> ## Documentation Index > Fetch the complete documentation index at: https://docs.lighton.ai/llms.txt > Use this file to discover all available pages before exploring further. # Create embeddings > This endpoint can be used to convert text chunks into embeddings. It is a simple proxy forwarding your requests to the desired model. **Model resolution:** The `model` field accepts either a model technical name or an alias technical name. If the value matches an alias technical name for the authenticated user's company, the alias routing is used to resolve the effective model (even if a model with the same technical name exists). Otherwise, the model is resolved directly by technical name. **Input Format:** - `input`: Text string to convert to embeddings - `encoding_format`: Format for returned embeddings (float or base64) - `dimensions`: Optional dimension specification for output embeddings ## OpenAPI ````yaml /api-reference/openapi-v3.yaml post /api/v3/embeddings openapi: 3.1.0 info: title: LightOn API version: 3.15.0 (v3) description: >- LightOn gives you an API to search, parse, and ingest documents at scale. Build knowledge-retrieval pipelines without managing vector databases or OCR models. servers: - url: https://paradigm.lighton.ai security: [] tags: - name: Agents description: Operations about agents - name: Threads description: Operations about agents conversation threads - name: Tools description: Operations about native tools - name: Models description: Operations about AI models - name: MCP description: Operations about MCP servers - name: Sources description: Operations about sources used by agents conversation threads - name: Artifacts description: Operations about artifacts generated by agents conversation threads - name: Agent description: >- Operations about agents (deprecated). Please use the 'Agents' API component instead. - name: Files description: Operations about files - name: Facets description: Operations about facets - name: Tags description: Operations about tags - name: Workspaces description: Operations about workspaces - name: Files Processing description: Operations about files processing - name: Users description: Operations about users - name: API Keys description: Operations about API keys - name: User Groups description: Operations about user groups - name: SCIM description: Operations about SCIM paths: /api/v3/embeddings: post: tags: - Models summary: Create embeddings description: >- This endpoint can be used to convert text chunks into embeddings. It is a simple proxy forwarding your requests to the desired model. **Model resolution:** The `model` field accepts either a model technical name or an alias technical name. If the value matches an alias technical name for the authenticated user's company, the alias routing is used to resolve the effective model (even if a model with the same technical name exists). Otherwise, the model is resolved directly by technical name. **Input Format:** - `input`: Text string to convert to embeddings - `encoding_format`: Format for returned embeddings (float or base64) - `dimensions`: Optional dimension specification for output embeddings operationId: api_v3_embeddings_create requestBody: content: application/json: schema: $ref: '#/components/schemas/EmbeddingsRequest' examples: BasicEmbeddingExample: value: model: multilingual-e5-large input: Hello, world! summary: Basic embedding example application/x-www-form-urlencoded: schema: $ref: '#/components/schemas/EmbeddingsRequest' multipart/form-data: schema: $ref: '#/components/schemas/EmbeddingsRequest' required: true responses: '200': content: application/json: schema: $ref: '#/components/schemas/EmbeddingsResponse' examples: BasicEmbeddingExample: value: model: multilingual-e5-large input: Hello, world! summary: Basic embedding example description: '' '503': description: >- API is under maintenance. Check `GET /api/v3/system/status` for active periods and retry. content: application/json: schema: $ref: '#/components/schemas/ServiceMaintenance503' security: - bearerAuth: [] components: schemas: EmbeddingsRequest: type: object description: Request serializer for embeddings endpoint (OpenAI-compatible). properties: model: type: string description: >- Model to use for generating embeddings, must exist and be configured from the admin input: type: string description: Input text to embed, encoded as a string or array of tokens encoding_format: allOf: - $ref: '#/components/schemas/EncodingFormatEnum' default: float description: |- The format to return the embeddings in * `float` - float * `base64` - base64 dimensions: type: integer description: The number of dimensions the resulting output embeddings should have user: type: string description: A unique identifier representing your end-user required: - input - model EmbeddingsResponse: type: object description: Response serializer for embeddings endpoint results. properties: object: type: string default: list description: The object type, which is always 'list' data: type: array items: $ref: '#/components/schemas/EmbeddingObject' description: The list of embeddings generated by the model model: type: string description: The model used for generating the embeddings usage: allOf: - $ref: '#/components/schemas/EmbeddingUsage' description: Usage statistics for the embeddings request required: - data - model ServiceMaintenance503: type: object description: >- Returned by the maintenance middleware when the requested endpoint is blocked. required: - detail - error - mode properties: detail: type: string example: System is under maintenance. error: type: string example: service_maintenance mode: type: string enum: - full_shutdown - warning_banner description: >- `full_shutdown` blocks all traffic; `warning_banner` also blocks and shows a dismissible toast. reason: type: string description: Operator-supplied maintenance reason, if any. started_at: type: string format: date-time endpoint_category_names: type: array items: type: string description: >- Non-empty only for category-scoped periods. Empty means all endpoints are affected. EncodingFormatEnum: enum: - float - base64 type: string description: |- * `float` - float * `base64` - base64 EmbeddingObject: type: object description: Serializer for individual embedding objects. properties: object: type: string default: embedding description: The object type, which is always 'embedding' embedding: type: array items: type: number format: double description: The embedding vector, which is a list of floats index: type: integer description: The index of the embedding in the list of embeddings required: - embedding - index EmbeddingUsage: type: object description: Serializer for token usage information in embeddings. properties: prompt_tokens: type: integer description: The number of tokens in the prompt total_tokens: type: integer description: The total number of tokens used by the request required: - prompt_tokens - total_tokens securitySchemes: bearerAuth: type: http scheme: bearer description: >- Bearer authentication header of the form `Bearer `, where `` is your auth token. ````