Embeddings
What Are Embeddings??
Embeddings transform text, images, and other media into dense numeric vectors that capture their underlying meaning. This makes it possible for machines to perform semantic search, similarity, recommendations, clustering, RAG, and more.
How Text & Image Embeddings Work
Text embeddings turn sentences or documents into high-dimensional vectors that capture meaning.
Example:
- “A young boy is playing soccer in a park.”
- “A child is kicking a football on a playground.”
These sentences get mapped to similar vectors, letting your AI recognize their meaning, regardless of wording.
Image embeddings use neural networks (like CNNs) or vision-language models to turn images into numeric vectors, capturing shapes, colors, and features. For example: A cat image → vector that is “close” to other cats and “far” from cars in vector space.
Supported Embedding Types
OpenAIEmbedding
Use OpenAI’s API to generate text embeddings.
Requires: OpenAI API Key.
MistralEmbedding
Use Mistral’s API for text embeddings.
Requires: Mistral API Key.
SentenceTransformerEncoder
VisionLanguageEmbedding
OpenAI’s vision models for image embeddings.
Requires: OpenAI API Key.
AzureOpenAI
Text embeddings from OpenAI models on Azure.
Requires: Azure OpenAI API Key.
TogetherEmbedding
Together AI’s hosted models for text embeddings.
Requires: Together AI API Key.
Usage Examples
Make sure you have the right API key set (OpenAI, Mistral, Azure, or Together) for the embedding backend you want to use.
Text Embeddings with OpenAI
Text Embeddings with Mistral
Local Sentence Transformers
Image Embeddings with Vision-Language Models
Text Embeddings with Azure OpenAI
Text Embeddings with Together AI
Pick a text embedding that matches your language, latency, and privacy needs. For multimodal use cases, use image or vision-language embeddings.