Moneta - an AI-Agentic Assistant for Insurance and Banking

Moneta is an AI-powered assistant designed to empower insurance and banking advisors. This Solution Accelerator provides a chat interface where advisors can interact with various AI agents specialized in different domains such as insurance policies, CRM, product information, funds, CIO insights, and news.

You can choose chich Agentic orchestration framework the Solution uses behind the scene by setting the approriate env variable. Choose from:

• Semantic Kernel
• Microsoft GBB AI EMEA - Vanilla Agents

Prerequisites

• Docker
• uv
• python 3.12
• pip

Features

• Agentic framework selection Support: Switch between Semantic Kernel and experimental MSFT GBB Vanilla Agents
• Multi-Use Case Support: Switch between insurance and banking use cases
• Agent Collaboration: Agents collaborate to provide the best answers
• Azure AD Authentication: Secure login with Microsoft Azure Active Directory
• Conversation History: Access and continue previous conversations

Implementation Details

• Python 3.12 or higher
• Streamlit (frontend app - chatGPT style)
• Microsoft Authentication Library (MSAL - if using authentication - optional)
• Azure AD application registration (if using authentication - optional)
• An Azure Container App hosting backend API endpoint
• CosmosDB to store user conversations and history

Use Cases

Insurance (SK)

• CRM: simulate fetching clients information from a CRM (DB, third-party API etc)
• Policies RAG: vector search with AI Search on various public available policy documents (product information)
• Responder: collects previous agents replies and respond to the user

Banking (SK)

• CRM: simulate fetching clients information from a CRM (DB, third-party API etc)
• Funds and ETF RAG: vector search with AI Search on few funds and ETF factsheets (product information)
• CIO: vector search with AI Search on in-house investments view and recommendations
• News: RSS online feed search on stock news
• Responder: collects previous agents replies and respond to the user

Note: GBB Vanilla agents are similar but differs a bit (no responder agent)

Project structure

• src

  • backend

    • gbb
      • agents
        • fsi_banking # agents files
        • fsi_insurance # agents files
      • genai_vanilla_agents # the framework source
    • sk
      • agents
        • banking # agents files
        • insurance # agents files
      • orchestrators
      • skills
    • app.py # exposes API

  • frontend

    • app.py # streamlit app

  • data

    • ai-search-index
      • cio-index
      • funds-index
      • ins-index
    • customer-profile

• infra

  • bicep file
  • infra modules

Azure deployment (automated)

To configure, follow these steps:

1. Make sure you AZ CLI is logged in in the right tenant. Optionally:

   az login --tenant your_tenant.onmicrosoft.com

2. Create a new azd environment:

   azd env new

   This will create a folder under .azure/ in your project to store the configuration for this deployment. You may have multiple azd environments if desired.

3. Set the AZURE_AUTH_TENANT_ID azd environment variable to the tenant ID you want to use for Entra authentication:

   azd env set AZURE_AUTH_TENANT_ID $(az account show --query tenantId -o tsv)

4. Login to the azd CLI with the Entra tenant ID:

   azd auth login --tenant-id $(azd env get-value AZURE_AUTH_TENANT_ID)

5. Proceed with AZD deployment:

   azd up

Data indexing

Demo data is loaded with the azd up process automatically.

Indexes are sourced from 'src/data/ai-search-index' folder. Each subfolder of the data folder will be a seperate index.

Customer profiles are sourced from 'src/data/customer-profiles'. Each subfolder of the data folder will be get a seperate index.

AZD deploy process will run setup_aisearch.py and setup_cosmosdb.py. DO NOT run those commands by hand unless you are confident you understand how the environment needs to be setup.

In case you need to reload the data, you can do it by running:

azd hooks run postdeploy

OBS! If you deploy from WSL mounted path, the postdeploy data init might fail. Please consider rerunning from WSL native path location.

Running the App locally - BACKEND

The python project is managed by pyproject.toml and the uv package manager. Install uv prior executing.

To run locally:

Mind the sample.env file - by default the application will try to read AZD environment variables and falls back on .env only when it does not find one.

Activate the .venv virtual environment or run the binary directly:

cd src/backend
uv sync
./.venv/bin/python app.py

Running the App locally - FRONTEND

The python project is managed by pyproject.toml and uv package manager. Install uv prior executing.

To run locally:

mind the sample.env file - by default the application will try to read AZD enviornment configuraiton and falls on .env only when it does not find one.

OBS! Activate .venv or run the binary directly.

cd src/frontend
uv sync
./.venv/bin/streamlit run app.py

Note: the web app is deployed with DISABLE_LOGIN=true configuration bypassing MSAL

Usage

1. Login: Click on "Log in with Microsoft" to authenticate via Azure AD (automatically skipped if DISABLE_LOGIN=True)
2. Select Use Case: Choose between fsi_insurance and fsi_banking from the sidebar
3. Start a Conversation: Click "Start New Conversation" or select an existing one
4. Chat: Use the chat input to ask questions. Predefined questions are available in a dropdown
5. Agents Online: View the available agents for the selected use case
6. Chat Histories: View and reload your past conversations