{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embedding Model\n",
    "\n",
    "SGLang supports embedding models in the same way as completion models. Here are some example models:\n",
    "\n",
    "- [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)\n",
    "- [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launch A Server\n",
    "\n",
    "The following code is equivalent to running this in the shell:\n",
    "```bash\n",
    "python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n",
    "    --port 30010 --host 0.0.0.0 --is-embedding --log-level error\n",
    "```\n",
    "\n",
    "Remember to add `--is-embedding` to the command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding server is ready. Proceeding with the next steps.\n"
     ]
    }
   ],
   "source": [
    "from sglang.utils import execute_shell_command, wait_for_server, terminate_process\n",
    "\n",
    "embedding_process = execute_shell_command(\n",
    "    \"\"\"\n",
    "python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n",
    "    --port 30010 --host 0.0.0.0 --is-embedding --log-level error\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "wait_for_server(\"http://localhost:30010\")\n",
    "\n",
    "print(\"Embedding server is ready. Proceeding with the next steps.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Curl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text embedding (first 10): [0.0083160400390625, 0.0006804466247558594, -0.00809478759765625, -0.0006995201110839844, 0.0143890380859375, -0.0090179443359375, 0.01238250732421875, 0.00209808349609375, 0.0062103271484375, -0.003047943115234375]\n"
     ]
    }
   ],
   "source": [
    "import subprocess, json\n",
    "\n",
    "text = \"Once upon a time\"\n",
    "\n",
    "curl_text = f\"\"\"curl -s http://localhost:30010/v1/embeddings \\\n",
    "  -H \"Content-Type: application/json\" \\\n",
    "  -H \"Authorization: Bearer None\" \\\n",
    "  -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"input\": \"{text}\"}}'\"\"\"\n",
    "\n",
    "text_embedding = json.loads(subprocess.check_output(curl_text, shell=True))[\"data\"][0][\n",
    "    \"embedding\"\n",
    "]\n",
    "\n",
    "print(f\"Text embedding (first 10): {text_embedding[:10]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using OpenAI Compatible API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text embedding (first 10): [0.00829315185546875, 0.0007004737854003906, -0.00809478759765625, -0.0006799697875976562, 0.01438140869140625, -0.00897979736328125, 0.0123748779296875, 0.0020923614501953125, 0.006195068359375, -0.0030498504638671875]\n"
     ]
    }
   ],
   "source": [
    "import openai\n",
    "\n",
    "client = openai.Client(base_url=\"http://127.0.0.1:30010/v1\", api_key=\"None\")\n",
    "\n",
    "# Text embedding example\n",
    "response = client.embeddings.create(\n",
    "    model=\"Alibaba-NLP/gte-Qwen2-7B-instruct\",\n",
    "    input=text,\n",
    ")\n",
    "\n",
    "embedding = response.data[0].embedding[:10]\n",
    "print(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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input IDs embedding (first 10): [0.00829315185546875, 0.0007004737854003906, -0.00809478759765625, -0.0006799697875976562, 0.01438140869140625, -0.00897979736328125, 0.0123748779296875, 0.0020923614501953125, 0.006195068359375, -0.0030498504638671875]\n"
     ]
    }
   ],
   "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-Qwen2-7B-instruct\")\n",
    "input_ids = tokenizer.encode(text)\n",
    "\n",
    "curl_ids = f\"\"\"curl -s http://localhost:30010/v1/embeddings \\\n",
    "  -H \"Content-Type: application/json\" \\\n",
    "  -H \"Authorization: Bearer None\" \\\n",
    "  -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-7B-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(f\"Input IDs embedding (first 10): {input_ids_embedding[:10]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(embedding_process)"
   ]
  }
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