> ## Documentation Index > Fetch the complete documentation index at: https://docs.aihubmix.com/llms.txt > Use this file to discover all available pages before exploring further. # Create an Embedding > Creates an embedding vector representing the input text. ## OpenAPI ````yaml /openapi.json post /v1/embeddings openapi: 3.1.0 info: title: AIHubMix Gateway API description: | Unified multi-protocol gateway. Exposes three native shapes side-by-side: - OpenAI (`/v1/chat/completions`, `/v1/responses`, ...) - Anthropic (`/v1/messages`) - Gemini (`/v1beta/models/{model}:generateContent`) Each vendor's native SDK connects directly. Gateway-only fields are marked with `x-source.authority: gateway`. contact: name: AIHubMix Team version: 2.1.1 x-upstream: vendorSpecs: packageVersion: 0.1.1 installedFrom: npm sources: openai/official: version: '2026-05-31' hash: >- sha256:a203971a0bc0cd3b903b9488e3ac40eaa161a274dfaf172c1da73eee029147a8 anthropic/official: version: '2026-05-31' hash: >- sha256:f9d488f9290d78a081bed62fa0c040476b5d009f3db92c06c8e279e69df73fc9 layers: - role: L0-base source: avs://openai/official protocol: openai authority: official - role: L0-base source: avs://anthropic/official protocol: anthropic authority: official - role: gateway-other source: gateway/anthropic-mappings.yml protocol: all authority: gateway - role: horizontal-auth source: gateway/auth.yml protocol: all authority: gateway - role: horizontal-errors source: gateway/errors.yml protocol: all authority: gateway - role: L3-gateway source: gateway/openai-gateway.yml protocol: openai authority: gateway - role: gateway-other source: gateway/openai-image-mappings.yml protocol: all authority: gateway - role: gateway-other source: gateway/openai-mappings.yml protocol: all authority: gateway - role: L2-passthrough source: gateway/openai-passthrough.yml protocol: openai authority: gateway servers: - url: https://aihubmix.com description: Production - url: https://api.inferera.com description: Backup (use when the primary domain is unreachable) security: - gatewayBearer: [] tags: - name: OpenAI Compatible description: >- OpenAI-compatible endpoints (chat, completions, embeddings, images, audio, videos, moderations). - name: Anthropic Compatible description: Anthropic-native endpoints (messages with Anthropic protocol). - name: Google Vertex AI Compatible description: Google Gemini / Vertex AI endpoints. paths: /v1/embeddings: post: tags: - OpenAI Compatible summary: Create an Embedding description: Creates an embedding vector representing the input text. operationId: createEmbedding requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/CreateEmbeddingRequest' responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/CreateEmbeddingResponse' '400': description: Invalid request content: application/json: schema: $ref: '#/components/schemas/GatewayError' '401': description: Authentication failed content: application/json: schema: $ref: '#/components/schemas/GatewayError' '403': description: Permission denied content: application/json: schema: $ref: '#/components/schemas/GatewayError' '404': description: Not found content: application/json: schema: $ref: '#/components/schemas/GatewayError' '429': description: Rate limited content: application/json: schema: $ref: '#/components/schemas/GatewayError' 5XX: description: Server / upstream error content: application/json: schema: $ref: '#/components/schemas/GatewayError' components: schemas: CreateEmbeddingRequest: type: object additionalProperties: false properties: input: description: > Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for all embedding models), cannot be an empty string, and any array must be 2048 dimensions or less. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens. In addition to the per-input token limit, all embedding models enforce a maximum of 300,000 tokens summed across all inputs in a single request. example: The quick brown fox jumped over the lazy dog oneOf: - type: string title: string description: The string that will be turned into an embedding. default: '' example: This is a test. - type: array title: array description: The array of strings that will be turned into an embedding. minItems: 1 maxItems: 2048 items: type: string default: '' example: '[''This is a test.'']' - type: array title: array description: The array of integers that will be turned into an embedding. minItems: 1 maxItems: 2048 items: type: integer example: '[1212, 318, 257, 1332, 13]' - type: array title: array description: >- The array of arrays containing integers that will be turned into an embedding. minItems: 1 maxItems: 2048 items: type: array minItems: 1 items: type: integer example: '[[1212, 318, 257, 1332, 13]]' model: description: > ID of the model to use. You can use the [List models](https://developers.openai.com/api/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](https://developers.openai.com/api/docs/models) for descriptions of them. example: text-embedding-3-small anyOf: - type: string - type: string enum: - text-embedding-ada-002 - text-embedding-3-small - text-embedding-3-large encoding_format: description: >- The format to return the embeddings in. Can be either `float` or [`base64`](https://pypi.org/project/pybase64/). example: float default: float type: string enum: - float - base64 dimensions: description: > The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models. type: integer minimum: 1 user: type: string example: user-1234 description: > A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](https://developers.openai.com/api/docs/guides/safety-best-practices#end-user-ids). required: - model - input x-source: from: avs://openai/official authority: official CreateEmbeddingResponse: type: object properties: data: type: array description: The list of embeddings generated by the model. items: $ref: '#/components/schemas/Embedding' model: type: string description: The name of the model used to generate the embedding. object: type: string description: The object type, which is always "list". enum: - list usage: type: object description: The usage information for the request. properties: prompt_tokens: type: integer description: The number of tokens used by the prompt. total_tokens: type: integer description: The total number of tokens used by the request. required: - prompt_tokens - total_tokens required: - object - model - data - usage x-source: from: avs://openai/official authority: official GatewayError: type: object description: | OpenAI-compatible error envelope. Top-level `error` object carrying `message` / `type` / `param` / `code`. `request_id` lives in the `X-Request-Id` response header, not in the body. The legacy `upstream` field is no longer emitted. x-source: from: gateway authority: gateway required: - error properties: error: type: object required: - message properties: message: type: string description: Human-readable error message. type: type: string nullable: true description: > Error category. OpenAI-compatible values include invalid_request_error / authentication_error / permission_error / not_found_error / rate_limit_error, etc. The upstream value is passed through verbatim; the enum is not fixed. param: type: string nullable: true description: Name of the offending field, when applicable. code: nullable: true description: | Sub-error code. May be a string or integer (the gateway treats `code` as an arbitrary type). Embedding: type: object description: | Represents an embedding vector returned by embedding endpoint. properties: index: type: integer description: The index of the embedding in the list of embeddings. embedding: type: array description: > The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the [embedding guide](https://developers.openai.com/api/docs/guides/embeddings). items: type: number format: float object: type: string description: The object type, which is always "embedding". enum: - embedding required: - index - object - embedding x-source: from: avs://openai/official authority: official securitySchemes: gatewayBearer: type: http scheme: bearer bearerFormat: API Key description: | Gateway-issued API key, formatted as `sk-gateway-xxxxxxxx`. Used by OpenAI-shaped endpoints (/v1/chat/completions, etc.). x-source: from: gateway authority: gateway ````