Refactor agent creation and update logic to improve error handling and default values; enhance logging for better traceability

application/vectorstore/base.py

@@ -1,3 +1,4 @@
+import logging
 import os
 from abc import ABC, abstractmethod
 
@@ -9,13 +10,27 @@ from application.core.settings import settings
 
 class EmbeddingsWrapper:
     def __init__(self, model_name, *args, **kwargs):
-        self.model = SentenceTransformer(
-            model_name,
-            config_kwargs={"allow_dangerous_deserialization": True},
-            *args,
-            **kwargs
-        )
-        self.dimension = self.model.get_sentence_embedding_dimension()
+        logging.info(f"Initializing EmbeddingsWrapper with model: {model_name}")
+        try:
+            kwargs.setdefault("trust_remote_code", True)
+            self.model = SentenceTransformer(
+                model_name,
+                config_kwargs={"allow_dangerous_deserialization": True},
+                *args,
+                **kwargs,
+            )
+            if self.model is None or self.model._first_module() is None:
+                raise ValueError(
+                    f"SentenceTransformer model failed to load properly for: {model_name}"
+                )
+            self.dimension = self.model.get_sentence_embedding_dimension()
+            logging.info(f"Successfully loaded model with dimension: {self.dimension}")
+        except Exception as e:
+            logging.error(
+                f"Failed to initialize SentenceTransformer with model {model_name}: {str(e)}",
+                exc_info=True,
+            )
+            raise
 
     def embed_query(self, query: str):
         return self.model.encode(query).tolist()
@@ -117,15 +132,29 @@ class BaseVectorStore(ABC):
                     embeddings_name, openai_api_key=embeddings_key
                 )
         elif embeddings_name == "huggingface_sentence-transformers/all-mpnet-base-v2":
-            if os.path.exists("./models/all-mpnet-base-v2"):
+            possible_paths = [
+                "/app/models/all-mpnet-base-v2",  # Docker absolute path
+                "./models/all-mpnet-base-v2",  # Relative path
+            ]
+            local_model_path = None
+            for path in possible_paths:
+                if os.path.exists(path):
+                    local_model_path = path
+                    logging.info(f"Found local model at path: {path}")
+                    break
+                else:
+                    logging.info(f"Path does not exist: {path}")
+            if local_model_path:
                 embedding_instance = EmbeddingsSingleton.get_instance(
-                    embeddings_name="./models/all-mpnet-base-v2",
+                    local_model_path,
                 )
             else:
+                logging.warning(
+                    f"Local model not found in any of the paths: {possible_paths}. Falling back to HuggingFace download."
+                )
                 embedding_instance = EmbeddingsSingleton.get_instance(
                     embeddings_name,
                 )
         else:
             embedding_instance = EmbeddingsSingleton.get_instance(embeddings_name)
-
         return embedding_instance