https://github.com/uwon0625/rag-agent-for-hospital
Retrieval Augmented Generation (RAG) Chatbot for Hospital
https://github.com/uwon0625/rag-agent-for-hospital
agents ai chatbot docker docker-compose etl etl-pipeline full-stack generative-ai healthcare hospital neo4j prompt-engineering rag streamlit
Last synced: 4 months ago
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Retrieval Augmented Generation (RAG) Chatbot for Hospital
- Host: GitHub
- URL: https://github.com/uwon0625/rag-agent-for-hospital
- Owner: uwon0625
- Created: 2024-05-19T17:27:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-19T17:29:38.000Z (over 1 year ago)
- Last Synced: 2025-03-14T20:36:19.932Z (7 months ago)
- Topics: agents, ai, chatbot, docker, docker-compose, etl, etl-pipeline, full-stack, generative-ai, healthcare, hospital, neo4j, prompt-engineering, rag, streamlit
- Language: Python
- Homepage:
- Size: 624 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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:
```.env
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](https://neo4j.com/cloud/platform/aura-graph-database/?ref=docs-nav-get-started) 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](https://docs.docker.com/compose/). You can install Docker Compose by following [these directions](https://docs.docker.com/compose/install/).
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:
```console
$ 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/`.