mirror of
https://github.com/trustgraph-ai/trustgraph.git
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150 lines
4.5 KiB
Python
Executable file
150 lines
4.5 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 qdrant_client import QdrantClient
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from qdrant_client.models import PointStruct
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from qdrant_client.models import Distance, VectorParams
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from .... schema import GraphEmbeddingsResponse
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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:6333'
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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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#optional api key
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api_key = params.get("api_key", None)
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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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"api_key": api_key,
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}
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)
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self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
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self.last_collection = None
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def ensure_collection_exists(self, collection, dim):
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"""Ensure collection exists, create if it doesn't"""
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if collection != self.last_collection:
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if not self.qdrant.collection_exists(collection):
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try:
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self.qdrant.create_collection(
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collection_name=collection,
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vectors_config=VectorParams(
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size=dim, distance=Distance.COSINE
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),
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)
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logger.info(f"Created collection: {collection}")
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except Exception as e:
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logger.error(f"Qdrant collection creation failed: {e}")
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raise e
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self.last_collection = collection
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def collection_exists(self, collection):
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"""Check if collection exists (no implicit creation)"""
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return self.qdrant.collection_exists(collection)
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def collection_exists(self, collection):
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"""Check if collection exists (no implicit creation)"""
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return self.qdrant.collection_exists(collection)
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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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entity_set = set()
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entities = []
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for vec in msg.vectors:
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# Use dimension suffix in collection name
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dim = len(vec)
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collection = f"t_{msg.user}_{msg.collection}_{dim}"
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# Check if collection exists - return empty if not
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if not self.collection_exists(collection):
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logger.info(f"Collection {collection} does not exist, skipping this vector")
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continue
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# Heuristic hack, get (2*limit), so that we have more chance
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# of getting (limit) entities
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search_result = self.qdrant.query_points(
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collection_name=collection,
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query=vec,
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limit=msg.limit * 2,
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with_payload=True,
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).points
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for r in search_result:
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ent = r.payload["entity"]
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# De-dupe entities
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if ent not in entity_set:
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entity_set.add(ent)
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entities.append(ent)
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# Keep adding entities until limit
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if len(entity_set) >= msg.limit: break
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# Keep adding entities until limit
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if len(entity_set) >= msg.limit: break
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ents2 = []
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for ent in entities:
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ents2.append(self.create_value(ent))
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entities = ents2
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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'Qdrant store URI (default: {default_store_uri})'
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)
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parser.add_argument(
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'-k', '--api-key',
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default=None,
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help=f'API key for qdrant (default: None)'
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)
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def run():
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Processor.launch(default_ident, __doc__)
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