> ## Documentation Index
> Fetch the complete documentation index at: https://urantia.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Export Embeddings

> Export embedding vectors for all paragraphs in a paper. The `paperId` query parameter is required.

Returns JSONL (default) or JSON. Each line/item contains `{ ref, embedding }`. A typical paper is 50-200 paragraphs (~1-5 MB).



## OpenAPI

````yaml GET /embeddings/export
openapi: 3.1.0
info:
  title: Urantia Papers API
  version: 1.0.0
  description: >-
    A developer and AI-agent friendly API for the Urantia Papers. Provides
    full-text search, structured content access, and audio URLs for all 17,000+
    paragraphs across 197 papers.
servers:
  - url: https://api.urantia.dev
    description: Production
  - url: http://localhost:3000
    description: Local development
security: []
paths:
  /embeddings/export:
    get:
      tags:
        - Embeddings
      summary: Bulk export embedding vectors
      description: >-
        Export embedding vectors for all paragraphs in a paper. The `paperId`
        query parameter is required.


        Returns JSONL (default) or JSON. Each line/item contains `{ ref,
        embedding }`. A typical paper is 50-200 paragraphs (~1-5 MB).
      operationId: exportEmbeddings
      parameters:
        - schema:
            type: string
            enum:
              - jsonl
              - json
            default: jsonl
          required: false
          name: format
          in: query
        - schema:
            type: string
          required: true
          name: paperId
          in: query
        - schema:
            type: string
            enum:
              - small
              - large
            default: large
          required: false
          name: model
          in: query
      responses:
        '200':
          description: Embedding vectors (JSONL or JSON)

````