> ## Documentation Index > Fetch the complete documentation index at: https://docs.obiguard.ai/llms.txt > Use this file to discover all available pages before exploring further. # Embeddings ## OpenAPI ````yaml post /embeddings openapi: 3.0.0 info: title: Obiguard API description: >- The Obiguard REST API. Please see https://docs.obiguard.ai/docs/api-reference for more details. version: 1.0.0 termsOfService: https://obiguard.ai/terms servers: - url: https://gateway.obiguard.ai/v1 security: [] tags: - name: Audio description: Turn audio into text or text into audio. - name: Chat description: >- Given a list of messages comprising a conversation, the model will return a response. - name: Completions description: >- Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position. - name: Embeddings description: >- Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms. - name: Fine-tuning description: Manage fine-tuning jobs to tailor a model to your specific training data. - name: Files description: >- Files are used to upload documents that can be used with features like Assistants and Fine-tuning. - name: Images description: Given a prompt and/or an input image, the model will generate a new image. paths: /embeddings: post: tags: - Embeddings summary: Embeddings 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' security: - Obiguard-API-Key: [] servers: - url: https://gateway.obiguard.ai/v1 x-code-samples: - lang: curl label: Default source: | curl https://gateway.obiguard.ai/v1/embeddings \ -H "x-obiguard-api-key: $OBIGUARD_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "input": "The food was delicious and the waiter...", "model": "text-embedding-ada-002", "encoding_format": "float" }' - lang: python label: Default source: | from obiguard import Obiguard client = Obiguard( obiguard_api_key="vk-obg***", # Your Obiguard virtual key ) client.embeddings.create( model="text-embedding-ada-002", input="The food was delicious and the waiter...", encoding_format="float" ) 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 `text-embedding-ada-002`), 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. 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]]' x-oaiExpandable: true model: description: > ID of the model to use. You can use the [List models](https://platform.openai.com/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](https://platform.openai.com/docs/models/overview) 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 x-oaiTypeLabel: string 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://platform.openai.com/docs/guides/safety-best-practices/end-user-ids). required: - model - input 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 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://platform.openai.com/docs/guides/embeddings). items: type: number object: type: string description: The object type, which is always "embedding". enum: - embedding required: - index - object - embedding x-code-samples: name: The embedding object example: | { "object": "embedding", "embedding": [ 0.0023064255, -0.009327292, .... (1536 floats total for ada-002) -0.0028842222, ], "index": 0 } securitySchemes: Obiguard-API-Key: type: apiKey in: header name: x-obiguard-api-key ````