InvalidArgument
google.api_core.exceptions.InvalidArgument: Context length exceeded for Gemini model
Stack trace
google.api_core.exceptions.InvalidArgument: 400 Context length exceeded for Gemini model at google.cloud.aiplatform_v1.services.prediction_service.client.predict(PredictionServiceClient.java:123) at user_code.py:45
Why it happens
Gemini models on Vertex AI have strict maximum context length limits. When the combined prompt, including system instructions and user input, exceeds this limit, the API rejects the request with an InvalidArgument error indicating context length exceeded. This prevents the model from processing overly large inputs.
Detection
Monitor API responses for InvalidArgument errors with messages about context length. Log prompt sizes before sending requests to catch inputs approaching the limit.
Causes & fixes
Prompt input text plus system instructions exceed Gemini model's max context length
Reduce prompt length by truncating user input or system messages to fit within the model's documented max token limit.
Concatenating multiple large documents or embeddings into a single prompt without chunking
Implement prompt chunking or summarization to keep each request under the context length limit.
Using an outdated or incorrect model version with smaller context window than expected
Verify and upgrade to the latest Gemini model version that supports larger context lengths.
Code: broken vs fixed
from google.cloud import aiplatform
client = aiplatform.gapic.PredictionServiceClient()
response = client.predict(
endpoint='projects/123/locations/us-central1/endpoints/456',
instances=[{'content': 'Very long prompt text exceeding context length...'}]
) # This line triggers the context length exceeded errorimport os
from google.cloud import aiplatform
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/path/to/creds.json' # Use env var for auth
client = aiplatform.gapic.PredictionServiceClient()
# Truncate prompt to max allowed length
max_length = 2048 # example max tokens for Gemini
prompt = 'Very long prompt text exceeding context length...'
truncated_prompt = prompt[:max_length]
response = client.predict(
endpoint='projects/123/locations/us-central1/endpoints/456',
instances=[{'content': truncated_prompt}]
)
print(response)Workaround
Catch the InvalidArgument exception, then programmatically truncate or split the prompt and retry the request with smaller input chunks.
Prevention
Implement prompt length checks and chunking logic in your application to guarantee all inputs remain within the Gemini model's documented context length limits before sending requests.