""" Accepts entity/vector pairs and writes them to a Milvus store. """ import logging from .... direct.milvus_graph_embeddings import EntityVectors from .... base import GraphEmbeddingsStoreService, CollectionConfigHandler from .... base import AsyncProcessor, Consumer, Producer from .... base import ConsumerMetrics, ProducerMetrics from .... schema import IRI, LITERAL # Module logger logger = logging.getLogger(__name__) def get_term_value(term): """Extract the string value from a Term""" if term is None: return None if term.type == IRI: return term.iri elif term.type == LITERAL: return term.value else: # For blank nodes or other types, use id or value return term.id or term.value default_ident = "graph-embeddings-write" default_store_uri = 'http://localhost:19530' class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService): def __init__(self, **params): store_uri = params.get("store_uri", default_store_uri) super(Processor, self).__init__( **params | { "store_uri": store_uri, } ) self.vecstore = EntityVectors(store_uri) # Register for config push notifications self.register_config_handler(self.on_collection_config) async def store_graph_embeddings(self, message): for entity in message.entities: entity_value = get_term_value(entity.entity) if entity_value != "" and entity_value is not None: for vec in entity.vectors: self.vecstore.insert( vec, entity_value, message.metadata.user, message.metadata.collection, chunk_id=entity.chunk_id or "", ) @staticmethod def add_args(parser): GraphEmbeddingsStoreService.add_args(parser) parser.add_argument( '-t', '--store-uri', default=default_store_uri, help=f'Milvus store URI (default: {default_store_uri})' ) async def create_collection(self, user: str, collection: str, metadata: dict): """ Create collection via config push - collections are created lazily on first write with the correct dimension determined from the actual embeddings. """ try: logger.info(f"Collection create request for {user}/{collection} - will be created lazily on first write") self.vecstore.create_collection(user, collection) except Exception as e: logger.error(f"Failed to create collection {user}/{collection}: {e}", exc_info=True) raise async def delete_collection(self, user: str, collection: str): """Delete the collection for graph embeddings via config push""" try: self.vecstore.delete_collection(user, collection) logger.info(f"Successfully deleted collection {user}/{collection}") except Exception as e: logger.error(f"Failed to delete collection {user}/{collection}: {e}", exc_info=True) raise def run(): Processor.launch(default_ident, __doc__)