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: 
{
    "type": "Title",
    "element_id": "fdbf5369-4485-453b-9701-1bb42c83b00b",
    "text": "THE CONSTITUTION of the United States",
    "metadata": {
        "filetype": "application/pdf",
        "languages": [
            "eng"
        ],
        "page_number": 1,
        "filename": "constitution.pdf",
        "data_source": {
            "record_locator": {
                "path": "/input/constitution.pdf"
            },
            "date_created": "1723069423.0536132",
            "date_modified": "1723069423.055078",
            "date_processed": "1725666244.571788",
            "permissions_data": [
                { 
                    "mode": 33188
                }
            ]
        }
    },
    "embeddings": [
        -0.06138836592435837,
        0.08634615689516068,
        -0.019471267238259315,
        "<full-results-omitted-for-brevity>",
        0.0895417109131813,
        0.05604064092040062,
        0.01376157347112894
    ]
}
Learn more.

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Generate embeddings

To generate embeddings, choose one of the available embedding providers and models in the Select Embedding Model section of an Embedder node in a workflow. When choosing an embedding model, be sure to pay attention to the number of dimensions listed next to each model. This number must match the number of dimensions in the embeddings field of your destination connector’s table, collection, or index.
You can change a workflow’s preconfigured provider only through Custom workflow settings.