> ## Documentation Index > Fetch the complete documentation index at: https://docs.mka1.com/llms.txt > Use this file to discover all available pages before exploring further. # Create text embeddings > Generates vector embeddings for single or multiple text inputs. Returns floating-point vectors along with token usage statistics. ## OpenAPI ````yaml https://apigw.mka1.com/speakeasy.json post /api/v1/llm/embeddings openapi: 3.1.1 info: title: MKA1 API version: 1.1.0 description: >- The MKA1 API is a RESTful API that provides access to the MKA1 platform. Learn how to get started with the API and the TypeScript SDK [here](https://mka1.apidocumentation.com/guides/getting-started). license: name: Proprietary servers: - url: https://apigw.mka1.com description: MKA1 API Gateway - url: / description: Relative server URL (configurable via SDK constructor) security: [] tags: - name: Resource Authorization description: >- Manage permissions for LLM resources. Create resources, grant/revoke permissions, and delete resources. Only resource owners can grant, revoke, or delete permissions. x-displayName: Resource Authorization - name: Embeddings description: >- Text embedding API endpoints for generating vector representations of text. Create semantic embeddings for search, clustering, and similarity matching using various embedding models. x-displayName: Embeddings - name: Feedback description: >- User feedback API for rating and commenting on chat completions. Collect thumbs up/down ratings and detailed feedback to improve model responses and track user satisfaction. x-displayName: Feedback - name: Images description: >- Image generation API endpoints for creating images from text descriptions. Generate images with control over size, quality, and style. x-displayName: Images - name: MCP Vault description: >- MCP vault API for storing user-owned MCP server configurations and encrypted credentials. Agents reference vault IDs so secrets are resolved only at tool execution time. x-displayName: MCP Vault - name: Speech description: >- Speech API endpoints for audio processing. Convert text to natural-sounding speech (TTS) or transcribe speech to text (STT) in different languages. x-displayName: Speech - name: Usage description: >- Usage tracking and analytics API for monitoring token consumption, request counts, and cost analysis. View detailed statistics per user, model, and time period. x-displayName: Usage - name: Extract description: >- Structured data extraction API for extracting information from files. Define JSON schemas to extract structured data from images, PDFs, and documents. Supports reusable schema templates. x-displayName: Extract - name: Text Classification description: >- Text classification API for categorizing text into predefined labels. Use AI models to classify text content for sentiment analysis, topic categorization, and content moderation. x-displayName: Text Classification - name: Responses description: >- Agent-powered responses API for creating AI agents with autonomous tool usage. Build conversational assistants that can use web search, file operations, image generation, code execution, computer use simulation, and MCP integrations. Supports background processing, streaming, and real-time status tracking. x-displayName: Responses - name: Files description: >- File management API for uploading, storing, and managing files with automatic expiration and S3 integration. Upload files that can be used with Assistants, Vector Stores, and other features. Files are stored in S3 with metadata tracked in PostgreSQL. Supports automatic cleanup of expired files. x-displayName: Files - name: Vector Stores description: >- Vector store API for storing and searching documents using embeddings. Create vector stores, upload files with automatic chunking and embedding generation, and perform semantic search. Files are processed asynchronously using Temporal workflows for durability. Supports automatic cleanup of expired stores and LanceDB for efficient vector storage. x-displayName: Vector Stores - name: Conversations description: >- Conversation management API for storing and retrieving conversation state across Response API calls. Create conversations, add items (user messages, assistant messages, system messages), and maintain conversation history. Supports metadata tracking and multi-turn dialogue state management. x-displayName: Conversations - name: Guardrails description: >- AI safety guardrails API for configuring content moderation and security policies. Set up ban word lists, prompt injection detection, and system prompt leakage prevention. Guardrails apply to all requests from an account and can be tested before deployment. x-displayName: Guardrails - name: Models description: >- Model listing API for discovering available models. Returns model IDs, ownership, and metadata for all registered models in the gateway. x-displayName: Models - name: Skills description: >- Skills API for managing versioned bundles of instructions and files following the Agent Skills standard. Create, version, and download reusable skill packages that include SKILL.md manifests for agent environments. x-displayName: Skills - name: Chat Completions description: >- **Deprecated: Use the Responses API (`/api/v1/llm/responses`) instead.** Chat completion endpoints with support for streaming, tool calls, and multiple providers. x-deprecated: true x-displayName: Chat Completions - name: Batches x-displayName: Batches - name: Evals x-displayName: Evals - name: Fine-Tuning x-displayName: Fine-Tuning - name: Memory Stores x-displayName: Memory Stores - name: Prompts x-displayName: Prompts - name: API Key x-displayName: API Key - name: Organization x-displayName: Organization - name: Cluster Admin x-displayName: Cluster Admin - name: Sessions description: Create, inspect, access, and terminate sandbox sessions. x-displayName: Sessions - name: Browser description: >- Connect to browser sessions through the gateway port proxy. Browser sessions expose a Chrome DevTools Protocol endpoint on port 9222. x-displayName: Browser - name: Execution description: Run shell commands and code inside an existing sandbox session. x-displayName: Execution - name: Workspace description: >- Inspect the workspace manifest, transfer files or archives, and download generated artifacts. x-displayName: Workspace - name: Sandbox Usage description: >- Aggregate sandbox usage statistics across sessions, execution, and workspace operations. x-displayName: Sandbox Usage - name: Sandbox Pricing description: >- Cluster-admin management of the sandbox compute rate card used for budgeted spend. x-displayName: Sandbox Pricing - name: Agents description: Create and manage reusable agent definitions. x-displayName: Agents - name: Agent Versions description: Inspect an agent's configuration history and roll back to a prior version. x-displayName: Agent Versions - name: Agent Runs description: Execute saved agents and inspect persisted run results. x-displayName: Agent Runs - name: Agent Connectors description: >- Connect saved agents to external messaging channels such as Telegram, including text, photo, and supported document exchange. x-displayName: Agent Connectors - name: Agent Schedules description: Create and manage scheduled or recurring saved agent runs. x-displayName: Agent Schedules - name: schema-4_other x-displayName: other - name: Budgets x-displayName: Budgets - name: Settings x-displayName: Settings - name: Deployments description: Long-lived inference servers. x-displayName: Deployments - name: Fine-Tune Jobs description: Submit, monitor, and cancel fine-tune jobs. x-displayName: Fine-Tune Jobs - name: Container Images description: Custom container images for deployments and jobs. x-displayName: Container Images - name: Serving Models description: Models registered for deployment and fine-tuning. x-displayName: Serving Models - name: Volumes description: Persistent storage for weights and checkpoints. x-displayName: Volumes - name: Secrets description: Credentials injected into your workloads. x-displayName: Secrets - name: Accelerators description: Available accelerator types (GPU, NPU, TPU). x-displayName: Accelerators paths: /api/v1/llm/embeddings: post: tags: - Embeddings summary: Create text embeddings description: >- Generates vector embeddings for single or multiple text inputs. Returns floating-point vectors along with token usage statistics. operationId: embed parameters: - name: X-On-Behalf-Of in: header required: false schema: type: string description: Optional external end-user identifier forwarded by the API gateway. requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/EmbeddingsRequest' example: model: auto input: The quick brown fox jumps over the lazy dog. encoding_format: float responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/EmbeddingsResponse' example: data: - object: embedding embedding: - 0.0023 - -0.0091 - 0.0156 - -0.0042 - 0.0089 index: 0 model: auto object: list usage: prompt_tokens: 8 total_tokens: 8 security: - bearerAuth: [] x-codeSamples: - lang: python label: Python (SDK) source: |- from meetkai_mka1 import SDK with SDK( bearer_auth="", ) as sdk: res = sdk.llm.embeddings.embed(input="The quick brown fox jumps over the lazy dog.", model="auto", encoding_format="float") # Handle response print(res) - lang: typescript label: Typescript (SDK) source: |- import { SDK } from "@meetkai/mka1"; const sdk = new SDK({ bearerAuth: "", }); async function run() { const result = await sdk.llm.embeddings.embed({ embeddingsRequest: { input: "The quick brown fox jumps over the lazy dog.", model: "auto", }, }); console.log(result); } run(); - lang: csharp label: CSharp (SDK) source: >- using MeetKai.MKA1; using MeetKai.MKA1.Types.Components; var sdk = new SDK(bearerAuth: ""); var res = await sdk.Llm.Embeddings.EmbedAsync(body: new EmbeddingsRequest() { Input = EmbeddingsRequestInput.CreateStr( "The quick brown fox jumps over the lazy dog." ), Model = "auto", }); // handle response components: schemas: EmbeddingsRequest: type: object properties: input: anyOf: - type: string minLength: 1 maxLength: 100000 description: A single text string to embed. Must not be empty. - type: array minItems: 1 maxItems: 10000 items: type: string minLength: 1 maxLength: 100000 description: An array of text strings to embed. Cannot be empty. description: >- The input text or array of texts to generate embeddings for. Can be a single string or an array of strings. Note: batch size and input length limits vary by model. See GET /embeddings/models for model-specific limits. model: type: string minLength: 1 description: >- ID of the model to use for generating embeddings. Use provider:model format. See GET /embeddings/models for available models and their limits. dimensions: type: integer minimum: 1 maximum: 9007199254740991 description: >- The number of dimensions the resulting output embeddings should have. Only supported in certain models. encoding_format: enum: - float - base64 type: string default: float description: >- The format to return the embeddings in. Can be either 'float' (array of numbers) or 'base64' (base64-encoded binary). user: type: string description: A unique identifier representing your end-user. required: - input - model description: >- Request parameters for creating embeddings. Generates vector representations of the input text(s). examples: - model: auto input: The quick brown fox jumps over the lazy dog. EmbeddingsResponse: type: object properties: data: type: array items: type: object properties: object: const: embedding default: embedding embedding: anyOf: - type: array items: type: number - type: string description: >- The embedding vector, either as an array of floats or a base64-encoded string index: type: number required: - embedding - index description: >- A list of embedding objects. Each object contains the embedding vector as an array of floating point numbers or base64-encoded string representing the semantic meaning of the input text. model: type: string description: The model used for generating the embeddings object: const: list default: list usage: type: object properties: prompt_tokens: type: number total_tokens: type: number required: - prompt_tokens - total_tokens description: Usage statistics for the embeddings request required: - data - model description: >- Response from the embeddings endpoint containing the generated embeddings and usage information. examples: - data: - object: embedding embedding: - 0.0023 - -0.0091 - 0.0156 - -0.0042 - 0.0089 index: 0 model: auto object: list usage: prompt_tokens: 8 total_tokens: 8 securitySchemes: bearerAuth: type: http scheme: bearer bearerFormat: API Key description: >- Gateway auth: send `Authorization: Bearer `. For multi-user server-side integrations, you can also send `X-On-Behalf-Of: `. ````