Implement logging strategy (#444)

* Logging strategy and convert all prints() to logging invocations

trustgraph-embeddings-hf/trustgraph/embeddings/hf/hf.py

@@ -4,10 +4,14 @@ Embeddings service, applies an embeddings model selected from HuggingFace.
 Input is text, output is embeddings vector.
 """
 
+import logging
 from ... base import EmbeddingsService
 
 from langchain_huggingface import HuggingFaceEmbeddings
 
+# Module logger
+logger = logging.getLogger(__name__)
+
 default_ident = "embeddings"
 
 default_model="all-MiniLM-L6-v2"
@@ -22,13 +26,13 @@ class Processor(EmbeddingsService):
             **params | { "model": model }
         )
 
-        print("Get model...", flush=True)
+        logger.info(f"Loading HuggingFace embeddings model: {model}")
         self.embeddings = HuggingFaceEmbeddings(model_name=model)
 
     async def on_embeddings(self, text):
 
         embeds = self.embeddings.embed_documents([text])
-        print("Done.", flush=True)
+        logger.debug("Embeddings generation complete")
         return embeds
 
     @staticmethod