List Retrievers

retrievers.list() -> RetrieverListResponse

GET/api/v1/retrievers

List Retrievers for a project.

ParametersExpand Collapse

organization_id: Optional[str]

project_id: Optional[str]

ReturnsExpand Collapse

List[Retriever]

id: str

Unique identifier

formatuuid

A name for the retriever tool. Will default to the pipeline name if not provided.

maxLength3000

minLength1

project_id: str

The ID of the project this retriever resides in.

formatuuid

created_at: Optional[datetime]

Creation datetime

formatdate-time

pipelines: Optional[List[RetrieverPipeline]]

The pipelines this retriever uses.

description: Optional[str]

A description of the retriever tool.

maxLength15000

A name for the retriever tool. Will default to the pipeline name if not provided.

maxLength3000

minLength1

pipeline_id: str

The ID of the pipeline this tool uses.

formatuuid

preset_retrieval_parameters: Optional[PresetRetrievalParams]

Parameters for retrieval configuration.

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], List[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]

List[object]

str

float

bool

sparse_similarity_top_k: Optional[int]

Number of nodes for sparse retrieval.

maximum100

minimum1

updated_at: Optional[datetime]

Update datetime

formatdate-time

List Retrievers

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
)
retrievers = client.retrievers.list()
print(retrievers)

[
  {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "name": "x",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "pipelines": [
      {
        "description": "description",
        "name": "x",
        "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "preset_retrieval_parameters": {
          "alpha": 0,
          "class_name": "class_name",
          "dense_similarity_cutoff": 0,
          "dense_similarity_top_k": 1,
          "enable_reranking": true,
          "files_top_k": 1,
          "rerank_top_n": 1,
          "retrieval_mode": "chunks",
          "retrieve_image_nodes": true,
          "retrieve_page_figure_nodes": true,
          "retrieve_page_screenshot_nodes": true,
          "search_filters": {
            "filters": [
              {
                "key": "key",
                "value": 0,
                "operator": "=="
              }
            ],
            "condition": "and"
          },
          "search_filters_inference_schema": {
            "foo": {
              "foo": "bar"
            }
          },
          "sparse_similarity_top_k": 1
        }
      }
    ],
    "updated_at": "2019-12-27T18:11:19.117Z"
  }
]

Returns Examples

[
  {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "name": "x",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "pipelines": [
      {
        "description": "description",
        "name": "x",
        "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "preset_retrieval_parameters": {
          "alpha": 0,
          "class_name": "class_name",
          "dense_similarity_cutoff": 0,
          "dense_similarity_top_k": 1,
          "enable_reranking": true,
          "files_top_k": 1,
          "rerank_top_n": 1,
          "retrieval_mode": "chunks",
          "retrieve_image_nodes": true,
          "retrieve_page_figure_nodes": true,
          "retrieve_page_screenshot_nodes": true,
          "search_filters": {
            "filters": [
              {
                "key": "key",
                "value": 0,
                "operator": "=="
              }
            ],
            "condition": "and"
          },
          "search_filters_inference_schema": {
            "foo": {
              "foo": "bar"
            }
          },
          "sparse_similarity_top_k": 1
        }
      }
    ],
    "updated_at": "2019-12-27T18:11:19.117Z"
  }
]