diff --git a/python/1.py b/python/1.py
new file mode 100644
index 0000000..0be0982
--- /dev/null
+++ b/python/1.py
@@ -0,0 +1,48 @@
+import json
+
+UNIFIED_PROMPT_TEMPLATE = (
+ "你是一个医疗知识图谱构建专家。请从以下文本中：\n"
+ "1. 提取所有医学实体（去重），仅返回名称列表；\n"
+ "2. 在这些实体之间抽取高质量、术语化的语义关系三元组。\n\n"
+ "### 输出规则\n"
+ "- 实体类型无需标注，只输出名称字符串（如 \"慢性淋巴细胞白血病\"）。\n"
+ "- 关系谓词必须是专业术语（2～6字），如：临床表现、诊断、相关疾病、禁忌症、治疗药物等。\n"
+ "- e1 和 e2 必须来自提取出的实体列表，且 e1 ≠ e2。\n"
+ "- 输出必须是纯 JSON，仅包含两个字段：\"entities\"（字符串列表）和 \"relations\"（对象列表，每个含 e1/r/e2）。\n"
+ "- 不要任何额外文本、解释或 Markdown。\n\n"
+ "文本：{input}\n\n输出："
+)
+
+with open("test_data.jsonl", "r", encoding="utf-8") as fin, \
+ open("sft_messages_format.jsonl", "w", encoding="utf-8") as fout:
+
+ for line in fin:
+ line = line.strip()
+ if not line:
+ continue
+ try:
+ item = json.loads(line)
+ input_text = item["input"]
+ output_obj = item["output"]
+
+ # system prompt 中的 {input} 占位符替换（可选，也可保留原样）
+ # 这里按你要求：system 保持模板不变，user 才放真实 input
+ system_content = UNIFIED_PROMPT_TEMPLATE # 不替换 {input}
+
+ user_content = input_text
+ # assistant content 必须是 JSON 字符串（带转义）
+ assistant_content = json.dumps(output_obj, ensure_ascii=False)
+
+ messages = [
+ {"role": "system", "content": system_content},
+ {"role": "user", "content": user_content},
+ {"role": "assistant", "content": assistant_content}
+ ]
+
+ fout.write(json.dumps({"messages": messages}, ensure_ascii=False) + "\n")
+
+ except Exception as e:
+ print(f"处理出错: {e}")
+ continue
+
+print("✅ 转换完成！文件已保存为 sft_messages_format.jsonl")
\ No newline at end of file
diff --git a/python/cmekg_aligner.py b/python/cmekg_aligner.py
new file mode 100644
index 0000000..900e7f4
--- /dev/null
+++ b/python/cmekg_aligner.py
@@ -0,0 +1,66 @@
+# cmekg_aligner.py
+
+from difflib import SequenceMatcher
+from typing import List, Dict, Optional, Tuple
+
+class CMeKGAligner:
+ def __init__(self, uri, user, password):
+ from neo4j import GraphDatabase
+ self.driver = GraphDatabase.driver(uri, auth=(user, password))
+
+ def infer_type_by_name(self, name: str) -> str:
+ # 你的类型推断逻辑（保持不变）
+ if "片" in name or "胶囊" in name or "注射" in name:
+ return "Drug"
+ # ... 其他规则
+ return "Unknown"
+
+ def find_entities_batch(self, terms: List[str]) -> Dict[str, Optional[Tuple[str, str]]]:
+ """
+ 批量对齐上万条实体，仅一次数据库查询
+ :param terms: 原始术语列表（允许重复）
+ :return: {原始词: (标准名, 类型) 或 None}
+ """
+ if not terms:
+ return {}
+
+ # 1. 去重并保留顺序（可选）
+ unique_terms = list(dict.fromkeys(terms)) # 保持首次出现顺序
+
+ # 2. 一次性从 Neo4j 获取所有可能的候选实体
+ with self.driver.session() as session:
+ result = session.run(
+ """
+ UNWIND $terms AS input_name
+ MATCH (e)
+ WHERE toLower(e.name) CONTAINS toLower(input_name)
+ OR toLower(input_name) CONTAINS toLower(e.name)
+ RETURN input_name, e.name AS std_name
+ """,
+ terms=unique_terms
+ )
+ # 构建 {input_name: [std_name1, std_name2, ...]}
+ candidates_map = {}
+ for record in result:
+ inp = record["input_name"]
+ std = record["std_name"]
+ if inp not in candidates_map:
+ candidates_map[inp] = []
+ candidates_map[inp].append(std)
+
+ # 3. 对每个输入词，从候选中选最相似的标准名
+ output = {}
+ for term in terms: # 遍历原始列表（含重复）
+ if term in output: # 已处理过（因重复）
+ continue
+
+ candidates = candidates_map.get(term, [])
+ if not candidates:
+ output[term] = None
+ else:
+ # 选与 term 最相似的标准名
+ best_std = max(candidates, key=lambda x: SequenceMatcher(None, term, x).ratio())
+ entity_type = self.infer_type_by_name(best_std)
+ output[term] = (best_std, entity_type)
+
+ return output
\ No newline at end of file
diff --git a/python/test_alignment.py b/python/test_alignment.py
new file mode 100644
index 0000000..a2c3e5d
--- /dev/null
+++ b/python/test_alignment.py
@@ -0,0 +1,36 @@
+# batch_test.py
+from cmekg_aligner import CMeKGAligner
+
+aligner = CMeKGAligner(
+ uri="bolt://localhost:7687",
+ user="neo4j",
+ password="your_password"
+)
+
+# 模拟上万条数据（实际可从文件读取）
+with open("input_terms.txt", "r", encoding="utf-8") as f:
+ terms = [line.strip() for line in f if line.strip()]
+
+print(f"🔍 开始批量对齐 {len(terms)} 条实体...")
+
+results = aligner.find_entities_batch(terms)
+
+# 输出结果
+for term in terms[:10]: # 只打印前10条示例
+ res = results[term]
+ if res:
+ print(f"✅ '{term}' → '{res[0]}', {res[1]}")
+ else:
+ print(f"❌ '{term}' → 未匹配")
+
+# 可选：保存到 CSV
+import csv
+with open("batch_alignment_result.csv", "w", encoding="utf-8", newline="") as f:
+ writer = csv.writer(f)
+ writer.writerow(["原始词", "标准名", "类型"])
+ for term in terms:
+ res = results[term]
+ if res:
+ writer.writerow([term, res[0], res[1]])
+ else:
+ writer.writerow([term, "", ""])
\ No newline at end of file
diff --git a/vue/src/system/GraphBuilder.vue b/vue/src/system/GraphBuilder.vue
index acef2d9..e5db356 100644
--- a/vue/src/system/GraphBuilder.vue
+++ b/vue/src/system/GraphBuilder.vue
@@ -20,12 +20,12 @@
{{msg.file.name}}
{{msg.file.size}}
- 
+
+
+
+
+
+