Unlock the Power of Langjin: A Comprehensive Beginner's Guide

Table of Contents

1. Introduction
2. What is Langjin?
3. Why is Langjin needed?
4. Components of Langjin
   • Models
   • Prompts
   • Indexes
   • Memory
   • Chains
   • Agents
5. Language Models and Embedding Models
6. Using Prompts and Prompt Templates
7. Few Shot Learning and Output Parsing
8. Example Selectors
9. Interacting with Chat Models
10. Using Your Own Data with Indexes
11. Retrieval with Vector Stores
12. Abstracting Model Calls with Chains
13. Creating Custom Chains
14. Using High Level Chains
15. The Power of Agents
16. Conclusion

What is Langjin and Why is it Needed?

Langjin is a popular framework in the field of AI, particularly in the realm of language models and text processing. It provides a way to develop applications on top of large language models like GPT. While sending requests to an API or model might work for simple applications, as the complexity increases, things become messy and repetitive. Langjin addresses this issue by offering components, such as document loaders, text splitters, vector databases, and more, that make it easier to connect language models to your own data and perform actions Based on those models' outputs.

In this article, we will explore the various components of Langjin, understand how language models and embedding models work, and learn how to use prompts, few shot learning, and output parsing to control the outputs of language models. We will also discuss example selectors, chat models, and integrating your own data using indexes. Furthermore, we will Delve into the concept of retrieval with vector stores, the abstraction of model calls using chains, and the power of agents in Langjin. By the end of this article, you will have a comprehensive understanding of Langjin and its significance in the field of AI.

What is Langjin?

Langjin is a framework used to develop applications on top of large language models, such as GPT, by providing components that simplify the process of connecting language models to your own data and performing various actions based on those models' outputs. It started as a Python Package, but there are also TypeScript and Ruby ports available.

Why is Langjin Needed?

While simple applications can rely on sending requests to an API or model, more complex scenarios, such as connecting language models to specific data sources or performing actions like sending emails or running code, require a more sophisticated approach. Langjin provides a solution to this problem by offering components that handle the complexities and repetitive tasks associated with these scenarios. It allows developers to easily swap models, test different vector databases, and ingest data from various sources with just a few lines of code.