{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "source": [ "# FastChat API using Google Colab\n", "\n", "[ggcr](https://github.com/ggcr)" ], "metadata": { "id": "1UDur96B5C7T" } }, { "cell_type": "code", "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" ], "metadata": { "id": "NQWpzwse8PrC" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "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:" ], "metadata": { "id": "97181RzwSjha" } }, { "cell_type": "code", "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" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BrhPP9ZggVL0", "outputId": "be510360-21ba-4f6f-d6b6-24c710bdff4d" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content\n" ] } ] }, { "cell_type": "code", "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()" ], "metadata": { "id": "3S8vDHy3gWUv" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "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" ], "metadata": { "id": "UAU097ymgbNf" }, "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "# Start API server thread\n", "api_server_thread = threading.Thread(target=run_api_server)\n", "api_server_thread.start()" ], "metadata": { "id": "bTqHMMr1gcQJ" }, "execution_count": 12, "outputs": [] }, { "cell_type": "markdown", "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." ], "metadata": { "id": "iBdjt9I6fuSn" } }, { "cell_type": "markdown", "source": [ "Try chat completion with" ], "metadata": { "id": "KtaxADXqhazs" } }, { "cell_type": "code", "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", " }'" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MZGd4y2SfBTT", "outputId": "066835bb-f7f0-4e16-f54a-2f74b0e2f9d9" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "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}}" ] } ] }, { "cell_type": "markdown", "source": [ "Try embeddings with" ], "metadata": { "id": "umgVIilThc6a" } }, { "cell_type": "code", "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", " }'" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VraqDkMahfAQ", "outputId": "18710c2c-1994-4f36-eff1-6aff5a2a83a4" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ 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] } ] }, { "cell_type": "markdown", "source": [ "Try text completion with" ], "metadata": { "id": "-U2SZWTghxzc" } }, { "cell_type": "code", "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", " }'" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "85T5NO7Wh03R", "outputId": "1a2c9568-2aa3-4a89-ecd8-8af496be1a41" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stdout", "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}}" ] } ] }, { "cell_type": "markdown", "source": [ "Try create_embeddings to analyze the prompts!" ], "metadata": { "id": "EDxLbQDKVLiQ" } }, { "cell_type": "code", "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": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bbrFoxgaplhK", "outputId": "48e23158-1468-445d-a4cd-b5bd67bd3bde" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "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" ] } ] } ] }