> ## Documentation Index
> Fetch the complete documentation index at: https://docs.seekr.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Embedding models

> Compare the embedding models available for vector databases by dimensions, input limits, and country of origin.

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An embedding model converts content into a fixed-length vector of numbers that represents its meaning. Content with similar meaning produces similar vectors, which is what allows a vector database to retrieve content by semantic similarity instead of keyword matching.

Most embedding models take text as input. Multimodal embedding models also accept images, which they represent as vectors in the same space as text.

Embedding models generate no language. They are used to build and query vector databases, and they cannot serve prompts or act as an agent's cognitive engine. For models that generate text, see [Generative models](/flow/components/models/generative-models).

Every embedding model in the catalog is available for inference and none can be fine-tuned.

`ibm-granite/granite-embedding-311m-multilingual-r2` is the default. Create a vector database without specifying a model and SeekrFlow uses the default.

<Warning>
  The embedding model is fixed when you create a vector database. Every vector in a database is stored in that model's vector space, and dimensions differ across models. An existing database cannot be switched to a different embedding model. To change models, create a new database and re-embed your documents.
</Warning>

## Supported embedding models

| Model                                                                                                          | Dimensions        | Max input tokens | Language support                                                            | Country of origin | Availability                                         |
| -------------------------------------------------------------------------------------------------------------- | ----------------- | ---------------- | --------------------------------------------------------------------------- | ----------------- | ---------------------------------------------------- |
| **Granite Embedding 311M Multilingual R2** (default)<br />`ibm-granite/granite-embedding-311m-multilingual-r2` | 768               | 32768            | 200+ languages, with enhanced support for 52 languages and programming code | United States     | All deployments                                      |
| **Granite Embedding 97M Multilingual R2**<br />`ibm-granite/granite-embedding-97m-multilingual-r2`             | 384               | 32768            | 200+ languages, with enhanced support for 52 languages and programming code | United States     | All deployments                                      |
| **Arctic Embed L v2.0**<br />`Snowflake/snowflake-arctic-embed-l-v2.0`                                         | 1024              | 8192             | Multilingual, with retained English performance                             | United States     | All deployments                                      |
| **E5-Mistral-7B-Instruct**<br />`intfloat/e5-mistral-7b-instruct`                                              | 4096              | 4096             | English (best); limited multilingual                                        | Mixed             | All deployments                                      |
| **Linq-Embed-Mistral**<br />`Linq-AI-Research/Linq-Embed-Mistral`                                              | 4096              |                  |                                                                             | South Korea       |                                                      |
| **BGE-M3**<br />`BAAI/bge-m3`                                                                                  | 1024              |                  |                                                                             | China             |                                                      |
| **BGE Base English v1.5**<br />`BAAI/bge-base-en-v1.5`                                                         |                   |                  | English                                                                     | China             |                                                      |
| **Titan Text Embeddings V2**<br />`bedrock:amazon.titan-embed-text-v2:0`                                       | 256, 512, or 1024 | 8192             | 100+ languages                                                              |                   | Self-hosted AWS/EKS only (recommended Bedrock model) |
| **Titan Text Embeddings V1**<br />`bedrock:amazon.titan-embed-text-v1`                                         | 1536              | 8192             | 25+ languages                                                               |                   | Self-hosted AWS/EKS only (legacy)                    |
| **Titan Text Embeddings G1**<br />`bedrock:amazon.titan-embed-g1-text-02`                                      | 1536              | 8192             | 25+ languages                                                               |                   | Self-hosted AWS/EKS only (legacy)                    |
| **Titan Multimodal Embeddings**<br />`bedrock:amazon.titan-embed-image-v1`                                     | 256, 384, or 1024 | 128              | Multimodal (text + image)                                                   |                   | Self-hosted AWS/EKS only                             |

**Mixed** means a model has no single country of origin. Empty cells are values the model catalog does not currently report. See [Country of origin](/flow/components/models#country-of-origin) and [Model attributes](/flow/components/models#model-attributes).

<Note>
  Bedrock embedding models are available for self-hosted AWS/EKS deployments only, and they are not listed in the SeekrFlow model catalog. See [Use AWS Bedrock for ingestion and inference](/flow/sdk/data-engine/aws-bedrock) for setup instructions.
</Note>

<Info>
  Models are added and updated over time. To retrieve the current models and their attributes, see [List models](/flow/reference/ml_models_v1_flow_models_get).
</Info>

## Select an embedding model

Weigh the following when choosing a model. Because the choice cannot be reversed without re-embedding, settle it before you create the database.

* **Dimensions** – Larger vectors capture more nuance and cost more to store and search. This value also defines the database's vector space.
* **Max input tokens** – Chunks longer than the model's limit are not fully represented. Keep your chunk size within the limit. Avoid inputs longer than a model's maximum input tokens.
* **Language support** – Match the model to the languages in your corpus. An English-only model degrades on multilingual content.
* **Country of origin** – Required if your deployment restricts model provenance. See [Country of origin](/flow/components/models#country-of-origin).

## Create a vector database

To choose an embedding model and create a vector database:

* In the UI, see [Vector stores](/flow/app/vector-stores).
* With the SDK, see [Create and populate a vector database](/flow/sdk/data-engine/create-and-populate-a-vector-database).