test: fix qdrant

Author: vemonet

compose.text2sparql.yml

@@ -78,5 +78,6 @@ services:
       - CORPORATE_URL=http://virtuoso-corporate:8890/sparql
       - VECTORDB_URL=http://vectordb:6334/
       - BENCH_MODEL=openrouter/openai/gpt-oss-120b
+      - PYTHONUNBUFFERED=1
     entrypoint: uv run
     command: uvicorn tests.text2sparql.api:app --host 0.0.0.0 --port 8765

tests/text2sparql/api.py

@@ -72,7 +72,7 @@ def get_dataset_id_from_iri(dataset_iri: str) -> str:
 """
 
 embedding_model = TextEmbedding(settings.embedding_model)
-vectordb = QdrantClient(url=settings.vectordb_url, prefer_grpc=True)
+qdrant_client = QdrantClient(url=settings.vectordb_url, prefer_grpc=True)
 
 # Statistics
 question_num = 0
@@ -102,7 +102,7 @@ async def get_answer(question: str, dataset: str):
     endpoint_url = DATASETS_ENDPOINTS[dataset]
     # Retrieve relevant queries
     question_embeddings = next(iter(embedding_model.embed([question])))
-    retrieved_queries = vectordb.query_points(
+    retrieved_queries = qdrant_client.query_points(
         collection_name=f"text2sparql-{get_dataset_id_from_iri(dataset)}",
         query=question_embeddings,
         limit=settings.default_number_of_retrieved_docs,
@@ -117,7 +117,7 @@ async def get_answer(question: str, dataset: str):
     )
 
     # Retrieve relevant classes
-    retrieved_classes = vectordb.query_points(
+    retrieved_classes = qdrant_client.query_points(
         collection_name=f"text2sparql-{get_dataset_id_from_iri(dataset)}",
         query=question_embeddings,
         limit=settings.default_number_of_retrieved_docs,

tests/text2sparql/index.py

@@ -6,8 +6,7 @@
 from qdrant_client import models
 from qdrant_client.http.models import Distance, VectorParams
 
-from sparql_llm.indexing.index_resources import embedding_model, qdrant_client
-from tests.text2sparql.api import DATASETS_ENDPOINTS, get_dataset_id_from_iri
+from tests.text2sparql.api import DATASETS_ENDPOINTS, embedding_model, get_dataset_id_from_iri, qdrant_client
 from tests.text2sparql.endpoint_schema import EndpointSchema
 
 QUERIES_FILE = os.path.join(os.path.abspath(os.path.dirname(__file__)), "queries.csv")