LangChain Core Workflow (Python)

LangChain Core Workflow (Python)

Overview

An engineer wires a four-stage LCEL chain: classify the question, retrieve context, format the prompt, invoke the LLM. It looks clean:

chain = (
    RunnablePassthrough.assign(category=classifier)
    | RunnablePassthrough.assign(docs=retriever)
    | prompt
    | llm
    | StrOutputParser()
)

chain.invoke({"question": "What's our refund policy?"})

The call returns this:

Traceback (most recent call last):
  ...
  File ".../runnables/base.py", line 3421, in _call_with_config
    output = call_func_with_variable_args(func, input, ...)
  File ".../prompts/chat.py", line 1021, in _format_messages
    return await ... await self.ainvoke({**kwargs})
KeyError: 'question'

Nothing in that stack says which stage produced the wrong dict shape. The RunnablePassthrough.assign(docs=retriever) call silently rebuilt the dict and — because retriever was itself a Runnable[str, list[Document]] that took the question string, not the dict — a mis-piped intermediate value overwrote the question key. The prompt template expected {question} and blew up. This is P06 in the pack's pain catalog: .pipe() on mismatched dict shape raises KeyError deep in runnable internals with no hint at the offending stage.