## Create Retriever

`Retriever retrievers().create(RetrieverCreateParamsparams, RequestOptionsrequestOptions = RequestOptions.none())`

**post** `/api/v1/retrievers`

Create a new Retriever.

### Parameters

- `RetrieverCreateParams params`

  - `Optional<String> organizationId`

  - `Optional<String> projectId`

  - `RetrieverCreate retrieverCreate`

### Returns

- `class Retriever:`

  An entity that retrieves context nodes from several sub RetrieverTools.

  - `String id`

    Unique identifier

  - `String name`

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

  - `String projectId`

    The ID of the project this retriever resides in.

  - `Optional<LocalDateTime> createdAt`

    Creation datetime

  - `Optional<List<RetrieverPipeline>> pipelines`

    The pipelines this retriever uses.

    - `Optional<String> description`

      A description of the retriever tool.

    - `Optional<String> name`

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

    - `String pipelineId`

      The ID of the pipeline this tool uses.

    - `Optional<PresetRetrievalParams> presetRetrievalParameters`

      Parameters for retrieval configuration.

      - `Optional<Double> alpha`

        Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval.

      - `Optional<String> className`

      - `Optional<Double> denseSimilarityCutoff`

        Minimum similarity score wrt query for retrieval

      - `Optional<Long> denseSimilarityTopK`

        Number of nodes for dense retrieval.

      - `Optional<Boolean> enableReranking`

        Enable reranking for retrieval

      - `Optional<Long> filesTopK`

        Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content).

      - `Optional<Long> rerankTopN`

        Number of reranked nodes for returning.

      - `Optional<RetrievalMode> retrievalMode`

        The retrieval mode for the query.

        - `CHUNKS("chunks")`

        - `FILES_VIA_METADATA("files_via_metadata")`

        - `FILES_VIA_CONTENT("files_via_content")`

        - `AUTO_ROUTED("auto_routed")`

      - `Optional<Boolean> retrieveImageNodes`

        Whether to retrieve image nodes.

      - `Optional<Boolean> retrievePageFigureNodes`

        Whether to retrieve page figure nodes.

      - `Optional<Boolean> retrievePageScreenshotNodes`

        Whether to retrieve page screenshot nodes.

      - `Optional<MetadataFilters> searchFilters`

        Metadata filters for vector stores.

        - `List<Filter> filters`

          - `class MetadataFilter:`

            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

            - `String key`

            - `Optional<Value> value`

              - `double`

              - `String`

              - `List<String>`

              - `List<double>`

              - `List<long>`

            - `Optional<Operator> operator`

              Vector store filter operator.

              - `EQUALS("==")`

              - `GREATER(">")`

              - `LESS("<")`

              - `NOT_EQUALS("!=")`

              - `GREATER_OR_EQUALS(">=")`

              - `LESS_OR_EQUALS("<=")`

              - `IN("in")`

              - `NIN("nin")`

              - `ANY("any")`

              - `ALL("all")`

              - `TEXT_MATCH("text_match")`

              - `TEXT_MATCH_INSENSITIVE("text_match_insensitive")`

              - `CONTAINS("contains")`

              - `IS_EMPTY("is_empty")`

          - `class MetadataFilters:`

            Metadata filters for vector stores.

        - `Optional<Condition> condition`

          Vector store filter conditions to combine different filters.

          - `AND("and")`

          - `OR("or")`

          - `NOT("not")`

      - `Optional<SearchFiltersInferenceSchema> searchFiltersInferenceSchema`

        JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference.

        - `class UnionMember0:`

        - `List<JsonValue>`

        - `String`

        - `double`

        - `boolean`

      - `Optional<Long> sparseSimilarityTopK`

        Number of nodes for sparse retrieval.

  - `Optional<LocalDateTime> updatedAt`

    Update datetime

### Example

```java
package com.llamacloud_prod.api.example;

import com.llamacloud_prod.api.client.LlamaCloudClient;
import com.llamacloud_prod.api.client.okhttp.LlamaCloudOkHttpClient;
import com.llamacloud_prod.api.models.retrievers.Retriever;
import com.llamacloud_prod.api.models.retrievers.RetrieverCreate;
import com.llamacloud_prod.api.models.retrievers.RetrieverCreateParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        LlamaCloudClient client = LlamaCloudOkHttpClient.fromEnv();

        RetrieverCreate params = RetrieverCreate.builder()
            .name("x")
            .build();
        Retriever retriever = client.retrievers().create(params);
    }
}
```

#### 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"
}
```