Run Search

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

Get retrieval results for a managed pipeline and a query

Path ParametersExpand Collapse

pipeline_id: string

Query ParametersExpand Collapse

organization_id: optional string

project_id: optional string

Cookie ParametersExpand Collapse

session: optional string

Body ParametersExpand Collapse

query: string

The query to retrieve against.

minLength1

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.

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:

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

ReturnsExpand Collapse

pipeline_id: string

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

formatuuid

retrieval_nodes: array of object { node, class_name, score }

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: optional string

embedding: optional array of number

Embedding of the node.

end_char_idx: optional number

End char index of the node.

excluded_embed_metadata_keys: optional array of string

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

excluded_llm_metadata_keys: optional array of string

Metadata keys that are excluded from text for the LLM.

extra_info: optional map[unknown]

A flat dictionary of metadata fields

id_: optional string

Unique ID of the node.

metadata_seperator: optional string

Separator between metadata fields when converting to string.

metadata_template: optional string

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

mimetype: optional string

MIME type of the node content.

relationships: optional map[object { node_id, class_name, hash, 2 more } or array of object { node_id, class_name, hash, 2 more } ]

A mapping of relationships to other node information.

Accepts one of the following:

RelatedNodeInfo = object { node_id, class_name, hash, 2 more }

node_id: string

class_name: optional string

hash: optional string

metadata: optional map[unknown]

node_type: optional "1" or "2" or "3" or 2 more or string

Accepts one of the following:

ObjectType = "1" or "2" or "3" or 2 more

Accepts one of the following:

"1"

"2"

"3"

"4"

"5"

UnionMember1 = string

UnionMember1 = array of object { node_id, class_name, hash, 2 more }

node_id: string

class_name: optional string

hash: optional string

metadata: optional map[unknown]

node_type: optional "1" or "2" or "3" or 2 more or string

Accepts one of the following:

ObjectType = "1" or "2" or "3" or 2 more

Accepts one of the following:

"1"

"2"

"3"

"4"

"5"

UnionMember1 = string

start_char_idx: optional number

Start char index of the node.

text: optional string

Text content of the node.

text_template: optional string

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

class_name: optional string

score: optional number

class_name: optional string

Deprecatedimage_nodes: optional array of 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: object { 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: optional map[unknown]

Metadata for the screenshot

score: number

The score of the screenshot node

class_name: optional string

inferred_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"

metadata: optional map[string]

Metadata associated with the retrieval execution

page_figure_nodes: optional array of PageFigureNodeWithScore { node, score, class_name }

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

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: 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

class_name: optional string

retrieval_latency: optional map[number]

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

Run Search

curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/retrieve \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '{
          "query": "x"
        }'

{
  "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
  }
}