rag1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "34dad867-8e2a-44a2-8cdc-59983ef7a7d7",
   "metadata": {},
   "source": [
    "Install Terraform\n",
    "https://www.hashicorp.com/official-packaging-guide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd67a7b5-3201-45e0-8d07-a76e06eae0d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%writefile installterraform.sh\n",
    "wget -O- https://rpm.releases.hashicorp.com/RHEL/hashicorp.repo | sudo tee /etc/yum.repos.d/hashicorp.repo\n",
    "wget -O- https://rpm.releases.hashicorp.com/AmazonLinux/hashicorp.repo | sudo tee /etc/yum.repos.d/hashicorp.repo\n",
    "yum -y install terraform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f425d2-5ea7-4727-9ef7-932015423ec3",
   "metadata": {},
   "source": [
    "Using terminal run the commands `sudo chmod +x installterraform.sh` and then `sudo ./installterraform.sh`\n",
    "\n",
    "Add following policy to sagemaker execution role:\n",
    "\n",
    "```json\n",
    "{\n",
    "    \"Version\": \"2012-10-17\",\n",
    "    \"Statement\": [\n",
    "        {\n",
    "            \"Action\": [\n",
    "                \"iam:*\",\n",
    "                \"secretsmanager:*\",\n",
    "                \"es:*\",\n",
    "                \"bedrock:*\",\n",
    "                \"sts:*\"\n",
    "            ],\n",
    "            \"Effect\": \"Allow\",\n",
    "            \"Resource\": \"*\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "{\n",
    "    \"Version\": \"2012-10-17\",\n",
    "    \"Statement\": [\n",
    "        {\n",
    "            \"Sid\": \"BedrockFullAccess\",\n",
    "            \"Effect\": \"Allow\",\n",
    "            \"Action\": \"bedrock:*\",\n",
    "            \"Resource\": \"*\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2b92829-de9c-4151-aae1-8ffe2b03a181",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!./download-beta-sdk.sh\n",
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af613bb4-0452-454d-a362-656f9c1a0e95",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, sys, json\n",
    "from utils import secret, opensearch, dataset, bedrock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0baf126b-d8d2-4a2c-aa98-960d4a506d3f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "opensearch_username =  opensearch_index = 'rag'\n",
    "early_stop_record_count = 100\n",
    "os.environ['AWS_DEFAULT_REGION'] = 'us-west-2'\n",
    "region = os.environ.get('AWS_DEFAULT_REGION')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28f9beb-d6f7-480a-b9dc-2ebc490b7e90",
   "metadata": {},
   "source": [
    "## Create Embeddings in OpenSearch using Titan Embeddings Model\n",
    "### Download Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cab5851-4ea9-423c-bd07-52bd4197b630",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def download_dataset():\n",
    "    dataset_url = \"https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/gooaq_pairs.jsonl.gz\"\n",
    "    archive = dataset.download_dataset(dataset_url)\n",
    "    if archive is not None:\n",
    "        file_path = dataset.decompress_dataset(archive)\n",
    "        dataset.delete_file(archive)\n",
    "    return file_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525b5b6e-da5f-43ef-a9cf-2e4713a3a87d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Initialize OpenSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cba27e17-f314-401d-8ee9-4faf1fe78f45",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "opensearch_secret = secret.get_secret(opensearch_username, region)\n",
    "opensearch_client = opensearch.get_opensearch_cluster_client(opensearch_index, opensearch_secret, region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "853b1baf-01c6-437b-8b2b-fb5d3581d2aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "response = opensearch.delete_opensearch_index(opensearch_client, opensearch_index)\n",
    "if response:\n",
    "    print(f'Deleted existing index: {opensearch_index}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beb283f2-a5ff-4d41-b88c-5ba25e3fa7e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "exists = opensearch.check_opensearch_index(opensearch_client, opensearch_index)  \n",
    "if not exists:\n",
    "    status = opensearch.create_index(opensearch_client, opensearch_index)\n",
    "    if status:\n",
    "        success = opensearch.create_index_mapping(opensearch_client, opensearch_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b81f72-3d3e-4493-8ca4-0a96260618d8",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Connect to Amazon Bedrock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27e114d1-6fc4-46fc-beed-c024099414bd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_bedrock_client(region, url):\n",
    "    module_path = \".\"\n",
    "    sys.path.append(os.path.abspath(module_path))\n",
    "    boto3_bedrock = bedrock.get_bedrock_client(\n",
    "        region = region,\n",
    "        url_override = url\n",
    "    )\n",
    "    return boto3_bedrock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56eb5896-ae2a-4c8f-ab46-db6dfaf3f79f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "endpoint_url = '<Your endpont url for bedrock>'\n",
    "bedrock_client = get_bedrock_client(region,endpoint_url)\n",
    "bedrock_client.list_foundation_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24a1aa8d-834f-484f-a255-6dcb95463533",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "dataset_path = download_dataset()\n",
    "if dataset_path is not None:\n",
    "    records = dataset.prep_for_put(dataset_path)\n",
    "\n",
    "dataset.delete_file(dataset_path)\n",
    "print(f'Record count: {len(records)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d7e9fe6-8e32-4924-af80-d15ab91c99bc",
   "metadata": {},
   "source": [
    "### Create Embeddings using Amazon Bedrock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f186ec7-f8e3-42f8-80e1-a00eee22058e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "embedded_records = []\n",
    "# Embeddings info https://aws.amazon.com/about-aws/whats-new/2023/09/amazon-titan-embeddings-generally-available/ \n",
    "# Base Model ID for titan embeddings https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html \n",
    "for record in records[:100]:\n",
    "\n",
    "    payload = {\"inputText\":f\"{record}\"}\n",
    "    body = json.dumps(payload)\n",
    "    modelId =\"amazon.titan-embed-text-v1\"  # amazon.titan-e1t-medium \n",
    "    accept = \"application/json\"\n",
    "    contentType = \"application/json\"\n",
    "    \n",
    "    response = bedrock_client.invoke_model(\n",
    "        body=body, modelId=modelId, accept=accept, contentType=contentType\n",
    "    )\n",
    "    response_body = json.loads(response.get(\"body\").read())\n",
    "\n",
    "    embedding = response_body.get(\"embedding\")\n",
    "    embedded_records.append(\n",
    "        {\"_index\": opensearch_index, \"text\": record, \"vector_field\": embedding}\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5834d7a-70ba-4297-8407-57e0f56330fd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "len(embedded_records)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c0e1d32-38fa-49a7-8108-7b15710ba46c",
   "metadata": {},
   "source": [
    "### Save embeddings to OpenSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c05e7c4f-8f7f-4eba-bb2c-e7baac68a63a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "success, failed = opensearch.put_bulk_in_opensearch(embedded_records, opensearch_client)\n",
    "print(f\"Documents saved {success}, documents failed to save {len(failed)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7188ac27-4aaa-4f52-8b4c-6ddbad0894e1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Store variables for reuse in rag2.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1c60072-896b-486c-aa2b-eadd928acb1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%store \\\n",
    "region endpoint_url opensearch_client opensearch_username opensearch_secret opensearch_index"
   ]
  }
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