post:
  tags:
    - Flow Services
  summary: Row Embeddings Query - semantic search on structured data
  description: |
    Query row embeddings to find similar rows by vector similarity on indexed fields.
    Enables fuzzy/semantic matching on structured data.

    ## Row Embeddings Query Overview

    Find rows whose indexed field values are semantically similar to a query:
    - **Input**: Query embedding vector, schema name, optional index filter
    - **Search**: Compare against stored row index embeddings
    - **Output**: Matching rows with index values and similarity scores

    Core component of semantic search on structured data.

    ## Use Cases

    - **Fuzzy name matching**: Find customers by approximate name
    - **Semantic field search**: Find products by description similarity
    - **Data deduplication**: Identify potential duplicate records
    - **Entity resolution**: Match records across datasets

    ## Process

    1. Obtain query embedding (via embeddings service)
    2. Query stored row index embeddings for the specified schema
    3. Calculate cosine similarity
    4. Return top N most similar index entries
    5. Use index values to retrieve full rows via GraphQL

    ## Response Format

    Each match includes:
    - `index_name`: The indexed field(s) that matched
    - `index_value`: The actual values for those fields
    - `text`: The text that was embedded
    - `score`: Similarity score (higher = more similar)

  operationId: rowEmbeddingsQueryService
  security:
    - bearerAuth: []
  parameters:
    - name: flow
      in: path
      required: true
      schema:
        type: string
      description: Flow instance ID
      example: my-flow
  requestBody:
    required: true
    content:
      application/json:
        schema:
          $ref: '../../components/schemas/embeddings-query/RowEmbeddingsQueryRequest.yaml'
        examples:
          basicQuery:
            summary: Find similar customer names
            value:
              vectors: [0.023, -0.142, 0.089, 0.234, -0.067, 0.156, 0.201, -0.178]
              schema_name: customers
              limit: 10
              user: alice
              collection: sales
          filteredQuery:
            summary: Search specific index
            value:
              vectors: [0.1, -0.2, 0.3, -0.4, 0.5]
              schema_name: products
              index_name: description
              limit: 20
  responses:
    '200':
      description: Successful response
      content:
        application/json:
          schema:
            $ref: '../../components/schemas/embeddings-query/RowEmbeddingsQueryResponse.yaml'
          examples:
            similarRows:
              summary: Similar rows found
              value:
                matches:
                  - index_name: full_name
                    index_value: ["John", "Smith"]
                    text: "John Smith"
                    score: 0.95
                  - index_name: full_name
                    index_value: ["Jon", "Smythe"]
                    text: "Jon Smythe"
                    score: 0.82
                  - index_name: full_name
                    index_value: ["Jonathan", "Schmidt"]
                    text: "Jonathan Schmidt"
                    score: 0.76
    '401':
      $ref: '../../components/responses/Unauthorized.yaml'
    '500':
      $ref: '../../components/responses/Error.yaml'