"This notebook shows how to get started using Hugging Face LLM's as chat models.\n",
"This notebook shows how to get started using `Hugging Face` LLM's as chat models.\n",
"\n",
"In particular, we will:\n",
"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",
@ -49,7 +49,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### `HuggingFaceTextGenInference`"
"### `HuggingFaceTextGenInference`"
]
},
{
@ -93,7 +93,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### `HuggingFaceEndpoint`"
"### `HuggingFaceEndpoint`"
]
},
{
@ -121,7 +121,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### `HuggingFaceHub`"
"### `HuggingFaceHub`"
]
},
{
@ -291,7 +291,7 @@
"source": [
"## 3. Take it for a spin as an agent!\n",
"\n",
"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",
"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",
"\n",
"> Note: To run this section, you'll need to have a [SerpAPI Token](https://serpapi.com/) saved as an environment variable: `SERPAPI_API_KEY`"
@ -58,31 +58,24 @@ See a [usage example](/docs/integrations/llms/huggingface_textgen_inference).
from langchain_community.llms import HuggingFaceTextGenInference
```
## Chat models
### Models from Hugging Face
## Document Loaders
### Hugging Face dataset
We can use the `Hugging Face` LLM classes or directly use the `ChatHuggingFace` class.
>[Hugging Face Hub](https://huggingface.co/docs/hub/index) is home to over 75,000
> [datasets](https://huggingface.co/docs/hub/index#datasets) in more than 100 languages
> that can be used for a broad range of tasks across NLP, Computer Vision, and Audio.
> They used for a diverse range of tasks such as translation, automatic speech
> recognition, and image classification.
We need to install `datasets` python package.
We need to install several python packages.
```bash
pip install datasets
pip install huggingface_hub
pip install transformers
```
See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
See a [usage example](/docs/integrations/chat/huggingface).
```python
from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
from langchain_community.chat_models.huggingface import ChatHuggingFace
```
## Embedding Models
### Hugging Face Hub
@ -126,6 +119,48 @@ See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
```
### Hugging Face Text Embeddings Inference (TEI)
>[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) 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`.
We need to install `huggingface-hub` python package.
```bash
pip install huggingface-hub
```
See a [usage example](/docs/integrations/text_embedding/text_embeddings_inference).
```python
from langchain_community.embeddings import HuggingFaceHubEmbeddings
```
## Document Loaders
### Hugging Face dataset
>[Hugging Face Hub](https://huggingface.co/docs/hub/index) is home to over 75,000
> [datasets](https://huggingface.co/docs/hub/index#datasets) in more than 100 languages
> that can be used for a broad range of tasks across NLP, Computer Vision, and Audio.
> They used for a diverse range of tasks such as translation, automatic speech
> recognition, and image classification.
We need to install `datasets` python package.
```bash
pip install datasets
```
See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
```python
from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
"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",
">[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) is a toolkit for deploying and serving open-source\n",
"> text embeddings and sequence classification models. `TEI` enables high-performance extraction for the most popular models,\n",
">including `FlagEmbedding`, `Ember`, `GTE` and `E5`.\n",
"\n",
"To use it within langchain, first install `huggingface-hub`."
Leonid Ganeline
5 months ago
committed by
GitHub