d978ae5438
feat(cache): add support for pandas and PIL Image objects caching fix(interface): import cache-related functions from base.py module test(cache): update import statements in cache-related test file
84 lines
2 KiB
Python
84 lines
2 KiB
Python
from pydantic import BaseModel, validator
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from langflow.graph.utils import extract_input_variables_from_prompt
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class CacheResponse(BaseModel):
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data: dict
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class Code(BaseModel):
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code: str
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class Prompt(BaseModel):
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template: str
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# Build ValidationResponse class for {"imports": {"errors": []}, "function": {"errors": []}}
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class CodeValidationResponse(BaseModel):
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imports: dict
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function: dict
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@validator("imports")
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def validate_imports(cls, v):
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return v or {"errors": []}
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@validator("function")
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def validate_function(cls, v):
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return v or {"errors": []}
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class PromptValidationResponse(BaseModel):
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input_variables: list
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INVALID_CHARACTERS = {
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" ",
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",",
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".",
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":",
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";",
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"!",
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"?",
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"/",
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"\\",
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"(",
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")",
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"[",
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"]",
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"{",
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"}",
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}
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def validate_prompt(template: str):
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input_variables = extract_input_variables_from_prompt(template)
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# Check if there are invalid characters in the input_variables
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input_variables = check_input_variables(input_variables)
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return PromptValidationResponse(input_variables=input_variables)
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def check_input_variables(input_variables: list):
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invalid_chars = []
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fixed_variables = []
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for variable in input_variables:
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new_var = variable
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for char in INVALID_CHARACTERS:
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if char in variable:
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invalid_chars.append(char)
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new_var = new_var.replace(char, "")
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fixed_variables.append(new_var)
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if new_var != variable:
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input_variables.remove(variable)
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input_variables.append(new_var)
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# If any of the input_variables is not in the fixed_variables, then it means that
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# there are invalid characters in the input_variables
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if any(var not in fixed_variables for var in input_variables):
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raise ValueError(
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f"Invalid input variables: {input_variables}. Please, use something like {fixed_variables} instead."
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)
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return input_variables
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