NVIDIA NIM

NVIDIA NIM Embeddings API

OpenAI-compatible embeddings endpoint (/v1/embeddings) backed by NVIDIA NeMo Retriever text embedding models including NV-Embed, NV-EmbedQA-E5, llama-3.2-nv-embedqa-1b, and BAAI BGE-M3. Returns dense float vectors for documents or queries to power RAG, semantic search, and clustering. Supports `input_type=passage|query` for asymmetric retrieval and the standard `dimensions` parameter on models that permit dimension reduction.

Documentation GitHub OpenAPI

Business capability

Artificial Intelligence Management BC-610.60

Operations 1

Each operation below carries the questions people ask an LLM about it and the instructions they give an agent to run it. Generated by API Evangelist overlay

POST /v1/embeddings Create embedding vectors for text · Create An Embedding #

Ask an LLM

“Can I turn a batch of text strings into embedding vectors for semantic search?”

“Should a query and a document be embedded with a different input type for retrieval models like NV-EmbedQA?”

Tell an agent

Embed the text {input} with the {model} embedding model.

Create {input_type} embeddings for {input} using {model}.

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OpenAPI Specification

openapi: 3.2.0
info:
  title: NVIDIA NIM Biology (BioNeMo) ASR Embeddings API
  description: NVIDIA BioNeMo NIMs for drug discovery and structural biology. Each model is a containerized microservice with its own task-specific payload but a consistent JSON contract. Includes protein structure prediction (AlphaFold2, ESMFold, OpenFold), protein generation (ProtGPT2, RFDiffusion), molecular property prediction (MolMIM), small molecule generation, and molecular docking (DiffDock).
  version: '2026-05-25'
  contact:
    name: NVIDIA Developer Support
    url: https://forums.developer.nvidia.com/c/ai-data-science/nemo-llm-service/
  license:
    name: NVIDIA AI Enterprise License
    url: https://www.nvidia.com/en-us/data-center/products/ai-enterprise/
servers:
- url: https://integrate.api.nvidia.com
  description: NVIDIA-hosted NIM endpoint
- url: http://localhost:8000
  description: Self-hosted NIM container default
security:
- BearerAuth: []
tags:
- name: Embeddings
  description: Dense vector embedding operations for RAG and semantic search
paths:
  /v1/embeddings:
    post:
      summary: Create An Embedding
      description: Generate embedding vectors for one or more input strings using a NeMo Retriever embedding model.
      operationId: createEmbedding
      tags:
      - Embeddings
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/EmbeddingRequest'
      responses:
        '200':
          description: Embedding response.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/EmbeddingResponse'
        '400':
          description: Invalid request.
        '401':
          description: Missing or invalid API key.
        '429':
          description: Rate limit exceeded.
components:
  schemas:
    EmbeddingResponse:
      type: object
      properties:
        object:
          type: string
          example: list
        data:
          type: array
          items:
            type: object
            properties:
              object:
                type: string
                example: embedding
              index:
                type: integer
              embedding:
                type: array
                items:
                  type: number
        model:
          type: string
        usage:
          type: object
          properties:
            prompt_tokens:
              type: integer
            total_tokens:
              type: integer
    EmbeddingRequest:
      type: object
      required:
      - model
      - input
      properties:
        model:
          type: string
          description: e.g. `nvidia/llama-3.2-nv-embedqa-1b-v2`, `nvidia/nv-embedqa-e5-v5`, `baai/bge-m3`.
        input:
          oneOf:
          - type: string
          - type: array
            items:
              type: string
        input_type:
          type: string
          enum:
          - query
          - passage
          description: Asymmetric retrieval hint for NV-EmbedQA-style models.
        encoding_format:
          type: string
          enum:
          - float
          - base64
          default: float
        truncate:
          type: string
          enum:
          - NONE
          - START
          - END
          default: NONE
        dimensions:
          type: integer
          description: Optional output dimensionality for models that support truncation (e.g. Matryoshka models).
        user:
          type: string
  securitySchemes:
    BearerAuth:
      type: http
      scheme: bearer
      bearerFormat: nvapi-...