openai_create_embedding
Convert text into numerical vector representations for semantic search, similarity comparison, and machine learning tasks using OpenAI embedding models.
Instructions
Create text embeddings using OpenAI embedding models via AceDataCloud.
Converts text into numerical vector representations that can be used for
semantic search, text similarity, clustering, and other ML tasks.
Use this when:
- You need to compare semantic similarity between texts
- You want to build a semantic search system
- You need vector representations for machine learning
Returns:
JSON response containing the embedding vectors and usage information.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Input text to embed. Can be a single string, an array of strings, or token arrays. The text to embed into a numerical vector representation. | |
| model | No | The embedding model to use. Options: 'text-embedding-3-small' (default, cost-efficient), 'text-embedding-3-large' (higher quality), 'text-embedding-ada-002' (legacy). | text-embedding-3-small |
| dimensions | No | Optional output embedding size. Supported by text-embedding-3 models. Reduces the embedding dimensions while maintaining quality. | |
| encoding_format | No | The format of the returned embeddings. 'float' returns floating-point numbers (default), 'base64' returns base64-encoded data. | float |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |