Author: Suvoraj Biswas

Due to the surge in large language model adoption in the Enterprises, GENERATIVE AI has opened a new pathway to unlock the various business potentials and use cases. One of the main architectural building block for GENERATIVE AI is the semantic search powered by the Vector database. Semantic search, as the name suggests is, essentially involves a "nearest neighbor" (A-NN or k-NN) search within a collection of vectors accompanied by metadata. This system having an index to find the nearest vector data in the vector storage is called Vector Database where query results are based on the relevancy of the query and not the exact match. This technique is widely used in the popular RAG (Retrieval Augmented Generation) pattern where the a similarity search is performed in the Vector database based on the user's input query, and the similar results or relevant information is augmented to the input of an Large Language Model so that the LLM doesn't hallucinate for a query outside of its knowledge boundary to generate an unique response for the user. This popular GENERATIVE AI based pattern called, RAG can't be implemented without the support of Vector database as one of the main core component in the architecture. Because of more and more increase in GENERATIVE-AI use cases, as an engineer working on transitioning an LLM-based prototype to production, it is extremely crucial to identify the right vector database during early stage of development. 

During the proof-of-concept phase, the choice of database may not be a critical concern for the engineering team. However, the entire perspective changes a lot as the team progresses toward the production phases. The volume of Embedding/vectors data can expand significantly as well as the requirement to integrate the security and compliance within the app. This requires a thoughtful considerations such as access control and data preservation in case of server failures. In this article we will explain a framework and evaluation parameters which should be considered while making the right selection of the Enterprise grade Vector database for the GENERATIVE-AI based use case considering both the Developer Experience as well as the technological experience combining into the Enterprise experience. We also need to keep in mind that numerous vector db products are available in the markets with closed or open source offering and, each catering to a specific use case, and no single solution fits all use cases. Therefore, it's essential to focus on the key aspects when deciding the most suitable option for your GENERATIVE AI based application.

In this article, I'll walk you through another important concept of Generative AI Prompt Engineering. Prompt Engineering in the field of AI, involves the creation of concise pieces of text or phrases designed according to specific principles. These prompts are then utilized with Large Language Models (LLMs) to generate output content effectively. The process of prompt engineering serves as a crucial building block because improperly constructed prompts can lead to LLM models like ChatGPT generating illogical, meaningless, or out-of-context responses. Therefore, it is considered a best practice to validate the input texts passed to the LLM model's API based on well-defined principles of prompt engineering.

Depending on the intent or purpose of the input phrases, the model can exhibit various capabilities. These may include summarizing extensive pools of texts or content, inferring or clarifying topics, transforming the input texts, or expanding upon the provided information. By adhering to the principles of prompt engineering, the AI model can harness its full potential while ensuring accurate and contextually relevant output.

In today's Generative AI world, Vector database has become one of the integral parts while designing LLM-based applications. Whether you are planning to build an application using OpenAI or Google's Generative AI or you are thinking to solve use cases like designing a recommendation engine or building a computer vision (CV) or Vector database, would be an important component to consider.

What Is Vector Database and Why Are They Different Than the Traditional Database?

In the machine learning world, Vector or Embeddings represent the numerical or mathematical representation of data, which can be text, images, or media contents (Audio or Video). LLM from OpenAI or others can transform the regular data into Vector Embeddings with high-level multi-dimensions and store them in the vector space. These numerical forms help determine the semantic meaning among data or identify patterns or clustering, or draw relationships. Regular columnar-based RDBMS or NoSQL databases are not equipped to store Vector Embeddings data with multi-dimensions and efficiently scaling if needed. This is where we need a Vector database, which is a special kind of database that is designed to handle and store this kind of Embeddings data and, at the same time, offers high performance and scalability.