Run Search

pipelines.retrieve(, ) -> PipelineRetrieveResponse

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

Get retrieval results for a managed pipeline and a query

ParametersExpand Collapse

pipeline_id: str

query: str

The query to retrieve against.

minLength1

organization_id: Optional[str]

project_id: Optional[str]

alpha: Optional[float]

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[str]

dense_similarity_cutoff: Optional[float]

Minimum similarity score wrt query for retrieval

maximum1

minimum0

dense_similarity_top_k: Optional[int]

Number of nodes for dense retrieval.

maximum100

minimum1

enable_reranking: Optional[bool]

Enable reranking for retrieval

files_top_k: Optional[int]

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

maximum5

minimum1

rerank_top_n: Optional[int]

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[bool]

Whether to retrieve image nodes.

retrieve_page_figure_nodes: Optional[bool]

Whether to retrieve page figure nodes.

retrieve_page_screenshot_nodes: Optional[bool]

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[Dict[str, Union[Dict[str, object], Iterable[object], str, 3 more]]]

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

Accepts one of the following:

Dict[str, object]

Iterable[object]

str

float

bool

sparse_similarity_top_k: Optional[int]

Number of nodes for sparse retrieval.

maximum100

minimum1

ReturnsExpand Collapse

class PipelineRetrieveResponse: …

Schema for the result of an retrieval execution.

pipeline_id: str

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

formatuuid

retrieval_nodes: List[RetrievalNode]

The nodes retrieved by the pipeline for the given query.

node: TextNode

Provided for backward compatibility.

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

class_name: Optional[str]

embedding: Optional[List[float]]

Embedding of the node.

end_char_idx: Optional[int]

End char index of the node.

excluded_embed_metadata_keys: Optional[List[str]]

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

excluded_llm_metadata_keys: Optional[List[str]]

Metadata keys that are excluded from text for the LLM.

extra_info: Optional[Dict[str, object]]

A flat dictionary of metadata fields

id: Optional[str]

Unique ID of the node.

metadata_seperator: Optional[str]

Separator between metadata fields when converting to string.

metadata_template: Optional[str]

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

mimetype: Optional[str]

MIME type of the node content.

relationships: Optional[Dict[str, Relationships]]

A mapping of relationships to other node information.

Accepts one of the following:

class RelationshipsRelatedNodeInfo: …

node_id: str

class_name: Optional[str]

hash: Optional[str]

metadata: Optional[Dict[str, object]]

node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]

Accepts one of the following:

Literal["1", "2", "3", 2 more]

Accepts one of the following:

"1"

"2"

"3"

"4"

"5"

str

List[RelationshipsUnionMember1]

node_id: str

class_name: Optional[str]

hash: Optional[str]

metadata: Optional[Dict[str, object]]

node_type: Optional[Union[Literal["1", "2", "3", 2 more], str, null]]

Accepts one of the following:

Literal["1", "2", "3", 2 more]

Accepts one of the following:

"1"

"2"

"3"

"4"

"5"

str

start_char_idx: Optional[int]

Start char index of the node.

text: Optional[str]

Text content of the node.

text_template: Optional[str]

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

class_name: Optional[str]

score: Optional[float]

class_name: Optional[str]

Deprecatedimage_nodes: Optional[List[PageScreenshotNodeWithScore]]

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

node: Node

file_id: str

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

formatuuid

image_size: int

The size of the image in bytes

minimum0

page_index: int

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

minimum0

metadata: Optional[Dict[str, object]]

Metadata for the screenshot

score: float

The score of the screenshot node

class_name: Optional[str]

inferred_search_filters: Optional[MetadataFilters]

Metadata filters for vector stores.

filters: List[Filter]

Accepts one of the following:

class FilterMetadataFilter: …

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: str

value: Union[float, str, List[str], 3 more]

Accepts one of the following:

float

str

List[str]

List[float]

List[int]

operator: Optional[Literal["==", ">", "<", 11 more]]

Vector store filter operator.

Accepts one of the following:

"=="

">"

"<"

"!="

">="

"<="

"in"

"nin"

"any"

"all"

"text_match"

"text_match_insensitive"

"contains"

"is_empty"

class MetadataFilters: …

Metadata filters for vector stores.

filters: List[Filter]

Accepts one of the following:

class FilterMetadataFilter: …

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: str

value: Union[float, str, List[str], 3 more]

Accepts one of the following:

float

str

List[str]

List[float]

List[int]

operator: Optional[Literal["==", ">", "<", 11 more]]

Vector store filter operator.

Accepts one of the following:

"=="

">"

"<"

"!="

">="

"<="

"in"

"nin"

"any"

"all"

"text_match"

"text_match_insensitive"

"contains"

"is_empty"

MetadataFilters

condition: Optional[Literal["and", "or", "not"]]

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

condition: Optional[Literal["and", "or", "not"]]

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

metadata: Optional[Dict[str, str]]

Metadata associated with the retrieval execution

page_figure_nodes: Optional[List[PageFigureNodeWithScore]]

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

node: Node

confidence: float

The confidence of the figure

maximum1

minimum0

figure_name: str

The name of the figure

figure_size: int

The size of the figure in bytes

minimum0

file_id: str

The ID of the file that the figure was taken from

formatuuid

page_index: int

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

minimum0

is_likely_noise: Optional[bool]

Whether the figure is likely to be noise

metadata: Optional[Dict[str, object]]

Metadata for the figure

score: float

The score of the figure node

class_name: Optional[str]

retrieval_latency: Optional[Dict[str, float]]

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

Run Search

import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
pipeline = client.pipelines.retrieve(
    pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    query="x",
)
print(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
  }
}