> ## Documentation Index > Fetch the complete documentation index at: https://docs.nugen.in/llms.txt > Use this file to discover all available pages before exploring further. # Generate Text Embeddings > Generate dense vector embeddings for input text. This endpoint creates numerical vector representations (embeddings) of text using specified embedding models. Embeddings are useful for semantic search, clustering, similarity comparisons, and RAG applications. **Request Body:** - `model`: Embedding model ID (required) - Name of the model to use for generating embeddings - `input`: Text or list of texts to embed (required) - Single string or array of strings. Cannot be empty and must not exceed max input tokens for the model - `dimensions` (optional): Number of dimensions for output embeddings (only applicable for Resizable Matryoshka Embedding models) **Returns:** - `id`: Unique identifier for the response - `object`: Object type (always `embedding`) - `data`: List of embedding objects, each containing: - `object`: Object type (always `embedding`) - `embedding`: Vector of floats representing the text (length depends on the model) - `index`: Position in the input array - `model`: Model ID used for generation - `created`: Unix timestamp (seconds) when response was generated - `usage`: Token usage statistics containing: - `total_tokens`: Total number of tokens used by the request **Example Request (Single Text):** ```json POST /api/v3/inference/embeddings Headers: {"Authorization": "Bearer "} { "model": "text-embedding-xyz", "input": "The quick brown fox jumps over the lazy dog" } ``` **Example Request (Multiple Texts):** ```json POST /api/v3/inference/embeddings Headers: {"Authorization": "Bearer "} { "model": "aligned-model-01kmqm4nrn9fw6r", "input": [ "First document for semantic search", "Second document for clustering", "Third document for similarity comparison" ] } ``` **Example Request (With Dimensions):** ```json POST /api/v3/inference/embeddings Headers: {"Authorization": "Bearer "} { "model": "aligned-model-01kmqm4nrn9fw6r", "input": "Sample text to embed", "dimensions": 512 } ``` **Example Response:** ```json { "id": "embed-abc123xyz", "object": "embedding", "data": [ { "object": "embedding", "embedding": [0.00658765, -0.008665467, -0.007653789, 0.0023, -0.0091, 0.0125], "index": 0 } ], "model": "text-embedding-ada-002", "created": 1705329600.0, "usage": { "total_tokens": 10 } } ``` **Notes:** - Input must not exceed the maximum input tokens supported by the model - Input cannot be an empty string - For batch processing, pass multiple texts as an array to the `input` field - The embedding vector length varies by model - check model documentation for specifics - Use `dimensions` parameter only with Matryoshka embedding models that support resizing ## OpenAPI ````yaml https://api.nugen.in/openapi-public.json post /api/v3/inference/embeddings openapi: 3.1.0 info: title: Nugen Intelligence API description: 'Nugen Intelligence : Powering Specialized Intelligence At Scale' version: 25.4.20 servers: [] security: [] paths: /api/v3/inference/embeddings: post: tags: - Inference summary: Generate Text Embeddings description: >- Generate dense vector embeddings for input text. This endpoint creates numerical vector representations (embeddings) of text using specified embedding models. Embeddings are useful for semantic search, clustering, similarity comparisons, and RAG applications. **Request Body:** - `model`: Embedding model ID (required) - Name of the model to use for generating embeddings - `input`: Text or list of texts to embed (required) - Single string or array of strings. Cannot be empty and must not exceed max input tokens for the model - `dimensions` (optional): Number of dimensions for output embeddings (only applicable for Resizable Matryoshka Embedding models) **Returns:** - `id`: Unique identifier for the response - `object`: Object type (always `embedding`) - `data`: List of embedding objects, each containing: - `object`: Object type (always `embedding`) - `embedding`: Vector of floats representing the text (length depends on the model) - `index`: Position in the input array - `model`: Model ID used for generation - `created`: Unix timestamp (seconds) when response was generated - `usage`: Token usage statistics containing: - `total_tokens`: Total number of tokens used by the request **Example Request (Single Text):** ```json POST /api/v3/inference/embeddings Headers: {"Authorization": "Bearer "} { "model": "text-embedding-xyz", "input": "The quick brown fox jumps over the lazy dog" } ``` **Example Request (Multiple Texts):** ```json POST /api/v3/inference/embeddings Headers: {"Authorization": "Bearer "} { "model": "aligned-model-01kmqm4nrn9fw6r", "input": [ "First document for semantic search", "Second document for clustering", "Third document for similarity comparison" ] } ``` **Example Request (With Dimensions):** ```json POST /api/v3/inference/embeddings Headers: {"Authorization": "Bearer "} { "model": "aligned-model-01kmqm4nrn9fw6r", "input": "Sample text to embed", "dimensions": 512 } ``` **Example Response:** ```json { "id": "embed-abc123xyz", "object": "embedding", "data": [ { "object": "embedding", "embedding": [0.00658765, -0.008665467, -0.007653789, 0.0023, -0.0091, 0.0125], "index": 0 } ], "model": "text-embedding-ada-002", "created": 1705329600.0, "usage": { "total_tokens": 10 } } ``` **Notes:** - Input must not exceed the maximum input tokens supported by the model - Input cannot be an empty string - For batch processing, pass multiple texts as an array to the `input` field - The embedding vector length varies by model - check model documentation for specifics - Use `dimensions` parameter only with Matryoshka embedding models that support resizing operationId: generate_text_embeddings requestBody: content: application/json: schema: $ref: '#/components/schemas/EmbeddingRequestSchema' required: true responses: '200': description: Vector embeddings and usage statistics content: application/json: schema: {} '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] components: schemas: EmbeddingRequestSchema: properties: input: anyOf: - items: type: string type: array - type: string title: Input description: > Input text to embed, encoded as a string or a list of strings. To embed multiple inputs in a single request, pass a list of strings. The input must not exceed the max input tokens for the model, cannot be an empty string. example: The quick brown fox jumped over the lazy dog model: type: string title: Model description: The name of the model used to generate the embedding. example: nugen-flash-embed dimensions: anyOf: - type: integer - type: 'null' title: Dimensions description: >- (Applicable for only Resizable Matryoshka Embedding models) The number of dimensions the resulting output embeddings should have. type: object required: - input - model title: EmbeddingRequestSchema HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError 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: HTTPBearer: type: http scheme: bearer ````