LangChain for LLM Apps
Large Language Models (LLMs) like GPT-4 have demonstrated immense capabilities in generating human-like text. However, simply accessing LLMs via APIs has limitations. Instead, combining them with other data sources and tools can enable more powerful applications. In this chapter, I will introduce LangChain as a way to overcome LLM limitations and build innovative language-based applications. I aim to demonstrate the potential of combining recent AI advancements with a robust framework like LangChain.
I will start by outlining some challenges faced when using LLMs on their own, like the lack of external knowledge, incorrect reasoning, and the inability to take action. LangChain provides solutions to these issues through different integrations and off-the-shelf components for specific tasks. I will walk through examples of how developers can use LangChain’s capabilities to create customized natural language processing solutions, outlining the components and concepts involved.
The goal is to illustrate how LangChain enables building dynamic, data-aware applications that go beyond what is possible by simply accessing LLMs via API calls. Lastly, I will talk about concepts related to LangChain, such as chains, action plan generation, and memory, which are important concepts for understanding how LangChain works.
The main sections of this chapter are:
- Going beyond stochastic parrots
- What is an LLM app?
- What is LangChain?
- Exploring key components of LangChain
- How does LangChain work?
- Comparing LangChain with other frameworks
