mirror of
https://github.com/trustgraph-ai/trustgraph.git
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f2ae0e8623
- Put scores in all responses - Remove unused 'middle' vector layer. Vector of texts -> vector of (vector embedding)
102 lines
2.6 KiB
Python
Executable file
102 lines
2.6 KiB
Python
Executable file
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"""
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Graph embeddings query service. Input is vector, output is list of
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entities
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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 .... schema import GraphEmbeddingsResponse, EntityMatch
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from .... schema import Error, Term, IRI, LITERAL
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from .... base import GraphEmbeddingsQueryService
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "graph-embeddings-query"
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default_store_uri = 'http://localhost:19530'
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class Processor(GraphEmbeddingsQueryService):
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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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def create_value(self, ent):
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if ent.startswith("http://") or ent.startswith("https://"):
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return Term(type=IRI, iri=ent)
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else:
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return Term(type=LITERAL, value=ent)
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async def query_graph_embeddings(self, msg):
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try:
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vec = msg.vector
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if not vec:
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return []
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# Handle zero limit case
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if msg.limit <= 0:
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return []
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resp = self.vecstore.search(
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vec,
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msg.user,
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msg.collection,
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limit=msg.limit * 2
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)
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entity_set = set()
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entities = []
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for r in resp:
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ent = r["entity"]["entity"]
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# Milvus returns distance, convert to similarity score
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distance = r.get("distance", 0.0)
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score = 1.0 - distance if distance else 0.0
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# De-dupe entities, keep highest score
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if ent not in entity_set:
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entity_set.add(ent)
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entities.append(EntityMatch(
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entity=self.create_value(ent),
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score=score,
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))
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# Keep adding entities until limit
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if len(entities) >= msg.limit:
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break
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logger.debug("Send response...")
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return entities
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except Exception as e:
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logger.error(f"Exception querying graph embeddings: {e}", exc_info=True)
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raise e
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@staticmethod
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def add_args(parser):
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GraphEmbeddingsQueryService.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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def run():
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Processor.launch(default_ident, __doc__)
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