How I Built a Customer Service Chatbot Using VectorShift (And How You Can Too!)

Discover the secrets to creating a powerful customer service chatbot with VectorShift. Your website visitors will thank you!

Hook: Ever wished your website could handle customer inquiries 24/7 without breaking a sweat? Well, buckle up, because we're about to make that dream a reality! 🚀

Building a Customer Service Chatbot: The Fun Way

Welcome back, tech enthusiasts! In our last session, we explored the magic of website analyzers. Today, we're diving into something even cooler—building a customer service chatbot that can answer user questions using your documentation. Sounds complicated? Fear not! With VectorShift, it's a walk in the park. 🌳

Step 1: Create a Vector Store

First things first, we need a place to store all our documentation—the Vector Store. Think of it as a digital library for our chatbot.

1. Go to the Storage Tab:
   • Click "Create New Vector Store."
2. Name and Describe Your Store:
   • For instance, "Travel Guide Chatbot" with a description that it provides up-to-date travel info.
3. Add Documentation:
   • Upload relevant documents. For this example, we’re using a travel agency's data. Check the resource section for sample files.

Boom! You’ve got yourself a Vector Store. 🏆

Step 2: Build the Chatbot Pipeline

Now, let's get our hands dirty with some pipeline magic.

1. Create a New Pipeline:
   • Go to the pipeline tab and click "Create Pipeline."
2. Set Up the Input Node:
   • Rename it to something like "User Question." This is where users will input their queries.
3. Connect to Vector Store:
   • Use the Vector Store Reader Node to link to your newly created store.
4. Add LLM (Large Language Model):
   • We’ll use OpenAI's LLM. Define system and prompt fields to guide the LLM on how to behave.
   • Create variables like {{User_Question}}, {{context}}, and {{conversational_history}}.
5. Incorporate Chat Memory:
   • This ensures our chatbot remembers past interactions. Connect it to the LLM.
6. Set the Output Node:
   • Label it "Result" and connect it to the LLM's response.

Step 3: Deploy and Test Your Chatbot

Finally, let's see our creation in action.

1. Run Within Pipeline Builder:
   • Input a question like "What should be considered while traveling abroad?" and watch the magic unfold.
2. Run as a Form:
   • Click the run button in the pipeline section to see the form option.
3. Generate API Calls:
   • Use the three-dot menu to create API calls for embedding your chatbot into websites.
4. Use as Backend for a Chatbot:
   • Configure the pipeline as a backend, name your chatbot, and voila!

The Result

Congratulations! You've built a chatbot that can handle customer inquiries like a pro. It's efficient, friendly, and always ready to help. 🌟

"The best way to predict the future is to create it." – Peter Drucker

With VectorShift, you’re not just predicting the future—you’re building it. Happy automating! 🚀