mirror of
https://github.com/trustgraph-ai/trustgraph.git
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1809c1f56d
* Structured data refactor - multi-index tables, remove need for manual mods to the Cassandra tables * Tech spec updated to track implementation
100 lines
3.1 KiB
Python
Executable file
100 lines
3.1 KiB
Python
Executable file
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"""
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Accepts entity/vector pairs and writes them to a Milvus store.
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"""
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import logging
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from .... direct.milvus_graph_embeddings import EntityVectors
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from .... base import GraphEmbeddingsStoreService, CollectionConfigHandler
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from .... base import AsyncProcessor, Consumer, Producer
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from .... base import ConsumerMetrics, ProducerMetrics
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from .... schema import IRI, LITERAL
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# Module logger
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logger = logging.getLogger(__name__)
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def get_term_value(term):
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"""Extract the string value from a Term"""
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if term is None:
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return None
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if term.type == IRI:
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return term.iri
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elif term.type == LITERAL:
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return term.value
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else:
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# For blank nodes or other types, use id or value
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return term.id or term.value
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default_ident = "graph-embeddings-write"
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default_store_uri = 'http://localhost:19530'
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class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
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def __init__(self, **params):
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store_uri = params.get("store_uri", default_store_uri)
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super(Processor, self).__init__(
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**params | {
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"store_uri": store_uri,
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}
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)
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self.vecstore = EntityVectors(store_uri)
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# Register for config push notifications
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self.register_config_handler(self.on_collection_config)
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async def store_graph_embeddings(self, message):
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for entity in message.entities:
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entity_value = get_term_value(entity.entity)
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if entity_value != "" and entity_value is not None:
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for vec in entity.vectors:
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self.vecstore.insert(
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vec, entity_value,
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message.metadata.user,
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message.metadata.collection
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)
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@staticmethod
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def add_args(parser):
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GraphEmbeddingsStoreService.add_args(parser)
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parser.add_argument(
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'-t', '--store-uri',
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default=default_store_uri,
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help=f'Milvus store URI (default: {default_store_uri})'
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)
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async def create_collection(self, user: str, collection: str, metadata: dict):
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"""
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Create collection via config push - collections are created lazily on first write
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with the correct dimension determined from the actual embeddings.
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"""
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try:
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logger.info(f"Collection create request for {user}/{collection} - will be created lazily on first write")
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self.vecstore.create_collection(user, collection)
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except Exception as e:
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logger.error(f"Failed to create collection {user}/{collection}: {e}", exc_info=True)
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raise
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async def delete_collection(self, user: str, collection: str):
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"""Delete the collection for graph embeddings via config push"""
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try:
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self.vecstore.delete_collection(user, collection)
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logger.info(f"Successfully deleted collection {user}/{collection}")
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except Exception as e:
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logger.error(f"Failed to delete collection {user}/{collection}: {e}", exc_info=True)
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raise
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def run():
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Processor.launch(default_ident, __doc__)
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