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Architecture Deep Dive

This document provides a detailed explanation of the [INSERT_PROJECT_NAME] architecture.

Architecture Diagram

Architecture Diagram

[PLACEHOLDER] Architecture diagram needed. Please create a diagram showing the complete system architecture and save it as docs/media/architecture.png

Architecture Flow

The following describes the step-by-step flow of how the system processes requests:

1. User Interaction

[INSERT_STEP_1_DESCRIPTION - Describe how users interact with the system, e.g., "User accesses the chatbot through the web interface"]

2. Request Processing

[INSERT_STEP_2_DESCRIPTION - Describe how requests are received and processed]

3. [INSERT_STEP_3_NAME]

[INSERT_STEP_3_DESCRIPTION]

4. [INSERT_STEP_4_NAME]

[INSERT_STEP_4_DESCRIPTION]

5. Response Generation

[INSERT_STEP_5_DESCRIPTION - Describe how responses are generated and returned to the user]

Cloud Services / Technology Stack

Frontend

  • Next.js: [INSERT_NEXTJS_USAGE_DESCRIPTION - e.g., "React framework for the web application interface"]
    • App Router for page routing
    • [INSERT_ADDITIONAL_FRONTEND_DETAILS]

Backend Infrastructure

  • AWS CDK: Infrastructure as Code for deploying AWS resources

    • Defines all cloud infrastructure in TypeScript
    • Enables reproducible deployments

  • Amazon API Gateway: [INSERT_API_GATEWAY_DESCRIPTION - e.g., "Acts as the front door for all API requests"]

    • [INSERT_API_GATEWAY_DETAILS]

  • AWS Lambda: Serverless compute for backend logic

    • [INSERT_LAMBDA_FUNCTION_1_NAME]: [INSERT_LAMBDA_FUNCTION_1_DESCRIPTION]
    • [INSERT_LAMBDA_FUNCTION_2_NAME]: [INSERT_LAMBDA_FUNCTION_2_DESCRIPTION]
    • [INSERT_LAMBDA_FUNCTION_3_NAME]: [INSERT_LAMBDA_FUNCTION_3_DESCRIPTION]

AI/ML Services

  • Amazon Bedrock: [INSERT_BEDROCK_DESCRIPTION - e.g., "Foundation model service for AI capabilities"]

    • Model: [INSERT_MODEL_NAME]
    • [INSERT_BEDROCK_USAGE_DETAILS]

  • [INSERT_ADDITIONAL_AI_SERVICE]: [INSERT_AI_SERVICE_DESCRIPTION]

Data Storage

  • Amazon S3: [INSERT_S3_USAGE_DESCRIPTION - e.g., "Object storage for documents and media"]

    • Bucket: [INSERT_BUCKET_PURPOSE]

  • Amazon DynamoDB: [INSERT_DYNAMODB_DESCRIPTION - if applicable]

    • Table: [INSERT_TABLE_PURPOSE]

Additional Services

  • [INSERT_SERVICE_NAME]: [INSERT_SERVICE_DESCRIPTION]
  • [INSERT_SERVICE_NAME]: [INSERT_SERVICE_DESCRIPTION]

Infrastructure as Code

This project uses AWS CDK (Cloud Development Kit) to define and deploy infrastructure.

CDK Stack Structure

backend/
├── bin/
│   └── backend.ts          # CDK app entry point
├── lib/
│   └── backend-stack.ts    # Main stack definition
└── lambda/
    └── [INSERT_LAMBDA_HANDLERS]

Key CDK Constructs

[INSERT_CDK_CONSTRUCTS_DESCRIPTION - Describe the main constructs used in the CDK stack]

  1. [INSERT_CONSTRUCT_1]: [INSERT_CONSTRUCT_1_DESCRIPTION]
  2. [INSERT_CONSTRUCT_2]: [INSERT_CONSTRUCT_2_DESCRIPTION]
  3. [INSERT_CONSTRUCT_3]: [INSERT_CONSTRUCT_3_DESCRIPTION]

Deployment Automation

[INSERT_DEPLOYMENT_AUTOMATION_DESCRIPTION - Describe any CI/CD or automated deployment processes]

Security Considerations

[INSERT_SECURITY_CONSIDERATIONS - Describe security measures implemented in the architecture]

  • Authentication: [INSERT_AUTH_DESCRIPTION]
  • Authorization: [INSERT_AUTHZ_DESCRIPTION]
  • Data Encryption: [INSERT_ENCRYPTION_DESCRIPTION]
  • Network Security: [INSERT_NETWORK_SECURITY_DESCRIPTION]

Scalability

[INSERT_SCALABILITY_DESCRIPTION - Describe how the architecture handles scaling]

  • Auto-scaling: [INSERT_AUTOSCALING_DETAILS]
  • Load Balancing: [INSERT_LOAD_BALANCING_DETAILS]
  • Caching: [INSERT_CACHING_DETAILS]

Architectural Decisions

This section documents key architectural decisions made during the project's development. Each decision includes the context, alternatives considered, and rationale for the chosen approach.

Note: Use the ADR Template when adding new decisions.

Decision 1: [INSERT_DECISION_TITLE]

Date: [INSERT_DATE]
Status: Accepted | Superseded | Deprecated
Deciders: [INSERT_NAMES]

Context:
[INSERT_CONTEXT - What problem were we trying to solve? What constraints existed?]

Decision:
[INSERT_DECISION - What did we decide to do?]

Alternatives Considered:

  1. [INSERT_ALTERNATIVE_1]: [INSERT_WHY_NOT_CHOSEN]
  2. [INSERT_ALTERNATIVE_2]: [INSERT_WHY_NOT_CHOSEN]
  3. [INSERT_ALTERNATIVE_3]: [INSERT_WHY_NOT_CHOSEN]

Rationale:
[INSERT_RATIONALE - Why did we choose this approach? What were the key factors?]

Consequences:

  • Positive: [INSERT_BENEFITS]
  • Negative: [INSERT_TRADEOFFS]
  • Neutral: [INSERT_NEUTRAL_IMPACTS]

Related Decisions: [INSERT_LINKS_TO_RELATED_DECISIONS]

Decision 2: [INSERT_DECISION_TITLE]

[Repeat structure above for additional decisions]

Example: Vector Search Implementation

Date: 2024-01-15
Status: Accepted
Deciders: Jane Doe, John Smith

Context:
The application requires semantic search capabilities over user-uploaded documents. We needed to choose a vector database solution that integrates well with AWS services, scales automatically, and minimizes operational overhead.

Decision:
Use Amazon OpenSearch Serverless with vector search capabilities instead of S3 with vector embeddings stored separately.

Alternatives Considered:

  1. S3 + DynamoDB for vectors: Store embeddings in DynamoDB, documents in S3. Rejected due to query complexity and lack of native vector search optimization.
  2. Amazon Kendra: Provides semantic search but higher cost for our use case and less control over embedding models.
  3. Self-managed OpenSearch on EC2: Full control but requires operational overhead (patching, scaling, monitoring).

Rationale:

  • OpenSearch Serverless provides native vector search with k-NN algorithms
  • Automatic scaling eliminates capacity planning
  • Integrated with Bedrock for embeddings
  • Pay-per-use pricing aligns with PoC/prototype nature
  • No infrastructure management required

Consequences:

  • Positive: Zero operational overhead, automatic scaling, optimized vector search performance
  • Negative: Vendor lock-in to AWS, cold start latency for infrequent queries, limited customization vs self-managed
  • Neutral: Learning curve for OpenSearch query syntax

Related Decisions: Decision 3 (Bedrock Model Selection)