post:
  tags:
    - Flow Services
  summary: Document RAG - retrieve and generate from documents
  description: |
    Retrieval-Augmented Generation over document embeddings.

    ## Document RAG Overview

    Document RAG combines:
    1. **Retrieval**: Search document embeddings using semantic similarity
    2. **Generation**: Use LLM to synthesize answer from retrieved documents

    This provides grounded answers based on your document corpus.

    ## Query Process

    1. Convert query to embedding
    2. Search document embeddings for most similar chunks
    3. Retrieve top N document chunks (configurable via doc-limit)
    4. Pass query + retrieved context to LLM
    5. Generate answer grounded in documents

    ## Streaming

    Enable `streaming: true` to receive the answer as it's generated:
    - Multiple messages with `response` content
    - Final message with `end-of-stream: true`

    Without streaming, returns complete answer in single response.

    ## Parameters

    - **doc-limit**: Controls retrieval depth (1-100, default 20)
      - Higher = more context but slower
      - Lower = faster but may miss relevant info
    - **collection**: Target specific document collection
    - **user**: Multi-tenant isolation

  operationId: documentRagService
  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/rag/DocumentRagRequest.yaml'
        examples:
          basicQuery:
            summary: Basic document query
            value:
              query: What are the key findings in the research papers?
              user: alice
              collection: research
          streamingQuery:
            summary: Streaming query
            value:
              query: Summarize the main conclusions
              user: alice
              collection: research
              doc-limit: 15
              streaming: true
          limitedRetrieval:
            summary: Query with limited retrieval
            value:
              query: What is quantum entanglement?
              doc-limit: 5
  responses:
    '200':
      description: Successful response
      content:
        application/json:
          schema:
            $ref: '../../components/schemas/rag/DocumentRagResponse.yaml'
          examples:
            completeResponse:
              summary: Complete non-streaming response
              value:
                response: |
                  The research papers present three key findings:
                  1. Quantum entanglement exhibits non-local correlations
                  2. Bell's inequality is violated in experimental tests
                  3. Applications in quantum cryptography are promising
                end-of-stream: false
            streamingChunk:
              summary: Streaming response chunk
              value:
                response: "The research papers present three"
                end-of-stream: false
            streamingComplete:
              summary: Streaming complete marker
              value:
                response: ""
                end-of-stream: true
    '401':
      $ref: '../../components/responses/Unauthorized.yaml'
    '500':
      $ref: '../../components/responses/Error.yaml'