Source code for langchain_experimental.comprehend_moderation.toxicity

import asyncio
import importlib
from typing import Any, List, Optional

from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (

[docs]class ComprehendToxicity: """Class to handle toxicity moderation."""
[docs] def __init__( self, client: Any, callback: Optional[Any] = None, unique_id: Optional[str] = None, chain_id: Optional[str] = None, ) -> None: self.client = client self.moderation_beacon = { "moderation_chain_id": chain_id, "moderation_type": "Toxicity", "moderation_status": "LABELS_NOT_FOUND", } self.callback = callback self.unique_id = unique_id
def _toxicity_init_validate(self, max_size: int) -> Any: """ Validate and initialize toxicity processing configuration. Args: max_size (int): Maximum sentence size defined in the configuration object. Raises: Exception: If the maximum sentence size exceeds the 5KB limit. Note: This function ensures that the NLTK punkt tokenizer is downloaded if not already present. Returns: None """ if max_size > 1024 * 5: raise Exception("The sentence length should not exceed 5KB.") try: nltk = importlib.import_module("nltk")"tokenizers/punkt") return nltk except ImportError: raise ModuleNotFoundError( "Could not import nltk python package. " "Please install it with `pip install nltk`." ) except LookupError:"punkt") def _split_paragraph( self, prompt_value: str, max_size: int = 1024 * 4 ) -> List[List[str]]: """ Split a paragraph into chunks of sentences, respecting the maximum size limit. Args: paragraph (str): The input paragraph to be split into chunks. max_size (int, optional): The maximum size limit in bytes for each chunk. Defaults to 1024. Returns: List[List[str]]: A list of chunks, where each chunk is a list of sentences. Note: This function validates the maximum sentence size based on service limits using the 'toxicity_init_validate' function. It uses the NLTK sentence tokenizer to split the paragraph into sentences. Example: paragraph = "This is a sample paragraph. It contains multiple sentences. ..." chunks = split_paragraph(paragraph, max_size=2048) """ # validate max. sentence size based on Service limits nltk = self._toxicity_init_validate(max_size) sentences = nltk.sent_tokenize(prompt_value) chunks = list() # type: ignore current_chunk = list() # type: ignore current_size = 0 for sentence in sentences: sentence_size = len(sentence.encode("utf-8")) # If adding a new sentence exceeds max_size # or current_chunk has 10 sentences, start a new chunk if (current_size + sentence_size > max_size) or (len(current_chunk) >= 10): if current_chunk: # Avoid appending empty chunks chunks.append(current_chunk) current_chunk = [] current_size = 0 current_chunk.append(sentence) current_size += sentence_size # Add any remaining sentences if current_chunk: chunks.append(current_chunk) return chunks
[docs] def validate(self, prompt_value: str, config: Any = None) -> str: """ Check the toxicity of a given text prompt using AWS Comprehend service and apply actions based on configuration. Args: prompt_value (str): The text content to be checked for toxicity. config (Dict[str, Any]): Configuration for toxicity checks and actions. Returns: str: The original prompt_value if allowed or no toxicity found. Raises: ValueError: If the prompt contains toxic labels and cannot be processed based on the configuration. """ chunks = self._split_paragraph(prompt_value=prompt_value) for sentence_list in chunks: segments = [{"Text": sentence} for sentence in sentence_list] response = self.client.detect_toxic_content( TextSegments=segments, LanguageCode="en" ) if self.callback and self.callback.toxicity_callback: self.moderation_beacon["moderation_input"] = segments # type: ignore self.moderation_beacon["moderation_output"] = response toxicity_found = False threshold = config.get("threshold") toxicity_labels = config.get("labels") if not toxicity_labels: for item in response["ResultList"]: for label in item["Labels"]: if label["Score"] >= threshold: toxicity_found = True break else: for item in response["ResultList"]: for label in item["Labels"]: if ( label["Name"] in toxicity_labels and label["Score"] >= threshold ): toxicity_found = True break if self.callback and self.callback.toxicity_callback: if toxicity_found: self.moderation_beacon["moderation_status"] = "LABELS_FOUND" asyncio.create_task( self.callback.on_after_toxicity( self.moderation_beacon, self.unique_id ) ) if toxicity_found: raise ModerationToxicityError return prompt_value