Question

You are building a LangChain (LCEL) chain that classifies incoming customer emails using a Databricks Foundation Model API endpoint. During a nightly job, you must process roughly 50,000 emails that are already loaded into a Python list. Your current code loops over the list and calls chain.invoke() once per email, but the job is too slow to finish within its window. Without changing the model or endpoint, what is the most appropriate way to improve throughput while keeping the same LCEL chain?

Accepted Answer

Replace the loop with a single call to chain.batch() passing the full list of inputs so the runnable processes them concurrently.

Answer

Wrap the chain in chain.stream() so tokens are returned incrementally and the job overlaps generation with I/O.

Answer

Call chain.invoke() inside a Python for-loop but set temperature=0 to make each request return faster.

Answer

Convert the chain to an agent with a classification tool so it can decide how to parallelize the work automatically.