medUseCase-3.ipynb

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   "source": [
    "# Create, update, invoking KBs + LLaMa based RAG models & Evaluating responses with open source frameworks for a product use case: Medical Health"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba499585-78f2-4429-abfa-06d68817bf9e",
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   "source": [
    "### Introduction\n",
    "\n",
    "#### This guide outlines how to leverage Amazon Bedrock's knowledge base and Retrieval Augmented Generation (RAG) capabilities for a medical health product. The process involves configuring a RAG-based application to query a knowledge base, generating responses augmented with retrieved information. This technique enhances the capabilities of Large Language Models (LLMs) like LLaMa, enabling more informed and context-rich responses.\n",
    "\n",
    "### Steps for Setup and Usage\n",
    "\n",
    "1. Configure Data Sources: Determine the medical information to be included in your knowledge base.\n",
    "\n",
    "2. Upload Data to Amazon S3: Transfer your prepared medical data to an S3 bucket for accessibility and storage.\n",
    "\n",
    "3. Data Ingestion: Utilize a foundation model to generate embeddings of your data. Store these embeddings in a supported vector store.\n",
    "\n",
    "4. Application/Agent Setup: Configure your application or agent to query the knowledge base, ensuring it returns responses augmented with the retrieved data.\n",
    "\n",
    "### How It Works\n",
    "\n",
    "#### 1. Retrieval Augmented Generation (RAG): Amazon Bedrock's knowledge base facilitates RAG by drawing information from data sources to supplement responses from LLMs.\n",
    "\n",
    "#### 2. Pre-processing Data: Documents are chunked, converted to embeddings, and stored in a vector index with a link to the original document.\n",
    "\n",
    "#### 3. Runtime Execution: User queries are converted to vectors, matched with document vectors from the knowledge base, and used to augment responses.\n"
   ]
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      "\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[0mNote: you may need to restart the kernel to use updated packages.\n",
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      "      Successfully uninstalled botocore-1.33.13\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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      "panel 0.13.1 requires bokeh<2.5.0,>=2.4.0, but you have bokeh 3.3.0 which is incompatible.\n",
      "sagemaker 2.199.0 requires urllib3<1.27, but you have urllib3 2.0.7 which is incompatible.\n",
      "sagemaker-datawrangler 0.4.3 requires sagemaker-data-insights==0.4.0, but you have sagemaker-data-insights 0.3.3 which is incompatible.\n",
      "spyder 5.3.3 requires ipython<8.0.0,>=7.31.1, but you have ipython 8.16.1 which is incompatible.\n",
      "spyder 5.3.3 requires pylint<3.0,>=2.5.0, but you have pylint 3.0.1 which is incompatible.\n",
      "spyder-kernels 2.3.3 requires ipython<8,>=7.31.1; python_version >= \"3\", but you have ipython 8.16.1 which is incompatible.\n",
      "spyder-kernels 2.3.3 requires jupyter-client<8,>=7.3.4; python_version >= \"3\", but you have jupyter-client 8.4.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed botocore-1.33.13 jmespath-1.0.1 python-dateutil-2.8.2 six-1.16.0 urllib3-2.0.7\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[0mNote: you may need to restart the kernel to use updated packages.\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "daal4py 2021.6.0 requires daal==2021.4.0, which is not installed.\n",
      "spyder 5.3.3 requires pyqt5<5.16, which is not installed.\n",
      "spyder 5.3.3 requires pyqtwebengine<5.16, which is not installed.\n",
      "awscli 1.29.73 requires botocore==1.31.73, but you have botocore 1.33.13 which is incompatible.\n",
      "awscli 1.29.73 requires s3transfer<0.8.0,>=0.7.0, but you have s3transfer 0.8.2 which is incompatible.\n",
      "botocore 1.33.13 requires urllib3<2.1,>=1.25.4; python_version >= \"3.10\", but you have urllib3 2.1.0 which is incompatible.\n",
      "distributed 2022.7.0 requires tornado<6.2,>=6.0.3, but you have tornado 6.3.3 which is incompatible.\n",
      "fastapi 0.95.2 requires pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2, but you have pydantic 2.5.2 which is incompatible.\n",
      "jupyterlab 3.4.4 requires jupyter-server~=1.16, but you have jupyter-server 2.7.3 which is incompatible.\n",
      "jupyterlab-server 2.10.3 requires jupyter-server~=1.4, but you have jupyter-server 2.7.3 which is incompatible.\n",
      "notebook 6.5.6 requires jupyter-client<8,>=5.3.4, but you have jupyter-client 8.4.0 which is incompatible.\n",
      "notebook 6.5.6 requires pyzmq<25,>=17, but you have pyzmq 25.1.1 which is incompatible.\n",
      "numba 0.55.1 requires numpy<1.22,>=1.18, but you have numpy 1.26.2 which is incompatible.\n",
      "panel 0.13.1 requires bokeh<2.5.0,>=2.4.0, but you have bokeh 3.3.0 which is incompatible.\n",
      "sagemaker 2.199.0 requires urllib3<1.27, but you have urllib3 2.1.0 which is incompatible.\n",
      "sagemaker-datawrangler 0.4.3 requires sagemaker-data-insights==0.4.0, but you have sagemaker-data-insights 0.3.3 which is incompatible.\n",
      "spyder 5.3.3 requires ipython<8.0.0,>=7.31.1, but you have ipython 8.16.1 which is incompatible.\n",
      "spyder 5.3.3 requires pylint<3.0,>=2.5.0, but you have pylint 3.0.1 which is incompatible.\n",
      "spyder-kernels 2.3.3 requires ipython<8,>=7.31.1; python_version >= \"3\", but you have ipython 8.16.1 which is incompatible.\n",
      "spyder-kernels 2.3.3 requires jupyter-client<8,>=7.3.4; python_version >= \"3\", but you have jupyter-client 8.4.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m\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[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#install knowledge base sdk\n",
    "%pip install --upgrade pip\n",
    "%pip install boto3 --force-reinstall\n",
    "%pip install botocore --force-reinstall\n",
    "%pip install botocore --force-reinstall\n",
    "%pip install langchain --force-reinstall --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1966a7-bd26-468f-bacc-dfcd8f9377bc",
   "metadata": {},
   "source": [
    "### Configure your bedrock client:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c213681e-bb5a-453a-8c1d-db9e18ea16eb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import boto3\n",
    "import pprint\n",
    "from botocore.client import Config\n",
    "from langchain.llms.bedrock import Bedrock\n",
    "from IPython.display import Markdown, display\n",
    "from langchain.embeddings import BedrockEmbeddings\n",
    "\n",
    "bedrock_config = Config(connect_timeout=120, read_timeout=120, retries={'max_attempts': 0})\n",
    "bedrock_client = boto3.client('bedrock-runtime')\n",
    "agent_client = boto3.client('bedrock-agent')\n",
    "bedrock_agent_client = boto3.client(\"bedrock-agent-runtime\",\n",
    "                              region_name='us-east-1',\n",
    "                              config=bedrock_config)\n",
    "\n",
    "# we will be using the Titan Embeddings Model to generate our Embeddings.\n",
    "embeddings = BedrockEmbeddings(model_id=\"amazon.titan-embed-g1-text-02\", client=bedrock_client)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "338be9d8-a4c7-4217-8ad7-a0d66d5c4109",
   "metadata": {},
   "source": [
    "## Creating the KB using the API for knowledge bases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bfd20e5a-27e5-4782-960b-058775fc9dfc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "role_arn = ''  # Replace with your Role ARN\n",
    "embedding_model_arn = ''  # Corrected Embedding Model ARN\n",
    "\n",
    "# Choose your vector store type and configure accordingly\n",
    "# Example for Amazon OpenSearch Service\n",
    "storage_configuration = {\n",
    "    'opensearchServerlessConfiguration': {\n",
    "        'collectionArn': '',  # Replace with your Collection ARN\n",
    "        'fieldMapping': {\n",
    "            'metadataField': '',  # Replace with your Metadata Field\n",
    "            'textField': '',  # Replace with your Text Field\n",
    "            'vectorField': ''  # Replace with your Vector Field\n",
    "        },\n",
    "        'vectorIndexName': ''  # Replace with your Vector Index Name\n",
    "    },\n",
    "    'type': ''  # Corrected type\n",
    "}\n",
    "\n",
    "# Creating the knowledge base\n",
    "try:\n",
    "    medical_KB = agent_client.create_knowledge_base(\n",
    "        name='medical_KB', \n",
    "        description='KB that contains information on medicines and health',\n",
    "        roleArn=role_arn,\n",
    "        knowledgeBaseConfiguration={\n",
    "            'type': 'VECTOR',  # Corrected type\n",
    "            'vectorKnowledgeBaseConfiguration': {\n",
    "                'embeddingModelArn': embedding_model_arn\n",
    "            }\n",
    "        },\n",
    "        storageConfiguration=storage_configuration\n",
    "    )\n",
    "\n",
    "    # Pretty print the response\n",
    "    pprint.pprint(medical_KB)\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"Error occurred: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbcb2be-209f-4a56-ae23-3926a0a32386",
   "metadata": {},
   "source": [
    "## Create a data source that you can attach to this KB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "099f0dc0-0a8e-4658-a33c-207e85e54314",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the S3 configuration for your data source\n",
    "s3_configuration = {\n",
    "    'bucketArn': 'arn:aws:s3:::medicaldata2039',  # Replace with the ARN of your S3 bucket\n",
    "    'inclusionPrefixes': ['*']  # Assuming you want to include all files in the bucket\n",
    "}\n",
    "\n",
    "# Define the data source configuration\n",
    "data_source_configuration = {\n",
    "    's3Configuration': s3_configuration,\n",
    "    'type': 'S3'  # Type of data source, in this case, S3\n",
    "}\n",
    "\n",
    "# Replace with your knowledge base ID\n",
    "knowledge_base_id = ''\n",
    "\n",
    "# Create the data source\n",
    "try:\n",
    "    data_source_response = agent_client.create_data_source(\n",
    "        knowledgeBaseId=knowledge_base_id,\n",
    "        name='MedicalDataSource',\n",
    "        description='DataSource for medical data',\n",
    "        dataSourceConfiguration=data_source_configuration\n",
    "    )\n",
    "\n",
    "    # Pretty print the response\n",
    "    pprint.pprint(data_source_response)\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"Error occurred: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa1e768-6181-4b1f-8a7e-4f3e5975a30f",
   "metadata": {},
   "source": [
    "Creating a data source in Amazon Bedrock involves configuring a connection to an external storage system where your data is hosted. In this case, the data is stored in an Amazon S3 bucket named medicaldata2039. The process starts by defining an S3 configuration, which includes the bucket's ARN and inclusion prefixes. The inclusion prefixes are used to specify which files in the bucket should be included; this can range from a specific file name to a wildcard '*' to include all files.\n",
    "\n",
    "Once the S3 configuration is set, it's incorporated into a larger data source configuration, which is then used to create the data source through the Amazon Bedrock API. The API call requires details like the name and description of the data source, as well as the ID of the knowledge base to which this data source is to be added.\n",
    "\n",
    "After the data source is successfully created and linked to the knowledge base, the next step is to initiate a synchronization process. This process involves Amazon Bedrock scanning the specified S3 bucket, based on the inclusion prefixes, and ingesting the data into the knowledge base. During this synchronization, the data is processed, and potentially, embeddings are created based on the configured embedding model. This makes the data searchable and retrievable through the knowledge base.\n",
    "\n",
    "Once synchronization is complete, the content from the S3 bucket is available in the knowledge base. You can then perform queries against this knowledge base to retrieve relevant information based on your search criteria. This integration allows for a seamless connection between your stored data in S3 and the powerful search and retrieval capabilities offered by Amazon Bedrock, making it a robust solution for managing and accessing large volumes of data efficiently."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b720edc7-b77f-40e0-9763-d89ce975e497",
   "metadata": {},
   "source": [
    "## Retrieving content and evaluating using OpenSource Frameworks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2b39dfc4-5e3b-48a0-90d4-9c7bc6ff676f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import boto3\n",
    "import json\n",
    "from botocore.auth import SigV4Auth\n",
    "from botocore.awsrequest import AWSRequest\n",
    "\n",
    "# Replace these variables with your actual values\n",
    "knowledge_base_id = ''\n",
    "api_endpoint = 'https://bedrock-agent-runtime.us-east-1.amazonaws.com'\n",
    "query_text = 'health spending'\n",
    "number_of_results = 3\n",
    "\n",
    "# Construct the URL\n",
    "url = f\"{api_endpoint}/knowledgebases/{knowledge_base_id}/retrieve\"\n",
    "\n",
    "# Construct the payload\n",
    "payload = {\n",
    "    \"retrievalConfiguration\": {\n",
    "        \"vectorSearchConfiguration\": {\n",
    "            \"numberOfResults\": number_of_results\n",
    "        }\n",
    "    },\n",
    "    \"retrievalQuery\": {\n",
    "        \"text\": query_text\n",
    "    }\n",
    "}\n",
    "\n",
    "# Create a request object\n",
    "request = AWSRequest(method=\"POST\", url=url, data=json.dumps(payload), headers={'Content-Type': 'application/json'})\n",
    "\n",
    "# Use Boto3 to get the current session's credentials\n",
    "session = boto3.Session()\n",
    "credentials = session.get_credentials()\n",
    "\n",
    "# Create a SigV4Auth object with 'bedrock' as the service name\n",
    "auth = SigV4Auth(credentials, 'bedrock', session.region_name)\n",
    "\n",
    "# Sign the request\n",
    "auth.add_auth(request)\n",
    "\n",
    "# Make the POST request with the signed headers\n",
    "response = requests.post(url, headers=dict(request.headers), data=request.body)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Process the response\n",
    "    results = response.json()\n",
    "    print(\"Retrieval Results:\", results)\n",
    "else:\n",
    "    print(\"Error:\", response.status_code, response.text)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d516a592-0f26-4331-8452-99220df59543",
   "metadata": {},
   "source": []
  }
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