Designing an Event-Driven AI Knowledge Platform for Campus Documents with AWS

College information is often scattered across PDFs, notices, question papers, event documents, and placement material. This article presents a proposed event-driven AWS architecture that transforms these documents into a searchable, AI-grounded knowledge platform using Amazon S3, AWS Lambda, Amazon SQS, Amazon Bedrock Knowledge Bases, Amazon DynamoDB, and API Gateway.

VINOD

Published Sep 25, 2026

Last modified Sep 25, 2026

Designing an Event-Driven AI Knowledge Platform for Campus Documents with AWS

Introduction

College information is rarely stored in one place.

Students may need to search through lecture notes, question papers, placement notices, club announcements, laboratory manuals, event documents, project reports, and other PDF files to find a single piece of information.

Traditional document repositories solve the storage problem, but they do not completely solve the knowledge discovery problem.

A student can search for a filename, but finding the actual answer often requires opening several documents and manually reading them.

This article presents a proposed solution called Campus Intelligence Platform: an event-driven, AI-powered knowledge system designed to transform unstructured campus documents into a searchable and context-aware knowledge source.

The proposed architecture uses Amazon S3 for document storage, Amazon SQS for asynchronous processing, AWS Lambda for serverless application logic, Amazon Bedrock Knowledge Bases for retrieval-augmented generation, Amazon DynamoDB for application metadata, Amazon API Gateway for the API layer, and AWS IAM and Amazon CloudWatch for security and observability.

The architecture described here is a reference design. It is not presented as a claim of an already deployed production system. Implementation results, performance measurements, and cost observations should be added after the system is actually built and tested.

The Problem

Consider a student looking for information about a placement drive.

The information might be distributed across:

A placement notification PDF
An eligibility document
A company job description
A registration notice
A preparation document
A previous announcement

A conventional file repository may allow the student to search for:

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placement

but it does not necessarily answer:

"What are the eligibility requirements for this placement opportunity?"

The student still has to locate the correct document, open it, read it, and interpret the information.

The same problem occurs with academic information.

For example:

"Which topics are covered in Unit 3 of this subject?"

The answer may exist inside a PDF, but finding it efficiently requires document-level searching.

The problem can therefore be summarized as:

How can scattered institutional documents be transformed into a searchable knowledge system that can provide answers grounded in the original source material?

Why Traditional Approaches Are Not Enough

A simple file-storage system provides persistence, but it does not provide contextual understanding.

A conventional approach might look like:

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Student
   |
   v
File Repository
   |
   v
Manual Search
   |
   v
Open Documents
   |
   v
Read and Interpret

This approach has several limitations.

1. Filename-based discovery

The student must already know what to search for.

2. Manual document inspection

Important information may be buried several pages inside a document.

3. Limited contextual understanding

A keyword search may find a document without understanding whether the document actually answers the question.

4. Information fragmentation

Related information may exist in multiple documents.

5. No natural-language interaction

Students cannot naturally ask questions such as:

"Which upcoming events are related to artificial intelligence?"

A knowledge-based AI architecture can address these limitations by retrieving relevant source information before generating a response.

Proposed Solution

The proposed Campus Intelligence Platform follows an event-driven architecture.

Its purpose is to:

Store campus documents securely.
Detect newly uploaded or updated documents.
Process documents asynchronously.
Make document content available to an AI knowledge system.
Retrieve relevant information when a student asks a question.
Generate an answer grounded in the retrieved source material.
Return the response through a controlled API.

The high-level workflow is:

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                   Student
                      |
                      v
                Web Application
                      |
                      v
                Amazon API Gateway
                      |
             +--------+--------+
             |                 |
             | Query           | Upload
             |                 |
             v                 v
      Retrieval Layer       Amazon S3
             |                 |
             |              Event
             |                 |
             |                 v
             |             Amazon SQS
             |                 |
             |                 v
             |            AWS Lambda
             |                 |
             |          Processing Workflow
             |                 |
             +--------+--------+
                      |
                      v
          Amazon Bedrock Knowledge Base
                      |
                      v
             Grounded AI Response
                      |
                      v
                   Student

Amazon Bedrock Knowledge Bases can use Amazon S3 as a data source and retrieve relevant content from the indexed documents. AWS also provides a RetrieveAndGenerate operation that combines retrieval with model-generated responses.

Why AWS Is Suitable

AWS is suitable for this architecture because different parts of the problem can be handled by managed services rather than requiring a continuously running backend server.

The architecture can be built using loosely coupled components.

For example:

Amazon S3 handles object storage.
Amazon SQS provides asynchronous message buffering.
AWS Lambda handles event-driven processing.
Amazon Bedrock provides managed generative AI capabilities.
Amazon DynamoDB stores application metadata.
Amazon API Gateway provides the API boundary.
Amazon CloudWatch provides operational visibility.
AWS IAM controls access between components.

This separation allows individual components to evolve independently.

It also allows the system to scale without immediately requiring a large always-on infrastructure footprint.

Architecture Overview

The proposed architecture consists of four major layers.

Layer 1: User Interface

Students interact with a web application.

The interface can provide:

Document upload
Natural-language question input
Search history
Source references
Document categories
Processing status

Layer 2: API Layer

Amazon API Gateway provides the entry point for application requests.

Example APIs could include:

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POST /documents/upload
GET  /documents
GET  /documents/{id}
POST /query
GET  /history

API Gateway forwards requests to the appropriate backend Lambda functions.

Layer 3: Event-Driven Processing

Uploaded documents are stored in Amazon S3.

The document-processing workflow can use an event-driven pattern:

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S3
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Event
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 v
SQS
 |
 v
Lambda
 |
 v
Processing

Amazon SQS acts as a buffer between document arrival and processing.

This prevents document uploads from being tightly coupled to the processing operation.

Layer 4: AI Knowledge Layer

Amazon Bedrock Knowledge Bases can use documents stored in Amazon S3 as a data source.

The knowledge base can ingest and index supported documents, including PDFs and other unstructured content.

When a user asks a question, relevant source chunks can be retrieved and used to generate a grounded response.

AWS Services and Their Responsibilities

Amazon S3

Why it is selected

Amazon S3 provides durable object storage for campus documents.

Example objects:

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documents/
├── academics/
│   ├── dbms/
│   ├── operating-systems/
│   └── computer-networks/
│
├── placements/
│   ├── company-notices/
│   └── job-descriptions/
│
├── events/
│   └── announcements/
│
└── clubs/
    └── activities/

What problem it solves

It separates document storage from application logic.

The application does not need to store large documents inside DynamoDB.

Communication

The application uploads objects to S3.

The S3 location can then be used as the source for an Amazon Bedrock Knowledge Base.

Failure considerations

If a document upload fails, the application should report the failure rather than marking the document as successfully uploaded.

Scalability

S3 is designed for large-scale object storage, allowing the document repository to grow without redesigning the storage layer.

Cost control

Only necessary documents should be retained, and lifecycle policies can be considered for older data.

Amazon SQS

Why it is selected

Document processing may take longer than the original upload request.

Instead of processing synchronously:

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Upload
 |
 v
Process
 |
 v
Response

the system can use:

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Upload
 |
 v
S3
 |
 v
SQS
 |
 v
Lambda
 |
 v
Process

This creates loose coupling between document ingestion and processing.

Failure handling

If a worker fails, the message can be retried according to the configured queue behavior.

A dead-letter queue can also be considered for messages that repeatedly fail processing.

Scalability

Additional Lambda executions can process queued work as demand increases, subject to service quotas and appropriate concurrency controls.

Cost control

Queue messages should contain only the information required to identify and process the document instead of duplicating large document contents.

AWS Lambda

Why it is selected

Lambda allows backend operations to execute in response to events or API requests without maintaining a continuously running application server.

Potential Lambda functions include:

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upload_handler
document_processor
query_handler
metadata_handler

Responsibilities

A Lambda function could:

Validate input
Generate pre-signed upload information
Process queue messages
Update document status
Query the knowledge base
Store metadata
Return API responses

Failure handling

Lambda functions should use structured error handling.

Example:

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try:
    process_document(document)
except Exception as error:
    logger.exception("Document processing failed")
    raise

The actual implementation should additionally distinguish between retryable and non-retryable failures.

Scalability

Lambda can execute multiple invocations as demand changes, subject to concurrency limits and downstream service capacity.

Cost control

Functions should remain small and focused.

Long-running or unnecessarily frequent processing should be avoided.

Amazon DynamoDB

DynamoDB would store application metadata rather than the original documents.

Example record:

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{
  "document_id": "doc-001",
  "title": "DBMS Unit 3",
  "category": "academic",
  "subject": "DBMS",
  "uploaded_by": "user-123",
  "status": "processed",
  "created_at": "2026-09-25T10:00:00Z"
}

Possible access patterns include:

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Get document by ID
List documents for a user
List documents by category
Track processing status
Store query history

The table design should be based on actual access patterns rather than treating DynamoDB like a relational database.

Amazon API Gateway

API Gateway acts as the controlled entry point for the frontend.

Example:

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POST /query

Request:

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{
  "question": "What are the topics covered in DBMS Unit 3?"
}

The API can invoke a Lambda function responsible for processing the request.

Security controls such as authentication, authorization, validation, and throttling should be applied according to the application requirements.

Amazon Bedrock Knowledge Bases

This is the central AI component.

Instead of sending an arbitrary question directly to a foundation model, the system can first retrieve relevant information from the campus documents.

The conceptual workflow is:

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User Question
      |
      v
Knowledge Base Retrieval
      |
      v
Relevant Document Chunks
      |
      v
Foundation Model
      |
      v
Grounded Response

Amazon Bedrock Knowledge Bases supports Amazon S3 as a data source and provides retrieval capabilities for information contained in the connected data.

AWS also documents RetrieveAndGenerate, which combines retrieval with response generation.

This approach helps keep the generated response connected to the application's document collection.

Complete System Workflow

Step 1: User authentication

A student signs into the application.

Amazon Cognito can be used to manage authentication.

Step 2: Document upload

The student selects a document.

Example:

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DBMS_Unit_3.pdf

The frontend requests an upload operation through the API.

The backend validates the request and stores the document in Amazon S3.

Step 3: Processing event

The new document generates an event-driven processing request.

The processing request is placed into Amazon SQS.

Step 4: Document processing

A Lambda worker consumes the queue message.

It can:

Validate the document.
Verify metadata.
Update DynamoDB.
Trigger or coordinate knowledge-base ingestion where required.
Record processing status.

Amazon Bedrock Knowledge Bases supports S3 data sources and incremental synchronization of added, modified, and deleted content.

Step 5: Student submits a question

Example:

"Explain normalization using the uploaded DBMS material."

The frontend sends the question to:

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API Gateway
      |
      v
Lambda

Step 6: Knowledge retrieval

The query handler interacts with the Bedrock Knowledge Base.

Relevant document chunks are retrieved.

Step 7: AI generation

The retrieved context is used to produce a natural-language response.

The intended behavior is:

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Question
+
Relevant campus information
        |
        v
AI model
        |
        v
Grounded answer

The system should instruct the model not to invent information when relevant source material cannot be found.

Step 8: Response

The application returns the response to the student.

A useful response format could be:

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Answer:
Normalization is the process of organizing...

Sources:
- DBMS_Unit_3.pdf
- DBMS_Notes.pdf

This makes the answer easier to verify.

Data Flow

The major data flows are:

Document flow

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Student
   ↓
Frontend
   ↓
API Gateway
   ↓
S3
   ↓
SQS
   ↓
Lambda
   ↓
Knowledge Base

Query flow

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Student
   ↓
Frontend
   ↓
API Gateway
   ↓
Lambda
   ↓
Bedrock Knowledge Base
   ↓
Relevant Context
   ↓
Foundation Model
   ↓
Response
   ↓
Student

Metadata flow

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Application
    ↓
Lambda
    ↓
DynamoDB

Suggested Project Structure

A possible implementation repository could be organized as:

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campus-intelligence-platform/
│
├── frontend/
│   ├── src/
│   ├── components/
│   └── services/
│
├── backend/
│   ├── upload/
│   │   └── handler.py
│   │
│   ├── query/
│   │   └── handler.py
│   │
│   ├── processor/
│   │   └── handler.py
│   │
│   └── metadata/
│       └── handler.py
│
├── infrastructure/
│   ├── iam/
│   ├── api/
│   ├── storage/
│   └── queues/
│
├── tests/
│   ├── unit/
│   └── integration/
│
├── docs/
│   └── architecture.md
│
└── README.md

The exact structure can change depending on whether the infrastructure is deployed manually, with AWS SAM, AWS CDK, Terraform, or another infrastructure-as-code approach.

Example Query Handler

A simplified Python example could look like:

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import boto3
import os

bedrock_agent_runtime = boto3.client(
    "bedrock-agent-runtime",
    region_name=os.environ["AWS_REGION"]
)

def lambda_handler(event, context):

    question = event.get("question", "").strip()

    if not question:
        return {
            "statusCode": 400,
            "body": "Question is required"
        }

    response = bedrock_agent_runtime.retrieve_and_generate(
        input={
            "text": question
        },
        retrieveAndGenerateConfiguration={
            "type": "KNOWLEDGE_BASE",
            "knowledgeBaseConfiguration": {
                "knowledgeBaseId": os.environ["KNOWLEDGE_BASE_ID"],
                "modelArn": os.environ["MODEL_ARN"]
            }
        }
    )

    return {
        "statusCode": 200,
        "body": response["output"]["text"]
    }

This is an example implementation, not a claim that the code has been deployed or tested in this project.

The exact API parameters and model configuration should be verified against the current Amazon Bedrock documentation before deployment.

Important Configuration

Configuration values should not be hard-coded into source code.

For example:

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AWS_REGION
KNOWLEDGE_BASE_ID
MODEL_ARN
DYNAMODB_TABLE
S3_BUCKET

These can be supplied through Lambda environment variables or an appropriate secrets/configuration mechanism.

Sensitive credentials should never be committed to GitHub.

Security Considerations

Security should be designed into the architecture from the beginning.

1. S3 access control

The document bucket should not be publicly accessible.

Public access should be blocked unless there is an explicit and justified requirement.

2. Encryption

Data should be encrypted at rest and transmitted using encrypted connections.

3. Authentication

Users should authenticate before accessing protected application functionality.

Amazon Cognito can be used for user authentication.

4. Input validation

The API should validate:

File type
File size
Question length
Required parameters
User permissions

5. Prompt safety

User input should not automatically be treated as trusted instructions.

The AI layer should be designed so that retrieved document content and user instructions are handled appropriately.

6. Sensitive information

Campus documents may contain personal or confidential information.

The system should avoid exposing documents to users who do not have permission to access them.

Amazon Bedrock Knowledge Bases provides document-level access-control capabilities for supported S3 data-source configurations, which can be considered when implementing access restrictions.

IAM Considerations

IAM policies should follow the principle of least privilege.

For example, a query Lambda should receive only the permissions required for its job.

Conceptually:

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Query Lambda
    |
    +--> Bedrock retrieval permissions
    |
    +--> DynamoDB read permissions

A document-processing Lambda might require:

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Processor Lambda
    |
    +--> S3 read access
    |
    +--> DynamoDB update access
    |
    +--> SQS receive/delete permissions

Avoid using:

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AdministratorAccess

for application Lambda functions.

Separate roles should be created where practical.

Error Handling

Distributed systems should assume that failures can occur.

Possible failures include:

S3 upload failure
Lambda execution failure
SQS processing failure
Knowledge Base synchronization failure
Bedrock request failure
DynamoDB throttling
API timeout

The application should expose meaningful states such as:

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UPLOADED
PROCESSING
READY
FAILED

For example:

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Document
   |
   v
UPLOADED
   |
   v
PROCESSING
   |
   +---- Failure ----> FAILED
   |
   v
READY

A retry mechanism can be used for appropriate transient failures.

A dead-letter queue can be considered for messages that repeatedly fail processing.

Scalability

The architecture is designed to separate components so that each part can scale independently.

For example:

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Many uploads
     |
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    S3
     |
     v
   SQS Queue
     |
     v
Lambda Workers

A queue allows incoming work to be buffered instead of forcing every upload to be processed immediately.

The API layer and processing layer can therefore be scaled independently.

However, scalability should not be interpreted as unlimited capacity. AWS service quotas, concurrency limits, model quotas, and downstream dependencies must be considered during a real deployment.

Reliability

Reliability should be considered at every layer.

S3

Provides durable object storage.

SQS

Provides asynchronous buffering and retry behavior.

Lambda

Can retry certain event-driven invocations depending on the event source and configuration.

DynamoDB

Stores application state separately from Lambda execution environments.

Bedrock

The application should handle model/API failures gracefully instead of assuming every request will succeed.

A reliable system should also make operations idempotent where possible.

For example, processing the same SQS message twice should not create inconsistent metadata.

Monitoring

Amazon CloudWatch can be used to monitor the system.

Useful metrics include:

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Lambda invocations
Lambda errors
Lambda duration
API request count
API latency
SQS queue depth
SQS processing failures
DynamoDB throttling
Application errors

Structured application logs should make it possible to trace a document or request through the system.

Example:

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request_id=abc123
document_id=doc001
operation=document_processing
status=success

Sensitive document contents should not be unnecessarily written to logs.

Cost Optimization

The architecture should be designed for a limited student budget.

The main cost drivers can include:

Amazon S3 storage and requests
Lambda execution
API Gateway requests
DynamoDB usage
SQS requests
Amazon Bedrock model inference
Bedrock Knowledge Base-related usage
Logging and monitoring

AWS pricing varies by region, service configuration, model, and usage and can change over time. Therefore, exact prices should be checked on the official AWS pricing pages before deployment.

Cost-control strategies

1. Delete unused resources

Development resources should be removed when no longer required.

2. Control document size

Large documents can increase storage and AI processing requirements.

3. Avoid unnecessary model calls

Not every operation needs an LLM.

Metadata operations can remain traditional application logic.

4. Limit test data

During development, use a small controlled document collection.

5. Monitor usage

Set appropriate billing and cost alerts where available.

6. Use asynchronous processing

Queue-based processing prevents unnecessary synchronous infrastructure from remaining active while work is being completed.

Testing and Evaluation

Because this architecture is proposed rather than presented as an already deployed system, actual performance numbers should be collected only after implementation.

A practical evaluation plan would include the following.

Functional testing

Test:

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Document upload
Document processing
Question submission
Knowledge retrieval
AI response
Authentication
Error handling

Retrieval testing

Prepare questions whose answers are known to exist in the document set.

For example:

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Question:
What topics are included in Unit 3?

Expected:
Topics listed in the source document.

Evaluate whether the retrieved content actually contains the required information.

Grounding evaluation

Check whether generated answers are supported by the retrieved source content.

Failure testing

Simulate:

Invalid documents
Missing documents
Failed processing
Repeated queue messages
Invalid API requests
Unauthorized requests

Performance evaluation

After deployment, measurements could include:

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Upload-to-processing time
API response latency
Knowledge retrieval latency
AI response latency
Queue processing time

These should be reported only after actual measurements are collected.

Expected Results

The intended outcome of the proposed architecture is a system where students can ask natural-language questions about an authorized collection of campus documents instead of manually searching through files.

For example:

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Student:
"What are the eligibility requirements mentioned
in the placement notice?"

System:
Retrieves relevant placement documents
        ↓
Extracts relevant context
        ↓
Generates grounded response
        ↓
Returns answer with source information

The expected benefits are:

Faster information discovery
Natural-language interaction
Centralized document knowledge
Reduced manual searching
Source-grounded responses
Event-driven processing
Independent scaling of application components

These are design objectives, not measured results.

Challenges

Several engineering challenges would need to be addressed during an actual implementation.

Challenge 1: Document quality

Poorly formatted PDFs, scanned documents, tables, or images can make information extraction more difficult.

Challenge 2: Retrieval relevance

A knowledge base can return technically related content that does not fully answer the user's question.

Chunking, metadata, filtering, and retrieval configuration therefore matter.

Challenge 3: Hallucination

A generative model may produce information that is not supported by the available documents.

The application should therefore emphasize grounded responses and clearly communicate when relevant information is unavailable.

Challenge 4: Access control

Different students may be allowed to access different documents.

Authorization must therefore be considered at both the application and knowledge-access layers.

Challenge 5: Cost

Generative AI requests can become a significant cost driver if the application makes unnecessary or excessively large model requests.

Future Improvements

The proposed architecture can be extended in several directions.

1. Automatic document classification

Documents could automatically be categorized as:

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Academic
Placement
Event
Club
Administration
Laboratory
Project

2. Multimodal knowledge

The knowledge system could be extended to handle supported multimodal content such as diagrams, images, and other document elements.

Amazon Bedrock documentation describes support for multimodal content in supported knowledge-base data-source configurations.

3. Personalized knowledge access

The application could provide different information depending on:

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Department
Year
Role
Course
Organization
Permissions

4. Automatic notifications

Important deadlines could be extracted from documents and connected to a notification workflow.

5. Analytics

The platform could measure:

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Most searched subjects
Frequently asked questions
Documents with highest usage
Unanswered questions

These analytics could help administrators identify information gaps.

6. Multi-campus architecture

The same architecture could eventually support multiple institutions or organizational tenants, with appropriate data isolation and authorization.

What I Would Measure After Deployment

If the proposed architecture is implemented, the following measurements would make the project more meaningful:

Metric	Purpose
Document processing success rate	Measures ingestion reliability
Retrieval relevance	Measures quality of retrieved information
Grounded answer rate	Measures whether responses are supported by sources
Average API latency	Measures application responsiveness
AI response latency	Measures model interaction time
Queue processing time	Measures asynchronous processing
Failure recovery rate	Measures resilience
Cost per query	Measures economic efficiency

These values should be reported only from actual experiments.

Conclusion

The Campus Intelligence Platform demonstrates how an ordinary document-storage problem can be redesigned as an event-driven cloud architecture.

Instead of treating AWS as a collection of unrelated services, each service has a specific responsibility:

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Amazon S3
    ↓
Document storage

Amazon SQS
    ↓
Asynchronous processing

AWS Lambda
    ↓
Serverless application logic

Amazon DynamoDB
    ↓
Application metadata

Amazon API Gateway
    ↓
API boundary

Amazon Bedrock Knowledge Bases
    ↓
Knowledge retrieval and grounded AI

Amazon Cognito
    ↓
Authentication

Amazon CloudWatch
    ↓
Monitoring

AWS IAM
    ↓
Security and access control

The most important design principle is that the AI model should not be treated as the entire application.

The AI component is one part of a larger system involving storage, event processing, retrieval, authorization, APIs, monitoring, error handling, and cost management.

That is what makes the architecture useful as a cloud engineering project.

The proposed platform can start with a small collection of academic documents and evolve into a broader knowledge system for educational institutions.

Most importantly, the architecture can be implemented incrementally. A developer can begin with S3, Lambda, API Gateway, and DynamoDB, introduce asynchronous processing with SQS, and then add Amazon Bedrock Knowledge Bases once the document and application layers are ready.

The result is not simply an AI chatbot.

It is a proposed event-driven knowledge platform built around real cloud engineering principles.

References and AWS Documentation

The following official AWS documentation should be consulted during implementation:

Amazon Bedrock Knowledge Bases — creating and configuring knowledge bases.
Amazon Bedrock Knowledge Bases — Amazon S3 data source.
Amazon Bedrock Knowledge Bases — retrieval and RetrieveAndGenerate.
Amazon Bedrock Knowledge Bases — supported data sources and multimodal content.
Amazon S3 documentation for object storage and access control.
AWS Lambda documentation for serverless application development.
Amazon SQS documentation for asynchronous and decoupled processing.
Amazon DynamoDB documentation for NoSQL data modeling.
Amazon API Gateway documentation for API management.
Amazon Cognito documentation for authentication.
AWS IAM documentation for least-privilege access control.
Amazon CloudWatch documentation for logging and monitoring.

Before implementing the architecture, the current AWS documentation and regional pricing should be checked because service capabilities, APIs, quotas, and pricing can change over time.

# amazon-bedrock # generative-ai # serverless # aws-lambda

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Designing an Event-Driven AI Knowledge Platform for Campus Documents with AWS

VINOD

Designing an Event-Driven AI Knowledge Platform for Campus Documents with AWS

Introduction

The Problem

Why Traditional Approaches Are Not Enough

1. Filename-based discovery

2. Manual document inspection

3. Limited contextual understanding

4. Information fragmentation

5. No natural-language interaction

Proposed Solution

Why AWS Is Suitable

Architecture Overview

Layer 1: User Interface

Layer 2: API Layer

Layer 3: Event-Driven Processing

Layer 4: AI Knowledge Layer

AWS Services and Their Responsibilities

Amazon S3

Why it is selected

What problem it solves

Communication

Failure considerations

Scalability

Cost control

Amazon SQS

Why it is selected

Failure handling

Scalability

Cost control

AWS Lambda

Why it is selected

Responsibilities

Failure handling

Scalability

Cost control

Amazon DynamoDB

Amazon API Gateway

Amazon Bedrock Knowledge Bases

Complete System Workflow

Step 1: User authentication

Step 2: Document upload

Step 3: Processing event

Step 4: Document processing

Step 5: Student submits a question

Step 6: Knowledge retrieval

Step 7: AI generation

Step 8: Response

Data Flow

Document flow

Query flow

Metadata flow

Suggested Project Structure

Example Query Handler

Important Configuration

Security Considerations

1. S3 access control

2. Encryption

3. Authentication

4. Input validation

5. Prompt safety

6. Sensitive information

IAM Considerations

Error Handling

Scalability

Reliability

S3

SQS

Lambda

DynamoDB

Bedrock

Monitoring

Cost Optimization

Cost-control strategies

1. Delete unused resources

2. Control document size

3. Avoid unnecessary model calls

4. Limit test data