Update Retriever

$ llamacloud-prod retrievers update

PUT/api/v1/retrievers/{retriever_id}

Update an existing Retriever.

ParametersExpand Collapse

--retriever-id: string

Path param

--pipeline: array of RetrieverPipeline { description, name, pipeline_id, preset_retrieval_parameters }

Body param: The pipelines this retriever uses.

--organization-id: optional string

Query param

--project-id: optional string

Query param

--name: optional string

Body param: A name for the retriever.

ReturnsExpand Collapse

retriever: object { id, name, project_id, 3 more }

An entity that retrieves context nodes from several sub RetrieverTools.

id: string

Unique identifier

name: string

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

project_id: string

The ID of the project this retriever resides in.

created_at: optional string

Creation datetime

pipelines: optional array of RetrieverPipeline { description, name, pipeline_id, preset_retrieval_parameters }

The pipelines this retriever uses.

description: string

A description of the retriever tool.

name: string

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

pipeline_id: string

The ID of the pipeline this tool uses.

preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }

Parameters for retrieval configuration.

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.

class_name: optional string

dense_similarity_cutoff: optional number

Minimum similarity score wrt query for retrieval

dense_similarity_top_k: optional number

Number of nodes for dense retrieval.

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).

rerank_top_n: optional number

Number of reranked nodes for returning.

retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"

The retrieval mode for the query.

"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.

filters: array of object { key, value, operator } or MetadataFilters { filters, condition }

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

union_member_0: number

union_member_1: string

union_member_2: array of string

union_member_3: array of number

union_member_4: array of number

operator: optional "==" or ">" or "<" or 11 more

Vector store filter operator.

"=="

">"

"<"

"!="

">="

"<="

"in"

"nin"

"any"

"all"

"text_match"

"text_match_insensitive"

"contains"

"is_empty"

metadata_filters

condition: optional "and" or "or" or "not"

Vector store filter conditions to combine different filters.

"and"

"or"

"not"

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.

union_member_0: map[unknown]

union_member_1: array of unknown

union_member_2: string

union_member_3: number

union_member_4: boolean

sparse_similarity_top_k: optional number

Number of nodes for sparse retrieval.

updated_at: optional string

Update datetime

Update Retriever

llamacloud-prod retrievers update \
  --api-key 'My API Key' \
  --retriever-id 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e \
  --pipeline '{description: description, name: x, pipeline_id: 182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e}'

{
  "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"
}