> ## Documentation Index
> Fetch the complete documentation index at: https://langchain.idochub.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Xorbits inference (Xinference)

This notebook goes over how to use Xinference embeddings within LangChain

## Installation

Install `Xinference` through PyPI:

```python theme={null}
%pip install -qU  "xinference[all]"
```

## Deploy Xinference Locally or in a Distributed Cluster

For local deployment, run `xinference`.

To deploy Xinference in a cluster, first start an Xinference supervisor using the `xinference-supervisor`. You can also use the option -p to specify the port and -H to specify the host. The default port is 9997.

Then, start the Xinference workers using `xinference-worker` on each server you want to run them on.

You can consult the README file from [Xinference](https://github.com/xorbitsai/inference) for more information.

## Wrapper

To use Xinference with LangChain, you need to first launch a model. You can use command line interface (CLI) to do so:

```python theme={null}
!xinference launch -n vicuna-v1.3 -f ggmlv3 -q q4_0
```

```output theme={null}
Model uid: 915845ee-2a04-11ee-8ed4-d29396a3f064
```

A model UID is returned for you to use. Now you can use Xinference embeddings with LangChain:

```python theme={null}
from langchain_community.embeddings import XinferenceEmbeddings

xinference = XinferenceEmbeddings(
    server_url="http://0.0.0.0:9997", model_uid="915845ee-2a04-11ee-8ed4-d29396a3f064"
)
```

```python theme={null}
query_result = xinference.embed_query("This is a test query")
```

```python theme={null}
doc_result = xinference.embed_documents(["text A", "text B"])
```

Lastly, terminate the model when you do not need to use it:

```python theme={null}
!xinference terminate --model-uid "915845ee-2a04-11ee-8ed4-d29396a3f064"
```