> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praxis-ai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Create a new embedding chunk

> Creates a new embedding chunk for an existing upload. The chunk text is vectorized
automatically using the institution's configured embedding model. The new chunk is
appended after the last existing chunk (highest chunkIndex + 1).

Use this to manually extend a file's RAG segments with additional text content
that wasn't captured during automatic ingestion.




## OpenAPI

````yaml /mdx/api-reference/runtime/runtime-api.json post /api/user/embedding
openapi: 3.0.0
info:
  title: Pria Runtime API
  version: 2.0.1
  description: >-
    Pria API Documentation Praxis's developer platform is a core part of our
    mission to empower organizations to grow better. Our APIs are designed to
    enable teams of any shape or size to build robust integrations that help
    them customize and get the most value out of Pria. All Pria APIs are built
    using REST conventions and designed to have a predictable URL structure.
    <br/>  <br/>They use many standard HTTP features, including methods (POST,
    GET, PUT, DELETE) and error response codes.  <br/> <br/>All API calls are
    made under https://hiimpria.ai/api and all responses return standard JSON.
    In these docs, you'll find lists of all available endpoints for a given API,
    along with interactive code blocks for building requests. For walkthroughs
    of basic usage for these APIs, check out the API guides.
servers:
  - url: https://pria.praxislxp.com
    description: Pria API Server
security: []
tags:
  - name: Authentication
    description: User authentication, registration, and password management (/api/auth)
  - name: OAuth
    description: OAuth authentication providers - Google, GitHub, SSO (/api/auth/oauth)
  - name: User
    description: User profile management and account operations (/api/user)
  - name: User Institutions
    description: User institution memberships and switching (/api/user/institution)
  - name: User Tools
    description: Available tools for authenticated users (/api/user/tools)
  - name: Institutions
    description: Institution settings and configuration (/api/user/institution)
  - name: Conversation
    description: AI conversation and Q&A endpoints (/api/ai)
  - name: Realtime
    description: Real-time voice AI and WebRTC sessions (/api/ai/rt)
  - name: Assistant
    description: AI assistant configuration and management (/api/user/assistant)
  - name: History
    description: Conversation history and favorites (/api/user/history)
  - name: RAG
    description: >-
      Document upload, embedding, and retrieval-augmented generation
      (/api/user/files, /api/user/rag)
  - name: Setting
    description: Instance variables and settings management (/api/user/setting)
  - name: Branding
    description: Digital twin branding and customization (/api/agent/branding)
  - name: Agent
    description: Agent engagement and session management (/api/agent)
  - name: SDK Launch
    description: >-
      SDK launch token signing and verification for secure iframe embedding
      (/api/auth/sdk-sign, /api/auth/sdk-verify)
  - name: Testing
    description: Health checks, diagnostics, and test endpoints (/api/test)
  - name: Admin Accounts
    description: Account management for super admins (/api/admin/account)
  - name: Admin Institutions
    description: Institution management for admins (/api/admin/institution)
  - name: Admin Users
    description: User management for admins (/api/admin/user)
  - name: Admin Entitlements
    description: >-
      User-institution relationships and permissions
      (/api/admin/userInstitution)
  - name: Admin Sessions
    description: Session management for admins (/api/admin/session)
  - name: Admin Histories
    description: Conversation history management and analytics (/api/admin/history)
  - name: Admin Assistants
    description: AI assistant management for admins (/api/admin/assistant)
  - name: Admin Questions
    description: Institution question and prompt management (/api/admin/question)
  - name: Admin Tools
    description: Tool configuration management (/api/admin/tool)
  - name: Admin AI Models
    description: AI model configuration (/api/admin/aimodel)
  - name: Admin MCP Servers
    description: Model Context Protocol server management (/api/admin/mcpserver)
  - name: Admin Feedbacks
    description: User feedback management (/api/admin/feedback)
  - name: Admin Uploads
    description: Upload management (/api/admin/upload)
  - name: Admin Charts
    description: Analytics and visualization chart management (/api/admin/chart)
  - name: Audio Notes
    description: Capture and ingest spoken notes into the personal vault
  - name: Memory
    description: User-facing memory parameters (personal + shared instance memory).
  - name: My Data
    description: >-
      GDPR controls — personal-scope counts, async ZIP-by-email export, and
      scoped soft-delete. Every endpoint pins `user = req.user._id` AND
      `institution: null`; institution-scoped data is governed by the
      institution's own retention policy and never reached from here.
  - name: Questions
    description: >-
      User-facing read of the onboarding question bank used by the "create a
      digital twin" wizard.
  - name: Transcription
    description: >-
      One-shot speech-to-text for in-place dictation. Audio blob in, transcript
      out — no Upload / History / RAG embeddings are persisted. Use
      `/audio-notes` for anything durable.
paths:
  /api/user/embedding:
    post:
      tags:
        - RAG
      summary: Create a new embedding chunk
      description: >
        Creates a new embedding chunk for an existing upload. The chunk text is
        vectorized

        automatically using the institution's configured embedding model. The
        new chunk is

        appended after the last existing chunk (highest chunkIndex + 1).


        Use this to manually extend a file's RAG segments with additional text
        content

        that wasn't captured during automatic ingestion.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateEmbeddingRequest'
      responses:
        '201':
          description: Embedding chunk created successfully
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateEmbeddingResponse'
        '400':
          description: Bad request - Missing upload ID or chunkText
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
        '401':
          description: Unauthorized - Invalid or missing access token
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
        '404':
          description: Upload not found
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
      security:
        - apiKeyAuth: []
components:
  schemas:
    CreateEmbeddingRequest:
      type: object
      required:
        - upload
        - chunkText
      properties:
        upload:
          type: string
          description: The ID of the upload to add the embedding chunk to
          example: 665a1b2c3d4e5f6789012300
        chunkText:
          type: string
          description: >-
            The text content for the new chunk (max ~32K characters). A vector
            embedding is generated automatically.
          example: This is a new manually-added paragraph for RAG search...
    CreateEmbeddingResponse:
      type: object
      properties:
        success:
          type: boolean
          example: true
        message:
          type: string
          example: Embedding created!
        data:
          $ref: '#/components/schemas/EmbeddingChunk'
    ErrorResponse:
      type: object
      properties:
        success:
          type: boolean
          example: false
        message:
          type: string
          description: Human-readable error message
        error:
          type: string
          description: Technical error details
    EmbeddingChunk:
      type: object
      properties:
        _id:
          type: string
          description: Unique identifier for the embedding chunk
          example: 665a1b2c3d4e5f6789012345
        chunkText:
          type: string
          description: The text content of this chunk (max ~32K characters)
          example: This is a paragraph from the uploaded document...
        chunkLen:
          type: integer
          description: Character length of the chunk text
          example: 512
        chunkIndex:
          type: integer
          description: Zero-based position of this chunk within the parent upload
          example: 0
        upload:
          type: string
          description: ID of the parent upload this chunk belongs to
          example: 665a1b2c3d4e5f6789012300
        chunkUrl:
          type: string
          description: >-
            Location reference within the source document (e.g., page number for
            PDFs, element ID for HTML)
          example: '#page=2'
        created:
          type: string
          format: date-time
          description: Timestamp when this chunk was created
        usage:
          type: integer
          description: Token count consumed when generating the vector embedding
          example: 128
  securitySchemes:
    apiKeyAuth:
      type: apiKey
      in: header
      name: x-access-token
      description: JWT token passed in x-access-token header

````