playground/FastChat_API_GoogleColab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1UDur96B5C7T"
      },
      "source": [
        "# FastChat API using Google Colab\n",
        "\n",
        "[ggcr](https://github.com/ggcr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NQWpzwse8PrC"
      },
      "outputs": [],
      "source": [
        "%cd /content/\n",
        "\n",
        "# clone FastChat\n",
        "!git clone https://github.com/lm-sys/FastChat.git\n",
        "\n",
        "# install dependencies\n",
        "%cd FastChat\n",
        "!python3 -m pip install -e \".[model_worker,webui]\" --quiet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97181RzwSjha"
      },
      "source": [
        "See [openai_api.md](https://github.com/lm-sys/FastChat/blob/main/docs/openai_api.md) from FastChat docs.\n",
        "\n",
        "Because in Google Colab we are limited in resources and executing things in the background is not stable, we will run each API process in a thread and communicate them via explicit addresses:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrhPP9ZggVL0",
        "outputId": "be510360-21ba-4f6f-d6b6-24c710bdff4d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "import subprocess\n",
        "import threading\n",
        "\n",
        "%cd /content/\n",
        "\n",
        "# Using 127.0.0.1 because localhost does not work properly in Colab\n",
        "\n",
        "def run_controller():\n",
        "    subprocess.run([\"python3\", \"-m\", \"fastchat.serve.controller\", \"--host\", \"127.0.0.1\"])\n",
        "\n",
        "def run_model_worker():\n",
        "    subprocess.run([\"python3\", \"-m\", \"fastchat.serve.model_worker\", \"--host\", \"127.0.0.1\", \"--controller-address\", \"http://127.0.0.1:21001\", \"--model-path\", \"lmsys/vicuna-7b-v1.5\", \"--load-8bit\"])\n",
        "\n",
        "def run_api_server():\n",
        "    subprocess.run([\"python3\", \"-m\", \"fastchat.serve.openai_api_server\", \"--host\", \"127.0.0.1\", \"--controller-address\", \"http://127.0.0.1:21001\", \"--port\", \"8000\"])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "3S8vDHy3gWUv"
      },
      "outputs": [],
      "source": [
        "# Start controller thread\n",
        "# see `controller.log` on the local storage provided by Colab\n",
        "controller_thread = threading.Thread(target=run_controller)\n",
        "controller_thread.start()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "UAU097ymgbNf"
      },
      "outputs": [],
      "source": [
        "# Start model worker thread\n",
        "\n",
        "# see `controller.log` on the local storage provided by Colab\n",
        "# important to wait until the checkpoint shards are fully downloaded\n",
        "model_worker_thread = threading.Thread(target=run_model_worker)\n",
        "model_worker_thread.start()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "bTqHMMr1gcQJ"
      },
      "outputs": [],
      "source": [
        "# Start API server thread\n",
        "api_server_thread = threading.Thread(target=run_api_server)\n",
        "api_server_thread.start()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iBdjt9I6fuSn"
      },
      "source": [
        "We now have the API running at http://127.0.0.1:8000/v1/ locally from Google Colab.\n",
        "\n",
        "We can run the examples from FastChat with curl."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtaxADXqhazs"
      },
      "source": [
        "Try chat completion with"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZGd4y2SfBTT",
        "outputId": "066835bb-f7f0-4e16-f54a-2f74b0e2f9d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\"id\":\"chatcmpl-3RViU5mrsEBNu8oSxexAEb\",\"object\":\"chat.completion\",\"created\":1705781842,\"model\":\"vicuna-7b-v1.5\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"Sure thing! Here's one for you:\\n\\nWhy did the tomato turn red?\\n\\nBecause it saw the salad dressing!\"},\"finish_reason\":\"stop\"}],\"usage\":{\"prompt_tokens\":50,\"total_tokens\":82,\"completion_tokens\":32}}"
          ]
        }
      ],
      "source": [
        "!curl http://127.0.0.1:8000/v1/chat/completions \\\n",
        "  -H \"Content-Type: application/json\" \\\n",
        "  -d '{ \\\n",
        "    \"model\": \"vicuna-7b-v1.5\", \\\n",
        "    \"messages\": [{\"role\": \"user\", \"content\": \"Hello, can you tell me a joke for me?\"}], \\\n",
        "    \"temperature\": 0.5 \\\n",
        "  }'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umgVIilThc6a"
      },
      "source": [
        "Try embeddings with"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VraqDkMahfAQ",
        "outputId": "18710c2c-1994-4f36-eff1-6aff5a2a83a4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            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          ]
        }
      ],
      "source": [
        "!curl http://127.0.0.1:8000/v1/embeddings \\\n",
        "  -H \"Content-Type: application/json\" \\\n",
        "  -d '{ \\\n",
        "    \"model\": \"vicuna-7b-v1.5\", \\\n",
        "    \"input\": \"Hello, can you tell me a joke for me?\" \\\n",
        "  }'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-U2SZWTghxzc"
      },
      "source": [
        "Try text completion with"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85T5NO7Wh03R",
        "outputId": "1a2c9568-2aa3-4a89-ecd8-8af496be1a41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\"id\":\"cmpl-kB3gg4KtgcGdif9V4eNbh6\",\"object\":\"text_completion\",\"created\":1705782008,\"model\":\"vicuna-7b-v1.5\",\"choices\":[{\"index\":0,\"text\":\", there was a little girl named Alice. Alice lived in a small village nestled in a valley\",\"logprobs\":null,\"finish_reason\":\"length\"}],\"usage\":{\"prompt_tokens\":5,\"total_tokens\":24,\"completion_tokens\":19}}"
          ]
        }
      ],
      "source": [
        "!curl http://127.0.0.1:8000/v1/completions \\\n",
        "  -H \"Content-Type: application/json\" \\\n",
        "  -d '{ \\\n",
        "    \"model\": \"vicuna-7b-v1.5\", \\\n",
        "    \"prompt\": \"Once upon a time\", \\\n",
        "    \"max_tokens\": 20, \\\n",
        "    \"temperature\": 0.5 \\\n",
        "  }'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDxLbQDKVLiQ"
      },
      "source": [
        "Try create_embeddings to analyze the prompts!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbrFoxgaplhK",
        "outputId": "48e23158-1468-445d-a4cd-b5bd67bd3bde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cosine similarity between 'The quick brown fox' and 'The quick brown dog': 0.90\n",
            "Cosine similarity between 'The quick brown fox' and 'The fast brown fox': 0.86\n",
            "Cosine similarity between 'The quick brown fox' and 'A completely different sentence': 0.58\n",
            "Cosine similarity between 'The quick brown dog' and 'The fast brown fox': 0.84\n",
            "Cosine similarity between 'The quick brown dog' and 'A completely different sentence': 0.66\n",
            "Cosine similarity between 'The fast brown fox' and 'A completely different sentence': 0.62\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "import numpy as np\n",
        "import requests\n",
        "from scipy.spatial.distance import cosine\n",
        "\n",
        "\n",
        "def get_embedding_from_api(word, model='vicuna-7b-v1.5'):\n",
        "    url = 'http://127.0.0.1:8000/v1/embeddings'\n",
        "    headers = {'Content-Type': 'application/json'}\n",
        "    data = json.dumps({\n",
        "        'model': model,\n",
        "        'input': word\n",
        "    })\n",
        "\n",
        "    response = requests.post(url, headers=headers, data=data)\n",
        "    if response.status_code == 200:\n",
        "        embedding = np.array(response.json()['data'][0]['embedding'])\n",
        "        return embedding\n",
        "    else:\n",
        "        print(f\"Error: {response.status_code} - {response.text}\")\n",
        "        return None\n",
        "\n",
        "\n",
        "def cosine_similarity(vec1, vec2):\n",
        "    return 1 - cosine(vec1, vec2)\n",
        "\n",
        "\n",
        "def print_cosine_similarity(embeddings, texts):\n",
        "    for i in range(len(texts)):\n",
        "        for j in range(i + 1, len(texts)):\n",
        "            sim = cosine_similarity(embeddings[texts[i]], embeddings[texts[j]])\n",
        "            print(f\"Cosine similarity between '{texts[i]}' and '{texts[j]}': {sim:.2f}\")\n",
        "\n",
        "\n",
        "texts = [\n",
        "    'The quick brown fox',\n",
        "    'The quick brown dog',\n",
        "    'The fast brown fox',\n",
        "    'A completely different sentence'\n",
        "]\n",
        "\n",
        "embeddings = {}\n",
        "for text in texts:\n",
        "    embeddings[text] = get_embedding_from_api(text)\n",
        "\n",
        "print_cosine_similarity(embeddings, texts)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}