How To Create An Accurate AI Knowledge Base

Summary

This video script discusses the critical role of a well-structured knowledge base in developing advanced AI agents, emphasizing that without it, the AI's performance can collapse. The speaker shares strategies for enhancing AI's knowledge-based responses, including document structuring, query improvement, and utilizing various document types. The tutorial also introduces Vector shift, a platform that offers advanced customization for knowledge bases, allowing for more accurate and context-rich AI responses through features like chunk size adjustment, chunk overlap, and hybrid keyword search.

Takeaways

• 📚 The foundation of building advanced AI agents is a well-structured knowledge base; without it, the system's responses may be inaccurate and confuse users.
• 🔍 Structuring documents properly is crucial for a knowledge base, as it ensures relevant information is provided to the AI, enhancing the accuracy of responses.
• 📈 The number of chunks used from the knowledge base can affect both the cost and accuracy of AI responses, with more chunks potentially leading to higher costs but better accuracy.
• 🔧 Voice flow, a no-code chatbot builder, allows for the uploading of documents to create a knowledge base, but it has limitations in functionality compared to more advanced platforms.
• 🛠️ Vector shift, a sponsor of the video, offers advanced customization for knowledge bases, including the ability to adjust chunk size and overlap for improved context and accuracy.
• 📝 Vector shift's CSV query function enables the querying of structured data, such as Excel sheets or CSV files, which is not typically supported by basic knowledge bases.
• 🖼️ Advanced querying features in Vector shift can detect and interpret elements like graphs and images, even when text is in image form, by using OCR technology.
• 🔑 Enabling 'hybrid keyword search' in Vector shift can provide additional context by combining traditional keyword searching with the more modern chunk-based approach.
• ❓ Vector shift offers 'transform query' to rephrase user questions into a form that is more likely to yield better responses from the knowledge base.
• 🔄 'Expand query' in Vector shift breaks down complex questions into multiple steps, allowing for a more structured and in-depth response from the knowledge base.
• 🗣️ 'Answer multiple questions' feature in Vector shift helps in handling situations where multiple questions are asked at once, ensuring clear and direct responses to each.

Q & A

What is considered the most integral part of building advanced AI agents?

The most integral part of building advanced AI agents is the knowledge base, which is crucial for providing accurate answers and avoiding confused users.

How does the speaker compare the knowledge base to a stack of playing cards?

The speaker compares the knowledge base to the bottom layer of a stack of playing cards, emphasizing that without a solid foundation, the entire structure collapses.

What platform is used to demonstrate the knowledge base functionality in the video?

The platform used to demonstrate the knowledge base functionality is VoiceFlow, a no-code chatbot builder.

What is the significance of structuring documents properly in a knowledge base?

Properly structuring documents ensures that the information is relevant and organized, which helps in providing more accurate and contextually rich responses to user queries.

What is the default number of chunks used by VoiceFlow's knowledge base?

The default number of chunks used by VoiceFlow's knowledge base is three, which can be increased up to 10 for more accuracy at a higher cost.

What is Vector shift, and how does it differ from VoiceFlow's knowledge base?

Vector shift is a platform that enables full customization of the knowledge base in storing and retrieving information. It offers more advanced features compared to VoiceFlow's knowledge base, such as changing chunk size, chunk overlap, and querying structured data.

What does chunk size refer to in the context of a knowledge base?

Chunk size refers to the amount of information included in a single chunk within the knowledge base, affecting the relevance and cost of the information provided in responses.

What is chunk overlap, and why is it important?

Chunk overlap is the number of characters that overlap between two chunks, providing more context for each chunk and making the knowledge responses more accurate.

How does Vector shift handle different document types, such as Excel sheets or CSV files?

Vector shift has a CSV query function that allows it to query structured data, unlike typical knowledge bases that only handle text-based documents.

What is the purpose of enabling 'hybrid keyword search' in Vector shift?

The purpose of enabling 'hybrid keyword search' is to use both keyword search and chunks to find relevant parts of a document, providing additional context and improving the accuracy of the AI's responses.

What are the three querying options available in Vector shift to improve response accuracy?

The three querying options in Vector shift are 'transform query', which rephrases user queries for better responses; 'expand query', which breaks down complex questions into simpler steps; and 'answer multiple questions', which handles multiple questions separately for clarity.