An AI-powered chatbot that bridges medical expertise with real-world patient experience.
TrustMed AI helps patients find clear, reliable information about Diabetes and Cardiovascular Disease. It combines authoritative medical content (Mayo Clinic, CDC, MedlinePlus) with patient discussions from Reddit to deliver answers that are accurate, empathetic, and cited.
Built as a Retrieval-Augmented Generation (RAG) system, it grounds every response in verifiable evidence — unlike systems that rely solely on clinical sources (hard to understand) or patient forums (unverified).
Dataset: 1,727 documents — 201 authoritative medical articles + 1,525 Reddit patient discussions Retrieval: Hybrid k-NN + BM25 search (Amazon OpenSearch Serverless) LLM: Meta Llama 3 8B Instruct via AWS Bedrock Evaluation: Context Relevance 0.72 | Answer Relevance 0.68 | Groundedness 0.74
graph TD
subgraph Data Pipeline
A1[Mayo Clinic\nCDC, Healthline\n+5 more] -->|scrape| P[Python\nPreprocessing]
A2[Reddit\nr/diabetes\nr/heartdisease\n+6 more] -->|scrape| P
P -->|1727 docs| S3[Amazon S3]
S3 --> KB[Bedrock Knowledge Base\nTitan Embeddings v2]
KB --> OS[OpenSearch Serverless\nVector Index]
end
subgraph RAG Flow
U[User Query] --> CL[Chainlit UI]
CL --> RG[Bedrock retrieve_and_generate\nHybrid k-NN + BM25\ntop-k=8]
OS --> RG
RG --> LLM[Llama 3 8B Instruct]
LLM --> R[Response + Citations]
R --> CL
end
| Layer | Technology |
|---|---|
| UI | Chainlit (streaming chat with citations) |
| RAG Orchestration | LangChain + AWS Bedrock retrieve_and_generate |
| LLM | Meta Llama 3 8B Instruct (AWS Bedrock) |
| Embeddings | Amazon Titan Text Embeddings G1 (1536-dim) |
| Vector DB | Amazon OpenSearch Serverless (k-NN + BM25 hybrid) |
| Data Storage | Amazon S3 |
| Data Collection | Python, Requests, BeautifulSoup, PRAW |
| Evaluation | TruLens-style metrics (Sentence Transformers) |
- Dual-source RAG — 65% authoritative sources, 35% patient forum content per query, automatically balanced by hybrid retrieval
- Transparent citations — every response includes inline citations with source name, type (clinical vs. forum), title, URL, and a content excerpt
- Hybrid search — combines semantic similarity (k-NN) with keyword matching (BM25) for better retrieval than either alone
- Evaluated quality — custom TruLens-style evaluation across 3 dimensions on 6 representative queries
| Metric | Score | What it measures |
|---|---|---|
| Context Relevance | 0.72 | Retrieved docs match the query |
| Answer Relevance | 0.68 | Response stays on topic |
| Groundedness | 0.74 | Claims are supported by retrieved sources |
Authoritative (201 articles): Mayo Clinic, CDC, Healthline, MedicalNewsToday, Medical Xpress, Johns Hopkins, MedlinePlus, WebMD
Patient Forums (1,525 threads): r/diabetes, r/diabetes_t2, r/type2diabetes, r/prediabetes, r/hypertension, r/heartdisease, r/cardiology, r/askcardiology
- Python 3.10+
- AWS account with Bedrock access (Llama 3 8B + Titan Embeddings enabled)
- Amazon OpenSearch Serverless collection
- S3 bucket with processed documents
cd app
pip install -r requirements.txtaws configurecd app
chainlit run app.pyOpen http://localhost:8000 in your browser.
.
├── app/
│ ├── app.py # Chainlit app + Bedrock RAG integration
│ └── chainlit.md # Welcome page content
├── data-collection/
│ ├── scripts/ # Scraping and preprocessing scripts
│ └── data/ # Collected medical articles and forum threads
├── evaluation/
│ ├── evaluations.py # TruLens-style evaluation framework
│ ├── consolidated_trulens.png # Metrics visualization
│ └── indi_query.png # Per-query analysis
├── docs/ # Final report, proposal, and presentation
├── knowledge-base/ # Processed documents for S3/Bedrock KB
└── requirements.txt
| Consolidated Metrics | Per-Query Analysis |
|---|---|
 |
 |
MIT License — see LICENSE