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- [Xorbits Inference(Xinference)](https://github.com/xorbitsai/inference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. Xinference supports a variety of GGML-compatible models including chatglm, whisper, and vicuna, and utilizes heterogeneous hardware and a distributed architecture for seamless cross-device and cross-server model deployment. - This PR integrates Xinference models and Xinference embeddings into LangChain. - Dependencies: To install the depenedencies for this integration, run `pip install "xinference[all]"` - Example Usage: To start a local instance of Xinference, run `xinference`. To deploy Xinference in a distributed cluster, first start an Xinference supervisor using `xinference-supervisor`: `xinference-supervisor -H "${supervisor_host}"` Then, start the Xinference workers using `xinference-worker` on each server you want to run them on. `xinference-worker -e "http://${supervisor_host}:9997"` To use Xinference with LangChain, you also need to launch a model. You can use command line interface (CLI) to do so. Fo example: `xinference launch -n vicuna-v1.3 -f ggmlv3 -q q4_0`. This launches a model named vicuna-v1.3 with `model_format="ggmlv3"` and `quantization="q4_0"`. A model UID is returned for you to use. Now you can use Xinference with LangChain: ```python from langchain.llms import Xinference llm = Xinference( server_url="http://0.0.0.0:9997", # suppose the supervisor_host is "0.0.0.0" model_uid = {model_uid} # model UID returned from launching a model ) llm( prompt="Q: where can we visit in the capital of France? A:", generate_config={"max_tokens": 1024}, ) ``` You can also use RESTful client to launch a model: ```python from xinference.client import RESTfulClient client = RESTfulClient("http://0.0.0.0:9997") model_uid = client.launch_model(model_name="vicuna-v1.3", model_size_in_billions=7, quantization="q4_0") ``` The following code block demonstrates how to use Xinference embeddings with LangChain: ```python from langchain.embeddings import XinferenceEmbeddings xinference = XinferenceEmbeddings( server_url="http://0.0.0.0:9997", model_uid = model_uid ) ``` ```python query_result = xinference.embed_query("This is a test query") ``` ```python doc_result = xinference.embed_documents(["text A", "text B"]) ``` Xinference is still under rapid development. Feel free to [join our Slack community](https://xorbitsio.slack.com/join/shared_invite/zt-1z3zsm9ep-87yI9YZ_B79HLB2ccTq4WA) to get the latest updates! - Request for review: @hwchase17, @baskaryan - Twitter handle: https://twitter.com/Xorbitsio --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
102 lines
2.5 KiB
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102 lines
2.5 KiB
Plaintext
# Xorbits Inference (Xinference)
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This page demonstrates how to use [Xinference](https://github.com/xorbitsai/inference)
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with LangChain.
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`Xinference` is a powerful and versatile library designed to serve LLMs,
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speech recognition models, and multimodal models, even on your laptop.
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With Xorbits Inference, you can effortlessly deploy and serve your or
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state-of-the-art built-in models using just a single command.
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## Installation and Setup
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Xinference can be installed via pip from PyPI:
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```bash
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pip install "xinference[all]"
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```
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## LLM
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Xinference supports various models compatible with GGML, including chatglm, baichuan, whisper,
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vicuna, and orca. To view the builtin models, run the command:
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```bash
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xinference list --all
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```
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### Wrapper for Xinference
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You can start a local instance of Xinference by running:
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```bash
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xinference
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```
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You can also deploy Xinference in a distributed cluster. To do so, first start an Xinference supervisor
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on the server you want to run it:
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```bash
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xinference-supervisor -H "${supervisor_host}"
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```
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Then, start the Xinference workers on each of the other servers where you want to run them on:
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```bash
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xinference-worker -e "http://${supervisor_host}:9997"
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```
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You can also start a local instance of Xinference by running:
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```bash
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xinference
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```
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Once Xinference is running, an endpoint will be accessible for model management via CLI or
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Xinference client.
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For local deployment, the endpoint will be http://localhost:9997.
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For cluster deployment, the endpoint will be http://${supervisor_host}:9997.
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Then, you need to launch a model. You can specify the model names and other attributes
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including model_size_in_billions and quantization. You can use command line interface (CLI) to
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do it. For example,
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```bash
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xinference launch -n orca -s 3 -q q4_0
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```
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A model uid will be returned.
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Example usage:
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```python
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from langchain.llms import Xinference
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llm = Xinference(
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server_url="http://0.0.0.0:9997",
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model_uid = {model_uid} # replace model_uid with the model UID return from launching the model
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)
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llm(
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prompt="Q: where can we visit in the capital of France? A:",
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generate_config={"max_tokens": 1024, "stream": True},
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)
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```
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### Usage
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For more information and detailed examples, refer to the
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[example notebook for xinference](../modules/models/llms/integrations/xinference.ipynb)
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### Embeddings
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Xinference also supports embedding queries and documents. See
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[example notebook for xinference embeddings](../modules/data_connection/text_embedding/integrations/xinference.ipynb)
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for a more detailed demo. |