# Open Source Embeddings

Open source embedding components provide access to locally-hosted and community-driven embedding models.

## Hugging Face Embeddings

This component loads embedding models from HuggingFace.

Use this component to generate embeddings using locally downloaded Hugging Face models. Ensure you have sufficient computational resources to run the models.

### Inputs

| Name          | Display Name       | Info                                          |
| ------------- | ------------------ | --------------------------------------------- |
| Cache Folder  | Cache Folder       | Folder path to cache HuggingFace models       |
| Encode Kwargs | Encoding Arguments | Additional arguments for the encoding process |
| Model Kwargs  | Model Arguments    | Additional arguments for the model            |
| Model Name    | Model Name         | Name of the HuggingFace model to use          |
| Multi Process | Multi-Process      | Whether to use multiple processes             |

### Outputs

| Name       | Display Name | Info                     |
| ---------- | ------------ | ------------------------ |
| embeddings | Embeddings   | The generated embeddings |

## Hugging Face Embeddings Inference

This component generates embeddings using [Hugging Face Inference API models](https://huggingface.co/) and requires a [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) to authenticate. Local inference models do not require an API key.

Use this component to create embeddings with Hugging Face's hosted models, or to connect to your own locally hosted models.

### Inputs

| Name          | Display Name       | Info                                                      |
| ------------- | ------------------ | --------------------------------------------------------- |
| API Key       | API Key            | The API key for accessing the Hugging Face Inference API. |
| API URL       | API URL            | The URL of the Hugging Face Inference API.                |
| Model Name    | Model Name         | The name of the model to use for embeddings.              |
| Cache Folder  | Cache Folder       | The folder path to cache Hugging Face models.             |
| Encode Kwargs | Encoding Arguments | Additional arguments for the encoding process.            |
| Model Kwargs  | Model Arguments    | Additional arguments for the model.                       |
| Multi Process | Multi-Process      | Whether to use multiple processes.                        |

### Outputs

| Name       | Display Name | Info                      |
| ---------- | ------------ | ------------------------- |
| embeddings | Embeddings   | The generated embeddings. |

## Ollama Embeddings

This component generates embeddings using [Ollama models](https://ollama.com/).

For a list of Ollama embeddings models, see the [Ollama documentation](https://ollama.com/search?c=embedding).

To use this component in a flow, connect BroxiAI to your locally running Ollama server and select an embeddings model.

1. In the Ollama component, in the **Ollama Base URL** field, enter the address for your locally running Ollama server. This value is set as the `OLLAMA_HOST` environment variable in Ollama. The default base URL is `http://127.0.0.1:11434`.
2. To refresh the server's list of models, click .
3. In the **Ollama Model** field, select an embeddings model. This example uses `all-minilm:latest`.
4. Connect the **Ollama** embeddings component to a flow. For example, this flow connects a local Ollama server running a `all-minilm:latest` embeddings model to a [Chroma DB](https://guidenai.gitbook.io/broxi/vector-databases/open-source-databases#chroma-db) vector store to generate embeddings for split text.

![Ollama embeddings connected to Chroma DB](https://docs.langflow.org/assets/images/component-ollama-embeddings-chromadb-c02d6ef9e753b61c274778d90f2a6eec.png)

For more information, see the [Ollama documentation](https://ollama.com/).

### Inputs

| Name              | Type   | Description                                                                             |
| ----------------- | ------ | --------------------------------------------------------------------------------------- |
| Ollama Model      | String | Name of the Ollama model to use (default: `llama2`)                                     |
| Ollama Base URL   | String | Base URL of the Ollama API (default: `http://localhost:11434`)                          |
| Model Temperature | Float  | Temperature parameter for the model. Adjusts the randomness in the generated embeddings |

### Outputs

| Name       | Type       | Description                                        |
| ---------- | ---------- | -------------------------------------------------- |
| embeddings | Embeddings | An instance for generating embeddings using Ollama |

## LM Studio Embeddings

This component generates embeddings using [LM Studio](https://lmstudio.ai/docs) models.

### Inputs

| Name        | Display Name       | Info                                                 |
| ----------- | ------------------ | ---------------------------------------------------- |
| model       | Model              | The LM Studio model to use for generating embeddings |
| base\_url   | LM Studio Base URL | The base URL for the LM Studio API                   |
| api\_key    | LM Studio API Key  | API key for authentication with LM Studio            |
| temperature | Model Temperature  | Temperature setting for the model                    |

### Outputs

| Name       | Display Name | Info                     |
| ---------- | ------------ | ------------------------ |
| embeddings | Embeddings   | The generated embeddings |

## Usage Notes

* **Cost-Effective**: No API costs after initial setup
* **Privacy**: Models run locally, keeping your data private
* **Customization**: Full control over model parameters and configurations
* **Offline Capability**: Works without internet connection once models are downloaded
* **Community Models**: Access to thousands of open source embedding models