dump_graph_data.py 4.0 KB

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  1. '''
  2. 这个脚本是用来从postgre数据库中导出图谱数据到json文件的。
  3. '''
  4. import sys,os
  5. current_path = os.getcwd()
  6. sys.path.append(current_path)
  7. from sqlalchemy import text
  8. from sqlalchemy.orm import Session
  9. import json
  10. #这个是数据库的连接
  11. from db.session import SessionLocal
  12. #两个会话,分别是读取节点和属性的
  13. db = SessionLocal()
  14. prop = SessionLocal()
  15. def get_props(ref_id):
  16. props = {}
  17. sql = """select prop_name, prop_value,prop_title from kg_props where ref_id=:ref_id"""
  18. result = prop.execute(text(sql), {'ref_id':ref_id})
  19. for record in result:
  20. prop_name, prop_value,prop_title = record
  21. props[prop_name] = prop_title + ":" +prop_value
  22. return props
  23. def get_entities():
  24. #COUNT_SQL = "select count(*) from kg_nodes where version=:version"
  25. COUNT_SQL = "select count(*) from kg_nodes where status=0"
  26. result = db.execute(text(COUNT_SQL))
  27. count = result.scalar()
  28. print("total nodes: ", count)
  29. entities = []
  30. batch = 100
  31. start = 1
  32. while start < count:
  33. #sql = """select id,name,category from kg_nodes where version=:version order by id limit :batch OFFSET :start"""
  34. sql = """select id,name,category from kg_nodes where status=0 order by id limit :batch OFFSET :start"""
  35. result = db.execute(text(sql), {'start':start, 'batch':batch})
  36. #["发热",{"type":"症状","description":"发热是诊断的主要目的,用于明确发热病因。"}]
  37. row_count = 0
  38. for row in result:
  39. id,name,category = row
  40. props = get_props(id)
  41. #description = props.get('description', props.get('standard_description', ''))
  42. entities.append([name,{'type':category, **props}])
  43. row_count += 1
  44. if row_count == 0:
  45. break
  46. start = start + row_count
  47. print("start: ", start, "row_count: ", row_count)
  48. with open(current_path+"\\web\\cached_data\\entities_med.json", "w", encoding="utf-8") as f:
  49. f.write(json.dumps(entities, ensure_ascii=False,indent=4))
  50. def get_names(src_id, dest_id):
  51. sql = """select name from kg_nodes where id = :src_id"""
  52. result = db.execute(text(sql), {'src_id':src_id})
  53. src_name = result.scalar()
  54. result = db.execute(text(sql), {'src_id':dest_id})
  55. dest_name = result.scalar()
  56. return (src_name, dest_name)
  57. def get_relationships():
  58. #COUNT_SQL = "select count(*) from kg_edges where version=:version"
  59. COUNT_SQL = "select count(*) from kg_edges"
  60. result = db.execute(text(COUNT_SQL))
  61. count = result.scalar()
  62. print("total edges: ", count)
  63. edges = []
  64. batch = 1000
  65. start = 1
  66. file_index = 1
  67. while start < count:
  68. #sql = """select id,name,category,src_id,dest_id from kg_edges where version=:version order by id limit :batch OFFSET :start"""
  69. sql = """select id,name,category,src_id,dest_id from kg_edges order by id limit :batch OFFSET :start"""
  70. result = db.execute(text(sql), {'start':start, 'batch':batch})
  71. #["发热",{"type":"症状","description":"发热是诊断的主要目的,用于明确发热病因。"}]
  72. row_count = 0
  73. for row in result:
  74. id,name,category,src_id,dest_id = row
  75. props = get_props(id)
  76. src_name, dest_name = get_names(src_id, dest_id)
  77. edges.append([src_name, dest_name,{'type':category, **props}])
  78. row_count += 1
  79. if row_count == 0:
  80. break
  81. start = start + row_count
  82. print("start: ", start, "row_count: ", row_count)
  83. if len(edges) > 10000:
  84. with open(current_path+f"\\web\\cached_data\\relationship_med_{file_index}.json", "w", encoding="utf-8") as f:
  85. f.write(json.dumps(edges, ensure_ascii=False,indent=4))
  86. edges = []
  87. file_index += 1
  88. with open(current_path+"\\web\\cached_data\\relationship_med.json", "w", encoding="utf-8") as f:
  89. f.write(json.dumps(edges, ensure_ascii=False,indent=4))
  90. #导出节点数据
  91. get_entities()
  92. #导出关系数据
  93. get_relationships()