{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "954c92d7-8b54-4e03-a202-168eb297b0a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting opensearch-py\n",
      "  Using cached opensearch_py-2.2.0-py2.py3-none-any.whl (291 kB)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from opensearch-py) (1.16.0)\n",
      "Requirement already satisfied: certifi>=2022.12.07 in /opt/conda/lib/python3.10/site-packages (from opensearch-py) (2022.12.7)\n",
      "Requirement already satisfied: urllib3<2,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from opensearch-py) (1.26.15)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.4.0 in /opt/conda/lib/python3.10/site-packages (from opensearch-py) (2.28.2)\n",
      "Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.10/site-packages (from opensearch-py) (2.8.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests<3.0.0,>=2.4.0->opensearch-py) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests<3.0.0,>=2.4.0->opensearch-py) (3.3)\n",
      "Installing collected packages: opensearch-py\n",
      "Successfully installed opensearch-py-2.2.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install opensearch-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "919f5038-98f3-4254-842e-c3682fecb99a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import boto3\n",
    "import json\n",
    "\n",
    "EMB_MODEL_ENDPOINT = \"st-paraphrase-mpnet-base-v2-2023-04-19-04-14-31-658-endpoint\"\n",
    "smr_client = boto3.client(\"sagemaker-runtime\")\n",
    "\n",
    "def get_st_embedding(smr_client, text_input, endpoint_name=EMB_MODEL_ENDPOINT):\n",
    "    parameters = {\n",
    "      #\"early_stopping\": True,\n",
    "      #\"length_penalty\": 2.0,\n",
    "      \"max_new_tokens\": 50,\n",
    "      \"temperature\": 0,\n",
    "      \"min_length\": 10,\n",
    "      \"no_repeat_ngram_size\": 2,\n",
    "    }\n",
    "\n",
    "    response_model = smr_client.invoke_endpoint(\n",
    "                EndpointName=endpoint_name,\n",
    "                Body=json.dumps(\n",
    "                {\n",
    "                    \"inputs\": [text_input],\n",
    "                    \"parameters\": parameters\n",
    "                }\n",
    "                ),\n",
    "                ContentType=\"application/json\",\n",
    "            )\n",
    "    \n",
    "    json_str = response_model['Body'].read().decode('utf8')\n",
    "    json_obj = json.loads(json_str)\n",
    "    embeddings = json_obj[\"sentence_embeddings\"]\n",
    "    \n",
    "    return embeddings[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1abcad24-a853-4774-bcd3-b93a9bb3f862",
   "metadata": {},
   "outputs": [],
   "source": [
    "from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, helpers\n",
    "import boto3\n",
    "import random\n",
    "\n",
    "# aos conf\n",
    "AOS_ENDPOINT = 'vpc-chatbot-knn-3qe6mdpowjf3cklpj5c4q2blou.us-east-1.es.amazonaws.com'\n",
    "INDEX_NAME = 'chatbot-knn-index'\n",
    "REGION='us-east-1'\n",
    "\n",
    "def WriteVecIndexToAOS(paragraph_array, smr_client, aos_endpoint=AOS_ENDPOINT, region=REGION, index_name=INDEX_NAME):\n",
    "    \"\"\"\n",
    "    write paragraph to AOS for Knn indexing.\n",
    "    :param paragraph_input : document content \n",
    "    :param aos_endpoint : AOS endpoint\n",
    "    :param index_name : AOS index name\n",
    "    :return None\n",
    "    \"\"\"\n",
    "    credentials = boto3.Session().get_credentials()\n",
    "    auth = AWSV4SignerAuth(credentials, region)\n",
    "\n",
    "    client = OpenSearch(\n",
    "        hosts = [{'host': aos_endpoint, 'port': 443}],\n",
    "        http_auth = auth,\n",
    "        use_ssl = True,\n",
    "        verify_certs = True,\n",
    "        connection_class = RequestsHttpConnection\n",
    "    )\n",
    "    \n",
    "    def get_embs():\n",
    "        for paragraph in paragraph_array:\n",
    "            yield { \"doc\" : paragraph, \"embedding\" : get_st_embedding(smr_client, paragraph)}\n",
    "\n",
    "#     # bulk operation\n",
    "#     def load_data(index_name, count):\n",
    "#         color = ('red', 'organge', 'green', 'blue', 'yellow', 'black', 'pickacl', 'spice')\n",
    "#         for paragraph,emb in emb_vec_arr:\n",
    "#             document = {\"color\": color[int(random.uniform(1, 8))], \"my_vector\": [float(random.uniform(0.0, 1.0)) for _ in range(128)]}\n",
    "#             yield {\"_index\": index_name, \"_source\": document}\n",
    "\n",
    "#     source_data = load_data(index_name, 1000000)\n",
    "    get_embs_func = get_embs()\n",
    "    \n",
    "    # for tup in get_embs_func:\n",
    "        # print(tup)\n",
    "\n",
    "    response = helpers.bulk(client, get_embs_func)\n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f356ae66-6642-4452-a607-ba6e5f2b893f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "paragraph_array = [\"Question: 在中国区是否可用？\\nAnswer: 目前没有落地中国区的时间表，已经在以下区域推出：美国东部（弗吉尼亚州北部）、美国东部（俄亥俄州）、美国西部（俄勒冈州）、亚太地区（首尔）、亚太地区（新加坡）、亚太地区（悉尼）、亚太地区（东京）、欧洲地区（法兰克福）、欧洲地区（爱尔兰）、欧洲地区（伦敦）和欧洲地区（斯德哥尔摩）\",\"Question: 目前可以支持什么数据源的接入？ \\nAnswer: 目前只支持S3，其他数据源近期没有具体计划。\"]\n",
    "WriteVecIndexToAOS(paragraph_array, smr_client)"
   ]
  }
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