## Run Search

**post** `/api/v1/pipelines/{pipeline_id}/retrieve`

Run a retrieval query against a managed pipeline.

Searches the pipeline's vector store using the provided query
and retrieval parameters. Supports dense, sparse, and hybrid
search modes with configurable top-k and reranking.

### Path Parameters

- `pipeline_id: string`

### Query Parameters

- `organization_id: optional string`

- `project_id: optional string`

### Cookie Parameters

- `session: optional string`

### Body Parameters

- `query: string`

  The query to retrieve against.

- `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

- `pipeline_id: string`

  The ID of the pipeline that the query was retrieved against.

- `retrieval_nodes: array of object { node, class_name, score }`

  The nodes retrieved by the pipeline for the given query.

  - `node: TextNode`

    Provided for backward compatibility.

    - `class_name: optional string`

    - `embedding: optional array of number`

      Embedding of the node.

    - `end_char_idx: optional number`

      End char index of the node.

    - `excluded_embed_metadata_keys: optional array of string`

      Metadata keys that are excluded from text for the embed model.

    - `excluded_llm_metadata_keys: optional array of string`

      Metadata keys that are excluded from text for the LLM.

    - `extra_info: optional map[unknown]`

      A flat dictionary of metadata fields

    - `id_: optional string`

      Unique ID of the node.

    - `metadata_seperator: optional string`

      Separator between metadata fields when converting to string.

    - `metadata_template: optional string`

      Template for how metadata is formatted, with {key} and {value} placeholders.

    - `mimetype: optional string`

      MIME type of the node content.

    - `relationships: optional map[object { node_id, class_name, hash, 2 more }  or array of object { node_id, class_name, hash, 2 more } ]`

      A mapping of relationships to other node information.

      - `RelatedNodeInfo = object { node_id, class_name, hash, 2 more }`

        - `node_id: string`

        - `class_name: optional string`

        - `hash: optional string`

        - `metadata: optional map[unknown]`

        - `node_type: optional "1" or "2" or "3" or 2 more or string`

          - `ObjectType = "1" or "2" or "3" or 2 more`

            - `"1"`

            - `"2"`

            - `"3"`

            - `"4"`

            - `"5"`

          - `string`

      - `array of object { node_id, class_name, hash, 2 more }`

        - `node_id: string`

        - `class_name: optional string`

        - `hash: optional string`

        - `metadata: optional map[unknown]`

        - `node_type: optional "1" or "2" or "3" or 2 more or string`

          - `ObjectType = "1" or "2" or "3" or 2 more`

            - `"1"`

            - `"2"`

            - `"3"`

            - `"4"`

            - `"5"`

          - `string`

    - `start_char_idx: optional number`

      Start char index of the node.

    - `text: optional string`

      Text content of the node.

    - `text_template: optional string`

      Template for how text is formatted, with {content} and {metadata_str} placeholders.

  - `class_name: optional string`

  - `score: optional number`

- `class_name: optional string`

- `image_nodes: optional array of PageScreenshotNodeWithScore`

  The image nodes retrieved by the pipeline for the given query. Deprecated - will soon be replaced with 'page_screenshot_nodes'.

  - `node: object { file_id, image_size, page_index, metadata }`

    - `file_id: string`

      The ID of the file that the page screenshot was taken from

    - `image_size: number`

      The size of the image in bytes

    - `page_index: number`

      The index of the page for which the screenshot is taken (0-indexed)

    - `metadata: optional map[unknown]`

      Metadata for the screenshot

  - `score: number`

    The score of the screenshot node

  - `class_name: optional string`

- `inferred_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"`

- `metadata: optional map[string]`

  Metadata associated with the retrieval execution

- `page_figure_nodes: optional array of PageFigureNodeWithScore`

  The page figure nodes retrieved by the pipeline for the given query.

  - `node: object { confidence, figure_name, figure_size, 4 more }`

    - `confidence: number`

      The confidence of the figure

    - `figure_name: string`

      The name of the figure

    - `figure_size: number`

      The size of the figure in bytes

    - `file_id: string`

      The ID of the file that the figure was taken from

    - `page_index: number`

      The index of the page for which the figure is taken (0-indexed)

    - `is_likely_noise: optional boolean`

      Whether the figure is likely to be noise

    - `metadata: optional map[unknown]`

      Metadata for the figure

  - `score: number`

    The score of the figure node

  - `class_name: optional string`

- `retrieval_latency: optional map[number]`

  The end-to-end latency for retrieval and reranking.

### Example

```http
curl https://api.cloud.llamaindex.ai/api/v1/pipelines/$PIPELINE_ID/retrieve \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" \
    -d '{
          "query": "x"
        }'
```

#### Response

```json
{
  "pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "retrieval_nodes": [
    {
      "node": {
        "class_name": "class_name",
        "embedding": [
          0
        ],
        "end_char_idx": 0,
        "excluded_embed_metadata_keys": [
          "string"
        ],
        "excluded_llm_metadata_keys": [
          "string"
        ],
        "extra_info": {
          "foo": "bar"
        },
        "id_": "id_",
        "metadata_seperator": "metadata_seperator",
        "metadata_template": "metadata_template",
        "mimetype": "mimetype",
        "relationships": {
          "foo": {
            "node_id": "node_id",
            "class_name": "class_name",
            "hash": "hash",
            "metadata": {
              "foo": "bar"
            },
            "node_type": "1"
          }
        },
        "start_char_idx": 0,
        "text": "text",
        "text_template": "text_template"
      },
      "class_name": "class_name",
      "score": 0
    }
  ],
  "class_name": "class_name",
  "image_nodes": [
    {
      "node": {
        "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "image_size": 0,
        "page_index": 0,
        "metadata": {
          "foo": "bar"
        }
      },
      "score": 0,
      "class_name": "class_name"
    }
  ],
  "inferred_search_filters": {
    "filters": [
      {
        "key": "key",
        "value": 0,
        "operator": "=="
      }
    ],
    "condition": "and"
  },
  "metadata": {
    "foo": "string"
  },
  "page_figure_nodes": [
    {
      "node": {
        "confidence": 0,
        "figure_name": "figure_name",
        "figure_size": 0,
        "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
        "page_index": 0,
        "is_likely_noise": true,
        "metadata": {
          "foo": "bar"
        }
      },
      "score": 0,
      "class_name": "class_name"
    }
  ],
  "retrieval_latency": {
    "foo": 0
  }
}
```