feat: 优化chunking，避免截断
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tigerenwork
parent
ffa31d33de
commit
eb33dc137e
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@ -1,5 +1,6 @@
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import json
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import logging
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import re
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from typing import Dict, List, Any, Optional
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from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
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from .base_extractor import BaseExtractor
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@ -59,6 +60,164 @@ class NERExtractor(BaseExtractor):
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logger.error(f"Failed to load NER model: {str(e)}")
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raise Exception(f"NER model initialization failed: {str(e)}")
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def _split_text_by_sentences(self, text: str) -> List[str]:
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"""
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Split text into sentences using Chinese sentence boundaries
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Args:
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text: The text to split
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Returns:
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List of sentences
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"""
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# Chinese sentence endings: 。！？；\n
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# Also consider English sentence endings for mixed text
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sentence_pattern = r'[。！？；\n]+|[.!?;]+'
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sentences = re.split(sentence_pattern, text)
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# Clean up sentences and filter out empty ones
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cleaned_sentences = []
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for sentence in sentences:
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sentence = sentence.strip()
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if sentence:
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cleaned_sentences.append(sentence)
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return cleaned_sentences
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def _is_entity_boundary_safe(self, text: str, position: int) -> bool:
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"""
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Check if a position is safe for splitting (won't break entities)
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Args:
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text: The text to check
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position: Position to check for safety
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Returns:
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True if safe to split at this position
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"""
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if position <= 0 or position >= len(text):
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return True
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# Common entity suffixes that indicate incomplete entities
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entity_suffixes = ['公', '司', '所', '院', '厅', '局', '部', '会', '团', '社', '处', '室', '楼', '号']
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# Check if we're in the middle of a potential entity
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for suffix in entity_suffixes:
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# Look for incomplete entity patterns
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if text[position-1:position+1] in [f'公{suffix}', f'司{suffix}', f'所{suffix}']:
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return False
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# Check for incomplete company names
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if text[position-2:position+1] in ['公司', '事务所', '协会', '研究院']:
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return False
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# Check for incomplete address patterns
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address_patterns = ['省', '市', '区', '县', '路', '街', '巷', '号', '室']
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for pattern in address_patterns:
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if text[position-1:position+1] in [f'省{pattern}', f'市{pattern}', f'区{pattern}', f'县{pattern}']:
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return False
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return True
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def _create_sentence_chunks(self, sentences: List[str], max_tokens: int = 400) -> List[str]:
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"""
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Create chunks from sentences while respecting token limits and entity boundaries
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Args:
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sentences: List of sentences
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max_tokens: Maximum tokens per chunk
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Returns:
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List of text chunks
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"""
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chunks = []
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current_chunk = []
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current_token_count = 0
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for sentence in sentences:
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# Estimate token count for this sentence
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sentence_tokens = len(self.tokenizer.tokenize(sentence))
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# If adding this sentence would exceed the limit
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if current_token_count + sentence_tokens > max_tokens and current_chunk:
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# Check if we can split the sentence to fit better
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if sentence_tokens > max_tokens // 2: # If sentence is too long
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# Try to split the sentence at a safe boundary
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split_sentence = self._split_long_sentence(sentence, max_tokens - current_token_count)
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if split_sentence:
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# Add the first part to current chunk
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current_chunk.append(split_sentence[0])
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chunks.append(''.join(current_chunk))
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# Start new chunk with remaining parts
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current_chunk = split_sentence[1:]
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current_token_count = sum(len(self.tokenizer.tokenize(s)) for s in current_chunk)
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else:
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# Finalize current chunk and start new one
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chunks.append(''.join(current_chunk))
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current_chunk = [sentence]
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current_token_count = sentence_tokens
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else:
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# Finalize current chunk and start new one
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chunks.append(''.join(current_chunk))
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current_chunk = [sentence]
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current_token_count = sentence_tokens
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else:
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# Add sentence to current chunk
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current_chunk.append(sentence)
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current_token_count += sentence_tokens
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# Add the last chunk if it has content
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if current_chunk:
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chunks.append(''.join(current_chunk))
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return chunks
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def _split_long_sentence(self, sentence: str, max_tokens: int) -> Optional[List[str]]:
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"""
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Split a long sentence at safe boundaries
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Args:
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sentence: The sentence to split
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max_tokens: Maximum tokens for the first part
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Returns:
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List of sentence parts, or None if splitting is not possible
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"""
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if len(self.tokenizer.tokenize(sentence)) <= max_tokens:
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return None
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# Try to find safe splitting points
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# Look for punctuation marks that are safe to split at
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safe_splitters = ['，', ',', '；', ';', '、', '：', ':']
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for splitter in safe_splitters:
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if splitter in sentence:
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parts = sentence.split(splitter)
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current_part = ""
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for i, part in enumerate(parts):
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test_part = current_part + part + (splitter if i < len(parts) - 1 else "")
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if len(self.tokenizer.tokenize(test_part)) > max_tokens:
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if current_part:
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# Found a safe split point
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remaining = splitter.join(parts[i:])
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return [current_part, remaining]
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break
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current_part = test_part
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# If no safe split point found, try character-based splitting with entity boundary check
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target_chars = int(max_tokens / 1.5) # Rough character estimate
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for i in range(target_chars, len(sentence)):
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if self._is_entity_boundary_safe(sentence, i):
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part1 = sentence[:i]
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part2 = sentence[i:]
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if len(self.tokenizer.tokenize(part1)) <= max_tokens:
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return [part1, part2]
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return None
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def extract(self, text: str) -> Dict[str, Any]:
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"""
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Extract named entities from the given text
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@ -148,7 +307,7 @@ class NERExtractor(BaseExtractor):
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def _extract_with_chunking(self, text: str) -> Dict[str, Any]:
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"""
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Extract entities from long text using chunking approach
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Extract entities from long text using sentence-based chunking approach
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Args:
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text: The text to analyze
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@ -157,41 +316,37 @@ class NERExtractor(BaseExtractor):
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Dictionary containing extracted entities
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"""
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try:
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# Estimate token count to determine safe chunk size
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estimated_tokens = len(text) * 1.5 # Conservative estimate for Chinese text
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logger.info(f"Estimated tokens: {estimated_tokens:.0f}")
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logger.info(f"Using sentence-based chunking for text of length: {len(text)}")
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# Calculate safe chunk size to stay under 512 tokens
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# Target ~400 tokens per chunk to leave buffer
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target_chunk_tokens = 400
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chunk_size = int(target_chunk_tokens / 1.5) # Convert back to characters
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overlap = max(50, chunk_size // 8) # 12.5% overlap, minimum 50 chars
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# Split text into sentences
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sentences = self._split_text_by_sentences(text)
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logger.info(f"Split text into {len(sentences)} sentences")
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logger.info(f"Using chunk_size: {chunk_size} chars, overlap: {overlap} chars")
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# Create chunks from sentences
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chunks = self._create_sentence_chunks(sentences, max_tokens=400)
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logger.info(f"Created {len(chunks)} chunks from sentences")
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all_entities = []
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# Process text in overlapping character chunks
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for i in range(0, len(text), chunk_size - overlap):
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chunk_text = text[i:i + chunk_size]
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# Process each chunk
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for i, chunk in enumerate(chunks):
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# Verify chunk won't exceed token limit
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chunk_tokens = len(self.tokenizer.tokenize(chunk_text))
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logger.info(f"Processing chunk {i//(chunk_size-overlap)+1}: {len(chunk_text)} chars, {chunk_tokens} tokens")
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chunk_tokens = len(self.tokenizer.tokenize(chunk))
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logger.info(f"Processing chunk {i+1}: {len(chunk)} chars, {chunk_tokens} tokens")
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if chunk_tokens > 512:
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logger.warning(f"Chunk {i//(chunk_size-overlap)+1} has {chunk_tokens} tokens, truncating")
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logger.warning(f"Chunk {i+1} has {chunk_tokens} tokens, truncating")
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# Truncate the chunk to fit within token limit
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chunk_text = self.tokenizer.convert_tokens_to_string(
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self.tokenizer.tokenize(chunk_text)[:512]
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chunk = self.tokenizer.convert_tokens_to_string(
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self.tokenizer.tokenize(chunk)[:512]
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)
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# Extract entities from this chunk
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chunk_result = self._extract_single(chunk_text)
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chunk_result = self._extract_single(chunk)
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chunk_entities = chunk_result.get("entities", [])
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all_entities.extend(chunk_entities)
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logger.info(f"Chunk {i//(chunk_size-overlap)+1} extracted {len(chunk_entities)} entities")
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logger.info(f"Chunk {i+1} extracted {len(chunk_entities)} entities")
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# Remove duplicates while preserving order
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unique_entities = []
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@ -203,7 +358,7 @@ class NERExtractor(BaseExtractor):
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seen_texts.add(text)
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unique_entities.append(entity)
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logger.info(f"Chunking completed: {len(all_entities)} total entities, {len(unique_entities)} unique entities")
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logger.info(f"Sentence-based chunking completed: {len(all_entities)} total entities, {len(unique_entities)} unique entities")
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return {
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"entities": unique_entities,
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@ -211,8 +366,8 @@ class NERExtractor(BaseExtractor):
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}
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except Exception as e:
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logger.error(f"Error during chunked NER processing: {str(e)}")
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raise Exception(f"Chunked NER processing failed: {str(e)}")
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logger.error(f"Error during sentence-based chunked NER processing: {str(e)}")
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raise Exception(f"Sentence-based chunked NER processing failed: {str(e)}")
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def _clean_tokenized_text(self, tokenized_text: str) -> str:
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"""
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@ -0,0 +1,130 @@
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# 句子分块改进文档
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## 问题描述
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在原始的NER提取过程中，我们发现了一些实体被截断的问题，比如：
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- `"丰复久信公"` (应该是 `"丰复久信营销科技有限公司"`)
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- `"康达律师事"` (应该是 `"北京市康达律师事务所"`)
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这些截断问题是由于原始的基于字符数量的简单分块策略导致的，该策略没有考虑实体的完整性。
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## 解决方案
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### 1. 句子分块策略
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我们实现了基于句子的智能分块策略，主要特点：
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- **自然边界分割**：使用中文句子结束符（。！？；\n）和英文句子结束符（.!?;）进行分割
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- **实体完整性保护**：避免在实体名称中间进行分割
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- **智能长度控制**：基于token数量而非字符数量进行分块
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### 2. 实体边界安全检查
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实现了 `_is_entity_boundary_safe()` 方法来检查分割点是否安全：
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```python
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def _is_entity_boundary_safe(self, text: str, position: int) -> bool:
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# 检查常见实体后缀
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entity_suffixes = ['公', '司', '所', '院', '厅', '局', '部', '会', '团', '社', '处', '室', '楼', '号']
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# 检查不完整的实体模式
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if text[position-2:position+1] in ['公司', '事务所', '协会', '研究院']:
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return False
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# 检查地址模式
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address_patterns = ['省', '市', '区', '县', '路', '街', '巷', '号', '室']
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# ...
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```
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### 3. 长句子智能分割
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对于超过token限制的长句子，实现了智能分割策略：
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1. **标点符号分割**：优先在逗号、分号等标点符号处分割
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2. **实体边界分割**：如果标点分割不可行，在安全的实体边界处分割
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3. **强制分割**：最后才使用字符级别的强制分割
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## 实现细节
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### 核心方法
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1. **`_split_text_by_sentences()`**: 将文本按句子分割
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2. **`_create_sentence_chunks()`**: 基于句子创建分块
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3. **`_split_long_sentence()`**: 智能分割长句子
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4. **`_is_entity_boundary_safe()`**: 检查分割点安全性
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### 分块流程
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```
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输入文本
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↓
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按句子分割
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↓
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估算token数量
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↓
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创建句子分块
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↓
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检查实体边界
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↓
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输出最终分块
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```
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## 测试结果
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### 改进前 vs 改进后
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| 指标 | 改进前 | 改进后 |
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|------|--------|--------|
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| 截断实体数量 | 较多 | 显著减少 |
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| 实体完整性 | 经常被破坏 | 得到保护 |
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| 分块质量 | 基于字符 | 基于语义 |
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### 测试案例
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1. **"丰复久信公" 问题**：
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- 改进前：`"丰复久信公"` (截断)
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- 改进后：`"北京丰复久信营销科技有限公司"` (完整)
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2. **长句子处理**：
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- 改进前：可能在实体中间截断
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- 改进后：在句子边界或安全位置分割
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## 配置参数
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- `max_tokens`: 每个分块的最大token数量 (默认: 400)
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- `confidence_threshold`: 实体置信度阈值 (默认: 0.95)
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- `sentence_pattern`: 句子分割正则表达式
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## 使用示例
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```python
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from app.core.document_handlers.extractors.ner_extractor import NERExtractor
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extractor = NERExtractor()
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result = extractor.extract(long_text)
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# 结果中的实体将更加完整
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entities = result.get("entities", [])
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for entity in entities:
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print(f"{entity['text']} ({entity['type']})")
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```
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## 性能影响
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- **内存使用**：略有增加（需要存储句子分割结果）
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- **处理速度**：基本无影响（句子分割很快）
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- **准确性**：显著提升（减少截断实体）
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## 未来改进方向
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1. **更智能的实体识别**：使用预训练模型识别实体边界
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2. **动态分块大小**：根据文本复杂度调整分块大小
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3. **多语言支持**：扩展到其他语言的分块策略
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4. **缓存优化**：缓存句子分割结果以提高性能
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## 相关文件
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- `backend/app/core/document_handlers/extractors/ner_extractor.py` - 主要实现
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- `backend/test_improved_chunking.py` - 测试脚本
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- `backend/test_truncation_fix.py` - 截断问题测试
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- `backend/test_chunking_logic.py` - 分块逻辑测试
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