This page demonstrates how to use Xinference with LangChain. Xinference is a powerful and versatile library designed to serve LLMs, speech recognition models, and multimodal models, even on your laptop. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command.

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Installation and Setup

Xinference can be installed via pip from PyPI: 
pip install "xinference[all]"

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LLM

Xinference supports various models compatible with GGML, including chatglm, baichuan, whisper, vicuna, and orca. To view the builtin models, run the command: 
xinference list --all

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Wrapper for Xinference

You can start a local instance of Xinference by running: 
xinference
You can also deploy Xinference in a distributed cluster. To do so, first start an Xinference supervisor on the server you want to run it: 
xinference-supervisor -H "${supervisor_host}"
Then, start the Xinference workers on each of the other servers where you want to run them on: 
xinference-worker -e "http://${supervisor_host}:9997"
You can also start a local instance of Xinference by running: 
xinference
Once Xinference is running, an endpoint will be accessible for model management via CLI or Xinference client. For local deployment, the endpoint will be http://localhost:9997. For cluster deployment, the endpoint will be http://${supervisor_host}:9997. Then, you need to launch a model. You can specify the model names and other attributes including model_size_in_billions and quantization. You can use command line interface (CLI) to do it. For example, 
xinference launch -n orca -s 3 -q q4_0
A model uid will be returned. Example usage: 
from langchain_community.llms import Xinference

llm = Xinference(
    server_url="http://0.0.0.0:9997",
    model_uid = {model_uid} # replace model_uid with the model UID return from launching the model
)

llm(
    prompt="Q: where can we visit in the capital of France? A:",
    generate_config={"max_tokens": 1024, "stream": True},
)

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Usage

For more information and detailed examples, refer to the example for xinference LLMs

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Embeddings

Xinference also supports embedding queries and documents. See example for xinference embeddings for a more detailed demo.

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Xinference LangChain partner package install

Install the integration package with: 
pip install langchain-xinference

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Chat Models

from langchain_xinference.chat_models import ChatXinference

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LLM

from langchain_xinference.llms import Xinference