Add initial implementation of txt2graph tool for knowledge graph generation

- Created a new Streamlit application for visualizing knowledge graphs.
- Implemented text extraction from PDF, Markdown, and TXT files.
- Developed graph building logic using Zep Cloud API.
- Added support for custom entity types and relationships.
- Included interactive HTML visualization for generated graphs.
- Updated .gitignore to include new directories and files.
- Added example environment configuration file (.env.example) for API key setup.
- Created README.md with installation and usage instructions.
- Introduced various utility scripts and styles for enhanced functionality.

txt2graph/text_extractor.py

@@ -0,0 +1,126 @@
+"""
+文本提取模块
+支持从 .md, .txt, .pdf 文件中提取纯文本
+"""
+
+import os
+from pathlib import Path
+from typing import Optional
+
+
+def extract_from_txt(file_path: str) -> str:
+    """从TXT文件提取文本"""
+    with open(file_path, 'r', encoding='utf-8') as f:
+        return f.read()
+
+
+def extract_from_md(file_path: str) -> str:
+    """从Markdown文件提取文本（保留原始格式，不转换HTML）"""
+    with open(file_path, 'r', encoding='utf-8') as f:
+        return f.read()
+
+
+def extract_from_pdf(file_path: str) -> str:
+    """从PDF文件提取文本"""
+    try:
+        import fitz  # PyMuPDF
+    except ImportError:
+        raise ImportError("请安装 PyMuPDF: pip install PyMuPDF")
+    
+    text_parts = []
+    with fitz.open(file_path) as doc:
+        for page_num, page in enumerate(doc):
+            text = page.get_text()
+            if text.strip():
+                text_parts.append(f"--- 第 {page_num + 1} 页 ---\n{text}")
+    
+    return "\n\n".join(text_parts)
+
+
+def extract_text(file_path: str) -> str:
+    """
+    根据文件扩展名自动选择提取方法
+    
+    Args:
+        file_path: 文件路径
+        
+    Returns:
+        提取的纯文本内容
+        
+    Raises:
+        ValueError: 不支持的文件格式
+        FileNotFoundError: 文件不存在
+    """
+    path = Path(file_path)
+    
+    if not path.exists():
+        raise FileNotFoundError(f"文件不存在: {file_path}")
+    
+    suffix = path.suffix.lower()
+    
+    extractors = {
+        '.txt': extract_from_txt,
+        '.md': extract_from_md,
+        '.markdown': extract_from_md,
+        '.pdf': extract_from_pdf,
+    }
+    
+    extractor = extractors.get(suffix)
+    if extractor is None:
+        supported = ', '.join(extractors.keys())
+        raise ValueError(f"不支持的文件格式: {suffix}。支持的格式: {supported}")
+    
+    return extractor(file_path)
+
+
+def split_text_into_chunks(text: str, max_chunk_size: int = 8000, overlap: int = 200) -> list[str]:
+    """
+    将长文本分割成多个小块，适合Zep处理
+    
+    Args:
+        text: 原始文本
+        max_chunk_size: 每个块的最大字符数
+        overlap: 块之间的重叠字符数
+        
+    Returns:
+        文本块列表
+    """
+    if len(text) <= max_chunk_size:
+        return [text]
+    
+    chunks = []
+    start = 0
+    
+    while start < len(text):
+        end = start + max_chunk_size
+        
+        # 尝试在句子边界处分割
+        if end < len(text):
+            # 查找最近的句子结束符
+            for sep in ['。', '！', '？', '\n\n', '. ', '! ', '? ']:
+                last_sep = text[start:end].rfind(sep)
+                if last_sep != -1 and last_sep > max_chunk_size * 0.5:
+                    end = start + last_sep + len(sep)
+                    break
+        
+        chunk = text[start:end].strip()
+        if chunk:
+            chunks.append(chunk)
+        
+        # 下一个块从重叠位置开始
+        start = end - overlap if end < len(text) else len(text)
+    
+    return chunks
+
+
+if __name__ == "__main__":
+    # 测试
+    import sys
+    if len(sys.argv) > 1:
+        file_path = sys.argv[1]
+        text = extract_text(file_path)
+        print(f"提取了 {len(text)} 个字符")
+        print(f"前500字符:\n{text[:500]}")
+
+
+