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Embeddings API scores (#671)

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- Put scores in all responses
- Remove unused 'middle' vector layer. Vector of texts -> vector of (vector embedding)
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cybermaggedon 2026-03-09 10:53:44 +00:00 • committed by GitHub
parent 4fa7cc7d7c
commit f2ae0e8623
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65 changed files with 1339 additions and 1292 deletions
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53
trustgraph-flow/trustgraph/query/graph_embeddings/milvus/service.py
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@ -7,7 +7,7 @@ entities
import logging
from .... direct.milvus_graph_embeddings import EntityVectors
from .... schema import GraphEmbeddingsResponse
from .... schema import GraphEmbeddingsResponse, EntityMatch
from .... schema import Error, Term, IRI, LITERAL
from .... base import GraphEmbeddingsQueryService
@ -41,42 +41,41 @@ class Processor(GraphEmbeddingsQueryService):
try:
entity_set = set()
entities = []
vec = msg.vector
if not vec:
return []
# Handle zero limit case
if msg.limit <= 0:
return []
for vec in msg.vectors:
resp = self.vecstore.search(
vec,
msg.user,
msg.collection,
limit=msg.limit * 2
)
resp = self.vecstore.search(
vec,
msg.user,
msg.collection,
limit=msg.limit * 2
)
entity_set = set()
entities = []
for r in resp:
ent = r["entity"]["entity"]
# De-dupe entities
if ent not in entity_set:
entity_set.add(ent)
entities.append(ent)
for r in resp:
ent = r["entity"]["entity"]
# Milvus returns distance, convert to similarity score
distance = r.get("distance", 0.0)
score = 1.0 - distance if distance else 0.0
# Keep adding entities until limit
if len(entity_set) >= msg.limit: break
# De-dupe entities, keep highest score
if ent not in entity_set:
entity_set.add(ent)
entities.append(EntityMatch(
entity=self.create_value(ent),
score=score,
))
# Keep adding entities until limit
if len(entity_set) >= msg.limit: break
ents2 = []
for ent in entities:
ents2.append(self.create_value(ent))
entities = ents2
if len(entities) >= msg.limit:
break
logger.debug("Send response...")
return entities