Building a Supercharged Customer Support System with Microsoft 365 and GPT-4

Hey there,

Today, I’m diving into the nitty-gritty of building a customer support system that’s not just smart, but supercharged. I’m talking about leveraging the power of Microsoft 365 and the wizardry of GPT-4 to create a Retrieval-Augmented Generation (RAG) system. Buckle up, because this ride is going to be both fun and enlightening!

Retrieval-Augmented Generation (RAG) | Created by Midjourney

What’s a RAG System Anyway?

Imagine having a system that doesn’t just generate responses from pre-existing knowledge but also fetches real-time data to ensure the answers are as accurate as they can get. That’s what a RAG system does – it combines the best of both worlds: retrieval of information and generation of responses. Sounds cool, right? Let’s break down how I can build one using Microsoft 365 and GPT-4.

Step 1: Set Up Your Microsoft 365 Environment

First things first, you need a Microsoft 365 account. If you’re already using it, great! If not, it’s time to get one. Make sure you have access to SharePoint, Outlook, and OneDrive, as these are going to be our playground.

Create a SharePoint Site:

1. Head over to SharePoint and create a new site. This will be your knowledge hub.
2. Upload all your customer support documents, FAQs, and any relevant manuals here. Organize them well – think of it as setting up a library where everything is in its place.

Step 2: Prep Your Knowledge Base

A messy library helps no one. Ensure your documents are well-organized and tagged with relevant metadata. This tagging will make it easier to retrieve the right information when needed.

Index Your Documents:

Use Microsoft Search or another search tool to index these documents. This step is crucial because a well-indexed document repository means faster and more accurate retrievals.

Step 3: Develop the Retrieval Mechanism

Here’s where things get technical. I’m going to use the Microsoft Graph API to query and retrieve documents from SharePoint. The Graph API is like our little robot librarian that fetches exactly what we need from the shelves.

Using Microsoft Graph API:

Get comfy with the [Microsoft Graph API documentation](https://docs.microsoft.com/en-us/graph/overview). You’ll learn how to make API calls and retrieve data.

Here’s a snippet to get you started.

python Code example:

import requests

def search_sharepoint(query, site_id, token):
    headers = {
        'Authorization': f'Bearer {token}',
        'Accept': 'application/json'
    }
    search_url = f'https://graph.microsoft.com/v1.0/sites/{site_id}/search/query'
    params = {
        'querytext': query,
        'trimduplicates': 'true'
    }
    response = requests.get(search_url, headers=headers, params=params)
    return response.json()

This function will help you fetch documents related to the user’s query from SharePoint.

Step 4: Integrate with GPT-4

Now comes the fun part – integrating GPT-4. If you don’t have access to GPT-4 yet, sign up with OpenAI and get your API key.

Create a Response Generation Function:

I need a function that takes the user query and the retrieved documents, then asks GPT-4 to generate a response.

Here’s an example:

import openai

def generate_response(query, retrieved_docs):
    combined_input = f"User query: {query}\n\nRetrieved information:\n"
    for doc in retrieved_docs['value']:
        combined_input += f"\nTitle: {doc['title']}\nContent: {doc['snippet']}\n"

    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=combined_input,
        max_tokens=150
    )
    return response.choices[0].text

This nifty function combines the retrieved information with the user query and lets GPT-4 do its magic.

Step 5: Build the Customer Support Interface

Time to give your system a face. I’ll create a simple web interface using Flask where users can input their queries.

Develop the Interface:

Create a Flask app that ties everything together.

Here’s a basic example:

from flask import Flask, request, jsonify
import openai
import requests

app = Flask(__name__)

@app.route('/query', methods=['POST'])
def query():
    data = request.json
    user_query = data['query']

    # Retrieve documents from SharePoint
    site_id = 'your_sharepoint_site_id'
    token = 'your_ms_graph_token'
    retrieved_docs = search_sharepoint(user_query, site_id, token)

    # Generate response using GPT-4
    response_text = generate_response(user_query, retrieved_docs)

    return jsonify({'response': response_text})

if __name__ == '__main__':
    app.run(debug=True)

This Flask app takes a user query, fetches relevant documents from SharePoint, and then uses GPT-4 to generate a detailed response.

Step 6: Test and Refine

No system is perfect from the get-go. Testing is crucial to ensure everything works smoothly.

Testing Instructions:

1. Prepare Test Cases:

• Create a variety of test queries that cover different aspects of customer support.
• Examples: “How do I reset my password?”, “What are the latest features of our product?”, “How can I contact support?”

2. Run Tests:

• Use the Flask app interface to input these test queries.
• Verify that the retrieved documents from SharePoint are relevant to the queries.
• Check the responses generated by GPT-4 for accuracy and relevance.

3. Analyze Results:

• Note down any issues such as irrelevant documents being retrieved or inaccurate responses from GPT-4.
• Evaluate the response time to ensure the system is performing efficiently.

4. Refine:

• Adjust the retrieval mechanism if irrelevant documents are frequently retrieved. This might involve tweaking search parameters or improving document tagging.
• Fine-tune the prompts and context provided to GPT-4 to improve response quality.

Step 7: Deploy and Maintain

Once you’re happy with your system, deploy your Flask app on a cloud service like AWS, Azure, or Heroku. Keep an eye on its performance and make adjustments as needed.

Step 8: Add a Microsoft Teams App for the Service Team

To give your service team the ability to track customer queries and analyze interactions, you can create a Microsoft Teams app. This app will allow the team to monitor ongoing conversations and take over the support if needed.

Create a Microsoft Teams App:

Set Up Your Teams Environment:

• Ensure your organization uses Microsoft Teams and that your service team has access.

Develop the Teams App:

• Use the Teams Toolkit to create a new app.
• The app will have tabs for tracking queries and analyzing interactions.

Add Functionality to the App:

Example: Tracking Customer Queries

from flask import Flask, request, jsonify
import requests

app = Flask(__name__)

queries = []

@app.route('/query', methods=['POST'])
def query():
    data = request.json
    user_query = data['query']
    user_id = data['user_id']
    
    # Retrieve documents from SharePoint
    site_id = 'your_sharepoint_site_id'
    token = 'your_ms_graph_token'
    retrieved_docs = search_sharepoint(user_query, site_id, token)

    # Generate response using GPT-4
    response_text = generate_response(user_query, retrieved_docs)
    
    # Track the query
    queries.append({
        'user_id': user_id,
        'query': user_query,
        'response': response_text
    })

    return jsonify({'response': response_text})

@app.route('/queries', methods=['GET'])
def get_queries():
    return jsonify({'queries': queries})

if __name__ == '__main__':
    app.run(debug=True)

Integrate with Teams:

• Use Microsoft Teams API to integrate the tracking functionality.
• Create tabs or messaging extensions to display ongoing queries and their status.

Enable Takeover Feature:

• Add functionality that allows service team members to take over communication.
• Implement a mechanism to switch from automated responses to human support seamlessly.

Example Takeover communication:

@app.route('/takeover', methods=['POST'])
def takeover():
    data = request.json
    query_id = data['query_id']
    team_member = data['team_member']
    
    # Find the query and mark it as taken over
    for query in queries:
        if query['id'] == query_id:
            query['taken_over_by'] = team_member
            break

    return jsonify({'status': 'success', 'message': 'Query taken over by ' + team_member})

This Flask app takes a user query, fetches relevant documents from SharePoint, and then uses GPT-4 to generate a detailed response.

Step 6: Test and Refine

No system is perfect from the get-go. Testing is crucial to ensure everything works smoothly.

Testing Instructions:

1. Prepare Test Cases.
2. Create a variety of test queries that cover different aspects of customer support.
3. Examples: “How do I reset my password?”, “What are the latest features of our product?”, “How can I contact support?”

2. Run Tests:

• Use the Flask app interface to input these test queries.
• Verify that the retrieved documents from SharePoint are relevant to the queries.
• Check the responses generated by GPT-4 for accuracy and relevance.

3. Analyze Results:

• Note down any issues such as irrelevant documents being retrieved or inaccurate responses from GPT-4.
• Evaluate the response time to ensure the system is performing efficiently.

4. Refine:

• Adjust the retrieval mechanism if irrelevant documents are frequently retrieved. This might involve tweaking search parameters or improving document tagging.
• Fine-tune the prompts and context provided to GPT-4 to improve response quality.

Step 7: Deploy and Maintain

Once you’re happy with your system, deploy your Flask app on a cloud service like AWS, Azure, or Heroku. Keep an eye on its performance and make adjustments as needed.

Wrapping Up

There you have it – a step-by-step guide to building a customer support RAG system using Microsoft 365 and GPT-4. This setup ensures your customers get accurate, up-to-date, and contextually relevant responses, making their support experience seamless and satisfying.

Go ahead and give it a try! And remember, the key to a successful RAG system is in the details – well-organized documents, fine-tuned retrieval queries, and a powerful generative model working in harmony.

Happy coding!