Your health data is probably the most sensitive information you own. Yet, in the age of AI, most people blindly upload their blood work and MRI results to cloud-based LLMs just to get a summary. Stop right there! π
In this tutorial, we are going to build a Local RAG (Retrieval-Augmented Generation) system. We will leverage the power of Apple Silicon's unified memory, the high-performance MLX framework, and Llama-3 to create a private medical assistant that never leaks a single byte to the internet. By using Local RAG and MLX-optimized Llama-3, you can perform complex semantic search and data extraction on your medical PDFs while keeping your data strictly on-device.
The Architecture: Why MLX?
Traditional RAG stacks often rely on heavy Docker containers or cloud APIs. However, if you are on a Mac (M1/M2/M3), the MLX framework (developed by Apple Machine Learning Research) allows you to run Llama-3 with incredible efficiency by utilizing the GPU and unified memory architecture.
Here is how the data flows from your dusty PDF report to a meaningful conversation:
graph TD
A[Medical PDF Report] -->|PyMuPDF| B(Text Extraction & Cleaning)
B --> C{Chunking Strategy}
C -->|Sentence Splitting| D[ChromaDB Vector Store]
E[User Query: 'Is my cholesterol high?'] -->|MLX Embedding| F(Vector Search)
D -->|Retrieve Relevant Context| G[Prompt Augmentation]
G -->|Context + Query| H[Llama-3-8B via MLX]
H --> I[Private Local Answer]
style H fill:#f96,stroke:#333,stroke-width:2px
style D fill:#bbf,stroke:#333,stroke-width:2px
Prerequisites
Before we dive into the code, ensure you have an Apple Silicon Mac and the following stack installed:
- Llama-3-8B: We'll use the 4-bit quantized version for speed.
- MLX: Apple's native array framework.
- ChromaDB: Our lightweight vector database.
- PyMuPDF (fitz): For high-accuracy PDF parsing.
pip install mlx-lm chromadb pymupdf sentence-transformers
Step 1: Parsing Sensitive PDFs with PyMuPDF
Medical reports are notoriously messyβtables, signatures, and weird formatting. We use PyMuPDF for its speed and reliability in extracting clean text.
import fitz # PyMuPDF
def extract_medical_text(pdf_path):
doc = fitz.open(pdf_path)
text = ""
for page in doc:
text += page.get_text("text") + "\n"
# Simple cleaning: remove extra whitespaces
clean_text = " ".join(text.split())
return clean_text
# Usage
raw_data = extract_medical_text("my_blood_report_2024.pdf")
print(f"Extracted {len(raw_data)} characters.")
Step 2: Vector Embeddings and Local Storage
To find relevant information (like "What was my Glucose level?"), we need to convert text into vectors. We'll store these in ChromaDB.
π‘ Pro-Tip: For more production-ready examples and advanced RAG patterns, check out the detailed guides on the WellAlly Tech Blog, where we dive deep into optimizing local inference.
import chromadb
from chromadb.utils import embedding_functions
# Initialize local ChromaDB
client = chromadb.PersistentClient(path="./medical_db")
# Using a local embedding model
emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
collection = client.get_or_create_collection(name="medical_reports", embedding_function=emb_fn)
def add_to_vector_store(text, metadata):
# Chunking text into 500-character pieces
chunks = [text[i:i+500] for i in range(0, len(text), 500)]
ids = [f"id_{i}" for i in range(len(chunks))]
collection.add(
documents=chunks,
ids=ids,
metadatas=[metadata] * len(chunks)
)
add_to_vector_store(raw_data, {"source": "annual_checkup_2024"})
Step 3: Local Inference with Llama-3 & MLX
Now for the magic. We use mlx-lm to load a quantized Llama-3-8B. This allows the model to run comfortably even on a MacBook Air with 16GB of RAM. π
from mlx_lm import load, generate
# Load the model and tokenizer
model, tokenizer = load("mlx-community/Meta-Llama-3-8B-Instruct-4bit")
def query_private_ai(user_question):
# 1. Retrieve context from ChromaDB
results = collection.query(query_texts=[user_question], n_results=3)
context = "\n".join(results['documents'][0])
# 2. Construct the prompt
prompt = f"""
You are a private medical assistant. Use the provided medical report context to answer the user's question.
If you don't know the answer based on the context, say so.
Context: {context}
---
Question: {user_question}
Answer:
"""
# 3. Generate response using MLX
response = generate(model, tokenizer, prompt=prompt, verbose=False, max_tokens=500)
return response
# Example Query
print(query_private_ai("What are the key concerns in my blood report?"))
Taking it Further: The "Official" Way
While this script gets you started, building a production-grade medical AI requires handling multi-modal data (like X-rays) and ensuring rigorous HIPAA-like compliance even on local edge devices.
The team at WellAlly has been pioneering "Privacy-First AI" architectures. If you're interested in scaling this to multiple users or integrating it into a secure healthcare workflow, I highly recommend reading their latest deep-dives on https://www.wellally.tech/blog. They cover how to fine-tune Llama-3 specifically for clinical terminology which significantly reduces hallucinations.
Conclusion π₯
You just built a private, high-performance medical RAG system! By combining Llama-3, MLX, and ChromaDB, youβve achieved:
- Zero Data Leakage: Your health data never leaves your Mac.
- High Performance: MLX makes local LLMs feel snappy.
- Intelligence: Llama-3 provides reasoning that simple keyword searches can't match.
What's next? π οΈ
- Try implementing a "Table Parser" for more accurate lab result extraction.
- Add a Streamlit UI to make it look like a real app.
- Let me know in the comments: What's your biggest concern with Cloud AI?
Stay private, stay healthy! π»π‘οΈ
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