AI-Powered Document Intelligence Platform

AWS AI Agent Global Hackathon Submission

🎯 Project Overview

This application transforms traditional OCR into an intelligent document analysis platform by combining AWS Textract with Amazon Bedrock's Claude AI. It demonstrates autonomous AI agent capabilities by not just extracting text, but understanding, categorizing, and structuring document content without human intervention.

What Makes This an AI Agent?

Autonomous Decision Making: The system automatically selects specialized analysis strategies based on document type
Complex Task Execution: Combines multiple AWS services (Textract → Bedrock → S3) in a coordinated workflow
Intelligent Reasoning: Uses Claude 3 Haiku to extract structured insights, identify entities, and understand context
Self-Service Operation: Processes documents end-to-end without human intervention once configured

✨ Key Features

🤖 AI-Powered Document Analysis

General Summary: Extracts key points, entities (people, organizations, dates, locations), and document type
Invoice/Receipt Analysis: Automatically extracts vendor, amounts, line items, dates, and tax information
Contract Review: Identifies parties, key terms, obligations, important clauses, and dates
Form Data Extraction: Structures form fields, checkboxes, and completeness status

📊 Document Processing

OCR with AWS Textract: High-accuracy text extraction from PDFs and images
Real-time Status Tracking: Live updates during document processing
CSV & JSON Export: Download raw text and structured analysis results
Document History: Access previous analyses without reprocessing

💼 Freemium Business Model

Free Tier: 5 documents/month, 2 LLM analyses, 3 pages max
Pro Tier: 200 documents/month, 50 LLM analyses, 50 pages max ($10/month)
Enterprise Tier: 1000 documents/month, 500 LLM analyses, API access ($99/month)

🔐 Security & Authentication

Google OAuth 2.0 integration
Secure S3 presigned URLs for downloads
API key authentication for Enterprise users

🏗️ Architecture

High-Level System Design

graph TB
    subgraph "Client Layer"
        User[👤 User Browser]
    end

    subgraph "Vercel Platform"
        subgraph "Flask Application"
            WebApp[🌐 Flask Web Server]
            LLM[🧠 LLM Analyzer<br/>Claude Integration]
            Auth[🔐 Authentication]
        end
    end

    subgraph "AWS Cloud Services"
        S3[📦 Amazon S3<br/>Document Storage]
        Textract[📄 AWS Textract<br/>OCR Processing]
        Bedrock[🤖 Amazon Bedrock<br/>Claude 3 Haiku]
    end

    subgraph "External Services"
        Google[🔑 Google OAuth]
        Stripe[💳 Stripe Payments]
        DB[(🗄️ PostgreSQL)]
    end

    User -->|Upload Document| WebApp
    WebApp -->|Store| S3
    WebApp -->|Extract Text| Textract
    Textract -->|Read| S3
    WebApp -->|Analyze| Bedrock
    Bedrock -->|Structured Insights| WebApp
    WebApp -->|Save Results| S3
    WebApp -->|Track Usage| DB
    User -->|Authenticate| Google
    User -->|Subscribe| Stripe

    classDef aws fill:#FF9900,stroke:#232F3E,stroke-width:2px
    classDef vercel fill:#000000,stroke:#FFFFFF,stroke-width:2px,color:#FFFFFF
    classDef external fill:#4285F4,stroke:#1967D2,stroke-width:2px,color:#FFFFFF
    
    class S3,Textract,Bedrock aws
    class WebApp,LLM,Auth vercel
    class Google,Stripe,DB external

AI Agent Workflow

1. Document Upload
   ↓
2. AWS Textract OCR
   ↓
3. Text Extraction
   ↓
4. [AI Agent Decision Point]
   ├─ Check user quota
   ├─ Determine analysis type
   └─ Select specialized prompt
   ↓
5. Amazon Bedrock (Claude 3 Haiku)
   ├─ Understand document context
   ├─ Extract structured data
   └─ Generate insights
   ↓
6. JSON Structure & Validation
   ↓
7. Store Results (S3 + Database)
   ↓
8. Present to User

🚀 AWS Services Used

1. Amazon Textract

Purpose: OCR text extraction from documents
Features Used:
- StartDocumentTextDetection for async processing
- GetDocumentTextDetection for result retrieval
- Multi-page document support
Cost: ~$0.0015 per page

2. Amazon Bedrock (Claude 3 Haiku)

Purpose: LLM-powered document intelligence
Features Used:
- Anthropic Claude 3 Haiku model
- Structured JSON output generation
- Low-temperature inference for consistency
Cost: ~$0.25 per 1M input tokens, ~$1.25 per 1M output tokens
Why Claude 3 Haiku: Fast, cost-effective, excellent at structured extraction

3. Amazon S3

Purpose: Document and result storage
Features Used:
- Document upload and storage
- CSV and JSON result hosting
- Presigned URLs for secure downloads
- 90-730 day retention policies
Cost: ~$0.023 per GB per month

4. AWS IAM

Purpose: Security and access control
Permissions Required:
- s3:PutObject, s3:GetObject
- textract:StartDocumentTextDetection, textract:GetDocumentTextDetection
- bedrock:InvokeModel

📋 Setup Instructions

Prerequisites

Python 3.9 or higher
AWS Account with Bedrock access (see setup steps below)
Google Cloud Project (for OAuth)
Stripe Account (for payments)
PostgreSQL database (Neon recommended)
AWS CLI (optional, for manual testing)

1. Clone the Repository

git clone <repository-url>
cd <project-directory>

2. Create Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

Note: All required dependencies are listed in requirements.txt, including:

boto3 - AWS SDK for Python (includes Bedrock support)
Flask - Web framework
psycopg2-binary - PostgreSQL adapter
stripe - Payment processing
cryptography - Secure key generation

4. Configure AWS Services

Enable Amazon Bedrock

Go to AWS Console → Amazon Bedrock
Navigate to "Model access" in the left sidebar
Click "Manage model access"
Enable "Anthropic Claude 3 Haiku"
Wait for access to be granted (usually instant)

Create IAM User

Go to AWS Console → IAM
Create new user with programmatic access
Attach the following policy (also available in .iampolicyaws.json):

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:PutObject",
        "s3:GetObject",
        "s3:ListBucket"
      ],
      "Resource": [
        "arn:aws:s3:::YOUR_BUCKET_NAME",
        "arn:aws:s3:::YOUR_BUCKET_NAME/*"
      ]
    },
    {
      "Effect": "Allow",
      "Action": [
        "textract:StartDocumentTextDetection",
        "textract:GetDocumentTextDetection"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel"
      ],
      "Resource": "arn:aws:bedrock:*::foundation-model/anthropic.claude-3-haiku-20240307-v1:0"
    }
  ]
}

Important: Replace YOUR_BUCKET_NAME with your actual S3 bucket name.

Save the Access Key ID and Secret Access Key

Minimum Required Permissions:

s3:PutObject, s3:GetObject, s3:ListBucket - For document storage
textract:StartDocumentTextDetection, textract:GetDocumentTextDetection - For OCR
bedrock:InvokeModel - For LLM analysis with Claude 3 Haiku

Create S3 Bucket

aws s3 mb s3://your-bucket-name --region us-east-1

5. Configure Google OAuth

Go to Google Cloud Console
Create a new project or select existing
Navigate to "APIs & Services" → "Credentials"
Create OAuth 2.0 Client ID
Add authorized redirect URIs:
- http://127.0.0.1:5000/login/callback (local)
- https://your-app.vercel.app/login/callback (production)
Save Client ID and Client Secret

6. Configure Stripe

Sign up at Stripe
Create products for Pro and Enterprise tiers
Get API keys from Dashboard
Set up webhook endpoint: https://your-app.vercel.app/stripe-webhook

7. Set Environment Variables

Create a .env file in the project root:

# AWS Configuration
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=us-east-1
S3_BUCKET=your-bucket-name

# Note: AWS_REGION is used for all AWS services (S3, Textract, and Bedrock)
# Ensure your region supports Amazon Bedrock with Claude 3 Haiku.
# Recommended regions for full feature support:
# - us-east-1 (N. Virginia) - RECOMMENDED
# - us-west-2 (Oregon)
# - ap-southeast-1 (Singapore)
# - eu-central-1 (Frankfurt)
# - eu-west-3 (Paris)
# 
# Note: us-west-1 (N. California) has limited Bedrock model availability
# For full list, see: https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-regions.html

# Google OAuth
GOOGLE_CLIENT_ID=your_client_id.apps.googleusercontent.com
GOOGLE_CLIENT_SECRET=your_client_secret
GOOGLE_DISCOVERY_URL=https://accounts.google.com/.well-known/openid-configuration

# Stripe Payment Configuration
STRIPE_SECRET_KEY=sk_test_...
STRIPE_PUBLISHABLE_KEY=pk_test_...
STRIPE_WEBHOOK_SECRET=whsec_...
STRIPE_SUBSCRIPTION_PRICE_ID=price_...  # Pro tier ($10/month)
STRIPE_ENTERPRISE_PRICE_ID=price_...    # Enterprise tier ($99/month)
STRIPE_ONETIME_PRICE_ID=price_...       # One-time payment option
STRIPE_MODE=payment                      # Default mode: 'payment' or 'subscription'

# Database
DATABASE_URL=postgresql://user:password@host:5432/dbname

# Flask Configuration
SECRET_KEY=your_secret_key_here
FLASK_ENV=development

# Admin Configuration (optional)
ADMIN_EMAIL=admin@example.com

Environment Variable Details

Variable	Required	Description
`AWS_ACCESS_KEY_ID`	Yes	AWS IAM user access key with S3, Textract, and Bedrock permissions
`AWS_SECRET_ACCESS_KEY`	Yes	AWS IAM user secret key
`AWS_REGION`	Yes	AWS region (must support Bedrock, e.g., us-east-1, us-west-2)
`S3_BUCKET`	Yes	S3 bucket name for document storage
`GOOGLE_CLIENT_ID`	Yes	Google OAuth 2.0 client ID
`GOOGLE_CLIENT_SECRET`	Yes	Google OAuth 2.0 client secret
`GOOGLE_DISCOVERY_URL`	Yes	Google OpenID discovery URL (usually constant)
`STRIPE_SECRET_KEY`	Yes	Stripe secret API key (use test key for development)
`STRIPE_PUBLISHABLE_KEY`	Yes	Stripe publishable API key
`STRIPE_WEBHOOK_SECRET`	Yes	Stripe webhook signing secret
`STRIPE_SUBSCRIPTION_PRICE_ID`	Yes	Stripe price ID for Pro tier subscription
`STRIPE_ENTERPRISE_PRICE_ID`	Yes	Stripe price ID for Enterprise tier subscription
`STRIPE_ONETIME_PRICE_ID`	No	Stripe price ID for one-time payments
`STRIPE_MODE`	No	Payment mode: 'payment' or 'subscription' (default: payment)
`DATABASE_URL`	Yes	PostgreSQL connection string
`SECRET_KEY`	Yes	Flask secret key for session management (generate random string)
`FLASK_ENV`	No	Flask environment: 'development' or 'production'
`ADMIN_EMAIL`	No	Admin email for special privileges

8. Verify AWS Permissions

Option 1: Automated Test Script (Recommended)

Run the provided test script to verify all AWS services:

python test_bedrock_connection.py

This script will test:

AWS credentials configuration
S3 bucket access
Amazon Bedrock API access
Claude 3 Haiku model invocation

Expected Output: All tests should pass (✅). If the Bedrock Invoke test fails, you need to:

Go to AWS Console → Amazon Bedrock → Model access
Click "Manage model access"
Enable "Anthropic Claude 3 Haiku"
Wait 1-2 minutes for access to be granted
Run the test script again

Option 2: Manual AWS CLI Tests

Test that your IAM user has the correct permissions:

# Test S3 access
aws s3 ls s3://your-bucket-name --region us-east-1

# Test Textract access (requires a test document in S3)
aws textract start-document-text-detection \
  --document-location '{"S3Object":{"Bucket":"your-bucket-name","Name":"test.pdf"}}' \
  --region us-east-1

# Test Bedrock access (list available models)
aws bedrock list-foundation-models --region us-east-1

# Verify Claude 3 Haiku is accessible
aws bedrock list-foundation-models \
  --by-provider anthropic \
  --region us-east-1 \
  --query 'modelSummaries[?modelId==`anthropic.claude-3-haiku-20240307-v1:0`]'

If any command fails, review your IAM policy and ensure:

The IAM user has the policy attached (see .iampolicyaws.json)
Amazon Bedrock model access is enabled in the AWS Console
Your region supports Bedrock (us-east-1, us-west-2, etc.)
Wait a few minutes after enabling model access for permissions to propagate

9. Initialize Database

flask --app api.index:create_app init-db

10. Run Locally

flask --app api.index:create_app run --debug

Visit http://127.0.0.1:5000

11. Deploy to Vercel

# Install Vercel CLI
npm install -g vercel

# Deploy
vercel

# Add environment variables in Vercel dashboard
# Settings → Environment Variables
# Copy all variables from your .env file

Important: When deploying to Vercel, ensure:

All environment variables are added to the Vercel project settings
AWS_REGION is set to a Bedrock-supported region
IAM credentials have all required permissions
Stripe webhook endpoint is updated to your production URL

🔌 Enterprise API Usage

Enterprise tier users get API access for programmatic document processing.

Generate API Key

Log in to the application
Upgrade to Enterprise tier
Navigate to Settings → API Keys
Click "Generate New Key"
Copy and save the key (shown only once)

API Endpoints

1. Analyze Document

POST /api/v1/analyze
Authorization: Bearer YOUR_API_KEY
Content-Type: multipart/form-data

# Parameters:
# - file: Document file (PDF or image)
# - analysis_type: general|invoice|contract|form (optional)
# - enable_llm: true|false (optional, default: true)

# Example with curl:
curl -X POST https://your-app.vercel.app/api/v1/analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@document.pdf" \
  -F "analysis_type=invoice" \
  -F "enable_llm=true"

# Response:
{
  "job_id": "abc123",
  "status": "processing",
  "status_url": "https://your-app.vercel.app/api/v1/status/abc123"
}

2. Check Status

GET /api/v1/status/{job_id}
Authorization: Bearer YOUR_API_KEY

# Response:
{
  "job_id": "abc123",
  "status": "completed",
  "result_url": "https://your-app.vercel.app/api/v1/result/abc123"
}

3. Get Results

GET /api/v1/result/{job_id}
Authorization: Bearer YOUR_API_KEY

# Response:
{
  "job_id": "abc123",
  "csv_url": "https://s3.amazonaws.com/...",
  "json_url": "https://s3.amazonaws.com/...",
  "analysis": {
    "vendor": "Acme Corp",
    "invoice_number": "INV-001",
    "total_amount": "1234.56",
    ...
  }
}

Rate Limits

100 requests per hour per API key
Returns 429 Too Many Requests when exceeded

📊 Example Analysis Outputs

General Document Analysis

{
  "summary": "Employment agreement between TechCorp and John Doe for Software Engineer position starting January 2024.",
  "key_points": [
    "Full-time employment as Software Engineer",
    "Annual salary of $120,000",
    "Standard benefits package included",
    "90-day probation period"
  ],
  "document_type": "employment_contract",
  "entities": {
    "people": ["John Doe"],
    "organizations": ["TechCorp Inc."],
    "dates": ["2024-01-15", "2024-04-15"],
    "locations": ["San Francisco, CA"]
  }
}

Invoice Analysis

{
  "vendor": "Office Supplies Co.",
  "invoice_number": "INV-2024-001",
  "date": "2024-01-15",
  "due_date": "2024-02-15",
  "total_amount": "1,234.56",
  "currency": "USD",
  "line_items": [
    {
      "description": "Printer Paper (10 reams)",
      "quantity": 10,
      "unit_price": "25.00",
      "total": "250.00"
    },
    {
      "description": "Ink Cartridges",
      "quantity": 5,
      "unit_price": "45.00",
      "total": "225.00"
    }
  ],
  "subtotal": "1,134.56",
  "tax": "100.00"
}

Contract Analysis

{
  "contract_type": "Service Agreement",
  "parties": ["ABC Corp", "XYZ Services LLC"],
  "effective_date": "2024-01-01",
  "expiration_date": "2025-01-01",
  "key_terms": [
    {
      "term": "Payment Terms",
      "details": "Net 30 days from invoice date"
    },
    {
      "term": "Termination",
      "details": "Either party may terminate with 30 days written notice"
    }
  ],
  "obligations": {
    "ABC Corp": [
      "Pay monthly service fee of $5,000",
      "Provide access to necessary systems"
    ],
    "XYZ Services LLC": [
      "Deliver monthly reports",
      "Maintain 99.9% uptime SLA"
    ]
  },
  "important_clauses": [
    "Confidentiality agreement",
    "Limitation of liability",
    "Dispute resolution through arbitration"
  ]
}

💰 Cost Analysis

Per-Document Processing Costs

Service	Cost per Document	Notes
AWS Textract	$0.0015	1 page average
Amazon Bedrock	$0.0001	~400 tokens
S3 Storage	$0.000023	Per MB per month
Total	~$0.0016	Per LLM analysis

Profitability by Tier

Tier	Monthly Price	Max LLM Analyses	AWS Cost	Margin
Free	$0	2	$0.0032	-100% (acquisition)
Pro	$10	50	$0.08	99.2%
Enterprise	$99	500	$0.80	99.2%

🎨 User Interface

Upload Interface

Clean, modern design with drag-and-drop support
Real-time quota display
Analysis type selector (General, Invoice, Contract, Form)
Enable/disable AI analysis toggle
Tier comparison table

Results Display

Tabbed interface: "Text" and "Analysis" views
Formatted JSON display with syntax highlighting
Download buttons for CSV and JSON
Analysis type badge
Processing time display

Document History

Table view of all processed documents
Sortable by date, filename, type
Quick access to previous results
Download links for CSV and JSON
Pagination for large histories

🔧 Troubleshooting

Common Issues

Bedrock Access Denied

Error: AccessDeniedException: User is not authorized to perform: bedrock:InvokeModel

Solutions:

Verify IAM policy includes Bedrock permissions (see .iampolicyaws.json)
Check that model access is enabled in AWS Console → Bedrock → Model access
Ensure your AWS region supports Bedrock
Wait a few minutes after enabling model access for permissions to propagate

Bedrock Model Not Found

Error: ValidationException: The provided model identifier is invalid

Solutions:

Verify you're using the correct model ID: anthropic.claude-3-haiku-20240307-v1:0
Check that Claude 3 Haiku is available in your region
Ensure model access is granted in the Bedrock console

Region Not Supported

Error: InvalidSignatureException or connection timeouts

Solutions:

Change AWS_REGION to a Bedrock-supported region:
- us-east-1 (N. Virginia) - Recommended
- us-west-2 (Oregon)
- ap-southeast-1 (Singapore)
- eu-central-1 (Frankfurt)
Update your S3 bucket to the same region for optimal performance

LLM Analysis Not Working

Symptoms: Documents process but no JSON analysis appears

Solutions:

Check browser console for JavaScript errors
Verify "Enable AI Analysis" checkbox is selected during upload
Check that you haven't exceeded your LLM quota
Review Flask logs for Bedrock API errors
Ensure api/llm_service.py exists and is properly imported

Database Migration Issues

Error: OperationalError: no such table: document_history

Solutions:

Run database initialization: flask --app api.index:create_app init-db
If using Flask-Migrate: flask db upgrade
Check DATABASE_URL is correct and accessible

🧪 Testing

Manual Testing Checklist

Sample Test Documents

Include test documents in test_documents/ directory:

sample_letter.pdf - General document
sample_invoice.pdf - Invoice with line items
sample_contract.pdf - Service agreement
sample_form.pdf - Application form

🔒 Security Considerations

API Keys: Cryptographically secure random generation
S3 Access: Presigned URLs with 5-minute expiration
Authentication: Google OAuth 2.0 with HTTPS required
Rate Limiting: 100 requests/hour for API endpoints
Input Validation: File type and size restrictions
SQL Injection: SQLAlchemy ORM with parameterized queries
XSS Protection: Jinja2 auto-escaping enabled

📈 Future Enhancements

Multi-language OCR support
Batch document processing
Webhook notifications for async results
Custom LLM prompts for Enterprise users
Document comparison and diff analysis
Integration with Slack, email, and business tools
Advanced analytics dashboard
Multi-model support (GPT-4, Gemini)

📄 License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

🙏 Acknowledgments

AWS: For providing Textract, Bedrock, and S3 services
Anthropic: For Claude 3 Haiku model
Vercel: For seamless Python hosting
Flask Community: For excellent web framework
Open Source Community: For supporting libraries

📞 Support

For questions, issues, or feature requests:

Open an issue on GitHub
Email: support@example.com
Documentation: https://docs.example.com

Built for the AWS AI Agent Global Hackathon 2024

Demonstrating autonomous AI agents that combine multiple AWS services to deliver intelligent document processing without human intervention.

Name		Name	Last commit message	Last commit date
Latest commit History 55 Commits
.kiro		.kiro
.vscode		.vscode
api		api
templates		templates
.gitignore		.gitignore
ENTERPRISE_SETUP.md		ENTERPRISE_SETUP.md
LICENSE		LICENSE
README-3.md		README-3.md
README.md		README.md
architecture-diagram.md		architecture-diagram.md
favicon.ico		favicon.ico
migrate_database.py		migrate_database.py
requirements.txt		requirements.txt
test_bedrock_connection.py		test_bedrock_connection.py
test_bedrock_simple.py		test_bedrock_simple.py
vercel.json		vercel.json

License

fapulito/vercel_textract

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AI-Powered Document Intelligence Platform

AWS AI Agent Global Hackathon Submission

🎯 Project Overview

What Makes This an AI Agent?

✨ Key Features

🤖 AI-Powered Document Analysis

📊 Document Processing

💼 Freemium Business Model

🔐 Security & Authentication

🏗️ Architecture

High-Level System Design

AI Agent Workflow

🚀 AWS Services Used

1. Amazon Textract

2. Amazon Bedrock (Claude 3 Haiku)

3. Amazon S3

4. AWS IAM

📋 Setup Instructions

Prerequisites

1. Clone the Repository

2. Create Virtual Environment

3. Install Dependencies

4. Configure AWS Services

Enable Amazon Bedrock

Create IAM User

Create S3 Bucket

5. Configure Google OAuth

6. Configure Stripe

7. Set Environment Variables

Environment Variable Details

8. Verify AWS Permissions

Option 1: Automated Test Script (Recommended)

Option 2: Manual AWS CLI Tests

9. Initialize Database

10. Run Locally

11. Deploy to Vercel

🔌 Enterprise API Usage

Generate API Key

API Endpoints

1. Analyze Document

2. Check Status

3. Get Results

Rate Limits

📊 Example Analysis Outputs

General Document Analysis

Invoice Analysis

Contract Analysis

💰 Cost Analysis

Per-Document Processing Costs

Profitability by Tier

🎨 User Interface

Upload Interface

Results Display

Document History

🔧 Troubleshooting

Common Issues

Bedrock Access Denied

Bedrock Model Not Found

Region Not Supported

LLM Analysis Not Working

Database Migration Issues

🧪 Testing

Manual Testing Checklist

Sample Test Documents

🔒 Security Considerations

📈 Future Enhancements

📄 License

🙏 Acknowledgments

📞 Support

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