExpand Collapse

PipelineRetrieveResponse { pipeline_id, retrieval_nodes, class_name, 5 more }

Schema for the result of an retrieval execution.

pipeline_id: string

The ID of the pipeline that the query was retrieved against.

formatuuid

retrieval_nodes: Array<RetrievalNode>

The nodes retrieved by the pipeline for the given query.

node: TextNode { class_name, embedding, end_char_idx, 11 more }

Provided for backward compatibility.

Note: we keep the field with the typo "seperator" to maintain backward compatibility for serialized objects.

class_name?: string

embedding?: Array<number> | null

Embedding of the node.

end_char_idx?: number | null

End char index of the node.

excluded_embed_metadata_keys?: Array<string>

Metadata keys that are excluded from text for the embed model.

excluded_llm_metadata_keys?: Array<string>

Metadata keys that are excluded from text for the LLM.

extra_info?: Record<string, unknown>

A flat dictionary of metadata fields

id_?: string

Unique ID of the node.

metadata_seperator?: string

Separator between metadata fields when converting to string.

metadata_template?: string

Template for how metadata is formatted, with {key} and {value} placeholders.

mimetype?: string

MIME type of the node content.

relationships?: Record<string, RelatedNodeInfo { node_id, class_name, hash, 2 more } | Array<UnionMember1>>

A mapping of relationships to other node information.

Accepts one of the following:

RelatedNodeInfo { node_id, class_name, hash, 2 more }

node_id: string

class_name?: string

hash?: string | null

metadata?: Record<string, unknown>

node_type?: "1" | "2" | "3" | 2 more | (string & {}) | null

Accepts one of the following:

"1" | "2" | "3" | 2 more

"1"

"2"

"3"

"4"

"5"

(string & {})

Array<UnionMember1>

node_id: string

class_name?: string

hash?: string | null

metadata?: Record<string, unknown>

node_type?: "1" | "2" | "3" | 2 more | (string & {}) | null

Accepts one of the following:

"1" | "2" | "3" | 2 more

"1"

"2"

"3"

"4"

"5"

(string & {})

start_char_idx?: number | null

Start char index of the node.

text?: string

Text content of the node.

text_template?: string

Template for how text is formatted, with {content} and {metadata_str} placeholders.

class_name?: string

score?: number | null

class_name?: string

Deprecatedimage_nodes?: Array<PageScreenshotNodeWithScore { node, score, class_name } >

The image nodes retrieved by the pipeline for the given query. Deprecated - will soon be replaced with 'page_screenshot_nodes'.

node: Node { file_id, image_size, page_index, metadata }

file_id: string

The ID of the file that the page screenshot was taken from

formatuuid

image_size: number

The size of the image in bytes

minimum0

page_index: number

The index of the page for which the screenshot is taken (0-indexed)

minimum0

metadata?: Record<string, unknown> | null

Metadata for the screenshot

score: number

The score of the screenshot node

class_name?: string

inferred_search_filters?: MetadataFilters { filters, condition } | null

Metadata filters for vector stores.

filters: Array<MetadataFilter { key, value, operator } | MetadataFilters { filters, condition } >

Accepts one of the following:

MetadataFilter { 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 | string | Array<string> | 2 more | null

Accepts one of the following:

number

string

Array<string>

Array<number>

operator?: "==" | ">" | "<" | 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 }

Metadata filters for vector stores.

filters: Array<MetadataFilter { key, value, operator } | MetadataFilters { filters, condition } >

Accepts one of the following:

MetadataFilter { 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 | string | Array<string> | 2 more | null

Accepts one of the following:

number

string

Array<string>

Array<number>

operator?: "==" | ">" | "<" | 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?: "and" | "or" | "not" | null

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

condition?: "and" | "or" | "not" | null

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

metadata?: Record<string, string>

Metadata associated with the retrieval execution

page_figure_nodes?: Array<PageFigureNodeWithScore { node, score, class_name } >

The page figure nodes retrieved by the pipeline for the given query.

node: Node { confidence, figure_name, figure_size, 4 more }

confidence: number

The confidence of the figure

maximum1

minimum0

figure_name: string

The name of the figure

figure_size: number

The size of the figure in bytes

minimum0

file_id: string

The ID of the file that the figure was taken from

formatuuid

page_index: number

The index of the page for which the figure is taken (0-indexed)

minimum0

is_likely_noise?: boolean

Whether the figure is likely to be noise

metadata?: Record<string, unknown> | null

Metadata for the figure

score: number

The score of the figure node

class_name?: string

retrieval_latency?: Record<string, number>

The end-to-end latency for retrieval and reranking.

Run Search

import LlamaCloud from '@llamaindex/llama-cloud';

const client = new LlamaCloud({
  apiKey: process.env['LLAMA_CLOUD_API_KEY'], // This is the default and can be omitted
});

const pipeline = await client.pipelines.retrieve('182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e', {
  query: 'x',
});

console.log(pipeline.pipeline_id);

{
  "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "retrieval_nodes": [
    {
      "node": {
        "class_name": "class_name",
        "embedding": [
          0
        ],
        "end_char_idx": 0,
        "excluded_embed_metadata_keys": [
          "string"
        ],
        "excluded_llm_metadata_keys": [
          "string"
        ],
        "extra_info": {
          "foo": "bar"
        },
        "id_": "id_",
        "metadata_seperator": "metadata_seperator",
        "metadata_template": "metadata_template",
        "mimetype": "mimetype",
        "relationships": {
          "foo": {
            "node_id": "node_id",
            "class_name": "class_name",
            "hash": "hash",
            "metadata": {
              "foo": "bar"
            },
            "node_type": "1"
          }
        },
        "start_char_idx": 0,
        "text": "text",
        "text_template": "text_template"
      },
      "class_name": "class_name",
      "score": 0
    }
  ],
  "class_name": "class_name",
  "image_nodes": [
    {
      "node": {
        "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "image_size": 0,
        "page_index": 0,
        "metadata": {
          "foo": "bar"
        }
      },
      "score": 0,
      "class_name": "class_name"
    }
  ],
  "inferred_search_filters": {
    "filters": [
      {
        "key": "key",
        "value": 0,
        "operator": "=="
      }
    ],
    "condition": "and"
  },
  "metadata": {
    "foo": "string"
  },
  "page_figure_nodes": [
    {
      "node": {
        "confidence": 0,
        "figure_name": "figure_name",
        "figure_size": 0,
        "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "page_index": 0,
        "is_likely_noise": true,
        "metadata": {
          "foo": "bar"
        }
      },
      "score": 0,
      "class_name": "class_name"
    }
  ],
  "retrieval_latency": {
    "foo": 0
  }
}

Returns Examples

{
  "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "retrieval_nodes": [
    {
      "node": {
        "class_name": "class_name",
        "embedding": [
          0
        ],
        "end_char_idx": 0,
        "excluded_embed_metadata_keys": [
          "string"
        ],
        "excluded_llm_metadata_keys": [
          "string"
        ],
        "extra_info": {
          "foo": "bar"
        },
        "id_": "id_",
        "metadata_seperator": "metadata_seperator",
        "metadata_template": "metadata_template",
        "mimetype": "mimetype",
        "relationships": {
          "foo": {
            "node_id": "node_id",
            "class_name": "class_name",
            "hash": "hash",
            "metadata": {
              "foo": "bar"
            },
            "node_type": "1"
          }
        },
        "start_char_idx": 0,
        "text": "text",
        "text_template": "text_template"
      },
      "class_name": "class_name",
      "score": 0
    }
  ],
  "class_name": "class_name",
  "image_nodes": [
    {
      "node": {
        "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "image_size": 0,
        "page_index": 0,
        "metadata": {
          "foo": "bar"
        }
      },
      "score": 0,
      "class_name": "class_name"
    }
  ],
  "inferred_search_filters": {
    "filters": [
      {
        "key": "key",
        "value": 0,
        "operator": "=="
      }
    ],
    "condition": "and"
  },
  "metadata": {
    "foo": "string"
  },
  "page_figure_nodes": [
    {
      "node": {
        "confidence": 0,
        "figure_name": "figure_name",
        "figure_size": 0,
        "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "page_index": 0,
        "is_likely_noise": true,
        "metadata": {
          "foo": "bar"
        }
      },
      "score": 0,
      "class_name": "class_name"
    }
  ],
  "retrieval_latency": {
    "foo": 0
  }
}