Retrieval Augmented Generation (RAG) Chatbot for Hospital

Build a RAG chatbot agent in LangChain that uses Neo4j to based on OpenAI API/LLM and retrieved hospital sample data(including patients, patient experiences, hospital locations, visits, insurance payers, and physicians).

Project Setup

Create a .env file in the root directory and add the following environment variables:

OPENAI_API_KEY=...

NEO4J_URI=...
NEO4J_USERNAME=...
NEO4J_PASSWORD=...

HOSPITALS_CSV_PATH=...
PAYERS_CSV_PATH=...
PHYSICIANS_CSV_PATH=...
PATIENTS_CSV_PATH=...
VISITS_CSV_PATH=...
REVIEWS_CSV_PATH=...

HOSPITAL_AGENT_MODEL=gpt-3.5-turbo-1106
HOSPITAL_CYPHER_MODEL=gpt-3.5-turbo-1106
HOSPITAL_QA_MODEL=gpt-3.5-turbo-0125

CHATBOT_URL=http://host.docker.internal:8000/hospital-rag-agent

The three NEO4J_ variables are used to connect to your Neo4j AuraDB instance. Follow the directions here to create a free instance.

When you have a running Neo4j instance, and have filled out all the environment variables in .env, you can run the entire project with Docker Compose. You can install Docker Compose by following these directions.

After you've filled in all of the environment variables, set up a Neo4j AuraDB instance, and installed Docker Compose, open a terminal and run:

$ docker-compose up --build

After each container finishes building, you'll be able to access the chatbot API at http://localhost:8000/docs and the Streamlit app at http://localhost:8501/.