mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
31 lines
1.2 KiB
Markdown
31 lines
1.2 KiB
Markdown
# Runhouse
|
|
|
|
This page covers how to use the [Runhouse](https://github.com/run-house/runhouse) ecosystem within LangChain.
|
|
It is broken into three parts: installation and setup, LLMs, and Embeddings.
|
|
|
|
## Installation and Setup
|
|
- Install the Python SDK with `pip install runhouse`
|
|
- If you'd like to use on-demand cluster, check your cloud credentials with `sky check`
|
|
|
|
## Self-hosted LLMs
|
|
For a basic self-hosted LLM, you can use the `SelfHostedHuggingFaceLLM` class. For more
|
|
custom LLMs, you can use the `SelfHostedPipeline` parent class.
|
|
|
|
```python
|
|
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
|
|
```
|
|
|
|
For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/llms/integrations/self_hosted_examples.ipynb)
|
|
|
|
## Self-hosted Embeddings
|
|
There are several ways to use self-hosted embeddings with LangChain via Runhouse.
|
|
|
|
For a basic self-hosted embedding from a Hugging Face Transformers model, you can use
|
|
the `SelfHostedEmbedding` class.
|
|
```python
|
|
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
|
|
```
|
|
|
|
For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
|
|
|
|
## |