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Table of Contents

Introduction

In recent years, the fields of artificial intelligence (AI) and machine learning have made significant strides. Notably, GPT (Generative Pre-trained Transformer), developed by OpenAI in 2018, has revolutionized the field of natural language processing (NLP). With the advent of GPT-3, the immense power of large language models became widely recognized, and GPT-4 further enhanced their capabilities.

Amidst this backdrop, the development of applications leveraging large language models (LLMs) is progressing rapidly. Companies and research institutions are racing to adopt these technologies to extract valuable information from vast amounts of text data and create new value. However, there has also been a growing recognition of the lack of tools and frameworks to effectively utilize LLMs.

In such an environment, "Flowise" was developed. Flowise is a tool designed to facilitate the simple and efficient development of applications using LLMs. The reasons behind the development of Flowise include:

1. Simplification of Complex Processes: Developing applications using LLMs requires advanced expertise and complex procedures. Flowise simplifies this process, making it intuitive and visual, enabling developers to quickly prototype and transition to production.
2. Integrated Workflow: There was a need for a framework that could manage the various phases of LLM application development, including data preprocessing, model training, evaluation, and deployment, in an integrated manner. Flowise achieves this, providing a consistent workflow.
3. Scalability and Flexibility: Flowise allows integration with various data sources, storage systems, and APIs, offering developers the flexibility to customize it to their needs.

By using Flowise, developers and data scientists can enjoy several advantages:

• Increased Efficiency: The intuitive UI and rich toolset significantly reduce development time, allowing more time to be spent on developing innovative applications and improving models.
• Ensured Reproducibility: Flowise's ability to record and manage each step of the workflow makes it easier to ensure the reproducibility of experimental results, enhancing project reliability and quality.
• Scalability: Designed to handle large datasets and models, Flowise addresses the challenges of scaling as projects grow.
• Community and Support: Flowise boasts an active community and extensive documentation, providing developers with the environment to quickly find the information they need.

This book offers concrete steps and best practices for building LLM applications using Flowise. Through this guide, we hope readers will be able to harness the latest AI technologies to develop innovative applications.

What You Will Learn in This Book

You will systematically learn the knowledge and skills necessary to practically build applications using this book. Specifically, you will acquire the following:

1. Basic Operations and Applications of Flowise
   • Detailed explanations from installing Flowise to basic operations, supporting the development of your first LLM application.
2. Understanding Flowise Nodes
   • Deep understanding of key Flowise nodes such as Chains, Language Models, Prompts, Output Parsers, and Memory, and how to effectively utilize them.