> ## 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 a vector store > Creates a new vector store for storing and searching through document embeddings. ## OpenAPI ````yaml https://apigw.mka1.com/speakeasy.json post /api/v1/llm/vector_stores 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/vector_stores: post: tags: - Vector Stores summary: Create a vector store description: >- Creates a new vector store for storing and searching through document embeddings. operationId: createVectorStore 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/CreateVectorStoreRequest' example: name: Product Documentation description: Vector store for product manuals and documentation file_ids: - file-abc123 expires_after: anchor: last_active_at days: 30 responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/VectorStore' example: id: vs_abc123 object: vector_store created_at: 1704067200 name: Product Documentation usage_bytes: 1024000 embedding_model: auto embedding_dimensions: 1536 file_counts: in_progress: 0 completed: 5 failed: 0 cancelled: 0 total: 5 status: completed last_active_at: 1704153600 last_used_at: 1704153600 metadata: department: engineering description: Vector store for product manuals and docs expires_at: 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.vector_stores.create(name="Product Documentation", description="Vector store for product manuals and documentation", file_ids=[ "file-abc123", ], expires_after={ "anchor": "last_active_at", "days": 30, }) # 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.vectorStores.create({ createVectorStoreRequest: { name: "Product Documentation", description: "Vector store for product manuals and documentation", fileIds: [ "file-abc123", ], expiresAfter: { anchor: "last_active_at", days: 30, }, }, }); console.log(result); } run(); - lang: csharp label: CSharp (SDK) source: >- using MeetKai.MKA1; using MeetKai.MKA1.Types.Components; using System.Collections.Generic; var sdk = new SDK(bearerAuth: ""); var res = await sdk.Llm.VectorStores.CreateAsync(body: new MeetKai.MKA1.Types.Components.CreateVectorStoreRequest() { Name = "Product Documentation", Description = "Vector store for product manuals and documentation", FileIds = new List() { "file-abc123", }, ExpiresAfter = new CreateVectorStoreRequestExpiresAfter() { Days = 30, }, }); // handle response components: schemas: CreateVectorStoreRequest: type: object properties: name: type: string description: The name of the vector store. description: type: string description: >- A description for the vector store. Can be used to describe the vector store's purpose. file_ids: type: array maxItems: 500 items: type: string description: >- A list of File IDs that the vector store should use. Useful for tools like file_search that can access files. At most 500 per request. expires_after: type: object properties: anchor: const: last_active_at description: >- Anchor timestamp after which the expiration policy applies. Supported anchors: last_active_at. days: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: >- The number of days after the anchor time that the vector store will expire. required: - anchor - days description: The expiration policy for a vector store. chunking_strategy: anyOf: - $ref: '#/components/schemas/AutoChunkingStrategy' - $ref: '#/components/schemas/StaticChunkingStrategy' description: >- The chunking strategy used to chunk the file(s). If not set, will use the auto strategy. Only applicable if file_ids is non-empty. metadata: type: object propertyNames: type: string maxLength: 64 additionalProperties: type: string maxLength: 512 description: >- Set of 16 key-value pairs that can be attached to an object. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters. embedding_model: type: string description: >- The embedding model to use. Defaults to the auto-configured model if not specified. embedding_dimensions: type: integer minimum: -9007199254740991 maximum: 9007199254740991 exclusiveMinimum: 0 description: >- The number of dimensions for the embedding vectors. Only supported for models with flexible dimensions. If not specified, uses the model's default dimensions. retrieval_mode: enum: - vector - graph type: string description: >- Retrieval mode, frozen at creation (cannot be changed later). 'vector' (default): standard vector similarity search. 'graph': GraphRAG — entities and relations are extracted from every chunk at ingest (metered against your usage) and search traverses the knowledge graph. extraction_model: type: string description: >- Model used for entity/relation extraction on graph stores (ingest-time triplet extraction and query-entity extraction). Defaults to the auto-configured model, resolved at creation time — same contract as embedding_model. Only valid when retrieval_mode is 'graph'. max_hops: type: integer minimum: 1 maximum: 4 description: >- Graph expansion depth for graph-mode queries (1-4, engine default 2). Only valid for graph stores. description: Request body for creating a vector store. examples: - name: Product Documentation description: Vector store for product manuals and documentation expires_after: anchor: last_active_at days: 30 - name: Compact Embeddings Store embedding_model: auto embedding_dimensions: 256 VectorStore: type: object properties: id: type: string description: The identifier, which can be referenced in API endpoints. object: const: vector_store description: The object type, which is always 'vector_store'. created_at: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: >- The Unix timestamp (in seconds) for when the vector store was created. name: anyOf: - type: string - type: 'null' description: The name of the vector store. usage_bytes: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: The total number of bytes used by the files in the vector store. embedding_model: anyOf: - type: string - type: 'null' description: >- The embedding model used for this vector store. Resolved at creation time from the requested or auto model. Null for legacy vector stores. embedding_dimensions: anyOf: - type: integer minimum: -9007199254740991 maximum: 9007199254740991 exclusiveMinimum: 0 - type: 'null' description: >- The number of dimensions for the embedding vectors in this vector store. Null for legacy vector stores. retrieval_mode: enum: - vector - hybrid - graph type: string description: >- Retrieval mode, frozen at creation. 'vector': standard vector similarity search. 'graph': GraphRAG — entities and relations are extracted at ingest and queries traverse the knowledge graph. 'hybrid' is reserved. extraction_model: anyOf: - type: string - type: 'null' description: >- The model used for entity/relation extraction on graph stores. Null for non-graph stores. max_hops: anyOf: - type: integer minimum: 1 maximum: 4 - type: 'null' description: >- Graph expansion depth for graph-mode queries. Null for non-graph stores. file_counts: $ref: '#/components/schemas/FileCounts' status: enum: - expired - in_progress - completed type: string description: >- The status of the vector store. 'expired' means the store has expired, 'in_progress' means files are still being processed, 'completed' indicates that the vector store is ready for use. expires_after: type: object properties: anchor: const: last_active_at description: >- Anchor timestamp after which the expiration policy applies. Supported anchors: last_active_at. days: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: >- The number of days after the anchor time that the vector store will expire. required: - anchor - days description: The expiration policy for a vector store. expires_at: anyOf: - type: integer minimum: -9007199254740991 maximum: 9007199254740991 - type: 'null' description: >- The Unix timestamp (in seconds) for when the vector store will expire. last_active_at: anyOf: - type: integer minimum: -9007199254740991 maximum: 9007199254740991 - type: 'null' description: >- The Unix timestamp (in seconds) for when the vector store was last active. metadata: type: object propertyNames: type: string maxLength: 64 additionalProperties: anyOf: - type: string maxLength: 512 - type: boolean - type: number description: >- Set of 16 key-value pairs that can be attached to an object. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters, booleans, or numbers. description: anyOf: - type: string - type: 'null' description: >- A description for the vector store. Can be used to describe the vector store's purpose. last_used_at: anyOf: - type: integer minimum: -9007199254740991 maximum: 9007199254740991 - type: 'null' description: >- The Unix timestamp (in seconds) for when the vector store was last used. required: - id - object - created_at - name - usage_bytes - embedding_model - embedding_dimensions - retrieval_mode - file_counts - status - last_active_at - metadata - last_used_at description: >- A vector store is a collection of processed files that can be used by the file_search tool. examples: - id: vs_abc123 object: vector_store created_at: 1704067200 name: Product Documentation usage_bytes: 1024000 embedding_model: auto embedding_dimensions: 1536 file_counts: in_progress: 0 completed: 5 failed: 0 cancelled: 0 total: 5 status: completed last_active_at: 1704153600 last_used_at: 1704153600 metadata: department: engineering AutoChunkingStrategy: type: object properties: type: const: auto description: Always 'auto'. The default chunking strategy. required: - type description: >- The default chunking strategy. This strategy currently uses a max_chunk_size_tokens of 800 and chunk_overlap_tokens of 400. StaticChunkingStrategy: type: object properties: type: const: static description: Always 'static'. static: type: object properties: chunk_overlap_tokens: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: >- The number of tokens that overlap between chunks. The default value is 400. Must not exceed half of max_chunk_size_tokens. max_chunk_size_tokens: type: integer minimum: 100 maximum: 4096 description: >- The maximum number of tokens in each chunk. The default value is 800. The minimum value is 100 and the maximum value is 4096. required: - chunk_overlap_tokens - max_chunk_size_tokens required: - type - static description: >- Customize your own chunking strategy by setting chunk size and chunk overlap. FileCounts: type: object properties: in_progress: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: The number of files that are currently being processed. completed: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: The number of files that have been successfully processed. failed: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: The number of files that have failed to process. cancelled: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: The number of files that were cancelled. total: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: The total number of files. required: - in_progress - completed - failed - cancelled - total description: File processing status counts. 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: `. ````