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from gradio_client import Client
import uuid
# Point to your deployed Gradio app
client = Client("IotaCluster/embedding-model")
def get_dense_embedding(text: str):
"""Calls the /embed_dense endpoint (MiniLM)."""
return _call_api(text, api_name="/embed_dense")
def get_sparse_embedding(text: str):
"""Calls the /embed_sparse endpoint (BM25)."""
return _call_api(text, api_name="/embed_sparse")
def get_late_embedding(text: str):
"""Calls the /embed_colbert endpoint (ColBERT late-interaction)."""
return _call_api(text, api_name="/embed_colbert")
def _call_api(text: str, api_name: str):
try:
result = client.predict(
text=text,
api_name=api_name
)
# Normalize response: return the first value in the dict or the list itself
if isinstance(result, dict):
return next(iter(result.values()))
elif isinstance(result, list):
return result
else:
print(f"Unexpected response from {api_name!r}:", result)
return None
except Exception as e:
print(f"API request to {api_name!r} failed:", e)
return None
def to_valid_qdrant_id(id_val):
"""Ensures the ID is a valid UUID (for Qdrant)."""
try:
return str(uuid.UUID(str(id_val)))
except Exception:
return str(uuid.uuid5(uuid.NAMESPACE_DNS, str(id_val)))
if __name__ == "__main__":
text = "Hello!!"
# dense = get_dense_embedding(text)
# print("Dense embedding:", dense)
# print("Length:", len(dense) if dense else None)
# sparse = get_sparse_embedding(text)
# print("\nSparse term-weights:", sparse)
late = get_late_embedding(text)
print("\nLate-interaction (ColBERT) embeddings:", late)
print("Tokens count:", len(late) if isinstance(late, list) else None)
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