post:
  tags:
    - Flow Services
  summary: Document Embeddings Query - find similar text chunks
  description: |
    Query document embeddings to find similar text chunks by vector similarity.

    ## Document Embeddings Query Overview

    Find document chunks semantically similar to a query vector:
    - **Input**: Query embedding vector
    - **Search**: Compare against stored chunk embeddings
    - **Output**: Most similar text chunks

    Core component of document RAG retrieval.

    ## Use Cases

    - **Document retrieval**: Find relevant passages
    - **Semantic search**: Search by meaning not keywords
    - **Context gathering**: Get text for RAG
    - **Similar content**: Discover related documents

    ## Process

    1. Obtain query embedding (via embeddings service)
    2. Query stored document chunk embeddings
    3. Calculate cosine similarity
    4. Return top N most similar chunks
    5. Use chunks as context for generation

    ## Chunking

    Documents are split into chunks during indexing:
    - Typical size: 200-1000 tokens
    - Overlap between chunks for continuity
    - Each chunk has own embedding

    Queries return individual chunks, not full documents.

    ## Similarity Scoring

    Uses cosine similarity:
    - Results ordered by similarity
    - No explicit scores in response
    - Limit controls result count

    ## Output Format

    Returns text chunks as strings:
    - Raw chunk text
    - No metadata (source, position, etc.)
    - Use for LLM context directly

  operationId: documentEmbeddingsQueryService
  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/DocumentEmbeddingsQueryRequest.yaml'
        examples:
          basicQuery:
            summary: Find similar chunks
            value:
              vectors: [0.023, -0.142, 0.089, 0.234, -0.067, 0.156, 0.201, -0.178]
              limit: 10
              user: alice
              collection: research
          largeQuery:
            summary: Larger result set
            value:
              vectors: [0.1, -0.2, 0.3, -0.4, 0.5]
              limit: 30
  responses:
    '200':
      description: Successful response
      content:
        application/json:
          schema:
            $ref: '../../components/schemas/embeddings-query/DocumentEmbeddingsQueryResponse.yaml'
          examples:
            similarChunks:
              summary: Similar document chunks
              value:
                chunks:
                  - "Quantum computing uses quantum mechanics principles like superposition and entanglement for computation. Unlike classical bits, quantum bits (qubits) can exist in multiple states simultaneously."
                  - "Neural networks are computing systems inspired by biological neural networks. They consist of interconnected nodes organized in layers that process information through weighted connections."
                  - "Machine learning algorithms learn patterns from data without being explicitly programmed. They improve their performance through experience and exposure to training data."
    '401':
      $ref: '../../components/responses/Unauthorized.yaml'
    '500':
      $ref: '../../components/responses/Error.yaml'