Medical Chatbot Strategy Comparison

Chatbot for answering medical questions.

Compares four LLM strategies on medical Q&A using PubMed abstracts.

Strategy	RAG	Memory
S1 — Single LLM	—	—
S2 — Single RAG	✓	—
S3 — Multi-Turn LLM	—	✓
S4 — Multi-Turn RAG	✓	✓

All strategies use llama-3.3-70b-versatile via Groq (free tier).

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Prerequisites

• Python 3.10+
• A Groq API key (free, no credit card)
• Your UIC email (required by NCBI Entrez for PubMed access)

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Local setup

1. Install dependencies

cd backend
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

2. Configure environment

cp .env.example .env

Open .env and fill in at minimum:

Variable	Description
GROQ_API_KEY	Get from console.groq.com
PUBMED_EMAIL	Your UIC email (e.g. netid@uic.edu)
NCBI_API_KEY	Optional — speeds up seeding (3→10 req/s)
CHROMA_BACKUP_URL	Optional — URL to pre-built chroma_db.tar.gz to skip seeding

3. Seed ChromaDB (run once)

ChromaDB is file-persisted — you only need to do this once per machine.

Option A — fast restore from backup (recommended):

CHROMA_BACKUP_URL is pre-filled in .env.example. The server downloads and restores the backup automatically on startup (~60s). Just start the server and wait.

Option B — seed from PubMed (~5h without NCBI key, ~1h with):

# From backend/ with venv active
python seed_db.py

Or trigger it via the API after the server is running:

curl -X POST http://localhost:8000/api/v1/ingestion/ingest \
  -H "Content-Type: application/json" \
  -d '{"query": "diabetes mellitus", "max_results": 100}'

Check seeding progress:

curl http://localhost:8000/api/v1/ingestion/status

4. Start the server

# From backend/ with venv active
uvicorn app.main:app --reload

API is available at http://localhost:8000 Interactive docs at http://localhost:8000/docs

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Running tests

cd backend
pytest                              # all tests
pytest tests/test_single_llm.py    # single file

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Running the evaluation

Install eval dependencies (from repo root):

pip install bert-score sentence-transformers httpx openpyxl pandas

Run evaluation against all 4 strategies:

python eval/run_evaluation.py \
  --input "reference-folder/Evaluation Dataset - CS 540 Project.xlsx" \
  --base-url http://localhost:8000 \
  --output eval/results/evaluation_results.xlsx

Add --member 2 to run only one member's sheet. Results are written to the output Excel file with one row per (session × turn × strategy).

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API overview

Endpoint	Strategy
POST /api/v1/chat/single-llm/	S1 — { "query": "..." }
POST /api/v1/chat/single-rag/	S2 — { "query": "...", "top_k": 5 }
POST /api/v1/chat/multi-turn-llm/	S3 — { "session_id": "<uuid>", "query": "..." }
POST /api/v1/chat/multi-turn-rag/	S4 — { "session_id": "<uuid>", "query": "...", "top_k": 5 }

For S3/S4, generate a session_id once per conversation (e.g. python -c "import uuid; print(uuid.uuid4())") and reuse it on every follow-up turn. DELETE the session endpoint to reset history.