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

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

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. 🌟