feat: 增强知识图谱关系的时序性支持

- 为关系和向量数据增加时间戳支持,记录知识获取的时间
- 优化混合查询策略,同时考虑语义相关性和时间顺序
- 增强提示词模板,指导LLM在处理冲突信息时考虑时间因素

lightrag/operate.py

@@ -30,6 +30,7 @@ from .base import (
     QueryParam,
 )
 from .prompt import GRAPH_FIELD_SEP, PROMPTS
+import time
 
 
 def chunking_by_token_size(
@@ -128,6 +129,9 @@ async def _handle_single_relationship_extraction(
         description=edge_description,
         keywords=edge_keywords,
         source_id=edge_source_id,
+        metadata={
+            "created_at": time.time()
+        }
     )
 
 
@@ -445,6 +449,9 @@ async def extract_entities(
                 + dp["src_id"]
                 + dp["tgt_id"]
                 + dp["description"],
+                "metadata": {
+                    "created_at": dp.get("metadata", {}).get("created_at", time.time())
+                }
             }
             for dp in all_relationships_data
         }
@@ -733,9 +740,13 @@ async def _get_node_data(
     entities_context = list_of_list_to_csv(entites_section_list)
 
     relations_section_list = [
-        ["id", "source", "target", "description", "keywords", "weight", "rank"]
+        ["id", "source", "target", "description", "keywords", "weight", "rank", "created_at"]
     ]
     for i, e in enumerate(use_relations):
+        created_at = e.get("created_at", "未知")
+        # 转换时间戳为可读格式
+        if isinstance(created_at, (int, float)):
+            created_at = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(created_at))
         relations_section_list.append(
             [
                 i,
@@ -745,6 +756,7 @@ async def _get_node_data(
                 e["keywords"],
                 e["weight"],
                 e["rank"],
+                created_at
             ]
         )
     relations_context = list_of_list_to_csv(relations_section_list)
@@ -882,6 +894,8 @@ async def _get_edge_data(
     if not len(results):
         return "", "", ""
 
+    # 从 KV 存储中获取完整的关系信息
+    edge_ids = [r["id"] for r in results]
     edge_datas = await asyncio.gather(
         *[knowledge_graph_inst.get_edge(r["src_id"], r["tgt_id"]) for r in results]
     )
@@ -892,7 +906,13 @@ async def _get_edge_data(
         *[knowledge_graph_inst.edge_degree(r["src_id"], r["tgt_id"]) for r in results]
     )
     edge_datas = [
-        {"src_id": k["src_id"], "tgt_id": k["tgt_id"], "rank": d, **v}
+        {
+            "src_id": k["src_id"], 
+            "tgt_id": k["tgt_id"], 
+            "rank": d, 
+            "created_at": k.get("__created_at__", None),  # 从 KV 存储中获取时间元数据
+            **v
+        }
         for k, v, d in zip(results, edge_datas, edge_degree)
         if v is not None
     ]
@@ -916,9 +936,13 @@ async def _get_edge_data(
     )
 
     relations_section_list = [
-        ["id", "source", "target", "description", "keywords", "weight", "rank"]
+        ["id", "source", "target", "description", "keywords", "weight", "rank", "created_at"]
     ]
     for i, e in enumerate(edge_datas):
+        created_at = e.get("created_at", "未知")
+        # 转换时间戳为可读格式
+        if isinstance(created_at, (int, float)):
+            created_at = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(created_at))
         relations_section_list.append(
             [
                 i,
@@ -928,6 +952,7 @@ async def _get_edge_data(
                 e["keywords"],
                 e["weight"],
                 e["rank"],
+                created_at
             ]
         )
     relations_context = list_of_list_to_csv(relations_section_list)
@@ -1259,9 +1284,15 @@ async def mix_kg_vector_query(
             chunks_ids = [r["id"] for r in results]
             chunks = await text_chunks_db.get_by_ids(chunks_ids)
 
-            valid_chunks = [
-                chunk for chunk in chunks if chunk is not None and "content" in chunk
-            ]
+            valid_chunks = []
+            for chunk, result in zip(chunks, results):
+                if chunk is not None and "content" in chunk:
+                    # 合并 chunk 内容和时间元数据
+                    chunk_with_time = {
+                        "content": chunk["content"],
+                        "created_at": result.get("created_at", None)
+                    }
+                    valid_chunks.append(chunk_with_time)
 
             if not valid_chunks:
                 return None
@@ -1275,7 +1306,15 @@ async def mix_kg_vector_query(
             if not maybe_trun_chunks:
                 return None
 
-            return "\n--New Chunk--\n".join([c["content"] for c in maybe_trun_chunks])
+            # 在内容中包含时间信息
+            formatted_chunks = []
+            for c in maybe_trun_chunks:
+                chunk_text = c["content"]
+                if c["created_at"]:
+                    chunk_text = f"[Created at: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(c['created_at']))}]\n{chunk_text}"
+                formatted_chunks.append(chunk_text)
+
+            return "\n--New Chunk--\n".join(formatted_chunks)
         except Exception as e:
             logger.error(f"Error in get_vector_context: {e}")
             return None