Generative AI / LLM - Document Retrieval and Question Answering

Introduction

Welcome back to my channel! In today's video, we will be diving into the world of generator Peri and how it's transforming document retrieval and question answering systems. With the advent of large language models, we now have the power to integrate domain-specific knowledge and answer questions like never before. This is particularly useful when dealing with data that the model hasn't been trained on, such as internal company data or a knowledge base.

This architecture, known as a retrieval augmentation generation architecture or generative question answering, can be used for a wide range of use cases. It reduces the time needed to interact with documents as there is no need to pass queries to search results. The large language model takes care of precisely finding the most relevant documents and generating answers directly from them.

One of the advantages of this approach is the ability to handle multiple languages. Many large language models are trained on multiple languages and can effectively answer questions even when the question and document are in different languages. The model can directly translate the answer found in an English document to a Spanish answer, for example. While our example in this video uses Google's Prime tool, which currently only supports English, more languages will be supported in the future.

When it comes to integrating domain-specific knowledge, there are two approaches: fine-tuning the model or using an external index for querying. Fine-tuning the model can be time-consuming and may require hours to incorporate new data. Additionally, there are context size limitations with most large language models, typically allowing around 4000 M tokens per request. On the other hand, using an external index allows the model to rely on unlimited data by retrieving only the relevant documents for a given question. It also enables the integration of internal document restrictions, making it easier to handle access control. Indexing is also a more cost-effective option as it doesn't require fine-tuning.

In this video, I'll show you how to implement this architecture using Google Cloud. The code and necessary resources can be found in the description below, and feel free to leave me a comment if you have any questions.

FAQ

Q: What is generative AI / LLM? Generative AI, or LLM (Large Language Models), refers to the use of large-scale language models to generate text or answer questions based on context. These models have the power to understand and generate human-like text, making them a valuable tool for various applications.

Q: How does document retrieval and question answering with LLM work? The retrieval augmentation generation architecture combines document retrieval and question answering using large language models. It involves retrieving relevant documents from a database using an index and generating answers based on those documents. This allows for precise and context-based answers to questions.

Q: How does indexing documents improve question answering with LLM? Indexing documents allows large language models to retrieve only the relevant documents for a given question, reducing retrieval time and improving accuracy. It also enables the integration of domain-specific knowledge and internal document restrictions.

Keywords

Generative AI, LLM, document retrieval, question answering, large language models, indexing, fine-tuning, domain-specific knowledge, data integration, context-based answers.