The video explains how LangChain Expression Language (LCEL) improves chain building by using runnables and the pipe operator for flexible, modular, and intuitive chaining of components, replacing the older LMChain method. It also demonstrates advanced features like parallel execution and passthrough runnables, showcasing LCEL’s ability to handle complex workflows efficiently.
In this chapter, the focus is on LangChain Expression Language (LCEL) and how it enhances the process of building chains in LangChain. The traditional approach using LMChain is introduced first, where a prompt template is combined with a language model (LM) and optionally an output parser. Although this method works, it is restrictive and deprecated. The preferred modern approach uses runnables and the pipe operator to chain components together, making the process more flexible and intuitive.
The pipe operator in LCEL is explained as a way to pass the output of one runnable as the input to the next. This operator is essentially syntactic sugar for the “or” method implemented in a custom Runnable class. By defining runnables as wrappers around functions, multiple runnables can be chained together using either the pipe operator or the “or” method, allowing for sequential processing of data. This approach is creative and simplifies chaining operations compared to traditional methods.
The video then demonstrates practical examples of chaining runnables, including generating a report on a topic, extracting facts, and replacing words within the generated text. These examples illustrate how multiple runnable operations can be combined to manipulate and refine outputs. It is also noted that functions with multiple arguments can be handled by passing a single dictionary-like argument and unpacking it within the function, providing flexibility in runnable design.
Further, the video introduces advanced runnable types such as RunnableParallel and RunnablePassthrough. RunnableParallel allows multiple runnables to execute concurrently, while RunnablePassthrough passes variables through the chain without modification. These are demonstrated using two separate vector stores containing different pieces of information, which are queried in parallel and combined to answer a question about a model’s architecture. This showcases LCEL’s capability to handle complex workflows involving parallel data retrieval and processing.
In conclusion, the chapter provides a comprehensive overview of LCEL’s core concepts and utilities, including runnables, the pipe operator, and parallel execution. It highlights the advantages of this approach over the older LMChain method, emphasizing flexibility, modularity, and clarity in building language model pipelines. The practical examples and explanations equip viewers with the knowledge to leverage LCEL effectively in their LangChain projects.