Production-style workflows you can walk through.

flowchart TB
  subgraph external["Outside AWS"]
    User["Demo user"]
    LangSmith["LangSmith Cloud"]
    Tavily["Tavily API optional"]
  end

  subgraph aws["AWS us-east-1"]
    CDK["CDK KubeRagLanggDemo"]

    subgraph vpc["VPC"]
      NAT["NAT Gateway"]
      ALB["ALB HTTPS Cognito edge"]

      subgraph eks["EKS kube-rag-langg"]
        subgraph showcase["namespace showcase"]
          UI["Showcase UI React FastAPI"]
        end
        subgraph agents_ns["namespace agents"]
          Agents["LangGraph research-agents"]
        end
        subgraph rag_ns["namespace rag"]
          Ingest["Ingestion API SWAG CRAG expansion"]
          Qdrant["Qdrant EBS 20Gi"]
          Redis["Redis in-cluster"]
        end
        Node["t3.large node group"]
      end

      S3["S3 documents"]
      ECR["ECR images"]
      Cognito["Cognito"]
      Bedrock["Bedrock Sonnet Haiku Titan"]
    end
  end

  User -->|HTTPS| ALB
  ALB --> Cognito
  ALB --> UI
  UI --> Agents
  UI --> Ingest
  Agents --> Ingest
  Ingest --> Qdrant
  Ingest --> S3
  Ingest --> Bedrock
  Agents --> Bedrock
  Agents -.-> LangSmith
  Agents -.-> Tavily
  CDK --> vpc
  CDK --> S3
  CDK --> ECR
  CDK --> Cognito

flowchart TB
  Q[User query] --> B[Baseline retrieve top_k]
  B --> CF{Vector confidence floor RAG_MIN_VECTOR_SCORE 0.12}
  CF -->|blocked non-CRAG modes| Z[0 hits confidence message]
  CF -->|pass or CRAG mode| M{mode}

  M -->|baseline| BL[Return baseline hits]
  M -->|swag| SW[Candidate pool rerank]
  M -->|crag| CR[Retrieve Haiku grade]
  M -->|swag_crag| SC[SWAG pool then CRAG grade]

  SW --> SE[Same-document sibling expansion]
  CR --> SE
  SC --> SE
  BL --> SE

  SE --> OUT[Response hits plus swag crag expansion meta]

flowchart LR
  C[Vector candidates about 20] --> P[Ensure SWAG profiles heuristic or cached]
  P --> D[Dedupe chunk-kind weights]
  D --> R[Blend vector SWAG metadata]
  R --> N[Noise gate diversity cap]
  N --> K[top_k hits]

sequenceDiagram
  actor User
  participant UI as Showcase UI
  participant LG as LangGraph agents
  participant Ing as Ingestion
  participant Q as Qdrant
  participant BR as Bedrock

  User->>UI: Research topic
  UI->>LG: SSE stream
  loop Researchers
    LG->>Ing: POST rag search swag_crag
    Ing->>Q: Vector candidates
    Ing->>Ing: SWAG rerank sibling expansion
    Ing->>BR: Haiku CRAG grade
    BR-->>Ing: Graded chunks
    Ing-->>LG: Hits or web_fallback
  end
  LG->>BR: Sonnet synthesis
  BR-->>LG: Report draft
  LG-->>UI: HITL checkpoints plan critique report
  UI-->>User: Live progress editable report

sequenceDiagram
  participant UI as Showcase UI
  participant Ing as Ingestion
  participant S3 as S3
  participant BR as Bedrock Titan
  participant Q as Qdrant

  UI->>Ing: Upload or URL ingest
  Ing->>S3: Raw object
  Ing->>Ing: Chunk classify kind document_id
  Ing->>BR: Embed batch
  Ing->>Ing: SWAG profile heuristic default
  Ing->>Q: Upsert vector plus payload

stateDiagram-v2
  [*] --> Parked: demo-park.sh
  Parked --> Running: infra bootstrap deploy
  Running --> Stopped: demo-stop.sh
  Stopped --> Running: demo-start.sh
  Running --> Parked: demo-park.sh

flowchart TB
  subgraph external["Outside AWS"]
    Visitor["Website visitor"]
    Owner["Sales owner inbox"]
  end

  subgraph aws["AWS us-east-1"]
    SAM["SAM template.yaml stack"]

    subgraph region["Serverless lead capture"]
      Web["Static form frontend"]
      API["API Gateway HTTP API"]
      Lambda["Lambda lead-processor Python 3.12"]
      DDB["DynamoDB leads table"]
      SES["Amazon SES"]
      Bedrock["Bedrock optional scoring"]
    end
  end

  Visitor --> Web
  Web -->|POST /lead| API
  API --> Lambda
  Lambda --> DDB
  Lambda --> SES
  Lambda -.-> Bedrock
  SES --> Owner
  SAM --> region

flowchart TB
  POST[POST /lead JSON] --> CORS[CORS preflight OPTIONS]
  CORS --> API[API Gateway HTTP API]
  API --> V{validate_lead fields}
  V -->|invalid| R[400 reject]
  V -->|ok| Q{qualify_lead}
  Q -->|rules path| S[save_lead DynamoDB]
  Q -->|model path| M[Bedrock score grade]
  M --> S
  S --> N[send_notification SES]
  N --> OUT[200 JSON grade summary]

sequenceDiagram
  actor Visitor
  participant Web as Static form
  participant API as HTTP API prod
  participant Lambda as lead-processor
  participant DDB as DynamoDB
  participant SES as Amazon SES
  participant Owner as Sales email
  participant BR as Bedrock optional

  Visitor->>Web: Fill form submit
  Web->>API: POST /lead JSON
  API->>Lambda: Invoke app.lambda_handler
  Lambda->>Lambda: validate_lead
  alt model assist enabled
    Lambda->>BR: Score and grade lead
    BR-->>Lambda: Qualification result
  else rules only
    Lambda->>Lambda: qualify_lead rules
  end
  Lambda->>DDB: PutItem lead fields
  Lambda->>SES: Send notification email
  SES->>Owner: Alert with grade summary
  Lambda-->>API: 200 response body
  API-->>Visitor: Confirmation JSON