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.
Go to the Storage Tab:
- Click "Create New Vector Store."
Name and Describe Your Store:
- For instance, "Travel Guide Chatbot" with a description that it provides up-to-date travel info.
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.
Create a New Pipeline:
- Go to the pipeline tab and click "Create Pipeline."
Set Up the Input Node:
- Rename it to something like "User Question." This is where users will input their queries.
Connect to Vector Store:
- Use the Vector Store Reader Node to link to your newly created store.
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}}
.
Incorporate Chat Memory:
- This ensures our chatbot remembers past interactions. Connect it to the LLM.
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.
Run Within Pipeline Builder:
- Input a question like "What should be considered while traveling abroad?" and watch the magic unfold.
Run as a Form:
- Click the run button in the pipeline section to see the form option.
Generate API Calls:
- Use the three-dot menu to create API calls for embedding your chatbot into websites.
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. 🌟