Insurance RAG demo

Claim management with LLMs in RAG and vector search | MongoDB

Instructions

Create a file named '.env' and store your AWS credentials and MongoDB connection string in it, follow this format:

AWS_ACCESS_KEY_ID=<your aws access key>
AWS_SECRET_ACCESS_KEY=<your aws secret key>
AWS_KEY_REGION=<your aws region>
MONGO_URI="mongodb+srv://<usr>:<pswd>@<cluster-name>.mongodb.net/?retryWrites=true&w=majority"

In MongoDB Atlas create a database called "demo_rag_insurance" and a collection called "claims_final", import the dataset "demo_rag_insurance.claims.json" into the collection. You have to create a Vector Search Index for "claimDescriptionEmbeddingCohere" called "vector_index_claim_description_cohere":

{
  "fields": [
    {
      "type": "vector",
      "path": "claimDescriptionEmbeddingCohere",
      "numDimensions": 1024,
      "similarity": "cosine"
    }
  ]
}

The application uses AWS Bedrock with the following models:

• Embeddings: Cohere Embed English v3 (cohere.embed-english-v3)
• LLM: Anthropic Claude 3 Haiku (anthropic.claude-3-haiku-20240307-v1:0)

Setup Instructions

Prerequisites

• AWS Account with Bedrock access
• MongoDB Atlas cluster
• Python 3.8+
• Node.js and npm

Run it Locally

Backend

1. (Optional) Set your project description and author information in the pyproject.toml file:

   description = "Your Description"
   authors = ["Your Name <you@example.com>"]

2. Open the project in your preferred IDE (the standard for the team is Visual Studio Code).
3. Open the Terminal within Visual Studio Code.
4. Ensure you are in the root project directory where the makefile is located.
5. Execute the following commands:

• Poetry start

  make poetry_start

• Poetry install

  make poetry_install

6. Verify that the .venv folder has been generated within the /backend directory.
7. Make sure to select the Python interpreter from the .venv folder. You can change this in Visual Studio Code by clicking on the Python version in the bottom left corner, or searching by Python: Select Interpreter in the command palette. For this project, the Python interpreter should be located at ./backend/.venv/bin/python.

Interact with the API

Start the server by running the following commands:

1. Make sure to be over /backend directory.

    cd backend

2. Start the backend server with the following command:

   poetry run uvicorn main:app --host 0.0.0.0 --port 8000

Note: The server will be running on http://localhost:8000.

API Documentation

You can access the API documentation by visiting the following URL:

http://localhost:<PORT_NUMBER>/docs

E.g. http://localhost:8000/docs

To connect with Frontend

Configure the Environment Variables for the frontend

1. In the frontend folder, create a .env.local file.
2. Add the URL for the API using the following format:

NEXT_PUBLIC_ASK_THE_PDF_API_URL="http://localhost:8000/querythepdf"

Run the Frontend

1. Navigate to the frontend folder.
2. Install dependencies by running:

npm install

3. Start the frontend development server with:

npm run dev

The frontend will now be accessible at http://localhost:3000 by default, providing a user interface to interact with the image vector search demo.

Run with Docker (Preferred)

Prerequisites:

• Docker Desktop installed on your machine.
• Docker Desktop running on your machine.

Docker Setup Instructions

To run the application using Docker, follow these setup steps:

Build the Application

NOTE: If you don’t have make installed, you can install it using sudo apt install make or brew install make

1. To run with Docker use the following command:

make build

2. To delete the container and image run:

make clean

Common errors

• Check that you've created an .env file that contains your valid (and working) API keys, environment and index variables.
• AWS CLI configured with appropriate credentials

You can request model access through the AWS Bedrock console if needed.