API Reference

Retrievers

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Create Retriever

POST/api/v1/retrievers

Create a new Retriever.

Query ParametersExpand Collapse

organization_id: optional string

project_id: optional string

Cookie ParametersExpand Collapse

session: optional string

Body ParametersExpand Collapse

name: string

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

maxLength3000

minLength1

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

ReturnsExpand Collapse

Retriever = object { id, name, project_id, 3 more }

An entity that retrieves context nodes from several sub RetrieverTools.

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

Create Retriever

HTTP

HTTP

TypeScript

Python

curl https://api.cloud.llamaindex.ai/api/v1/retrievers \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '{
          "name": "x"
        }'

200 example

{
  "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

200 example

{
  "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"
}