add everyting for docs

docs/extras/integrations/text_embedding/xinference.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Xorbits inference (Xinference)\n",
+    "\n",
+    "This notebook goes over how to use Xinference embeddings within LangChain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "\n",
+    "Install `Xinference` through PyPI:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install \"xinference[all]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploy Xinference Locally or in a Distributed Cluster.\n",
+    "\n",
+    "For local deployment, run `xinference`. \n",
+    "\n",
+    "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.\n",
+    "\n",
+    "Then, start the Xinference workers using `xinference-worker` on each server you want to run them on. \n",
+    "\n",
+    "You can consult the README file from [Xinference](https://github.com/xorbitsai/inference) for more information.\n",
+    "\n",
+    "## Wrapper\n",
+    "\n",
+    "To use Xinference with LangChain, you need to first launch a model. You can use command line interface (CLI) to do so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model uid: 915845ee-2a04-11ee-8ed4-d29396a3f064\n"
+     ]
+    }
+   ],
+   "source": [
+    "!xinference launch -n vicuna-v1.3 -f ggmlv3 -q q4_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A model UID is returned for you to use. Now you can use Xinference embeddings with LangChain:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import XinferenceEmbeddings\n",
+    "\n",
+    "xinference = XinferenceEmbeddings(\n",
+    "    server_url=\"http://0.0.0.0:9997\",\n",
+    "    model_uid = \"915845ee-2a04-11ee-8ed4-d29396a3f064\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = xinference.embed_query(\"This is a test query\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_result = xinference.embed_documents([\"text A\", \"text B\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lastly, terminate the model when you do not need to use it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!xinference terminate --model-uid \"915845ee-2a04-11ee-8ed4-d29396a3f064\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}