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fix: cache SentenceTransformer model at startup instead of per-request #190

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fix: cache SentenceTransformer model at startup instead of per-request #190

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10 changes: 7 additions & 3 deletions server-https/app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,11 @@
MILVUS_VECTOR_FIELD = os.getenv("MILVUS_VECTOR_FIELD", "vector")
EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "sentence-transformers/all-mpnet-base-v2")

# Initialize embedding model once at module level to avoid reloading on every request
print(f"Loading embedding model: {EMBEDDING_MODEL}...")
_embedding_model = SentenceTransformer(EMBEDDING_MODEL)
print("Embedding model loaded successfully.")

# System prompt (same as WebSocket version)
SYSTEM_PROMPT = """
You are the Kubeflow Docs Assistant.
Expand Down Expand Up @@ -119,9 +124,8 @@ def milvus_search(query: str, top_k: int = 5) -> Dict[str, Any]:
collection = Collection(MILVUS_COLLECTION)
collection.load()

# Encoder (same model as pipeline)
encoder = SentenceTransformer(EMBEDDING_MODEL)
query_vec = encoder.encode(query).tolist()
# Encode query using cached model
query_vec = _embedding_model.encode(query).tolist()

search_params = {"metric_type": "COSINE", "params": {"nprobe": 32}}
results = collection.search(
Expand Down
10 changes: 7 additions & 3 deletions server/app.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,11 @@
MILVUS_VECTOR_FIELD = os.getenv("MILVUS_VECTOR_FIELD", "vector")
EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "sentence-transformers/all-mpnet-base-v2")

# Initialize embedding model once at module level to avoid reloading on every request
print(f"Loading embedding model: {EMBEDDING_MODEL}...")
_embedding_model = SentenceTransformer(EMBEDDING_MODEL)
print("Embedding model loaded successfully.")

# System prompt
SYSTEM_PROMPT = """
You are the Kubeflow Docs Assistant.
Expand Down Expand Up @@ -73,9 +78,8 @@ def milvus_search(query: str, top_k: int = 5) -> Dict[str, Any]:
collection = Collection(MILVUS_COLLECTION)
collection.load()

# Encoder (same model as pipeline)
encoder = SentenceTransformer(EMBEDDING_MODEL)
query_vec = encoder.encode(query).tolist()
# Encode query using cached model
query_vec = _embedding_model.encode(query).tolist()

search_params = {"metric_type": "COSINE", "params": {"nprobe": 32}}
results = collection.search(
Expand Down