LLMs such as GPT have certain limitations. They may not have up-to-date information due to their knowledge cutoff date for training. This poses a significant challenge when we want our AI models to provide accurate, context-aware, and timely responses. Imagine asking an LLM about the latest technology trends or seeking real-time updates on a breaking news event; traditional language models might fall short in these scenarios.
In this blog, we will introduce you to a game-changing technique called retrieval-augmented generation (RAG). This unique approach empowers language models such as GPT to bridge the gap between their static knowledge and the dynamic real world. With RAG, we’ll show you how to equip your generative AI applications with the ability to pull in fresh information, ground your organizational data, cross-reference facts to address hallucinations and stay contextually aware, all in real-time.
Generative AI technology has the potential to greatly enhance education in the health sector, particularly in fields like anatomy and physiology. This is because AI platforms can create highly detailed and interactive models of the human body, making complex systems like the cardiovascular or nervous systems easier to understand than with traditional methods.
Another benefit of generative AI is its ability to personalize the learning experience. By analyzing a student's performance, the AI can identify areas where the student needs improvement and generate customized practice questions to target those areas. Additionally, generative AI can simulate patient interactions, which is essential in enhancing diagnostic skills.
This blog will show how generative AI using Azure AI studio prompt flow with Multi-Round Q&A on Your Data chat can make anatomy and physiology education more interactive, engaging, and effective and help students prepare for their healthcare careers.
You need an Azure AI Search resource to index your data for your copilot solution. This will let you use custom data in a prompt flow.
3. Wait for your Azure AI Search resource deployment to be completed.
Now you’re ready to create an Azure AI Studio project and the Azure AI resources to support it.
3. Wait for your project to be created.
To implement your solution, you will require two models.
The data for your copilot consists of a set of Essentials of Anatomy and Physiology in PDF format designed to provide a comprehensive introduction to human anatomy and physiology. Let’s add it to the project.
Now that you’ve added a data source to your project, you can use it to create an index in your Azure AI Search resource.
Before using your index in a RAG-based prompt flow, let’s verify that it can be used to affect generative AI responses.
In this example, I’ll use a ready-made template from the Prompt Flow page. I’ll choose the “Multi-Round Q&A on Your Data” template, which applies an augmented generation method to the retrieved Essentials of Anatomy and Philosophy book, based on a user’s question.
You can easily clone this Flow and start exploring it.
Let us analyze the individual parts that comprise it and delve into their unique characteristics:
Before the question gets embedded, modify_query_with_history adds context to the question by including instructions and the chat history. This is the default prompt:
Input: Hi, I'm struggling to understand how the nervous system interacts with other body systems. Can you help?
Output: Absolutely! The nervous system interacts with other body systems primarily through nerve impulses that control and coordinate actions. For example, the nervous system sends signals to the muscular system to initiate movement.
Input: That makes sense. Can you explain how the nervous system affects the digestive system?
Output: Sure! The nervous system regulates the speed at which food is digested through signals that increase or decrease the rate of muscle contractions in the gastrointestinal tract. This is crucial for efficient digestion and nutrient absorption.
Input: Thanks! How about the cardiovascular system? How does the nervous system interact with that?
Output: The nervous system controls the heart rate and blood pressure. It can send signals to the heart to speed up or slow down based on the body's needs, like during exercise or rest.
We use lookup to link our data to the model. The data must be indexed first, which is done in the Azure AI Studio ‘Indexes’ component on the side bar.
In the lookup section, set the following parameter values:
To get the right path, go to Build / your_project_name / Data / your_index_name, click on Index Data and copy the Data connection URI from the Data links section
generate_prompt_context receives a list of search result entities as input and turns them into a string with content and source information for each document. This function enhances the intelligence level by adding pertinent details to the prompt, enabling more knowledgeable and context-sensitive responses.
With prompt_variants, you can make different versions of prompts to get more variety in the questions you ask.
chat_with_context uses the context created by generate_prompt_context to improve the conversation. It takes into account the previous context and the related document chunks, which helps it to reply more logically and correctly.
Let's test the chat to see how it reacts.
After creating the flow, we can deploy it as a managed endpoint, which can be consumed through REST API by clicking the "Deploy" button on the flow page.
After that, you will need to choose a virtual machine that will be used to facilitate the deployment process :
Note that there is a feature available that you can opt to enable, called Inferencing data collection (currently in preview). When enabled, it automatically collects inputs and outputs as a data asset within your Azure AI Studio. This can be used later as a test dataset.
After deploying your flow in Azure AI Studio, you can consume it as a managed endpoint. To access this feature, simply navigate to the "Deployments" tab and click on your flow's name. From there, you can also test your flow to ensure it's working properly before consumption.
We can use streamlit in VS Code to write the code that will view your Endpoint and Keys. Go to the consume tab and copy and paste them into your code.
import streamlit as st
import urllib.request
import json
import os
import ssl
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
AZURE_ENDPOINT_KEY = os.environ['AZURE_ENDPOINT_KEY'] = 'xxxxxxxxxxxxxxxxxxxxxx'
def allowSelfSignedHttps(allowed):
# Bypass the server certificate verification on the client side
if allowed and not os.environ.get('PYTHONHTTPSVERIFY', '') and getattr(ssl, '_create_unverified_context', None):
ssl._create_default_https_context = ssl._create_unverified_context
# Streamlit UI components
st.image("education.png", width=600)
st.title(' Welcome to your Essential of Anantomy and Physiology Assistant!')
st.sidebar.title(" Copilot for Anantomy and Physiology !")
st.sidebar.caption("Made by an Pascal Burume")
st.sidebar.info("""
Generative AI technology has the potential to greatly enhance education in the health sector, particularly in fields like anatomy and physiology. This is because AI platforms can create highly detailed and interactive models of the human body, making complex systems like the cardiovascular or nervous systems easier to understand than with traditional methods.
""")
def main():
allowSelfSignedHttps(True)
# Initialize chat history
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
# Display chat history
for interaction in st.session_state.chat_history:
if interaction["inputs"]["chat_input"]:
with st.chat_message("user"):
st.write(interaction["inputs"]["chat_input"])
if interaction["outputs"]["chat_output"]:
with st.chat_message("assistant"):
st.write(interaction["outputs"]["chat_output"])
# React to user input
if user_input := st.chat_input("Ask me anything..."):
# Display user message in chat message container
st.chat_message("user").markdown(user_input)
# Query API
data = {"chat_history": st.session_state.chat_history, 'chat_input': user_input}
body = json.dumps(data).encode('utf-8')
url = 'https://xxxxxxxxxxxxxxxxxxxxx.ml.azure.com/score'
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {AZURE_ENDPOINT_KEY}',
'azureml-model-deployment': 'xxxxxxxxxx-1'
}
req = urllib.request.Request(url, body, headers)
try:
response = urllib.request.urlopen(req)
response_data = json.loads(response.read().decode('utf-8'))
# Check if 'chat_output' key exists in the response_data
if 'chat_output' in response_data:
with st.chat_message("assistant"):
st.markdown(response_data['chat_output'])
st.session_state.chat_history.append(
{"inputs": {"chat_input": user_input},
"outputs": {"chat_output": response_data['chat_output']}}
)
else:
st.error("The response data does not contain a 'chat_output' key.")
except urllib.error.HTTPError as error:
st.error(f"The request failed with status code: {error.code}")
if __name__ == "__main__":
main()
Samples prompts:
You can also use the backend of Azure Monitor to view metrics for your flow, in the “Monitoring” tab.
As we wrap up this exploration into the transformative capabilities of generative AI technologies, particularly within the realms of education and healthcare, it's clear that the potential for innovation is immense. By leveraging retrieval-augmented generation (RAG), we have unlocked a path that bridges the gap between static data and the dynamic needs of real-world applications. This blog has outlined not just the theoretical possibilities but also practical steps to implement these technologies using Azure AI Studio.
Thank you for joining us on this insightful journey through the capabilities of modern AI technologies. We are excited about the future possibilities as we continue to push the boundaries of what AI can achieve in educational contexts. Let’s move forward into a future where technology and education merge to create enriching, empowering learning experiences.