🛡️ Insurance Specialist AI System

A sophisticated AI-powered insurance assistant system that combines PostgreSQL database management, vector embeddings with Milvus, and specialized AI agents for insurance domain expertise.

🌟 Key Features

• Multi-Database Integration: PostgreSQL for structured data + Milvus for vector embeddings
• Real-time Sync: Continuous database synchronization between PostgreSQL and Milvus
• Specialized AI Agents: Domain-specific insurance experts (Life, Home, Auto)
• Intelligent Routing: Smart query routing to appropriate specialists
• Streamlit UI: Beautiful, interactive web interface

🛠️ Prerequisites

📦 Docker Desktop Installation

1. Download and install Docker Desktop
2. Ensure Docker is running in administrator mode

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📥 Milvus Installation via Docker

# Download the installation script
Invoke-WebRequest https://raw.githubusercontent.com/milvus-io/milvus/refs/heads/master/scripts/standalone_embed.bat -OutFile standalone.bat

# Start Milvus container
standalone.bat start
Wait for Milvus starting...
Start successfully.

# Verify Milvus is running (port 19530 should be listening)
docker ps

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⚙️ Milvus Management Commands

# Stop Milvus
standalone.bat stop
Stop successfully.

# Delete Milvus container and data
standalone.bat delete
Delete Milvus container successfully.
Delete successfully.

# Restart Milvus
standalone.bat start

🏗️ System Architecture

graph TD
    A[PostgreSQL Database] -->|Stores Insurance Data| B[Sync Process]
    B -->|Generates Embeddings| C[Milvus Vector Database]
    D[Streamlit UI] -->|User Queries| E[Coordinator Agent]
    E -->|Routes to| F[Life Insurance Agent]
    E -->|Routes to| G[Home Insurance Agent]
    E -->|Routes to| H[Auto Insurance Agent]
    C -->|Provides Context| F
    C -->|Provides Context| G
    C -->|Provides Context| H

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📁 File Structure

insurance-ai-system/
├── creating_postgres_database.py
├── first_vector_embedding.py
├── syncing_databases.py
├── insurance_agents_main.py
├── .env.example
└── README.md

⚙️ Installation

1. Clone the repository

git clone https://github.com/yourusername/insurance-ai-system.git
cd insurance-ai-system

2. Set up environment variables

cp .env.example .env

Edit the .env file with your credentials:

PG_DB_NAME=insurance_db
PG_USER=postgres
PG_PASSWORD=yourpassword
PG_HOST=localhost
PG_PORT=5432
MILVUS_HOST=localhost
MILVUS_PORT=19530
OPENAI_API_KEY=your_openai_key
GROQ_API_KEY=your_groq_key

3. Install dependencies

pip install -r requirements.txt

4. Initialize the system

python creating_postgres_database.py
python first_vector_embedding.py

🚀 Usage

1. Start the database sync process

python syncing_databases.py

2. Launch the Streamlit app

streamlit run insurance_agents_main.py

🧠 AI Agent Specializations

Agent Type	Specialization	Example Capabilities
Life Insurance	Mortality risk assessment	Term vs whole life analysis
Home Insurance	Property valuation	HO-3 vs HO-5 comparisons
Auto Insurance	State compliance	Liability coverage analysis

🔍 Technical Highlights

• PostgreSQL Schema Design: Comprehensive data model with constraints

• Vector Embeddings: OpenAI's text-embedding-3-large

• Real-time Triggers: PostgreSQL LISTEN/NOTIFY

• Agent Specialization: Strict domain boundaries

• Streamlit UI: Custom CSS styling

🧮 Performance Metrics

• Embedding Generation: ~100 records in 2 seconds
• Query Response Time: 1.5–3 seconds average
• Database Sync: Sub-second latency

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🤝 Contributing

1. Fork the project
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

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📜 License

Distributed under the MIT License.

📧 Contact

Project Maintainer - Sam Joe Silvano Putti

Project Link: https://github.com/SamJoeSilvano/Insurance-Agentic-AI

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🖼️ Diagram

Reality Augmented Generation(RAG) Pipeline Overview

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🔮 Future Enhancements

• Multi-language support
• Voice interaction
• PDF policy analysis