Mastering LCEL: Advanced Patterns in LangChain

The introduction of LangChain Expression Language (LCEL) marked a paradigm shift in how we build LLM applications. It provides a declarative way to compose chains, offering built-in support for streaming, batching, and async execution.

1. The Power of Pipe (|)

LCEL allows you to “pipe” different components together like a Unix terminal.

// A simple LCEL chain
const chain = prompt.pipe(model).pipe(outputParser);

// Parallel execution with RunnableMap
const mapChain = RunnableMap.from({
  context: retriever.pipe(formatDocumentsAsString),
  question: new RunnablePassthrough(),
}).pipe(prompt).pipe(model);

2. Dynamic Routing

Using RunnableBranch, you can route inputs to different chains based on logic (e.g., routing a user query to a “Technical” chain or a “General” chain).

3. Self-Querying Retrievers

Standard vector search only uses semantic similarity. A Self-Querying Retriever uses an LLM to extract metadata filters from the user’s query, enabling complex filtered searches (e.g., “Find documents about ‘Security’ written in 2023”).

4. Custom Tool Callbacks

For production systems, you need to track exactly what your agents are doing. LangChain’s callback system allows you to hook into every start, end, and error of a tool call.

Summary

Advanced LangChain is about Composition. By mastering LCEL and the Runnable interface, you can build AI systems that are not just series of prompts, but robust, streaming, and observable software architectures.