adding genai chatbot architecture

genai_chatbot_diagram.py

@@ -0,0 +1,105 @@
+from diagrams import Diagram, Cluster, Edge
+from diagrams.aws.compute import Lambda
+from diagrams.aws.network import APIGateway, CloudFront, Route53
+from diagrams.aws.storage import S3
+from diagrams.aws.database import Dynamodb
+from diagrams.aws.ml import Sagemaker, Bedrock
+from diagrams.aws.security import Cognito, SecretsManager
+from diagrams.aws.integration import Eventbridge, SQS, SNS, StepFunctions
+from diagrams.aws.analytics import KinesisDataAnalytics
+from diagrams.onprem.client import Users
+
+# Configuration for the diagram
+graph_attr = {
+    "fontsize": "14",
+    "bgcolor": "white",
+    "pad": "0.5",
+}
+
+with Diagram(
+    "AWS GenAI LLM Chatbot Architecture",
+    filename="aws_genai_chatbot_architecture",
+    show=False,
+    direction="TB",
+    graph_attr=graph_attr
+):
+
+    # Users
+    user = Users("Users")
+
+    with Cluster("Frontend Layer"):
+        dns = Route53("Route 53")
+        cdn = CloudFront("CloudFront")
+        webapp = S3("Web App\n(S3 Static Site)")
+
+    with Cluster("API Layer"):
+        apigw = APIGateway("API Gateway")
+        websocket_apigw = APIGateway("WebSocket API")
+
+    with Cluster("Authentication"):
+        auth = Cognito("Cognito\nUser Pool")
+
+    with Cluster("Application Logic"):
+        with Cluster("Lambda Functions"):
+            api_lambda = Lambda("API Handler")
+            websocket_lambda = Lambda("WebSocket\nHandler")
+            chat_lambda = Lambda("Chat Processing")
+            orchestrator = Lambda("Orchestrator")
+
+    with Cluster("AI/ML Services"):
+        bedrock = Bedrock("Amazon Bedrock\n(LLM Models)")
+        sagemaker = Sagemaker("SageMaker\n(Custom Models)")
+
+    with Cluster("Data Storage"):
+        dynamodb = Dynamodb("DynamoDB\n(Chat History)")
+        s3_data = S3("S3\n(Documents)")
+        secrets = SecretsManager("Secrets Manager")
+
+    with Cluster("Message Processing"):
+        queue = SQS("SQS Queue")
+        eventbus = Eventbridge("EventBridge")
+        step_func = StepFunctions("Step Functions\n(Workflow)")
+
+    with Cluster("Monitoring & Analytics"):
+        analytics = KinesisDataAnalytics("Kinesis\nAnalytics")
+        notifications = SNS("SNS\nNotifications")
+
+    # Flow connections
+    user >> Edge(label="HTTPS") >> dns >> cdn >> webapp
+
+    webapp >> Edge(label="REST API") >> apigw
+    webapp >> Edge(label="WebSocket") >> websocket_apigw
+
+    apigw >> auth
+    websocket_apigw >> auth
+
+    auth >> api_lambda
+    auth >> websocket_lambda
+
+    api_lambda >> chat_lambda
+    websocket_lambda >> chat_lambda
+
+    chat_lambda >> orchestrator
+    orchestrator >> queue
+    queue >> step_func
+
+    step_func >> bedrock
+    step_func >> sagemaker
+    step_func >> s3_data
+
+    chat_lambda >> dynamodb
+    orchestrator >> dynamodb
+
+    bedrock >> Edge(label="Response") >> orchestrator
+    sagemaker >> Edge(label="Response") >> orchestrator
+
+    orchestrator >> Edge(label="Store") >> dynamodb
+    s3_data >> Edge(label="RAG Context") >> orchestrator
+
+    secrets >> api_lambda
+    secrets >> chat_lambda
+
+    eventbus >> analytics
+    chat_lambda >> eventbus
+
+    analytics >> notifications