Python + n8n Starter Guide for Doctors Who Code

Introduction

Welcome to your Python + n8n Starter Guide! This guide is designed for tech-savvy doctors who want to build AI-driven solutions for their medical practice using Python, Pinecone.io, and n8n.

By the end of this guide, you’ll have the tools and templates to:

• Set up Pinecone.io for vector search
• Convert clinical notes into AI-readable vector embeddings
• Automate workflows using n8n

Part 1: Setting Up Pinecone.io for Vector Search

Step 1: Install Required Python Libraries

You’ll need a few libraries to get started:

pip install pinecone-client sentence-transformers

Step 2: Initialize Pinecone.io

import pinecone

# Initialize Pinecone with your API key
pinecone.init(api_key="YOUR_API_KEY", environment="us-west1-gcp")

# Create an index for clinical notes
index = pinecone.Index("medical-notes")

Step 3: Create Vector Embeddings for Clinical Notes

Use SentenceTransformers to convert text into vector embeddings.

from sentence_transformers import SentenceTransformer

# Load a pre-trained transformer model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Sample clinical note
note = "Patient presents with fever, sore throat, and fatigue."

# Generate an embedding for the note
embedding = model.encode(note)
print(embedding)

Step 4: Index the Embedding in Pinecone

# Index the embedding with a unique identifier
index.upsert([("patient_123", embedding)])

# Confirm the embedding is indexed
print(index.describe_index_stats())