> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Embedding
For the Unstructured open source library version of this page, see [Embedding for open source](/open-source/core-functionality/embedding).
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](https://python.langchain.com/v0.2/docs/concepts/#embedding-models) 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](https://www.pinecone.io/learn/what-is-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](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
on Hugging Face:
```json theme={null}
{
"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,
"",
0.0895417109131813,
0.05604064092040062,
0.01376157347112894
]
}
```
[Understanding embedding models: make an informed choice for your RAG](https://unstructured.io/blog/understanding-embedding-models-make-an-informed-choice-for-your-rag).
## Generate embeddings
To have Unstructured generate embeddings, do the following:
* For **Unstructured UI** users, add an [Embedder node](/ui/workflows#custom-workflow-node-types)
to an Unstructured [custom workflow](/ui/workflows#create-a-custom-workflow).
* For **Unstructured API** users, add an [Embedder node](/api-reference/workflow/nodes/embedder/embedder)
as either as an object in a `workflow_nodes` array
(for curl) or as a `WorkflowNode` in a `WorkflowNodes` collection (for Python) whenever you
[create a workflow](/api-reference/api/workflow/create-workflow),
[update a workflow](/api-reference/api/workflow/update-workflow), or
[create an on-demand workflow job](/api-reference/api/job/create-job).
When choosing an embedding model, be sure to pay attention to the number of dimensions for the model you choose. This number must match the number of dimensions in the
embeddings field of your destination connector's table, collection, or index.
## Chunk sizing and embedding models
If your workflow has an [Embedder](/concepts/embedding) node, your workflow's [Chunker](/concepts/chunking) node settings must stay within the selected embedding model's token limits.
Exceeding these limits will cause workflow failures.
Set your **Chunker** node's **Max Characters** to a value at or below Unstructured's recommended maximum chunk size for your selected embedding model,
as listed in the following table's last column.
The following list applies only to Unstructured **Let's Go** and **Pay-As-You-Go** accounts.
For Unstructured **Business** accounts, see your Unstructured account administrator for your list of available embedding models.
To add more embedding models to your list, contact your Unstructured account administrator or Unstructured sales representative,
or email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
| Embedding model | Dimensions | Tokens | Chunker Max Characters\* |
| ------------------------------ | ---------- | ------ | ----------------------------------- |
| *Amazon Bedrock* | | | |
| Cohere Embed English | 1024 | 512 | 1792 |
| Cohere Embed Multilingual | 1024 | 512 | 1792 |
| Titan Embeddings G1 - Text | 1536 | 8192 | 28672 |
| Titan Multimodal Embeddings G1 | 1024 | 256 | 896 |
| Titan Text Embeddings V2 | 1024 | 8192 | 28672 |
| *Azure OpenAI* | | | |
| Text Embedding 3 Large | 3072 | 8192 | 28672 |
| Text Embedding 3 Small | 1536 | 8192 | 28672 |
| Text Embedding Ada 002 | 1536 | 8192 | 28672 |
| *Together AI* | | | |
| M2-Bert 80M 32K Retrieval | 768 | 8192 | 28672 |
| *Voyage AI* | | | |
| Voyage 3 | 1024 | 32000 | 112000 |
| Voyage 3 Large | 1024 | 32000 | 112000 |
| Voyage 3 Lite | 512 | 32000 | 112000 |
| Voyage Code 2 | 1536 | 16000 | 56000 |
| Voyage Code 3 | 1024 | 32000 | 112000 |
| Voyage Finance 2 | 1024 | 32000 | 112000 |
| Voyage Law 2 | 1024 | 16000 | 56000 |
| Voyage Multimodal 3 | 1024 | 32000 | 112000 |
\* This is an approximate value, determined by multiplying the embedding model's token limit by 3.5.