## Upsert Retriever **put** `/api/v1/retrievers` Upsert a new Retriever. ### Query Parameters - `organization_id: optional string` - `project_id: optional string` ### Cookie Parameters - `session: optional string` ### Body Parameters - `name: string` A name for the retriever tool. Will default to the pipeline name if not provided. - `pipelines: optional array of RetrieverPipeline` 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 PresetRetrievalParams` 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 RetrievalMode` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_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` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `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` - `number` - `string` - `array of string` - `array of number` - `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"` - `MetadataFilters = object { filters, condition }` Metadata filters for vector stores. - `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. - `map[unknown]` - `array of unknown` - `string` - `number` - `boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. ### Returns - `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` 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 PresetRetrievalParams` 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 RetrievalMode` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_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` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `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` - `number` - `string` - `array of string` - `array of number` - `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"` - `MetadataFilters = object { filters, condition }` Metadata filters for vector stores. - `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. - `map[unknown]` - `array of unknown` - `string` - `number` - `boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `updated_at: optional string` Update datetime ### Example ```http curl https://api.cloud.llamaindex.ai/api/v1/retrievers \ -X PUT \ -H 'Content-Type: application/json' \ -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \ -d '{ "name": "x" }' ``` #### Response ```json { "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" } ```