Architecture patterns for building Generative AI Applications

In this blog post I will take you through some of the most common usage patterns we are seeing with customers for Generative AI. We will explore techniques for generating text and images, creating value for organizations by improving productivity. This is achieved by leveraging foundation models to help in composing emails, summarizing text, answering questions, building chatbots, and creating images.

Below are few categories on which the Architecture Pattern will be discussed.

Architecture Pattern for Text Generation

But before getting into the details lets try to understand LANGCHAIN it is an open source library for orchestrating language models( which are stateless) into workflows that may keep memory and combine range of tools.

Text Generation with Simple Prompt

Text Generation with LangChain

Text Generation with Context and LangChain

Architecture Pattern for Text Summarization

Text Summarization with small files

Text Summarization with Large Files and LangChain

The pattern is useful for summarizing documents which are much larger than the maximum token limit of the OpenAI models involved in the summarization process.

Architecture Pattern for Question Answer

Question Answer with Simple Prompt

Question Answer with Context

Question Answering with Retrieval-Augmented Generation(RAG) via Self-Managed Vector Store

This pattern addresses the needs to leverage/convert data retrieved from existing systems to generate a new output (structured or unstructured) to be passed to downstream processes or other parties. This pattern is discussed and implemented in detail under

Question Answering with Retrieval-Augmented Generation(RAG) via Search Engine

Architecture Pattern for Chatbot

Basic Chatbot

Chatbot with Context

The list is not exhaustive but my sincere effort to bring few architecture patterns in the emerging field of Generative AI