ValueError
langchain.chains.base.ChainValueError
Stack trace
Traceback (most recent call last):
File "app.py", line 25, in <module>
result = chain.run({"question": "What is AI?"}) # Missing required input 'context'
File "/usr/local/lib/python3.9/site-packages/langchain/chains/base.py", line 120, in run
raise ValueError(f"Missing input variables: {missing_vars}")
ValueError: Missing input variables: {'context'} Why it happens
LangChain chains require all input variables defined in their input schema to be provided when calling run or invoke. If any required input variable is missing from the input dictionary, the chain raises a ValueError to prevent incomplete or invalid execution.
Detection
Before calling chain.run(), assert that all required input keys are present in your input dictionary or catch ValueError and log the missing keys for debugging.
Causes & fixes
Input dictionary passed to chain.run() is missing one or more required keys defined in the chain's input variables.
Ensure the input dictionary includes all required keys exactly as defined in the chain's input_variables list before calling run.
Mismatch between input variable names expected by the chain and the keys used in the input dictionary (e.g., case sensitivity or typos).
Verify that input variable names in your input dictionary match exactly the chain's expected input variable names, including case.
Using a chain composed of multiple subchains without properly mapping or passing all required inputs to each subchain.
When using nested or composite chains, explicitly map and provide all required inputs for each subchain to avoid missing variables.
Code: broken vs fixed
from langchain.chains import LLMChain
from langchain.llms import OpenAI
llm = OpenAI()
chain = LLMChain(llm=llm, prompt="What is {context}? Answer briefly.", input_variables=["context"])
# Missing 'context' key in input dict triggers ValueError
result = chain.run({"question": "What is AI?"}) # This line raises ValueError
print(result)import os
from langchain.chains import LLMChain
from langchain.llms import OpenAI
os.environ["OPENAI_API_KEY"] = "your_api_key_here" # Set your API key securely
llm = OpenAI()
chain = LLMChain(llm=llm, prompt="What is {context}? Answer briefly.", input_variables=["context"])
# Provide the required 'context' key exactly as expected
result = chain.run({"context": "AI"}) # Fixed input dictionary
print(result)Workaround
Wrap the chain.run() call in try/except ValueError, catch the missing input variable error, log the missing keys, and prompt the user or fallback to default values before retrying.
Prevention
Always check and validate input dictionaries against the chain's input_variables list before execution, or use typed input models to enforce input completeness at development time.