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API Reference
Retrievers

List Retrievers

GET/api/v1/retrievers

List Retrievers for a project.

Query ParametersExpand Collapse
name: optional string
organization_id: optional string
project_id: optional string
Cookie ParametersExpand Collapse
session: optional string
ReturnsExpand Collapse
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: optional string

Creation datetime

formatdate-time
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.

maxLength15000
name: string

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: optional PresetRetrievalParams { 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.

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.

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"
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
updated_at: optional string

Update datetime

formatdate-time

List Retrievers

curl https://api.cloud.llamaindex.ai/api/v1/retrievers \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
[
  {
    "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"
  }
]