hit_testing_service.py 4.1 KB

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  1. import logging
  2. import time
  3. from core.rag.datasource.retrieval_service import RetrievalService
  4. from core.rag.models.document import Document
  5. from core.rag.retrieval.retrival_methods import RetrievalMethod
  6. from extensions.ext_database import db
  7. from models.account import Account
  8. from models.dataset import Dataset, DatasetQuery, DocumentSegment
  9. default_retrieval_model = {
  10. 'search_method': RetrievalMethod.SEMANTIC_SEARCH.value,
  11. 'reranking_enable': False,
  12. 'reranking_model': {
  13. 'reranking_provider_name': '',
  14. 'reranking_model_name': ''
  15. },
  16. 'top_k': 2,
  17. 'score_threshold_enabled': False
  18. }
  19. class HitTestingService:
  20. @classmethod
  21. def retrieve(cls, dataset: Dataset, query: str, account: Account, retrieval_model: dict, limit: int = 10) -> dict:
  22. if dataset.available_document_count == 0 or dataset.available_segment_count == 0:
  23. return {
  24. "query": {
  25. "content": query,
  26. "tsne_position": {'x': 0, 'y': 0},
  27. },
  28. "records": []
  29. }
  30. start = time.perf_counter()
  31. # get retrieval model , if the model is not setting , using default
  32. if not retrieval_model:
  33. retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
  34. all_documents = RetrievalService.retrieve(retrival_method=retrieval_model.get('search_method', 'semantic_search'),
  35. dataset_id=dataset.id,
  36. query=cls.escape_query_for_search(query),
  37. top_k=retrieval_model.get('top_k', 2),
  38. score_threshold=retrieval_model.get('score_threshold', .0)
  39. if retrieval_model['score_threshold_enabled'] else None,
  40. reranking_model=retrieval_model.get('reranking_model', None),
  41. reranking_mode=retrieval_model.get('reranking_mode')
  42. if retrieval_model.get('reranking_mode') else 'reranking_model',
  43. weights=retrieval_model.get('weights', None),
  44. )
  45. end = time.perf_counter()
  46. logging.debug(f"Hit testing retrieve in {end - start:0.4f} seconds")
  47. dataset_query = DatasetQuery(
  48. dataset_id=dataset.id,
  49. content=query,
  50. source='hit_testing',
  51. created_by_role='account',
  52. created_by=account.id
  53. )
  54. db.session.add(dataset_query)
  55. db.session.commit()
  56. return cls.compact_retrieve_response(dataset, query, all_documents)
  57. @classmethod
  58. def compact_retrieve_response(cls, dataset: Dataset, query: str, documents: list[Document]):
  59. i = 0
  60. records = []
  61. for document in documents:
  62. index_node_id = document.metadata['doc_id']
  63. segment = db.session.query(DocumentSegment).filter(
  64. DocumentSegment.dataset_id == dataset.id,
  65. DocumentSegment.enabled == True,
  66. DocumentSegment.status == 'completed',
  67. DocumentSegment.index_node_id == index_node_id
  68. ).first()
  69. if not segment:
  70. i += 1
  71. continue
  72. record = {
  73. "segment": segment,
  74. "score": document.metadata.get('score', None),
  75. }
  76. records.append(record)
  77. i += 1
  78. return {
  79. "query": {
  80. "content": query,
  81. },
  82. "records": records
  83. }
  84. @classmethod
  85. def hit_testing_args_check(cls, args):
  86. query = args['query']
  87. if not query or len(query) > 250:
  88. raise ValueError('Query is required and cannot exceed 250 characters')
  89. @staticmethod
  90. def escape_query_for_search(query: str) -> str:
  91. return query.replace('"', '\\"')