Explained: The Voiceflow Knowledge Base (Retrieval Augmented Generation)

Summary

This video script introduces 'Retrieval Augmented Generation' (REG), a key feature in AI assistants that allows them to answer questions based on uploaded documents. The script explains how documents are broken into chunks and stored in a vector database, which the AI then uses to find relevant information to answer user queries. It also covers the technical aspects of implementing REG in Voice Flow, including the use of AI models, system prompts, and chunk limits to optimize the accuracy and efficiency of the AI's responses.

Takeaways

• 📚 The script introduces Retrieval-Augmented Generation (RAG), a feature in AI tools that allows AI to answer questions based on uploaded documents.
• 🛠️ The process involves using a vector database to break documents into chunks and match them with user queries to provide relevant information.
• 🔍 Voice Flow's knowledge base is a key feature that enables the creation of AI assistants that can access and utilize uploaded documents to answer questions.
• 📈 The script explains the technical process of how documents are broken into chunks, stored in a vector database, and then matched to user queries.
• 📝 Chunks are small snippets of text from the original document, which are used to provide context to the AI model when assembling answers.
• 🧲 The AI model uses the similarity of concepts within the question to find relevant chunks from the vector database to answer the user's query.
• 🔑 The script highlights the importance of choosing the right AI model, adjusting settings like temperature, max tokens, and chunk limit for optimal results.
• 🔄 The accuracy of the AI's answers depends on the quality of the chunks provided, and the script suggests testing and refining the knowledge base for better accuracy.
• 🛑 The script mentions the ability to debug and inspect the process, including viewing the API information and similarity scores of chunks used in forming answers.
• 🔧 The script discusses the importance of optimizing chunk usage and the trade-off between accuracy and token usage when increasing the chunk limit.
• 🔗 The script concludes with a mention of Voice Flow's API documentation, which allows developers to build custom solutions and integrate document management with the knowledge base.

Q & A

What is Retrieval-Augmented Generation (RAG) and why is it important for AI assistants?

Retrieval-Augmented Generation (RAG) is a function that allows AI assistants to upload documents and answer questions based on those documents. It is important because it enables AI to provide valuable and contextually accurate answers by referencing specific information from the uploaded documents.

What is the role of a vector database in RAG?

A vector database is used in RAG to store and manage the chunks of text from uploaded documents as vectors. These vectors represent the content of the chunks and help the AI model to identify and retrieve the most relevant information when answering questions.

How does Voice Flow's knowledge base differ from a traditional AI's base layer of knowledge?

Voice Flow's knowledge base allows users to upload specific documents that the AI can then reference for answering questions. Unlike a traditional AI's base layer, which relies on general knowledge from its training data, Voice Flow's knowledge base is tailored to the user's provided documents, offering more customized and specific answers.

What happens when a document is uploaded to Voice Flow's knowledge base?

When a document is uploaded, Voice Flow breaks it down into smaller sections called chunks. These chunks are then processed and stored in a vector database, where they are turned into vectors that represent the content of the document.

How does Voice Flow determine which chunks of information to use when answering a question?

Voice Flow uses a model to compare the user's question with the chunks in the vector database, identifying the most similar chunks based on the concepts within the question. It then selects the most relevant chunks to include in the AI model's response.

What is the significance of the chunk limit setting in Voice Flow?

The chunk limit setting determines how many of the most similar chunks are selected to answer a question. Increasing the chunk limit can improve the accuracy of the answer by providing the AI model with more information, but it also increases the number of tokens used per response.

How can the quality of answers from Voice Flow's knowledge base be improved?

The quality of answers can be improved by ensuring the chunks in the knowledge base are accurate and relevant. This involves testing the knowledge base, reviewing the chunks used in answers, and updating or removing documents that do not provide correct information.

What is the purpose of the system prompt in Voice Flow's knowledge base?

The system prompt is used to frame the response from the AI model. It can be customized to influence the length, format, or style of the answer, such as requiring a certain number of sentences or a specific structure like bullet points.

How does Voice Flow handle the token usage when multiple chunks are used to answer a question?

Token usage in Voice Flow is influenced by both the input (number of chunks) and the output (length of the answer). When more chunks are used, the input token count increases, which can affect the overall token usage for each response.

What are some of the developer tools available for Voice Flow's knowledge base?

Voice Flow provides APIs for uploading, deleting, and replacing documents in the knowledge base, as well as for retrieving answers. These APIs can be used to build custom services or widgets that integrate with Voice Flow, allowing for automated updates to the knowledge base.

How can Voice Flow's knowledge base be used to create custom solutions?

Developers can use Voice Flow's knowledge base APIs to create custom solutions, such as widgets for uploading documents directly into the knowledge base or services that automatically update the knowledge base with the latest documentation from a CMS.