> ## 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. # Get embeddings usage > Retrieve usage metrics for embedding operations. ## OpenAPI ````yaml https://apigw.mka1.com/speakeasy.json get /api/v1/llm/usage/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/usage/embeddings: get: tags: - Usage summary: Get embeddings usage description: Retrieve usage metrics for embedding operations. operationId: getEmbeddingsUsage parameters: - name: start_time in: query required: true schema: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: Start time (Unix seconds), inclusive examples: - 1704067200 allowEmptyValue: true example: 1704067200 - name: end_time in: query required: false schema: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: End time (Unix seconds), exclusive examples: - 1704153600 allowEmptyValue: true example: 1704153600 - name: bucket_width in: query required: false schema: $ref: '#/components/schemas/BucketWidth' description: Width of each time bucket (1m, 1h, 1d) examples: - 1d allowEmptyValue: true example: 1d - name: limit in: query required: false schema: type: integer minimum: 1 maximum: 1440 description: Number of buckets to return examples: - 30 allowEmptyValue: true example: 30 - name: page in: query required: false schema: type: string description: Pagination cursor examples: - cursor_abc123 allowEmptyValue: true example: cursor_abc123 - name: user_ids in: query required: false schema: description: >- Filter by platform user IDs (the same dimension group_by=user_id reports) examples: - - user_123 - user_456 type: array items: type: string allowEmptyValue: true example: - user_123 - user_456 - name: external_user_ids in: query required: false schema: description: >- Filter by delegated end-user IDs (externalUserId, i.e. X-On-Behalf-Of subs) examples: - - sub_123 type: array items: type: string allowEmptyValue: true example: - sub_123 - name: all_orgs in: query required: false schema: type: string description: Aggregate usage across all organizations (cluster admins only) allowEmptyValue: true - name: org_ids in: query required: false schema: description: >- Filter by organization IDs (cluster admins only, implies cluster scope) examples: - - org_123 type: array items: type: string allowEmptyValue: true example: - org_123 - name: group_by in: query required: false schema: description: Group by specified fields type: array items: $ref: '#/components/schemas/EmbeddingsGroupBy' allowEmptyValue: true example: - model - name: models in: query required: false schema: description: Filter by model names type: array items: type: string allowEmptyValue: true example: - auto - name: X-On-Behalf-Of in: header required: false schema: type: string description: Optional external end-user identifier forwarded by the API gateway. responses: '200': description: OK content: application/json: schema: type: object properties: object: const: page data: type: array items: type: object properties: object: const: bucket start_time: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: Unix timestamp of bucket start (inclusive) end_time: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: Unix timestamp of bucket end (exclusive) results: type: array items: anyOf: - {} - type: 'null' description: Array of usage results for this time bucket required: - object - start_time - end_time - results has_more: type: boolean description: Whether there are more pages next_page: anyOf: - type: string - type: 'null' description: Cursor for next page, null if no more pages required: - object - data - has_more - next_page example: object: page data: - object: bucket start_time: 1704067200 end_time: 1704153600 results: - object: organization.usage.embeddings.result input_tokens: 6400 num_model_requests: 64 project_id: null user_id: user_123 api_key_id: key_abc123 model: auto has_more: false next_page: null security: - bearerAuth: [] x-codeSamples: - lang: python label: Python (SDK) source: |- from meetkai_mka1 import SDK with SDK( bearer_auth="", ) as sdk: res = sdk.llm.usage.embeddings(start_time=1704067200, end_time=1704153600, bucket_width="1d", limit=30, page="cursor_abc123", user_ids=[ "user_123", "user_456", ], external_user_ids=[ "sub_123", ], org_ids=[ "org_123", ], group_by=[ "model", ], models=[ "auto", ]) # 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.usage.embeddings({ startTime: 1704067200, endTime: 1704153600, limit: 30, page: "cursor_abc123", userIds: [ "user_123", "user_456", ], externalUserIds: [ "sub_123", ], orgIds: [ "org_123", ], groupBy: [ "model", ], models: [ "auto", ], }); console.log(result); } run(); - lang: csharp label: CSharp (SDK) source: |- using MeetKai.MKA1; using MeetKai.MKA1.Types.Components; using MeetKai.MKA1.Types.Requests; using System.Collections.Generic; var sdk = new SDK(bearerAuth: ""); GetEmbeddingsUsageRequest req = new GetEmbeddingsUsageRequest() { StartTime = 1704067200, EndTime = 1704153600, Limit = 30, Page = "cursor_abc123", UserIds = new List() { "user_123", "user_456", }, ExternalUserIds = new List() { "sub_123", }, OrgIds = new List() { "org_123", }, GroupBy = new List() { EmbeddingsGroupBy.Model, }, Models = new List() { "auto", }, }; var res = await sdk.Llm.Usage.EmbeddingsAsync(req); // handle response components: schemas: BucketWidth: enum: - 1m - 1h - 1d type: string default: 1d EmbeddingsGroupBy: enum: - user_id - api_key_id - org_id - model type: string 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: `. ````