04-encode-passages.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "id": "b985c5b5-1d65-4e4a-82d1-2dfc9768d97d",
   "metadata": {},
   "source": [
    "## Encode legal passages and create embeddings index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57aa4ada-a686-43de-bc90-0f4107f95ce1",
   "metadata": {},
   "source": [
    "##### Prerequisites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faf12933-564a-41a3-9a9e-c02a9437310e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture \n",
    "\n",
    "!pip install PyYAML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52706ec1-fd47-42d4-af0f-33f0a03f654d",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81eea5f6-50e3-4398-80eb-11b680d026b4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from requests.auth import HTTPBasicAuth\n",
    "from tqdm import tqdm\n",
    "import requests\n",
    "import logging \n",
    "import boto3\n",
    "import yaml\n",
    "import json\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ec6d42-db7a-4c4c-8322-95963806f987",
   "metadata": {},
   "source": [
    "##### Setup logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fcddd51c-9251-4428-9931-b2700a71142a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "logger = logging.getLogger('sagemaker')\n",
    "logger.setLevel(logging.DEBUG)\n",
    "logger.addHandler(logging.StreamHandler())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a81f78f-675d-4009-9228-8fa3cfc559b8",
   "metadata": {},
   "source": [
    "##### Log versions of dependencies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75474334-8b19-4491-abb9-58468bc33329",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using requests==2.28.2\n",
      "Using pyyaml==6.0\n"
     ]
    }
   ],
   "source": [
    "logger.info(f'Using requests=={requests.__version__}')\n",
    "logger.info(f'Using pyyaml=={yaml.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c52e3b3f-f770-4b90-bc35-12cc0f793604",
   "metadata": {},
   "source": [
    "#### Setup essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a25a24c9-b504-45a0-888d-fe507b34402d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "TEXT_EMBEDDING_MODEL_ENDPOINT_NAME = 'huggingface-textembedding-gpt-j-6b-fp16-1680825746'\n",
    "CHUNKS_DIR_PATH = './data/chunks'\n",
    "\n",
    "sagemaker_client = boto3.client('runtime.sagemaker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b2b1f73-2576-412f-ad38-13638db59281",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('config.yml', 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "es_username = config['credentials']['username']\n",
    "es_password = config['credentials']['password']\n",
    "\n",
    "domain_endpoint = config['domain']['endpoint']\n",
    "domain_index = config['domain']['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e558272-b128-47da-8e68-7cdc79aca465",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "URL for OpenSearch index = https://search-semantic-search-hryn56c5jy43yryimohz4ajvyi.us-east-1.es.amazonaws.com/legal-passages\n"
     ]
    }
   ],
   "source": [
    "URL = f'{domain_endpoint}/{domain_index}'\n",
    "logger.info(f'URL for OpenSearch index = {URL}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f72086-5050-40c3-b4e8-32341cbe071c",
   "metadata": {},
   "source": [
    "#### Define the index mapping with a k-NN vector field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "45c097ca-4501-42c4-ba1f-d10b52fc025e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mapping = {\n",
    "    'settings': {\n",
    "        'index': {\n",
    "            'knn': True  # Enable k-NN search for this index\n",
    "        }\n",
    "    },\n",
    "    'mappings': {\n",
    "        'properties': {\n",
    "            'embedding': {  # k-NN vector field\n",
    "                'type': 'knn_vector',\n",
    "                'dimension': 4096  # Dimension of the vector\n",
    "            },\n",
    "            'passage_id': {\n",
    "                'type': 'long'\n",
    "            },\n",
    "            'passage': {\n",
    "                'type': 'text'\n",
    "            },\n",
    "            'doc_id': {\n",
    "                'type': 'keyword'\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a33e70-1e5f-44d7-aec4-4c374d2a8e3d",
   "metadata": {},
   "source": [
    "#### Create the index with the specified mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06601622-e982-42b2-910a-71de86e56475",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Index created: {\"acknowledged\":true,\"shards_acknowledged\":true,\"index\":\"legal-passages\"}\n"
     ]
    }
   ],
   "source": [
    "# Check if the index exists using an HTTP HEAD request\n",
    "response = requests.head(URL, auth=HTTPBasicAuth(es_username, es_password))\n",
    "\n",
    "# If the index does not exist (status code 404), create the index\n",
    "if response.status_code == 404:\n",
    "    response = requests.put(URL, auth=HTTPBasicAuth(es_username, es_password), json=mapping)\n",
    "    logger.info(f'Index created: {response.text}')\n",
    "else:\n",
    "    logger.error('Index already exists!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15360977-8631-4a21-a599-b91c91c8c893",
   "metadata": {},
   "source": [
    "#### Encode passages (chunks) using JumpStart's GPT-J text embedding model and ingest to OpenSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e38760a-4a5d-453b-8270-d1557e1b664e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def chunk_iterator(dir_path: str):\n",
    "    for root, _, filenames in os.walk(dir_path):\n",
    "        for filename in filenames:\n",
    "            file_path = os.path.join(root, filename)\n",
    "            if os.path.isfile(file_path):\n",
    "                with open(file_path, 'r') as file:\n",
    "                    file_contents = file.read()\n",
    "                    yield filename, file_contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6adb976e-f7ab-4952-92e8-2a73aad8b501",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "378it [01:55,  3.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.13 s, sys: 451 ms, total: 8.58 s\n",
      "Wall time: 1min 55s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "i = 1\n",
    "for chunk_name, chunk in tqdm(chunk_iterator(CHUNKS_DIR_PATH)):\n",
    "    doc_id, chunk_id = chunk_name.split('_')\n",
    "    payload = {'text_inputs': [chunk]}\n",
    "    payload = json.dumps(payload).encode('utf-8')\n",
    "    \n",
    "    response = sagemaker_client.invoke_endpoint(EndpointName=TEXT_EMBEDDING_MODEL_ENDPOINT_NAME, \n",
    "                                                ContentType='application/json',  \n",
    "                                                Body=payload)\n",
    "    \n",
    "    model_predictions = json.loads(response['Body'].read())\n",
    "    embedding = model_predictions['embedding'][0]\n",
    "   \n",
    "    document = { \n",
    "        'doc_id': doc_id, \n",
    "        'passage_id': chunk_id,\n",
    "        'passage': chunk, \n",
    "        'embedding': embedding}\n",
    "    \n",
    "    response = requests.post(f'{URL}/_doc/{i}', auth=HTTPBasicAuth(es_username, es_password), json=document)\n",
    "    i += 1\n",
    "    \n",
    "    if response.status_code not in [200, 201]:\n",
    "        logger.error(response.status_code)\n",
    "        logger.error(response.text)\n",
    "        break"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "17ef4711-ec69-4628-b829-64e25d8e290e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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