Chatbots Made Easy with Vectara: How I Built an RFK Jr. Chatbot

Introduction

I'm captivated by the potential of large language models (LLMs) to transform real-world applications. In the field of educational technology, for instance, chatbots have emerged as powerful tools for delivering engaging, hands-on learning experiences.

With the 2024 elections on the horizon, I recognized a unique opportunity to explore an educational use case. I aimed to create a dynamic learning experience that would enable users to deeply understand the policies of political candidates, particularly those from third-party candidates whose platforms are often less visible. My latest project, a chatbot dedicated to RFK Jr., offers an interactive way to engage with his political views, utilizing a wealth of information from his campaign website and various interviews.

Chatbot in the Making

Since the announcement of OpenAI's ChatGPT, the generative AI landscape has expanded significantly, introducing new tools and capabilities. Retrieval-Augmented Generation (RAG) has become the go-to technique for enhancing LLM responses by grounding them in external knowledge sources.

My journey through this evolving ecosystem included mastering orchestration frameworks like LangChain and LlamaIndex, which offer abstractions and utilities that simplify the development of RAG applications. These tools continue to evolve, making the development of LLM applications increasingly accessible.

Alongside these advancements, packaged solutions for managing LLMs have also emerged. I was particularly intrigued when I discovered Vectara—a platform designed to efficiently manage complex RAG tasks behind the scenes.

RFK Jr. Bot was the perfect opportunity to test-drive Vectara. While Vectara integrates seamlessly with many technologies, including LlamaIndex and LangChain, my goal was to explore its standalone capabilities.

I set out to develop a chatbot entirely powered by Vectara, supplemented by its supporting open-source tools. Below is a high-level overview of how I did this:

Starting with content from RFK Jr.'s campaign website and YouTube videos of his interviews, I created a chatbot that espouses his viewpoints. Vectara is central to this project, streamlining the entire RAG process. It automatically handles indexing by splitting source text, creating embeddings, and storing them in a vector database. The platform also manages retrieval, orchestrates generation by integrating with LLMs, and maintains conversational memory—critical components for a chatbot that I previously had to manage step-by-step.

I used open-source tools from Vectara for web crawling and React website creation; to support YouTube video ingestion, I reused the data preparation pipeline that I had previously developed.

Vectara in Action

In the video below, you can see the Vectara-powered RFK Jr. Bot in action. I pose three questions to the chatbot and demonstrate that it correctly retrieves relevant information. The video is narrated using my ElevenLabs voice clone. The end of the video is silent.

As you see, the chatbot responds thoughtfully and accurately, providing well-rounded answers that cite specific sources for a rich, informed interaction. It draws from RFK Jr.’s campaign website and over 10 hours of YouTube interviews, including discussions on global issues.

For convenience, screenshots of the questions answered during the demo are shown below:

When asked about Ukraine, the chatbot efficiently cuts through the vast content in my Vectara corpus to identify relevant insights and generates a cohesive, informed response. Pretty cool, right?

When asked about vaccine safety—a topic where RFK Jr. is well-known for his views—the chatbot again draws on a rich mix of textual and video information to provide a cohesive answer. This response is particularly informed by extensive content from his campaign website and his discussion with Lex Fridman.

RFK Bot's Secret Sauce

If you’re interested in developing your own chatbots and want to quickly move from an initial idea to a fully functioning prototype or even a complete product, keep reading. I’ll explain how I efficiently turned my concept into a practical learning tool using Vectara.

Step 1: Creating a Vectara Corpus

After setting up an account with Vectara, I prepared an empty corpus (a container that will contain data for querying) for the chatbot. I enabled the Chat feature for my corpus, which automatically provisioned conversational management and memory capabilities.

Step 2: YouTube Content Acquisition

RFK Jr. has appeared on numerous long-form YouTube podcasts. I harvested content from these videos, where he discusses his platform in interviews with podcasters like Lex Fridman and Theo Von. This process was enabled by the YouTube-to-JSON pipeline I'd previously developed, which includes yt-dlp for downloading YouTube videos and metadata, Simple Diarizer for speaker identification, and Whisper via Hugging Face for voice transcription. After modifying my JSON slightly to format it as a Vectara document object, I easily ingested it using Vectara's API.

Step 3: Crawling Kennedy24.com with Vectara-Ingest

I needed a quick and efficient way to crawl RFK Jr.'s campaign website. That's when I discovered Ofer Mendelevitch 's open-source project, Vectara-ingest. The project walks you through the process of creating a crawler and data ingestion tool that runs inside a Docker container. In addition to the website template that I used, Vectara-ingest offers crawler templates for RSS feeds, Notion, Discord, and many more.

Step 4: Tuning Vectara Retrieval Parameters

Vectara’s advanced retrieval options, including keyword search and MMR (maximal marginal relevance) reranking, allowed for finely tuned search capabilities. I configured these settings to ensure that the chatbot’s responses were accurate and contextually relevant.

Step 5: Building the Chatbot UI

Vectara's open-source Create-UI React code generator enabled me to quickly deploy a React web application that interacts with my Vectara corpus. The application interface is featured in the above demo.

Conclusion

This project has been an enriching journey into using AI to create engaging, hands-on learning experiences that make political discourse more accessible. I encourage fellow tech enthusiasts and developers to explore how these tools can be applied across various informative domains. If you're interested in developing similar solutions or expanding on this project, let’s connect and explore the possibilities of conversational AI together!