from flask import current_app from core.embedding.cached_embedding import CacheEmbedding from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig from core.index.vector_index.vector_index import VectorIndex from core.model_providers.model_factory import ModelFactory from models.dataset import Dataset class IndexBuilder: @classmethod def get_index(cls, dataset: Dataset, indexing_technique: str, ignore_high_quality_check: bool = False): if indexing_technique == "high_quality": if not ignore_high_quality_check and dataset.indexing_technique != 'high_quality': return None embedding_model = ModelFactory.get_embedding_model( tenant_id=dataset.tenant_id, model_provider_name=dataset.embedding_model_provider, model_name=dataset.embedding_model ) embeddings = CacheEmbedding(embedding_model) return VectorIndex( dataset=dataset, config=current_app.config, embeddings=embeddings ) elif indexing_technique == "economy": return KeywordTableIndex( dataset=dataset, config=KeywordTableConfig( max_keywords_per_chunk=10 ) ) else: raise ValueError('Unknown indexing technique')