mirror of
https://github.com/trustgraph-ai/trustgraph.git
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141 lines
4.6 KiB
Python
Executable file
141 lines
4.6 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_doc_embeddings import DocVectors
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from .... base import DocumentEmbeddingsStoreService
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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 StorageManagementRequest, StorageManagementResponse, Error
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from .... schema import vector_storage_management_topic, storage_management_response_topic
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "de-write"
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default_store_uri = 'http://localhost:19530'
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class Processor(DocumentEmbeddingsStoreService):
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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 = DocVectors(store_uri)
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# Set up metrics for storage management
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storage_request_metrics = ConsumerMetrics(
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processor=self.id, flow=None, name="storage-request"
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)
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storage_response_metrics = ProducerMetrics(
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processor=self.id, flow=None, name="storage-response"
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)
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# Set up consumer for storage management requests
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self.storage_request_consumer = Consumer(
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taskgroup=self.taskgroup,
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client=self.pulsar_client,
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flow=None,
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topic=vector_storage_management_topic,
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subscriber=f"{self.id}-storage",
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schema=StorageManagementRequest,
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handler=self.on_storage_management,
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metrics=storage_request_metrics,
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)
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# Set up producer for storage management responses
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self.storage_response_producer = Producer(
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client=self.pulsar_client,
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topic=storage_management_response_topic,
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schema=StorageManagementResponse,
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metrics=storage_response_metrics,
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)
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async def start(self):
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"""Start the processor and its storage management consumer"""
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await super().start()
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await self.storage_request_consumer.start()
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await self.storage_response_producer.start()
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async def store_document_embeddings(self, message):
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for emb in message.chunks:
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if emb.chunk is None or emb.chunk == b"": continue
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chunk = emb.chunk.decode("utf-8")
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if chunk == "": continue
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for vec in emb.vectors:
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self.vecstore.insert(
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vec, chunk,
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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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DocumentEmbeddingsStoreService.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 on_storage_management(self, message, consumer, flow):
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"""Handle storage management requests"""
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logger.info(f"Storage management request: {message.operation} for {message.user}/{message.collection}")
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try:
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if message.operation == "delete-collection":
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await self.handle_delete_collection(message)
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else:
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response = StorageManagementResponse(
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error=Error(
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type="invalid_operation",
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message=f"Unknown operation: {message.operation}"
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)
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)
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await self.storage_response_producer.send(response)
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except Exception as e:
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logger.error(f"Error processing storage management request: {e}", exc_info=True)
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response = StorageManagementResponse(
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error=Error(
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type="processing_error",
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message=str(e)
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)
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)
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await self.storage_response_producer.send(response)
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async def handle_delete_collection(self, message):
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"""Delete the collection for document embeddings"""
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try:
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self.vecstore.delete_collection(message.user, message.collection)
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# Send success response
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response = StorageManagementResponse(
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error=None # No error means success
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)
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await self.storage_response_producer.send(response)
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logger.info(f"Successfully deleted collection {message.user}/{message.collection}")
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except Exception as e:
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logger.error(f"Failed to delete collection: {e}")
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raise
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def run():
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Processor.launch(default_ident, __doc__)
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