sgl-project--sglang/docs_new/docs/basic_usage/openai_api_embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenAI APIs - Embedding\n",
    "\n",
    "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n",
    "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/embeddings).\n",
    "\n",
    "This tutorial covers the embedding APIs for embedding models. For a list of the supported models see the [corresponding overview page](../supported_models/retrieval_ranking/embedding_models.md)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launch A Server\n",
    "\n",
    "Launch the server in your terminal and wait for it to initialize. Remember to add `--is-embedding` to the command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sglang.test.doc_patch import launch_server_cmd\n",
    "from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
    "\n",
    "embedding_process, port = launch_server_cmd(\"\"\"\n",
    "python3 -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-1.5B-instruct \\\n",
    "    --host 0.0.0.0 --is-embedding --log-level warning\n",
    "\"\"\")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\", process=embedding_process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using cURL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess, json\n",
    "\n",
    "text = \"Once upon a time\"\n",
    "\n",
    "curl_text = f\"\"\"curl -s http://localhost:{port}/v1/embeddings \\\n",
    "  -H \"Content-Type: application/json\" \\\n",
    "  -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", \"input\": \"{text}\"}}'\"\"\"\n",
    "\n",
    "result = subprocess.check_output(curl_text, shell=True)\n",
    "\n",
    "print(result)\n",
    "\n",
    "text_embedding = json.loads(result)[\"data\"][0][\"embedding\"]\n",
    "\n",
    "print_highlight(f\"Text embedding (first 10): {text_embedding[:10]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Python Requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "text = \"Once upon a time\"\n",
    "\n",
    "response = requests.post(\n",
    "    f\"http://localhost:{port}/v1/embeddings\",\n",
    "    json={\"model\": \"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", \"input\": text},\n",
    ")\n",
    "\n",
    "text_embedding = response.json()[\"data\"][0][\"embedding\"]\n",
    "\n",
    "print_highlight(f\"Text embedding (first 10): {text_embedding[:10]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using OpenAI Python Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "\n",
    "client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
    "\n",
    "# Text embedding example\n",
    "response = client.embeddings.create(\n",
    "    model=\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\",\n",
    "    input=text,\n",
    ")\n",
    "\n",
    "embedding = response.data[0].embedding[:10]\n",
    "print_highlight(f\"Text embedding (first 10): {embedding}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Input IDs\n",
    "\n",
    "SGLang also supports `input_ids` as input to get the embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n",
    "input_ids = tokenizer.encode(text)\n",
    "\n",
    "curl_ids = f\"\"\"curl -s http://localhost:{port}/v1/embeddings \\\n",
    "  -H \"Content-Type: application/json\" \\\n",
    "  -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", \"input\": {json.dumps(input_ids)}}}'\"\"\"\n",
    "\n",
    "input_ids_embedding = json.loads(subprocess.check_output(curl_ids, shell=True))[\"data\"][\n",
    "    0\n",
    "][\"embedding\"]\n",
    "\n",
    "print_highlight(f\"Input IDs embedding (first 10): {input_ids_embedding[:10]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(embedding_process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Modal Embedding Model\n",
    "Please refer to [Multi-Modal Embedding Model](../supported_models/retrieval_ranking/embedding_models.md)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}