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, we will introduce LangChain as a way to overcome LLM limitations and build innovative language-based applications. We aim to demonstrate the potential of combining recent AI advancements with a robust framework like LangChain.

We 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. We will walk through examples of how developers can use LangChain’s capabilities to create customized natural language processing solutions, outlining the components...

Going beyond stochastic parrots

LLMs have gained significant attention and popularity due to their ability to generate human-like text and understand natural language, which makes them useful in scenarios that revolve around content generation, text classification, and summarization. However, their apparent fluency obscures serious deficiencies that constrain real-world utility. The concept of stochastic parrots helps to elucidate this fundamental issue.

Stochastic parrots refers to LLMs that can produce convincing language but lack any true comprehension of the meaning behind words. Coined by researchers Emily Bender, Timnit Gebru, Margaret Mitchell, and Angelina McMillan-Major in their influential paper On the Dangers of Stochastic Parrots (2021), the term critiques models that mindlessly mimic linguistic patterns. Without being grounded in the real world, models can produce responses that are inaccurate, irrelevant, unethical, or make little logical sense.

Simply scaling...

What is LangChain?

Created in 2022 by Harrison Chase, LangChain is an open-source Python framework for building LLM-powered applications. It provides developers with modular, easy-to-use components for connecting language models with external data sources and services. The project has attracted millions in venture capital funding from the likes of Sequoia Capital and Benchmark, who supplied funding to Apple, Cisco, Google, WeWork, Dropbox, and many other successful companies.

LangChain simplifies the development of sophisticated LLM applications by providing reusable components and pre-assembled chains. Its modular architecture abstracts access to LLMs and external services into a unified interface. Developers can combine these building blocks to carry out complex workflows.

Building impactful LLM apps involves challenges like prompt engineering, bias mitigation, productionizing, and integrating external data. LangChain reduces this learning curve through its abstractions...

Exploring key components of LangChain

Chains, agents, memory, and tools enable the creation of sophisticated LLM applications that go beyond basic API calls to a single LLM. In the following dedicated subsections on these key concepts, we’ll consider how they enable the development of capable systems by combining language models with external data and services.

We won’t dive into implementation patterns in this chapter; however, we will discuss in more detail what some of these components are good for. By the end, you should have the level of understanding that’s required to architect systems with LangChain. Let’s start with chains!

What are chains?

Chains are a critical concept in LangChain for composing modular components into reusable pipelines. For example, developers can put together multiple LLM calls and other components in a sequence to create complex applications for things like chatbot-like social interactions, data extraction, and...

How does LangChain work?

The LangChain framework simplifies building sophisticated LLM applications by providing modular components that facilitate connecting language models with other data and services. The framework organizes capabilities into modules spanning from basic LLM interaction to complex reasoning and persistence.

These components can be combined into pipelines also called chains that sequence the following actions:

• Loading documents
• Embedding for retrieval
• Querying LLMs
• Parsing outputs
• Writing memory

Chains match modules to application goals, while agents leverage chains for goal-directed interactions with users. They repeatedly execute actions based on observations, plan optimal logic chains, and persist memory across conversations.

The modules, ranging from simple to advanced, are:

• LLMs and chat models: Provide interfaces to connect and query language models like GPT-3. Support async, streaming, and batch...

Comparing LangChain with other frameworks

LLM application frameworks have been developed to provide specialized tooling that can harness the power of LLMs effectively to solve complex problems. A few libraries have emerged that meet the requirements of effectively combining generative AI models with other tools to build LLM applications.

There are several open-source frameworks for building dynamic LLM applications. They all offer value in developing cutting-edge LLM applications. This graph shows their popularity over time (data source: GitHub star history, https://star-history.com/):

Figure 2.11: Comparison of popularity between different frameworks in Python

We can see the number of stars on GitHub over time for each project. Haystack is the oldest of the compared frameworks, having started in early 2020 (as per the earliest GitHub commits). It is also the least popular in terms of stars on GitHub. LangChain, LlamaIndex (previously called GPTIndex), and SuperAGI...

Summary

LLMs produce convincing language but have significant limitations in terms of reasoning, knowledge, and access to tools. The LangChain framework simplifies the building of sophisticated applications powered by LLMs that can mitigate these shortcomings. It provides developers with modular, reusable building blocks like chains for composing pipelines and agents for goal-oriented interactions. These building blocks fit together as LLM apps that come with extended capabilities.

As we saw in this chapter, chains allow sequencing calls to LLMs, databases, APIs, and more to accomplish multi-step workflows. Agents leverage chains to take actions based on observations for managing dynamic applications. Memory persists information across executions to maintain state. Together, these concepts enable developers to overcome the limitations of individual LLMs by integrating external data, actions, and context. In other words, LangChain reduces complex orchestration into customizable...

Questions

Please see if you can come up with answers to these questions. I’d recommend you go back to the corresponding sections of this chapter if you are unsure about any of them:

1. What are the limitations of LLMs?
2. What are stochastic parrots?
3. What are LLM applications?
4. What is LangChain and why should you use it?
5. What are LangChain’s key features?
6. What is a chain in LangChain?
7. What is an agent?
8. What is memory and why do we need it?
9. What kind of tools are available in LangChain?
10. How does LangChain work?

Join our community on Discord

Join our community’s Discord space for discussions with the authors and other readers:

https://packt.link/lang