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

@@ -129,9 +129,7 @@ async def _handle_single_relationship_extraction(
         description=edge_description,
         keywords=edge_keywords,
         source_id=edge_source_id,
-        metadata={
+        metadata={"created_at": time.time()},
-            "created_at": time.time()
-        }
     )
 
 
@@ -451,7 +449,7 @@ async def extract_entities(
                 + dp["description"],
                 "metadata": {
                     "created_at": dp.get("metadata", {}).get("created_at", time.time())
-                }
+                },
             }
             for dp in all_relationships_data
         }
@@ -740,11 +738,20 @@ 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", "created_at"]
+        [
+            "id",
+            "source",
+            "target",
+            "description",
+            "keywords",
+            "weight",
+            "rank",
+            "created_at",
+        ]
     ]
     for i, e in enumerate(use_relations):
-        created_at = e.get("created_at", "未知")
+        created_at = e.get("created_at", "UNKNOWN")
-        # 转换时间戳为可读格式
+        # Convert timestamp to readable format
         if isinstance(created_at, (int, float)):
             created_at = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(created_at))
         relations_section_list.append(
@@ -756,7 +763,7 @@ async def _get_node_data(
                 e["keywords"],
                 e["weight"],
                 e["rank"],
-                created_at
+                created_at,
             ]
         )
     relations_context = list_of_list_to_csv(relations_section_list)
@@ -894,8 +901,6 @@ 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]
     )
@@ -907,11 +912,11 @@ async def _get_edge_data(
     )
     edge_datas = [
         {
-            "src_id": k["src_id"], 
+            "src_id": k["src_id"],
-            "tgt_id": k["tgt_id"], 
+            "tgt_id": k["tgt_id"],
-            "rank": d, 
+            "rank": d,
             "created_at": k.get("__created_at__", None),  # 从 KV 存储中获取时间元数据
-            **v
+            **v,
         }
         for k, v, d in zip(results, edge_datas, edge_degree)
         if v is not None
@@ -936,11 +941,20 @@ async def _get_edge_data(
     )
 
     relations_section_list = [
-        ["id", "source", "target", "description", "keywords", "weight", "rank", "created_at"]
+        [
+            "id",
+            "source",
+            "target",
+            "description",
+            "keywords",
+            "weight",
+            "rank",
+            "created_at",
+        ]
     ]
     for i, e in enumerate(edge_datas):
-        created_at = e.get("created_at", "未知")
+        created_at = e.get("created_at", "Unknown")
-        # 转换时间戳为可读格式
+        # Convert timestamp to readable format
         if isinstance(created_at, (int, float)):
             created_at = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(created_at))
         relations_section_list.append(
@@ -952,7 +966,7 @@ async def _get_edge_data(
                 e["keywords"],
                 e["weight"],
                 e["rank"],
-                created_at
+                created_at,
             ]
         )
     relations_context = list_of_list_to_csv(relations_section_list)
@@ -1287,10 +1301,10 @@ async def mix_kg_vector_query(
             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
+                    # Merge chunk content and time metadata
                     chunk_with_time = {
                         "content": chunk["content"],
-                        "created_at": result.get("created_at", None)
+                        "created_at": result.get("created_at", None),
                     }
                     valid_chunks.append(chunk_with_time)
 
@@ -1306,7 +1320,7 @@ async def mix_kg_vector_query(
             if not maybe_trun_chunks:
                 return None
 
-            # 在内容中包含时间信息
+            # Include time information in content
             formatted_chunks = []
             for c in maybe_trun_chunks:
                 chunk_text = c["content"]

lightrag/storage.py

@@ -88,7 +88,7 @@ class NanoVectorDBStorage(BaseVectorStorage):
         if not len(data):
             logger.warning("You insert an empty data to vector DB")
             return []
-        
+
         current_time = time.time()
         list_data = [
             {
@@ -137,11 +137,11 @@ class NanoVectorDBStorage(BaseVectorStorage):
         )
         results = [
             {
-                **dp, 
+                **dp,
-                "id": dp["__id__"], 
+                "id": dp["__id__"],
                 "distance": dp["__metrics__"],
-                "created_at": dp.get("__created_at__")
+                "created_at": dp.get("__created_at__"),
-            } 
+            }
             for dp in results
         ]
         return results