Unlocking the Power of Open Source AI Chatbots

Table of Contents

1. Introduction
2. Building a Viral Open-Source AI Chatbot
3. Who am I?
4. Background of Quiver
5. Challenges Faced during Development
   1. Challenge 1: Technical Limitations
   2. Challenge 2: Pleasing Everyone
   3. Challenge 3: Supporting Multiple LMs
   4. Challenge 4: Lack of Documentation and Tooling
   5. Challenge 5: Lack of Planning for Growth
6. Testing and Improving the Application
7. Future Developments and Exciting Opportunities
8. Conclusion

Building a Viral Open-Source AI Chatbot

Welcome to my talk on building a viral open-source AI chatbot. In this article, I will discuss the Journey of building Quiver, a virtual Second brain in the cloud. Quiver is an open-source project that gained significant popularity on GitHub, with over 22,000 stars and trending as the number one project for two weeks. I will share the challenges we faced during development, including technical limitations, the struggle to please everyone, supporting multiple language models, the lack of documentation and tooling, and the failure to plan for growth. Additionally, I will discuss the importance of testing and improving the application, as well as the exciting opportunities that lie ahead in the future. So, let's dive in and explore the fascinating world of open-source AI chatbots!

Introduction

Building a successful open-source AI chatbot involves overcoming various challenges and constantly evolving with new developments in the field. In this article, we will explore the journey of building Quiver, a viral open-source AI chatbot that serves as a virtual second brain in the cloud. We will discuss the technical limitations, the struggle to please everyone, supporting multiple language models, the lack of documentation and tooling, and the failure to plan for growth. We will also Delve into the importance of testing and improving the application and highlight exciting opportunities for the future.

Building a Viral Open-Source AI Chatbot

Quiver is an open-source project that quickly gained popularity on Github, accumulating over 22,000 stars. It offers users a unique chatbot experience with the ability to teach it new things. Unlike other chatbots, Quiver allows users to upload a vast amount of documents and utilizes a full Knowledge Graph for comprehensive document analysis. With its appealing features and functionality, Quiver became a sensation on Github, attracting a significant user base and receiving widespread Attention.

Who am I?

Before diving deeper into Quiver, allow me to introduce myself. I am an AWS Community Builder and a consultant at AliOurselves, focusing mainly on open-source development with AWS. I am passionate about open-source projects and enjoy sharing my experiences with others. I organize the Service London Meetup in London and the JennAI Days events in London, Paris, and New York. I have extensive experience in the field and have encountered numerous challenges while building Quiver.

Background of Quiver

Quiver was developed to be a personal assistant for users, allowing them to easily retrieve information from their uploaded documents. It aimed to provide a condensed, paraphrased representation of the Relevant content in response to user queries. Quiver gained Momentum quickly, reaching 18,000 regular users and 800 daily users. However, with increasing popularity came a new set of challenges that needed to be addressed.

Challenges Faced during Development

Building Quiver was not without its challenges. In this section, we will discuss the major hurdles encountered during the development process and how we overcame them.

Challenge 1: Technical Limitations

One of the primary challenges we faced was the limitation imposed by being an open-source project. We wanted Quiver to be accessible to a wide range of users, regardless of their technical expertise or available resources. As a result, we avoided relying on serverless functions or requiring users to have specific cloud accounts. Instead, we opted for a more user-friendly approach, enabling users to run Quiver locally with a single command. This decision eliminated the need for specialized knowledge or hardware, making Quiver accessible to a broader audience.

Challenge 2: Pleasing Everyone

With Quiver's growing popularity, we received a significant amount of user feedback in the form of pull requests and issues. While we appreciated the contributions, it became challenging to balance the desires of the user community with our own vision for the project. Striking a balance between incorporating user feedback and maintaining the integrity of the product was a delicate task. We had to carefully assess each suggestion and decide whether it aligned with our long-term goals.

Challenge 3: Supporting Multiple Language Models

Quiver was designed to support various language models (LMs) to provide users with the flexibility to choose the model that best suited their needs. Initially, we attempted to support every available LM, but this proved to be an overwhelming task for our small team. The different LM APIs and their unique Context window sizes posed significant challenges. We eventually partnered with Langchain to Create Genos GPT, an AI model that allowed users to replace OpenAI with a single line of code. This collaboration offered users more flexibility while streamlining our development process.

Challenge 4: Lack of Documentation and Tooling

Throughout Quiver's development, we encountered a severe lack of documentation and tooling around open-source AI. This made it difficult for us to navigate the intricacies of the field and hindered our progress. While some resources existed, such as newsletters and workshops, they were not comprehensive enough to address our specific challenges. Consequently, we had to rely on our own ingenuity and problem-solving skills to overcome these obstacles.

Challenge 5: Lack of Planning for Growth

Initially, we did not anticipate the tremendous growth Quiver would experience. As a result, we did not adequately plan for scaling the application to accommodate a larger user base. We faced significant technical debt and relied heavily on Superbase, a back-end as a service, for database and authentication functionality. While Superbase was an excellent tool initially, it became evident that our application required more robust infrastructure. We learned the importance of planning for growth and the need to establish strong governance and roadmaps to guide future development.

Testing and Improving the Application

Ensuring the reliability and accuracy of Quiver was of utmost importance. However, testing a non-deterministic application like Quiver, which relied on Generative AI models, presented unique challenges. We had to develop a comprehensive testing framework to verify the application's responsiveness and accuracy. This framework incorporated embeddings models, semantic similarity calculations, and human review processes. By thoroughly testing the application, we were able to improve its performance and provide a better user experience.

Future Developments and Exciting Opportunities

Looking ahead, there are numerous exciting developments in the field of open-source AI chatbots. AWS Bedrock, a foundational model, is expected to become widely available, enabling enterprises to leverage AI models with greater confidence. Additionally, advancements in DevTooling, such as function calling and prompt engineering, will enhance the user and developer experience. Opportunities for future startups abound as the field continues to evolve, with countless possibilities waiting to be explored.

Conclusion

Building a viral open-source AI chatbot like Quiver requires navigating various challenges, from technical limitations to user satisfaction. By overcoming these hurdles and continuously improving the application, Quiver has garnered popularity and established itself as a reliable tool. The future holds promising opportunities for the development of open-source AI chatbots and the advancement of the field as a whole. As developers and enthusiasts, it is essential to stay informed, tackle challenges head-on, and contribute to the growth and innovation in this exciting domain.

Highlights

• Building a viral open-source AI chatbot called Quiver.
• Overcoming challenges such as technical limitations, user satisfaction, supporting multiple language models, lack of documentation and tooling, and failure to plan for growth.
• Importance of testing and improving the application to ensure reliability and accuracy.
• Exciting opportunities in the future, including AWS Bedrock and advancements in DevTooling.
• The need to navigate challenges and contribute to the growth and innovation in the open-source AI chatbot domain.

FAQ

Q: What is Quiver?

A: Quiver is a viral open-source AI chatbot that serves as a virtual second brain in the cloud. It allows users to upload and analyze a vast amount of documents, providing condensed and paraphrased information in response to user queries.

Q: How popular is Quiver?

A: Quiver gained significant popularity on GitHub, accumulating over 22,000 stars and trending as the number one project on the platform for two weeks.

Q: What were the challenges faced during the development of Quiver?

A: The challenges included technical limitations, the struggle to please everyone, supporting multiple language models, the lack of documentation and tooling, and the failure to plan for growth.

Q: How was the testing process for Quiver conducted?

A: Testing Quiver, a non-deterministic application, required the development of a comprehensive testing framework. This framework incorporated embeddings models, semantic similarity calculations, and human review processes to ensure responsiveness and accuracy.

Q: What exciting opportunities lie ahead in the field of open-source AI chatbots?

A: The future holds promising developments such as AWS Bedrock, advancements in DevTooling (function calling, prompt engineering), and opportunities for startups to explore the evolving field of open-source AI chatbots.