Chat Your Way to Knowledge: Integrating AI with your data

In business today, there's growing interest in integrating artificial intelligence into various processes. A prime example is the AI-powered knowledge base, a novel approach to accessing information within companies. This technology, still in experimental stages and not broadly available, offers exciting possibilities. I've combined my learnings to draft a preliminary architecture for such a system.

Consider this system as an AI knowledge base integrated with Slack. It uses the LLM as its AI engine, processing queries and generating responses. Next, we need a 'middleware app' that serves as an intermediary, facilitating communication between the Slack app and the AI core. Users interact with the system through the Slack app, submitting prompts and receiving AI-generated responses directly within Slack. The knowledge base is dynamic, drawing from various data providers such as Box and Google Docs for cloud file storage, Slack’s own chat history for contextual information, and Confluence for knowledge management. The architecture suggests that the system is designed for easy scalability, capable of incorporating additional data sources as needed. This design allows for a seamless flow of information, making it a powerful tool for knowledge management and retrieval in a collaborative workspace.

My initial experiments with a small-scale AI knowledge base, focused on a glossary of terms and acronyms, yielded promising results. However, scaling up to handle a larger, more diverse dataset introduces complexity in achieving accurate retrieval.

Delving deeper into the question, I've found that the AI community has made significant progress in developing tools for managing data and enhancing retrieval and generation capabilities. There are two popular frameworks for building apps with LLMs: LangChain and LlamaIndex. The choice depends on your final goal. If it's primarily an intelligent search tool, LlamaIndex is ideal. However, LangChain allows you to leverage multiple instances of ChatGPT, provide them with memory, and even utilize multiple instances of LlamaIndex. With LangChain, you can build agents that perform multiple tasks, such as executing Python code while simultaneously searching Google. Essentially, LlamaIndex serves as a smart storage mechanism, while LangChain is a framework designed to integrate multiple tools.

Recently, I watched a tutorial on how to build an intelligent search tool with LlamaIndex. What surprised me was how easily it can be built.

I believe that if you already have experience in experimenting with Large Language Models locally or through APIs, your next step could be to develop more advanced pipelines using frameworks specifically designed for LLMs.