05-dense-passage-retrieval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "af87c930-cd58-4c86-ba10-62b4bc5e8aaa",
   "metadata": {},
   "source": [
    "## Dense Passage Retrieval: Retrieve Top K matching passages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62768906-3358-4fd5-839e-0877cd808d19",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "!pip install cohere-sagemaker"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05878dcc-720e-45d7-840a-63763809700e",
   "metadata": {},
   "source": [
    "#### Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a8da780-4f13-4783-a649-95e312e5eb73",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from cohere_sagemaker import Client\n",
    "from requests.auth import HTTPBasicAuth\n",
    "import requests\n",
    "import logging \n",
    "import boto3\n",
    "import yaml\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe6a76a-162b-4705-923d-451f6f9b96fb",
   "metadata": {},
   "source": [
    "##### Setup logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "599e748a-7155-4711-add4-8b50e3527a48",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "logger = logging.getLogger('sagemaker')\n",
    "logger.setLevel(logging.DEBUG)\n",
    "logger.addHandler(logging.StreamHandler())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32757848-2fa0-4a1f-91e2-ed55c47b516e",
   "metadata": {},
   "source": [
    "##### Log versions of dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5cdda0a0-1fc4-4fa9-8a67-b62a1309997c",
   "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": "f46123b2-fee8-4564-a11b-707eb1a1d253",
   "metadata": {},
   "source": [
    "#### Setup essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91b3d627-cadc-4aac-8bb5-ad56ceb894d4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "TEXT_EMBEDDING_MODEL_ENDPOINT_NAME = 'huggingface-textembedding-gpt-j-6b-fp16-1680825746'\n",
    "TEXT_GENERATION_MODEL_ENDPOINT_NAME = 'cohere-medium-1680827379'\n",
    "\n",
    "CHUNKS_DIR_PATH = './data/chunks'\n",
    "sagemaker_client = boto3.client('runtime.sagemaker')\n",
    "cohere_client = Client(endpoint_name=TEXT_GENERATION_MODEL_ENDPOINT_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fad5a8ab-254a-4e43-bb27-d46532109d31",
   "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": "a413ec33-e9cf-4d3a-8994-4fd2aa5fcbb1",
   "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/_search\n"
     ]
    }
   ],
   "source": [
    "URL = f'{domain_endpoint}/{domain_index}/_search'\n",
    "logger.info(f'URL for OpenSearch index = {URL}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "906b14ec-6121-4ff0-a904-e87c84b55bf9",
   "metadata": {},
   "source": [
    "Refer to https://docs.aws.amazon.com/opensearch-service/latest/developerguide/knn.html for more info."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad933c2c-f7d1-4b46-8818-a8cd3ba3d1a6",
   "metadata": {},
   "source": [
    "#### Encode question using SageMaker JumpStart's text embedding model endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "125be0d6-2ea8-4c1c-949d-3bb038446f3e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prompt = 'What is the definition of crime of battery?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f893136-8bed-4b54-8b0f-8df66100f400",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "payload = {'text_inputs': [prompt]}\n",
    "payload = json.dumps(payload).encode('utf-8')\n",
    "response = sagemaker_client.invoke_endpoint(EndpointName=TEXT_EMBEDDING_MODEL_ENDPOINT_NAME, \n",
    "                                            ContentType='application/json', \n",
    "                                            Body=payload)\n",
    "body = json.loads(response['Body'].read())\n",
    "embedding = body['embedding'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7302717d-58ff-46db-863b-6cd7c99d494c",
   "metadata": {},
   "source": [
    "#### Find top k (k=3) matching passages aligned in context to the encoded question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46d9f622-b720-492c-aa1b-effdccdd48aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "K = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "47fe35bb-7936-4d92-8bb2-71dff13a215e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "query = {\n",
    "    'size': K,\n",
    "    'query': {\n",
    "        'knn': {\n",
    "          'embedding': {\n",
    "            'vector': embedding,\n",
    "            'k': K\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "26a6d5ac-edc0-4d23-941a-7bb9df2f41d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "response = requests.post(URL, auth=HTTPBasicAuth(es_username, es_password), json=query)\n",
    "response_json = response.json()\n",
    "hits = response_json['hits']['hits']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba964331-7588-42f4-8ffb-16eac1f6eeb2",
   "metadata": {},
   "source": [
    "#### Generate answers using SageMaker JumpStart's text generation model by leveraging the previously matched passages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bd7ecd7b-02d8-4b53-91bb-4cecd457cad4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Answer:\n",
      "The crime of battery is committed when a person intentionally touches someone else in a way that causes injury.\n",
      "Reference:\n",
      "Document = 005 | Passage = 45 | Score = 0.64828235\n",
      "Answer:\n",
      "The crime of battery is committed when a person intentionally touches another person in a harmful or offensive way.\n",
      "Reference:\n",
      "Document = 005 | Passage = 44 | Score = 0.6335997\n",
      "Answer:\n",
      "The crime of battery is defined as \"an unlawful and intentional application of force upon another person.\"\n",
      "Reference:\n",
      "Document = 005 | Passage = 7 | Score = 0.6297361\n",
      "Answer:\n",
      "The crime of battery is committed when a person intentionally touches another person against their will.\n",
      "Reference:\n",
      "Document = 005 | Passage = 74 | Score = 0.6294636\n",
      "Answer:\n",
      "The crime of battery is defined as \"willfully and maliciously causing harm to another person.\"\n",
      "Reference:\n",
      "Document = 007 | Passage = 12 | Score = 0.6268758\n"
     ]
    }
   ],
   "source": [
    "for hit in hits:\n",
    "    score = hit['_score']\n",
    "    passage = hit['_source']['passage']\n",
    "    doc_id = hit['_source']['doc_id']\n",
    "    passage_id = hit['_source']['passage_id']\n",
    "    qa_prompt = f'Context={passage}\\nQuestion={prompt}\\nAnswer='\n",
    "    \n",
    "    response = cohere_client.generate(prompt=qa_prompt, \n",
    "                                      max_tokens=512, \n",
    "                                      temperature=0.25, \n",
    "                                      return_likelihoods='GENERATION')\n",
    "    \n",
    "    answer = response.generations[0].text.strip().replace('\\n', '')\n",
    "    logger.info(f'Answer:\\n{answer}')\n",
    "    logger.info(f'Reference:\\nDocument = {doc_id} | Passage = {passage_id} | Score = {score}')\n",
    "    \n",
    "if not hits:\n",
    "    logger.warn('No matching documents found!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a7e2d3a-6108-427f-9ff3-df39308ce59f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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