> ## Documentation Index > Fetch the complete documentation index at: https://developers.telnyx.com/llms.txt > Use this file to discover all available pages before exploring further. # Create embeddings > Creates an embedding vector representing the input text. This endpoint is compatible with the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) and may be used with the OpenAI JS or Python SDK by setting the base URL to `https://api.telnyx.com/v2/ai/openai`. ## OpenAPI ````yaml /openapi/source/external/inference/inference-embedding.json post /ai/openai/embeddings openapi: 3.1.0 info: version: 2.0.0 title: Telnyx API x-latency-category: responsive x-endpoint-cost: light description: SIP trunking, SMS, MMS, Call Control and Telephony Data Services. contact: email: support@telnyx.com servers: - url: https://api.telnyx.com/v2 description: Version 2.0.0 of the Telnyx API security: - bearerAuth: [] tags: - name: Chat description: Generate text with LLMs - name: Assistants description: Configure AI assistant specifications - name: Conversations description: Manage historical AI assistant conversations - name: File-based Text-to-Speech description: Turn audio into text or text into audio. - name: Embeddings description: Embed documents and perform text searches - name: Clusters description: Identify common themes and patterns in your embedded documents - name: Fine Tuning description: Customize LLMs for your unique needs - name: OpenAI Embeddings description: >- OpenAI-compatible embeddings endpoints for generating vector representations of text paths: /ai/openai/embeddings: post: tags: - OpenAI Embeddings summary: Create embeddings description: >- Creates an embedding vector representing the input text. This endpoint is compatible with the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) and may be used with the OpenAI JS or Python SDK by setting the base URL to `https://api.telnyx.com/v2/ai/openai`. operationId: create_openai_embeddings requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/OpenAIEmbeddingRequest' example: input: The quick brown fox jumps over the lazy dog model: thenlper/gte-large responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/OpenAIEmbeddingResponse' example: object: list data: - object: embedding embedding: - 0 index: 0 model: string usage: prompt_tokens: 0 total_tokens: 0 '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' x-codeSamples: - lang: JavaScript source: >- import Telnyx from 'telnyx'; const client = new Telnyx({ apiKey: process.env['TELNYX_API_KEY'], // This is the default and can be omitted }); const response = await client.ai.openai.embeddings.createEmbeddings({ input: 'The quick brown fox jumps over the lazy dog', model: 'thenlper/gte-large', }); console.log(response.data); - lang: Python source: | import os from telnyx import Telnyx client = Telnyx( api_key=os.environ.get("TELNYX_API_KEY"), # This is the default and can be omitted ) response = client.ai.openai.embeddings.create_embeddings( input="The quick brown fox jumps over the lazy dog", model="thenlper/gte-large", ) print(response.data) - lang: Go source: "package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\n\t\"github.com/team-telnyx/telnyx-go\"\n\t\"github.com/team-telnyx/telnyx-go/option\"\n)\n\nfunc main() {\n\tclient := telnyx.NewClient(\n\t\toption.WithAPIKey(\"My API Key\"),\n\t)\n\tresponse, err := client.AI.OpenAI.Embeddings.NewEmbeddings(context.TODO(), telnyx.AIOpenAIEmbeddingNewEmbeddingsParams{\n\t\tInput: telnyx.AIOpenAIEmbeddingNewEmbeddingsParamsInputUnion{\n\t\t\tOfString: telnyx.String(\"The quick brown fox jumps over the lazy dog\"),\n\t\t},\n\t\tModel: \"thenlper/gte-large\",\n\t})\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\tfmt.Printf(\"%+v\\n\", response.Data)\n}\n" - lang: Java source: >- package com.telnyx.sdk.example; import com.telnyx.sdk.client.TelnyxClient; import com.telnyx.sdk.client.okhttp.TelnyxOkHttpClient; import com.telnyx.sdk.models.ai.openai.embeddings.EmbeddingCreateEmbeddingsParams; import com.telnyx.sdk.models.ai.openai.embeddings.EmbeddingCreateEmbeddingsResponse; public final class Main { private Main() {} public static void main(String[] args) { TelnyxClient client = TelnyxOkHttpClient.fromEnv(); EmbeddingCreateEmbeddingsParams params = EmbeddingCreateEmbeddingsParams.builder() .input("The quick brown fox jumps over the lazy dog") .model("thenlper/gte-large") .build(); EmbeddingCreateEmbeddingsResponse response = client.ai().openai().embeddings().createEmbeddings(params); } } - lang: Ruby source: |- require "telnyx" telnyx = Telnyx::Client.new(api_key: "My API Key") response = telnyx.ai.openai.embeddings.create_embeddings( input: "The quick brown fox jumps over the lazy dog", model: "thenlper/gte-large" ) puts(response) - lang: PHP source: >- ai->openai->embeddings->createEmbeddings( input: 'The quick brown fox jumps over the lazy dog', model: 'thenlper/gte-large', dimensions: 0, encodingFormat: 'float', user: 'user', ); var_dump($response); } catch (APIException $e) { echo $e->getMessage(); } - lang: CLI source: |- telnyx ai:openai:embeddings create-embeddings \ --api-key 'My API Key' \ --input 'The quick brown fox jumps over the lazy dog' \ --model thenlper/gte-large components: schemas: OpenAIEmbeddingRequest: type: object properties: input: oneOf: - type: string description: A single text string to embed - type: array items: type: string description: An array of text strings to embed description: Input text to embed. Can be a string or array of strings. model: type: string description: >- ID of the model to use. Use the List embedding models endpoint to see available models. example: thenlper/gte-large encoding_format: type: string enum: - float - base64 default: float description: The format to return the embeddings in. dimensions: type: integer description: >- The number of dimensions the resulting output embeddings should have. Only supported in some models. user: type: string description: >- A unique identifier representing your end-user for monitoring and abuse detection. required: - input - model title: OpenAIEmbeddingRequest OpenAIEmbeddingResponse: type: object properties: object: type: string default: list description: The object type, always 'list' data: type: array items: $ref: '#/components/schemas/OpenAIEmbeddingData' description: List of embedding objects model: type: string description: The model used for embedding usage: $ref: '#/components/schemas/OpenAIEmbeddingUsage' required: - object - data - model - usage title: OpenAIEmbeddingResponse HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError example: detail: - loc: - body - name msg: Field required type: missing OpenAIEmbeddingData: type: object properties: object: type: string default: embedding description: The object type, always 'embedding' embedding: type: array items: type: number description: The embedding vector index: type: integer description: The index of the embedding in the list of embeddings required: - object - embedding - index title: OpenAIEmbeddingData OpenAIEmbeddingUsage: type: object properties: prompt_tokens: type: integer description: Number of tokens in the input total_tokens: type: integer description: Total number of tokens used required: - prompt_tokens - total_tokens title: OpenAIEmbeddingUsage ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError securitySchemes: bearerAuth: type: http scheme: bearer ````