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docs: Hugging Face update (#16490)
- added missed integrations to the platform page - updated integration examples: added links and fixed formats
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Leonid Ganeline
GitHub
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@ -4,9 +4,9 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Hugging Face Chat Wrapper\n",
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"# Hugging Face\n",
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"\n",
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"This notebook shows how to get started using Hugging Face LLM's as chat models.\n",
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"This notebook shows how to get started using `Hugging Face` LLM's as chat models.\n",
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"\n",
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"In particular, we will:\n",
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"1. Utilize the [HuggingFaceTextGenInference](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_text_gen_inference.py), [HuggingFaceEndpoint](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_endpoint.py), or [HuggingFaceHub](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_hub.py) integrations to instantiate an `LLM`.\n",
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@ -49,7 +49,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### `HuggingFaceTextGenInference`"
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"### `HuggingFaceTextGenInference`"
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]
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},
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{
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@ -93,7 +93,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### `HuggingFaceEndpoint`"
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"### `HuggingFaceEndpoint`"
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]
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},
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{
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@ -121,7 +121,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### `HuggingFaceHub`"
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"### `HuggingFaceHub`"
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]
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},
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{
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@ -291,7 +291,7 @@
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"source": [
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"## 3. Take it for a spin as an agent!\n",
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"\n",
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"Here we'll test out `Zephyr-7B-beta` as a zero-shot ReAct Agent. The example below is taken from [here](https://python.langchain.com/docs/modules/agents/agent_types/react#using-chat-models).\n",
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"Here we'll test out `Zephyr-7B-beta` as a zero-shot `ReAct` Agent. The example below is taken from [here](https://python.langchain.com/docs/modules/agents/agent_types/react#using-chat-models).\n",
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"\n",
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"> Note: To run this section, you'll need to have a [SerpAPI Token](https://serpapi.com/) saved as an environment variable: `SERPAPI_API_KEY`"
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]
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@ -448,7 +448,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.5"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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@ -58,31 +58,24 @@ See a [usage example](/docs/integrations/llms/huggingface_textgen_inference).
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from langchain_community.llms import HuggingFaceTextGenInference
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```
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## Chat models
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### Models from Hugging Face
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## Document Loaders
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We can use the `Hugging Face` LLM classes or directly use the `ChatHuggingFace` class.
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### Hugging Face dataset
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>[Hugging Face Hub](https://huggingface.co/docs/hub/index) is home to over 75,000
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> [datasets](https://huggingface.co/docs/hub/index#datasets) in more than 100 languages
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> that can be used for a broad range of tasks across NLP, Computer Vision, and Audio.
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> They used for a diverse range of tasks such as translation, automatic speech
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> recognition, and image classification.
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We need to install `datasets` python package.
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We need to install several python packages.
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```bash
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pip install datasets
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pip install huggingface_hub
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pip install transformers
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```
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See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
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See a [usage example](/docs/integrations/chat/huggingface).
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```python
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from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
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from langchain_community.chat_models.huggingface import ChatHuggingFace
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```
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## Embedding Models
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### Hugging Face Hub
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@ -126,6 +119,48 @@ See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
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from langchain_community.embeddings import HuggingFaceBgeEmbeddings
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```
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### Hugging Face Text Embeddings Inference (TEI)
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>[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) is a toolkit for deploying and serving open-source
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> text embeddings and sequence classification models. `TEI` enables high-performance extraction for the most popular models,
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>including `FlagEmbedding`, `Ember`, `GTE` and `E5`.
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We need to install `huggingface-hub` python package.
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```bash
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pip install huggingface-hub
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```
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See a [usage example](/docs/integrations/text_embedding/text_embeddings_inference).
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```python
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from langchain_community.embeddings import HuggingFaceHubEmbeddings
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```
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## Document Loaders
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### Hugging Face dataset
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>[Hugging Face Hub](https://huggingface.co/docs/hub/index) is home to over 75,000
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> [datasets](https://huggingface.co/docs/hub/index#datasets) in more than 100 languages
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> that can be used for a broad range of tasks across NLP, Computer Vision, and Audio.
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> They used for a diverse range of tasks such as translation, automatic speech
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> recognition, and image classification.
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We need to install `datasets` python package.
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```bash
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pip install datasets
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```
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See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
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```python
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from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
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```
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## Tools
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@ -7,7 +7,9 @@
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"source": [
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"# Text Embeddings Inference\n",
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"\n",
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"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.\n",
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">[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) is a toolkit for deploying and serving open-source\n",
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"> text embeddings and sequence classification models. `TEI` enables high-performance extraction for the most popular models,\n",
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">including `FlagEmbedding`, `Ember`, `GTE` and `E5`.\n",
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"\n",
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"To use it within langchain, first install `huggingface-hub`."
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]
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@ -21,7 +23,7 @@
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},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet huggingface-hub -q"
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"%pip install --upgrade huggingface-hub"
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]
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},
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{
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@ -146,9 +148,9 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "conda_python3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "conda_python3"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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@ -160,7 +162,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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