API Reference

Retrievers

Copy Markdown

Open in Claude

Open in ChatGPT

Open in Cursor

---

Copy Markdown

View as Markdown

Update Retriever

retrievers.update(strretriever_id, RetrieverUpdateParams**kwargs) -> Retriever

PUT/api/v1/retrievers/{retriever_id}

Update an existing Retriever.

ParametersExpand Collapse

retriever_id: str

pipelines: Optional[Iterable[RetrieverPipelineParam]]

The pipelines this retriever uses.

description: Optional[str]

A description of the retriever tool.

maxLength15000

name: Optional[str]

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.

search_filters: Optional[MetadataFilters]

Metadata filters for vector stores.

filters: List[Filter]

Accepts one of the following:

class FilterMetadataFilter: …

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: str

value: Union[float, str, List[str], 3 more]

Accepts one of the following:

float

str

List[str]

List[float]

List[int]

operator: Optional[Literal["==", ">", "<", 11 more]]

Vector store filter operator.

Accepts one of the following:

"=="

">"

"<"

"!="

">="

"<="

"in"

"nin"

"any"

"all"

"text_match"

"text_match_insensitive"

"contains"

"is_empty"

class MetadataFilters: …

Metadata filters for vector stores.

filters: List[Filter]

Accepts one of the following:

class FilterMetadataFilter: …

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: str

value: Union[float, str, List[str], 3 more]

Accepts one of the following:

float

str

List[str]

List[float]

List[int]

operator: Optional[Literal["==", ">", "<", 11 more]]

Vector store filter operator.

Accepts one of the following:

"=="

">"

"<"

"!="

">="

"<="

"in"

"nin"

"any"

"all"

"text_match"

"text_match_insensitive"

"contains"

"is_empty"

MetadataFilters

condition: Optional[Literal["and", "or", "not"]]

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

condition: Optional[Literal["and", "or", "not"]]

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

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

name: Optional[str]

A name for the retriever.

ReturnsExpand Collapse

class Retriever: …

An entity that retrieves context nodes from several sub RetrieverTools.

id: str

Unique identifier

formatuuid

name: str

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

name: Optional[str]

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.

search_filters: Optional[MetadataFilters]

Metadata filters for vector stores.

filters: List[Filter]

Accepts one of the following:

class FilterMetadataFilter: …

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: str

value: Union[float, str, List[str], 3 more]

Accepts one of the following:

float

str

List[str]

List[float]

List[int]

operator: Optional[Literal["==", ">", "<", 11 more]]

Vector store filter operator.

Accepts one of the following:

"=="

">"

"<"

"!="

">="

"<="

"in"

"nin"

"any"

"all"

"text_match"

"text_match_insensitive"

"contains"

"is_empty"

class MetadataFilters: …

Metadata filters for vector stores.

filters: List[Filter]

Accepts one of the following:

class FilterMetadataFilter: …

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: str

value: Union[float, str, List[str], 3 more]

Accepts one of the following:

float

str

List[str]

List[float]

List[int]

operator: Optional[Literal["==", ">", "<", 11 more]]

Vector store filter operator.

Accepts one of the following:

"=="

">"

"<"

"!="

">="

"<="

"in"

"nin"

"any"

"all"

"text_match"

"text_match_insensitive"

"contains"

"is_empty"

MetadataFilters

condition: Optional[Literal["and", "or", "not"]]

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

condition: Optional[Literal["and", "or", "not"]]

Vector store filter conditions to combine different filters.

Accepts one of the following:

"and"

"or"

"not"

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

Update Retriever

Python

HTTP

TypeScript

Python

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
)
retriever = client.retrievers.update(
    retriever_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    pipelines=[{
        "description": "description",
        "name": "x",
        "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    }],
)
print(retriever.id)

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"
}