After partitioning, chunking, and summarizing, the embedding step creates arrays of numbers known as vectors, representing the text that is extracted by Unstructured. These vectors are stored or embedded next to the text itself. These vector embeddings are generated by an embedding model that is provided by an embedding provider.
You typically save these embeddings in a vector store. When a user queries a retrieval augmented generation (RAG) application, the application can use a vector database to perform a similarity search in that vector store and then return the items whose embeddings are the closest to that user’s query.
Here is an example of a document element generated by Unstructured, along with its vector embeddings generated by the embedding model sentence-transformers/all-MiniLM-L6-v2 on Hugging Face:
To generate embeddings, choose one of the following embedding providers and models in the Select Embedding Model section of an Embedder node in a workflow:
Azure OpenAI: Use Azure OpenAI to generate embeddings with one of the following models:
text-embedding-ada-002
), with 1536 dimensions.Amazon Bedrock: Use Amazon Bedrock to generate embeddings with one of the following models:
TogetherAI: Use TogetherAI to generate embeddings with one of the following models:
Voyage AI: Use Voyage AI to generate embeddings with one of the following models:
After partitioning, chunking, and summarizing, the embedding step creates arrays of numbers known as vectors, representing the text that is extracted by Unstructured. These vectors are stored or embedded next to the text itself. These vector embeddings are generated by an embedding model that is provided by an embedding provider.
You typically save these embeddings in a vector store. When a user queries a retrieval augmented generation (RAG) application, the application can use a vector database to perform a similarity search in that vector store and then return the items whose embeddings are the closest to that user’s query.
Here is an example of a document element generated by Unstructured, along with its vector embeddings generated by the embedding model sentence-transformers/all-MiniLM-L6-v2 on Hugging Face:
To generate embeddings, choose one of the following embedding providers and models in the Select Embedding Model section of an Embedder node in a workflow:
Azure OpenAI: Use Azure OpenAI to generate embeddings with one of the following models:
text-embedding-ada-002
), with 1536 dimensions.Amazon Bedrock: Use Amazon Bedrock to generate embeddings with one of the following models:
TogetherAI: Use TogetherAI to generate embeddings with one of the following models:
Voyage AI: Use Voyage AI to generate embeddings with one of the following models: