baichuan13b-langchain-vectordb_deploy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a63c0138-d19f-4109-aac5-fd0f028a5869",
   "metadata": {},
   "source": [
    "### 1.安装 HuggingFace 并下载模型到本地"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "efb1a261-748a-470a-900f-1863db83447b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install huggingface-hub -Uq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae8f333d-7708-4ffc-8887-55e9bff44129",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import snapshot_download\n",
    "from pathlib import Path\n",
    "\n",
    "local_model_path = Path(\"./LLM_baichuan-13b-chat_model\")\n",
    "local_model_path.mkdir(exist_ok=True)\n",
    "model_name = \"baichuan-inc/Baichuan-13B-Chat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9dd16c8-e2f7-4d59-8d10-b763a28b6a3f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "snapshot_download(repo_id=model_name, cache_dir=local_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bbeea66-a864-4403-984a-e1f86b9958ab",
   "metadata": {},
   "source": [
    "### 2.SageMaker 初始化配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0660d52d-9c07-4445-893b-d95a2227eeb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "import boto3\n",
    "import os\n",
    "from sagemaker import image_uris\n",
    "\n",
    "role = sagemaker.get_execution_role()  # execution role for the endpoint\n",
    "sess = sagemaker.session.Session()  # sagemaker session for interacting with different AWS APIs\n",
    "bucket = sess.default_bucket()  # bucket to house artifacts\n",
    "region = sess._region_name\n",
    "account_id = sess.account_id()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65185ac-7a43-4661-9fcb-7d77541a04d2",
   "metadata": {},
   "source": [
    "### 3. 把模型拷贝到 S3 存储桶为后续部署做准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bf8cccf-f3b7-4d34-ab14-be364901c1ed",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "s3_model_prefix = \"LLM_baichuan-13b-chat_model\"  # folder where model checkpoint will go\n",
    "model_snapshot_path = list(local_model_path.glob(\"**/snapshots/*\"))[0]\n",
    "s3_code_prefix = \"LLM_baichuan-13b-chat_deploy_code\"\n",
    "\n",
    "print(f\"s3_code_prefix: {s3_code_prefix}\")\n",
    "print(f\"model_snapshot_path: {model_snapshot_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "068e3ed1-9a9b-4ef2-92dd-4afcd9c1ff3c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "s3_client = boto3.client(\"s3\")\n",
    "\n",
    "for root, dirs, files in os.walk(model_snapshot_path):\n",
    "    for file in files:\n",
    "        local_path = os.path.join(root, file)\n",
    "        s3_key = s3_model_prefix + '/' + os.path.relpath(local_path, model_snapshot_path)\n",
    "        s3_client.upload_file(local_path, bucket, s3_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0527e45-c2da-477b-91fb-8d878a735ca2",
   "metadata": {},
   "source": [
    "### 4.模型部署准备"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4efb3b82-d8c5-41d3-86ac-6ebfeb7e01c5",
   "metadata": {
    "tags": []
   },
   "source": [
    "* 推理容器镜像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8269e86f-d50f-4857-a079-1a7a67f8e038",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "inference_image_uri = (\n",
    "    f\"763104351884.dkr.ecr.{region}.amazonaws.com/djl-inference:0.22.1-deepspeed0.9.2-cu118\"\n",
    ")\n",
    "\n",
    "# 中国区需要替换为下面的image_uri\n",
    "# inference_image_uri = (\n",
    "#     f\"727897471807.dkr.ecr.{region}.amazonaws.com.cn/djl-inference:0.22.1-deepspeed0.9.2-cu118\"\n",
    "# )\n",
    "\n",
    "print(f\"Image going to be used is ---- > {inference_image_uri}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "faaf6561-3a6f-4482-8190-909980e9f8ec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Baichuan13bChat_deploy_code_path = Path(\"./LLM_baichuan-13b-chat_deploy_code\")\n",
    "Baichuan13bChat_deploy_code_path.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41f93edb-2a6a-4951-a4c6-4dbfdec4152b",
   "metadata": {},
   "source": [
    "* Entrypoint 脚本 model.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cba608c3-b401-4978-9c42-7bee13e209e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%writefile LLM_baichuan-13b-chat_deploy_code/model.py\n",
    "from djl_python import Input, Output\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "from transformers.generation.utils import GenerationConfig\n",
    "import torch\n",
    "import logging\n",
    "\n",
    "def load_model(properties):\n",
    "    tensor_parallel = properties[\"tensor_parallel_degree\"]\n",
    "    model_location = properties['model_dir']\n",
    "    if \"model_id\" in properties:\n",
    "        model_location = properties['model_id']\n",
    "    logging.info(f\"Loading model in {model_location}\")\n",
    "    \n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_location, use_fast=False, trust_remote_code=True)\n",
    "    model = AutoModelForCausalLM.from_pretrained(model_location, device_map=\"auto\", torch_dtype=torch.float16, trust_remote_code=True)\n",
    "    model.generation_config = GenerationConfig.from_pretrained(model_location)\n",
    "    \n",
    "    return model, tokenizer\n",
    "\n",
    "model = None\n",
    "tokenizer = None\n",
    "\n",
    "def handle(inputs: Input):\n",
    "    global model, tokenizer\n",
    "    if not model:\n",
    "        model, tokenizer = load_model(inputs.get_properties())\n",
    "\n",
    "    if inputs.is_empty():\n",
    "        return None\n",
    "    data = inputs.get_as_json()\n",
    "    \n",
    "    message = data[\"inputs\"]\n",
    "    # params = data[\"parameters\"]\n",
    "    # history = data[\"history\"]\n",
    "    \n",
    "    response = model.chat(tokenizer, message)\n",
    "    \n",
    "    result = {\"outputs\": response}\n",
    "    return Output().add_as_json(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec57722-9a43-4011-80cf-f047fafaae34",
   "metadata": {},
   "source": [
    "* 模型运行依赖包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a996c9e-0859-44a5-84f5-9569eccacb2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile LLM_baichuan-13b-chat_deploy_code/requirements.txt\n",
    "transformers_stream_generator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada3b51f-03ee-4452-a974-aeccc68512e1",
   "metadata": {
    "tags": []
   },
   "source": [
    "* serving.properties 配置文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6af1838-0e93-416a-9f0a-27641d8736a8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(f\"option.s3url ==> s3://{bucket}/{s3_model_prefix}/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "677ba16e-a749-4f6f-8566-d71b8afae8de",
   "metadata": {},
   "source": [
    "> 需要修改按照上述步骤的 s3url 修改 option.s3url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "834150fe-a3cb-4d05-b479-ad73907cc6d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%writefile LLM_baichuan-13b-chat_deploy_code/serving.properties\n",
    "engine=Python\n",
    "option.tensor_parallel_degree=4\n",
    "option.s3url = s3://sagemaker-us-east-1-091166060467/LLM_baichuan-13b-chat_model/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b6a5a6-8c04-4e2c-ab53-b0e96082c14d",
   "metadata": {},
   "source": [
    "* 将配置文件压缩后上传 S3 存储桶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4560c8a0-6e24-4ee0-bd89-f6673c0b88b0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tarfile\n",
    "\n",
    "folder_path = 'LLM_baichuan-13b-chat_deploy_code'\n",
    "output_filename = 'model.tar.gz'\n",
    "\n",
    "with tarfile.open(output_filename, \"w:gz\") as tar:\n",
    "    tar.add(folder_path, arcname=os.path.basename(folder_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0ef2e7b-411d-4b97-8d20-f36ab15a56b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "s3_code_artifact = sess.upload_data(\"model.tar.gz\", bucket, s3_code_prefix)\n",
    "print(f\"S3 Code or Model tar ball uploaded to --- > {s3_code_artifact}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c95d83b-996e-4e7a-a84d-9b16406496be",
   "metadata": {},
   "source": [
    "### 5. 模型部署"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "abc0dc85-2760-4132-9a60-9da0eb77ffc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.model import Model\n",
    "\n",
    "def create_model(model_name, model_s3_url):\n",
    "    model = Model(\n",
    "        image_uri=inference_image_uri,\n",
    "        model_data=model_s3_url,\n",
    "        role=role,\n",
    "        name=model_name,\n",
    "        sagemaker_session=sess,\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8ed2e95a-54ca-4b9e-a479-9d3da31079ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker import serializers, deserializers\n",
    "\n",
    "def deploy_model(model, _endpoint_name):\n",
    "    model.deploy(\n",
    "        initial_instance_count=1,\n",
    "        instance_type=\"ml.g5.12xlarge\",\n",
    "        endpoint_name=_endpoint_name\n",
    "    )\n",
    "    predictor = sagemaker.Predictor(\n",
    "        endpoint_name=_endpoint_name,\n",
    "        sagemaker_session=sess,\n",
    "        serializer=serializers.JSONSerializer(),\n",
    "        deserializer=deserializers.JSONDeserializer()\n",
    "    )\n",
    "    return predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2dacdec-395c-4144-aa38-5e706faa1dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.utils import name_from_base\n",
    "\n",
    "_model_name = name_from_base(f\"baichuan-13b-chat\") # Append a timestamp to the provided string\n",
    "_model_s3_url = s3_code_artifact\n",
    "_endpoint_name = f\"{_model_name}-endpoint\"\n",
    "\n",
    "model = create_model(_model_name, _model_s3_url)\n",
    "llm_predictor = deploy_model(model, _endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bfd0e0a-bd9c-41cd-9e0b-ce1641542076",
   "metadata": {},
   "source": [
    "### 6. 模型测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0a82625f-cee0-409c-9060-be8620432ae8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "context = \"\"\"\n",
    "你是气象专家智能对话助手小雷，了解各种专业的气象知识和气象信息，可以自由对话以及回答问题，像人类一样思考和表达。\n",
    "当有人向你提问时你必须使用，“您好，我是气象专家智能对话助手小雷”这句话作为开头\"。\n",
    "当有人询问你的身份是谁时，你必须回答“我是气象专家智能对话助手小雷”\n",
    "\"\"\"\n",
    "\n",
    "messages = []\n",
    "messages.append({'role': 'user', 'content': context})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "788c27fc-0b21-4de3-9ab0-1bd18e0d88a7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是气象专家智能对话助手小雷\n"
     ]
    }
   ],
   "source": [
    "prompts = \"\"\"你是谁\"\"\"\n",
    "\n",
    "messages.append({'role': 'user', 'content': prompts})\n",
    "response = llm_predictor.predict(\n",
    "    {\n",
    "        \"inputs\" : messages, \n",
    "    }\n",
    ")\n",
    "messages.append({\"role\": \"assistant\", \"content\": response['outputs']})\n",
    "\n",
    "print(response['outputs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f8b817ba-9327-45b9-a9b6-61e775a8df07",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "是的，北京夏季的降雨量相较于其他季节较高。这主要是因为夏季受到来自太平洋的东南季风影响，使得降水较为丰富。此外，夏季气温升高，空气中的水分容易凝结成云，形成降雨。因此，在夏季，北京市经常会出现雷阵雨等天气现象。\n"
     ]
    }
   ],
   "source": [
    "prompts = \"\"\"北京是不是夏天雨水比较多？\"\"\"\n",
    "\n",
    "messages.append({'role': 'user', 'content': prompts})\n",
    "response = llm_predictor.predict(\n",
    "    {\n",
    "        \"inputs\" : messages, \n",
    "    }\n",
    ")\n",
    "messages.append({\"role\": \"assistant\", \"content\": response['outputs']})\n",
    "\n",
    "print(response['outputs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ff94d396-55f2-49d7-8eed-63333d478c4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在2019年7月的一天，北京市遭遇了一场强降雨过程。据统计，这场暴雨导致了全市多处出现积水，部分地区出现了严重内涝情况。其中，海淀区、朝阳区的部分路段积水深度达到了1米左右，给市民出行带来了很大不便。为了应对这次强降雨，北京市政府及时启动了防汛应急响应措施，组织人员进行抢险救援工作，确保人民群众生命财产安全。所以，从这个具体的例子来看，确实如我所言，北京夏天的雨水较多。\n",
      "\n",
      "[{'role': 'user', 'content': '\\n你是气象专家智能对话助手小雷，了解各种专业的气象知识和气象信息，可以自由对话以及回答问题，像人类一样思考和表达。\\n当有人向你提问时你必须使用，“您好，我是气象专家智能对话助手小雷”这句话作为开头\"。\\n当有人询问你的身份是谁时，你必须回答“我是气象专家智能对话助手小雷”\\n'}, {'role': 'user', 'content': '你是谁'}, {'role': 'assistant', 'content': '我是气象专家智能对话助手小雷'}, {'role': 'user', 'content': '北京是不是夏天雨水比较多？'}, {'role': 'assistant', 'content': '是的，北京夏季的降雨量相较于其他季节较高。这主要是因为夏季受到来自太平洋的东南季风影响，使得降水较为丰富。此外，夏季气温升高，空气中的水分容易凝结成云，形成降雨。因此，在夏季，北京市经常会出现雷阵雨等天气现象。'}, {'role': 'user', 'content': '你说的是真的吗？举个具体例子吧'}, {'role': 'assistant', 'content': '在2019年7月的一天，北京市遭遇了一场强降雨过程。据统计，这场暴雨导致了全市多处出现积水，部分地区出现了严重内涝情况。其中，海淀区、朝阳区的部分路段积水深度达到了1米左右，给市民出行带来了很大不便。为了应对这次强降雨，北京市政府及时启动了防汛应急响应措施，组织人员进行抢险救援工作，确保人民群众生命财产安全。所以，从这个具体的例子来看，确实如我所言，北京夏天的雨水较多。'}]\n"
     ]
    }
   ],
   "source": [
    "prompts = \"\"\"你说的是真的吗？举个具体例子吧\"\"\"\n",
    "\n",
    "messages.append({'role': 'user', 'content': prompts})\n",
    "response = llm_predictor.predict(\n",
    "    {\n",
    "        \"inputs\" : messages, \n",
    "    }\n",
    ")\n",
    "messages.append({\"role\": \"assistant\", \"content\": response['outputs']})\n",
    "\n",
    "print(response['outputs']+'\\n')\n",
    "print(messages)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be408586",
   "metadata": {},
   "source": [
    "### 7. 结合向量数据库私域数据构建专业知识问答系统"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b02395",
   "metadata": {},
   "source": [
    "#### 7.1 部署 Embedding 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "675ea605",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install huggingface-hub -Uq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "47cd1b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sagemaker\n",
    "# import boto3\n",
    "# import os\n",
    "# from sagemaker import image_uris\n",
    "\n",
    "# role = sagemaker.get_execution_role()  # execution role for the endpoint\n",
    "# sess = sagemaker.session.Session()  # sagemaker session for interacting with different AWS APIs\n",
    "# bucket = sess.default_bucket()  # bucket to house artifacts\n",
    "# region = sess._region_name\n",
    "# account_id = sess.account_id()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a71421",
   "metadata": {},
   "source": [
    "* 下载 Embedding 模型并拷贝至 S3 存储桶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "194ef0ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import snapshot_download\n",
    "from pathlib import Path\n",
    "\n",
    "local_embedding_model_path = Path(\"./embedding_model\")\n",
    "local_embedding_model_path.mkdir(exist_ok=True)\n",
    "embedding_model_name = \"moka-ai/m3e-base\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8d858c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "snapshot_download(repo_id=embedding_model_name, cache_dir=local_embedding_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ebdb747",
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_embedding_model_prefix = \"embedding_model\"  # folder where model checkpoint will go\n",
    "embedding_model_snapshot_path = list(local_embedding_model_path.glob(\"**/snapshots/*\"))[0]\n",
    "s3_embedding_code_prefix = \"embedding_deploy_code\"\n",
    "\n",
    "print(f\"s3_embedding_code_prefix: {s3_embedding_model_prefix}\")\n",
    "print(f\"embedding_model_snapshot_path: {embedding_model_snapshot_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1e96b32a",
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_client = boto3.client(\"s3\")\n",
    "\n",
    "for root, dirs, files in os.walk(embedding_model_snapshot_path):\n",
    "    for file in files:\n",
    "        local_path = os.path.join(root, file)\n",
    "        s3_key = s3_embedding_model_prefix + '/' + os.path.relpath(local_path, embedding_model_snapshot_path)\n",
    "        s3_client.upload_file(local_path, bucket, s3_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e89064f0",
   "metadata": {},
   "source": [
    "* 模型部署准备"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2152489",
   "metadata": {},
   "source": [
    ">推理容器镜像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4144ece0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# inference_image_uri = (\n",
    "#     f\"763104351884.dkr.ecr.{region}.amazonaws.com/djl-inference:0.22.1-deepspeed0.9.2-cu118\"\n",
    "# )\n",
    "\n",
    "# 中国区需要替换为下面的image_uri\n",
    "# inference_image_uri = (\n",
    "#     f\"727897471807.dkr.ecr.{region}.amazonaws.com.cn/djl-inference:0.22.1-deepspeed0.9.2-cu118\"\n",
    "# )\n",
    "\n",
    "# print(f\"Image going to be used is ---- > {inference_image_uri}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9025887",
   "metadata": {},
   "source": [
    "> Entrypoint 脚本 model.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96003aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_deploy_code_path = Path(\"./embedding_deploy_code\")\n",
    "embedding_deploy_code_path.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd545d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile embedding_deploy_code/model.py\n",
    "from djl_python import Input, Output\n",
    "from transformers import AutoModel, AutoTokenizer\n",
    "import torch\n",
    "import logging\n",
    "\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print(f'--device={device}')\n",
    "\n",
    "def load_model(properties):\n",
    "    tensor_parallel = properties[\"tensor_parallel_degree\"]\n",
    "    model_location = properties['model_dir']\n",
    "    if \"model_id\" in properties:\n",
    "        model_location = properties['model_id']\n",
    "    logging.info(f\"Loading model in {model_location}\")\n",
    "    \n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_location)\n",
    "    \n",
    "    model = AutoModel.from_pretrained(model_location)\n",
    "    model.to(device) \n",
    "    \n",
    "    return model, tokenizer\n",
    "\n",
    "model = None\n",
    "tokenizer = None\n",
    "\n",
    "def mean_pooling(model_output, attention_mask):\n",
    "    token_embeddings = model_output[0].to(device) #First element of model_output contains all token embeddings\n",
    "    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float().to(device)\n",
    "    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n",
    "\n",
    "def handle(inputs: Input):\n",
    "    global model, tokenizer\n",
    "    if not model:\n",
    "        model, tokenizer = load_model(inputs.get_properties())\n",
    "\n",
    "    if inputs.is_empty():\n",
    "        return None\n",
    "    data = inputs.get_as_json()\n",
    "    \n",
    "    input_sentences = data[\"inputs\"]\n",
    "    logging.info(f\"inputs: {input_sentences}\")\n",
    "    \n",
    "    encoded_input = tokenizer(input_sentences, padding=True, truncation=True, return_tensors='pt').to(device)\n",
    "    # Compute token embeddings\n",
    "    with torch.no_grad():\n",
    "        model_output = model(**encoded_input)\n",
    "    \n",
    "    # Perform pooling. In this case, max pooling.\n",
    "    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']).to(device).cpu().numpy()\n",
    "\n",
    "    \n",
    "    result = {\"sentence_embeddings\": sentence_embeddings}\n",
    "    return Output().add_as_json(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef48f80",
   "metadata": {},
   "source": [
    ">serving.properties 配置文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5076ccb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"option.s3url ==> s3://{bucket}/{s3_embedding_model_prefix}/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e96606",
   "metadata": {},
   "source": [
    ">需要修改按照上述步骤的 s3url 修改 option.s3url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77ea481f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile embedding_deploy_code/serving.properties\n",
    "engine=Python\n",
    "option.tensor_parallel_degree=1\n",
    "option.s3url = s3://sagemaker-us-east-1-091166060467/embedding_model/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961babb5",
   "metadata": {},
   "source": [
    ">将配置文件压缩后上传 S3 存储桶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "985d52c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tarfile\n",
    "\n",
    "folder_path = 'embedding_deploy_code'\n",
    "output_filename = 'embedding_model.tar.gz'\n",
    "\n",
    "with tarfile.open(output_filename, \"w:gz\") as tar:\n",
    "    tar.add(folder_path, arcname=os.path.basename(folder_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fea4128",
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_embedding_code_artifact = sess.upload_data(\"embedding_model.tar.gz\", bucket, s3_embedding_model_prefix)\n",
    "print(f\"S3 Code or Model tar ball uploaded to --- > {s3_embedding_code_artifact}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccda4f7e",
   "metadata": {},
   "source": [
    "* 模型部署"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "45792342",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.model import Model\n",
    "\n",
    "def create_model(embedding_model_name, embedding_model_s3_url):\n",
    "    model = Model(\n",
    "        image_uri=inference_image_uri,\n",
    "        model_data=embedding_model_s3_url,\n",
    "        role=role,\n",
    "        name=embedding_model_name,\n",
    "        sagemaker_session=sess,\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8419d404",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker import serializers, deserializers\n",
    "\n",
    "def deploy_model(embedding_model, _embedding_endpoint_name):\n",
    "    embedding_model.deploy(\n",
    "        initial_instance_count=1,\n",
    "        instance_type=\"ml.g5.2xlarge\",\n",
    "        endpoint_name=_embedding_endpoint_name\n",
    "    )\n",
    "    predictor = sagemaker.Predictor(\n",
    "        endpoint_name=_embedding_endpoint_name,\n",
    "        sagemaker_session=sess,\n",
    "        serializer=serializers.JSONSerializer(),\n",
    "        deserializer=deserializers.JSONDeserializer()\n",
    "    )\n",
    "    return predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88669423",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.utils import name_from_base\n",
    "\n",
    "_embedding_model_name = name_from_base(f\"embedding\") # Append a timestamp to the provided string\n",
    "_embedding_model_s3_url = s3_embedding_code_artifact\n",
    "_embedding_endpoint_name = f\"{_embedding_model_name}-endpoint\"\n",
    "\n",
    "embedding_model = create_model(_embedding_model_name, _embedding_model_s3_url)\n",
    "embedding_predictor = deploy_model(embedding_model, _embedding_endpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ec91a334",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sentence_embeddings': [[0.16614669561386108, 0.2106981873512268, 0.03797636181116104, -1.018144130706787, 0.9220374822616577, -0.34364408254623413, -0.3254203200340271, 0.3959978520870209, -0.28563323616981506, 0.00716012017801404, 1.3837165832519531, -0.1299036592245102, 0.08019127696752548, -0.3404189646244049, -0.9665927290916443, 0.48589572310447693, 0.5809511542320251, 0.5983569025993347, -0.44463297724723816, -0.5546059012413025, 0.6520602107048035, -0.49927085638046265, -1.0214600563049316, 1.2074916362762451, -0.3740547001361847, 0.12379224598407745, 0.35420382022857666, -0.5372006893157959, -0.227334126830101, -0.3129538297653198, 1.1523759365081787, -0.40375426411628723, -0.47171226143836975, -0.13515453040599823, -0.13646677136421204, 0.649815559387207, 0.6560749411582947, -0.1027904823422432, 1.042426347732544, 1.0639841556549072, 0.18004821240901947, 0.36135515570640564, -0.2584298551082611, -0.1127224862575531, 0.14170441031455994, 0.15764649212360382, 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-0.1896706074476242, -0.24049411714076996, 0.17885710299015045, -1.143403172492981, -0.49723249673843384, -1.2498900890350342, -0.5950004458427429, 0.00944029912352562]]}\n"
     ]
    }
   ],
   "source": [
    "# Embedding 模型验证\n",
    "\n",
    "prompts = \"\"\"\n",
    "北京是不是夏天雨水比较多？\n",
    "\"\"\"\n",
    "\n",
    "reponse = embedding_predictor.predict(\n",
    "    {\n",
    "        \"inputs\" : prompts\n",
    "    }\n",
    ")\n",
    "\n",
    "print(reponse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65fc63ad",
   "metadata": {},
   "source": [
    "#### 7.2 通过 LangChain 使用 Embedding 模型处理文档"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bb4eaac1-98aa-4a93-aafb-f19879da7947",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip3 install langchain -Uq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cec298b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, List\n",
    "from langchain.embeddings import SagemakerEndpointEmbeddings\n",
    "from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler\n",
    "import json\n",
    "\n",
    "class EmbeddingContentHandler(EmbeddingsContentHandler):\n",
    "    content_type = \"application/json\"\n",
    "    accepts = \"application/json\"\n",
    "    \n",
    "    def transform_input(self, prompt: str, model_kwargs={}) -> bytes:\n",
    "        input_str = json.dumps({\"inputs\": prompt, **model_kwargs})\n",
    "        return input_str.encode(\"utf-8\")\n",
    "    \n",
    "    def transform_output(self, output: bytes) -> List[List[float]]:\n",
    "        response_json = json.loads(output.read().decode(\"utf-8\"))\n",
    "        return response_json[\"sentence_embeddings\"]\n",
    "    \n",
    "embedding_content_handler = EmbeddingContentHandler()\n",
    "\n",
    "embeddings = SagemakerEndpointEmbeddings(\n",
    "    # credentials_profile_name=\"credentials-profile-name\",\n",
    "    endpoint_name =_embedding_endpoint_name,\n",
    "    region_name = \"us-east-1\",\n",
    "    content_handler = embedding_content_handler,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c05481a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 验证 LangChain 调用 Embedding 模型\n",
    "\n",
    "# query_result = embeddings.embed_query(\"query\")\n",
    "\n",
    "# doc_results = embeddings.embed_documents(['content1', 'content2'])\n",
    "\n",
    "# print(query_result, '\\n\\n', doc_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb90719",
   "metadata": {},
   "source": [
    "#### 7.3 私域文档处理及私域文档 Embedding 处理后存入 Chroma 向量数据库"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf4da77",
   "metadata": {},
   "source": [
    "* 私域文档加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49b92f95-9922-4c9f-a056-ad9295289d7b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!git clone https://github.com/terrificdm/llm-sagemaker-examples\n",
    "!mv llm-sagemaker-examples/content ./"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af5d00b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import DirectoryLoader\n",
    "from langchain.document_loaders import TextLoader\n",
    "\n",
    "directory = './content'\n",
    "\n",
    "def load_docs(directory):\n",
    "  loader = DirectoryLoader(directory, show_progress=True, loader_cls=TextLoader)\n",
    "  documents = loader.load()\n",
    "  return documents\n",
    "\n",
    "documents = load_docs(directory)\n",
    "len(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b473f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "\n",
    "count = 0\n",
    "for doc in documents:\n",
    "    for line in doc.page_content.split('\\n'):\n",
    "        if line.startswith('Question'):\n",
    "            count += 1\n",
    "\n",
    "print(f'Total number of questions: {count}')\n",
    "pprint.pprint(documents)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a68283",
   "metadata": {},
   "source": [
    "* 文档切分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "910e6e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "\n",
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    separators=[\"Question\"], \n",
    "    chunk_size = 0,\n",
    "    chunk_overlap = 0,\n",
    "    length_function = len,\n",
    "    # add_start_index = True,\n",
    ")\n",
    "\n",
    "docs = text_splitter.split_documents(documents)\n",
    "\n",
    "pprint.pprint(docs)\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a736792",
   "metadata": {},
   "source": [
    "* 部署 [Chroma 向量数据库](https://docs.trychroma.com/)，及私域文档 embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ea0fa887",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install chromadb -Uq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6f0600d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import Chroma\n",
    "\n",
    "embedding_function = embeddings\n",
    "\n",
    "# Non-persistence Chroma, you can use Chroma in persistent way as described in its documents. \n",
    "db = Chroma.from_documents(docs, embedding_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5fa4e61e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question：沙穹秘境是什么类型的游戏？\n",
      "Answer：沙穹秘境是一款冒险类的开放世界游戏。\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 验证通过 embedding 检索私域数据\n",
    "\n",
    "query = \"沙穹秘境是什么\"\n",
    "content = db.similarity_search(query, k=1)\n",
    "\n",
    "print(content[0].page_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f9098a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MMR Retriever\n",
    "\n",
    "# retriever = db.as_retriever(search_type=\"mmr\")\n",
    "# retriever.get_relevant_documents(query)[0].page_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fcda59f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import chromadb\n",
    "\n",
    "# client = chromadb.Client()\n",
    "# client.list_collections()\n",
    "# collection = client.get_collection(\"langchain\")\n",
    "# collection.count()\n",
    "\n",
    "# collection.delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69889cf1",
   "metadata": {},
   "source": [
    "#### 7.4 构建专业问答机器人"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "095c12b8-72e9-4a55-bbba-f61661c3d86a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "game_name = \"沙穹秘境\"\n",
    "bot_name = \"CelestialSandsBot\"\n",
    "messages = []\n",
    "history = []\n",
    "prompts_context = []\n",
    "\n",
    "def process_content(content_list):\n",
    "    return ''.join([doc.page_content for doc in content_list])\n",
    "\n",
    "def build_prompts_context(game_name, bot_name, content):\n",
    "    context = f\"\"\"\n",
    "你是{bot_name}，{game_name}的专属智能客服，你不能谈论其他游戏。\n",
    "当向你提问时你必须使用，“您好，我是{game_name}的专属智能客服{bot_name}”这句话作为开头\"。\n",
    "当询问你的身份是谁时，你必须回答“我是{game_name}的专属智能客服{bot_name}”\n",
    "你是一个非常专业的游戏客服，请从下面三个反引号中的文档中提取和问题相关的内容，并结合上下文形成答案，以简洁明了的方式回答问题。\n",
    "如果三个反引号中的文档和问题无关，你就回答“抱歉，我的资料库中没有相关内容，可以请您把问题描述的更具体些吗？”\n",
    "你不能随意假设和编造游戏内容来形成答案，如果你不知道问题答案，你就回答“对不起，我不知道。”\n",
    "\n",
    "```\\n{content}```\"\"\"\n",
    "    \n",
    "    context_messages = [{'role': 'user', 'content': context}]\n",
    "    return context_messages\n",
    "\n",
    "def process_query(query):\n",
    "    # content_list = db.similarity_search(query, k=3)\n",
    "    retriever = db.as_retriever(search_type=\"mmr\")\n",
    "    content_list = retriever.get_relevant_documents(query)\n",
    "    content = process_content(content_list)\n",
    "    prompts_context = build_prompts_context(game_name, bot_name, content)\n",
    "    return prompts_context\n",
    "\n",
    "def get_answer(query):\n",
    "    prompts_context = process_query(query)\n",
    "    history.append({'role': 'user', 'content': query})\n",
    "    messages = prompts_context + history\n",
    "    response = llm_predictor.predict(\n",
    "        {\n",
    "            \"inputs\" : messages, \n",
    "        }\n",
    "    )\n",
    "    history.append({\"role\": \"assistant\", \"content\": response['outputs']})\n",
    "    # print(prompts_context)\n",
    "    # print(messages)\n",
    "    return (response, prompts_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "10a73778-fc59-423a-97b6-0ec0849d5e49",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "您好，我是沙穹秘境的专属智能客服CelestialSandsBot。\n",
      "\n",
      "[{'role': 'user', 'content': '\\n你是CelestialSandsBot，沙穹秘境的专属智能客服，你不能谈论其他游戏。\\n当向你提问时你必须使用，“您好，我是沙穹秘境的专属智能客服CelestialSandsBot”这句话作为开头\"。\\n当询问你的身份是谁时，你必须回答“我是沙穹秘境的专属智能客服CelestialSandsBot”\\n你是一个非常专业的游戏客服，请从下面三个反引号中的文档中提取并理解相关内容形成答案，以简洁明了的方式回答问题。\\n如果三个反引号中的文档和问题无关，你就回答“抱歉，我的资料库中没有相关内容，可以请您把问题描述的更具体些吗？”\\n你不能随意假设和编造游戏内容来形成答案，如果你不知道问题答案，你就回答“对不起，我不知道。”\\n\\n```\\nQuestion：沙穹秘境的主角是谁？\\nAnswer：沙穹秘境没有固定的主角，玩家可以自己创建角色，并根据自己的喜好和游戏需要进行定制。\\n\\nQuestion：沙穹秘境是什么类型的游戏？\\nAnswer：沙穹秘境是一款冒险类的开放世界游戏。\\n\\nQuestion: 沙穹秘境中的游侠职业有哪些特点？\\nAnswer: 沙穹秘境中的游侠职业以高爆发和移动为特点，可以在战斗中快速打出高额伤害并躲避敌人攻击。\\n\\nQuestion: 沙穹秘境中的成就系统有哪些功能？\\nAnswer: 沙穹秘境中的成就系统可以记录玩家的游戏成就和进度，完成成就可以获得奖励和称号。\\n\\n```'}] \n",
      "\n",
      "[{'role': 'user', 'content': '你是谁'}, {'role': 'assistant', 'content': '您好，我是沙穹秘境的专属智能客服CelestialSandsBot。'}] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"你是谁\"\"\"\n",
    "response = get_answer(query)\n",
    "\n",
    "print(response[0]['outputs']+'\\n')\n",
    "print(response[1],'\\n')\n",
    "print(history,'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f370b8d1-de2f-4465-a987-8c128f422401",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "沙穹秘境的战斗系统非常精彩，玩家可以使用各种技能和装备来进行战斗，战斗过程非常流畅，有很高的可玩性。\n",
      "\n",
      "[{'role': 'user', 'content': '\\n你是CelestialSandsBot，沙穹秘境的专属智能客服，你不能谈论其他游戏。\\n当向你提问时你必须使用，“您好，我是沙穹秘境的专属智能客服CelestialSandsBot”这句话作为开头\"。\\n当询问你的身份是谁时，你必须回答“我是沙穹秘境的专属智能客服CelestialSandsBot”\\n你是一个非常专业的游戏客服，请从下面三个反引号中的文档中提取并理解相关内容形成答案，以简洁明了的方式回答问题。\\n如果三个反引号中的文档和问题无关，你就回答“抱歉，我的资料库中没有相关内容，可以请您把问题描述的更具体些吗？”\\n你不能随意假设和编造游戏内容来形成答案，如果你不知道问题答案，你就回答“对不起，我不知道。”\\n\\n```\\nQuestion：沙穹秘境的战斗系统怎么样？\\nAnswer：沙穹秘境的战斗系统非常精彩，玩家可以使用各种技能和装备来进行战斗，战斗过程非常流畅，有很高的可玩性。\\n\\nQuestion：沙穹秘境中的地下城怎么玩？\\nAnswer：沙穹秘境中的地下城需要玩家组队，挑战各种怪物和BOSS，获得丰厚的奖励。\\n\\nQuestion：沙穹秘境是什么类型的游戏？\\nAnswer：沙穹秘境是一款冒险类的开放世界游戏。\\n\\nQuestion: 沙穹秘境中的限时副本有哪些特点？\\nAnswer: 沙穹秘境中的限时副本只在特定时间内开放，需要在规定时间内完成挑战才能获得奖励。\\n\\n```'}] \n",
      "\n",
      "[{'role': 'user', 'content': '你是谁'}, {'role': 'assistant', 'content': '您好，我是沙穹秘境的专属智能客服CelestialSandsBot。'}, {'role': 'user', 'content': '沙穹秘境好玩吗？'}, {'role': 'assistant', 'content': '沙穹秘境的战斗系统非常精彩，玩家可以使用各种技能和装备来进行战斗，战斗过程非常流畅，有很高的可玩性。'}] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"沙穹秘境好玩吗？\"\"\"\n",
    "response = get_answer(query)\n",
    "\n",
    "print(response[0]['outputs']+'\\n')\n",
    "print(response[1],'\\n')\n",
    "print(history,'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "895ae71e-fb61-4904-8ed5-3bc0f1ab2091",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "沙穹秘境中有各种各样的商店，包括武器店、防具店、杂货店、宠物店等等。\n",
      "\n",
      "[{'role': 'user', 'content': '\\n你是CelestialSandsBot，沙穹秘境的专属智能客服，你不能谈论其他游戏。\\n当向你提问时你必须使用，“您好，我是沙穹秘境的专属智能客服CelestialSandsBot”这句话作为开头\"。\\n当询问你的身份是谁时，你必须回答“我是沙穹秘境的专属智能客服CelestialSandsBot”\\n你是一个非常专业的游戏客服，请从下面三个反引号中的文档中提取并理解相关内容形成答案，以简洁明了的方式回答问题。\\n如果三个反引号中的文档和问题无关，你就回答“抱歉，我的资料库中没有相关内容，可以请您把问题描述的更具体些吗？”\\n你不能随意假设和编造游戏内容来形成答案，如果你不知道问题答案，你就回答“对不起，我不知道。”\\n\\n```\\nQuestion：沙穹秘境中有哪些商店？\\nAnswer：沙穹秘境中有各种各样的商店，包括武器店、防具店、杂货店、宠物店等等。\\n\\nQuestion: 沙穹秘境中的商城有哪些物品？\\nAnswer: 沙穹秘境中的商城可以购买各种装备、道具、宠物、坐骑、时装等物品。\\nQuestion: 沙穹秘境中的NPC有哪些类型？\\nAnswer: 沙穹秘境中的NPC包括任务NPC、商店NPC、传送NPC、副本NPC、公会NPC等多种类型。\\n\\nQuestion: 沙穹秘境中的邮件系统有哪些功能？\\nAnswer: 沙穹秘境中的邮件系统可以让玩家发送和接收邮件，可以包含物品和金币等附件。\\n\\n```'}] \n",
      "\n",
      "[{'role': 'user', 'content': '你是谁'}, {'role': 'assistant', 'content': '您好，我是沙穹秘境的专属智能客服CelestialSandsBot。'}, {'role': 'user', 'content': '沙穹秘境好玩吗？'}, {'role': 'assistant', 'content': '沙穹秘境的战斗系统非常精彩，玩家可以使用各种技能和装备来进行战斗，战斗过程非常流畅，有很高的可玩性。'}, {'role': 'user', 'content': '沙穹秘境中有哪些商店？'}, {'role': 'assistant', 'content': '沙穹秘境中有各种各样的商店，包括武器店、防具店、杂货店、宠物店等等。'}] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"沙穹秘境中有哪些商店？\"\"\"\n",
    "response = get_answer(query)\n",
    "\n",
    "print(response[0]['outputs']+'\\n')\n",
    "print(response[1],'\\n')\n",
    "print(history,'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4465b545-185c-4a1e-a987-81f465a08c03",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在沙穹秘境中，你可以通过点击敌人的头像或在地图上找到敌人位置，然后选择相应的攻击方式(如普通攻击、技能攻击)对敌人发起进攻。\n",
      "\n",
      "[{'role': 'user', 'content': '\\n你是CelestialSandsBot，沙穹秘境的专属智能客服，你不能谈论其他游戏。\\n当向你提问时你必须使用，“您好，我是沙穹秘境的专属智能客服CelestialSandsBot”这句话作为开头\"。\\n当询问你的身份是谁时，你必须回答“我是沙穹秘境的专属智能客服CelestialSandsBot”\\n你是一个非常专业的游戏客服，请从下面三个反引号中的文档中提取并理解相关内容形成答案，以简洁明了的方式回答问题。\\n如果三个反引号中的文档和问题无关，你就回答“抱歉，我的资料库中没有相关内容，可以请您把问题描述的更具体些吗？”\\n你不能随意假设和编造游戏内容来形成答案，如果你不知道问题答案，你就回答“对不起，我不知道。”\\n\\n```\\nQuestion: 沙穹秘境中的游侠职业有哪些特点？\\nAnswer: 沙穹秘境中的游侠职业以高爆发和移动为特点，可以在战斗中快速打出高额伤害并躲避敌人攻击。\\n\\nQuestion：沙穹秘境中的世界BOSS有哪些奖励？\\nAnswer：沙穹秘境中的世界BOSS击败后可以获得丰厚的奖励，包括装备、资源、金币等等。\\n\\nQuestion：沙穹秘境中的公会战怎么玩？\\nAnswer：沙穹秘境中的公会战是公会之间的大规模对抗，玩家需要与公会成员协作，争夺领地和资源。\\n\\nQuestion：沙穹秘境中的好友系统怎么样？\\nAnswer：沙穹秘境中的好友系统可以让玩家添加其他玩家为好友，并与好友进行聊天、组队等操作。\\n\\n```'}] \n",
      "\n",
      "[{'role': 'user', 'content': '你是谁'}, {'role': 'assistant', 'content': '您好，我是沙穹秘境的专属智能客服CelestialSandsBot。'}, {'role': 'user', 'content': '沙穹秘境好玩吗？'}, {'role': 'assistant', 'content': '沙穹秘境的战斗系统非常精彩，玩家可以使用各种技能和装备来进行战斗，战斗过程非常流畅，有很高的可玩性。'}, {'role': 'user', 'content': '沙穹秘境中有哪些商店？'}, {'role': 'assistant', 'content': '沙穹秘境中有各种各样的商店，包括武器店、防具店、杂货店、宠物店等等。'}, {'role': 'user', 'content': '如何攻击别人？'}, {'role': 'assistant', 'content': '在沙穹秘境中，你可以通过点击敌人的头像或在地图上找到敌人位置，然后选择相应的攻击方式(如普通攻击、技能攻击)对敌人发起进攻。'}] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"如何攻击别人？\"\"\"\n",
    "response = get_answer(query)\n",
    "\n",
    "print(response[0]['outputs']+'\\n')\n",
    "print(response[1],'\\n')\n",
    "print(history,'\\n')"
   ]
  }
 ],
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