🏥 HOSTOBOT

LangChain	Neo4j	Gemini	OpenAI	MistralAI

🚀 Overview

HOSTOBOT is an advanced Agentic Microservices demonstration combining Large Language Models (LLMs) with a Graph Database (Neo4j) to deliver a powerful Hybrid RAG (Retrieval-Augmented Generation) system.

Traditional RAG systems often struggle with structured aggregation and relationship-heavy queries. HOSTOBOT addresses this limitation using a Router Agent that dynamically selects the best tool:

• ⚙️ Python Tools → real-time simulations and computations
• 🔎 Vector Search → qualitative insights (patient sentiment)
• 🧠 Graph Cypher Generation → quantitative analytics

This platform empowers users to effortlessly request complex hospital-related data using natural language queries.

The code is written in Python 🐍.

---

🏗️ Architecture

The intelligent core of this system is the 🏥 Hospital RAG Agent, which dynamically routes queries using the following advanced tools:

• 🛠️ Current Hospitals (get_current_hospitals_tools) — Retrieves the active list of hospitals directly from the Neo4j database.
• 🛠️ Wait Times (get_current_wait_times) — Generates a simulated current wait time for patient emergency visits.
• 🛠️ Most Available Hospital (get_most_available_hospital) — Suggests the most optimal hospital with the highest available capacity.
• 🔗🛠️ Experiences & Reviews (get_reviews) — Leverages Vector Search capabilities on patient "Review" nodes via a Neo4jVectorSearchChain, seamlessly integrating qualitative semantic search into the populated graph database.
• 🔗🛠️ Graph Querying (get_graph) — Dynamically translates natural language questions into precise Cypher queries using a CypherChain, enabling direct quantitative question-answering against the complex graph structure.

---

⚙️ Configuration

To run the application, you need the following prerequisites:

• 🔑 NEO4J Credentials: URI, Username, and Password.
• 🔑 AI API Key: Choose between OpenAI, Gemini, or Mistral.

📝 Action: Provide these credentials in your .env file and ensure Docker Desktop 🐳 is running before proceeding.

---

📂 Project Structure

LLM_RAG_ETL_Showcase-Hostobot/
├── .env                        # Critical environment configurations
├── docker-compose.yml          # Orchestration for all 4 services
│
├── img/                        # Images to display in the Readme
│   └── dbscheme.png
│   
├── hospital_neo4j_etl/         # DATA LAYER
│   ├── src/
│   │   ├── hospital_bulk_csv_write.py  # Bulk loader & relationship mapper
│   │   └── entrypoint.sh               # Execution script
│   └── Dockerfile
│
├── chatbot_api/                # LOGIC LAYER (FastAPI)
│   ├── src/
│   │   ├── main.py                     # API entry point & routes
│   │   ├── agents/
│   │   │   └── hospital_rag_agent.py   # Agent & Tool definitions
│   │   ├── chains/
│   │   │   ├── hospital_cypher_chain.py # Cypher generation logic
│   │   │   └── hospital_review_chain.py # Vector search logic
│   │   ├── tools/
│   │   │   └── wait_times.py            # Simulated real-time tool
│   │   ├── models/
│   │   │   └── hospital_rag_query.py    # Pydantic schemas
│   │   └── utils/
│   │       └── async_utils.py          # Retry decorators
│   └── Dockerfile
│
└── chatbot_frontend/           # PRESENTATION LAYER
    ├── src/
    │   └── main.py                     # Streamlit UI logic
    └── Dockerfile

---

📊 Knowledge Graph Schema

The Neo4j database is modeled to handle multi-hop questions.

Nodes

• 🏥 Hospital: {id, name, state_name}
• 💰 Payer: {id, name}
• 👨‍⚕️ 🩺 Physician: {id, name, dob, school, salary}
• 🧑‍🤝‍🧑 🤕 Patient: {id, name, sex, blood_type}
• 📅 Visit: {id, room_number, admission_type, status, diagnosis}
• 📝 Review: {id, text, patient_name, physician_name}

Relationships

• (Patient)-[:HAS]->(Visit)
• (Physician)-[:TREATS]->(Visit)
• (Visit)-[:AT]->(Hospital)
• (Visit)-[:COVERED_BY]->(Payer)
• (Visit)-[:WRITES]->(Review)
• (Hospital)-[:EMPLOYS]->(Physician)

_{Graph Ontology: Mapping relationships between Patients, Visits, and Providers.}

---

🛠️ Installation & Execution

Start the entire microservices stack with a single command:

docker compose up --build

🌐 Local Access Points

Once the Docker containers are built and healthy, you can access the localized services:

• 📖 API Documentation (FastAPI): http://localhost:8000/docs
• 💬 Chatbot UI (Streamlit): http://localhost:8501/

---

❓ Example Questions to Ask

Try testing the agent with some of these complex, natural language questions:

• Which hospitals are part of the hospital network?
• What’s the current wait time at Wallace-Hamilton Hospital?
• At which hospitals are patients reporting issues related to billing or insurance?
• What’s the average length in days for completed emergency visits?
• How are patients describing the nursing team at Castaneda-Hardy?
• What was the total amount billed to each payer during 2023?
• What is the average charge for visits covered by Medicaid?
• Which doctor has the shortest average visit duration?
• What is the total billed amount for patient 789's hospital stay?
• Which state saw the biggest percentage increase in Medicaid visits from 2022 to 2023?
• What’s the average daily billing amount for patients with Aetna coverage?
• How many patient reviews have been submitted from Florida?
• For visits that include a chief complaint, what percentage also have a review?
• What percentage of visits at each hospital include patient reviews?
• Which physician has received the highest number of reviews for their visits?
• What is the unique identifier for Dr. James Cooper?
• Show all reviews associated with visits handled by physician 270 — include every one.