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114 lines
4.2 KiB
Python
114 lines
4.2 KiB
Python
import logging
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import time
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from core.rag.datasource.retrieval_service import RetrievalService
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from core.rag.models.document import Document
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from core.rag.retrieval.retrival_methods import RetrievalMethod
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from extensions.ext_database import db
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from models.account import Account
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from models.dataset import Dataset, DatasetQuery, DocumentSegment
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default_retrieval_model = {
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'search_method': RetrievalMethod.SEMANTIC_SEARCH.value,
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'reranking_enable': False,
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'reranking_model': {
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'reranking_provider_name': '',
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'reranking_model_name': ''
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},
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'top_k': 2,
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'score_threshold_enabled': False
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}
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class HitTestingService:
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@classmethod
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def retrieve(cls, dataset: Dataset, query: str, account: Account, retrieval_model: dict, limit: int = 10) -> dict:
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if dataset.available_document_count == 0 or dataset.available_segment_count == 0:
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return {
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"query": {
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"content": query,
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"tsne_position": {'x': 0, 'y': 0},
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},
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"records": []
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}
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start = time.perf_counter()
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# get retrieval model , if the model is not setting , using default
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if not retrieval_model:
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retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
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all_documents = RetrievalService.retrieve(retrival_method=retrieval_model.get('search_method', 'semantic_search'),
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dataset_id=dataset.id,
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query=cls.escape_query_for_search(query),
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top_k=retrieval_model.get('top_k', 2),
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score_threshold=retrieval_model.get('score_threshold', .0)
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if retrieval_model['score_threshold_enabled'] else None,
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reranking_model=retrieval_model.get('reranking_model', None)
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if retrieval_model['reranking_enable'] else None,
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reranking_mode=retrieval_model.get('reranking_mode')
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if retrieval_model.get('reranking_mode') else 'reranking_model',
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weights=retrieval_model.get('weights', None),
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)
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end = time.perf_counter()
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logging.debug(f"Hit testing retrieve in {end - start:0.4f} seconds")
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dataset_query = DatasetQuery(
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dataset_id=dataset.id,
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content=query,
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source='hit_testing',
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created_by_role='account',
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created_by=account.id
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)
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db.session.add(dataset_query)
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db.session.commit()
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return cls.compact_retrieve_response(dataset, query, all_documents)
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@classmethod
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def compact_retrieve_response(cls, dataset: Dataset, query: str, documents: list[Document]):
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i = 0
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records = []
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for document in documents:
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index_node_id = document.metadata['doc_id']
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segment = db.session.query(DocumentSegment).filter(
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DocumentSegment.dataset_id == dataset.id,
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DocumentSegment.enabled == True,
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DocumentSegment.status == 'completed',
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DocumentSegment.index_node_id == index_node_id
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).first()
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if not segment:
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i += 1
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continue
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record = {
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"segment": segment,
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"score": document.metadata.get('score', None),
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}
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records.append(record)
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i += 1
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return {
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"query": {
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"content": query,
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},
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"records": records
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}
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@classmethod
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def hit_testing_args_check(cls, args):
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query = args['query']
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if not query or len(query) > 250:
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raise ValueError('Query is required and cannot exceed 250 characters')
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@staticmethod
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def escape_query_for_search(query: str) -> str:
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return query.replace('"', '\\"')
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