Build an AI Website Chatbot with Vectorshift
Summary
The video demonstrates building a chatbot on the Vector Shift platform by creating a new pipeline structure. It explains how the chatbot uses LM to formulate responses based on user queries and chat memory. Viewers are guided on connecting the knowledge base, Vector Shift, and adding relevant data for a seamless chatbot experience, with final steps including naming, exporting, and deployment options. The tutorial showcases the capabilities of Vector Shift, including data querying and sources, inviting users to seek assistance as needed.
Chapters
Introduction to Building a Website Chatbot
Input Node and Querying Knowledge Base
Formulating Responses and Output
Connecting Knowledge Base and Adding Data
Variables and Output Fields
Creating Input Node and Output Fields
Connecting Response and Output
Finalizing Pipeline and Exporting Chatbot
Using Vector Shift and Reaching Out
Introduction to Building a Website Chatbot
Explanation of building a website chatbot using the Vector Shift platform and creating a new pipeline structure for the chatbot.
Input Node and Querying Knowledge Base
Description of the input node querying a knowledge base, specifically about Vector Shift, in the chatbot pipeline.
Formulating Responses and Output
Explaining how the chatbot formulates responses based on user questions and chat memory using LM (Large Language Model) in the pipeline.
Connecting Knowledge Base and Adding Data
Instructions on connecting the knowledge base, Vector Shift, and adding relevant data about the Vector Shift homepage.
Variables and Output Fields
Details on using variables in Vector Shift to represent data and the output fields in the chatbot pipeline.
Creating Input Node and Output Fields
Guidance on creating the input node with the Variable Builder tool and connecting output fields like text and chat memory in the pipeline.
Connecting Response and Output
Explanation on sending responses from the LM back to the user, connecting message responses to the output, and streamlining text output.
Finalizing Pipeline and Exporting Chatbot
Final steps in completing the pipeline, naming and exporting the chatbot, and options for deployment like embedding into websites and using Slack.
Using Vector Shift and Reaching Out
Discussing the capabilities of Vector Shift, including citations, sources, and data, and encouraging users to reach out for assistance.
FAQ
Q: What is the role of the input node in the chatbot pipeline?
A: The input node queries a knowledge base, specifically about Vector Shift, to gather relevant information for formulating responses.
Q: How does the chatbot use the Large Language Model (LM) in the pipeline?
A: The chatbot formulates responses based on user questions and chat memory by leveraging the Large Language Model (LM) in the pipeline.
Q: What is the purpose of using variables in Vector Shift?
A: Variables in Vector Shift are used to represent data and output fields in the chatbot pipeline, allowing for dynamic responses and personalized interaction.
Q: How can one create the input node using the Variable Builder tool?
A: To create the input node, one can utilize the Variable Builder tool to define variables and connect them to the knowledge base and relevant data sources like the Vector Shift homepage.
Q: What are the final steps in completing the chatbot pipeline?
A: The final steps involve naming and exporting the chatbot, as well as considering deployment options such as embedding into websites or using platforms like Slack for interaction.
Q: What are some capabilities of Vector Shift mentioned in the file?
A: Vector Shift offers capabilities such as accessing citations, sources, and data for enriching the chatbot responses, while also providing assistance to users who require further support.
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