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78 lines
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78 lines
4.6 KiB
Plaintext
---
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title: Hugging Face
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slug: /bundles-huggingface
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---
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import Icon from "@site/src/components/icon";
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import PartialParams from '@site/docs/_partial-hidden-params.mdx';
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<Icon name="Blocks" aria-hidden="true" /> [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
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The components in the **Hugging Face** bundle require access to Hugging Face APIs.
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For more information about Hugging Face features and functionality used by Hugging Face components, see the [Hugging Face documentation](https://huggingface.co/docs).
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## Hugging Face text generation
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The **Hugging Face** component generates text using a specified model by sending requests to the Hugging Face API, which is a hosted inference API for models hosted on Hugging Face.
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Authentication is required.
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This component can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
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Specifically, the **Language Model** output is an instance of [`ChatHuggingFace`](https://docs.langchain.com/oss/python/integrations/chat/huggingface) configured according to the component's parameters.
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Use the **Language Model** output when you want to use a Hugging Face model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
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For more information, see [Language model components](/components-models).
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### Hugging Face text generation parameters
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<PartialParams />
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| Name | Type | Description |
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|------|------|-------------|
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| model_id | String | Input parameter. The model ID from Hugging Face Hub. For example, "gpt2", "facebook/bart-large". |
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| huggingfacehub_api_token | SecretString | Input parameter. Your [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) for authentication. |
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| temperature | Float | Input parameter. Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7. |
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| max_new_tokens | Integer | Input parameter. Maximum number of tokens to generate. Default: 512. |
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| top_p | Float | Input parameter. Nucleus sampling parameter. Range: [0.0, 1.0]. Default: 0.95. |
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| top_k | Integer | Input parameter. Top-k sampling parameter. Default: 50. |
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| model_kwargs | Dictionary | Input parameter. Additional keyword arguments to pass to the model. |
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## Hugging Face Embeddings Inference
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Use the **Hugging Face Embeddings Inference** component to create embeddings with Hugging Face's hosted models or with your own locally hosted models.
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The component generates embeddings using [Hugging Face Inference API models](https://huggingface.co/models).
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Authentication is required when not using a local model.
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For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models) and [Use a local Hugging Face embeddings model](#local-hugging-face-model).
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### Hugging Face Embeddings Inference parameters
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| Name | Display Name | Info |
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|------|--------------|------|
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| api_key | API Key | Input parameter. Your [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) for accessing the Hugging Face Inference API, if required. Local inference models don't require an API key. |
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| api_url | API URL | Input parameter. The URL of the Hugging Face Inference API. |
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| model_name | Model Name | Input parameter. The name of the model to use for embeddings. |
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### Use a local Hugging Face embeddings model {#local-hugging-face-model}
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To connect the local Hugging Face model to the **Hugging Face Embeddings Inference** component and use it in a flow, follow these steps:
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1. Run a [local Hugging Face embeddings inference](https://huggingface.co/docs/text-embeddings-inference/local_cpu).
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2. For this example, create a flow from the **Vector Store RAG** template.
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3. Replace the two **OpenAI Embeddings** components with **Hugging Face Embeddings Inference** components.
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Make sure to reconnect the **Embedding Model** ports from each **Embeddings Inference** component to its corresponding **Astra DB** component.
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4. Configure the **Astra DB** components to connect to your Astra organization, or replace both **Astra DB** components with other vector store components.
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5. Connect each **Hugging Face Embeddings Inference** component to your local inference model:
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* **Inference Endpoint**: Enter the URL of your local inference model.
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* **API Key**: Can be empty for local inference.
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* **Model Name**: Enter the name of your local inference model if it isn't automatically detected.
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6. To test the flow, click **Playground**, and then enter some text to generate an embedding. |