added 3 more samples

clinical-note-similarity-py/.env.example

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+DOCUMENTDB_URI=mongodb://<username>:<password>@localhost:10260/?tls=true&tlsAllowInvalidCertificates=true&authMechanism=SCRAM-SHA-256
+DOCUMENTDB_DATABASE=clinicaldb
+DOCUMENTDB_COLLECTION=notes
+OLLAMA_BASE_URL=http://127.0.0.1:11434
+OLLAMA_EMBEDDING_MODEL=nomic-embed-text
+FLASK_PORT=5001
+NEAREST_NEIGHBORS=5
+EMBEDDING_DIMENSIONS=768

clinical-note-similarity-py/.gitignore

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+.env
+__pycache__/
+*.pyc
+*.pyo
+*.egg-info/
+dist/
+build/
+.venv/
+venv/

clinical-note-similarity-py/README.md

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+# Clinical Note Similarity Explorer — Python
+
+> **Important:** All clinical notes in this sample are fictional and de-identified. This tool is for demonstration purposes only and is not intended for use with real patient data.
+
+A Flask web application that stores de-identified clinical notes as documents with vector embeddings in **DocumentDB OSS**. Clinicians and researchers can search for similar cases using natural language descriptions — finding notes by clinical meaning rather than exact keyword matches. Built entirely on open-source tools with no cloud accounts required.
+
+## Use cases
+
+- Search by symptoms: *"patient with acute chest pain radiating to the left arm with ST elevation"*
+- Search by presentation: *"shortness of breath not responding to bronchodilators with wheeze"*
+- Search by findings: *"sudden onset facial droop, arm weakness, and slurred speech"*
+- Research patterns: *"abdominal pain with right lower quadrant tenderness and fever"*
+
+## How it works
+
+```
+Clinician Query (natural language)
+         │
+         ▼
+  Embed with nomic-embed-text (768-dim)
+         │
+         ▼
+  DocumentDB $search ──── cosine similarity ────► Similar Cases
+  (vector-ivf index)      by clinical meaning      ranked by score
+         │
+    optional $match
+    (specialty filter)
+```
+
+At ingest time, each note's chief complaint and clinical text are concatenated and embedded into a 768-dimensional vector using `nomic-embed-text`. At search time, the clinician's query is embedded with the same model and DocumentDB returns the most semantically similar cases — even when different clinical terminology is used.
+
+## Open-source stack
+
+| Component | Tool |
+|---|---|
+| Embedding model | [Ollama](https://ollama.com) `nomic-embed-text` (768 dimensions, runs locally) |
+| Vector database | [DocumentDB OSS](https://github.com/microsoft/documentdb) via Docker |
+| MongoDB driver | [PyMongo](https://pymongo.readthedocs.io/) |
+| Web framework | [Flask](https://flask.palletsprojects.com/) |
+| Language | Python 3.10+ |
+
+## Prerequisites
+
+- **Python 3.10+** — [python.org](https://python.org)
+- **Docker Desktop** — [docker.com/products/docker-desktop](https://www.docker.com/products/docker-desktop)
+- **Ollama** — [ollama.com/download](https://ollama.com/download)
+
+After installing Ollama, pull the embedding model:
+
+```bash
+ollama pull nomic-embed-text
+```
+
+## Setup
+
+### 1. Start DocumentDB OSS
+
+**macOS / Linux / Git Bash:**
+```bash
+docker run -dt \
+  -p 10260:10260 \
+  -e USERNAME=docdbuser \
+  -e PASSWORD=Admin100! \
+  ghcr.io/microsoft/documentdb/documentdb-local:latest
+```
+
+**Windows PowerShell:**
+```powershell
+docker run -dt `
+  -p 10260:10260 `
+  -e USERNAME=docdbuser `
+  -e PASSWORD=Admin100! `
+  ghcr.io/microsoft/documentdb/documentdb-local:latest
+```
+
+### 2. Install dependencies
+
+```bash
+cd clinical-note-similarity-py
+pip install -r requirements.txt
+```
+
+### 3. Configure environment variables
+
+```bash
+cp .env.example .env
+```
+
+Edit `.env` with your DocumentDB credentials:
+
+| Variable | Default | Description |
+|---|---|---|
+| `DOCUMENTDB_URI` | — | Full MongoDB connection string |
+| `DOCUMENTDB_DATABASE` | `clinicaldb` | Database name |
+| `DOCUMENTDB_COLLECTION` | `notes` | Collection name |
+| `OLLAMA_BASE_URL` | `http://127.0.0.1:11434` | Ollama server URL |
+| `OLLAMA_EMBEDDING_MODEL` | `nomic-embed-text` | Embedding model |
+| `FLASK_PORT` | `5001` | Port for the Flask web app |
+| `NEAREST_NEIGHBORS` | `5` | Default number of results |
+| `EMBEDDING_DIMENSIONS` | `768` | Must match the embedding model |
+
+### 4. Upload clinical notes
+
+Embeds all 20 sample notes and stores them in DocumentDB with a vector index:
+
+```bash
+python upload_notes.py
+```
+
+Expected output:
+```
+Loaded 20 clinical notes
+
+Cleared existing collection
+
+Generating embeddings and uploading...
+  [ 1/20] [Cardiology           ] Acute ST-Elevation Myocardial Infarction...
+  [ 2/20] [Cardiology           ] Unstable Angina...
+  ...
+  [20/20] [Gastroenterology     ] Crohn's Disease Flare...
+
+Inserted 20 note documents
+Vector index created (vector-ivf, dimensions: 768, similarity: COS)
+```
+
+### 5. Start the web app
+
+```bash
+python app.py
+```
+
+Open your browser at `http://localhost:5001`.
+
+## Sample queries to try
+
+| Query | Expected specialty |
+|---|---|
+| `Sudden severe chest pain with ST elevation, diaphoresis, left arm radiation` | Cardiology |
+| `Wheezing, dyspnea not responding to albuterol inhaler` | Pulmonology |
+| `Sudden onset facial droop, arm weakness, and speech difficulty` | Neurology |
+| `Abdominal pain migrating to right lower quadrant with fever` | Gastroenterology |
+| `Knee pop after pivoting with immediate swelling and instability` | Orthopedics |
+| `Fatigue, weight gain, cold intolerance, hair thinning, and constipation` | Endocrinology |
+| `Spreading skin redness with warmth and fever after minor skin break` | Dermatology |
+
+## Document schema
+
+Each clinical note document stored in DocumentDB:
+
+| Field | Type | Description |
+|---|---|---|
+| `note_id` | string | Unique identifier (e.g. `CN001`) |
+| `specialty` | string | Medical specialty |
+| `diagnosis` | string | Primary diagnosis |
+| `age_group` | string | `18-35`, `36-50`, `51-65`, `65+` |
+| `sex` | string | `M` or `F` |
+| `chief_complaint` | string | Presenting complaint in one sentence |
+| `clinical_note` | string | De-identified clinical summary (3-5 sentences) |
+| `icd_code` | string | ICD-10 diagnosis code |
+| `outcome` | string | `admitted`, `discharged`, `referred`, `follow-up` |
+| `embedding` | array | 768-dimensional vector (excluded from search results) |
+
+## Specialties covered
+
+The sample dataset includes 20 notes across 7 specialties:
+
+| Specialty | Notes | Diagnoses |
+|---|---|---|
+| Cardiology | 4 | STEMI, Unstable Angina, Atrial Fibrillation, Heart Failure |
+| Pulmonology | 3 | Pneumonia, Asthma Exacerbation, COPD Exacerbation |
+| Neurology | 3 | Migraine, Ischemic Stroke, Seizure |
+| Gastroenterology | 3 | Appendicitis, GERD, Crohn's Disease |
+| Orthopedics | 3 | Distal Radius Fracture, Lumbar Disc Herniation, ACL Tear |
+| Endocrinology | 2 | Type 2 Diabetes, Hashimoto's Thyroiditis |
+| Dermatology | 2 | Cellulitis, Plaque Psoriasis |
+
+## Project structure
+
+```
+clinical-note-similarity-py/
+├── data/
+│   └── clinical_notes.json        # 20 fictional de-identified clinical notes
+├── utils/
+│   ├── __init__.py
+│   ├── db.py                      # MongoDB client factory
+│   └── embeddings.py              # Ollama embedding helper
+├── templates/
+│   ├── index.html                 # Search page
+│   └── note.html                  # Full note detail page
+├── static/
+│   └── style.css                  # Styles
+├── upload_notes.py                # Seeds DocumentDB with embeddings + vector index
+├── cleanup.py                     # Drops the notes collection
+├── app.py                         # Flask web application
+├── requirements.txt
+├── .env.example                   # Template — copy to .env
+├── .gitignore
+└── README.md
+```
+
+## Cleanup
+
+Drop the notes collection when you are done:
+
+```bash
+python cleanup.py
+```
+
+## Disclaimer
+
+This sample uses entirely fictional, de-identified clinical notes generated for demonstration purposes. It is not a medical device, clinical decision support system, or suitable for use with real patient data. Always consult qualified healthcare professionals for medical decisions.

clinical-note-similarity-py/app.py

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+import os
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from flask import Flask, render_template, request
+from utils.db import get_client, get_collection
+from utils.embeddings import get_embedding
+
+app = Flask(__name__)
+
+_client = None
+_col = None
+
+
+def get_col():
+    global _client, _col
+    if _col is None:
+        _client = get_client()
+        _col = get_collection(_client)
+    return _col
+
+
+def similarity_search(query: str, specialty: str, num_results: int) -> list:
+    col = get_col()
+    k = num_results if specialty == "all" else num_results * 4
+
+    embedding = get_embedding(query)
+
+    pipeline = [
+        {
+            "$search": {
+                "cosmosSearch": {
+                    "vector": embedding,
+                    "path": "embedding",
+                    "k": k,
+                },
+                "returnStoredSource": True,
+            }
+        },
+        {
+            "$addFields": {"similarityScore": {"$meta": "searchScore"}}
+        },
+        {
+            "$project": {"embedding": 0}
+        },
+    ]
+
+    if specialty and specialty != "all":
+        pipeline.append({"$match": {"specialty": specialty}})
+
+    pipeline.append({"$limit": num_results})
+
+    return list(col.aggregate(pipeline))
+
+
+def get_specialties() -> list:
+    col = get_col()
+    return sorted(col.distinct("specialty"))
+
+
+@app.route("/")
+def index():
+    specialties = get_specialties()
+    return render_template("index.html", specialties=specialties)
+
+
+@app.route("/search", methods=["POST"])
+def search():
+    query = request.form.get("query", "").strip()
+    specialty = request.form.get("specialty", "all")
+    num_results = int(request.form.get("num_results", 5))
+    specialties = get_specialties()
+
+    results = []
+    error = None
+
+    if query:
+        try:
+            results = similarity_search(query, specialty, num_results)
+        except Exception as e:
+            error = str(e)
+
+    return render_template(
+        "index.html",
+        query=query,
+        results=results,
+        specialty=specialty,
+        num_results=num_results,
+        specialties=specialties,
+        error=error,
+    )
+
+
+@app.route("/note/<note_id>")
+def note_detail(note_id):
+    col = get_col()
+    doc = col.find_one({"note_id": note_id}, {"embedding": 0})
+    if not doc:
+        return "Note not found", 404
+    return render_template("note.html", note=doc)
+
+
+if __name__ == "__main__":
+    port = int(os.getenv("FLASK_PORT", 5001))
+    print(f"Starting Clinical Note Similarity Explorer on http://localhost:{port}")
+    app.run(debug=True, port=port)

clinical-note-similarity-py/cleanup.py

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+from dotenv import load_dotenv
+
+load_dotenv()
+
+from utils.db import get_client, get_collection
+
+
+def main():
+    client = get_client()
+    col = get_collection(client)
+    col.drop()
+    print(f"Dropped collection: {col.full_name}")
+    client.close()
+
+
+if __name__ == "__main__":
+    main()