> ## 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.
# Get embedding chunks for an upload
> Retrieves all embedding chunks for a specific IP Vault upload, sorted by chunk index.
Each uploaded file is split into text chunks and converted into vector embeddings for
retrieval-augmented generation (RAG). This endpoint returns the chunk metadata and text
content (up to 1000 chunks per request).
## OpenAPI
````yaml /mdx/api-reference/runtime/runtime-api.json post /api/user/embeddings
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.
They use many standard HTTP features, including methods (POST,
GET, PUT, DELETE) and error response codes.
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/embeddings:
post:
tags:
- RAG
summary: Get embedding chunks for an upload
description: >
Retrieves all embedding chunks for a specific IP Vault upload, sorted by
chunk index.
Each uploaded file is split into text chunks and converted into vector
embeddings for
retrieval-augmented generation (RAG). This endpoint returns the chunk
metadata and text
content (up to 1000 chunks per request).
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/GetEmbeddingsRequest'
responses:
'200':
description: Embedding chunks retrieved successfully
content:
application/json:
schema:
$ref: '#/components/schemas/GetEmbeddingsResponse'
'400':
description: Bad request - Missing or invalid upload ID
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'401':
description: Unauthorized - Invalid or missing access token
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
security:
- apiKeyAuth: []
components:
schemas:
GetEmbeddingsRequest:
type: object
required:
- upload
properties:
upload:
type: string
description: The ID of the upload whose embedding chunks to retrieve
example: 665a1b2c3d4e5f6789012300
GetEmbeddingsResponse:
type: object
properties:
success:
type: boolean
example: true
data:
type: array
description: Embedding chunks sorted by chunkIndex (max 1000)
items:
$ref: '#/components/schemas/EmbeddingChunk'
embeddingModel:
type: object
nullable: true
description: >-
Model actually used to generate these chunks' vectors. Null when the
upload has no recorded embedding model.
properties:
name:
type: string
example: text-embedding-3-small
provider:
type: string
example: openai_cli
maxInputTokens:
type: integer
example: 8191
currentEmbeddingModel:
type: object
nullable: true
description: >-
The institution's currently-configured embedding model (what a
re-ingestion would use). Compare against `embeddingModel` to detect
drift.
properties:
name:
type: string
example: text-embedding-3-small
provider:
type: string
example: openai_cli
chunkMaxChars:
type: integer
description: >-
Soft cap (characters) for segment edit/create UI. Matches the
server-side RAG chunk-size constant so admin edits stay in lockstep
with new ingestion output.
example: 8000
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
````