> ## Documentation Index > Fetch the complete documentation index at: https://docs.atoma.ai/llms.txt > Use this file to discover all available pages before exploring further. # Create confidential embeddings > This endpoint follows the OpenAI API format for generating vector embeddings from input text, but with confidential processing (through AEAD encryption and TEE hardware). The handler receives pre-processed metadata from middleware and forwards the request to the selected node. ## Returns * `Ok(Response)` - The embeddings response from the processing node * `Err(AtomaProxyError)` - An error status code if any step fails ## Errors * `INTERNAL_SERVER_ERROR` - Processing or node communication failures ## OpenAPI ````yaml cloud-api-reference/openapi.yml post /v1/confidential/embeddings openapi: 3.1.0 info: title: atoma-proxy description: '' license: name: Apache-2.0 identifier: Apache-2.0 version: 0.1.0 servers: - url: https://api.atoma.network security: [] tags: - name: Completions description: OpenAI's API completions v1 endpoint - name: Confidential Completions description: Atoma's API confidential completions v1 endpoint - name: Chat description: OpenAI's API chat completions v1 endpoint - name: Confidential Chat description: Atoma's API confidential chat completions v1 endpoint - name: Confidential Embeddings description: Atoma's API confidential embeddings v1 endpoint - name: Confidential Images description: Atoma's API confidential images v1 endpoint - name: Embeddings description: OpenAI's API embeddings v1 endpoint - name: Health description: Health check - name: Images description: OpenAI's API images v1 endpoint - name: Models description: OpenAI's API models v1 endpoint - name: Nodes description: Nodes Management - name: Node Public Key Selection description: Node public key selection paths: /v1/confidential/embeddings: post: tags: - Confidential Embeddings summary: Create confidential embeddings description: >- This endpoint follows the OpenAI API format for generating vector embeddings from input text, but with confidential processing (through AEAD encryption and TEE hardware). The handler receives pre-processed metadata from middleware and forwards the request to the selected node. ## Returns * `Ok(Response)` - The embeddings response from the processing node * `Err(AtomaProxyError)` - An error status code if any step fails ## Errors * `INTERNAL_SERVER_ERROR` - Processing or node communication failures operationId: confidential_embeddings_create requestBody: content: application/json: schema: $ref: '#/components/schemas/ConfidentialComputeRequest' required: true responses: '200': description: Confidential embeddings generated successfully content: application/json: schema: $ref: '#/components/schemas/ConfidentialComputeResponse' '400': description: Bad request '401': description: Unauthorized '500': description: Internal server error security: - bearerAuth: [] x-codeSamples: - lang: typescript label: confidential_embeddings_create source: |- import { AtomaSDK } from "atoma-sdk"; const atomaSDK = new AtomaSDK({ bearerAuth: process.env["ATOMASDK_BEARER_AUTH"] ?? "", }); async function run() { const result = await atomaSDK.confidentialEmbeddings.create({ model: "intfloat/multilingual-e5-large-instruct", input: "The quick brown fox jumped over the lazy dog", encoding_format: "float", }); // Handle the result console.log(result); } run(); - lang: python label: confidential_embeddings_create source: |- from atoma_sdk import AtomaSDK import os with AtomaSDK( bearer_auth=os.getenv("ATOMASDK_BEARER_AUTH", ""), ) as atoma_sdk: res = atoma_sdk.confidential_embeddings.create( input_="The quick brown fox jumped over the lazy dog", model="intfloat/multilingual-e5-large-instruct", encoding_format="float" ) print(res) components: schemas: ConfidentialComputeRequest: type: object description: >- A request for confidential computation that includes encrypted data and associated cryptographic parameters required: - ciphertext - stack_small_id - nonce - salt - client_dh_public_key - node_dh_public_key - plaintext_body_hash - model_name properties: ciphertext: type: string description: The encrypted payload that needs to be processed (base64 encoded) client_dh_public_key: type: string description: Client's public key for Diffie-Hellman key exchange (base64 encoded) model_name: type: string description: Model name node_dh_public_key: type: string description: Node's public key for Diffie-Hellman key exchange (base64 encoded) nonce: type: string description: Cryptographic nonce used for encryption (base64 encoded) num_compute_units: type: - integer - 'null' format: int64 description: >- Number of compute units to be used for the request, for image generations, as this value is known in advance (the number of pixels to generate) minimum: 0 plaintext_body_hash: type: string description: >- Hash of the original plaintext body for integrity verification (base64 encoded) salt: type: string description: Salt value used in key derivation (base64 encoded) stack_small_id: type: integer format: int64 description: Unique identifier for the small stack being used minimum: 0 stream: type: - boolean - 'null' description: Indicates whether this is a streaming request ConfidentialComputeResponse: type: object description: Represents a response from a confidential compute request required: - ciphertext - nonce properties: ciphertext: type: string description: Encrypted response body (base64 encoded) nonce: type: string description: Nonce used for encryption (base64 encoded) response_hash: type: - string - 'null' description: Hash of the response body (base64 encoded) signature: type: - string - 'null' description: Signature of the response body (base64 encoded) usage: oneOf: - type: 'null' - $ref: '#/components/schemas/Usage' description: Usage statistics for the request Usage: type: object required: - prompt_tokens - completion_tokens - total_tokens - completion_tokens_details - prompt_tokens_details properties: completion_tokens: type: integer format: int32 description: The number of completion tokens used example: 10 minimum: 0 completion_tokens_details: $ref: '#/components/schemas/CompletionTokensDetails' description: The details of the completion tokens prompt_tokens: type: integer format: int32 description: The number of prompt tokens used example: 10 minimum: 0 prompt_tokens_details: $ref: '#/components/schemas/PromptTokensDetails' description: The details of the prompt tokens total_tokens: type: integer format: int32 description: The total number of tokens used example: 20 minimum: 0 CompletionTokensDetails: type: object description: The details of the completion tokens required: - accepted_prediction_tokens - audio_tokens - reasoning_tokens - rejected_prediction_tokens properties: accepted_prediction_tokens: type: integer format: int32 description: The number of tokens in the completion example: 10 minimum: 0 audio_tokens: type: integer format: int32 description: The number of audio tokens example: 0 minimum: 0 reasoning_tokens: type: integer format: int32 description: The number of reasoning tokens example: 10 minimum: 0 rejected_prediction_tokens: type: integer format: int32 description: The number of rejected prediction tokens example: 0 minimum: 0 PromptTokensDetails: type: object required: - audio_tokens - cached_tokens properties: audio_tokens: type: integer format: int32 description: The number of audio tokens example: 0 minimum: 0 cached_tokens: type: integer format: int32 description: The number of cached tokens example: 10 minimum: 0 securitySchemes: bearerAuth: type: http scheme: bearer ````