List Retrievers

client.retrievers.list(?, ?): RetrieverListResponse { id, name, project_id, 3 more }

GET/api/v1/retrievers

List Retrievers for a project.

ParametersExpand Collapse

query: RetrieverListParams { name, organization_id, project_id }

name?: string | null

organization_id?: string | null

project_id?: string | null

ReturnsExpand Collapse

RetrieverListResponse = Array<Retriever { id, name, project_id, 3 more } >

id: string

Unique identifier

formatuuid

name: string

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

maxLength3000

minLength1

project_id: string

The ID of the project this retriever resides in.

formatuuid

created_at?: string | null

Creation datetime

formatdate-time

pipelines?: Array<RetrieverPipeline { description, name, pipeline_id, preset_retrieval_parameters } >

The pipelines this retriever uses.

description: string | null

A description of the retriever tool.

maxLength15000

name: string | null

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

maxLength3000

minLength1

pipeline_id: string

The ID of the pipeline this tool uses.

formatuuid

preset_retrieval_parameters?: PresetRetrievalParams { alpha, class_name, dense_similarity_cutoff, 11 more }

Parameters for retrieval configuration.

alpha?: number | null

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

dense_similarity_cutoff?: number | null

Minimum similarity score wrt query for retrieval

maximum1

minimum0

dense_similarity_top_k?: number | null

Number of nodes for dense retrieval.

maximum100

minimum1

enable_reranking?: boolean | null

Enable reranking for retrieval

files_top_k?: number | null

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

maximum5

minimum1

rerank_top_n?: number | null

Number of reranked nodes for returning.

maximum100

minimum1

retrieval_mode?: RetrievalMode

The retrieval mode for the query.

Accepts one of the following:

"chunks"

"files_via_metadata"

"files_via_content"

"auto_routed"

Deprecatedretrieve_image_nodes?: boolean

Whether to retrieve image nodes.

retrieve_page_figure_nodes?: boolean

Whether to retrieve page figure nodes.

retrieve_page_screenshot_nodes?: 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:

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

Number of nodes for sparse retrieval.

maximum100

minimum1

updated_at?: string | null

Update datetime

formatdate-time

List Retrievers

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 retrievers = await client.retrievers.list();

console.log(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"
  }
]