Try Gemini 1.5 models, our newest multimodal models in Vertex AI, and see what you can build with a 1M token context window.Try Gemini 1.5 models, our newest multimodal models in Vertex AI, and see what you can build with a 1M token context window.
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Convert natural language queries and constraints to regex constructs
You can query a model directly and test the results returned when using
different parameter values with the Cloud console, or by calling the
Vertex AI API directly.
The string should be alphanumeric and can contain - also.
The string must start as well as end with alphanumeric characters only.
Response
^[a-zA-Z0-9]{1}[a-zA-Z0-9-]{0,61}[a-zA-Z0-9]{1}$
Explanation:
^: Matches the beginning of the string.
[a-zA-Z0-9]{1}: Matches one alphanumeric character (a-z, A-Z, 0-9) at the beginning of the string.
[a-zA-Z0-9\-]{0,61}: Matches zero to 61 alphanumeric characters or hyphens (-) in the middle of the string. This ensures the string has a total length of 2-63 characters.
[a-zA-Z0-9]{1}: Matches one alphanumeric character at the end of the string.
$: Matches the end of the string.
Examples:
Valid strings:
abc
123-abc
abc-123
a-b-c-123
Invalid strings:
-abc (Starts with a hyphen)
abc- (Ends with a hyphen)
abc123- (Ends with a hyphen)
1234567890123456789012345678901234567890 (More than 63 characters)
Python Code:
import re
regex = r"^[a-zA-Z0-9]{1}[a-zA-Z0-9\-]{0,61}[a-zA-Z0-9]{1}$"
test_strings = [
"abc",
"123-abc",
"abc-123",
"a-b-c-123",
"-abc",
"abc-",
"abc123-",
"1234567890123456789012345678901234567890",
]
for string in test_strings:
match = re.match(regex, string)
if match:
print(f"'{string}' is a valid string.")
else:
print(f"'{string}' is an invalid string.")
This code will print whether each test string is valid or invalid based on the regex pattern.
