I'm in my master's degree program right now, and I've always wanted to find ways to reduce my learning hours every day. Voila! Here's my solution: creating a study companion using Amazon Bedrock.
We will leverage Amazon Bedrock to harness the power of foundation models (FMs) such as GPT-4 or T5.
These models will help us create a generative AI that can answer user queries on various topics in my master's program such as Quantum Physics, Machine Learning and more. We’ll explore how to fine-tune the model, implement advanced prompt engineering, and leverage Retrieval-Augmented Generation (RAG) to provide accurate answers to students.
Let's get into it!
Step 1: Setting Up Your Environment on AWS
To begin with, ensure that your AWS account is set up with the necessary permissions to access Amazon Bedrock, S3, and Lambda (I learned that the hard way after I found out I had to put in my debit card :( ). You’ll be working with AWS services like Amazon S3, Lambda, and Bedrock.
- Create an S3 Bucket to store your study materials
- This will allow the model to access materials for fine-tuning and retrieval.
- Go to the Amazon S3 Console and create a new bucket, e.g., "study-materials".
Upload Educational Content to S3. In my case, I created synthetic data to add that's relevant to my master's program. You can create your own based on your needs or add other datasets from Kaggle.
[
{
"topic": "Advanced Economics",
"question": "How does the Lucas Critique challenge traditional macroeconomic policy analysis?",
"answer": "The Lucas Critique argues that traditional macroeconomic models' parameters are not policy-invariant because economic agents adjust their behavior based on expected policy changes, making historical relationships unreliable for policy evaluation."
},
{
"topic": "Quantum Physics",
"question": "Explain quantum entanglement and its implications for quantum computing.",
"answer": "Quantum entanglement is a physical phenomenon where pairs of particles remain fundamentally connected regardless of distance. This property enables quantum computers to perform certain calculations exponentially faster than classical computers through quantum parallelism and superdense coding."
},
{
"topic": "Advanced Statistics",
"question": "What is the difference between frequentist and Bayesian approaches to statistical inference?",
"answer": "Frequentist inference treats parameters as fixed and data as random, using probability to describe long-run frequency of events. Bayesian inference treats parameters as random variables with prior distributions, updated through data to form posterior distributions, allowing direct probability statements about parameters."
},
{
"topic": "Machine Learning",
"question": "How do transformers solve the long-range dependency problem in sequence modeling?",
"answer": "Transformers use self-attention mechanisms to directly model relationships between all positions in a sequence, eliminating the need for recurrent connections. This allows parallel processing and better capture of long-range dependencies through multi-head attention and positional encodings."
},
{
"topic": "Molecular Biology",
"question": "What are the implications of epigenetic inheritance for evolutionary theory?",
"answer": "Epigenetic inheritance challenges the traditional neo-Darwinian model by demonstrating that heritable changes in gene expression can occur without DNA sequence alterations, suggesting a Lamarckian component to evolution through environmentally-induced modifications."
},
{
"topic": "Advanced Computer Architecture",
"question": "How do non-volatile memory architectures impact traditional memory hierarchy design?",
"answer": "Non-volatile memory architectures blur the traditional distinction between storage and memory, enabling persistent memory systems that combine storage durability with memory-like performance, requiring fundamental redesign of memory hierarchies and system software."
}
]
Step 2: Leverage Amazon Bedrock for Foundation Models
Launch Amazon Bedrock then:
- Go to the Amazon Bedrock Console.
- Create a new project and select your desired foundation model (e.g., GPT-3, T5).
- Choose your use case, in this case, a study companion.
- Select the Fine-tuning option (if needed) and upload the dataset (your educational content from S3) for fine-tuning.
- Fine-tuning the Foundation Model:
Bedrock will automatically fine-tune the foundation model on your dataset. For instance, if you're using GPT-3, Amazon Bedrock will adapt it to better understand educational content and generate accurate answers for specific topics.
Here's a quick Python code snippet using Amazon Bedrock SDK to fine-tune the model:
import boto3
# Initialize Bedrock client
client = boto3.client("bedrock-runtime")
# Define S3 path for your dataset
dataset_path = 's3://study-materials/my-educational-dataset.json'
# Fine-tune the model
response = client.start_training(
modelName="GPT-3",
datasetLocation=dataset_path,
trainingParameters={"batch_size": 16, "epochs": 5}
)
print(response)
Save Fine-tuned Model: After fine-tuning, the model is saved and ready for deployment. You can find it in your Amazon S3 bucket under a new folder called fine-tuned-model.
Step 3: Implement Retrieval-Augmented Generation (RAG)
1. Set Up an Amazon Lambda Function:
- Lambda will handle the request and interact with the fine-tuned model to generate responses.
- The Lambda function will fetch relevant study materials from S3 based on the user query and use RAG to generate an accurate answer.
Lambda Code for Answer Generation: Here's an example of how you might configure a Lambda function to use the fine-tuned model for generating answers:
import json
import boto3
from transformers import GPT2LMHeadModel, GPT2Tokenizer
s3 = boto3.client('s3')
model_s3_path = 's3://study-materials/fine-tuned-model'
# Load model and tokenizer
def load_model():
s3.download_file(model_s3_path, 'model.pth')
tokenizer = GPT2Tokenizer.from_pretrained('model.pth')
model = GPT2LMHeadModel.from_pretrained('model.pth')
return tokenizer, model
tokenizer, model = load_model()
def lambda_handler(event, context):
query = event['query']
topic = event['topic']
# Retrieve relevant documents from S3 (RAG)
retrieved_docs = retrieve_documents_from_s3(topic)
# Generate response
prompt = f"Topic: {topic}\nQuestion: {query}\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs['input_ids'], max_length=150)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
return {
'statusCode': 200,
'body': json.dumps({'answer': answer})
}
def retrieve_documents_from_s3(topic):
# Fetch study materials related to the topic from S3
# Your logic for document retrieval goes here
pass
3. Deploy the Lambda Function: Deploy this Lambda function on AWS. It will be invoked through API Gateway to handle real-time user queries.
Step 4: Expose the Model via API Gateway
Create an API Gateway:
Go to the API Gateway Console and create a new REST API.
Set up a POST endpoint to invoke your Lambda function that handles the generation of answers.
Deploy the API:
Deploy the API and make it publicly accessible by using a custom domain or default URL from AWS.
Step 5: Build a Streamlit Interface
Finally, build a simple Streamlit app to allow users to interact with your study companion.
import streamlit as st
import requests
st.title("Personalized Study Companion")
topic = st.text_input("Enter Study Topic:")
query = st.text_input("Enter Your Question:")
if st.button("Generate Answer"):
response = requests.post("https://your-api-endpoint", json={"topic": topic, "query": query})
answer = response.json().get("answer")
st.write(answer)
You can host this Streamlit app on AWS EC2 or Elastic Beanstalk.
If everything works well congratulations. You just made your study companion. If I had to evaluate this project, I could add some more examples for my synthetic data (duh??) or get another educational dataset that perfectly aligns with my goals.
Thanks for reading! Let me know what do you think!
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