data_ingestion_to_vectordb/data_ingestion_to_documentdb.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "id": "cc4ed4d8-6dd1-4422-9f17-99259212fbf1",
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   "source": [
    "# Ingest data to a Vector DB (Amazon DocumentDB (with MongoDB Compability))\n",
    "**_Use of Amazon DocumentDB (with MongoDB Compability) as a vector database to store embeddings_**\n",
    "\n",
    "This notebook works well with the `Data Science 2.0` kernel on a SageMaker Studio `ml.t3.medium` instance.\n",
    "\n",
    "Here is a list of packages that are used in this notebook.\n",
    "```\n",
    "!pip freeze | grep -E -w \"langchain|pymongo|pypdf\"\n",
    "---------------------------------------------------\n",
    "langchain==0.2.6\n",
    "langchain-aws==0.1.9\n",
    "langchain-community==0.2.6\n",
    "langchain-core==0.2.11\n",
    "langchain-text-splitters==0.2.2\n",
    "pymongo==4.6.3\n",
    "pypdf==4.2.0\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "066f83f2-7eeb-486d-88a9-6d6e0ffa50ed",
   "metadata": {},
   "source": [
    "## Step 1: Set up\n",
    "Install the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f76c51d6-46d8-423d-819e-8e29595b1b6f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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    "tags": []
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   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "\n",
    "!pip install -U langchain==0.2.6\n",
    "!pip install -U langchain-community==0.2.6\n",
    "!pip install -U langchain-aws==0.1.9\n",
    "!pip install -U pypdf==4.2.0\n",
    "!pip install -U pymongo==4.6.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51671f14-f746-474c-af39-76238b570152",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip list | grep -E -w \"langchain|pymongo|pypdf\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da3c829-43a9-45ee-8d44-d50ddbd5a93b",
   "metadata": {},
   "source": [
    "## Step 2: Download the data from the web\n",
    "\n",
    "In this step we use `wget` to download the pdf version of Amazon DocumentDB (MongoDB Compatability) developer guide.\n",
    "\n",
    "**This data download would take a few minutes**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a58a6013-7b8b-409f-91c2-9dbcb98abeee",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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    "tags": []
   },
   "outputs": [],
   "source": [
    "%%sh\n",
    "mkdir -p data\n",
    "wget -O data/documentdb-guide.pdf https://docs.aws.amazon.com/documentdb/latest/developerguide/developerguide.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb04316-214e-4b9c-9cf1-f364ad5474f6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Step 3: Load data into Amazon DocumentDB (with MongoDB Compatability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ef0be97-8832-4920-b62e-888f72f46d09",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import boto3\n",
    "\n",
    "aws_region = boto3.Session().region_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1059eaf1-dcff-4507-ae5a-c6f9451c3c26",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from typing import List\n",
    "\n",
    "def get_cfn_outputs(stackname: str, region_name: str) -> List:\n",
    "    cfn = boto3.client('cloudformation', region_name=region_name)\n",
    "    outputs = {}\n",
    "    for output in cfn.describe_stacks(StackName=stackname)['Stacks'][0]['Outputs']:\n",
    "        outputs[output['OutputKey']] = output['OutputValue']\n",
    "    return outputs\n",
    "\n",
    "\n",
    "def get_credentials(secret_id: str, region_name: str) -> str:\n",
    "    client = boto3.client('secretsmanager', region_name=region_name)\n",
    "    response = client.get_secret_value(SecretId=secret_id)\n",
    "    secrets_value = json.loads(response['SecretString'])\n",
    "    return secrets_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4981fbb4-c405-47e0-ae7a-37e86c33d855",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CFN_STACK_NAME = 'RAGDocDBStack'\n",
    "\n",
    "cfn_stack_outputs = get_cfn_outputs(CFN_STACK_NAME, aws_region)\n",
    "docdb_secret_name = cfn_stack_outputs['DocDBSecret']\n",
    "docdb_host = cfn_stack_outputs['DocumentDBCluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dabcc415-e621-4574-8be1-47f4e38f9e59",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "creds = get_credentials(docdb_secret_name, aws_region)\n",
    "USER, PASSWORD = creds['username'], creds['password']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10cfec83",
   "metadata": {},
   "source": [
    "\n",
    "##### Get Amazon DocumentDB Certificate Authority (CA) certificate\n",
    " Download the Amazon DocumentDB Certificate Authority (CA) certificate required to authenticate to your instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be5efd5b-26d9-49f3-b360-b46e7da92339",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!wget https://truststore.pki.rds.amazonaws.com/global/global-bundle.pem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6df6882a-4f73-45e6-a149-498eca05d326",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pymongo\n",
    "\n",
    "client = pymongo.MongoClient(\n",
    "    host=docdb_host,\n",
    "    port=27017,\n",
    "    username=USER,\n",
    "    password=PASSWORD,\n",
    "    retryWrites=False,\n",
    "    tls='true',\n",
    "    tlsCAFile=\"global-bundle.pem\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "732f6835-8aba-48a1-8838-330981db31b9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pymongo.errors import ConnectionFailure\n",
    "\n",
    "try:\n",
    "    client.admin.command('ping')\n",
    "    print('Server available')\n",
    "except ConnectionFailure as ex:\n",
    "    import tracebak\n",
    "    print('Server not available')\n",
    "    traceback.print_exc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c497cd57-cbec-48f6-8c06-1ae869b81970",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db_name = \"ragdemo\" # name the database\n",
    "collection_name = \"rag\" # name the collection\n",
    "\n",
    "db = client[db_name] # create a database object\n",
    "collection = db[collection_name] # create a collection object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdd0d744-07dc-42d0-954e-cd8de7b4e74b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders.pdf import PyPDFLoader\n",
    "from langchain_text_splitters.character import RecursiveCharacterTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d39db9b7-5d5c-4cfe-b2f1-5a7ce3bc5f55",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pdf_path = './data/documentdb-guide.pdf'\n",
    "\n",
    "loader = PyPDFLoader(file_path=pdf_path)\n",
    "\n",
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    separators=[\"\\n\\n\", \"\\n\", \".\", \" \"],\n",
    "    chunk_size=1000,\n",
    "    chunk_overlap=100\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc20b40a-e368-4135-94bf-02667d75d30e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 15s, sys: 125 ms, total: 1min 15s\n",
      "Wall time: 1min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "chunks = loader.load_and_split(text_splitter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce15007c-1032-4495-a3bb-cf105822e212",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import BedrockEmbeddings\n",
    "\n",
    "embeddings = BedrockEmbeddings(\n",
    "    region_name=aws_region\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dba9cc5b-21ed-4968-9ce4-86a69328506e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.33 s, sys: 426 ms, total: 7.75 s\n",
      "Wall time: 4min 24s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from langchain_community.vectorstores import DocumentDBVectorSearch\n",
    "\n",
    "# Using MongoDB Langchain integration as DocumentDB is compatible with MongoDB insert API\n",
    "vectorstore = DocumentDBVectorSearch.from_documents(\n",
    "    documents=chunks,\n",
    "    embedding=embeddings,\n",
    "    collection=collection\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9a31c96-c7a9-4e3e-a9e3-e9b1f57675e9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Count of events\n",
    "collection.count_documents({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d452ba2b-1f36-43ca-9c8d-8f1421ebba09",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.48 ms, sys: 59 µs, total: 3.54 ms\n",
      "Wall time: 1.39 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'vectorSearchIndex'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Below is creating a vector index named on field \"vectorContent\". By default, Langchain will insert chunks with the following fields: vectorContent, source, page, textContent\n",
    "# see the following for vector options in creating an index https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html#w5aac21c11c11\n",
    "collection.create_index(\n",
    "    [(\"vectorContent\", \"vector\")],\n",
    "    vectorOptions= {\n",
    "        \"type\": \"hnsw\",\n",
    "        \"similarity\": \"cosine\",\n",
    "        \"dimensions\": 1536,\n",
    "        \"m\": 16,\n",
    "        \"efConstruction\": 64\n",
    "    },\n",
    "    name=vectorstore.get_index_name()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2f2e634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SON([('v', 4), ('key', SON([('_id', 1)])), ('name', '_id_'), ('ns', 'ragdemo.rag')])\n",
      "SON([('v', 4), ('key', SON([('vectorContent', 'vector')])), ('name', 'vectorSearchIndex'), ('vectorOptions', SON([('type', 'hnsw'), ('dimensions', 1536), ('similarity', 'cosine'), ('m', 16), ('efConstruction', 64)])), ('ns', 'ragdemo.rag')])\n"
     ]
    }
   ],
   "source": [
    "for index in collection.list_indexes():\n",
    "    print(index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08266e3e-f547-40ed-8050-c4a33fd8bd5f",
   "metadata": {},
   "source": [
    "## Step 4: Do a similarity search for user input to documents (embeddings) in Amazon DocumentDB (with MongoDB Compatibility)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3ba3c8e-5493-485c-8aff-c91ba643053f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "query = \"What is the company's strategy for generative AI?\"\n",
    "\n",
    "embedded_query = embeddings.embed_query(query)\n",
    "\n",
    "pipeline = [\n",
    "    {\"$match\": {}},\n",
    "    {\n",
    "        \"$search\": {\n",
    "            \"vectorSearch\" : {\n",
    "                \"vector\" : embedded_query,\n",
    "                \"path\": \"vectorContent\",\n",
    "                \"similarity\": \"cosine\",\n",
    "                \"k\": 2,\n",
    "                \"efSearch\": 40\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "]\n",
    "\n",
    "docs = collection.aggregate(pipeline)\n",
    "\n",
    "results = [doc['textContent'] for doc in docs]\n",
    "for i, e in enumerate(results):\n",
    "    print(f\"[doc-{i}]\\n\", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f7ed2a",
   "metadata": {},
   "source": [
    "## Clean up\n",
    "\n",
    "To avoid incurring future charges, delete the resources. You can do this by deleting the CloudFormation template used to create the IAM role and SageMaker notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ce355c",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this notebook we were able to see how to use Amazon Bedrock to generate embeddings and then ingest those embeddings into Amazon DocumentDB (with MongoDB Compatibility) and finally do a similarity search for user input to the documents (embeddings) stored in Amazon DocumentDB (with MongoDB Compatibility). We used langchain as an abstraction layer to talk to both Amazon Bedrock as well as Amazon DocumentDB (with MongoDB Compatibility)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41bcd79b-fc36-46ad-b8b4-77f39fcd8b77",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "- [Vector search for Amazon DocumentDB](https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html)\n",
    "- [Amazon DocumentDB (with MongoDB compatibility) samples](https://github.com/aws-samples/amazon-documentdb-samples/)\n",
    "- [LangChain Providers - AWS](https://python.langchain.com/docs/integrations/platforms/aws/) - The `LangChain` integrations related to `Amazon AWS` platform."
   ]
  }
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