Merge f0e936ae43 into 96096ea0ff
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Sam
GitHub
commit
b8c5ddc6a3
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@ -9,6 +9,7 @@ import re
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from typing import Dict, Any, List, Optional
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from ..utils.llm_client import LLMClient
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from ..utils.locale import get_language_instruction
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from ..utils.file_parser import split_text_into_chunks
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logger = logging.getLogger(__name__)
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@ -227,6 +228,10 @@ class OntologyGenerator:
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# 传给 LLM 的文本最大长度（5万字）
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MAX_TEXT_LENGTH_FOR_LLM = 50000
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LONG_TEXT_CHUNK_SIZE = 8000
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LONG_TEXT_CHUNK_OVERLAP = 200
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MAX_LONG_TEXT_CHUNKS = 60
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MIN_LONG_TEXT_EXCERPT = 400
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def _build_user_message(
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self,
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@ -236,14 +241,7 @@ class OntologyGenerator:
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) -> str:
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"""构建用户消息"""
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# 合并文本
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combined_text = "\n\n---\n\n".join(document_texts)
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original_length = len(combined_text)
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# 如果文本超过5万字，截断（仅影响传给LLM的内容，不影响图谱构建）
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if len(combined_text) > self.MAX_TEXT_LENGTH_FOR_LLM:
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combined_text = combined_text[:self.MAX_TEXT_LENGTH_FOR_LLM]
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combined_text += f"\n\n...(原文共{original_length}字，已截取前{self.MAX_TEXT_LENGTH_FOR_LLM}字用于本体分析)..."
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combined_text = self._build_document_context(document_texts)
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message = f"""## 模拟需求
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@ -273,6 +271,142 @@ class OntologyGenerator:
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"""
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return message
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def _build_document_context(self, document_texts: List[str]) -> str:
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"""构建用于本体分析的文档上下文，长文本按全局分块抽样而不是只截取开头。"""
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combined_text = "\n\n---\n\n".join(document_texts)
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original_length = len(combined_text)
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if original_length <= self.MAX_TEXT_LENGTH_FOR_LLM:
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return combined_text
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chunks = self._collect_document_chunks(document_texts)
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if not chunks:
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return ""
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selected_chunks = self._select_representative_chunks(chunks)
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excerpt_budget = self._calculate_excerpt_budget(len(selected_chunks))
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context = self._render_chunked_context(
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selected_chunks=selected_chunks,
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original_length=original_length,
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total_chunks=len(chunks),
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excerpt_limit=excerpt_budget,
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)
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while len(context) > self.MAX_TEXT_LENGTH_FOR_LLM and excerpt_budget > self.MIN_LONG_TEXT_EXCERPT:
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excerpt_budget = max(self.MIN_LONG_TEXT_EXCERPT, int(excerpt_budget * 0.85))
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context = self._render_chunked_context(
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selected_chunks=selected_chunks,
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original_length=original_length,
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total_chunks=len(chunks),
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excerpt_limit=excerpt_budget,
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)
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if len(context) > self.MAX_TEXT_LENGTH_FOR_LLM:
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marker = "\n\n...(分块上下文已压缩到本体分析长度限制内)..."
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context = context[:self.MAX_TEXT_LENGTH_FOR_LLM - len(marker)] + marker
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return context
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def _collect_document_chunks(self, document_texts: List[str]) -> List[Dict[str, Any]]:
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"""按文档收集分块，保留文档和分块编号方便提示词定位。"""
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all_chunks: List[Dict[str, Any]] = []
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for doc_index, text in enumerate(document_texts, 1):
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doc_chunks = split_text_into_chunks(
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text,
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chunk_size=self.LONG_TEXT_CHUNK_SIZE,
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overlap=self.LONG_TEXT_CHUNK_OVERLAP,
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)
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total_doc_chunks = len(doc_chunks)
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for chunk_index, chunk in enumerate(doc_chunks, 1):
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all_chunks.append({
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"document_index": doc_index,
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"chunk_index": chunk_index,
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"total_document_chunks": total_doc_chunks,
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"text": chunk,
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})
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return all_chunks
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def _select_representative_chunks(self, chunks: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""从全部分块中等距抽样，覆盖长文开头、中段和结尾。"""
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if len(chunks) <= self.MAX_LONG_TEXT_CHUNKS:
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return chunks
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if self.MAX_LONG_TEXT_CHUNKS <= 1:
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return [chunks[0]]
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last_index = len(chunks) - 1
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selected_indexes = {
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round(i * last_index / (self.MAX_LONG_TEXT_CHUNKS - 1))
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for i in range(self.MAX_LONG_TEXT_CHUNKS)
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}
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return [chunks[i] for i in sorted(selected_indexes)]
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def _calculate_excerpt_budget(self, selected_count: int) -> int:
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"""根据选中的分块数量为每块分配字符预算。"""
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header_budget = 600
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chunk_header_budget = 120 * selected_count
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available = max(
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self.MIN_LONG_TEXT_EXCERPT * selected_count,
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self.MAX_TEXT_LENGTH_FOR_LLM - header_budget - chunk_header_budget,
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)
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return max(self.MIN_LONG_TEXT_EXCERPT, available // max(selected_count, 1))
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def _render_chunked_context(
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self,
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selected_chunks: List[Dict[str, Any]],
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original_length: int,
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total_chunks: int,
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excerpt_limit: int,
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) -> str:
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"""渲染长文本分块上下文。"""
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lines = [
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(
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f"【长文本自动分块摘要】原文共{original_length}字，"
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f"已分为{total_chunks}个文本块用于全局覆盖分析。"
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),
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(
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f"以下展示其中{len(selected_chunks)}个代表性文本块的摘录，"
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"覆盖开头、中段和结尾；请基于这些跨全文线索设计本体，不要只依赖第一段内容。"
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),
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]
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for chunk in selected_chunks:
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excerpt = self._excerpt_text(chunk["text"], excerpt_limit)
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lines.append(
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"\n".join([
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(
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f"--- 文档 {chunk['document_index']} / "
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f"分块 {chunk['chunk_index']}/{chunk['total_document_chunks']} ---"
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),
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excerpt,
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])
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)
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return "\n\n".join(lines)
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@staticmethod
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def _excerpt_text(text: str, char_limit: int) -> str:
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"""长分块保留首尾，避免每个分块内部再次变成只看开头。"""
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text = text.strip()
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if len(text) <= char_limit:
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return text
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marker = "\n...(本分块中间内容省略)...\n"
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if char_limit <= len(marker) + 20:
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return text[:char_limit]
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remaining = char_limit - len(marker)
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head_len = remaining // 2
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tail_len = remaining - head_len
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return f"{text[:head_len].rstrip()}{marker}{text[-tail_len:].lstrip()}"
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def _validate_and_process(self, result: Dict[str, Any]) -> Dict[str, Any]:
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"""验证和后处理结果"""
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@ -503,4 +637,3 @@ class OntologyGenerator:
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code_lines.append('}')
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return '\n'.join(code_lines)
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@ -0,0 +1,52 @@
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from app.services.ontology_generator import OntologyGenerator
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def _generator_for_test() -> OntologyGenerator:
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generator = OntologyGenerator(llm_client=object())
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generator.MAX_TEXT_LENGTH_FOR_LLM = 2000
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generator.LONG_TEXT_CHUNK_SIZE = 500
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generator.LONG_TEXT_CHUNK_OVERLAP = 0
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generator.MAX_LONG_TEXT_CHUNKS = 3
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generator.MIN_LONG_TEXT_EXCERPT = 120
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return generator
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def test_short_ontology_context_keeps_original_text():
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generator = _generator_for_test()
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context = generator._build_document_context(["short document body"])
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assert context == "short document body"
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assert "长文本自动分块摘要" not in context
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def test_long_ontology_context_samples_across_document():
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generator = _generator_for_test()
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long_text = "BEGIN" + ("a" * 1050) + "MIDDLE" + ("b" * 1050) + "END"
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context = generator._build_document_context([long_text])
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assert len(context) <= generator.MAX_TEXT_LENGTH_FOR_LLM
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assert "长文本自动分块摘要" in context
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assert "BEGIN" in context
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assert "MIDDLE" in context
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assert "END" in context
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assert "分块 1/" in context
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assert "分块 3/" in context
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assert "分块 5/" in context
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def test_very_long_ontology_context_selects_representative_chunks():
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generator = _generator_for_test()
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chunks = ["BEGIN"] + [
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f"CHUNK{i:02d}-" + (str(i) * 490)
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for i in range(12)
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] + ["FINALEND"]
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long_text = "".join(chunks)
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context = generator._build_document_context([long_text])
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assert len(context) <= generator.MAX_TEXT_LENGTH_FOR_LLM
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assert "BEGIN" in context
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assert "FINALEND" in context
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assert context.count("--- 文档 1 / 分块") == generator.MAX_LONG_TEXT_CHUNKS
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