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Fix cloud inference MRL dimensions option and bump qdrant-client #14

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Merged
Thierry Damiba (thierrypdamiba) merged 2 commits into from
Mar 2, 2026
Merged

Fix cloud inference MRL dimensions option and bump qdrant-client #14

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99ef759
Fix cloud inference MRL dimensions option and bump qdrant-client
mrscoopers Mar 1, 2026
257758e
resolve merge conflict: accept deletion of query_vectorstore_service
thierrypdamiba Mar 2, 2026
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2 changes: 1 addition & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ dependencies = [
"openai>=1.0.0",
"pydantic>=2.12.0",
"pydantic-settings>=2.11.0",
"qdrant-client>=1.16.2",
"qdrant-client>=1.17.0",
"uvicorn>=0.34.0",
]

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10 changes: 5 additions & 5 deletions src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
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Expand Up @@ -32,7 +32,7 @@ async def retrieve_papers_dense(self, query: str, top_k: int = 5) -> list[dict]:
"""
if self.qdrant_client.cloud_inference:
dense_vector = self.qdrant_client._define_openai_vectors(
query, dimensions=self.qdrant_client.embedding_dimension
query, mrl_dimensions=self.qdrant_client.embedding_dimension
)
else:
dense_vector = await self.qdrant_client._get_openai_vectors(
Expand Down Expand Up @@ -76,10 +76,10 @@ async def retrieve_papers_hybrid(self, query: str, top_k: int = 5) -> list[dict]
"""
if self.qdrant_client.cloud_inference:
retriever_vector = self.qdrant_client._define_openai_vectors(
query, dimensions=self.qdrant_client.embedding_dimension
query, mrl_dimensions=self.qdrant_client.embedding_dimension
)
reranker_vector = self.qdrant_client._define_openai_vectors(
query, dimensions=self.qdrant_client.reranker_embedding_dimension
query, mrl_dimensions=self.qdrant_client.reranker_embedding_dimension
)
else:
openai_vector = await self.qdrant_client._get_openai_vectors(
Expand Down Expand Up @@ -152,7 +152,7 @@ async def recommend_papers_based_on_constraints(
if positive_examples:
positive_vectors = [
self.qdrant_client._define_openai_vectors(
pos_example, dimensions=self.qdrant_client.embedding_dimension
pos_example, mrl_dimensions=self.qdrant_client.embedding_dimension
)
for pos_example in positive_examples
]
Expand All @@ -161,7 +161,7 @@ async def recommend_papers_based_on_constraints(
if negative_examples:
negative_vectors = [
self.qdrant_client._define_openai_vectors(
neg_example, dimensions=self.qdrant_client.embedding_dimension
neg_example, mrl_dimensions=self.qdrant_client.embedding_dimension
)
for neg_example in negative_examples
]
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10 changes: 5 additions & 5 deletions src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py
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Expand Up @@ -103,14 +103,14 @@ async def _get_openai_vectors(self, text: str, dimensions: int) -> list[float]:
logger.error(f"❌ Failed to create embedding: {e}")
raise

def _define_openai_vectors(self, text: str, dimensions: int = 1536) -> models.Document:
def _define_openai_vectors(self, text: str, mrl_dimensions: int = 1536) -> models.Document:
"""
Wrap text in models.Document to handle OpenAI embeddings inference
through Qdrant's Cloud.

Args:
text (str): Input text.
dimensions (int): Number of dimensions for the embedding. https://platform.openai.com/docs/api-reference/embeddings/create#embeddings-create-dimensions
mrl_dimensions (int): Number of MRL dimensions for the embedding. https://platform.openai.com/docs/api-reference/embeddings/create#embeddings-create-dimensions
Returns:
models.Document: Document object.
"""
Expand All @@ -119,7 +119,7 @@ def _define_openai_vectors(self, text: str, dimensions: int = 1536) -> models.Do
model=f"openai/{settings.qdrant.embedding_model}",
options={
"openai-api-key": settings.openai.api_key.get_secret_value(),
"dimensions": dimensions,
"mrl": mrl_dimensions,
},
)

Expand Down Expand Up @@ -243,10 +243,10 @@ async def upsert_points(

if self.cloud_inference:
retriever_vector = self._define_openai_vectors(
abstract, dimensions=self.embedding_dimension
abstract, mrl_dimensions=self.embedding_dimension
)
reranker_vector = self._define_openai_vectors(
abstract, dimensions=self.reranker_embedding_dimension
abstract, mrl_dimensions=self.reranker_embedding_dimension
)
else:
openai_vector = await self._get_openai_vectors(abstract, dimensions=self.reranker_embedding_dimension) # MRL, https://platform.openai.com/docs/guides/embeddings#use-cases
Expand Down